Physical and chemical water quality characteristics in six wetlands of Lake Tana, Ethiopia

Physical and chemical water quality characteristics in six wetlands of Lake Tana, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Physical and chemical water quality characteristics in six wetlands of Lake Tana, Ethiopia Hailu Mazengia, Horst Kaiser, Minwuyelet Mengist This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3993010/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Physical and chemical water quality characteristics were studied in six of Lake Tana. The purpose of the study was to explore how different methods describe the “health” of the wetlands and how different approaches relate to each other. The physicochemical parameters were measured in-situ with portable multimeter and nutrients and chlorophyll a were determined by following the standard procedures outlined in the United States Environmental Protection Agency using UV/Visible photometer (Spectrophotometer). The trophic state index (TSI) of wetlands was determined using trophic state variable and Carlson model. The lake water quality index (WQI) was also evaluated using data from multiple water quality parameters into a mathematical equation to express the overall water quality at each study wetland and season. The water quality datasets were subjected to four multivariate statistical techniques, namely, univariate analysis of variance (univariate ANOVA), cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA). Analysis of the physicochemical dataset using univariate analysis indicated a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of dissolved oxygen, electrical conductivity, Secchi depth a.m., and p.m., salinity, nitrate, total ammonia, total nitrogen, total phosphorous, and Chlorophyll-a while water temperature, water depth, soluble reactive phosphorous were not affected (ANOVA, p > 0.05) by the interaction between wetland by season. Spatial diversity and site grouping based on water quality characteristics using CA, PCA and FA analysis grouped the 6-wetlands into four clusters based on the similarity of water quality characteristics. The four clusters displayed in the dendrogram were grouped into least polluted cluster 1 (WO and RA), slightly polluted cluster 2 (MRM). moderately polluted cluster 3 ( GRM and ZG ) and highly polluted cluster 1 (AV). There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of total trophic state index (TOT TSI ), total nitrogen trophic state index (TSI TN ), total phosphorous trophic state index (TSI TP,), total chlorophyll-a trophic state index (TSI Chla ) ,and total Secchi depth trophic state index (TSI STD ). However, there was no a significant interaction between wetland and season (ANOVA, p > 0.05) for the mean value of WQI. In conclusion, ranking of the pollution status of wetlands of Lake Tana using different approaches in this study using multivariate statistics, Carlson TSI, and WQI model suggest that some wetlands did not fit completely in the same category The current study on water quality variables of Lake Tana recommends that top priority should be given to regular water quality monitoring, in conjunction with biodiversity and fish health assessment. Wetland Lake Tana Ethiopia water quality Trophic State Index Water quality Index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The physical and chemical characteristics of a water body are potentially limiting factors in the biological productivity of an aquatic ecosystem (Bhateria and Jain 2016; Elnaggar and El-Alfy 2016 ; Charoula et al. 2020 ). The maintenance of a healthy aquatic ecosystem is dependent on the physicochemical parameters of the water (Cairns, McCormick, and Niederlehner 1993 ; Wepener 2008 ; Arenas-Sánchez, Rico, and Vighi 2016; Riddell et al. 2019 ). In aquatic environments, different physicochemical factors may produce various biochemical and physiological parameters that can be used as biomarkers (Abalaka 2017 ; Ha et al. 2020 ). Many environmental factors affect ecological biodiversity, and consequently the use of bioindicators and biomarkers, and it is thus important that the physicochemical and ecological conditions of the ecosystem be characterized in such studies (Bartell 2006 ; Wepener 2008 ; Naigaga 2012 ; Abalaka 2017 ; Lomartire, Marques et al. 2021 ; Marinović et al. 2021 ). A review on eutrophication and nutrient release in aquatic environments of Sub-Saharan Africa reported that wastewaters from sewage and industries are often discharged into the environment untreated, and that this is becoming a major source of nutrients, which cause eutrophication of surface water bodies (Naigaga 2012 ). This is a particular problem in Lake Tana (Vijverberg, Sibbing, and Dejen 2009; Karlberg et al. 2015 ; Dejen, Anteneh, and Vijverberg 2017; Goshu et al. 2017; Goshu and Aynalem 2017; Akhtar et al. 2021 ). Much of Lake Tana’s shoreline is covered by extensive wetlands, often dominated by dense papyrus stands that extend out over the lake waters, and these wetlands play a role in the physical, chemical, and biological conditions of the inshore waters (Wepener 2008 ; Assefa et al. 2020 ). The water quality status of the shoreline waters is therefore an important indicator of the environmental status of the whole lake. As water quality deteriorates, ecosystem services may be lost, and organisms will begin to suffer. For example, the fluctuation of the physical and chemical characteristics of a lake have an impact on the diversity and abundance of organisms such as macroinvertebrates, and fish assemblage composition (Czerniawska-Kusza, 2005 ; Moog, 2015; Aragaw, 2021 ). In-flowing rivers carry heavy loads of suspended silt into the lake, thereby increasing the turbidity of the lake water and reducing primary production (Gebresllassie et al., 2014; Dejen et al., 2017; Gebremedhin et al., 2018 ; Wondie, 2018 ;Aragaw, 2021 ). It is, therefore, important to have reliable information on trends of water quality as a prerequisite for planning the prevention and control of the lake pollution and the sustainability of an effective water management program (Zelalem and Prokin 2017; Kassa and Tibebe 2019). The steady increase in human population size puts increasing pressure on catchment resources required for settlement, agriculture and urban and industrial infrastructure development, which in turn exacerbates water pollution problems as more wastewater is discharged into the lake (Gebresllassie et al., 2014; Karlberg et al., 2015 ; Dejen et al., 2017; Gebremedhin et al., 2018 ; Wondie, 2018 ). The use of bioindicators and biomarkers to evaluate environmental quality therefore necessitates that the ecosystem be characterized in terms of physicochemical as well as ecological characteristics. Given the eutrophication pollution challenge in Lake Tana and its urban wetlands, physicochemical parameters including nutrient levels were assessed in the present study. This study sites were characterized according to sixteen commonly recorded variables that have been used to assess Lake Tana’s waters (Aragaw et al. 2022 ; Kassa and Tibebe 2019; Wondie 2010 ; Zelalem and Prokin 2017). The results will be used in the proceeding chapters to relate the biological indicators and biomarkers to water quality. The main objective of this the study was to explore how different methods describe the “health” of the wetlands and how different approaches relate to each other. Description of the study area Data and samples for this study were collected in the bays of six wetlands located in four administrative zones under the same agroecology along the shoreline of Lake Tana. The wetland ecotones include Wonjeta, Zewdie Girar, Gumara River mouth, Megech River mouth, Avaj and Ras Abbay. Two wetlands of Avaj and Ras Abbay, are located in Bahir Dar municipality, Amhara Regional Staes’s capital and business center. Avaj is situated approximately 3 km North of Bahir Dar city. Ras Abbay wetland is located approximately 5 km northeast of Bahir Dar city. Gumara River mouth wetland is located Dera district. Dera is a rural district located approximately 40 km northeast of Bahir Dar city. Megech River mouth is situated in a rural district Dembia, approximately 90 km North of Bahir Dar city and approximately 40 km South of Gondar city. Wonjeta wetland is situated northwest of Bahir Dar city and it is approximately 5 km from Bahir Dar city. Zewdie Girar is located North Achefer district. North Achefer is a rural district approximately 50 km west of Bahir Dar city. Gumara river mouth wetland is under pressure of catchment agriculture thus receives agricultural effluent from adjacent forming lands (Fig. 1.). Materials and methods Measurement of physicochemical variables Physicochemical variables measured in this study were temperature, dissolved oxygen, conductivity, pH, secchi disk depth a.m., and p.m., total dissolved substance, salinity, nitrite (NO 2 ), nitrate (NO 3 ), soluble reactive phosphorous, total ammonia (NH 4 + NH 3 ), total phosphorus (TP), total nitrogen (TN), and chlorophyll a (Chl-a). These variables were monitored because they define the status and quality of the water and phosphorous can be directly harmful to fish in concentrations beyond the normal ranges (Zhang et al., 2022 ; Dar et al., 2021 ; Lopes, 2021 ; Holmes & Taylor, 2015). Temperature affects the speed of chemical reactions, the rate of photosynthesis, the metabolic rate of aquatic organisms, as well as how pollutants, parasites, and other pathogens interact with aquatic residents. Temperature also influences the solubility of dissolved oxygen (DO) and other molecules in the water column such as ammonia (Ha et al. 2020 ). Oxygen influences inorganic chemical reactions and is required for aerobic metabolism (Carr and Neary 2006 ). The pH of an aquatic ecosystem is important because it is closely linked to biological productivity. Secchi disk depth determines water transparency, a measure of water quality that quantifies the depth of light penetration in a body of water. Water bodies with high transparency typically have good water quality (Ha et al. 2020 ). Specific conductivity measures how well the water conducts an electrical current, a property that is proportional to the concentration and strength of ions in solution and can also be used to detect pollution sources (Moreira et al., 2016 ; Pal et al., 2015 ). Nutrients were considered because they regulate the productivity and define the trophic status of aquatic ecosystems. Phosphorus and nitrogen are reported to be the primary drivers of eutrophication of aquatic ecosystems, where increased nutrient concentrations leads to increased primary productivity ((Jarosiewicz, Ficek, and Zapadka 2011; Berzina and Sudars 2010; J. Ndungu et al. 2013 ; Ozbek et al. 2018 ). These two elements together with Secchi disk depth were relevant in the calculation of Carlson’s trophic index (Carlson 1977 ). The samples were analyzed for NH 4 + , soluble reactive phosphorous, NO 2 − , and NO 3 − , using a photometer while TN and TP were analyzed using spectrophotometer. Physicochemical variables, their units and methods of analysis are summarised in Table 1 . Table 1 Summary of physicochemical variables, units and analytical methods used in the wetlands of Lake Tana Parameter Abbreviation Units Analytical Tools Temperature Temp o C Portable meter Dissolved Oxygen DO mg/l Portable meter Electrical conductivity EC µS/cm Portable meter Ph pH Portable meter Secchi Depth a.m. SD a.m M Secchi disk Secchi Depth p.m. SD p.m M Secchi disk Water depth WD M Tape mounted on stick Total dissolved substance TDS g/l Portable meters Salinity S Ppm Portable meters Nitrite nitrogen NO 2 mg/l Palin test (Photometer) Nitrate nitrogen NO 3 mg/l Palin test (Photometer Soluble reactive phosphorus SRP mg/l Palin test (Photometer) Total ammonia NH 4 mg/l Palin test (Photometer) Total Nitrogen TN mg/l Ammonium Molybdate (Spectrophotometer) Total phosphorus TP mg/l Ammonium Molybdate (Spectrophotometer) Chlorophyll-a Chl-a ml/l Fluorometer Assessment of trophic status of the sampling sites Based on the value of TSI, aquatic ecosystems are classified into trophic categories (Carlson, 1977 ). Carlson TSI of wetlands of lake Tana was calculated using information of data sets of SDT, chlorophyll-a (Chla), concentrations of phosphorous (P) and total phosphorus (TP). TSI TN = 54.45 + 14.43 ∗ ln (TN) (mg/L) TSI TP = 14.42 ∗ ln (TP) + 4.15 (µg/L) TSI Chla = 9.81∗ ln (chl a) + 30.6 (µg/L) TSI SDT = 60 − 14.41 ∗ ln (SD) (m) TOT TSI = (TSI TN + TSI TP + TSI Chla + TSI SDT Where: TSI TN corresponding to concentration (mg/L) total nitrogen, TSI SDT is TSI corresponding to depth (m) of Secchi disc transparency, TSI TP is TSI corresponding to the concentration (µg /L) of total phosphorus, TSI Chla is TSI corresponding to the concentration (µg /L) of cholorophyll-a, and TOT TSI is total TSI, i.e., the average TSI SDT , TSI TP , TSI TN and TSI Chla . Generally, TSI values below 40 correspond to an oligotrophic, from 40–60 to mesotrophic, from 60–80 to eutrophic, and above 80 to a hypertrophic status of the lake (Jarosiewicz, Ficek, and Zapadka 2011). Assessment of the water quality index of the sampling sites The water quality index (WQI) was calculated from the data sets for assessing the spatio-temporal change in water quality parameters (Rubio-Arias et al. 2012 ). WQI is a ranking that replicates the composite impact of various water quality parameters and its appearance above a certain threshold limits the numerous uses of the water. The water quality parameters were assigned different weights from 1 to 5 based on their importance for the overall water quality. The estimated WQI values are classified into five categories from 300 water unfit for various purposes (Yidana and Yidana 2010 ). The WQI is computed as; Wi = \(\frac{\text{w}\text{i}}{\sum \text{w}\text{i}}\) where ∑Wi is the sum of the weights of all the parameters. In this study, ∑wi was 50. Table 3.2. presents the wi, Wi, and US Environmental Protection Agency (EPA) standard for each chemical parameter used in this study. A quality rating scale, qi, was computed for each parameter using the equation qi = (Ci/Si) x 100 where Ci and Si respectively refer to the concentration and the US EPA standard for each parameter, in mg/l. The water quality subindex, SIi was then calculated for each parameter using the equation WQI =∑Sli Table 2 The weights and relative weights of each of the water quality parameters used for the Water Quality Index determination. Where US EPA-US Environmental Protection Agency US EPA Weight (wi) Relative weight (Wi) Temperature 25 5 0.1 Dissolved oxygen (mg/L) 5 5 0.1 Electrical conductivity (µs/cm) 1000 4 0.08 Ph 7.5 4 0.08 Secchi depth a.m. (m) NA 1 0.02 Secchi depth p.m. (m) NA 1 0.02 Water depth(m) NA 1 0.02 Total dissolved solids (g/L) 0.1 4 0.08 Salinity (mg/L) 0.1 4 0.08 Nitrite (mg/L) 0.001 2 0.04 Nitrate (mg/L) 0.1 5 0.1 Soluble reactive phosphorous (mg/L) 0.02 3 0.06 Total ammonia (mg/L) 1.5 3 0.06 Total nitrogen (mg/L) 1.1 3 0.06 Total phosphorous (mg/L) 1.0 3 0.06 Chlorophyll-a a(mg/L) 2.0 2 0.04 Sum 50 1 Statistical analysis Descriptive statistics comprising the means, standard deviations and ranges for each parameter were derived. Data were not normally distributed hence the Kruskal-Wallis nonparametric ANOVA was used to compare the water quality variables between sampling locations. In order to evaluate spatial variation in water quality and to characterise the study sites according to their water quality status, subsequently defining their degree of contamination, the water quality datasets were subjected to four multivariate statistical techniques, namely, univariate analysis of variance (univariate ANOVA), cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA). Multivariate analysis was applied to test the significance, effect sizes, and powers of physicochemical properties in the six wetlands across four seasons. Between-subject effects for each dependent variable were analysed using univariate analysis. The cut-off value for determining statistical significance was chosen as p < 0.05 (Sharma and Sood 2022 ) three sampling site per off shore side each wetland were considered. Wetlands were independently ranked based on Carlson TSI and WQI. Carlson TSI was calculated using information of data sets of SDT, chlorophyll-a (Chla), concentration of phosphorous (P) and the concentration of total phosphorus (TP). WQIs were calculated for each study locations and wetlands and were ranked accordingly Results Characteristics of study wetlands of Lake Tana Six wetlands belonging to five types, i.e., one riverine, two lacustrine, two river mouth, and one urban wetland were considered in this study. The river mouth wetlands were Gumara River Mouth and Megech River mouth. Gumara River Mouth wetland is under high pressure due to catchment agriculture in harvesting, livestock grazing, irrigation developments, sedimentation, water extraction and the introduction of alien species. It receives agricultural effluents from adjacent farming lands of the South Gondar administrative zone. This wetland has been dominated by water hyacinth since 2012. Megech River Mouth is dominated by water hyacinth and receives combined effluent from agricultural lands the in North Gondar administrative zone and municipal effluent discharge with no wastewater pre-treatment facility from Gondar city. Ras Abbay wetland is a riverine wetland dominated by Cyperus papyrus and forest trees. Ras Abbay wetland receives industrial and domestic effluent from point and non-point sources. The Blue Nile River crossing Bahir Dar city receives untreated municipal and industrial wastewater and then drains into a mosaic of mixed wetland habitats of Ras Abbay. Avaj is an urban wetland that receives domestic wastewater generated from hotels, hospitals and fish landing sites from the surrounding communities. Wonjeta wetland is papyrus-dominated and it is a type of wetland originating from springs. It is known for its papyrus and tree natural forests. It is surrounded by rural settlements, and the spring water is pumped for domestic purposes and small-scale irrigation. Zewdie Girar is a lacustrine wetland that is rich in reed swamps and surrounded by a mountain. This wetland is under a relatively low pressure due to harvesting, sedimentation, water extraction and the introduction of alien species. Geographical locations and wetland characteristics of the study wetlands are shown in Table 2 . Table 2 Summary of the characteristics of study wetlands in Lake Tana. GRM - Gumara River mouth, MRM - Megech River mouth, RA - Ras Abbay, AV - Avaj, WO - Wongeta, and ZG - Zewdie Girar Wetland Location Latitude /Longitude Altitude (masl) Area (ha) Origin of water quality deterioration Type of vegetation Type of wetland Reference GRM Located southeast of the lake, approximately 50 km from Bahir Dar city 37 0 29’684’’N /11 0 53’’949’’ E 1850 1500 Catchment agriculture Eichhornia- dominated River mouth wetland (Kahsay et al., 2023 ; Wondie, 2018 ) MRM Located in the northern part of the lake, approximately 90 km from Bahir Dar city and 40 km from Gondar city 37 0 24’245’’ N /12 0 16’337’’E 1794 1816 Catchment agriculture and untreated municipal effluent Eichhornia- dominated River mouth wetland (Dersseh et al., 2020; Kahsay et al., 2023 ; Wondie, 2018 ) RA Located in south of the lake, approximately 5 km from Bahir Dar city 37 0 24’682’’ N /11 0 36’140’’ E 1769–1785 1114.5 Domestic wastewater Grass and trees Riverine wetland (Kahsay et al., 2023 ; Wondie, 2018 ) AV Located south of the lake, approximately 2 km from Bahir Dar city 37 0 22’464’’ N /11 0 36’679’’ E 1792 200 Untreated municipal and industrial wastewater Papyrus and tree natural forests Urban (Kahsay et al., 2023 ; Wondie, 2018 ) WO Located south of the lake, approximately 5 km from Bahir Dar city 37 0 17’832’’N /11 0 39’241’’ E 1806 300 Relatively low pressure from farming and wastewater Papyrus- dominated Lacustrine (Kahsay et al., 2023 ; Wondie, 2018 ) ZG Located southwest of the lake, approximately 4 km rom Bahir Dar city 36 0 59’812’’ N / 11 0 54’262’’ E 1795 250 Relatively low pressure from farming and wastewater Papyrus- dominated Lacustrine (Kahsay et al., 2023 ; Wondie, 2018 ) Assessment of spatio-temporal variations of selected water physicochemical properties Tests of between-subject effects for each dependent variable using univariate analysis are shown in table 3. Wetland had a significant effect (ANOVA, p < 0.05) on electrical conductivity, pH, Secchi depth a.m., Secchi depth p.m., WD, salinity, nitrate, total ammonia and chlorophyll-a while water temperature, dissolved oxygen, total dissolved solids, nitrite, soluble reactive phosphorous, total nitrogen, total phosphorous and total nitrogen to total phosphorous ration did not differ among the six wetlands (ANOVA, p > 0.05). On the other hand, season had a significant effect (ANOVA, p < 0.05) on water temperature, dissolved oxygen, electrical conductivity, Secchi depth a.m., Secchi depth p.m., water depth, nitrate, soluble reactive phosphorous, total ammonia, total nitrogen and Chlorophyll-a while pH, total dissolved solids, salinity, nitrite, and total nitrogen did not differ among seasons (ANOVA, p > 0.05). There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of dissolved oxygen, electrical conductivity, pH, Secchi depth a.m., Secchi depth p.m., salinity, nitrate, total ammonia, total nitrogen, total phosphorous, total nitrogen to total phosphorous ratio and Chlorophyll-a while water temperature, water depth, total dissolved solids, nitrate and soluble reactive phosphorous were not affected (ANOVA, p > 0.05) by the interaction between wetland by season. Table.3. Univariate tests of significance and powers of test for physicochemical properties in lake Tana wetlands Effect Variable Df SS MS F P Wetland Temperature ( 0 C) 5 11.74 2.35 1.36 0.26 Dissolved oxygen(mg/L) 5 2.589 0.518 0.96 0.45 Electrical conductivity µS/cm) 5 22458 4492 13.05 0.00 pH 5 5.909 1.182 2.884 0.02 Secchi depth a.m.(m) 5 24.0054 4.80108 17.84 0.00 Secchi depth p.m. (m) 5 19.20174 3.84035 15.56 0.00 Water depth (m) 5 42.6236 8.5247 9.24 0.00 Total dissolved solids (g/L) 5 0.144555 0.028911 0.75 0.59 Salinity (ppm) 5 0.004996 0.000999 8.88 0.00 Nitrite (mg/L) 5 0.054599 0.010920 1.30 0.28 Nitrate (mg/L) 5 0.31938 0.06388 5.34 0.00 Soluble reactive phosphorous (mg/L) 5 0.60925 0.12185 1.56 0.18 Total ammonia (mg/L) 5 0.368185 0.073637 5.23 0.00 Total nitrogen (mg/L) 5 25.5614 5.1123 0.54 0.74 Total phosphorous (mg/L) 5 6.60024 1.32005 1.84 0.12 Total nitrogen: Total phosphorous ratio 5 392.679 78.536 0.82919 0.53 Chlorophyll-a (mg/L) 5 266.903 53.381 3.0407 0.02 Season Temperature ( 0 C) 3 29.19 9.73 5.62 0.00 Dissolved oxygen(mg/L) 3 18.200 6.067 11.240 0.00 Electrical conductivity µS/cm) 3 3418 1139 3.313 0.03 pH 3 0.815 0.272 0.663 0.58 Secchi depth a.m.(m) 3 21.0783 7.02609 26.1061 0.00 Secchi depth p,m (m) 3 15.14610 5.04870 20.4560 0.00 Water depth (m) 3 36.5502 12.1834 13.2047 0.00 Total dissolved solids (g/L) 3 0.237585 0.079195 2.06663 0.12 Salinity (ppm) 3 0.000749 0.000250 2.218 0.10 Nitrite (mg/L) 3 0.030913 0.010304 1.228009 0.31 Nitrate (mg/L) 3 1.59503 0.53168 44.937 0.00 Soluble reactive phosphorous (mg/L) 3 1.08765 0.36255 4.7604 0.00 Total ammonia (mg/L) 3 1.076676 0.358892 25.5014 0.00 Total nitrogen (mg/L) 3 105.9809 35.3270 3.74764 0.02 Total phosphorous (mg/L) 3 5.79351 1.93117 2.68797 0.06 Total nitrogen: Total phosphorous ratio 3 400.579 133.526 1.40978 0.25 Chlorophyll-a (mg/L) 3 161.819 53.940 3.0725 0.04 Wetland x Season Temperature ( 0 C) 15 34.73 2.32 1.34 0.22 Dissolved oxygen(mg/L) 15 18.531 1.235 2.289 0.01 Electrical conductivity µS/cm) 15 27947 1863 5.417 0.00 pH 15 21.137 1.409 3.438 0.00 Secchi depth a.m.(m) 15 60.7273 4.04849 15.0425 0.00 Secchi depth p,m (m) 15 47.22013 3.14801 12.7549 0.00 Water depth (m) 15 8.7683 0.5846 0.6336 0.83 Total dissolved solids (g/L) 15 0.446392 0.029759 0.77659 0.69 Salinity (ppm) 15 0.008543 0.000570 5.063 0.00 Nitrite (mg/L) 15 0.176072 0.011738 1.398861 0.19 Nitrate (mg/L) 15 1.94689 0.12979 10.970 0.00 Soluble reactive phosphorous (mg/L) 15 1.43713 0.09581 1.2580 0.26 Total ammonia (mg/L) 15 1.195415 0.079694 5.6627 0.00 Total nitrogen (mg/L) 15 363.3852 24.2257 2.56997 0.01 Total phosphorous (mg/L) 15 20.86332 1.39089 1.93596 0.04 Total nitrogen: Total phosphorous ratio 15 2669.906 177.994 1.87927 0.05 Chlorophyll-a (mg/L) 15 669.931 44.662 2.5440 0.00 The water quality management policy for surface waters currently relies on a wide variety of physical and chemical parameters. To provide a similar wide range of data, the measured values of the chemical and physical parameters collected from six wetlands across four seasons are presented in Fig. 3 . Temperature The overall mean value of water temperature in this study was 24.02°C ± 1.49. Mean temperature did not differ among wetlands, ranging from 21.02 to 25.00 ° C (mean: 23.34 ± 0.34) in GRM and from 22.79 to 26.59 ° C (mean: 24.63 ± 0.37) in WO (ANOVA, p > 0.05). In contrast, the mean value of temperature differed among seasons ranging from 21.02 to 25.69 ° C (mean: 23.18 ± 1.12) in rainy season and from 22.82 to 27.92 ° C (mean: 24.79 ± 1.60) in early rainy season (ANOVA, p < 0.05) (figure.1a). Dissolved oxygen (DO) There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of DO. Oxygen concentrations ranged from 4.77 to 5.04 (mean: 4.89 ± 0.14) in WO during dry season and from 6.86 to 8.64 (mean: 7.62 ± 0.92 mg/L) in GRM during the rainy season. Significant differences between combinations of wetlands and season are indicated in Fig. 1b. Electrical conductivity (EC) There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of EC. Values ranged from 89 to 108 (mean: 96.67 ± 10.02 µS/cm) in GRM during rainy season and from 196 to 327 (mean: 250 ± 68.47 µS/cm) in MRM during late rainy season. Figure 1c shows differences between mean values. pH The mean value of pH in this study was 6.81 ± 0.82. