Spatio‑temporal assessment of nutrient pollution and water quality in Lake Hashenge, Ethiopia

Spatio‑temporal assessment of nutrient pollution and water quality in Lake Hashenge, 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 Spatio‑temporal assessment of nutrient pollution and water quality in Lake Hashenge, Ethiopia Berhanu Menasbo Tegegne, Emiru Birhane, Fasil Ejigu Eregno, Ståle Lief Haaland, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8035197/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 Freshwater quality is increasingly threatened by nutrient pollution, especially in closed-basin lakes like Lake Hashenge (LH), Ethiopia. This study explored how nutrient levels change across space and time between 2022 and 2025 and how they affect water quality, using the Drivers-Pressures-State-Impact-Response (DPSIR) framework. We collected 180 water samples from 15 sites during both wet and dry seasons and analyzed key physico-chemical and nutrient parameters using standard methods. Statistical tools—Principal Component Analysis (PCA) and Cluster Analysis (CA)—helped identify pollution sources and group sites by pollution status, while the Water Quality Index (WQI) and Comprehensive Pollution Index (CPI) provided an overall picture of ecological health. Results showed clear seasonal differences: nutrients tended to accumulate more during the dry season when dilution was limited. PCA pointed to nutrient-driven eutrophication, particularly at Debir, Endedo, and Abakiros, linked to human activities. Chlorophyll-a levels confirmed hypertrophic conditions throughout the year. CA revealed three pollution categories, highlighting spatial variability. While overall pollution was moderate, local hotspots stress the need for targeted watershed management, including land-use planning, buffer zones, and community awareness programs. nutrient pollution WQI multivariate analysis eutrophication DPSIR seasonal variation Ethiopia Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Freshwater ecosystems play a critical role in maintaining biodiversity, ensuring water security, and underpinning socioeconomic development. However, these systems worldwide are increasingly threatened by pressures such as population growth, rapid urbanization, intensified agriculture, and climate variability, all of which contribute to the accelerated decline of water quality (Bănăduc et al., 2022; Manashree, 2023). Of particular concern are anthropogenic nutrient inputs, especially nitrogen (N) and phosphorus (P), which drive eutrophication, harmful algal blooms, and consequent ecological disruptions in aquatic environments (Akhtar et al., 2021; Khatri & Tyagi, 2015). Closed-basin lakes in semi-arid and highland regions like LH in Northern Ethiopia are especially susceptible due to their limited hydrological exchange and reliance on seasonal water cycles (Gebreslase, 2015a; Yazew et al., 2013). In Ethiopia, lakes hold significant ecological and economic value by providing vital ecosystem services such as fisheries, irrigation, and cultural amenities. Despite this, they face unprecedented pressures from agricultural intensification, land-use changes, wastewater discharge, and livestock grazing, which substantially contribute to nutrient enrichment (Soares et al., 2017; Xu et al., 2010). The application of fertilizers, particularly in steep highland catchments, heightens the risks of nutrient runoff, sedimentation, and algal proliferation. These challenges are further intensified by climate change, which alters precipitation regimes, heightens drought and flood occurrences, and accelerates nutrient cycling (Geris et al., 2022; Havens & Jeppesen, 2018; Markandya, 2010). As a result, water bodies such as LH are witnessing declining water quality, hypertrophic conditions, and reduced suitability for aquatic organisms and human use (Ayele, 2021; Fetahi, 2019; Menberu et al., 2021). Effective water quality monitoring and assessment are essential for mitigating these threats, yet traditional approaches often require considerable resources and may have limited coverage (Carr G.M. and Neary J.P, 2019) . Multivariate statistical methods, including Principal Component Analysis (PCA) and Cluster Analysis (CA), provide robust means to simplify complex datasets, identify pollution sources, and classify water bodies based on contamination levels (Bhattrai et al., 2017; Zeinalzadeh & Rezaei, 2017). Additionally, composite indices such as the Water Quality Index (WQI) and Comprehensive Pollution Index (CPI) integrate multiple water quality parameters into accessible formats that facilitate communication with policymakers and stakeholders (Al-Mayah & Mashaanrabee, 2018; Chidiac et al., 2023; Howladar et al., 2018). These analytical tools are particularly valuable in data-limited settings like Ethiopia, where monitoring capacity is constrained but management demands are high. The Drivers Pressures State Impact Response (DPSIR) framework offers a comprehensive perspective linking human activities with environmental changes, ecological impacts, and management responses (Geris et al., 2022; Kifle Arsiso et al., 2017; Markandya, 2010; Mouratiadou et al., 2016). Applying this framework to nutrient pollution allows for the identification of critical land-use drivers and lake ecosystem responses, thereby informing prioritized interventions such as buffer zone implementation, riparian vegetation restoration, and the adoption of sustainable agricultural practices. Integrating DPSIR with PCA, CA, WQI, and CPI enables a holistic approach to understanding and managing nutrient pollution in LH. Despite its ecological significance, LH has received limited systematic study concerning nutrient loading, seasonal water quality trends, and pollution sources. Prior research has focused on localized hydrological and land-use assessments but has not delivered comprehensive spatio-temporal analyses incorporating physical, chemical, and biological indicators (Gebreslase, 2015a; Tibebe et al., 2022). Given the rapid land-use changes and emerging climate pressures in Northern Ethiopia, addressing this knowledge gap is crucial for preserving the lake's ecological integrity and the socioeconomic benefits it provides. Accordingly, this study investigates the spatio-temporal dynamics of nutrient pollution in LH from 2022 to 2025, employing an integrative framework that combines multivariate statistical analyses, composite water quality indices, and DPSIR modeling. The specific objectives are to (i) evaluate spatial and seasonal variations in key water quality parameters; (ii) identify primary nutrient pollution sources using PCA and CA; (iii) analyze the relationships between land use and pollution dynamics within the DPSIR framework; and (iv) calculate and interpret WQI and CPI values to assess ecological status across different sites and seasons. The results aim to support the development of cost-effective monitoring strategies and evidence-based management interventions, thereby promoting sustainable governance of LH and other vulnerable freshwater ecosystems across Ethiopia and beyond. Materials and methods Description of the study area LH, is a high-altitude, closed-basin lake located near Korem town in Northern Ethiopia, positioned between 1,386,000–1,400,000m N and 550,000–560,000m E UTM (Fig. 1 ) . The lake lies at an elevation of 2,440 meters above sea level (Table 1 ), with a surface area of approximately 20 km² and an average depth of 16 m (Yazew et al., 2013 ). The watershed covers 80.8 km² with a 33% average slope and consists of cultivated land, forests, grazing areas, and settlements (Shimbahri, 2015 ) and has a shoreline of 14 km. The surrounding mountains from 2,440 to 3,600 m.a.s.l. (Table 1 ) drain seasonal streams into the lake, contributing to non-point source pollution from farmlands and grazing fields. Cultivated land covers 25.7 km² (31.8% of the watershed), with N and P runoff being major concerns (Tegegne M. et al., 2025). The area receives 788.7 mm of annual rainfall (Fig. 2 ), with a mean temperature of 15.3 C (Yazew E. et al., 2013 ).The geographic coordinates are latitude 38˚45–48'N and longitude 128˚42–44'. Table 1 Water quality monitoring stations location and coordinates Sampling site Code North East Elevation (m) Debir D1 12 o 35’43.67’’ 39 o 29’26.86’’ 2409 D2 12 o 35’34.34’’ 39 o 29’32.33’’ 2409 D3 12 o 35’29.04’’ 39 o 29’38.25’’ 2409 Endedo E1 12 o 36’6.60’’ 39 o 39’25.98’’ 2409 E2 12 o 36’3.74’’ 39 o 30’22.10’’ 2409 E3 12 o 35’07.54’’ 39 o 30’18.88’’ 2409 Abakiros Ab1 12 o 34’52.13’’ 39 o 31’6.80’’ 2409 Ab2 12 o 34’49.38’’ 39 o 30’55.19’’ 2409 Ab3 12 o 34’46.17’’ 39 o 30’43.73’’ 2409 Adigolo Ag1 12 o 33’26.63’’ 39 o 29’45.37’’ 2409 Ag2 12 o 33’40.52’’ 39 o 29’45.51’’ 2409 Ag3 12 o 33’58.20’’ 39 o 29’48.65’’ 2409 Adiminda Am1 12 o 34’33.44’’ 39 o 28’54.45’’ 2409 Am2 12 o 34’32.53’’ 39 o 28’59.10’’ 2409 Am3 12 o 34’32.06’’ 39 o 29’61’’ 2409 Water resources in LH catchment are rich as the rain is available all year round. Still, water managers face a series of difficulties as the awareness of global climate impacts on precipitation patterns increases for example 83.55% of the outflow water is lost by climate change evaporation (Dore, 2005 ; Haider, 2019 ; He et al., 2012 ; Yazew et al., 2013 ). Meanwhile, with rapid economic development and the population explosion in the basin, human activities, including deforestation and land-use change, significantly affect the water supply and demand patterns(Abay et al., 2014b ), while simultaneously being exacerbated by increased pollutant loading(Tegegne Berhanu et al., 2025 ). Elevation and Land use land cover of the LH Catchment Catchment characterization is a critical step in identifying the sources of pollution affecting lakes and reservoirs. In the case of LH, key physical features such as elevation, land use, and land cover (Fig. 3 ) serve as important precursors for understanding nutrient and sediment inflows. This process involves integrating qualitative indicators, such as soil type, vegetation cover, and visible signs of active erosion, with quantitative data on slope gradients, rainfall intensity, and total catchment area. Such combined analysis provides essential input for estimating sediment loads, which are a major contributor to water quality degradation in lakes (Gebreslase, 2015). These sediments not only reduce water clarity but also act as carriers of nutrients and pollutants, accelerating eutrophication and threatening aquatic ecosystems. Sampling and laboratory analysis Sampling and analyses Water samples were collected during both wet and dry seasons between 2020 and 2023 across fifteen sampling stations distributed among five main villages-Debir, Endedo, Abakiros, Adigolo, and Adiminda- (Fig. 1 ). Each site was selected to represent littoral, riverine, and profundal zones, thereby capturing both spatial and seasonal heterogeneity in water quality conditions. 