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of pH. The mean value of pH ranged from 4.67 to 6.15 (mean: 5.62 ± 0.82) in WO during late rainy season and from 6.63 to 9.41 (mean: 8.41 ± 1.55) in RA during the dry season. Significant differences between combinations of wetlands and season are indicated in Fig. 1d). Secchi depth (SD) a.m. The mean value of SD a.m. in this study was 1.02 ± 1.29 m. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of SD a.m. Values ranged from 0.02 to 0.12 m (mean: 0.077 ± 0.05) in MRM during early rainy season and from 4.1 to 7.8 m (mean: 6.3 ± 1.96) in GRM during dry season. Figure 1e shows differences between mean values. Secchi depth (SD) p.m. The mean value of SD p.m. in this study was 0.94 ± 1.14 m. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of SD p.m. Values ranged from 0.0.02 to 0.12 m (mean: 0.07 ± 0.0.05) in MRM during late rainy season and from 3.38 to 6.90 m (mean: 5.59 ± 1.93) in GRM during dry season. Figure 1f shows differences between mean values. Water depth (WD) The mean value of WD in this study was 2.22 ± 1.36 m. The mean value of WD differed among wetlands, ranging from 0.28 m to 1.87m (mean: 0.78 ± 0.14) in MRM and from 2.05 to 4.10 m (mean: 3.08 ± 0.21) in RA (ANOVA, p < 0.05). Season had effect on the mean value of WD ranging from 0.26 to 2.85 m (mean:1.17 ± 0.86) during early rainy season and from 0.22 to 6.10m (mean:2.90 ± 1.34) during rainy season (ANOVA, p < 0.05) (Fig. 1g) Total dissolved solids (TDS) The mean value of TDS in this study was 0.13 ± 0.19 g/L. The mean value of TDS did not differ among wetlands, ranging from 0.01 to 0.15 g/L (mean: 0.12 ± 0.04 ) AV and from 0.01 to 0.98 g/L (mean: 0.21 ± 0.10 ) in RA (ANOVA, p > 0.05).Likewise, the mean value of TDS did not differ among seasons ranging from 0.01 to 0.10 g/L (mean:0.04 ) during early rainy season and from 0.01 to 0.9 g/L (mean:0.19 ± 0.29 ) during dry season (ANOVA, p > 0.05) (figure.1h). Salinity The mean value of salinity in this study was 0.07 ± 0.02 ppm. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of Salinity. The mean value of salinity ranged from 0.04 to 0.09 ppm (mean: 0.04 ± 0.0.005) in GRM during rainy season and from 0.02 to 0.06 ppm (mean: 0.05 ± 0.02) in GRM during the rainy season. Significant differences between combinations of wetlands and season are indicated in Fig. 1i. Nitrite The mean value of nitrite in this study was 0.02 ± 0.09 mg/L. The mean value of nitrite did not differ among wetlands, ranging from non-detectable to 0.013 mg/L (mean: 0.003 ± 0.001 mg/L) in WO and from non-detectable to 0.819 mg/L (mean: 0.078 ± 0.068) in MRM (ANOVA, p > 0.05). Likewise, the mean value of nitrite did not differ among season ranging from non-detectable to 0.017 mg/L (0.005 ± 0.007) during early rainy season and from non-detectable to 0.819 mg/L (mean:0.052 ± 0.193) during late rainy season (ANOVA, p > 0.05) (figure.1j). Nitrate The mean concentration of nitrate in this study was 0.46 ± 0.25 mg/l. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of nitrate. Nitrate concentrations ranged from 0.001 to 0.054 mg/L (mean: 0.02 ± 0.0.027) in ZG during early rainy season and from 0.76 to 0.98 mg/L (mean: 0.89 ± 0.11) in ZG during the dry season. Significant differences between combinations of wetlands and season are indicated in Fig. 1k). Soluble reactive phosphorus (SRP) The mean value of SRP in this study was 0.54 ± 0.31 mg/L. The mean value of SRP did not differ among wetlands, ranging from 0.083 to 1.350 mg/L (mean: 0.443 ± 0.093) in ZG and from 0.060to 1.850 mg/L (mean: 0.734 ± 0.142) in RA (ANOVA, p > 0.05). In contrast, the mean value of SRP differed among seasons ranging from 0.03 to 1.35 mg/L (mean:0.35 ± 0.34) during early rainy season and from 0.39 to 1.85 mg/L (mean:0.66 ± 0.36) during late rainy season (ANOVA, p < 0.05) (figure.1l). Total ammonia The mean value of ammonia in this study was 0.17 ± 0.21 mg/l. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of total ammonia. Ammonia concentrations ranged from non-detectable to 0.01 mg/L (mean:0.003 ± 0.006) in ZG during dry season and from 0.68 to 0.98 mg/L (mean: 0.79 ± 0.15mg/L) in AV during the late rainy season. Significant differences between combinations of wetlands and season are indicated in Fig. 1m). Total nitrogen (TN) The mean value of ammonia in this study was 2.23 ± 3.65 mg/L There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of TN. TN concentrations ranged from 0.128 to 0.306 mg/L (mean: .308 ± 0.090) in GRM during early rainy season and from 4.00 to 14.00 mg/L (mean: 8.333 5.132) in WO during the dry season. Significant differences between combinations of wetlands and season are indicated in Fig. 1n). Total phosphorous (TP) The mean value of TP in was 0.89 ± 0.98 mg/L. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of TP. Concentration of TP ranged from 0.05 to 0.18 mg/L (mean: 0.12 ± 0.06) in WO during early rainy season and from 0.80 to 6.00 (mean: 3.27 ± 2.61) in AV during late rainy season. Figure 1o shows differences between mean values. Total Nitrogen to Total Phosphorus (TN:TP) Ratio The mean value of total phosphorous did not differ among wetlands, ranging from 0.37 to 16.2 (mean: 3.8 ± 5.2) in the GRM and from 0.37 to 75.0 (mean: 11 18. ± 21.28 in RA (ANOVA, p > 0.05). Likewise, the mean value of TN:TP did not differ among seasons ranging from 0.507 to 15.000 (mean:3.915 ± 3.257) during rainy season and from 0.167 to 75.000 (mean:10.018 ± 19.363) RA (ANOVA, p > 0.05) (Fig. 1p). Chlorophyll-a (Chl-a) The mean value of Chl-a in this study was 5.15 ± 5.23 mg/L. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of chl-a. Chl-a concentrations ranged with mean value of 1.00 ± 0.00) in ZG during dry season and from 9.00 to 33.00 mg/L (mean: 718.67 ± 12.67 ) in MRM during the rainy season. Significant differences between combinations of wetlands and season are indicated in Fig. 1q). Of particular note are the mean value of dissolved oxygen, electrical conductivity, Secchi depth. a.m, Secchi depth. p.m., salinity, nitrate, ammonia, total nitrogen, total nitrogen and chl-a showed significant variation by wetland by season interactions that delineated study wetlands into different clusters. The mean, standard error, minimum and maximum spatio-temporal variations of physico-chemical properties of Lake Tana in four seasons are given Fig. 1. Spatial diversity and site grouping based on water quality characteristics Hierarchical cluster analysis grouped the 6-wetlands into four clusters based on the similarity of water quality characteristics (Fig. 2 ). The four clusters displayed in the dendrogram (Fig. 2 and Table 4 ) could be grouped into cluster 1 (WO and RA), clusters 2 (MRM). moderately polluted cluster (clusters 3) and least polluted cluster (cluster 1). The least polluted cluster comprised WO, and RA wetlands and the slightly polluted cluster comprised MRM while moderately polluted cluster comprised GRM and ZG wetlands. The highly polluted cluster comprised AV wetland. Relationship between sampling sites and physicochemical variables The results of the PCA based on normalized data of the physicochemical components are shown in Table 3.5, and the wetlands are shown in Fig. 3 .2. Two components of PCA loaded eigenvalues greater than 1, with Component one (X-axis) registering 42.24% of the total variance and Component two (Y-axis) explained 20.80% of the total variance (table 3.4). Altogether, the first two components explained 64.04% of total variance and 10.2 of the 16 eigenvalues. The PCA plot brought out the four clusters observed in the hierarchical cluster analysis dendrogram above (Fig. 3 ). Cluster 1, the highly polluted cluster (AV) correlated with higher values of, electrical conductivity, ammonia, total nitrogen., total phosphorous and total nitrogen total phosphorous ratio. In contrast, Cluster 4 (WO and RA), the least polluted cluster, was highly associated with l lower values of nutrients Clusters, 2 and 3, the slightly and moderately polluted clusters, lay between Clusters 1 and 4 (Fig. 3 ). Table 4 Eigenvalues, cumulative eigenvalues, percent of total variance and cumulative percent of the total variance of correlation PCA for physicochemical variable, (n = 6) in the study sites Eigenvalues Cumulative Total % of Total Variance Cumulative % of Total Variance 1 7.180538 7.18054 42.23846 42.2385 2 3.706058 10.88660 21.80034 64.0388 3 2.536499 13.42310 14.92058 78.9594 4 2.276251 15.69935 13.38971 92.3491 5 1.300654 17.00000 7.65091 100.0000 Latent factors influencing water quality in the study sites Three factors were extracted, explaining 64% of the total variance in the water quality data set (Table 5 ). Eigenvalues > 1 were taken as the criterion for the extraction of the factors required for explaining the source of variances in the data set under Kaiser Normalization (Alkarkhi et al 2008 ; Panda et al. 2006 ; Rohe 2020 ; Sarmento 2017 ; Sayadi et al 2014 ; Varol et al. 2012 ). The parameter loadings for the three identified factors, the factor eigenvalues, their percentage variance, and cumulative percentage variance are given in Table 5 . The loading coincided with the correlation coefficients between water quality variables and the factors (Alkarkhi et al 2008 ; Naigaga 2012 ; Varol et al. 2012 ). Factor 1 accounted for 43% of the total variance and was positively correlated (loading > 0.70) with EC, salinity, NO 2 and NO 3 while it was negatively correlated with SD. A.m, SD. p.m., water depth and NH 4 (Table 6 ). Factor 2 explained 21% of the total variance and was positively loaded with temperature and Chl-a while it was negatively loaded with dissolved oxygen. Factor 1 loading represented the changes and water quality status in the highly polluted Cluster 1 (AV), while the Factor 2 loading explained the changes in the least polluted cluster 4 (WO and RA), while Cluster 2 (GRM and ZG) and cluster 3 (MRM) were represented as moderately and slightly polluted wetlands. Table 5 R-mode varimax rotated factor analysis of water quality variables (number of variables = 16) factor loadings > 0.70 in bold Variable Factor 1 Factor 2 Temperature 0.1279 0.4325 Dissolved oxygen -0.1244 -0.3049 Electrical conductivity 0.8281 0.4280 Ph -0.1974 0.4098 Secchi depth a.m. -0.8965 0.1316 Secchi depth p.m. -0.9033 0.1435 Water depth -0.8135 0.4510 Total dissolved substance -0.2403 0.6088 Salinity 0.8846 0.3908 Nitrite 0.8588 0.0846 Nitrate 0.7515 -0.5049 Soluble reactive phosphorous -0.2901 0.7997 Total ammonia -0.7818 0.3075 Total nitrogen 0.6455 -0.2155 Total phosphorous 0.5993 0.3801 Total nitrogen-total phosphorous ratio -0.4319 -0.8445 Chlorophyll-a 0.5843 0.6398 Eigenvalues 7.1805 3.7060 % Variance 42.2385 21.8003 Cumulative Variance 42.2385 64.0388 Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization. Rotation converged in 16 iterations. Spatio-temporal variations of trophic status indices using multivariate analysis Tests between-subject effects for each dependent variable using univariate analysis are shown in Table 6 . Wetland had significant effect (ANOVA, p 0.05) among the six wetlands. On the other hand, season had significant effect (ANOVA, p 0.05) across four seasons. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of TOT TSI , TSI TN , TSI TP, TSI Chla , and TSI STD . Table 6 Univariate tests of significance and powers of Carlson Trophic Status Indices in Lake Tana wetlands Effect Variable Df SS MS F P Wetland Total nitrogen trophic state index 5 2069.3 413.9 2.146 0.08 Total phosphorous trophic state index 5 1847.23 369.45 2.8345 0.02 Chlorophyll-a trophic state index 5 670.4 134.1 4.207 0.00 Secchi disc transparency trophic state index 5 8369.7 1673.9 38.611 0.00 Total trophic state index 5 1338.9 267.8 8.025 0.00 Season Total nitrogen trophic state index 3 4404.8 1468.3 7.613 0.00 Total phosphorous trophic state index 3 1409.24 469.75 3.6040 0.02 Chlorophyll-a trophic state index 3 256.0 85.3 2.678 0.06 Secchi disc transparency trophic state index 3 1824.1 608.0 14.025 0.00 Total trophic state index 3 442.5 147.5 4.420 0.01 Wetland x Season Total nitrogen trophic state index 15 11629.9 775.3 4.020 0.00 Total phosphorous trophic state index 15 4519.97 301.33 2.3119 0.01 Chlorophyll-a trophic state index 15 1267.7 84.5 2.652 0.00 Secchi disc transparency trophic state index 15 4201.4 280.1 6.461 0.00 Total trophic state index 15 1832.0 122.1 3.660 0.00 Total trophic state index (TOT TSI ) The overall mean value of TOT TSI in this study was 64.4 ± 8.7. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of TOT TSI . Values ranged from 43.74 to 62.53 (mean: 50.69 ± 10.30) in GRM during late rainy season and from 77.18 to 86.81 (mean: 81.43 ± 4.91) in MRM during late rainy season. Figure 3.3a shows differences between mean values. Total nitrogen trophic state index (TSI TN ) The overall mean of TSI TN in this study was 94.2 ± 19.6. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of TSI TN . The mean value ranged from 48.67 to 87.68 (mean: 63.15 ± 21.36) in GRM during late rainy season and from 107.68 to 125.76 (mean: 116.86 ± 9.06) in WO during the dry season. Significant differences between combinations of wetlands and season are indicated in Fig. 3.3b). Total phosphorous trophic state index (TSI TP ) The overall mean of TSI TN in this study was 29.7 ± 14.0. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of TSI TP . Values ranged from 8.45 to 12.63 (mean: 4.52 ± 9.44) in WO during early rainy season and from 34.14 to 63.19 (mean: 50.17 ± 14.76) in AV during late rainy season. Figure 3.3c shows differences between mean values. Chlorophyll-a trophic state index (TSI Chla ) The overall mean of TSI TN in this study was 66.3 ± 7.2. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of TSI Chla . TSI Chla . The mean value of TSI Chla was 52.19 ± 0.00 in GRM during late rainy season and from 53.19 to 70.76 (mean: 80.44 ± 80.86) in MRM during the rainy season. Significant differences between combinations of wetlands and season are indicated in Fig. 3.3 d. Secchi disc transparency trophic state index (TSI STD ) The overall mean of TSI TN in this study was 67.6 ± 15.2. There was a significant interaction between wetland and season (ANOVA, p < 0.05) for the mean value of TSI STD . Values ranged from 34.26 to 40.99 (mean: .34.87 ± 5.33) in GRM during dry season and from 90.33 to 116.37 (mean: 100.54 ± 13.86 .) in MRM during late rainy season. Figure 3.3 e shows differences between mean values. Spatio-temporal variations in water quality indices (WQI) of Lake Tana wetlands The overall mean WQI value in this study was 56.88 ± 100.80. There was no a significant interaction between wetland and season (ANOVA, p > 0.05) for the mean value of WQI. Likewise, mean value of WQI did not differ (ANOVA, p > 0.05) among wetlands ranging from 14.76 to 61.69 (mean: 39.76 ± 14.53) in GRM and from 17.43 to 880.19 (mean:117.23 ± 242.27) in MRM. Season had no effect (ANOVA, p > 0.05) on mean value of WQI ranging from 10.63 to 81.31 (mean: 27.68 ± 18.15) in the early rainy season and from 30.72 to 880.19 (mean:97.94 ± 197.21) in the late rainy season. WO, GRM, AV, and ZG were under the category of excellent water while RA is under good water. In contrast, MRM is under the category of poor water (Fig. 3.4). Discussion Evaluation of physico-chemical variables using multivariate analysis The physicochemical parameters of wetlands had clear spatiotemporal patterns. Based on seventeen physicochemical variables, there was a significant interaction between wetland and season for the mean value of dissolved oxygen, pH, Secchi depth a.m., Secchi depth p.m., salinity, nitrate, ammonia, total nitrogen, total phosphorous and chlorophyll a. These findings were corroborated with the findings of earlier studies in different water bodies of Ethiopia. For example, studies on water quality variables in Ethiopia revealed spatial and seasonal variability of physicochemical variables and nutrients in Lake Tana (Getnet, Mengistou, and Warkineh 2020; Tibebe et al. 2019 ; Wondim and Mosa 2015), Lake Beseka of Ethiopian Rift Valley (Umer et al., 2020 ), Lake Ziway (Tibebe et al. 2022 ), and Lake Shalla (Wagaw and Getahun 2021b). Dagne et al., (2021) reported recent trends in some physicochemical features of Abaya and Chamo Lakes. Recent study by Saturday et al., (2021) on spatiotemporal variations in physicochemical water quality parameters of Lake Bunyonyi, Southwestern Uganda showed temporal variations in water quality variables. Assessment of water quality condition and spatiotemporal patterns in selected wetlands of Punjab, India (Singh et al., 2022 ) also revealed that physicochemical parameters of selected wetlands showed spatiotemporal patterns. Assessment of spatiotemporal variations of physical parameters of Lake Tana Temperature is an important factor that regulates the biogeochemical activities in the aquatic environment. Although the mean value of surface water temperature did not differ among wetlands, the mean temperature observed in this study (table 3.8) was higher than the water temperature recorded in Lake Tana (Tibebe et al., 2019 ; Wondim et al., 2016 ; Vijverberg et al., 2009; Wondie et al., 2007), Lake Navaishia Ndungu, 2014 ), and Soda lakes (Melese and Debella 2023 ), Lake Ziway (Abnet and Seyoum 2020) and Eleyele Lake (Ayoade and Ikulala 2007 ). The mean water temperature in this study showed significant variation across seasons with maximum mean values in early rainy season. The higher water temperatures in during the early season could be attributed to high air temperatures (Atobatele and Ugwumba 2008; Vajravelu et al. 2018 ) Seasonal variability of African lake temperatures has been reported by many studies (Damo and Icka 2013) .Umer et al., ( 2020 ) reported maximum temperatures in the rainy and dry season of 35.5°C and 28.4°C., respectively in Beseka in the Rift Valley of Ethiopia (table 3.8). Therefore, higher water temperature detected in WO and RA wetlands. The variations of water temperature among these studies might be explained by the differences in atmospheric temperature of the regions, sampling seasons and heat absorption potential of the lakes. On the other hand, the significant effect of season on RA, GRM and MRM might be associated with the presence of the floating macrophytes and the water hyacinth mat which restrained the increase of the water temperature (Van de Moortel et al. 2010). Seasonal variability of African lake temperatures has been reported by many studies (Damo and Icka 2013). Table 7 Comparison of the physico-chemical parameters and nutrients of Lake Tana with other tropical lakes for nutrients. Lake Temp DO EC pH SD WD TDS SAL NO 2 NO 3 SRP NH 4 TN TP Chl-a Reference Ziway 23 5 404 8.1 0.2 - - - - 0.21 0.006 - - 0.311 - (Tibebe et al., 2022 ) Hawasa 23.5 5–7 846 8.66 0.8-5 - - - - 0.025 0.015 - - 0.034 - (Girma Tilahun and Ahlgren 2010) Chamo 26.3 5–9 1910 8.84 0.1-8 - - - - 0.033 0.118 - - 0.182 - (Girma Tilahun and Ahlgren 2010) Hayq 18.2 1-8.4 910 9 2.7 0.042 0.022 0.058 - (Fetahi 2010 ) Abaya - - 623 8.9 - - - - 0.04 - - - (Wondie & Mengistou, 2006) Langano - - 1810 9.4 - - - -- - - 0.09 - - - - (Wood & Talling, 1988 ) Bishoftu - - 1830 9.2 - - - - - - 0.005-0.1 - - - - (Wood & Talling, 1988 ) Abijata - - 15,800 10.2 - - - - - - 0.005 - - - - (Wood & Talling, 1988 ) Shala - - 19,200 9.9 0–20 - - - - - 0.76 - - - - (Melese & Debella, 2023 ; Wood & Talling, 1988 ) Beseka 28.4–35.5 1407–3321 7.5–10.9 - - - -- - - - - - - - (Umer, Assefa, and Fito 2020 ) Chitu - - 28,600 9.8 - - - - -- - 1.7 - - - - (Wood & Talling, 1988 ) Tana 23.2 6.7 132.8 7.7 0.05–0.8 2.0–2.5 0.02–0.5 0.01 0-3.4 0.003-4.7 0.326-1.6 0.0-6.6 1.1 0.5 6.4–9.9 (Melaku and Yalew 2022 ; Wondim, Mosa, and Alehegn 2016; Vijverberg, Sibbing, and Dejen 2009; Wondie et al. 2007a ) Tana 24.02 6.1 153.8 6.8 0.9 2.2 1.3 0.07 0.02 0.5 0.5 0.2 2.2 0.9 5.1 Present study The overall mean DO concentration in this study (6.08 ± 0.9 mg/L) (table 3.8) is similar to the value reported in Ethiopian lakes (Fetahi, 2010 ; Tilahun & Ahlgren, 2010; Vijverberg et al., 2009; Wondie et al., 2007). In contrast, the overall mean DO concentration in this study was lower than the value reported in Ziway by (Rado 2008 ) (8.72 mg /l ) and in Hawassa by (Abate et al., 2015) (11.2-21.42 mg/l) which is very may be due to a result of hypereutrophication combined with measuring in the afternoon, and Soda lakes by Melese & Debella, ( 2023 ) while Tibebe et al., ( 2022 ) reported lower mean (5 mg/L) DO value in Ziway. The lowest DO values in dry season at WO was attributed to human impacts like fishing and human washing while the lowest DO values in MRM was attributed to its muddy water from agricultural and urban waste runoff (Tibebe et al., 2022 ). The highest values of DO in AV, RA, GRM, and ZG wetlands in the rainy season may be due to high dilution. The mean concentration of DO in the present study was greater than the minimum requirement for the survival of aquatic life (i.e. >5 mg/L) (Johansen et al. 2006 ). Concentrations below 4.0 mg/L adversely affect aquatic life (FEPA, 2003). The value of DO in this study is within the (EU Directive, 1998 ) and (USEPA,2000) permissible limits. According to (EU Directive, 1998 ) and (EPA 2015 ), the standard for DO value for fisheries and aquatic life is between 5.0 to 9.0 mg /L(table 3.8). The EC at the six wetlands ranged from 89 µS/cm to 327 µS/cm with highest values in MRM in the late rainy season (table 3.8). This finding is lower than the values from previous reports in Ethiopian water bodies (Tibebe, Zewge, et al., 2022 ; Umer et al., 2020 ; Fetahi, 2010 ; Tilahun & Ahlgren, 2010; Wood & Talling, 1988 ). The mean value of EC in the present study is lower than that reported by Melese & Debella, ( 2023 ) in Soda lakes. Significant seasonal variation was noted during the study with maximum EC during the rainy season of 327µs/cm. The seasonal variation of the EC of lake Tana may be due to different anthropogenic and naturally induced pollutants such as inorganic and organic pollutants from run-off. According to (EEPA 2003), EC levels above 1000 µs/cm limit the use of water for drinking. The mean value of EC in this study was within the normal ranges mentioned in EU and WHO guidelines. The conductivity of most freshwater bodies ranges from 10–1000 µS /cm (Rice et al. 2012 ), but may exceed 1000 µS /cm, especially in polluted waters, or those receiving large quantities of land run-off (Eaton, Clesceri, and Greenberg 1995 ). The overall mean pH value of the lake water was 6.81 ± 0.82 (table 3.8) which is lower than the previous data reported by Rado ( 2008 ) (8.39), by Girma Tilahun and Ahlgren (2010) (8.65), by Tamire and Mengistou (2013) (8.44), by Tibebe et al., ( 2022 ) (8.1) and, by Melese and Debella ( 2023 ) (> 8.5) respectively. The maximum and lower values of pH in WO and RA can be attributed to the high alkalinity of the lake due to different ions for example, K + , Na + , Ca 2+ , Mg 2+ (Umer, Assefa, and Fito 2020 ). However, no significant seasonal variation was noted during the study. This is not in agreement with the report by Umer et al., ( 2020 ) in lake Beseka with maximum pH values for the rainy and dry season, respectively. The pH value could mainly be controlled by freshwater swamp exudates that regulate the acidity of the water body. A pH range of 6 to 8.5 is normal according to (Rice et al. 2012 ). In general, the pH of lake Tana water is within the acceptable range according to (EPA 2015 ). The mean SD value (a.m. and p.m.) of 0.94 ± 1.29 m (table 3.8) is in agreement with other studies in Ethiopia (Melese & Debella, 2023 ; Wondim et al., 2016 ; Tilahun & Ahlgren, 2010; Vijverberg et al., 2009; Wondie et al., 2007a ). However, the mean value of SD in this study is higher than values of 0.21 m in previous studies in Lake Ziway by Tibebe, et al., ( 2022 ) while it is lower than the mean SD in Hayq by (Fetahi 2010 ) (2.7 m). The minimum SD in MRM may be attributed to the accumulation of sediments from agriculture and urban effluents drained by Megech River. Season had an effect on mean values of SD in WO, ZG and GRM can be mainly attributed to lower level of catchment degradation and siltation in these wetlands. The declining trend in SD is one of the indications that suggest an increasing trend in turbidity of the lake, which can be mainly attributed to catchment degradation and siltation. In general, the SD was found to be at its lowest during the rainy season, and highest during the dry season at all sampling sites, due to the large amount of run-off silt that enters the lake in the wet season. (Ndungu, 2014 ) reported that SD increased during dry season in tropical freshwater water bodies. This may be due to the undisturbed watershed, which keeps the soil system intact during the dry season. The mean value of WD in this study was 2.22 ± 1.36 m (table 3.8) which is in agreement with previous studies in Lake Tana by (Wondim et al., 2016 ; Vijverberg et al., 2009; Wondie et al., 2007). However, the mean value of WD in this study is lower the than the values in previous studies in Shala by Melese & Debella ( 2023 ) of 0-20m and lower than the depth in Ziway by Tibebe, et al., ( 2022 ) (0.21m). The highest mean value of water depth in RA may be attributed with lower load of sediment drained into this wetland. The significant effect of season on the mean value of WD in WO, AV, RA, and ZG may be associated with the complex pattern of water losses and inputs that can cause large daily and seasonal water level fluctuations. Water levels are highest at the end of the main-rainy season and during the post-rainy period, slowly decreasing to a minimum around the end of the dry season (Vijverberg, Sibbing, and Dejen 2009). The lower mean value of water depth in MRM is likely associated with high amount of sediment load from agricultural and urban effluents. Total dissolved solids (TDS) is one of the most important water quality parameters. The mean value of TDS in this study was 0.13 g ± 0.19 g/l (table 3.11). This was higher than the previous report in Lake Tana by Wondim et al., ( 2016 ) (0.02–0.5 g/l). In contrast, the mean value of TDS in this study was lower than Lake Tana, 0.065g/l -0.77 g/L) and in Lake Beseka (Umer et al., 2020 , 0.74–1.598 g/L). When compared with the TDS values of Lake Naivasha in Kenya (values ranged from 1.24 to 2.05 mg/L, with an mean of 1.52) by Ndungu ( 2014 ) and the mean of TDS value of Lake Hawassa in the southern part of Ethiopia (with the highest value of 4.556 mg/L) by Adimasu ( 2015 ), the TDS value of Lake Tana was very much low. The mean value of salinity in this study was 0.07 ± 0.02 ppm (table 3.8) which is in line with results in Lake Tana by (Kassa et al. 2021 ; Kassa and Tibebe 2019; Tibebe et al. 2022 ) with ranges of 0.07 ppm − 0.16 ppm). However, the mean value of salinity in this study is lower than previous reports in Lake Tana by (Wondie et al., 2007) (0.1ppm). The high level of salinity in the in MRM in the rainy season is in agreement with previous reports in Soda lakes of Ethiopia by Melese & Debella, ( 2023 ), by Wagaw et al., (2021) in Lake Shala, and by Kihwele et al., ( 2015 ) in Manyara Lake. Alkalinity levels were high during the post-rainy season in Lakes Beseka and Chittu, and during the dry season in Lake Shala (Melese and Debella 2023 ). Overall, the salinity levels were still low and within normal ranges of FEPA and WHO guidelines (500 mg/L). Spatio-temporal variations of nutrients in Lake Tana The mean nitrite value of 0.02 ± 0.09 mg/L (table 3.8) was higher than values reported in previous studies. For instance, (Beneberu and Mengistou 2009) and (Tamire and Mengistou 2013) reported 0.06 and 0.01 mg/L nitrite, respectively. However, the mean value of nitrite in the present study was lower than the reports for Lake Tana by Wondim, ( 2016 ) (0.2mg/l), Shitaw et al., ( 2018 ), (0.418), and in Lake Adele of eastern Ethiopia (30.67 mg/L), and by Tibebe, Zewge, et al., ( 2022 ) (0.5 mg/L). Relatively higher nitrite concentrations were measured in MRM in the late rainy season which could be due to the application of high amount of fertilizer for crop production in the adjacent farming lands of Megech River catchment. The presence of nitrite in water may mainly result from excessive application of fertilizers. The overall mean nitrite levels were still low and above the normal ranges of FEPA and USEPA (0.001 mg/L). The mean nitrate value found in this study (0.46 ± 0.25 mg/L) was higher than values of 0.21 0.17, 0.003, and 0.06 mg/L reported by (Tamire and Mengistou 2013; Tibebe et al. 2022 ; G. Tilahun 1988 ; Girma Tilahun and Ahlgren 2010), respectively (table 3.8). The high concentration of nitrate in ZG and MRM in the rainy and late rainy seasons is probably because of nutrient enrichment of the littoral zone of the lake from agriculture effluents sources from the catchment area. The observed nitrate concentration in this study is within normal ranges of FEPA and USEPA (50 mg/L). The mean SRP concentration (0.54 ± 0.31 mg/L) (table 3.8) was higher than in previous reports in fresh water lakes of Ethiopia (Tibebe, Zewge, et al. 2022 ; Tibebe et al. 2019 ; Tamire and Mengistou 2013; Wondie and Mengistou 2006; Gebre-Mariam and Desta 2002; Jeppesen et al. 2000 ; Kebede and Ahlgren 1994; Tilahun 1988 ) was higher than that of the pervious reported which was 0.016, 0.01, 0.059 and 0.029, 0.06 ,and 0.326 mg/L respectively. The high value of SRP in RA wetland during early rainy season may be attributed to organic and non-organic discharge of water from domestic sources around the wetland vicinity. The measured concentration is also beyond the range of its threshold (0.05 to 0.1mg/L ) as a nutrient for natural waters (Jeppesen et al., 2000 ; Wondie & Mengistou, 2006). The mean SRP concentration (0.660 ± 0.084 mg/L ) (table 3.8) in the late rainy season was higher than the values reported by (Gebre-Mariam and Desta 2002; Kebede, Mariam, and Ahlgren 1994; Tamire and Mengistou 2013; Girma Tilahun and Ahlgren 2010) for other Ethiopian Lakes (0.016, 0.035, 0.01 and 0.029 mg/L). However, this value is lower than recently reported by (Melaku and Yalew 2022 ) (1.6 mg/L). The maximum allowable concentration of phosphorous which should be permissible in environmental waters is 1 mg/L (USEPA 2000 ; WHO, 2009 ). The high value of phosphate in RA during the late rainy season may be due to excessive use of chemicals like detergents and waste from car wash which organic and inorganic pollutants are released and discharged in water from domestic sources into the lake. The mean concentration of total ammonia (0.17 ± 0.21 mg/L ) (table 3.11) is similar to relatively recent reports by (Tibebe, et al., 2022 ) (0.121 mg/l ) (Tilahun 1988 ) (0.111 mg/l ), and (Tamire and Mengistou 2013) (0.143 mg/l) but higher than reported by for example, by (Kebede et al., 1994) (0.036 mg/l). However, the mean value of ammonia in this study was lower than in reports for Lake Tana Wondim, ( 2016 ) (0.0-6.6 mg/l) and (Melese and Debella 2023 ) ammonium nitrogen had the highest value (56.39-161.93) in Lake Arenguade and the lowest and in Lake Beseka. When compared with the mean total ammonia values of Lake Naivasha in Kenya (the mean varied between 0.045 to 0.085 mg/L with a mean of 0.063 mg/l by Ndungu ( 2014 ), the total ammonia value of Lake Tana was found to be higher. The high value of total ammonia in AV and GRM during late rainy season may be attributed to hospital effluent discharged into AV, and to agricultural effluent discharged into GRM, respectively. The mean TN concentration of 2.23 mg/L (table 3.8) was lower than that reported in lakes of Ethiopai (Tibebe et al., 2018 ; Tibebe, et al., 2022 ). The relatively high concentration of TN in AV and MRM in the late rainy season could be due to chemicals from the hospital in AV and application of fertilizers on crop land and decomposition of organic matters washed off into MRM. Season did not influence TN values, which is not in line with reports by (Tibebe, et al., 2018 ; Tibebe, et al., 2022 ). The agricultural office report indicates that the application of diammonium phosphate (DAP) and urea fertilizer for rain-fed and irrigation agriculture is increasing in farm lands adjacent to Lake Tana (Dersseh et al. ( 2019 ). The observed TN concentration value in this study is within normal ranges of FEPA and USEPA (1.1 mg/l). The mean TP value of lake water was (0.89 ± 0.98 mg/L) (table 3.8) which is higher than in reports in fresh water lakes of Ethiopia ( Tibebe, et al., 2022 ),by Melaku and Yalew 2022 ), Kebede et al., 1994), and Tilahun 1988 ), which were 0.311, 0.48, 0.069 and 0.219 mg/L, respectively. A higher TP concentration was also measured in this study as compared to that of other Ethiopian rift valley lakes like Lake Awasa and Chamo ( Tilahun 1988 ). In contrast, the mean value of TP in this study is lower the values in Soda Lakes of Ethiopia (Melese and Debella 2023 ) (0.75–2.41 mg/L). However, the mean value of TP in this study is lower than in the report by (Dersseh et al. 2020) (0.01–1.8 mg/L) in Lake Tana. The increasing trend in TP is probably due to nutrient enrichment of the lake from agricultural activities around the lake watershed (Ayele & Atlabachew, 2021; Goshu & Aynalem, 2017; Wondie, 2018 ). The observed TP concentration value in this study is outside the normal ranges of FEPA and USEPA (0.05 to 0.1 mg/L). The mean value of Chl-a in this study was 5.15 ± 5.23 mg/L which was lower than the report by in Lake Hayq by Aragaw et al., ( 2022 ) (3.5mg/L), in Lake Tana by Melaku and Yalew ( 2022 ), (0.99 mg/L), by Mucheye et al. ( 2022 ) (2.52 mg/L), by Kahsay et al. ( 2022 ) (1.0–4.0 mg/L), by Tibebe et al. ( 2019 ) (8.0 mg/L) and by Wondie & Mengistu, (2017) (0.03-13 mg/L) by Vijverberg, Sibbing, and Dejen (2009), (0.64 mg/L), by Wondie et al., (2007) (0.61 mg/L) and by Dejen et al. ( 2004 ) (0.64 mg/L) (table 3.8). The highest value of Chl-a concentration in MRM in the late rainy may be attributed to the influx of sediment and nutrient load from the upper catchment. Lake Tana water Chlorophyll–a levels were above the permissible level (0.3mg/L) (Trodden and O’Boyle 2020 ). The TN:TP ratio in lakes and reservoirs is a key element as it gives an idea of which of these nutrients are either in excess or limiting to growth, and it was used to estimate the nutrient limitation in the lake. According to (Smith, 1962 ) blue-green algae (cyanobacteria) had a capacity to dominate in the lake section when the TN:TP ratio was less than 29 and it tends to be rare in the lake when TN:TP > 29. The mean value of the TN:TP ratio was 6.1 ± 10.6, which was lower than the report in Lake Ziway by (Tibebe et al., 2018 ) (48:1), in Lake Hawass by (Lencha, et al., 2021 ) (31:1). Even though, there was no significant difference in the TN:TP ratio among the six wetlands and among the four seasons, Lake Tana wetlands are hypereutrophic lakes (Downing and McCauley 1992). The underlying reasons for such spatial and temporal variations in the water quality parameters are likely unsustainable anthropogenic activities such as agricultural activities, urbanization, and discharge of waste into the lake. Most of the water parameters in the disturbed wetlands revealed lower qualities in the rainy and late rainy season, which can be associated with a high influx of effluents from agricultural lands. Significant differences were recorded in the concentration of nutrients (nitrate, ammonia, and total nitrogen) between seasons. The higher level of nitrate and total nitrogen in the dry season may be attributed to a lower dilution effect in the dry season. Similar reports on the seasonal distribution of nitrate and nitrite levels in the wetlands of Nigeria were reported by Nwankwoala et al. (2010) and Udom et al. ( 2018 ). The higher phosphate and ammonia levels recorded during the late rainy season could be attributed to additional discharge from the catchment areas, such as sewage discharge from Bahir Dar and Gondar towns, as well as runoff from the surrounding farmlands due to heavy rainfall. The seasonal influx of allochthonous organic and inorganic materials during the rainy and late rainy seasons is characteristic in most tropical wetlands (Angello, Tränckner, and Behailu 2020; Bagalwa et al. 2021 ; Nwankwoala, Pabon, and Amadi 2010; Saturday et al. 2021; Soro et al. 2020 ). Generally, a pattern of low mean concentrations of SRP, ammonia, nitrite, nitrate, TN, TP in the dry season have higher means in the rainy and late rainy seasons. This indicates point source pollution for these parameters, which might be associated with industrial effluents, human interference, and agricultural and urban effluents (Tibebe, et al., 2022 ). During dry season both decreased precipitation and increased agricultural crop lands contributed to lower flows of those nutrients, however, SRP, nitrite, nitrate, TN, and TP all had higher concentrations during rainy and late rainy sesaons. Similarly (Wondie & Mengistou, 2006) noted that nutrients that have a higher concentration during dry season than in the wet season tend to come from point sources whose supply is constant, whereas the inverse pattern can be attributed to non-point sources that are mobilized by high run-off during wet periods. The analysis of water quality data in Lake Tana revealed that there has been a progressive increase in the concentration of various parameters like EC, TDS, nitrate, ammonia, total nitrogen, and total phosphorous when compared to earlier records ((Dersseh et al., 2022 ; Vijverberg et al., 2009; Wondie et al., 2007b; Wondim, 2016 ) temperature increased from 23.2 0 C to 24.02 0 C, EC increased from 132.8 µS/l to 153.8 µS/l, TDS increased from 0.3 g/l to 1.3 g/l, TN increased from 1.1 mg/l to 2.2 mg/l and TP increased from 0.5 mg/ to 0.9 mg/l between 2009 to 2020. Spatial diversity and site grouping based on water quality characteristics The six study wetlands / clusters of least polluted (WO and RA), slightly polluted (MRM), moderately polluted (GRM and ZG) and highly polluted (AV) were investigated for their water quality physicochemical characteristics following different multivariate analyses. The groups were first ranked using hierarchical clustering based on the similarity of their physicochemical characteristics, which was then confirmed by PCA and FA. The relatively highly polluted cluster comprised one wetland, AV, and correlated highly with electrical conductivity, salinity, ammonia, total nitrogen, total phosphorous and chlorophyll-a on the PCA plot. This cluster was confirmed following the varimax rotation factor analysis and was linked to the positive loading of electrical conductivity, salinity, TN, TN and in Chl-a on Factor one. This registered higher water conductivity, salinity, ammonia, total nitrogen, total phosphorous and chlorophyl-a, implying that there was potentially higher photosynthetic activity and algal growth. High water conductivity, salinity, nitrite, total nitrogen, total phosphorous and chlorophyll-a point to the fact that the nutrients in the highly polluted site could be mainly attributed to non-point-sources effluents from urban waste from hospital in Bahir Dar city. This concurs with the fact that municipal and industrial discharges can contribute ions to receiving waters, increasing the conductivity and nutrients of the receiving waters (Moges et al. 2017 ; Wondim, Mosa, and Alehegn 2016; Zelalem and Prokin 2017; Kassa and Tibebe 2019; Engdaw, Hein, and Beneberu 2022 ). These studies also reported that specific physical, chemical and biological parameters were used to detect pollution sources (Goshu, Byamukama, et al., 2010; Aragaw, 2021 ; Mushi et al., 2021 ). Overall, these results reflect the high dissolved nutrients and organic pollution originating from the different catchment activities, implying that Factor one originates from industrial and municipal anthropogenic activities. This concurs with other research that traced the causes of pollution during different study periods in Lake Tana (Mucheye, Yitaferu, and Zenebe 2018 ; Zimale et al. 2018 ; Kebedew et al. 2020 ; Ayele and Atlabachew 2021b; Dersseh et al. 2022 ). The slightly polluted cluster, Cluster 3, comprised one wetland, MRM, was with electrical conductivity, salinity, nitrite, nitrate, total nitrogen, total phosphorous and chlorophyll-a on the PCA plot. This cluster was confirmed following the varimax rotation factor analysis and was linked to the positive loading of electrical conductivity, salinity, nitrite, nitrate, TN, TN and in Chl-a on Factor one. The high-value water conductivity, salinity, nitrite, nitrate, total nitrogen, total phosphorous and chlorophyll-a points to the fact that the nutrients in the highly polluted site could be mainly attributed to non-point-sources from agriculture and urban effluent from Gondar city. The catchment in MRM is dominated by subsistence agriculture and urban effluent from Gondar town, and this may contribute to the organic matter in the lake. This concurs with many studies in which it was found that catchment agriculture and urban effluent contributed more to water quality deterioration than municipal and industrial effluent (Setegn et al. 2009 ; Taffese et al. 2014; Assefa et al. 2020 ; Kebedew et al. 2020 ; Engdaw, Hein, and Beneberu 2022 ). As with the highly polluted wetland findings, high nutrient levels have also been observed in studies on other lakes in the tropics (Namugize and Nsengimana 2010; Naigaga 2012 ; Samanta et al. 2015 ; Assefa et al. 2020 ; Obubu et al. 2022 ). The Secchi depth a.m. and p.m., total depth and ammonia in MRM were relatively low compared with other wetlands studies. This could be attributed to the fact that the wetland contains high amounts of particles, which could be from algae or eroded sediment from agriculture farmlands in the catchment (Setegn et al., 2009 ; Gebremedhin et al., 2018 ; Wondie, 2018 ; Zimale et al., 2018 ; Kebedew et al., 2020 ; Engdaw et al., 2022 ). These authors observed relatively high sediments in bays which received any effluent from catchments. However, this study was limited to shallow coastline bays (coastal wetland areas) and could not confirm how nutrient levels compare with those in open waters. The moderately polluted cluster, Clusters 2 i.e. GRM and ZG. These clusters could not be well explained by PCA but were confirmed following the varimax rotation factor analysis under Factor three. Factors 1 and 2 had the same positive and negative variables as Factor 3, but with low loadings of pH, salinity, SRP and nitrate which explains and confirms the moderate pollution in these sampling locations. This difference was attributed to the dilution effect of the sewage effluent as it moves off the shoreline. This finding is in agreement with studies by (Ademe 2014 ; Goshu et al. 2010 ; Wondim, Mosa, and Alehegn 2016), who pointed to a stronger eutrophication effect in the inshore areas of Lake Tana wetlands. The water quality in other highland lakes of Ethiopia have shown that sewage discharge reduces water quality, depending on the degree of dilution, the degree of treatment of the original material, their composition and the response of the ecosystem (Assefa et al. 2020 ; Dersseh et al. 2022 ). In the least polluted cluster, Cluster four, the sampling locations under this group included WO and RA. This cluster correlated highly with high temperature, DO, and WD on the PCA plot, and these were confirmed by the positive loadings of these variables following the varimax rotation factor analysis under Factor two. The higher and stable values of temperature and the higher values of secchi depth a.m. and secchi depth p.m. in WO and RA were higher than the values for the rest of the wetlands studied. The higher temperature could be attributed to the fact that WO and RA did not experience wastewater cooling effects as there is no wastewater inlet, and the higher secchi depth a.m. and p.m. may be attributed to low amounts of sediment particles. This is in line with previous finding by various researchers (Namugize and Nsengimana 2010; Moges et al. 2017 ; Dallas 2018 ; Mucheye, Yitaferu, and Zenebe 2018 ). Overall, the study showed that AV wetland, which receives urban and domestic wastewater discharges, was more polluted than the rest of the sites, emphasizing the impact of discharges to water quality. This is in line with findings by many researchers who point to urban effluents as an important underlying factor responsible for surface water quality deterioration in Lake Tana (Ayele and Atlabachew 2021b; Engdaw, Hein, and Beneberu 2022 ; Goshu et al. 2020; Kebedew et al. 2020 ; Mucheye, Yitaferu, and Zenebe 2018 ; Setegn et al. 2009 ; Wondim, Mosa, and Alehegn 2016; Zimale et al. 2018 ). Results from the cluster analysis, principal component analysis and factor analysis complemented each other and led to the establishment of the six clusters. This synchronization in results concurs with previous studies on water quality, which have all recommended the application of different multivariate statistical techniques when dealing with environmental data (Panda et al. 2006 ; Landau and Chis Ster 2010 ; Varol et al. 2012 ; Liu, Ren, and Cai 2020). Spatio-temporal status in trophic status of Lake Tana using Carlson trophic state index model Wetland, season and the interaction of wetland aby season had effects on TSI of Lake Tana with a higher power of test (54%) for the interaction of wetland by season than wetland or season This may be attributed to each wetland receiving different loads of effluents from agriculture and urban. The average TOT TSI , TSI TN , TSI TP , TSI STD and TSI Chl−a, values were 64.4 ± 8.7, 94.2 ± 19.6, 29.7 ± 14.0, 66.3 ± 7.2 and 67.6 ± 15.2, respectively. TOT TSI ranked WO, ZG, GRM, AV and RA under the category of the eutrophic level while it ranked MRM under category of hypereutrophic level. The findings of this study were different from that of (Lencha, Tränckner, and Dananto 2021; Zemed, Beshah, and Reddythota 2021) whose finding was hypertrophic as the assessment result depended only on the Secchi depth and also (Worako, 2015) who found an average TSI of 72.6 (hypereutrophic) for Lake Hawassa. Eutrophication causes the impairment of activities, discomfort and visual unpleasantness that hamper the recreational use of water severely (Breen, Curtis, and Hynes 2018 ). Melaku & Yalew, ( 2022 ) reported trophic state index value of Lake Tana according to the three parameters of trophic state (TSIC) Lake Tana was eutrophic Lake. The overall average value of the Trophic State Index (TSI) of Lake Tana was 69.77. This TSI value, based on Carlson's trophic state classification criteria (Kratzer and Brezonik 1981 ; Jarosiewicz, Ficek, and Zapadka 2011), suggests that Lake Tana is eutrophic during the early rainy, rainy and late rainy seasons. When comparing this TSI value to the OECD's standard (Vollenweider and Kerekes 1982 ), it can be seen that Lake Tana is in an hypereutrophic state. Similarly, Tibebe et al., ( 2019 ), Teshale, ( 2003 ) and Wondie et al., (2007), reported that the lake is above the eutrophic threshold values, placing it in mesotrophic and oligotrophic states, respectively. This could be due to the current anthropomorphic activities around lake, as well as the seasons of study. The trophic state index of SD exhibited a higher trophic state, probably due to water transparency being the variable most affected by rainfall variations (Klippel, Macêdo, and Branco 2020). MRM, which receives municipal, industrial, and agricultural effluents, may be considered hypertrophic. This concurs with (Wondim, Mosa, and Alehegn 2016; Dersseh et al. 2020; Enyew, Assefa, and Gezie 2020; Ayele and Atlabachew 2021b; Damtie, Mengistu, and Meshesha 2021; Dersseh et al. 2022 ), who studied three shallow bays along the Lake Tana shoreline and reported the highest eutrophication in the agriculture-impacted bays due to invasion of the wetlands and inshore lake by water hyacinth. The findings also agree with studies carried out in other African Lakes, for example, in the Lake Kyoga basin of Uganda by Obubu et al. ( 2022 ), Lake Victoria of Kenya by Otieno et al. ( 2022 ), in Lake Victoria of Uganda by Wanda et al. (2015), and in the African Great Lakes (Plisnier et al. 2022 ). The presence of water hyacinth ( Eichhornia crassipes ) in the agricultural-impacted wetlands (GRM and MRM) may be the cause of water quality changes, with higher levels of EC, pH, SRP, TP, NH 4 + and Chl-a in the impacted wetlands, compared to the least impacted wetlands. Similarly, the water quality values across seasons showed lower values of water transparency and higher values of NO 3, SRP, NH 4 + , TP and Chl-a in the early rainy, rainy and late rainy seasons compared to the dry seasons in the agricultural-impacted wetlands (GRM and MRM), which are infested with water hyacinth. This finding is in line with the report by (Mucheye et al. 2022 ), who found seasonal variation in the invasive water hyacinth as well as changes in water quality values (Chl-a and TDS ) at the end of the main rainy season. Several reports have indicated that the plausible reason for infestation of the lake by water hyacinth could be changes in the physicochemical characteristics of the lake (Wubie, Assen, and Nicolau 2016; Gebremedhin et al. 2018 ; Damtie and Mengistu 2022 ). In addition, Dersseh et al., ( 2022 ) demonstrated that water hyacinths appeared in Lake Tana around 2010 after the nitrogen assimilation capacity of the lake was exceeded. This trend was seen mainly in the northeastern part of Lake Tana during rainy seasons, although nutrient concentrations are suitable for growing water hyacinths throughout the lake. The area covered by water hyacinth has increased significantly and positively correlates with the seasonal lake level fluctuation (E. Asmare 2017 ; T. Asmare et al. 2020 ; Dersseh et al. 2019 , 2020). Similarly, Kipng’eno ( 2019 ) reported the spread of water hyacinth in Lake Victoria using satellite imagery, and demonstrated that growth in urban areas with high effluent was proportional to the amount of spread in the water hyacinth. Spatio-temporal variations in water quality indices (WQI) of Lake Tana wetlands Wetland, season and the interaction of wetland aby season had effects on WQI of Lake Tana with higher power of test (73%) for interaction of wetland by season. The overall mean WQI value was 57.7 ± 101.1 was lower than the mean WQI in Lake Tinishu Abaya by Enawgaw & Lemma, (188–222), In Lake Hawassa by (Zemed, Beshah, and Reddythota 2021) (120.06–228.29), by (Ghebremedhin and Gupta 2023 ) in Lake Chamo (102.9-359.5). However, a recent report for Ribb reservior by Mekonnen et al., ( 2023 ) (65.42 -101.96) is comparable with this finding. The high mean value WQI in WO, RA, GRM and MRM during rainy and late rainy season is line with the report by (Teshome et al. 2015 ) in Hawssa. Therefore, the cumulative result of WQI for drinking, aquatic life and recreational uses showed that the environmental situation has become worse in the last few decades, Hence, Lake Tana watershed has been polluted and frequent monitoring of the watershed is necessary for proper management. In conclusion, ranking of the pollution status of wetlands of Lake Tana using different approaches in this study, multivariate statistics, Carlson’s trophic state index, and water quality index model suggest that some wetlands did not fit completely in the same category The current study on water quality variables of lake Tana recommends that top priority should be given to regular water quality monitoring, in conjunction with biodiversity and fish health assessment (as indicated in following chapters), and cleaner production technologies should be adopted to improve water quality in water bodies of Ethiopia. 