180 samples were collected in acid-washed high-density polyethylene (HDPE) bottles (1,000ml capacity), pre-rinsed with Milli-Q water and sample water to minimize contamination(Rama et al., 2013 ). Samples were immediately stored in ice-cooled containers and transported to the laboratory within 72 hours. Samples were stored at 4 C before analyses, and all analyses were finished within seventy-two hours of their collection, except in situ parameters (American Public Health Association, 2001). Water quality of the collected samples was assessed for thirteen parameters viz. dissolved oxygen (DO), total dissolves solid (TDS), turbidity, temperature(temp.), nitrate (NO 3 -N), pH, soluble reactive phosphorous (SRP), iron (Fe), silicate (SiO 2 -Si), total nitrogen (T-N), total phosphorous (T-P) and ammonium (NH 4 -N), and chlorophyll-a (chl-a) following standard procedures(American Public Health Association, 2001; Environmental et al., 2003 ; Health et al., 1994 ). Temp., pH, turbidity, and DO were measured in situ at the sites during sample collection. All the chemicals were of analytical grade and purchased from Mekelle, Ethiopia. Milli-Q water was used for the preparation of all reagents and standards. The analytical methods for surface water samples and all the laboratory work is summarized using standard references (Table 2 ) and done in School of Earth Science, Mekelle University. Table 2 Water quality parameters, abbreviations, analytical techniques and remarks for analysis Parameter Analytical Technique Method Reference Remarks Chlorophyll-a (Chl-a) Spectrophotometry or Fluorometry APHA 10200 H or EPA 445.0 Extracted with acetone; Soluble Reactive Phosphorus (SRP) Molybdenum Blue Method (Colorimetry) APHA 4500-P E or EPA 365.1 Filtered sample; measures orthophosphate only Total Phosphorus (T-P) Persulfate Digestion + Molybdenum Blue Method (Colorimetry) APHA 4500-P B & E or EPA 365.1 Includes particulate and dissolved forms Ammonium-Nitrogen (NH₄⁺-N) Indophenol Blue Method (Colorimetry) APHA 4500-NH₃ G or EPA 350.1 Can also use ion-selective electrode Nitrate-Nitrogen (NO₃⁻-N) Cadmium Reduction Method (Colorimetry) or Ion Chromatography APHA 4500-NO₃ E or EPA 353.2 May require filtration and preservation with H₂SO₄ Total Nitrogen (TN) Persulfate Digestion + Nitrate Measurement (Colorimetry) APHA 4500-N B or EPA 351.2 Measures organic and inorganic N forms Silicate (SiO₂) Molybdosilicate Method (Colorimetry) APHA 4500-Si D or EPA 200.7 Filtered sample; use within 24 hours Iron (Fe²⁺/Fe³⁺) Atomic Absorption Spectrophotometry (AAS) APHA 3111 B or EPA 200.9 Acidified sample; can also be measured by colorimetry using phenanthroline Multivariate statistical methods Multivariate statistical analyses were employed to evaluate water quality variability and pollution sources. Cluster Analysis (CA ) is a way to group similar monitoring points. Points in the same group are closely related, while those in different groups are less similar, with hierarchical clustering being the most common method. A dendrogram helps visualize these groups (Dutta et al., 2018 ; Gu et al., 2016 ; Khattree & Naik, 2012 ; Tibebe et al., 2022 ). In this study, Ward’s method with squared Euclidean distances was used on normalized data for clustering according to their pollution profiles. Principal Component Analysis (PCA) was conducted to reduce dataset dimensionality and identify the major factors contributing to spatio-temporal variation in water quality(Gradilla-hernández et al., 2020 ; Khattree & Naik, 2012 ; Rahman et al., 2021 ; Zhong & Zhang, 2018 ). PCA helps find relationships between features in a dataset. It breaks data into factor loadings, factor scores, and residuals. Since fewer factors are extracted than the original features, it reduces data complexity. After rotating the factor loadings, the factors can often be linked to their sources(Tibebe et al., 2022 ). Eigenvalues greater than 1 and factor loadings above 0.6 were considered significant, and Varimax rotation was applied to enhance interpretability. All statistical analyses were performed using the IBM SPSS (Version 20) and OriginPro 2025 statistical software. A Comprehensive Pollution Index (CPI) To integrate multiple physicochemical parameters into a single pollution measure, the Comprehensive Pollution Index (CPI) was calculated following Zhao et al. ( 2012 ). The pollution index for each parameter is calculated using the formula: $$\:{P}_{i}=\frac{{C}_{i}}{{S}_{i}}$$ ……………………… 1 Where: P i is the pollution index for parameter i, C i ​ is the measured concentration of parameter i in water, and S i​ is the standard or permissible limit for parameter i (as per water quality guidelines). The overall CPI is determined by summing the individual pollution indices as follows (Tibebe et al., 2022 ; Zhao et al., 2012 ). $$\:CPI=\frac{1}{n}{\sum\:}_{n=1}^{n}{P}_{i}\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\dots\:\:\:\:\:\:\:\:\:\left(2\right)$$ Where n is the number of parameters considered. The computed P values (Table 4 ), serve as a basis for categorizing the lake’s water quality status. Table 4 The Factor loading values and explained variance of water quality in two seasons (positive and negative strong correlations are marked bold) Parameters a. Wet season b. Dry Season PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 PC5 Total nitrogen, T-N 0.94 0.15 0.54 0.17 0.55 0.15 0.92 0.11 -0.04 Ammonium, NH 4 -N 0.93 0.22 -0.06 0.12 0.61 0.41 0.09 0.47 0.47 Nitrate, NO 3 -N 0.91 0.04 -0.11 0.06 0.71 0.27 -0.51 0.66 -0.19 pH 0.78 -0.07 0.37 -0.06 0.76 0.18 -0.09 0.09 -0.14 Dissolved oxygen, DO 0.68 0.21 0.59 -0.24 0.91 0.11 0.03 -0.21 -0.08 Total dissolved solids, TDS 0.82 0.9 -0.17 -0.14 0.66 0.81 0.12 0.56 -0.21 Total phosphorous, T-P 0.76 0.84 -0.03 -0.04 0.58 0.52 -0.06 -0.04 0.44 Soluble reactive P, SRP 0.38 0.69 0.1 0.24 0.54 0.85 -0.06 0.10 0.34 Silicate, SiO2-Si - 0.55 0.66 -0.24 -0.16 -0.71 0.07 -0.33 -0.07 0.31 Iron, Fe -0.30 0.65 0.38 0.36 -0.36 -0.39 -0.09 -0.05 0.76 Temperature, Temp. 0.55 -0.16 0.84 0.21 0.51 -0.33 0.53 0.17 -0.08 Chlorophyll-a, Chl-a -0.23 -0.14 -0.12 -0.86 -0.14 -0.04 -0.30 -0.87 -0.02 Turbidity 0.37 -0.40 0.01 0.48 0.74 -0.11 -0.03 0.35 0.165 Eigenvalue 6.63 3.16 1.49 1.05 5.96 3.02 2.04 1.31 1.10 % of variance 38.87 22.78 11.44 8.07 34.32 15.53 10.75 10.09 8.43 % of Cumulative variance 38.87 61.66 73.09 81.2 34.32 49.85 60.60 70.69 79.12 When the CPI is below 1 (Table 3 ), the water is considered suitable for both human consumption and sustaining aquatic ecosystems. However, if the CPI exceeds 3, it signals possible environmental hazards, necessitating immediate pollution mitigation strategies. This approach plays a crucial role in detecting contamination-prone areas, directing remediation initiatives, and shaping effective water resource management policies. Table 3 Standard of surface water quality classification (WHO, 1996) CPI Range Water Quality Status Level 2.01 Severe pollution V Statistical analysis Lake water quality data were examined using CA and PCA (Gradilla-hernández et al., 2020 ; Rahman et al., 2021 ; Zhong & Zhang, 2018 ), along with other statistical methods. To evaluate variations across and within sampling sites, Analysis of Variance (ANOVA) was performed at a 95% confidence level using SPSS (version 20) and Origin Pro 2025 software. Spatial variation was assessed by comparing differences among sites, while temporal variation was evaluated by examining differences across seasons. Statistical significance was considered at p < 0.05. Result Spatial and temporal water quality variation of LH The physical parameters The physicochemical characteristics of the lake showed seasonal variations among sites (Figs. 4 and 5 ). Temperature, pH, turbidity, dissolved oxygen (DO), total dissolved solids (TDS), and chlorophyll-a (Chl-a) all showed significant differences between the wet and dry seasons, demonstrating the impact of hydrology and climate on water quality (Baig et al., 2017 ; Li et al., 2019 ; G. Yang et al., 2016 ). Temperature and pH During the wet season, surface temperatures ranged from 19.9 C (Abakiros) to 21.4 C (Debir), and increased slightly in the dry season (20.7 to 22.1 C), reflecting the stronger solar radiation and reduced cloud cover (Dai et al., 1999 ; Shinohara et al., 2021 ; Weng & Fu, 2014 ; K. Yang et al., 2020 ). The water remained alkaline throughout the year, with pH levels ranging from 8.9 to 9.8 during the wet season and from 9.0 to 10.0 during the dry season. Debir consistently had the highest pH, most likely because of increased photosynthesis and less dilution (Maberly, 1996 ; Tank et al., 2009 ). Turbidity and TDS Turbidity was normally low, but increased during the rainy season (0.7 to 2.5 NTU), indicating runoff-driven sediment input at Endodo and Debir. During the dry season, readings plummeted to 0.3 to 1.3 NTU, indicating limited runoff. TDS followed the same pattern, peaking at 951 mg/L in Abakiros during the wet season and decreasing to 414 to 664 mg/L during the dry. Dissolved Oxygen (DO) Higher levels of DO were found during the rainy season. For instance, Debir rose from 6.87 mg/L (dry) to 7.3 mg/L (wet), most likely as a result of increased oxygen solubility brought on by low temperatures, precipitation, and wind-driven mixing. Chlorophyll-a Levels of chlorophyll-a, which indicate the biomass of phytoplankton, vary by location but not by season. Abakiros, Ab3 showed milder fluctuations from 92.48 µg/L to 89.98 µg/L, suggesting either seasonal nutrient enrichment or variability in light conditions and grazing pressure. In contrast, Adiminda maintained the highest amounts (157 µg/L dry, 155 µg/L wet), showing steady algal productivity. Nutrients : Ammonium (NH₄-N) peaked during the wet season (0.6–3.8 mg/L), particularly at Debir, due to runoff and mineralization (Figs. 1 and 2 ). During the dry season (Tables 3 and 4 ), values dropped to 0.1–1.0 mg/L, likely due to nitrification and reduced loading. Nitrate (NO₃-N) remained low year-round (≤ 0.5 mg/L), reflecting rapid uptake and denitrification. Total nitrogen (T-N), however, rose sharply in the dry season (up to 13.9 mg/L at Endodo), pointing to sediment release under stagnant conditions. Phosphorus remained relatively stable across seasons: soluble reactive phosphorus (SRP) was 0.1 to 0.4 mg/L, and total phosphorus (T-P) 0.2 to 0.9 mg/L, with slightly higher wet-season values. Silica and Iron Silica was highest in the wet season (2.0–5.0 mg/L), especially at Adigolo, due to runoff from silicate-rich soils (Kabeto et al., 2012 ). Concentrations declined to 0.3 to 2.5 mg/L in the dry season, likely reflecting uptake by diatoms. Iron levels were low overall but rose locally in the dry season (up to 0.5 mg/L at Adiminda), possibly from sediment release under low-oxygen conditions. Trophic Status Chl-a concentrations confirmed hypertrophic conditions year-round. Wet-season values ranged from 55.8 µg/L (Adigolo) to 112.8 µg/L (Adiminda), closely linked with NH₄-N enrichment. In the dry season, levels remained similarly high (58.3 to 115.3 µg/L). This persistence indicates strong internal nutrient recycling and favorable conditions for algal growth (Tegegne et al., 2025). Site-Specific Patterns Each site around LH reflected its own story of nutrient dynamics and ecological pressures. At Debir, water consistently showed higher levels of pH, NH 4 -N, T-N, and T-P, pointing to continuous inputs from farming and settlements nearby. Endedo displayed a clear seasonal shift: N and Fe were more pronounced in the dry months, while P and turbidity rose during the rains, likely due to sediment disturbance and runoff. Abakiros remained relatively balanced during the dry season but experienced sharp increases in silica, TDS, and turbidity once rainfall began, underscoring its vulnerability to catchment erosion. Adigolo stood out as the most stable site, with little seasonal fluctuation, suggesting that its surrounding landscape or lake setting provides some natural shielding. Adiminda, on the other hand, was remarkable for its consistently high chlo-a levels in both seasons, slightly higher in the dry period, showing