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Wondie, Ayalew, Seyoum Mengistu, Jacobus Vijverberg, and Eshete Dejen. 2007a. “Seasonal Variation in Primary Production of a Large High Altitude Tropical Lake (Lake Tana, Ethiopia): Effects of Nutrient Availability and Water Transparency.” Aquatic Ecology 41(2): 195–207. doi:10.1007/s10452-007-9080-8. ———. 2007b. “Seasonal Variation in Primary Production of a Large High Altitude Tropical Lake (Lake Tana, Ethiopia): Effects of Nutrient Availability and Water Transparency.” Aquatic Ecology 41(2): 195–207. Wondim, Yirga Kebede. 2016. “Water Quality Status of Lake Tana, Ethiopia.” Civil and Environmental Research . Wondim, Yirga Kebede, and Hassen Muhabaw Mosa. 2015. “Spatial Variation of Sediment Physicochemical Characteristics of Lake Tana, Ethiopia.” : 17. Wondim, Yirga Kebede, Hassen Muhabaw Mosa, and Manalebesh Asmara Alehegn. 2016. “Physico-Chemical Water Quality Assessment of Gilgel Abay River in the Lake Tana Basin, Ethiopia.” Civil and Environmental Research : 9. Wood, R. B., and J. F. Talling. 1988. “Chemical and Algal Relationships in a Salinity Series of Ethiopian Inland Waters.” In Saline Lakes , Springer, 29–67. Wubie, Mesfin Anteneh, Mohammed Assen, and Melanie D. Nicolau. 2016. “Patterns, Causes and Consequences of Land Use/Cover Dynamics in the Gumara Watershed of Lake Tana Basin, Northwestern Ethiopia.” Environmental Systems Research 5(1): 1–12. Yidana, Sandow Mark, and Adadow Yidana. 2010. “Assessing Water Quality Using Water Quality Index and Multivariate Analysis.” Environmental Earth Sciences 59: 1461–73. Zelalem, Wondie, and Alexander Prokin. 2017. “PHYSICO-CHEMICAL CHARACTERISTICS AND MACROZOOBENTHOS ABUNDANCE IN THE GULF OF LAKE TANA.” : 29. Zemed, Menberu, Mogesse Beshah, and Daniel Reddythota. 2021. “Evaluation of Water Quality and Eutrophication Status of Hawassa Lake Based on Different Water Quality Indices.” Applied Water Science (3). Zhang, Honglu et al. 2022. “Evolution of Habitat Quality and Analysis of Influencing Factors in the Yellow River Delta Wetland from 1986 to 2020.” Frontiers in Ecology and Evolution 10: 1075914. Zimale, Fasikaw A. et al. 2018. “Budgeting Suspended Sediment Fluxes in Tropical Monsoonal Watersheds with Limited Data: The Lake Tana Basin.” Journal of Hydrology and Hydromechanics 66(1): 65–78. doi:10.1515/johh-2017-0039. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3993010","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275299044,"identity":"6642acf6-c371-4f41-bac5-f8054c7637b2","order_by":0,"name":"Hailu Mazengia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBACAwbGxgNwXgKDTQIDiMuDX0sDspY0hBZc2gyA+AAS/zBhLebshxsO8zBsk5dv7z384kHN+Ty+GwmMD962McjZ49Bi2ZMI0nLbsLHnXJpFwrHbxZI3EpgN57YxGON02AGIFsZmiRwzgwS224kbbiSwSfO2MST24NJy/iFYi32b/Bugln/nQFrYfwO11OPUcgNiS2KPBI/xg8S2A2BbmIFaEnA67MbDhoNzDG4nz+DJMWNI7EsuljzzsFlyzjkJw54DuByW/vDBm4rbtvPbzxh//PHNLo/vePLBD2/KbOTZG3BYA9EIJtkkIDxGkFoJfOrhgPkDUcpGwSgYBaNgxAEAxAxjj7Yj8R0AAAAASUVORK5CYII=","orcid":"","institution":"Bahir Dar University","correspondingAuthor":true,"prefix":"","firstName":"Hailu","middleName":"","lastName":"Mazengia","suffix":""},{"id":275299045,"identity":"f81defd2-b9c5-47d9-b7fd-a96082c02049","order_by":1,"name":"Horst Kaiser","email":"","orcid":"","institution":"Rhdoes University","correspondingAuthor":false,"prefix":"","firstName":"Horst","middleName":"","lastName":"Kaiser","suffix":""},{"id":275299046,"identity":"d68fad05-d648-4688-9cc9-f7d8d2fc4740","order_by":2,"name":"Minwuyelet Mengist","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Minwuyelet","middleName":"","lastName":"Mengist","suffix":""}],"badges":[],"createdAt":"2024-02-27 06:03:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3993010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3993010/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51801216,"identity":"9b8ae196-9620-4d9a-a187-73ce6b174a92","added_by":"auto","created_at":"2024-02-29 09:11:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":451345,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area and Location of the study wetlands.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3993010/v1/e99e26e35e2b810c9f2be25e.png"},{"id":51801232,"identity":"5a9b69d7-2f71-41e3-ba83-152d2c09e229","added_by":"auto","created_at":"2024-02-29 09:11:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":232602,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1 Spatio-temporal variability plot for sixteen physicochemical properties in the wetlands of Lake Tana across four seasons. WO-Wonjeta, ZG-Zewdie Girar, GRM-Gumara river mouth, MRM-Megech river mouth, AV-Avaj, RA-Ras Abbay, D-Dry. ER-Early rainy-rainy, LR-Late rainy\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3993010/v1/1a39d76d32b70c09e9cfa346.png"},{"id":51801222,"identity":"75202794-076f-46b6-ab19-7a55cb380b37","added_by":"auto","created_at":"2024-02-29 09:11:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14574,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2. Hierarchical Cluster Analysis Dendrogram showing four physicochemical clusters in the wetlands of Lake Tana. Each cluster indicates sites with similar physicochemical characteristics. Homogeneity within clusters was based on Euclidean distance and the heterogeneity between clusters was based on Ward’s.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3993010/v1/24bd3de840426fa398531c5d.png"},{"id":51801212,"identity":"218f68ab-5367-4ae4-90c4-7eb41f1c0e86","added_by":"auto","created_at":"2024-02-29 09:11:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21897,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3. PCA plot correlating sampling wetland scores in Lake Tana with water quality vectors of the 16 physicochemical variables for plot component one (X-axis) and plot component two (Y-axis). Note the grouping of the four clusters.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3993010/v1/e71d53e25f28fe3488a65e36.png"},{"id":51801219,"identity":"db903062-6ecd-400b-9bf0-20a914cae007","added_by":"auto","created_at":"2024-02-29 09:11:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102473,"visible":true,"origin":"","legend":"\u003cp\u003eFigure\u003cem\u003e \u003c/em\u003e4. Spatio-temporal variability plot of Trophic State Indices in the wetlands of Lake Tana. Were, WO-Wonjeta, AV-Avai, RA-Ras Abbay, GRM-Gumara river mouth, MRM-Megech river mouth,ZG-Zedie Girar, D-Dry, ER-Early rainy, R-Rainy, LR-Late rainy. Standard TSI criteria: \u0026lt; 40 = Oligotrophic, 40 – 50 = Mesotrophic, 50 – 70 = Eutrophic, \u0026gt; 70 = Hypereutrophic. N = 24.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3993010/v1/d849b25762230a5424446d7a.png"},{"id":51801218,"identity":"cd55b89a-f935-4347-9367-245ad87502aa","added_by":"auto","created_at":"2024-02-29 09:11:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23684,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4. Spatio-temporal variability plot of Water Quality Index (WQI) in the wetlands of Lake Tana. Where, WO-Wonjeta, AV-Avai, RA-Ras Abbay, GRM-Gumara river mouth, MRM-Megech river mouth,ZG-Zedie Girar, D-Dry, ER-Early rainy, R-Rainy, LR-Late rainy.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3993010/v1/fb313a9c26a642ffcbbcb68b.png"},{"id":53449623,"identity":"abb8b831-3807-4415-baf5-de90bf17dfdc","added_by":"auto","created_at":"2024-03-26 06:20:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1382826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3993010/v1/abd0c0b9-050f-4f46-8af0-44d50050d11e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physical and chemical water quality characteristics in six wetlands of Lake Tana, Ethiopia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe physical and chemical characteristics of a water body are potentially limiting factors in the biological productivity of an aquatic ecosystem (Bhateria and Jain 2016; Elnaggar and El-Alfy \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Charoula et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The maintenance of a healthy aquatic ecosystem is dependent on the physicochemical parameters of the water (Cairns, McCormick, and Niederlehner \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Wepener \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Arenas-S\u0026aacute;nchez, Rico, and Vighi 2016; Riddell et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In aquatic environments, different physicochemical factors may produce various biochemical and physiological parameters that can be used as biomarkers (Abalaka \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ha et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Many environmental factors affect ecological biodiversity, and consequently the use of bioindicators and biomarkers, and it is thus important that the physicochemical and ecological conditions of the ecosystem be characterized in such studies (Bartell \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wepener \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Naigaga \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Abalaka \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lomartire, Marques et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marinović et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA review on eutrophication and nutrient release in aquatic environments of Sub-Saharan Africa reported that wastewaters from sewage and industries are often discharged into the environment untreated, and that this is becoming a major source of nutrients, which cause eutrophication of surface water bodies (Naigaga \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This is a particular problem in Lake Tana (Vijverberg, Sibbing, and Dejen 2009; Karlberg et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dejen, Anteneh, and Vijverberg 2017; Goshu et al. 2017; Goshu and Aynalem 2017; Akhtar et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMuch of Lake Tana\u0026rsquo;s shoreline is covered by extensive wetlands, often dominated by dense papyrus stands that extend out over the lake waters, and these wetlands play a role in the physical, chemical, and biological conditions of the inshore waters (Wepener \u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Assefa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The water quality status of the shoreline waters is therefore an important indicator of the environmental status of the whole lake. As water quality deteriorates, ecosystem services may be lost, and organisms will begin to suffer. For example, the fluctuation of the physical and chemical characteristics of a lake have an impact on the diversity and abundance of organisms such as macroinvertebrates, and fish assemblage composition (Czerniawska-Kusza, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Moog, 2015; Aragaw, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn-flowing rivers carry heavy loads of suspended silt into the lake, thereby increasing the turbidity of the lake water and reducing primary production (Gebresllassie et al., 2014; Dejen et al., 2017; Gebremedhin et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e;Aragaw, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is, therefore, important to have reliable information on trends of water quality as a prerequisite for planning the prevention and control of the lake pollution and the sustainability of an effective water management program (Zelalem and Prokin 2017; Kassa and Tibebe 2019). The steady increase in human population size puts increasing pressure on catchment resources required for settlement, agriculture and urban and industrial infrastructure development, which in turn exacerbates water pollution problems as more wastewater is discharged into the lake (Gebresllassie et al., 2014; Karlberg et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dejen et al., 2017; Gebremedhin et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The use of bioindicators and biomarkers to evaluate environmental quality therefore necessitates that the ecosystem be characterized in terms of physicochemical as well as ecological characteristics. Given the eutrophication pollution challenge in Lake Tana and its urban wetlands, physicochemical parameters including nutrient levels were assessed in the present study.\u003c/p\u003e \u003cp\u003eThis study sites were characterized according to sixteen commonly recorded variables that have been used to assess Lake Tana\u0026rsquo;s waters (Aragaw et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kassa and Tibebe 2019; Wondie \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zelalem and Prokin 2017). The results will be used in the proceeding chapters to relate the biological indicators and biomarkers to water quality. The main objective of this the study was to explore how different methods describe the \u0026ldquo;health\u0026rdquo; of the wetlands and how different approaches relate to each other.\u003c/p\u003e\n\u003ch3\u003eDescription of the study area\u003c/h3\u003e\n\u003cp\u003e \u003c/p\u003e \u003cp\u003eData and samples for this study were collected in the bays of six wetlands located in four administrative zones under the same agroecology along the shoreline of Lake Tana. The wetland ecotones include Wonjeta, Zewdie Girar, Gumara River mouth, Megech River mouth, Avaj and Ras Abbay. Two wetlands of Avaj and Ras Abbay, are located in Bahir Dar municipality, Amhara Regional Staes\u0026rsquo;s capital and business center. Avaj is situated approximately 3 km North of Bahir Dar city. Ras Abbay wetland is located approximately 5 km northeast of Bahir Dar city. Gumara River mouth wetland is located Dera district. Dera is a rural district located approximately 40 km northeast of Bahir Dar city. Megech River mouth is situated in a rural district Dembia, approximately 90 km North of Bahir Dar city and approximately 40 km South of Gondar city. Wonjeta wetland is situated northwest of Bahir Dar city and it is approximately 5 km from Bahir Dar city. Zewdie Girar is located North Achefer district. North Achefer is a rural district approximately 50 km west of Bahir Dar city. Gumara river mouth wetland is under pressure of catchment agriculture thus receives agricultural effluent from adjacent forming lands (Fig.\u0026nbsp;1.).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eMeasurement of physicochemical variables\u003c/p\u003e \u003cp\u003ePhysicochemical variables measured in this study were temperature, dissolved oxygen, conductivity, pH, secchi disk depth a.m., and p.m., total dissolved substance, salinity, nitrite (NO\u003csub\u003e2\u003c/sub\u003e), nitrate (NO\u003csub\u003e3\u003c/sub\u003e), soluble reactive phosphorous, total ammonia (NH\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;NH\u003csub\u003e3\u003c/sub\u003e), total phosphorus (TP), total nitrogen (TN), and chlorophyll a (Chl-a). These variables were monitored because they define the status and quality of the water and phosphorous can be directly harmful to fish in concentrations beyond the normal ranges (Zhang et al., \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dar et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lopes, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Holmes \u0026amp; Taylor, 2015). Temperature affects the speed of chemical reactions, the rate of photosynthesis, the metabolic rate of aquatic organisms, as well as how pollutants, parasites, and other pathogens interact with aquatic residents. Temperature also influences the solubility of dissolved oxygen (DO) and other molecules in the water column such as ammonia (Ha et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Oxygen influences inorganic chemical reactions and is required for aerobic metabolism (Carr and Neary \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pH of an aquatic ecosystem is important because it is closely linked to biological productivity. Secchi disk depth determines water transparency, a measure of water quality that quantifies the depth of light penetration in a body of water. Water bodies with high transparency typically have good water quality (Ha et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specific conductivity measures how well the water conducts an electrical current, a property that is proportional to the concentration and strength of ions in solution and can also be used to detect pollution sources (Moreira et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pal et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Nutrients were considered because they regulate the productivity and define the trophic status of aquatic ecosystems. Phosphorus and nitrogen are reported to be the primary drivers of eutrophication of aquatic ecosystems, where increased nutrient concentrations leads to increased primary productivity ((Jarosiewicz, Ficek, and Zapadka 2011; Berzina and Sudars 2010; J. Ndungu et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ozbek et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These two elements together with Secchi disk depth were relevant in the calculation of Carlson\u0026rsquo;s trophic index (Carlson \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). The samples were analyzed for NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, soluble reactive phosphorous, NO\u003csub\u003e2\u003c/sub\u003e \u003csup\u003e\u0026minus;\u003c/sup\u003e, and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, using a photometer while TN and TP were analyzed using spectrophotometer. Physicochemical variables, their units and methods of analysis are summarised in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of physicochemical variables, units and analytical methods used in the wetlands of Lake Tana\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnalytical Tools\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003csup\u003eo\u003c/sup\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePortable meter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePortable meter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrical conductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026micro;S/cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePortable meter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePortable meter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecchi Depth a.m.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD a.m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecchi disk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecchi Depth p.m.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD p.m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSecchi disk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTape mounted on stick\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal dissolved substance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePortable meters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePortable meters\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrite nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePalin test (Photometer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrate nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePalin test (Photometer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoluble reactive phosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePalin test (Photometer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal ammonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePalin test (Photometer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmmonium Molybdate (Spectrophotometer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal phosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAmmonium Molybdate (Spectrophotometer)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorophyll-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChl-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eml/l\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFluorometer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssessment of trophic status of the sampling sites\u003c/p\u003e \u003cp\u003eBased on the value of TSI, aquatic ecosystems are classified into trophic categories (Carlson, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Carlson TSI of wetlands of lake Tana was calculated using information of data sets of SDT, chlorophyll-a (Chla), concentrations of phosphorous (P) and total phosphorus (TP).\u003c/p\u003e \u003cp\u003eTSI \u003csub\u003eTN\u003c/sub\u003e = 54.45\u0026thinsp;+\u0026thinsp;14.43 \u0026lowast; ln (TN) (mg/L)\u003c/p\u003e \u003cp\u003eTSI\u003csub\u003eTP\u003c/sub\u003e = 14.42 \u0026lowast; ln (TP)\u0026thinsp;+\u0026thinsp;4.15 (\u0026micro;g/L)\u003c/p\u003e \u003cp\u003eTSI \u003csub\u003eChla\u003c/sub\u003e = 9.81\u0026lowast; ln (chl a)\u0026thinsp;+\u0026thinsp;30.6 (\u0026micro;g/L)\u003c/p\u003e \u003cp\u003eTSI \u003csub\u003eSDT\u003c/sub\u003e = 60\u0026thinsp;\u0026minus;\u0026thinsp;14.41 \u0026lowast; ln (SD) (m)\u003c/p\u003e \u003cp\u003eTOT\u003csub\u003eTSI\u003c/sub\u003e = \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(TSI\u003c/span\u003e \u003csub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTN\u003c/span\u003e\u003c/sub\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e+ TSI\u003c/span\u003e\u003csub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTP\u003c/span\u003e\u003c/sub\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e+ TSI\u003c/span\u003e \u003csub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChla\u003c/span\u003e\u003c/sub\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e+ TSI\u003c/span\u003e \u003csub\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSDT\u003c/span\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eWhere: TSI\u003csub\u003eTN\u003c/sub\u003e corresponding to concentration (mg/L) total nitrogen, TSI\u003csub\u003eSDT\u003c/sub\u003e is TSI corresponding to depth (m) of Secchi disc transparency, TSI\u003csub\u003eTP\u003c/sub\u003e is TSI corresponding to the concentration (\u0026micro;g /L) of total phosphorus, TSI\u003csub\u003eChla\u003c/sub\u003e is TSI corresponding to the concentration (\u0026micro;g /L) of cholorophyll-a, and TOT\u003csub\u003eTSI\u003c/sub\u003e is total TSI, i.e., the average TSI\u003csub\u003eSDT\u003c/sub\u003e, TSI\u003csub\u003eTP\u003c/sub\u003e, TSI\u003csub\u003eTN\u003c/sub\u003e and TSI\u003csub\u003eChla\u003c/sub\u003e. Generally, TSI values below 40 correspond to an oligotrophic, from 40\u0026ndash;60 to mesotrophic, from 60\u0026ndash;80 to eutrophic, and above 80 to a hypertrophic status of the lake (Jarosiewicz, Ficek, and Zapadka 2011).