that phytoplankton productivity is strong and sustained throughout the year, likely supported by internal nutrient recycling. Multivariate analysis in wet and dry seasons Principal Component Analysis Principal Component Analysis (PCA) was conducted on water quality data collected from LH to reduce dimensionality and identify the major variables driving water quality variability in wet and dry seasons. The scree plot (Fig. 7 ) displayed the eigenvalues in descending order, indicating that after the 4th PC in the wet season and the 5th PC in the dry season, the curve begins to flatten. This suggests that subsequent components contribute minimally to the overall variance and can be disregarded. In the wet season, the first four PCs explained a cumulative 81.2% of the total variance in water quality data (Table 4 a ) . The first PC alone explained 38.87% of the variation across sites and was primarily associated with nutrient parameters (T-N, NH₄-N, and NO₃-N), pH, and TDS. The second PC contributed 22.78% of the total variance and was strongly correlated with T-P, SRP, SiO₂-Si, Fe, and TDS. The third PC, explaining 11.44% of the variation, was defined by high loadings of Chl-a, temperature, DO, and pH ( Table SM1 ) In the dry season, 4 PCs together explained 79.12% of the total variance. The first PC explained 34.32% of the variation and included the same key parameters ( Figure SM1) , was heavily loaded by DO, NO₃-N, pH, and turbidity, indicating the dominance of oxygenation and nitrogen-related processes (Table 4 b). PC2 (15.53%) represented mineral and phosphorus influence, similar to the wet season, with high positive loadings for TDS and SRP. PC3 (10.75%) was associated with T-N, while PC4 (10.09%) captured temperature variability. PC5 (8.43%) exhibited that dry season-specific component features a strong loading for Fe (0.76), underscoring the increased influence of geological or sedimentary sources during low inflow periods. The absence of this component in the wet season may reflect dilution effects or less pronounced sediment interaction. The bi-plot of PCs during the wet season (Fig. 6 a) shows that the Debir sampling sites (D1, D2, and D3) were characterized by nutrient-related variables (NH₃-N, NO₃-N, and T-N), as well as DO, pH, and turbidity, with strong associations along both axes. In the dry season (Fig. 6 b), the inimitability of the Debir sites was primarily influenced by nutrient parameters (NH₄-N, NO₃-N, T-N, SPR, and T-P), along with pH, TDS, temperature, and turbidity, which were mainly aligned with the horizontal axis. Meanwhile, in the dry season, variation in the Adiminda and Adigolo sites was driven predominantly by SiO₃-Si along the vertical axis and Chl-a along the horizontal axis. This indicates that the ecological dynamics of the Debir sites are significantly affected by nutrient availability during the dry season, whereas the Adiminda and Adigolo sites exhibit a different set of influences. Understanding these variations is crucial for developing targeted conservation strategies and managing the water quality effectively in these regions. Cluster analysis Hierarchical cluster analysis was performed to assess the spatial variation of water quality parameters across different sampling sites in LH during both dry and wet seasons. The resulting dendrogram revealed three major clusters for each season, indicating consistent spatial groupings but with seasonal shifts in cluster membership. A dendrogram of sampling sites were obtained using Ward’s method (Fig. 8 ). Fifteen sampling sites were divided into three groups. In the dry season, the dendrogram identified three distinct cluster sites, grouped according to similarities in physico-chemical and nutrient parameters. These clusters reflect relative homogeneity within each group and pronounced differences between groups, suggesting the influence of localized pollution sources, internal nutrient dynamics, and hydrological isolation during the dry months. During the wet season, the structure of the clusters changed. While three main groupings still emerged, the composition of sites within each cluster varied compared to the dry season. This indicates the role of seasonal hydrological processes such as runoff, sediment transport, and nutrient influx in altering water quality conditions and the spatial relationships among sites. Cluster 1 corresponded to the site riverine (D1, Ab1, E1, and Ag1), which was located on the lake shore of LH. Cluster 2 included site littoral part of the lake (D2, Ab2, E2, and Am1), which were located in the peripheral part of the lake. Cluster 3 contained sites in the profundal part of the lake in Debir, Endedo, Abakiros, Adigolo, and Adiminda. Discussion Spatial and temporal patterns of LH physico-chemical parameters Physico-chemical patterns of LH LH showed minimal seasonal variation in temperature (Table 5 ), averaging 20.7 C in the wet season and 21.5 C in the dry, consistent with other Ethiopian crater lakes (Tibebe et al. ( 2022 ). Its depth (16 m) stabilizes thermal conditions, with only slight warming during prolonged dry-season sunshine. Table 5 Comparative analysis of Lake Hashenge's physico-chemical and nutrient characteristics (mgL) with other tropical lakes. Lakes Temp( o C) DO pH EC SRP TP NO3-N SiO2-Si SD References Hawassa 23.5 5–7 8.66 846 0.0015 0.034 0.025 37.6 0.85 (Tilahun & Ahlgren, 2010a ) Chamo 26.3 5–9 8.84 1910 0.118 0182 0.033 1 0.18 (Tilahun & Ahlgren, 2010a ) Hayq 18.2 1-8.4 9 910 0.022 0.058 0.042 3.7 2.7 (Fetahi, 2010 ) Tana 20–27 5.9-7 7.3–8.5 115–148 1.8 - 0.1-1 - 0.51–1.82 (Wondie & Mengistou, 2006 ) Abaya - - 8.9 623 0.04 - - 40 - (Wood & Talling, 1988 ) Langano - - 9.4 1810 0.09 - - 48 - (Wood & Talling, 1988 ) Bishoftu - - 9.2 1830 0.1 - - 38 - (Wood & Talling, 1988 ) Abjata - - 10.2 13800 0.05 - - 128 - (Wood & Talling, 1988 ) Shala - - 9.9 19200 0.76 - - 112 -- (Wood & Talling, 1988 ) Chitu - - 9.8 28600 1.7 - - 320 - (Wood & Talling, 1988 ) Ziway 23 5 8.1 404 0.06 0.311 0.21 40.7 0.2 (Tibebe et al., 2022 ) Hashenge 19–21 5.9–7.1 8.9–10 207.3-475.5 0.1–9.4 0.2–0.9 0.01–0.5 0.3-5 1.3 Present study Water pH is an important measure because it shapes both chemical toxicity and biological activity in aquatic systems. In Lake Hashenge, the pH stayed alkaline in all seasons, ranging from 9.0–10.0 during the dry season and 8.9–9.8 in the wet season (Fig. 4 ). Although slightly on the higher side, these values generally fit within the WHO’s safe guideline of 6.5–9.5 (Tilahun & Ahlgren, 2010b ; WHO, 2007)(Table 5 ). The consistent alkalinity can be traced to the lake’s geochemical setting (Ghaemi & Noshadi, 2022 ) and strong photosynthetic activity, as algae absorb CO₂ and push pH upward (Hamdhani, 2024 ; Talling J.F., 2009). This suggests that natural biogeochemical processes play the dominant role in controlling pH year-round. While the usual freshwater range is between 6.0 and 8.5, the lake’s levels still remain within acceptable ecological limits (Herschy, 2012 ). DO showed clear seasonal and site-based differences across the lake. The average concentration (6.3 mg/L) was consistent with earlier findings (Teame et al., 2017 ). Lower DO values were recorded in the dry season, likely influenced by human activities such as fishing and washing (Fig. 4 d). In contrast, sites like Debir and Adiminda had higher DO, probably linked to greater growth of macrophytes and phytoplankton (Goshu, 2007 ). During the wet season, rainfall and dilution appeared to boost oxygen levels. Overall, DO remained within Ethiopia’s guideline range for aquatic life (5.0–9.0 mg/L) (Environmental et al., 2003 ), unlike heavily polluted lakes such as Ziway, where levels can drop to 1.4 mg/L near floriculture effluent (Tadele, 2012 ). TDS showed seasonal variation, averaging 531.5 mg/L in the dry season and rising to 830.2 mg/L in the wet season (Fig. 4 e). These values remain below the WHO guideline of 960 mg/L (Environmental et al., 2003 ) but are still elevated compared to ideal conditions. The increase is largely linked to runoff from degraded farmlands (Gebreslase, 2015b ), and concentration effects from evaporation. Although lower than previously reported values (Park et al., 2019 ; T et al., 2016), such levels may still pose long-term ecological risks (Dutta et al., 2018 ). Earlier studies characterized Lake Hashenge as highly turbid and eutrophic, with low water transparency of about 0.7 m due to catchment degradation and siltation (Gebreslase, 2015b ; Teame et al., 2016, 2017 ). In contrast, the present study recorded much lower turbidity, ranging from 0.3 to 2.5 NTU (Fig. 4 c), is well within the recommended limit of 5 NTU (Bhavan et al., 1991). This suggests some improvement in water clarity compared to past conditions. Nutrient Dynamics and Spatial Heterogeneity of LH Nutrient temporal dynamics of LH Nutrient levels in LH changed significantly by season (Fig. 5 ). The dry season exhibited a rise in most parameters, including NH₄-N, NO₃-N, T-N, SRP, T-P, and Fe. This was attributed to lower water volume, limited flushing, and increased evaporation (Alemayehu et al., 2020 ; Tadesse et al., 2018 ; Wetzel, 2001 ). Agricultural runoff and animal activities, particularly in Debir, Endedo, and Abakiros, contributed to the enrichment. Under intense sun radiation, these factors increase the risk of eutrophication (Bhateria & Jain, 2016 ; Bhattarai et al., 2017 ; Singh et al., 2004 ). In contrast, nutrient concentrations declined in the wet season due to dilution and enhanced flushing (Pant et al., 2021 ). However, SiO₂-Si levels were consistently higher during this period, likely due to increased runoff and sediment re-suspension rather than internal cycling (Sharpley et al., 1994 ). Sites such as Endedo, Adigolo, and Adiminda recorded silica concentrations exceeding 5 mg/L, reflecting strong catchment influence. Notably, Adiminda maintained lower levels of most nutrients in both seasons, suggesting limited external inputs and better ecological stability. In the wet season, most nutrient concentrations decreased due to dilution and flushing (Pant et al., 2021 ). SiO₂-Si, however, rose with runoff and sediment re-suspension (Sharpley et al., 1994 ), exceeding 5 mg/L at Endedo, Adigolo, and Adiminda. Adiminda consistently showed lower nutrient levels in both seasons, indicating limited external inputs and greater ecological stability. These results highlight the need for continuous seasonal monitoring, particularly during rainy periods when runoff shifts nutrient balance and influences phytoplankton and diatom communities. Maintaining water quality will depend on stronger watershed management and better land-use practices to reduce nutrient loading (Nafeza et al., 2023 ; Zeinalzadeh, K. & Rezaei, 2017 ). Productivity and Geochemical Influences Silica, iron, and chlorophyll-a levels showed evident seasonal variations related to hydrology and internal lake dynamics (Fig. 9 ). SiO₂-Si levels were highest in the wet season at Adigolo and Abakiros due to runoff from silicate-rich soils, but decreased in the dry season due to lower inflow and diatom uptake. Elevated silica levels (> 10 mg/L) in African lakes may promote diatom productivity and disrupt ecological equilibrium (Tibebe et al., 2022 ). Iron levels remained largely low, with only minor increases at Endedo and Adiminda throughout the dry season, most likely due to sediment release under low-oxygen circumstances. Chlorophyll-a levels climbed throughout the dry season, indicating nutrient enrichment and algae development. Chl-a concentrations, a proxy for phytoplankton biomass, remained consistently high across seasons, with peak levels recorded at Adiminda. This sustained elevation indicates ongoing productivity, likely driven by internal nutrient recycling and favorable climatic conditions that may promote non-native phytoplankton growth (Flores-Moreno et al., 2016 ; Justic et al., 2009 ). However, the weak correlation between Chl-a and nutrient levels suggests that factors such as light availability, grazing pressure, or micronutrient limitation may exert stronger control on algal biomass (Ayele, 2021 ). Spatial Heterogeneity of nutrients in LH Seasonal water quality patterns in LH reflect both catchment inputs and internal processes. At Debir, persistently high N and P indicate continuous external loading from agriculture and grazing, with wet-season rainfall intensifying N transport (Molla et al., 2024). Endodo showed seasonal contrasts, with elevated total nitrogen in the dry season and higher SRP and turbidity in the wet season, pointing to sediment interactions and phosphorus release under anoxic conditions (Alemayehu et al., 2023). At Abakiros, wet-season peaks in turbidity, silica, and TDS suggest runoff-driven erosion and mineral inputs, consistent with patterns in other Ethiopian lakes (Gebremedhin et al., 2022; Molla et al., 2024). Adigolo remained stable across seasons, likely due to its small, well-buffered catchment (Fenta & Belete, 2022). In contrast, Adiminda consistently recorded the highest chl-a levels, with slightly higher values in the dry season, reflecting sustained algal productivity supported by internal nutrient recycling and favorable post-rainfall conditions (Alemayehu et al., 2023; Gebremedhin et al., 2022). These findings highlight the need for targeted lake management - reducing nutrient input at Debir, controlling sediment and P at Endodo and Abakiros, and managing algal blooms at Adiminda. Preserving natural buffers and adapting to climate and land use changes are crucial for maintaining ecosystem resilience (Houghton et al., 2001 )(IPCC, 2021). Multivariate analysis of nutrient pollution in LH Principal Component Analysis (PCA) We used Principal Component Analysis (PCA) to pinpoint the main drivers of eutrophication in Lake Hashenge (LH). The dataset was suitable for PCA, with a Kaiser–Meyer–Olkin (KMO) value of 0.683 and a highly significant Bartlett’s test (p < 0.0001), indicating strong relationships among variables. Variables with low communalities (< 0.5) were removed, and only those with strong factor loadings (≥ 0.6) were kept (Dharmarathna & Galagedara, 2024 ; Tibebe et al., 2022 ). PCA revealed five key components during the dry season and four during the wet season, with Varimax rotation applied to make the patterns clearer and easier to interpret. In the wet season, four principal components explained 81.2% of the variation in Lake Hashenge. The first component, influenced by nutrients such as T-N, NH₄-N, NO₃-N, and T-P, along with pH, TDS, and DO, was most evident at Debir, Ag1, and E1. These patterns suggest strong human impacts—agricultural runoff, grazing, fertilizer use, and domestic wastewater—with phosphorus likely from soils and nitrogen from fertilizers and organic matter, reflecting the lake’s overall eutrophic state (Barnard et al., 2012 ; Hamdhani, 2024 ; Magdoff, 1993 ; Ndungu et al., 2015 ; Tibebe et al., 2022 ). The rotated component matrix (Table 4 ) highlights how different water quality parameters are grouped across the PCs. In the wet season, PC1 emphasizes nutrient inputs, whereas PC2, with high loadings for TDS, SRP, SiO₂-Si, and Fe, points to a combination of domestic sources (e.g., detergents) and geological contributions. Silica likely comes from bedrock weathering, and iron may be released through redox processes or sediment disturbance, indicating additional non-point sources (Hamdhani, 2024 ; Tibebe et al., 2022 ). PC3 was dominated by temperature and DO, reflecting seasonal thermal dynamics, while PC4 had negative loadings for Chl-a at Adiminda (Am1–Am2), Abakiros (Ab1–Ab2), and E1, possibly due to algal nutrient uptake or senescence. The PCA results and biplot (Fig. 6 a) for the wet season highlight how human activities and natural processes shape water quality in LH. Nutrient enrichment was most pronounced at Debir, Ag1, and E1, where high levels of T-N, NH₄-N, NO₃-N, and T-P point to strong influences from agriculture, grazing, fertilizer application, and domestic wastewater (Barnard et al., 2012 ; Hamdhani, 2024 ; Magdoff, 1993 ; Ndungu et al., 2015 ; Tibebe et al., 2022 ). In contrast, PC2 suggested that some water quality patterns are linked to natural geological sources, with silica originating from bedrock weathering and iron mobilized through sediment disturbance or redox processes (Hamdhani, 2024 ; Tibebe et al., 2022 ). Temperature and dissolved oxygen, represented by PC3, reflected the seasonal thermal dynamics of the lake, while negative Chl-a loadings in PC4 at sites like Adiminda and Abakiros likely indicate periods of algal nutrient uptake or senescence. The wet-season biplot further emphasized these trends, showing nutrient hotspots at Debir, Ag1, and E1, geological influences at Am1 and Ab2, and localized algal activity at Am2 and Ag2. Profundal sites such as Am3, Ab3, and E3 appeared largely insulated from surface pollutants, except for iron, underscoring the spatial variability in nutrient and contaminant dynamics across the lake. Overall, these findings suggest that LH’s water quality is shaped by a complex interplay of human-induced nutrient loading and natural geological processes, with clear spatial patterns that reflect both catchment activities and in-lake ecological responses. During the dry season, five principal components explained 79.12% of total variance (Table 4 ), with PC1 accounting for 43.3%. PC1 measured a variety of variables, including NH₃-N, NO₃-N, SRP, T-P, TDS, pH, DO, and Chl-a, indicating persistent nutrient pollution at Debir, Ag1, and E1 during low inflow. The reduced dilution in this season likely amplified nutrient concentrations from livestock waste, domestic uses, and shoreline activities. PC2, with high loadings of SRP and TDS, particularly at Debir D1, indicated pollution from fertilizers and pesticides at the lake’s edge (Hamdhani, 2024 ; Tibebe et al., 2022 ). PC3 and PC4 isolated the influence of T-N and Fe, respectively, pointing to localized nitrogen enrichment and possible internal loading or sediment interaction. The fifth component had minor influence but added nuance to site-specific variation In the dry season, Chl-a loaded positively with key nutrient indicators, reflecting an algal response to nutrient-rich yet relatively stable water conditions (Table 4 ). The consistent influence of TDS and SRP across principal components further indicates the sustained impact of both natural processes and human activities. This pattern is clearly illustrated in the PCA biplot for the dry season (Fig. 6 b). Similar to the wet season, Debir and E1 were influenced by several nutrient vectors—particularly TDS, NH₃-N, and SRP. However, the shorter vector lengths suggest seasonally weaker correlations, likely resulting from reduced runoff or limited internal nutrient recycling during the dry period. Meanwhile, Am2 and Ag2 again aligned closely with Chl-a, highlighting potential zones of algal proliferation, whereas profundal sites remained largely detached from the major pollution vectors—except for Fe, which continued to exert some influence. Cluster Analysis (CA) Cluster analysis (CA) using Ward’s method grouped the fifteen sampling sites in LH into three statistically significant clusters (Fig. 5 ), reflecting distinct spatial variations in water quality. These groupings were influenced by factors such as natural background features, land use/land cover, and anthropogenic activities (Tibebe et al., 2022 ). Cluster I primarily included sites such as Ab1, Ag1, D1, and El (excluding Am1 during the wet season) and Ab1, Ag1, Am1, D1, and El (excluding Ab2 and D2 in the dry season). These sites are mostly located along river inlets and the lake shore, where runoff from surrounding agricultural fields and grazing areas is prevalent. As a result, Cluster I sites represent highly polluted (HP) zones, influenced heavily by agrochemical inputs and livestock activities (Abay et al., 2014a ; Gebreslase, 2015b ). Cluster II comprised sites like Ab2, D2, and E2 in the wet season (excluding Am1) and only D3 in the dry season. These sites are positioned around the littoral zone of the lake and showed moderate pollution (MP) levels. The placement of only one site (D3) under Cluster II during the dry season may be attributed to the lake’s flat bathymetric and contour profile, which results in hydrological similarities between the littoral and surrounding zones (Yazew, Mesfin, GebreSamueal, et al., 2013). Cluster III included Ab3, Ag3, Am3, D3, and E3 in the wet season (excluding Ag2 and Am2) and similar sites in the dry season. These sites, generally situated in the deeper (profundal) part of the lake, represent relatively less polluted (LP) areas, with limited direct anthropogenic disturbance. The exclusion of more impacted sites suggests these central zones are hydrologically more stable, as supported by similar findings from other Ethiopian lakes (Tibebe et al., 2022 ). Spatial variations were evident, with the grouping of E1 in Cluster II and Ab2 and D2 in Cluster I during the wet season, indicating some overlap in water quality characteristics between clusters. Nevertheless, the consistent placement of central sites (e.g., Ab3, Ag3, Am3, and E3) in Cluster III across both seasons confirms better water quality in the lake’s profundal region. The CA results suggest that the technique provides a reliable spatial classification of water quality, enabling a more targeted and cost-effective monitoring strategy. This approach can guide the selection of representative sites, reducing redundancy without compromising data quality. Similar utility of CA for spatial optimization has been reported in other studies (Bhattarai et al., 2017 ; Khattree & Naik, 2012 ; Singh et al., 2004 ). Moreover, the integration of CA and PCA proved valuable in source apportionment and understanding parameter associations, as noted in comparable studies. For instance, Zhao et al. ( 2012 ) used these techniques to assess nutrient sources in Baiyangdian Lake, highlighting runoff-driven pollution during the wet season and point-source inputs in the dry season. Ndungu et al. ( 2015 ) applied PCA and CA to Lake Naivasha and found that river-influenced regions displayed distinct water quality patterns. Their observations align with the current study, particularly the seasonal shifts in parameter concentrations—higher in the dry season due to evaporation and lower during the wet season due to dilution by rainfall (Tibebe et al., 2022 ). Synthesis of Seasonal Trends Across both seasons, Debir (D1–D3), Ag1, and E1 consistently emerged as nutrient pollution hotspots, driven largely by runoff from agriculture and household activities. The spatial clustering (Figs. 6 a and 6 b) and the consistent loadings (Table 4 ) provide strong evidence that eutrophication in LH is nutrient-driven and seasonally amplified. While physicochemical parameters such as pH, DO, and temperature influenced nutrient dynamics—especially NH₄-N and NO₃-N—TDS and SRP were critical in shaping the pollution profile, particularly at littoral and inflow-affected