\u003c/p\u003e \u003cp\u003eAssessment of the water quality index of the sampling sites\u003c/p\u003e \u003cp\u003eThe water quality index (WQI) was calculated from the data sets for assessing the spatio-temporal change in water quality parameters (Rubio-Arias et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). WQI is a ranking that replicates the composite impact of various water quality parameters and its appearance above a certain threshold limits the numerous uses of the water.\u003c/p\u003e \u003cp\u003eThe water quality parameters were assigned different weights from 1 to 5 based on their importance for the overall water quality. The estimated WQI values are classified into five categories from \u0026lt;\u0026thinsp;50 representing excellent water quality, 50\u0026ndash;100 good water, 100\u0026ndash;200 poor water, 200\u0026ndash;300 very poor water, and \u0026gt;\u0026thinsp;300 water unfit for various purposes (Yidana and Yidana \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The WQI is computed as;\u003c/p\u003e \u003cp\u003eWi = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{w}\\text{i}}{\\sum \\text{w}\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u0026sum;Wi is the sum of the weights of all the parameters. In this study, \u0026sum;wi was 50.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;3.2. presents the wi, Wi, and US Environmental Protection Agency (EPA) standard for each chemical parameter used in this study. A quality rating scale, qi, was computed for each parameter using the equation\u003c/p\u003e \u003cp\u003eqi = (Ci/Si) x 100\u003c/p\u003e \u003cp\u003ewhere Ci and Si respectively refer to the concentration and the US EPA standard for each parameter, in mg/l.\u003c/p\u003e \u003cp\u003eThe water quality subindex, SIi was then calculated for each parameter using the equation\u003c/p\u003e \u003cp\u003eWQI =\u0026sum;Sli\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe weights and relative weights of each of the water quality parameters used for the Water Quality Index determination. Where US EPA-US Environmental Protection Agency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUS EPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeight (wi)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelative weight (Wi)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissolved oxygen (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrical conductivity (\u0026micro;s/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecchi depth a.m. (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecchi depth p.m. (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater depth(m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal dissolved solids (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalinity (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrite (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrate (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoluble reactive phosphorous (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal ammonia (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal nitrogen (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal phosphorous (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorophyll-a a(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics comprising the means, standard deviations and ranges for each parameter were derived. Data were not normally distributed hence the Kruskal-Wallis nonparametric ANOVA was used to compare the water quality variables between sampling locations. In order to evaluate spatial variation in water quality and to characterise the study sites according to their water quality status, subsequently defining their degree of contamination, the water quality datasets were subjected to four multivariate statistical techniques, namely, univariate analysis of variance (univariate ANOVA), cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA). Multivariate analysis was applied to test the significance, effect sizes, and powers of physicochemical properties in the six wetlands across four seasons. Between-subject effects for each dependent variable were analysed using univariate analysis. The cut-off value for determining statistical significance was chosen as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Sharma and Sood \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) three sampling site per off shore side each wetland were considered. Wetlands were independently ranked based on Carlson TSI and WQI. Carlson TSI was calculated using information of data sets of SDT, chlorophyll-a (Chla), concentration of phosphorous (P) and the concentration of total phosphorus (TP). WQIs were calculated for each study locations and wetlands and were ranked accordingly\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eCharacteristics of study wetlands of Lake Tana\u003c/p\u003e \u003cp\u003eSix wetlands belonging to five types, i.e., one riverine, two lacustrine, two river mouth, and one urban wetland were considered in this study. The river mouth wetlands were Gumara River Mouth and Megech River mouth. Gumara River Mouth wetland is under high pressure due to catchment agriculture in harvesting, livestock grazing, irrigation developments, sedimentation, water extraction and the introduction of alien species. It receives agricultural effluents from adjacent farming lands of the South Gondar administrative zone. This wetland has been dominated by water hyacinth since 2012. Megech River Mouth is dominated by water hyacinth and receives combined effluent from agricultural lands the in North Gondar administrative zone and municipal effluent discharge with no wastewater pre-treatment facility from Gondar city. Ras Abbay wetland is a riverine wetland dominated by \u003cem\u003eCyperus papyrus\u003c/em\u003e and forest trees. Ras Abbay wetland receives industrial and domestic effluent from point and non-point sources. The Blue Nile River crossing Bahir Dar city receives untreated municipal and industrial wastewater and then drains into a mosaic of mixed wetland habitats of Ras Abbay. Avaj is an urban wetland that receives domestic wastewater generated from hotels, hospitals and fish landing sites from the surrounding communities. Wonjeta wetland is papyrus-dominated and it is a type of wetland originating from springs. It is known for its papyrus and tree natural forests. It is surrounded by rural settlements, and the spring water is pumped for domestic purposes and small-scale irrigation. Zewdie Girar is a lacustrine wetland that is rich in reed swamps and surrounded by a mountain. This wetland is under a relatively low pressure due to harvesting, sedimentation, water extraction and the introduction of alien species. Geographical locations and wetland characteristics of the study wetlands are shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the characteristics of study wetlands in Lake Tana. GRM - Gumara River mouth, MRM - Megech River mouth, RA - Ras Abbay, AV - Avaj, WO - Wongeta, and ZG - Zewdie Girar\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003cp\u003e/Longitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003cp\u003e(masl)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOrigin of water quality deterioration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eType of vegetation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eType of wetland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocated southeast of the lake, approximately 50 km from Bahir Dar city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003csup\u003e0\u003c/sup\u003e29\u0026rsquo;684\u0026rsquo;\u0026rsquo;N\u003c/p\u003e \u003cp\u003e/11\u003csup\u003e0\u003c/sup\u003e53\u0026rsquo;\u0026rsquo;949\u0026rsquo;\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCatchment agriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEichhornia-\u003c/em\u003e dominated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRiver mouth wetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Kahsay et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocated in the northern part of the lake, approximately 90 km from Bahir Dar city and 40 km from Gondar city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003csup\u003e0\u003c/sup\u003e24\u0026rsquo;245\u0026rsquo;\u0026rsquo; N\u003c/p\u003e \u003cp\u003e/12\u003csup\u003e0\u003c/sup\u003e16\u0026rsquo;337\u0026rsquo;\u0026rsquo;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCatchment agriculture and untreated municipal effluent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eEichhornia-\u003c/em\u003e dominated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRiver mouth wetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Dersseh et al., 2020; Kahsay et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocated in south of the lake, approximately 5 km from Bahir Dar city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003csup\u003e0\u003c/sup\u003e24\u0026rsquo;682\u0026rsquo;\u0026rsquo; N\u003c/p\u003e \u003cp\u003e/11\u003csup\u003e0\u003c/sup\u003e36\u0026rsquo;140\u0026rsquo;\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1769\u0026ndash;1785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1114.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDomestic wastewater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGrass and trees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRiverine wetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Kahsay et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocated south of the lake, approximately 2 km from Bahir Dar city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003csup\u003e0\u003c/sup\u003e22\u0026rsquo;464\u0026rsquo;\u0026rsquo; N\u003c/p\u003e \u003cp\u003e/11\u003csup\u003e0\u003c/sup\u003e36\u0026rsquo;679\u0026rsquo;\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUntreated municipal and industrial wastewater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePapyrus and tree natural forests\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Kahsay et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocated south of the lake, approximately 5 km from Bahir Dar city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003csup\u003e0\u003c/sup\u003e17\u0026rsquo;832\u0026rsquo;\u0026rsquo;N\u003c/p\u003e \u003cp\u003e/11\u003csup\u003e0\u003c/sup\u003e39\u0026rsquo;241\u0026rsquo;\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively low pressure from farming and wastewater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePapyrus- dominated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLacustrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Kahsay et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocated southwest of the lake, approximately 4 km rom Bahir Dar city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003csup\u003e0\u003c/sup\u003e59\u0026rsquo;812\u0026rsquo;\u0026rsquo; N\u003c/p\u003e \u003cp\u003e/ 11\u003csup\u003e0\u003c/sup\u003e54\u0026rsquo;262\u0026rsquo;\u0026rsquo; E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRelatively low pressure from farming and wastewater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePapyrus- dominated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLacustrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(Kahsay et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssessment of spatio-temporal variations of selected water physicochemical properties\u003c/p\u003e \u003cp\u003eTests of between-subject effects for each dependent variable using univariate analysis are shown in table 3. Wetland had a significant effect (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on electrical conductivity, pH, Secchi depth a.m., Secchi depth p.m., WD, salinity, nitrate, total ammonia and chlorophyll-a while water temperature, dissolved oxygen, total dissolved solids, nitrite, soluble reactive phosphorous, total nitrogen, total phosphorous and total nitrogen to total phosphorous ration did not differ among the six wetlands (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). On the other hand, season had a significant effect (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on water temperature, dissolved oxygen, electrical conductivity, Secchi depth a.m., Secchi depth p.m., water depth, nitrate, soluble reactive phosphorous, total ammonia, total nitrogen and Chlorophyll-a while pH, total dissolved solids, salinity, nitrite, and total nitrogen did not differ among seasons (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of dissolved oxygen, electrical conductivity, pH, Secchi depth a.m., Secchi depth p.m., salinity, nitrate, total ammonia, total nitrogen, total phosphorous, total nitrogen to total phosphorous ratio and Chlorophyll-a while water temperature, water depth, total dissolved solids, nitrate and soluble reactive phosphorous were not affected (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) by the interaction between wetland by season.\u003c/p\u003e \u003cp\u003eTable.3. Univariate tests of significance and powers of test for physicochemical properties in lake Tana wetlands\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDissolved oxygen(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectrical conductivity \u0026micro;S/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi depth a.m.(m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.80108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi depth p.m. (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.20174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.84035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater depth (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.6236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.5247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal dissolved solids (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalinity (ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrite (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.054599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrate (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoluble reactive phosphorous (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal ammonia (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.368185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.073637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.5614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal phosphorous (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.60024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen: Total phosphorous ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e392.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChlorophyll-a (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDissolved oxygen(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectrical conductivity \u0026micro;S/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi depth a.m.(m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.0783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.02609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.1061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi depth p,m (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.14610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.04870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.4560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater depth (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.5502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.1834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.2047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal dissolved solids (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.06663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalinity (ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrite (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.228009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrate (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoluble reactive phosphorous (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.7604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal ammonia (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.076676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.358892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.5014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105.9809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.74764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal phosphorous (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.79351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.93117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.68797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen: Total phosphorous ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e400.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.40978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChlorophyll-a (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland x Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature (\u003csup\u003e0\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDissolved oxygen(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectrical conductivity \u0026micro;S/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi depth a.m.(m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.7273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.04849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.0425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi depth p,m (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.22013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.14801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.7549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater depth (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.7683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal dissolved solids (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.446392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.77659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalinity (ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrite (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.176072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.398861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrate (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoluble reactive phosphorous (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.09581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal ammonia (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.195415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.6627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363.3852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.2257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.56997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal phosphorous (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.86332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.93596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen: Total phosphorous ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2669.906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e177.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChlorophyll-a (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e669.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe water quality management policy for surface waters currently relies on a wide variety of physical and chemical parameters. To provide a similar wide range of data, the measured values of the chemical and physical parameters collected from six wetlands across four seasons are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTemperature The overall mean value of water temperature in this study was 24.02\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49. Mean temperature did not differ among wetlands, ranging from 21.02 to 25.00\u003csup\u003e\u0026deg;\u003c/sup\u003eC (mean: 23.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34) in GRM and from 22.79 to 26.59\u003csup\u003e\u0026deg;\u003c/sup\u003eC (mean: 24.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37) in WO (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, the mean value of temperature differed among seasons ranging from 21.02 to 25.69\u003csup\u003e\u0026deg;\u003c/sup\u003eC (mean: 23.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12) in rainy season and from 22.82 to 27.92\u003csup\u003e\u0026deg;\u003c/sup\u003eC (mean: 24.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60) in early rainy season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (figure.1a).\u003c/p\u003e \u003cp\u003eDissolved oxygen (DO)\u003c/p\u003e \u003cp\u003eThere was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of DO. Oxygen concentrations ranged from 4.77 to 5.04 (mean: 4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14) in WO during dry season and from 6.86 to 8.64 (mean: 7.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 mg/L) in GRM during the rainy season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;1b.\u003c/p\u003e \u003cp\u003eElectrical conductivity (EC)\u003c/p\u003e \u003cp\u003eThere was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of EC. Values ranged from 89 to 108 (mean: 96.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02 \u0026micro;S/cm) in GRM during rainy season and from 196 to 327 (mean: 250\u0026thinsp;\u0026plusmn;\u0026thinsp;68.47 \u0026micro;S/cm) in MRM during late rainy season. Figure\u0026nbsp;1c shows differences between mean values.\u003c/p\u003e \u003cp\u003epH\u003c/p\u003e \u003cp\u003eThe mean value of pH in this study was 6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of pH. The mean value of pH ranged from 4.67 to 6.15 (mean: 5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82) in WO during late rainy season and from 6.63 to 9.41 (mean: 8.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55) in RA during the dry season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;1d).\u003c/p\u003e \u003cp\u003eSecchi depth (SD) a.m.\u003c/p\u003e \u003cp\u003eThe mean value of SD a.m. in this study was 1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 m. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of SD a.m. Values ranged from 0.02 to 0.12 m (mean: 0.077\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05) in MRM during early rainy season and from 4.1 to 7.8 m (mean: 6.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96) in GRM during dry season. Figure\u0026nbsp;1e shows differences between mean values.\u003c/p\u003e \u003cp\u003eSecchi depth (SD) p.m.\u003c/p\u003e \u003cp\u003eThe mean value of SD p.m. in this study was 0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14 m. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of SD p.m. Values ranged from 0.0.02 to 0.12 m (mean: 0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0.05) in MRM during late rainy season and from 3.38 to 6.90 m (mean: 5.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93) in GRM during dry season. Figure\u0026nbsp;1f shows differences between mean values.\u003c/p\u003e \u003cp\u003eWater depth (WD)\u003c/p\u003e \u003cp\u003eThe mean value of WD in this study was 2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 m. The mean value of WD differed among wetlands, ranging from 0.28 m to 1.87m (mean: 0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14) in MRM and from 2.05 to 4.10 m (mean: 3.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21) in RA (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Season had effect on the mean value of WD ranging from 0.26 to 2.85 m (mean:1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86) during early rainy season and from 0.22 to 6.10m (mean:2.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34) during rainy season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1g)\u003c/p\u003e \u003cp\u003eTotal dissolved solids (TDS)\u003c/p\u003e \u003cp\u003eThe mean value of TDS in this study was 0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 g/L. The mean value of TDS did not differ among wetlands, ranging from 0.01 to 0.15 g/L (mean: 0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 ) AV and from 0.01 to 0.98 g/L (mean: 0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 ) in RA (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).Likewise, the mean value of TDS did not differ among seasons ranging from 0.01 to 0.10 g/L (mean:0.04 ) during early rainy season and from 0.01 to 0.9 g/L (mean:0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 ) during dry season (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (figure.1h).\u003c/p\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003cp\u003eThe mean value of salinity in this study was 0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 ppm. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of Salinity. The mean value of salinity ranged from 0.04 to 0.09 ppm (mean: 0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0.005) in GRM during rainy season and from 0.02 to 0.06 ppm (mean: 0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02) in GRM during the rainy season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;1i.\u003c/p\u003e \u003cp\u003eNitrite\u003c/p\u003e \u003cp\u003eThe mean value of nitrite in this study was 0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 mg/L. The mean value of nitrite did not differ among wetlands, ranging from non-detectable to 0.013 mg/L (mean: 0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001 mg/L) in WO and from non-detectable to 0.819 mg/L (mean: 0.078\u0026thinsp;\u0026plusmn;\u0026thinsp;0.068) in MRM (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Likewise, the mean value of nitrite did not differ among season ranging from non-detectable to 0.017 mg/L (0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007) during early rainy season and from non-detectable to 0.819 mg/L (mean:0.052\u0026thinsp;\u0026plusmn;\u0026thinsp;0.193) during late rainy season (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (figure.1j).\u003c/p\u003e \u003cp\u003eNitrate\u003c/p\u003e \u003cp\u003eThe mean concentration of nitrate in this study was 0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 mg/l. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of nitrate. Nitrate concentrations ranged from 0.001 to 0.054 mg/L (mean: 0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0.027) in ZG during early rainy season and from 0.76 to 0.98 mg/L (mean: 0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11) in ZG during the dry season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;1k).\u003c/p\u003e \u003cp\u003eSoluble reactive phosphorus (SRP)\u003c/p\u003e \u003cp\u003eThe mean value of SRP in this study was 0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 mg/L. The mean value of SRP did not differ among wetlands, ranging from 0.083 to 1.350 mg/L (mean: 0.443\u0026thinsp;\u0026plusmn;\u0026thinsp;0.093) in ZG and from 0.060to 1.850 mg/L (mean: 0.734\u0026thinsp;\u0026plusmn;\u0026thinsp;0.142) in RA (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, the mean value of SRP differed among seasons ranging from 0.03 to 1.35 mg/L (mean:0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34) during early rainy season and from 0.39 to 1.85 mg/L (mean:0.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36) during late rainy season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (figure.1l).\u003c/p\u003e \u003cp\u003eTotal ammonia\u003c/p\u003e \u003cp\u003eThe mean value of ammonia in this study was 0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 mg/l. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of total ammonia. Ammonia concentrations ranged from non-detectable to 0.01 mg/L (mean:0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006) in ZG during dry season and from 0.68 to 0.98 mg/L (mean: 0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15mg/L) in AV during the late rainy season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;1m).\u003c/p\u003e \u003cp\u003eTotal nitrogen (TN)\u003c/p\u003e \u003cp\u003eThe mean value of ammonia in this study was 2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.65 mg/L There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of TN. TN concentrations ranged from 0.128 to 0.306 mg/L (mean: .308\u0026thinsp;\u0026plusmn;\u0026thinsp;0.090) in GRM during early rainy season and from 4.00 to 14.00 mg/L (mean: 8.333 5.132) in WO during the dry season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;1n).\u003c/p\u003e \u003cp\u003eTotal phosphorous (TP)\u003c/p\u003e \u003cp\u003eThe mean value of TP in was 0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98 mg/L. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of TP. Concentration of TP ranged from 0.05 to 0.18 mg/L (mean: 0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06) in WO during early rainy season and from 0.80 to 6.00 (mean: 3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61) in AV during late rainy season. Figure\u0026nbsp;1o shows differences between mean values.\u003c/p\u003e \u003cp\u003eTotal Nitrogen to Total Phosphorus (TN:TP) Ratio\u003c/p\u003e \u003cp\u003eThe mean value of total phosphorous did not differ among wetlands, ranging from 0.37 to 16.2 (mean: 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2) in the GRM and from 0.37 to 75.0 (mean: 11 18. \u0026plusmn; 21.28 in RA (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Likewise, the mean value of TN:TP did not differ among seasons ranging from 0.507 to 15.000 (mean:3.915\u0026thinsp;\u0026plusmn;\u0026thinsp;3.257) during rainy season and from 0.167 to 75.000 (mean:10.018\u0026thinsp;\u0026plusmn;\u0026thinsp;19.363) RA (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1p).\u003c/p\u003e \u003cp\u003eChlorophyll-a (Chl-a)\u003c/p\u003e \u003cp\u003eThe mean value of Chl-a in this study was 5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23 mg/L. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of chl-a. Chl-a concentrations ranged with mean value of 1.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00) in ZG during dry season and from 9.00 to 33.00 mg/L (mean: 718.67\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67 ) in MRM during the rainy season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;1q).\u003c/p\u003e \u003cp\u003eOf particular note are the mean value of dissolved oxygen, electrical conductivity, Secchi depth. a.m, Secchi depth. p.m., salinity, nitrate, ammonia, total nitrogen, total nitrogen and chl-a showed significant variation by wetland by season interactions that delineated study wetlands into different clusters. The mean, standard error, minimum and maximum spatio-temporal variations of physico-chemical properties of Lake Tana in four seasons are given Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eSpatial diversity and site grouping based on water quality characteristics\u003c/p\u003e \u003cp\u003eHierarchical cluster analysis grouped the 6-wetlands into four clusters based on the similarity of water quality characteristics (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The four clusters displayed in the dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) could be grouped into cluster 1 (WO and RA), clusters 2 (MRM). moderately polluted cluster (clusters 3) and least polluted cluster (cluster 1). The least polluted cluster comprised WO, and RA wetlands and the slightly polluted cluster comprised MRM while moderately polluted cluster comprised GRM and ZG wetlands. The highly polluted cluster comprised AV wetland.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRelationship between sampling sites and physicochemical variables\u003c/p\u003e \u003cp\u003eThe results of the PCA based on normalized data of the physicochemical components are shown in Table\u0026nbsp;3.5, and the wetlands are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.2. Two components of PCA loaded eigenvalues greater than 1, with Component one (X-axis) registering 42.24% of the total variance and Component two (Y-axis) explained 20.80% of the total variance (table 3.4). Altogether, the first two components explained 64.04% of total variance and 10.2 of the 16 eigenvalues. The PCA plot brought out the four clusters observed in the hierarchical cluster analysis dendrogram above (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCluster 1, the highly polluted cluster (AV) correlated with higher values of, electrical conductivity, ammonia, total nitrogen., total phosphorous and total nitrogen total phosphorous ratio. In contrast, Cluster 4 (WO and RA), the least polluted cluster, was highly associated with l lower values of nutrients Clusters, 2 and 3, the slightly and moderately polluted clusters, lay between Clusters 1 and 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEigenvalues, cumulative eigenvalues, percent of total variance and cumulative percent of the total variance of correlation PCA for physicochemical variable, (n\u0026thinsp;=\u0026thinsp;6) in the study sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalues\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCumulative Total\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% of Total Variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCumulative % of Total Variance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.180538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.18054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.23846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.2385\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.706058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.88660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.80034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.0388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.536499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.42310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.92058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.9594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.276251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.69935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.38971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92.3491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.300654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.00000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.65091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLatent factors influencing water quality in the study sites\u003c/p\u003e \u003cp\u003eThree factors were extracted, explaining 64% of the total variance in the water quality data set (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1 were taken as the criterion for the extraction of the factors required for explaining the source of variances in the data set under Kaiser Normalization (Alkarkhi et al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Panda et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rohe \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sarmento \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sayadi et al \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Varol et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The parameter loadings for the three identified factors, the factor eigenvalues, their percentage variance, and cumulative percentage variance are given in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The loading coincided with the correlation coefficients between water quality variables and the factors (Alkarkhi et al \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Naigaga \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Varol et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Factor 1 accounted for 43% of the total variance and was positively correlated (loading\u0026thinsp;\u0026gt;\u0026thinsp;0.70) with EC, salinity, NO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e3\u003c/sub\u003e while it was negatively correlated with SD. A.m, SD. p.m., water depth and NH\u003csub\u003e4\u003c/sub\u003e (Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Factor 2 explained 21% of the total variance and was positively loaded with temperature and Chl-a while it was negatively loaded with dissolved oxygen. Factor 1 loading represented the changes and water quality status in the highly polluted Cluster 1 (AV), while the Factor 2 loading explained the changes in the least polluted cluster 4 (WO and RA), while Cluster 2 (GRM and ZG) and cluster 3 (MRM) were represented as moderately and slightly polluted wetlands.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eR-mode varimax rotated factor analysis of water quality variables (number of variables\u0026thinsp;=\u0026thinsp;16) factor loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.70 in bold\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissolved oxygen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.3049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrical conductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecchi depth a.m.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.8965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecchi depth p.m.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.9033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.8135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal dissolved substance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoluble reactive phosphorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal ammonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.7818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal nitrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2155\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal phosphorous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal nitrogen-total phosphorous ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.8445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorophyll-a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6398\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEigenvalues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.1805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% Variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.2385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCumulative Variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.2385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.0388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExtraction Method: Principal Axis Factoring.\u003c/p\u003e \u003cp\u003eRotation Method: Varimax with Kaiser Normalization.\u003c/p\u003e \u003cp\u003eRotation converged in 16 iterations.\u003c/p\u003e \u003cp\u003eSpatio-temporal variations of trophic status indices using multivariate analysis\u003c/p\u003e \u003cp\u003eTests between-subject effects for each dependent variable using univariate analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Wetland had significant effect (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on TOT\u003csub\u003eTSI\u003c/sub\u003e, TSI\u003csub\u003eTP,\u003c/sub\u003e TSI\u003csub\u003eChla\u003c/sub\u003e, and TSI\u003csub\u003eSTD\u003c/sub\u003e while TSI\u003csub\u003eTN\u003c/sub\u003e did not differ (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among the six wetlands. On the other hand, season had significant effect (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) on TOT\u003csub\u003eTSI\u003c/sub\u003e, TSI\u003csub\u003eTP\u003c/sub\u003e and TSI\u003csub\u003eSDT\u003c/sub\u003e while TSI\u003csub\u003eChla\u003c/sub\u003e did not differ (univariate ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) across four seasons. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of TOT\u003csub\u003eTSI\u003c/sub\u003e, TSI\u003csub\u003eTN\u003c/sub\u003e, TSI\u003csub\u003eTP,\u003c/sub\u003e TSI\u003csub\u003eChla\u003c/sub\u003e, and TSI\u003csub\u003eSTD\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate tests of significance and powers of Carlson Trophic Status Indices in Lake Tana wetlands\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2069.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e413.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal phosphorous trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1847.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e369.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChlorophyll-a trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e670.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi disc transparency trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8369.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1673.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1338.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4404.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1468.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal phosphorous trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1409.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e469.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChlorophyll-a trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e256.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi disc transparency trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1824.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e608.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e442.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWetland x Season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal nitrogen trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11629.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e775.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal phosphorous trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4519.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e301.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.3119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChlorophyll-a trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1267.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecchi disc transparency trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4201.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e280.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal trophic state index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1832.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTotal trophic state index (TOT\u003csub\u003eTSI\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eThe overall mean value of TOT\u003csub\u003eTSI\u003c/sub\u003e in this study was 64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of TOT\u003csub\u003eTSI\u003c/sub\u003e. Values ranged from 43.74 to 62.53 (mean: 50.69\u0026thinsp;\u0026plusmn;\u0026thinsp;10.30) in GRM during late rainy season and from 77.18 to 86.81 (mean: 81.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91) in MRM during late rainy season. Figure\u0026nbsp;3.3a shows differences between mean values.\u003c/p\u003e \u003cp\u003eTotal nitrogen trophic state index (TSI\u003csub\u003eTN\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eThe overall mean of TSI\u003csub\u003eTN\u003c/sub\u003e in this study was 94.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of TSI\u003csub\u003eTN\u003c/sub\u003e. The mean value ranged from 48.67 to 87.68 (mean: 63.15\u0026thinsp;\u0026plusmn;\u0026thinsp;21.36) in GRM during late rainy season and from 107.68 to 125.76 (mean: 116.86\u0026thinsp;\u0026plusmn;\u0026thinsp;9.06) in WO during the dry season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;3.3b).\u003c/p\u003e \u003cp\u003eTotal phosphorous trophic state index (TSI\u003csub\u003eTP\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eThe overall mean of TSI\u003csub\u003eTN\u003c/sub\u003e in this study was 29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of TSI\u003csub\u003eTP\u003c/sub\u003e. Values ranged from 8.45 to 12.63 (mean: 4.52\u0026thinsp;\u0026plusmn;\u0026thinsp;9.44) in WO during early rainy season and from 34.14 to 63.19 (mean: 50.17\u0026thinsp;\u0026plusmn;\u0026thinsp;14.76) in AV during late rainy season. Figure\u0026nbsp;3.3c shows differences between mean values.\u003c/p\u003e \u003cp\u003eChlorophyll-a trophic state index (TSI\u003csub\u003eChla\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eThe overall mean of TSI\u003csub\u003eTN\u003c/sub\u003e in this study was 66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of TSI\u003csub\u003eChla\u003c/sub\u003e. TSI\u003csub\u003eChla\u003c/sub\u003e. The mean value of TSI\u003csub\u003eChla\u003c/sub\u003e was 52.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 in GRM during late rainy season and from 53.19 to 70.76 (mean: 80.44\u0026thinsp;\u0026plusmn;\u0026thinsp;80.86) in MRM during the rainy season. Significant differences between combinations of wetlands and season are indicated in Fig.\u0026nbsp;3.3 d.\u003c/p\u003e \u003cp\u003eSecchi disc transparency trophic state index (TSI\u003csub\u003eSTD\u003c/sub\u003e)\u003c/p\u003e \u003cp\u003eThe overall mean of TSI\u003csub\u003eTN\u003c/sub\u003e in this study was 67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2. There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of TSI\u003csub\u003eSTD\u003c/sub\u003e. Values ranged from 34.26 to 40.99 (mean: .34.87\u0026thinsp;\u0026plusmn;\u0026thinsp;5.33) in GRM during dry season and from 90.33 to 116.37 (mean: 100.54\u0026thinsp;\u0026plusmn;\u0026thinsp;13.86 .) in MRM during late rainy season. Figure\u0026nbsp;3.3 e shows differences between mean values.\u003c/p\u003e \u003cp\u003eSpatio-temporal variations in water quality indices (WQI) of Lake Tana wetlands\u003c/p\u003e \u003cp\u003eThe overall mean WQI value in this study was 56.88\u0026thinsp;\u0026plusmn;\u0026thinsp;100.80. There was no a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for the mean value of WQI. Likewise, mean value of WQI did not differ (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among wetlands ranging from 14.76 to 61.69 (mean: 39.76\u0026thinsp;\u0026plusmn;\u0026thinsp;14.53) in GRM and from 17.43 to 880.19 (mean:117.23\u0026thinsp;\u0026plusmn;\u0026thinsp;242.27) in MRM. Season had no effect (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) on mean value of WQI ranging from 10.63 to 81.31 (mean: 27.68\u0026thinsp;\u0026plusmn;\u0026thinsp;18.15) in the early rainy season and from 30.72 to 880.19 (mean:97.94\u0026thinsp;\u0026plusmn;\u0026thinsp;197.21) in the late rainy season. WO, GRM, AV, and ZG were under the category of excellent water while RA is under good water. In contrast, MRM is under the category of poor water (Fig.\u0026nbsp;3.4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEvaluation of physico-chemical variables using multivariate analysis\u003c/p\u003e \u003cp\u003eThe physicochemical parameters of wetlands had clear spatiotemporal patterns. Based on seventeen physicochemical variables, there was a significant interaction between wetland and season for the mean value of dissolved oxygen, pH, Secchi depth a.m., Secchi depth p.m., salinity, nitrate, ammonia, total nitrogen, total phosphorous and chlorophyll a. These findings were corroborated with the findings of earlier studies in different water bodies of Ethiopia. For example, studies on water quality variables in Ethiopia revealed spatial and seasonal variability of physicochemical variables and nutrients in Lake Tana (Getnet, Mengistou, and Warkineh 2020; Tibebe et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wondim and Mosa 2015), Lake Beseka of Ethiopian Rift Valley (Umer et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Lake Ziway (Tibebe et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Lake Shalla (Wagaw and Getahun 2021b). Dagne et al., (2021) reported recent trends in some physicochemical features of Abaya and Chamo Lakes. Recent study by Saturday et al., (2021) on spatiotemporal variations in physicochemical water quality parameters of Lake Bunyonyi, Southwestern Uganda showed temporal variations in water quality variables. Assessment of water quality condition and spatiotemporal patterns in selected wetlands of Punjab, India (Singh et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also revealed that physicochemical parameters of selected wetlands showed spatiotemporal patterns.\u003c/p\u003e \u003cp\u003eAssessment of spatiotemporal variations of physical parameters of Lake Tana\u003c/p\u003e \u003cp\u003eTemperature is an important factor that regulates the biogeochemical activities in the aquatic environment. Although the mean value of surface water temperature did not differ among wetlands, the mean temperature observed in this study (table 3.8) was higher than the water temperature recorded in Lake Tana (Tibebe et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wondim et al., \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vijverberg et al., 2009; Wondie et al., 2007), Lake Navaishia Ndungu, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and Soda lakes (Melese and Debella \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Lake Ziway (Abnet and Seyoum 2020) and Eleyele Lake (Ayoade and Ikulala \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The mean water temperature in this study showed significant variation across seasons with maximum mean values in early rainy season. The higher water temperatures in during the early season could be attributed to high air temperatures (Atobatele and Ugwumba 2008; Vajravelu et al. \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) Seasonal variability of African lake temperatures has been reported by many studies (Damo and Icka 2013) .Umer et al., (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported maximum temperatures in the rainy and dry season of 35.5\u0026deg;C and 28.4\u0026deg;C., respectively in Beseka in the Rift Valley of Ethiopia (table 3.8). Therefore, higher water temperature detected in WO and RA wetlands. The variations of water temperature among these studies might be explained by the differences in atmospheric temperature of the regions, sampling seasons and heat absorption potential of the lakes. On the other hand, the significant effect of season on RA, GRM and MRM might be associated with the presence of the floating macrophytes and the water hyacinth mat which restrained the increase of the water temperature (Van de Moortel et al. 2010). Seasonal variability of African lake temperatures has been reported by many studies (Damo and Icka 2013).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the physico-chemical parameters and nutrients of Lake Tana with other tropical lakes for nutrients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" 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colname=\"c1\"\u003e \u003cp\u003eZiway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Tibebe et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHawasa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Girma Tilahun and Ahlgren 2010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChamo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Girma Tilahun and Ahlgren 2010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHayq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Fetahi \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Wondie \u0026amp; Mengistou, 2006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLangano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Wood \u0026amp; Talling, \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBishoftu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.005-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Wood \u0026amp; Talling, \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbijata\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Wood \u0026amp; Talling, \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Melese \u0026amp; Debella, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wood \u0026amp; Talling, \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeseka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.4\u0026ndash;35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1407\u0026ndash;3321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5\u0026ndash;10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Umer, Assefa, and Fito \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChitu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Wood \u0026amp; Talling, \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u0026ndash;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0\u0026ndash;2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u0026ndash;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0-3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003-4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.326-1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.0-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e6.4\u0026ndash;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e(Melaku and Yalew \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wondim, Mosa, and Alehegn 2016; Vijverberg, Sibbing, and Dejen 2009; Wondie et al. \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003ePresent study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe overall mean DO concentration in this study (6.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9 mg/L) (table 3.8) is similar to the value reported in Ethiopian lakes (Fetahi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tilahun \u0026amp; Ahlgren, 2010; Vijverberg et al., 2009; Wondie et al., 2007). In contrast, the overall mean DO concentration in this study was lower than the value reported in Ziway by (Rado \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) (8.72 mg /l ) and in Hawassa by (Abate et al., 2015) (11.2-21.42 mg/l) which is very may be due to a result of hypereutrophication combined with measuring in the afternoon, and Soda lakes by Melese \u0026amp; Debella, (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) while Tibebe et al., (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported lower mean (5 mg/L) DO value in Ziway. The lowest DO values in dry season at WO was attributed to human impacts like fishing and human washing while the lowest DO values in MRM was attributed to its muddy water from agricultural and urban waste runoff (Tibebe et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The highest values of DO in AV, RA, GRM, and ZG wetlands in the rainy season may be due to high dilution. The mean concentration of DO in the present study was greater than the minimum requirement for the survival of aquatic life (i.e. \u0026gt;5 mg/L) (Johansen et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Concentrations below 4.0 mg/L adversely affect aquatic life (FEPA, 2003). The value of DO in this study is within the (EU Directive, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and (USEPA,2000) permissible limits. According to (EU Directive, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and (EPA \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the standard for DO value for fisheries and aquatic life is between 5.0 to 9.0 mg /L(table 3.8).\u003c/p\u003e \u003cp\u003eThe EC at the six wetlands ranged from 89 \u0026micro;S/cm to 327 \u0026micro;S/cm with highest values in MRM in the late rainy season (table 3.8). This finding is lower than the values from previous reports in Ethiopian water bodies (Tibebe, Zewge, et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Umer et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fetahi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tilahun \u0026amp; Ahlgren, 2010; Wood \u0026amp; Talling, \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The mean value of EC in the present study is lower than that reported by Melese \u0026amp; Debella, (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in Soda lakes. Significant seasonal variation was noted during the study with maximum EC during the rainy season of 327\u0026micro;s/cm. The seasonal variation of the EC of lake Tana may be due to different anthropogenic and naturally induced pollutants such as inorganic and organic pollutants from run-off. According to (EEPA 2003), EC levels above 1000 \u0026micro;s/cm limit the use of water for drinking. The mean value of EC in this study was within the normal ranges mentioned in EU and WHO guidelines. The conductivity of most freshwater bodies ranges from 10\u0026ndash;1000 \u0026micro;S /cm (Rice et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), but may exceed 1000 \u0026micro;S /cm, especially in polluted waters, or those receiving large quantities of land run-off (Eaton, Clesceri, and Greenberg \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe overall mean pH value of the lake water was 6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82 (table 3.8) which is lower than the previous data reported by Rado (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) (8.39), by Girma Tilahun and Ahlgren (2010) (8.65), by Tamire and Mengistou (2013) (8.44), by Tibebe et al., (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (8.1) and, by Melese and Debella (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (\u0026gt;\u0026thinsp;8.5) respectively. The maximum and lower values of pH in WO and RA can be attributed to the high alkalinity of the lake due to different ions for example, K\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e, Ca\u003csup\u003e2+\u003c/sup\u003e, Mg\u003csup\u003e2+\u003c/sup\u003e (Umer, Assefa, and Fito \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, no significant seasonal variation was noted during the study. This is not in agreement with the report by Umer et al., (\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in lake Beseka with maximum pH values for the rainy and dry season, respectively. The pH value could mainly be controlled by freshwater swamp exudates that regulate the acidity of the water body. A pH range of 6 to 8.5 is normal according to (Rice et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In general, the pH of lake Tana water is within the acceptable range according to (EPA \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mean SD value (a.m. and p.m.) of 0.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 m (table 3.8) is in agreement with other studies in Ethiopia (Melese \u0026amp; Debella, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wondim et al., \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tilahun \u0026amp; Ahlgren, 2010; Vijverberg et al., 2009; Wondie et al., \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e2007a\u003c/span\u003e). However, the mean value of SD in this study is higher than values of 0.21 m in previous studies in Lake Ziway by Tibebe, et al., (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) while it is lower than the mean SD in Hayq by (Fetahi \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) (2.7 m). The minimum SD in MRM may be attributed to the accumulation of sediments from agriculture and urban effluents drained by Megech River. Season had an effect on mean values of SD in WO, ZG and GRM can be mainly attributed to lower level of catchment degradation and siltation in these wetlands. The declining trend in SD is one of the indications that suggest an increasing trend in turbidity of the lake, which can be mainly attributed to catchment degradation and siltation. In general, the SD was found to be at its lowest during the rainy season, and highest during the dry season at all sampling sites, due to the large amount of run-off silt that enters the lake in the wet season. (Ndungu, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reported that SD increased during dry season in tropical freshwater water bodies. This may be due to the undisturbed watershed, which keeps the soil system intact during the dry season.\u003c/p\u003e \u003cp\u003eThe mean value of WD in this study was 2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 m (table 3.8) which is in agreement with previous studies in Lake Tana by (Wondim et al., \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vijverberg et al., 2009; Wondie et al., 2007). However, the mean value of WD in this study is lower the than the values in previous studies in Shala by Melese \u0026amp; Debella (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) of 0-20m and lower than the depth in Ziway by Tibebe, et al., (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (0.21m). The highest mean value of water depth in RA may be attributed with lower load of sediment drained into this wetland. The significant effect of season on the mean value of WD in WO, AV, RA, and ZG may be associated with the complex pattern of water losses and inputs that can cause large daily and seasonal water level fluctuations. Water levels are highest at the end of the main-rainy season and during the post-rainy period, slowly decreasing to a minimum around the end of the dry season (Vijverberg, Sibbing, and Dejen 2009). The lower mean value of water depth in MRM is likely associated with high amount of sediment load from agricultural and urban effluents.\u003c/p\u003e \u003cp\u003eTotal dissolved solids (TDS) is one of the most important water quality parameters. The mean value of TDS in this study was 0.13 g\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 g/l (table 3.11). This was higher than the previous report in Lake Tana by Wondim et al., (\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (0.02\u0026ndash;0.5 g/l). In contrast, the mean value of TDS in this study was lower than Lake Tana, 0.065g/l -0.77 g/L) and in Lake Beseka (Umer et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, 0.74\u0026ndash;1.598 g/L). When compared with the TDS values of Lake Naivasha in Kenya (values ranged from 1.24 to 2.05 mg/L, with an mean of 1.52) by Ndungu (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and the mean of TDS value of Lake Hawassa in the southern part of Ethiopia (with the highest value of 4.556 mg/L) by Adimasu (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the TDS value of Lake Tana was very much low.\u003c/p\u003e \u003cp\u003eThe mean value of salinity in this study was 0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 ppm (table 3.8) which is in line with results in Lake Tana by (Kassa et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kassa and Tibebe 2019; Tibebe et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with ranges of 0.07 ppm \u0026minus;\u0026thinsp;0.16 ppm). However, the mean value of salinity in this study is lower than previous reports in Lake Tana by (Wondie et al., 2007) (0.1ppm). The high level of salinity in the in MRM in the rainy season is in agreement with previous reports in Soda lakes of Ethiopia by Melese \u0026amp; Debella, (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), by Wagaw et al., (2021) in Lake Shala, and by Kihwele et al., (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in Manyara Lake. Alkalinity levels were high during the post-rainy season in Lakes Beseka and Chittu, and during the dry season in Lake Shala (Melese and Debella \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Overall, the salinity levels were still low and within normal ranges of FEPA and WHO guidelines (500 mg/L).\u003c/p\u003e \u003cp\u003eSpatio-temporal variations of nutrients in Lake Tana\u003c/p\u003e \u003cp\u003eThe mean nitrite value of 0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 mg/L (table 3.8) was higher than values reported in previous studies. For instance, (Beneberu and Mengistou 2009) and (Tamire and Mengistou 2013) reported 0.06 and 0.01 mg/L nitrite, respectively. However, the mean value of nitrite in the present study was lower than the reports for Lake Tana by Wondim, (\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (0.2mg/l), Shitaw et al., (\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), (0.418), and in Lake Adele of eastern Ethiopia (30.67 mg/L), and by Tibebe, Zewge, et al., (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (0.5 mg/L). Relatively higher nitrite concentrations were measured in MRM in the late rainy season which could be due to the application of high amount of fertilizer for crop production in the adjacent farming lands of Megech River catchment. The presence of nitrite in water may mainly result from excessive application of fertilizers. The overall mean nitrite levels were still low and above the normal ranges of FEPA and USEPA (0.001 mg/L).\u003c/p\u003e \u003cp\u003eThe mean nitrate value found in this study (0.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 mg/L) was higher than values of 0.21 0.17, 0.003, and 0.06 mg/L reported by (Tamire and Mengistou 2013; Tibebe et al. \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; G. Tilahun \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Girma Tilahun and Ahlgren 2010), respectively (table 3.8). The high concentration of nitrate in ZG and MRM in the rainy and late rainy seasons is probably because of nutrient enrichment of the littoral zone of the lake from agriculture effluents sources from the catchment area. The observed nitrate concentration in this study is within normal ranges of FEPA and USEPA (50 mg/L).\u003c/p\u003e \u003cp\u003eThe mean SRP concentration (0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31 mg/L) (table 3.8) was higher than in previous reports in fresh water lakes of Ethiopia (Tibebe, Zewge, et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tibebe et al. \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tamire and Mengistou 2013; Wondie and Mengistou 2006; Gebre-Mariam and Desta 2002; Jeppesen et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Kebede and Ahlgren 1994; Tilahun \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) was higher than that of the pervious reported which was 0.016, 0.01, 0.059 and 0.029, 0.06 ,and 0.326 mg/L respectively. The high value of SRP in RA wetland during early rainy season may be attributed to organic and non-organic discharge of water from domestic sources around the wetland vicinity. The measured concentration is also beyond the range of its threshold (0.05 to 0.1mg/L ) as a nutrient for natural waters (Jeppesen et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wondie \u0026amp; Mengistou, 2006).\u003c/p\u003e \u003cp\u003eThe mean SRP concentration (0.660\u0026thinsp;\u0026plusmn;\u0026thinsp;0.084 mg/L ) (table 3.8) in the late rainy season was higher than the values reported by (Gebre-Mariam and Desta 2002; Kebede, Mariam, and Ahlgren 1994; Tamire and Mengistou 2013; Girma Tilahun and Ahlgren 2010) for other Ethiopian Lakes (0.016, 0.035, 0.01 and 0.029 mg/L). However, this value is lower than recently reported by (Melaku and Yalew \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (1.6 mg/L). The maximum allowable concentration of phosphorous which should be permissible in environmental waters is 1 mg/L (USEPA \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; WHO, \u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The high value of phosphate in RA during the late rainy season may be due to excessive use of chemicals like detergents and waste from car wash which organic and inorganic pollutants are released and discharged in water from domestic sources into the lake.\u003c/p\u003e \u003cp\u003eThe mean concentration of total ammonia (0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 mg/L ) (table 3.11) is similar to relatively recent reports by (Tibebe, et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (0.121 mg/l ) (Tilahun \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) (0.111 mg/l ), and (Tamire and Mengistou 2013) (0.143 mg/l) but higher than reported by for example, by (Kebede et al., 1994) (0.036 mg/l). However, the mean value of ammonia in this study was lower than in reports for Lake Tana Wondim, (\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (0.0-6.6 mg/l) and (Melese and Debella \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) ammonium nitrogen had the highest value (56.39-161.93) in Lake Arenguade and the lowest and in Lake Beseka. When compared with the mean total ammonia values of Lake Naivasha in Kenya (the mean varied between 0.045 to 0.085 mg/L with a mean of 0.063 mg/l by Ndungu (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), the total ammonia value of Lake Tana was found to be higher. The high value of total ammonia in AV and GRM during late rainy season may be attributed to hospital effluent discharged into AV, and to agricultural effluent discharged into GRM, respectively.\u003c/p\u003e \u003cp\u003eThe mean TN concentration of 2.23 mg/L (table 3.8) was lower than that reported in lakes of Ethiopai (Tibebe et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tibebe, et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The relatively high concentration of TN in AV and MRM in the late rainy season could be due to chemicals from the hospital in AV and application of fertilizers on crop land and decomposition of organic matters washed off into MRM. Season did not influence TN values, which is not in line with reports by (Tibebe, et al., \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tibebe, et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The agricultural office report indicates that the application of diammonium phosphate (DAP) and urea fertilizer for rain-fed and irrigation agriculture is increasing in farm lands adjacent to Lake Tana (Dersseh et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The observed TN concentration value in this study is within normal ranges of FEPA and USEPA (1.1 mg/l).\u003c/p\u003e \u003cp\u003eThe mean TP value of lake water was (0.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98 mg/L) (table 3.8) which is higher than in reports in fresh water lakes of Ethiopia ( Tibebe, et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e),by Melaku and Yalew \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Kebede et al., 1994), and Tilahun \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), which were 0.311, 0.48, 0.069 and 0.219 mg/L, respectively. A higher TP concentration was also measured in this study as compared to that of other Ethiopian rift valley lakes like Lake Awasa and Chamo ( Tilahun \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). In contrast, the mean value of TP in this study is lower the values in Soda Lakes of Ethiopia (Melese and Debella \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (0.75\u0026ndash;2.41 mg/L). However, the mean value of TP in this study is lower than in the report by (Dersseh et al. 2020) (0.01\u0026ndash;1.8 mg/L) in Lake Tana. The increasing trend in TP is probably due to nutrient enrichment of the lake from agricultural activities around the lake watershed (Ayele \u0026amp; Atlabachew, 2021; Goshu \u0026amp; Aynalem, 2017; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The observed TP concentration value in this study is outside the normal ranges of FEPA and USEPA (0.05 to 0.1 mg/L).\u003c/p\u003e \u003cp\u003eThe mean value of Chl-a in this study was 5.15\u0026thinsp;\u0026plusmn;\u0026thinsp;5.23 mg/L which was lower than the report by in Lake Hayq by Aragaw et al., (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (3.5mg/L), in Lake Tana by Melaku and Yalew (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), (0.99 mg/L), by Mucheye et al. (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (2.52 mg/L), by Kahsay et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (1.0\u0026ndash;4.0 mg/L), by Tibebe et al. (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) (8.0 mg/L) and by Wondie \u0026amp; Mengistu, (2017) (0.03-13 mg/L) by Vijverberg, Sibbing, and Dejen (2009), (0.64 mg/L), by Wondie et al., (2007) (0.61 mg/L) and by Dejen et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) (0.64 mg/L) (table 3.8). The highest value of Chl-a concentration in MRM in the late rainy may be attributed to the influx of sediment and nutrient load from the upper catchment. Lake Tana water Chlorophyll\u0026ndash;a levels were above the permissible level (0.3mg/L) (Trodden and O\u0026rsquo;Boyle \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe TN:TP ratio in lakes and reservoirs is a key element as it gives an idea of which of these nutrients are either in excess or limiting to growth, and it was used to estimate the nutrient limitation in the lake. According to (Smith, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e1962\u003c/span\u003e) blue-green algae (cyanobacteria) had a capacity to dominate in the lake section when the TN:TP ratio was less than 29 and it tends to be rare in the lake when TN:TP\u0026thinsp;\u0026gt;\u0026thinsp;29. The mean value of the TN:TP ratio was 6.1\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6, which was lower than the report in Lake Ziway by (Tibebe et al., \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (48:1), in Lake Hawass by (Lencha, et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (31:1). Even though, there was no significant difference in the TN:TP ratio among the six wetlands and among the four seasons, Lake Tana wetlands are hypereutrophic lakes (Downing and McCauley 1992).