sites. Interestingly, turbidity had little influence in the wet season, while temperature, EC, and DO had a more pronounced role in both biogeochemical cycling and algal behavior. Profundal sites, notably Am3, Ab3, and E3, remained minimally affected by surface pollution, suggesting a spatial buffer from anthropogenic inputs. Implications for Management Seasonal PCA results confirm that both organic and inorganic agrochemicals are major contributors to water quality deterioration in LH, particularly in catchment-facing and riverine zones. The contamination patterns demand integrated watershed management, which focuses on reducing nutrient inputs from farming, improving sanitation, and regulating lakeshore activities. Seasonal monitoring of SRP, T-P, and Chl-a—along with control of sediment mobilization—will be crucial in mitigating eutrophication risks. Water Quality Index (WQI) The nutrient-based Water Quality Index (WQI) and the Composite Pollution Index (CPI) for LH were assessed using key parameters, including NH₄⁺-N, NO₃⁻-N, T-N, SRP, T-P, and DO. These parameters exhibited notable seasonal and spatial variation across the sampling sites (Table 6 ). The dry season recorded significantly higher WQI values (CPI = 0.67) compared to the wet season (CPI = 0.36), indicating greater nutrient accumulation and pollutant concentration during periods of reduced water flow. This trend can be attributed to decreased dilution capacity due to lower water levels and intensified anthropogenic inputs such as runoff from agriculture and domestic waste discharge (Bhattaria, 2016 ; Wetzel, 2001 ). Table 6 Single and comprehensive pollution index of five sampling sites in some selected water quality parameters in dry and wet seasons of Lake Hashenge. Site Dry season Wet season Sample pts. P NH4N P NO3N P TN P SRP P TP P DO PCI P NH4N P NO3N P TN P SRP P TP P DO PCI Debir D1 1.20 0.02 1.47 0.40 0.48 1.22 0.80 0.36 0.08 0.51 0.14 0.36 0.68 0.36 D2 1.45 0.01 1.08 0.23 0.78 1.15 0.78 0.24 0.06 0.32 0.56 0.06 0.43 0.28 D3 1.35 0.01 1.93 0.40 0.53 1.18 0.90 0.85 0.33 1.82 0.37 0.57 0.46 0.73 Endedo E1 1.35 0.00 1.77 0.60 0.70 1.09 0.92 0.86 0.21 1.43 0.61 0.8 1.58 0.92 E2 0.75 0.00 0.91 0.60 0.63 1.06 0.66 0.46 0.09 0.54 0.65 0.43 0.49 0.44 E3 1.20 0.00 0.56 0.40 0.80 1.04 0.67 0.31 0.05 0.58 0.17 0.43 0.47 0.34 Abakiros Ab1 0.65 0.03 0.05 0.05 0.31 1.06 0.36 0.18 0.09 0.39 0.33 0.57 0.78 0.39 Ab2 1.00 0.10 1.08 0.03 0.67 1.02 0.65 0.29 0.07 0.36 0.32 0.29 0.51 0.31 Ab3 0.10 0.05 0.30 0.05 0.70 1.02 0.37 0.3 0.04 0.52 0.14 0.5 0.49 0.33 Adigolo Ag1 0.90 0.02 1.04 0.29 0.43 1.10 0.63 0.28 0.08 0.54 0.33 0.42 0.66 0.39 Ag2 1.70 0.07 1.03 0.27 0.54 1.02 0.77 0.14 0.08 0.33 0.27 0.46 0.48 0.29 Ag3 0.45 0.05 0.63 0.08 0.19 1.04 0.41 0.37 0.03 0.49 0.17 0.9 0.02 0.33 Adiminda Am1 1.10 0.03 1.02 0.16 1.23 1.13 0.78 0.39 0.04 0.44 0.4 0.5 0.51 0.38 Am2 0.70 0.01 0.82 0.10 0.34 1.12 0.52 0.26 0.02 0.37 0.35 0.26 0.51 0.30 Am3 1.55 0.01 1.31 0.05 0.70 1.01 0.77 0.12 0.02 0.24 0.14 0.15 0.3 0.16 The mean Secchi depth (SD) values observed in this study (0.21 m in the dry season and 0.16 m in the wet season) were comparable to those reported, 0.19 m for Lake Zeway (Tibebe et al., 2022 ). However, the SD ranges recorded here (0.15–0.25 m in the dry season and 0.08–0.25 m in the wet season; Table SM5 and SM6) were narrower than those reported elsewhere, which varied between 0.20–0.35 m and 0.40–1.06 m (Assefa et al., 2012 ; Teame et al., 2016). In contrast, Assefa et al. ( 2012 ) reported considerably higher mean SD values for LH, reaching 3.1 m in the dry period and 1.7 m in the wet period. The gradual increase in SD observed in recent years suggests declining turbidity, likely reflecting the positive effects of soil and water conservation measures implemented in the catchment. CPI values further revealed that most sites experienced moderate pollution (level III) during the dry season, ranging from 0.36 to 0.92. The highest CPI values were observed at sites E1 (0.92) and D3 (0.9), likely influenced by effluents from small-scale horticultural activities and livestock grazing around Debir and Endedo. During the wet season, CPI values ranged from 0.33 to 1.02, suggesting slightly polluted (level II) conditions across most sites except Ag3, which showed minimal contamination. The observed pollution sources include agricultural runoff, soil erosion, and waste from grazing by livestock, swimming, and fishing-related activities. Comparable findings were reported by Tibebe et al. ( 2022 ), who applied the CPI model to Lake Ziway. Their results showed a broader pollution range - from low to severe - indicating that Lake Ziway is more polluted than LH. Nevertheless, the moderate pollution status of LH underscores growing anthropogenic pressures and early signs of ecological degradation that warrant preventive management interventions. Conclusion LH exhibits clear seasonal and spatial variations in water quality. During the dry season, nutrients accumulate and dilution is limited, increasing eutrophication risks, especially at Debir and Endedo, while the wet season partially alleviates these effects but introduces phosphorus and silica through runoff. PCA and cluster analysis highlighted nitrogen and phosphorus as the main pollutants, with hotspots near inflows and along the shoreline, whereas deeper zones remained relatively stable. Persistent high chlorophyll-a levels indicate ongoing hypertrophic conditions fueled by both external inputs and internal nutrient recycling. Overall, the lake is moderately polluted, with localized hotspots that could disrupt its ecological balance. Implementing integrated watershed management—reducing nutrient loading, controlling erosion, enhancing riparian buffers, and maintaining seasonal monitoring—is critical to safeguard LH’s ecological integrity and socio-economic value amid growing human and climate pressures. Abbreviations Title Spatio-temporal variations of nutrient dynamics in Lake Hashenge Declarations Clinical trial number Not applicable (this is an environmental study). Ethics approval All authors have read, understood, and complied as applicable with the statement on “Ethical responsibilities of Authors”. Competing Interests The authors declare no competing interests. Funding Research funds in this study were obtained from Phase IV of the Institutional Collaboration Program of Mekelle and Hawassa Universities (in Ethiopia) and the Norwegian University of Life Science (in Norway) (MU-HU-NMBU phase IV). Author Contribution All authors took part in developing the study’s concept and design. Berhanu Menasbo and Professor Abraha Gebrekidan carried out the material preparation, data collection, and laboratory analyses. The first draft of the manuscript was prepared by Berhanu Menasbo and Emiru Birhane, with all authors providing feedback on earlier versions. 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1","display":"","copyAsset":false,"role":"figure","size":1141254,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Lake Hashenge showing location and monitoring stations in Ethiopia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/c1338078cca48f46d443cd13.png"},{"id":95360609,"identity":"276cdd9b-fd56-4694-84b2-f65e6761507e","added_by":"auto","created_at":"2025-11-07 07:24:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126345,"visible":true,"origin":"","legend":"\u003cp\u003eTotal annual rainfall and mean temperature of Lake Hashenge Basin 1994 to 2024.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/25eed25fe88a83bdf2d66ce9.png"},{"id":95360611,"identity":"6266aeee-30ce-45eb-aa2c-f2279507b07a","added_by":"auto","created_at":"2025-11-07 07:24:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2567252,"visible":true,"origin":"","legend":"\u003cp\u003eElevation and land use map of Lake Hashenge catchment 2024.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/bf1c3dc57b5443a2a56589f2.png"},{"id":95360638,"identity":"90644c32-c703-4c34-b8f8-d6bff18ebc12","added_by":"auto","created_at":"2025-11-07 07:24:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1927905,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph comparing physico-chemical parameter concentrations (Turbidity in NTU, Temperature in degree C, and DO and TDS in mg/L) at sampling sites during wet and dry seasons in Lake Hashenge\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/8f59fef42f1fee81c6392759.png"},{"id":95360614,"identity":"edc878d7-98e5-411a-897c-d9653948ddbe","added_by":"auto","created_at":"2025-11-07 07:24:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":868512,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph showing seasonal variation of nutrient concentrations (NH4-N, NO3-N,T-N, SPR, and T-P in mg/L) at each sampling site in Lake Hashenge.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/925b3647f97c86f75f9113fd.png"},{"id":95360655,"identity":"a127735b-d3cb-4344-8c66-8cf745d1a52d","added_by":"auto","created_at":"2025-11-07 07:24:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":316191,"visible":true,"origin":"","legend":"\u003cp\u003eFactor loadings and variance explained for water quality parameters during wet and dry seasons, with strong positive and negative loadings highlighted in bold.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/b8decde405c242c46b04ab89.png"},{"id":95525532,"identity":"f926482c-0400-4298-abe9-84aefd549eef","added_by":"auto","created_at":"2025-11-10 10:05:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1177996,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot for the eigenvalues greater than 1 in Wet and dry seasons.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/94f74c6c05bf803bb830fcf3.png"},{"id":95360622,"identity":"e12dd608-acd5-4591-b2b9-49bbdcb7a839","added_by":"auto","created_at":"2025-11-07 07:24:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":329324,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram illustrating Ward’s method of agglomerative hierarchical clustering of PCA scores in wet and dry seasons.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/2f0c7fa75424a15d345421fb.png"},{"id":95524710,"identity":"cea8d803-2347-4a9e-8d60-c46b2e4b483f","added_by":"auto","created_at":"2025-11-10 10:03:18","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":906956,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph showing variation of silicate, iron, and chlorophyll-a at each sampling site during wet and dry seasons in Lake Hashenge.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/4d7f3206659c791c8310bfac.png"},{"id":95796911,"identity":"b04d2d58-0a7f-4ad8-bd2b-47759114c82d","added_by":"auto","created_at":"2025-11-13 07:58:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10598428,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/f7d3088e-5cfe-4b8c-8fc1-fd4459d541e0.pdf"},{"id":95524956,"identity":"14c3da35-9f8d-4db1-afc2-dbf68555cadc","added_by":"auto","created_at":"2025-11-10 10:03:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2827267,"visible":true,"origin":"","legend":"","description":"","filename":"BerhanuSuplementraymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8035197/v1/e75f9264eb8da4dc4dd09b7c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatio‑temporal assessment of nutrient pollution and water quality in Lake Hashenge, Ethiopia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFreshwater ecosystems play a critical role in maintaining biodiversity, ensuring water security, and underpinning socioeconomic development. However, these systems worldwide are increasingly threatened by pressures such as population growth, rapid urbanization, intensified agriculture, and climate variability, all of which contribute to the accelerated decline of water quality\u0026nbsp;(Bănăduc et al., 2022; Manashree, 2023). Of particular concern are anthropogenic nutrient inputs, especially nitrogen (N) and phosphorus (P), which drive eutrophication, harmful algal blooms, and consequent ecological disruptions in aquatic environments\u0026nbsp;(Akhtar et al., 2021; Khatri \u0026amp; Tyagi, 2015).\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClosed-basin lakes in semi-arid and highland regions like LH in Northern Ethiopia are especially susceptible due to their limited hydrological exchange and reliance on seasonal water cycles\u0026nbsp;(Gebreslase, 2015a; Yazew et al., 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Ethiopia, lakes hold significant ecological and economic value by providing vital ecosystem services such as fisheries, irrigation, and cultural amenities. Despite this, they face unprecedented pressures from agricultural intensification, land-use changes, wastewater discharge, and livestock grazing, which substantially contribute to nutrient enrichment (Soares et al., 2017; Xu et al., 2010). The application of fertilizers, particularly in steep highland catchments, heightens the risks of nutrient runoff, sedimentation, and algal proliferation. These challenges are further intensified by climate change, which alters precipitation regimes, heightens drought and flood occurrences, and accelerates nutrient cycling \u0026nbsp;(Geris et al., 2022; Havens \u0026amp; Jeppesen, 2018; Markandya, 2010). As a result, water bodies such as LH are witnessing declining water quality, hypertrophic conditions, and reduced suitability for aquatic organisms and human use (Ayele, 2021; Fetahi, 2019; Menberu et al., 2021).\u003c/p\u003e\n\u003cp\u003eEffective water quality monitoring and assessment are essential for mitigating these threats, yet traditional approaches often require considerable resources and may have limited coverage (Carr G.M. and Neary J.P, 2019)\u003cstrong\u003e.\u003c/strong\u003e Multivariate statistical methods, including Principal Component Analysis (PCA) and Cluster Analysis (CA), provide robust means to simplify complex datasets, identify pollution sources, and classify water bodies based on contamination levels\u0026nbsp;(Bhattrai et al., 2017; Zeinalzadeh \u0026amp; Rezaei, 2017). Additionally, composite indices such as the Water Quality Index (WQI) and Comprehensive Pollution Index (CPI) integrate multiple water quality parameters into accessible formats that facilitate communication with policymakers and stakeholders\u0026nbsp;(Al-Mayah \u0026amp; Mashaanrabee, 2018; Chidiac et al., 2023; Howladar et al., 2018).\u0026nbsp;These analytical tools are particularly valuable in data-limited settings like Ethiopia, where monitoring capacity is constrained but management demands are high.\u003c/p\u003e\n\u003cp\u003eThe Drivers Pressures State Impact Response (DPSIR) framework offers a comprehensive perspective linking human activities with environmental changes, ecological impacts, and management responses (Geris et al., 2022; Kifle Arsiso et al., 2017; Markandya, 2010; Mouratiadou et al., 2016). Applying this framework to nutrient pollution allows for the identification of critical land-use drivers and lake ecosystem responses, thereby informing prioritized interventions such as buffer zone implementation, riparian vegetation restoration, and the adoption of sustainable agricultural practices. Integrating DPSIR with PCA, CA, WQI, and CPI enables a holistic approach to understanding and managing nutrient pollution in LH.\u003c/p\u003e\n\u003cp\u003eDespite its ecological significance, LH has received limited systematic study concerning nutrient loading, seasonal water quality trends, and pollution sources. Prior research has focused on localized hydrological and land-use assessments but has not delivered comprehensive spatio-temporal analyses incorporating physical, chemical, and biological indicators (Gebreslase, 2015a; Tibebe et al., 2022). Given the rapid land-use changes and emerging climate pressures in Northern Ethiopia, addressing this knowledge gap is crucial for preserving the lake's ecological integrity and the socioeconomic benefits it provides.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccordingly, this study investigates the spatio-temporal dynamics of nutrient pollution in LH from 2022 to 2025, employing an integrative framework that combines multivariate statistical analyses, composite water quality indices, and DPSIR modeling. The specific objectives are to (i) evaluate spatial and seasonal variations in key water quality parameters; (ii) identify primary nutrient pollution sources using PCA and CA; (iii) analyze the relationships between land use and pollution dynamics within the DPSIR framework; and (iv) calculate and interpret WQI and CPI values to assess ecological status across different sites and seasons. The results aim to support the development of cost-effective monitoring strategies and evidence-based management interventions, thereby promoting sustainable governance of LH and other vulnerable freshwater ecosystems across Ethiopia and beyond.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eDescription of the study area\u003c/h2\u003e\u003cp\u003eLH, is a high-altitude, closed-basin lake located near Korem town in Northern Ethiopia, positioned between 1,386,000\u0026ndash;1,400,000m N and 550,000\u0026ndash;560,000m E UTM (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The lake lies at an elevation of 2,440 meters above sea level (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with a surface area of approximately 20 km\u0026sup2; and an average depth of 16 m (Yazew et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The watershed covers 80.8 km\u0026sup2; with a 33% average slope and consists of cultivated land, forests, grazing areas, and settlements (Shimbahri, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and has a shoreline of 14 km. The surrounding mountains from 2,440 to 3,600 m.a.s.l. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) drain seasonal streams into the lake, contributing to non-point source pollution from farmlands and grazing fields. Cultivated land covers 25.7 km\u0026sup2; (31.8% of the watershed), with N and P runoff being major concerns (Tegegne M. et al., 2025). The area receives 788.7 mm of annual rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with a mean temperature of 15.3 C (Yazew E. et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).The geographic coordinates are latitude 38˚45\u0026ndash;48'N and longitude 128˚42\u0026ndash;44'.\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\u003eWater quality monitoring stations location and coordinates\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSampling site\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNorth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEast\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eElevation (m)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDebir\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eD1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e35\u0026rsquo;43.67\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e29\u0026rsquo;26.86\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e35\u0026rsquo;34.34\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e29\u0026rsquo;32.33\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e35\u0026rsquo;29.04\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e29\u0026rsquo;38.25\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEndedo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e36\u0026rsquo;6.60\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e39\u0026rsquo;25.98\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e36\u0026rsquo;3.74\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e30\u0026rsquo;22.10\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e35\u0026rsquo;07.54\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e30\u0026rsquo;18.88\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAbakiros\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAb1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e34\u0026rsquo;52.13\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e31\u0026rsquo;6.80\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAb2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e34\u0026rsquo;49.38\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e30\u0026rsquo;55.19\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAb3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e34\u0026rsquo;46.17\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e30\u0026rsquo;43.73\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAdigolo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAg1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e33\u0026rsquo;26.63\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e29\u0026rsquo;45.37\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAg2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e33\u0026rsquo;40.52\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e29\u0026rsquo;45.51\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAg3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e33\u0026rsquo;58.20\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e29\u0026rsquo;48.65\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eAdiminda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAm1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e34\u0026rsquo;33.44\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e28\u0026rsquo;54.45\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAm2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e34\u0026rsquo;32.53\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e28\u0026rsquo;59.10\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAm3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u003csup\u003eo\u003c/sup\u003e34\u0026rsquo;32.06\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39\u003csup\u003eo\u003c/sup\u003e29\u0026rsquo;61\u0026rsquo;\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2409\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\u003eWater resources in LH catchment are rich as the rain is available all year round. Still, water managers face a series of difficulties as the awareness of global climate impacts on precipitation patterns increases for example 83.55% of the outflow water is lost by climate change evaporation (Dore, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Haider, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; He et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Yazew et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Meanwhile, with rapid economic development and the population explosion in the basin, human activities, including deforestation and land-use change, significantly affect the water supply and demand patterns(Abay et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e), while simultaneously being exacerbated by increased pollutant loading(Tegegne Berhanu et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eElevation and Land use land cover of the LH Catchment\u003c/h2\u003e\u003cp\u003eCatchment characterization is a critical step in identifying the sources of pollution affecting lakes and reservoirs. In the case of LH, key physical features such as elevation, land use, and land cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e3\u003c/span\u003e) serve as important precursors for understanding nutrient and sediment inflows. This process involves integrating qualitative indicators, such as soil type, vegetation cover, and visible signs of active erosion, with quantitative data on slope gradients, rainfall intensity, and total catchment area. Such combined analysis provides essential input for estimating sediment loads, which are a major contributor to water quality degradation in lakes (Gebreslase, 2015). These sediments not only reduce water clarity but also act as carriers of nutrients and pollutants, accelerating eutrophication and threatening aquatic ecosystems.