\u003c/p\u003e \u003cp\u003eThe underlying reasons for such spatial and temporal variations in the water quality parameters are likely unsustainable anthropogenic activities such as agricultural activities, urbanization, and discharge of waste into the lake. Most of the water parameters in the disturbed wetlands revealed lower qualities in the rainy and late rainy season, which can be associated with a high influx of effluents from agricultural lands. Significant differences were recorded in the concentration of nutrients (nitrate, ammonia, and total nitrogen) between seasons. The higher level of nitrate and total nitrogen in the dry season may be attributed to a lower dilution effect in the dry season. Similar reports on the seasonal distribution of nitrate and nitrite levels in the wetlands of Nigeria were reported by Nwankwoala et al. (2010) and Udom et al. (\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The higher phosphate and ammonia levels recorded during the late rainy season could be attributed to additional discharge from the catchment areas, such as sewage discharge from Bahir Dar and Gondar towns, as well as runoff from the surrounding farmlands due to heavy rainfall. The seasonal influx of allochthonous organic and inorganic materials during the rainy and late rainy seasons is characteristic in most tropical wetlands (Angello, Tr\u0026auml;nckner, and Behailu 2020; Bagalwa et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nwankwoala, Pabon, and Amadi 2010; Saturday et al. 2021; Soro et al. \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, a pattern of low mean concentrations of SRP, ammonia, nitrite, nitrate, TN, TP in the dry season have higher means in the rainy and late rainy seasons. This indicates point source pollution for these parameters, which might be associated with industrial effluents, human interference, and agricultural and urban effluents (Tibebe, et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). During dry season both decreased precipitation and increased agricultural crop lands contributed to lower flows of those nutrients, however, SRP, nitrite, nitrate, TN, and TP all had higher concentrations during rainy and late rainy sesaons. Similarly (Wondie \u0026amp; Mengistou, 2006) noted that nutrients that have a higher concentration during dry season than in the wet season tend to come from point sources whose supply is constant, whereas the inverse pattern can be attributed to non-point sources that are mobilized by high run-off during wet periods. The analysis of water quality data in Lake Tana revealed that there has been a progressive increase in the concentration of various parameters like EC, TDS, nitrate, ammonia, total nitrogen, and total phosphorous when compared to earlier records ((Dersseh et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Vijverberg et al., 2009; Wondie et al., 2007b; Wondim, \u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) temperature increased from 23.2\u003csup\u003e0\u003c/sup\u003eC to 24.02 \u003csup\u003e0\u003c/sup\u003eC, EC increased from 132.8 \u0026micro;S/l to 153.8 \u0026micro;S/l, TDS increased from 0.3 g/l to 1.3 g/l, TN increased from 1.1 mg/l to 2.2 mg/l and TP increased from 0.5 mg/ to 0.9 mg/l between 2009 to 2020.\u003c/p\u003e \u003cp\u003eSpatial diversity and site grouping based on water quality characteristics\u003c/p\u003e \u003cp\u003eThe six study wetlands / clusters of least polluted (WO and RA), slightly polluted (MRM), moderately polluted (GRM and ZG) and highly polluted (AV) were investigated for their water quality physicochemical characteristics following different multivariate analyses. The groups were first ranked using hierarchical clustering based on the similarity of their physicochemical characteristics, which was then confirmed by PCA and FA. The relatively highly polluted cluster comprised one wetland, AV, and correlated highly with electrical conductivity, salinity, ammonia, total nitrogen, total phosphorous and chlorophyll-a on the PCA plot. This cluster was confirmed following the varimax rotation factor analysis and was linked to the positive loading of electrical conductivity, salinity, TN, TN and in Chl-a on Factor one. This registered higher water conductivity, salinity, ammonia, total nitrogen, total phosphorous and chlorophyl-a, implying that there was potentially higher photosynthetic activity and algal growth. High water conductivity, salinity, nitrite, total nitrogen, total phosphorous and chlorophyll-a point to the fact that the nutrients in the highly polluted site could be mainly attributed to non-point-sources effluents from urban waste from hospital in Bahir Dar city. This concurs with the fact that municipal and industrial discharges can contribute ions to receiving waters, increasing the conductivity and nutrients of the receiving waters (Moges et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wondim, Mosa, and Alehegn 2016; Zelalem and Prokin 2017; Kassa and Tibebe 2019; Engdaw, Hein, and Beneberu \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These studies also reported that specific physical, chemical and biological parameters were used to detect pollution sources (Goshu, Byamukama, et al., 2010; Aragaw, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mushi et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, these results reflect the high dissolved nutrients and organic pollution originating from the different catchment activities, implying that Factor one originates from industrial and municipal anthropogenic activities. This concurs with other research that traced the causes of pollution during different study periods in Lake Tana (Mucheye, Yitaferu, and Zenebe \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zimale et al. \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kebedew et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ayele and Atlabachew 2021b; Dersseh et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe slightly polluted cluster, Cluster 3, comprised one wetland, MRM, was with electrical conductivity, salinity, nitrite, nitrate, total nitrogen, total phosphorous and chlorophyll-a on the PCA plot. This cluster was confirmed following the varimax rotation factor analysis and was linked to the positive loading of electrical conductivity, salinity, nitrite, nitrate, TN, TN and in Chl-a on Factor one. The high-value water conductivity, salinity, nitrite, nitrate, total nitrogen, total phosphorous and chlorophyll-a points to the fact that the nutrients in the highly polluted site could be mainly attributed to non-point-sources from agriculture and urban effluent from Gondar city. The catchment in MRM is dominated by subsistence agriculture and urban effluent from Gondar town, and this may contribute to the organic matter in the lake. This concurs with many studies in which it was found that catchment agriculture and urban effluent contributed more to water quality deterioration than municipal and industrial effluent (Setegn et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Taffese et al. 2014; Assefa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kebedew et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Engdaw, Hein, and Beneberu \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As with the highly polluted wetland findings, high nutrient levels have also been observed in studies on other lakes in the tropics (Namugize and Nsengimana 2010; Naigaga \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Samanta et al. \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Assefa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Obubu et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Secchi depth a.m. and p.m., total depth and ammonia in MRM were relatively low compared with other wetlands studies. This could be attributed to the fact that the wetland contains high amounts of particles, which could be from algae or eroded sediment from agriculture farmlands in the catchment (Setegn et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Gebremedhin et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wondie, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zimale et al., \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kebedew et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Engdaw et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These authors observed relatively high sediments in bays which received any effluent from catchments. However, this study was limited to shallow coastline bays (coastal wetland areas) and could not confirm how nutrient levels compare with those in open waters.\u003c/p\u003e \u003cp\u003eThe moderately polluted cluster, Clusters 2 i.e. GRM and ZG. These clusters could not be well explained by PCA but were confirmed following the varimax rotation factor analysis under Factor three. Factors 1 and 2 had the same positive and negative variables as Factor 3, but with low loadings of pH, salinity, SRP and nitrate which explains and confirms the moderate pollution in these sampling locations. This difference was attributed to the dilution effect of the sewage effluent as it moves off the shoreline. This finding is in agreement with studies by (Ademe \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Goshu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wondim, Mosa, and Alehegn 2016), who pointed to a stronger eutrophication effect in the inshore areas of Lake Tana wetlands. The water quality in other highland lakes of Ethiopia have shown that sewage discharge reduces water quality, depending on the degree of dilution, the degree of treatment of the original material, their composition and the response of the ecosystem (Assefa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dersseh et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the least polluted cluster, Cluster four, the sampling locations under this group included WO and RA. This cluster correlated highly with high temperature, DO, and WD on the PCA plot, and these were confirmed by the positive loadings of these variables following the varimax rotation factor analysis under Factor two. The higher and stable values of temperature and the higher values of secchi depth a.m. and secchi depth p.m. in WO and RA were higher than the values for the rest of the wetlands studied. The higher temperature could be attributed to the fact that WO and RA did not experience wastewater cooling effects as there is no wastewater inlet, and the higher secchi depth a.m. and p.m. may be attributed to low amounts of sediment particles. This is in line with previous finding by various researchers (Namugize and Nsengimana 2010; Moges et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dallas \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mucheye, Yitaferu, and Zenebe \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the study showed that AV wetland, which receives urban and domestic wastewater discharges, was more polluted than the rest of the sites, emphasizing the impact of discharges to water quality. This is in line with findings by many researchers who point to urban effluents as an important underlying factor responsible for surface water quality deterioration in Lake Tana (Ayele and Atlabachew 2021b; Engdaw, Hein, and Beneberu \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Goshu et al. 2020; Kebedew et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mucheye, Yitaferu, and Zenebe \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Setegn et al. \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wondim, Mosa, and Alehegn 2016; Zimale et al. \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResults from the cluster analysis, principal component analysis and factor analysis complemented each other and led to the establishment of the six clusters. This synchronization in results concurs with previous studies on water quality, which have all recommended the application of different multivariate statistical techniques when dealing with environmental data (Panda et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Landau and Chis Ster \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Varol et al. \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liu, Ren, and Cai 2020).\u003c/p\u003e \u003cp\u003eSpatio-temporal status in trophic status of Lake Tana using Carlson trophic state index model\u003c/p\u003e \u003cp\u003eWetland, season and the interaction of wetland aby season had effects on TSI of Lake Tana with a higher power of test (54%) for the interaction of wetland by season than wetland or season This may be attributed to each wetland receiving different loads of effluents from agriculture and urban. The average TOT\u003csub\u003eTSI\u003c/sub\u003e, TSI\u003csub\u003eTN\u003c/sub\u003e, TSI\u003csub\u003eTP\u003c/sub\u003e, TSI\u003csub\u003eSTD\u003c/sub\u003e and TSI\u003csub\u003eChl\u0026minus;a,\u003c/sub\u003e values were 64.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7, 94.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.6, 29.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0, 66.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2 and 67.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2, respectively. TOT\u003csub\u003eTSI\u003c/sub\u003e ranked WO, ZG, GRM, AV and RA under the category of the eutrophic level while it ranked MRM under category of hypereutrophic level. The findings of this study were different from that of (Lencha, Tr\u0026auml;nckner, and Dananto 2021; Zemed, Beshah, and Reddythota 2021) whose finding was hypertrophic as the assessment result depended only on the Secchi depth and also (Worako, 2015) who found an average TSI of 72.6 (hypereutrophic) for Lake Hawassa. Eutrophication causes the impairment of activities, discomfort and visual unpleasantness that hamper the recreational use of water severely (Breen, Curtis, and Hynes \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Melaku \u0026amp; Yalew, (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported trophic state index value of Lake Tana according to the three parameters of trophic state (TSIC) Lake Tana was eutrophic Lake.\u003c/p\u003e \u003cp\u003eThe overall average value of the Trophic State Index (TSI) of Lake Tana was 69.77. This TSI value, based on Carlson's trophic state classification criteria (Kratzer and Brezonik \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Jarosiewicz, Ficek, and Zapadka 2011), suggests that Lake Tana is eutrophic during the early rainy, rainy and late rainy seasons. When comparing this TSI value to the OECD's standard (Vollenweider and Kerekes \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), it can be seen that Lake Tana is in an hypereutrophic state. Similarly, Tibebe et al., (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Teshale, (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and Wondie et al., (2007), reported that the lake is above the eutrophic threshold values, placing it in mesotrophic and oligotrophic states, respectively. This could be due to the current anthropomorphic activities around lake, as well as the seasons of study. The trophic state index of SD exhibited a higher trophic state, probably due to water transparency being the variable most affected by rainfall variations (Klippel, Mac\u0026ecirc;do, and Branco 2020).\u003c/p\u003e \u003cp\u003eMRM, which receives municipal, industrial, and agricultural effluents, may be considered hypertrophic. This concurs with (Wondim, Mosa, and Alehegn 2016; Dersseh et al. 2020; Enyew, Assefa, and Gezie 2020; Ayele and Atlabachew 2021b; Damtie, Mengistu, and Meshesha 2021; Dersseh et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who studied three shallow bays along the Lake Tana shoreline and reported the highest eutrophication in the agriculture-impacted bays due to invasion of the wetlands and inshore lake by water hyacinth. The findings also agree with studies carried out in other African Lakes, for example, in the Lake Kyoga basin of Uganda by Obubu et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Lake Victoria of Kenya by Otieno et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), in Lake Victoria of Uganda by Wanda et al. (2015), and in the African Great Lakes (Plisnier et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe presence of water hyacinth (\u003cem\u003eEichhornia crassipes\u003c/em\u003e) in the agricultural-impacted wetlands (GRM and MRM) may be the cause of water quality changes, with higher levels of EC, pH, SRP, TP, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and Chl-a in the impacted wetlands, compared to the least impacted wetlands. Similarly, the water quality values across seasons showed lower values of water transparency and higher values of NO\u003csub\u003e3,\u003c/sub\u003e SRP, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003csub\u003e,\u003c/sub\u003e TP and Chl-a in the early rainy, rainy and late rainy seasons compared to the dry seasons in the agricultural-impacted wetlands (GRM and MRM), which are infested with water hyacinth. This finding is in line with the report by (Mucheye et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found seasonal variation in the invasive water hyacinth as well as changes in water quality values (Chl-a and TDS ) at the end of the main rainy season. Several reports have indicated that the plausible reason for infestation of the lake by water hyacinth could be changes in the physicochemical characteristics of the lake (Wubie, Assen, and Nicolau 2016; Gebremedhin et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Damtie and Mengistu \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, Dersseh et al., (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that water hyacinths appeared in Lake Tana around 2010 after the nitrogen assimilation capacity of the lake was exceeded. This trend was seen mainly in the northeastern part of Lake Tana during rainy seasons, although nutrient concentrations are suitable for growing water hyacinths throughout the lake. The area covered by water hyacinth has increased significantly and positively correlates with the seasonal lake level fluctuation (E. Asmare \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; T. Asmare et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dersseh et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, 2020). Similarly, Kipng\u0026rsquo;eno (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported the spread of water hyacinth in Lake Victoria using satellite imagery, and demonstrated that growth in urban areas with high effluent was proportional to the amount of spread in the water hyacinth.\u003c/p\u003e \u003cp\u003eSpatio-temporal variations in water quality indices (WQI) of Lake Tana wetlands\u003c/p\u003e \u003cp\u003eWetland, season and the interaction of wetland aby season had effects on WQI of Lake Tana with higher power of test (73%) for interaction of wetland by season. The overall mean WQI value was 57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;101.1 was lower than the mean WQI in Lake Tinishu Abaya by Enawgaw \u0026amp; Lemma, (188\u0026ndash;222), In Lake Hawassa by (Zemed, Beshah, and Reddythota 2021) (120.06\u0026ndash;228.29), by (Ghebremedhin and Gupta \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in Lake Chamo (102.9-359.5). However, a recent report for Ribb reservior by Mekonnen et al., (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (65.42 -101.96) is comparable with this finding. The high mean value WQI in WO, RA, GRM and MRM during rainy and late rainy season is line with the report by (Teshome et al. \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in Hawssa. Therefore, the cumulative result of WQI for drinking, aquatic life and recreational uses showed that the environmental situation has become worse in the last few decades, Hence, Lake Tana watershed has been polluted and frequent monitoring of the watershed is necessary for proper management.\u003c/p\u003e \u003cp\u003eIn conclusion, ranking of the pollution status of wetlands of Lake Tana using different approaches in this study, multivariate statistics, Carlson\u0026rsquo;s trophic state index, and water quality index model suggest that some wetlands did not fit completely in the same category The current study on water quality variables of lake Tana recommends that top priority should be given to regular water quality monitoring, in conjunction with biodiversity and fish health assessment (as indicated in following chapters), and cleaner production technologies should be adopted to improve water quality in water bodies of Ethiopia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHailu Mazengia- PhD student and Corresponding AuthorProf. Horst Kaiser- Major Supervisor Dr. Minweyelet Mengist-Cosuperviser\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbalaka, S.E. 2017. \u0026ldquo;Histopathological Evaluation of \u003cem\u003eOreochromis Mossambicus\u003c/em\u003e Gills and Liver as Biomarkers of Earthen Pond Water Pollution.\u0026rdquo; \u003cem\u003eSokoto Journal of Veterinary Sciences\u003c/em\u003e 15(1): 57. doi:10.4314/sokjvs.v15i1.8.\u003c/li\u003e\n\u003cli\u003eAbate, Begashaw, Admasu Woldesenbet, and Daniel Fitamo. 2015. \u0026ldquo;Water Quality Assessment of Lake Hawassa for Multiple Designated Water Uses.\u0026rdquo; \u003cem\u003eWater Utility Journal\u003c/em\u003e 9: 47\u0026ndash;60.\u003c/li\u003e\n\u003cli\u003eAbnet, Woldesenbe, and Mengistou Seyoum. 2020. \u0026ldquo;Evaluation of Multi-Assemblage Metrics and Temperate Indices as Indicators of Human Impact in Lake Ziway, Ethiopia.\u0026rdquo; \u003cem\u003eEthiopian Journal of Biological Sciences\u003c/em\u003e 19(1): 61-80-61\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eAdeme, Arega Shumetie. 2014. \u0026ldquo;Source and Determinants of Water Pollution in Ethiopia: Distributed Lag Modeling Approach.\u0026rdquo; \u003cem\u003eIntellectual Property Rights: Open Access\u003c/em\u003e 2(2). doi:10.4172/2375-4516.1000110.\u003c/li\u003e\n\u003cli\u003eAdimasu, Woldesenbet Worako. 2015. \u0026ldquo;Physicochemical and Biological Water Quality Assessment of Lake Hawassa for Multiple Designated Water Uses.\u0026rdquo; \u003cem\u003eJournal of Urban and Environmental Engineering (JUEE)\u003c/em\u003e 9(2): 146\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eAkhtar, Naseem et al. 2021. \u0026ldquo;Modification of the Water Quality Index (WQI) Process for Simple Calculation Using the Multi-Criteria Decision-Making (MCDM) Method: A Review.\u0026rdquo; \u003cem\u003eWater\u003c/em\u003e 13(7): 905. doi:10.3390/w13070905.\u003c/li\u003e\n\u003cli\u003eAlkarkhi et al. 2008. \u0026ldquo;Evaluation of Spatial and Temporal Variation in River Water Quality.\u0026rdquo; \u003cem\u003eInt. 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The purpose of the study was to explore how different methods describe the \u0026ldquo;health\u0026rdquo; of the wetlands and how different approaches relate to each other. The physicochemical parameters were measured in-situ with portable multimeter and nutrients and chlorophyll \u003cem\u003ea\u003c/em\u003e were determined by following the standard procedures outlined in the United States Environmental Protection Agency using UV/Visible photometer (Spectrophotometer). The trophic state index (TSI) of wetlands was determined using trophic state variable and Carlson model. The lake water quality index (WQI) was also evaluated using data from multiple water quality parameters into a mathematical equation to express the overall water quality at each study wetland and season. The water quality datasets were subjected to four multivariate statistical techniques, namely, univariate analysis of variance (univariate ANOVA), cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA). Analysis of the physicochemical dataset using univariate analysis indicated a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of dissolved oxygen, electrical conductivity, Secchi depth a.m., and p.m., salinity, nitrate, total ammonia, total nitrogen, total phosphorous, and Chlorophyll-a while water temperature, water depth, soluble reactive phosphorous were not affected (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) by the interaction between wetland by season. Spatial diversity and site grouping based on water quality characteristics using CA, PCA and FA analysis grouped the 6-wetlands into four clusters based on the similarity of water quality characteristics. The four clusters displayed in the dendrogram were grouped into least polluted cluster 1 (WO and RA), slightly polluted cluster 2 (MRM). moderately polluted cluster 3 ( GRM and ZG ) and highly polluted cluster 1 (AV). There was a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for the mean value of total trophic state index (TOT\u003csub\u003eTSI\u003c/sub\u003e), total nitrogen trophic state index (TSI\u003csub\u003eTN\u003c/sub\u003e), total phosphorous trophic state index (TSI\u003csub\u003eTP,),\u003c/sub\u003e total chlorophyll-a trophic state index (TSI\u003csub\u003eChla\u003c/sub\u003e) ,and total Secchi depth trophic state index (TSI\u003csub\u003eSTD\u003c/sub\u003e). However, there was no a significant interaction between wetland and season (ANOVA, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for the mean value of WQI. In conclusion, ranking of the pollution status of wetlands of Lake Tana using different approaches in this study using multivariate statistics, Carlson TSI, and WQI model suggest that some wetlands did not fit completely in the same category The current study on water quality variables of Lake Tana recommends that top priority should be given to regular water quality monitoring, in conjunction with biodiversity and fish health assessment.\u003c/p\u003e","manuscriptTitle":"Physical and chemical water quality characteristics in six wetlands of Lake Tana, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 09:10:49","doi":"10.21203/rs.3.rs-3993010/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3f6a92f3-bafb-40bd-9ff7-4eb581bd601f","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-26T06:12:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-29 09:10:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3993010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3993010","identity":"rs-3993010","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}