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling and laboratory analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eSampling and analyses\u003c/h2\u003e\u003cp\u003eWater samples were collected during both wet and dry seasons between 2020 and 2023 across fifteen sampling stations distributed among five main villages-Debir, Endedo, Abakiros, Adigolo, and Adiminda- (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Each site was selected to represent littoral, riverine, and profundal zones, thereby capturing both spatial and seasonal heterogeneity in water quality conditions. 180 samples were collected in acid-washed high-density polyethylene (HDPE) bottles (1,000ml capacity), pre-rinsed with Milli-Q water and sample water to minimize contamination(Rama et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Samples were immediately stored in ice-cooled containers and transported to the laboratory within 72 hours.\u003c/p\u003e\u003cp\u003eSamples were stored at 4 C before analyses, and all analyses were finished within seventy-two hours of their collection, except in situ parameters (American Public Health Association, 2001). Water quality of the collected samples was assessed for thirteen parameters viz. dissolved oxygen (DO), total dissolves solid (TDS), turbidity, temperature(temp.), nitrate (NO\u003csub\u003e3\u003c/sub\u003e-N), pH, soluble reactive phosphorous (SRP), iron (Fe), silicate (SiO\u003csub\u003e2\u003c/sub\u003e-Si), total nitrogen (T-N), total phosphorous (T-P) and ammonium (NH\u003csub\u003e4\u003c/sub\u003e-N), and chlorophyll-a (chl-a) following standard procedures(American Public Health Association, 2001; Environmental et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Health et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Temp., pH, turbidity, and DO were measured in situ at the sites during sample collection. All the chemicals were of analytical grade and purchased from Mekelle, Ethiopia. Milli-Q water was used for the preparation of all reagents and standards. The analytical methods for surface water samples and all the laboratory work is summarized using standard references (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and done in School of Earth Science, Mekelle University.\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\u003eWater quality parameters, abbreviations, analytical techniques and remarks for analysis\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\u003eAnalytical Technique\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMethod Reference\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRemarks\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChlorophyll-a (Chl-a)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpectrophotometry or Fluorometry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPHA 10200 H or EPA 445.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExtracted with acetone;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoluble Reactive Phosphorus (SRP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMolybdenum Blue Method (Colorimetry)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPHA 4500-P E or EPA 365.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFiltered sample; measures orthophosphate only\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Phosphorus\u003c/p\u003e\u003cp\u003e(T-P)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePersulfate Digestion\u0026thinsp;+\u0026thinsp;Molybdenum Blue Method (Colorimetry)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPHA 4500-P B \u0026amp; E or EPA 365.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncludes particulate and dissolved forms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmmonium-Nitrogen\u003c/p\u003e\u003cp\u003e(NH₄⁺-N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndophenol Blue Method (Colorimetry)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPHA 4500-NH₃ G or EPA 350.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCan also use ion-selective electrode\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrate-Nitrogen (NO₃⁻-N)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCadmium Reduction Method (Colorimetry) or Ion Chromatography\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPHA 4500-NO₃ E or EPA 353.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMay require filtration and preservation with H₂SO₄\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Nitrogen (TN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePersulfate Digestion\u0026thinsp;+\u0026thinsp;Nitrate Measurement (Colorimetry)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPHA 4500-N B or EPA 351.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMeasures organic and inorganic N forms\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSilicate (SiO₂)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMolybdosilicate Method (Colorimetry)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPHA 4500-Si D or EPA 200.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFiltered sample; use within 24 hours\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron (Fe\u0026sup2;⁺/Fe\u0026sup3;⁺)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAtomic Absorption Spectrophotometry (AAS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAPHA 3111 B or EPA 200.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAcidified sample; can also be measured by colorimetry using phenanthroline\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMultivariate statistical methods\u003c/h3\u003e\n\u003cp\u003eMultivariate statistical analyses were employed to evaluate water quality variability and pollution sources. Cluster Analysis (CA ) is a way to group similar monitoring points. Points in the same group are closely related, while those in different groups are less similar, with hierarchical clustering being the most common method. A dendrogram helps visualize these groups (Dutta et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Khattree \u0026amp; Naik, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, Ward\u0026rsquo;s method with squared Euclidean distances was used on normalized data for clustering according to their pollution profiles.\u003c/p\u003e\u003cp\u003ePrincipal Component Analysis (PCA) was conducted to reduce dataset dimensionality\u003c/p\u003e\u003cp\u003eand identify the major factors contributing to spatio-temporal variation in water quality(Gradilla-hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Khattree \u0026amp; Naik, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhong \u0026amp; Zhang, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). PCA helps find relationships between features in a dataset. It breaks data into factor loadings, factor scores, and residuals. Since fewer factors are extracted than the original features, it reduces data complexity. After rotating the factor loadings, the factors can often be linked to their sources(Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Eigenvalues greater than 1 and factor loadings above 0.6 were considered significant, and Varimax rotation was applied to enhance interpretability. All statistical analyses were performed using the IBM SPSS (Version 20) and OriginPro 2025 statistical software.\u003c/p\u003e\n\u003ch3\u003eA Comprehensive Pollution Index (CPI)\u003c/h3\u003e\n\u003cp\u003eTo integrate multiple physicochemical parameters into a single pollution measure,\u003c/p\u003e\u003cp\u003ethe Comprehensive Pollution Index (CPI) was calculated following Zhao et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe pollution index for each parameter is calculated using the formula:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{P}_{i}=\\frac{{C}_{i}}{{S}_{i}}$$\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003cp\u003eWhere: P\u003csub\u003ei\u003c/sub\u003e is the pollution index for parameter i, C\u003csub\u003ei\u003c/sub\u003e​ is the measured concentration of parameter \u003cb\u003ei\u003c/b\u003e in water, and S\u003csub\u003ei​\u003c/sub\u003e is the standard or permissible limit for parameter \u003cb\u003ei\u003c/b\u003e (as per water quality guidelines).\u003c/p\u003e\u003cp\u003eThe overall CPI is determined by summing the individual pollution indices as follows (Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CPI=\\frac{1}{n}{\\sum\\:}_{n=1}^{n}{P}_{i}\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\dots\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cb\u003en\u003c/b\u003e is the number of parameters considered. The computed \u003cb\u003eP\u003c/b\u003e values (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), serve as a basis for categorizing the lake\u0026rsquo;s water quality status.\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Factor loading values and explained variance of water quality in two seasons (positive and negative strong correlations are marked bold)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003ea. Wet season\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c10\" namest=\"c6\"\u003e\u003cp\u003eb. Dry Season\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ePC1\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ePC2\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003ePC3\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ePC4\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003ePC1\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003ePC2\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003ePC3\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003ePC4\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003ePC5\u003c/b\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal nitrogen, T-N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.94\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmmonium, NH\u003csub\u003e4\u003c/sub\u003e-N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.93\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrate, NO\u003csub\u003e3\u003c/sub\u003e-N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.19\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=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDissolved oxygen, DO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal dissolved solids, TDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal phosphorous, T-P\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoluble reactive P, SRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.85\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSilicate, SiO2-Si\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e-0.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIron, Fe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature, Temp.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChlorophyll-a, Chl-a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e-0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEigenvalue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% of variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e10.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e10.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e8.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e% of Cumulative variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e49.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e60.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e70.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e79.12\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\u003eWhen the CPI is below 1 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the water is considered suitable for both human consumption and sustaining aquatic ecosystems. However, if the CPI exceeds 3, it signals possible environmental hazards, necessitating immediate pollution mitigation strategies. This approach plays a crucial role in detecting contamination-prone areas, directing remediation initiatives, and shaping effective water resource management policies.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStandard of surface water quality classification (WHO, 1996)\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\u003eCPI Range\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater Quality Status\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLevel\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClean (no pollution)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.21 \u0026minus;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSub-clean (Low pollution)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0.41\u0026ndash;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.01\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHeavy pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSevere pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eV\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=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eLake water quality data were examined using CA and PCA (Gradilla-hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahman et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhong \u0026amp; Zhang, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), along with other statistical methods. To evaluate variations across and within sampling sites, Analysis of Variance (ANOVA) was performed at a 95% confidence level using SPSS (version 20) and Origin Pro 2025 software. Spatial variation was assessed by comparing differences among sites, while temporal variation was evaluated by examining differences across seasons. Statistical significance was considered at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eSpatial and temporal water quality variation of LH\u003c/h2\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003eThe physical parameters\u003c/h2\u003e\u003cp\u003eThe physicochemical characteristics of the lake showed seasonal variations among sites (Figs.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Temperature, pH, turbidity, dissolved oxygen (DO), total dissolved solids (TDS), and chlorophyll-a (Chl-a) all showed significant differences between the wet and dry seasons, demonstrating the impact of hydrology and climate on water quality (Baig et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; G. Yang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTemperature and pH\u003c/strong\u003e\u003cp\u003eDuring the wet season, surface temperatures ranged from 19.9 C (Abakiros) to 21.4 C (Debir), and increased slightly in the dry season (20.7 to 22.1 C), reflecting the stronger solar radiation and reduced cloud cover (Dai et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Shinohara et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Weng \u0026amp; Fu, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; K. Yang et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The water remained alkaline throughout the year, with pH levels ranging from 8.9 to 9.8 during the wet season and from 9.0 to 10.0 during the dry season. Debir consistently had the highest pH, most likely because of increased photosynthesis and less dilution (Maberly, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Tank et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTurbidity and TDS\u003c/strong\u003e\u003cp\u003eTurbidity was normally low, but increased during the rainy season (0.7 to 2.5 NTU), indicating runoff-driven sediment input at Endodo and Debir. During the dry season, readings plummeted to 0.3 to 1.3 NTU, indicating limited runoff. TDS followed the same pattern, peaking at 951 mg/L in Abakiros during the wet season and decreasing to 414 to 664 mg/L during the dry.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDissolved Oxygen (DO)\u003c/strong\u003e\u003cp\u003eHigher levels of DO were found during the rainy season. For instance, Debir rose from 6.87 mg/L (dry) to 7.3 mg/L (wet), most likely as a result of increased oxygen solubility brought on by low temperatures, precipitation, and wind-driven mixing.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eChlorophyll-a\u003c/strong\u003e\u003cp\u003eLevels of chlorophyll-a, which indicate the biomass of phytoplankton, vary by location but not by season. Abakiros, Ab3 showed milder fluctuations from 92.48 \u0026micro;g/L to 89.98 \u0026micro;g/L, suggesting either seasonal nutrient enrichment or variability in light conditions and grazing pressure. In contrast, Adiminda maintained the highest amounts (157 \u0026micro;g/L dry, 155 \u0026micro;g/L wet), showing steady algal productivity.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eNutrients\u003c/em\u003e: Ammonium (NH₄-N) peaked during the wet season (0.6\u0026ndash;3.8 mg/L), particularly at Debir, due to runoff and mineralization (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e2\u003c/span\u003e). During the dry season (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), values dropped to 0.1\u0026ndash;1.0 mg/L, likely due to nitrification and reduced loading. Nitrate (NO₃-N) remained low year-round (\u0026le;\u0026thinsp;0.5 mg/L), reflecting rapid uptake and denitrification. Total nitrogen (T-N), however, rose sharply in the dry season (up to 13.9 mg/L at Endodo), pointing to sediment release under stagnant conditions. Phosphorus remained relatively stable across seasons: soluble reactive phosphorus (SRP) was 0.1 to 0.4 mg/L, and total phosphorus (T-P) 0.2 to 0.9 mg/L, with slightly higher wet-season values.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSilica and Iron\u003c/strong\u003e\u003cp\u003eSilica was highest in the wet season (2.0\u0026ndash;5.0 mg/L), especially at Adigolo, due to runoff from silicate-rich soils (Kabeto et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Concentrations declined to 0.3 to 2.5 mg/L in the dry season, likely reflecting uptake by diatoms. Iron levels were low overall but rose locally in the dry season (up to 0.5 mg/L at Adiminda), possibly from sediment release under low-oxygen conditions.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrophic Status\u003c/strong\u003e\u003cp\u003eChl-a concentrations confirmed hypertrophic conditions year-round. Wet-season values ranged from 55.8 \u0026micro;g/L (Adigolo) to 112.8 \u0026micro;g/L (Adiminda), closely linked with NH₄-N enrichment. In the dry season, levels remained similarly high (58.3 to 115.3 \u0026micro;g/L). This persistence indicates strong internal nutrient recycling and favorable conditions for algal growth (Tegegne et al., 2025).\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSite-Specific Patterns\u003c/h2\u003e\u003cp\u003eEach site around LH reflected its own story of nutrient dynamics and ecological pressures. At Debir, water consistently showed higher levels of pH, NH\u003csub\u003e4\u003c/sub\u003e-N, T-N, and T-P, pointing to continuous inputs from farming and settlements nearby. Endedo displayed a clear seasonal shift: N and Fe were more pronounced in the dry months, while P and turbidity rose during the rains, likely due to sediment disturbance and runoff. Abakiros remained relatively balanced during the dry season but experienced sharp increases in silica, TDS, and turbidity once rainfall began, underscoring its vulnerability to catchment erosion. Adigolo stood out as the most stable site, with little seasonal fluctuation, suggesting that its surrounding landscape or lake setting provides some natural shielding. Adiminda, on the other hand, was remarkable for its consistently high chlo-a levels in both seasons, slightly higher in the dry period, showing that phytoplankton productivity is strong and sustained throughout the year, likely supported by internal nutrient recycling.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMultivariate analysis in wet and dry seasons\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003ePrincipal Component Analysis\u003c/h2\u003e\u003cp\u003ePrincipal Component Analysis (PCA) was conducted on water quality data collected from LH to reduce dimensionality and identify the major variables driving water quality variability in wet and dry seasons. The scree plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e7\u003c/span\u003e) displayed the eigenvalues in descending order, indicating that after the 4th PC in the wet season and the 5th PC in the dry season, the curve begins to flatten. This suggests that subsequent components contribute minimally to the overall variance and can be disregarded.\u003c/p\u003e\u003cp\u003eIn the wet season, the first four PCs explained a cumulative 81.2% of the total variance in water quality data (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e. The first PC alone explained 38.87% of the variation across sites and was primarily associated with nutrient parameters (T-N, NH₄-N, and NO₃-N), pH, and TDS. The second PC contributed 22.78% of the total variance and was strongly correlated with T-P, SRP, SiO₂-Si, Fe, and TDS. The third PC, explaining 11.44% of the variation, was defined by high loadings of Chl-a, temperature, DO, and pH (\u003cb\u003eTable SM1\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eIn the dry season, 4 PCs together explained 79.12% of the total variance. The first PC explained 34.32% of the variation and included the same key parameters (\u003cb\u003eFigure SM1)\u003c/b\u003e, was heavily loaded by DO, NO₃-N, pH, and turbidity, indicating the dominance of oxygenation and nitrogen-related processes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). PC2 (15.53%) represented mineral and phosphorus influence, similar to the wet season, with high positive loadings for TDS and SRP. PC3 (10.75%) was associated with T-N, while PC4 (10.09%) captured temperature variability. PC5 (8.43%) exhibited that dry season-specific component features a strong loading for Fe (0.76), underscoring the increased influence of geological or sedimentary sources during low inflow periods. The absence of this component in the wet season may reflect dilution effects or less pronounced sediment interaction.\u003c/p\u003e\u003cp\u003eThe bi-plot of PCs during the wet season (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) shows that the Debir sampling sites (D1, D2, and D3) were characterized by nutrient-related variables (NH₃-N, NO₃-N, and T-N), as well as DO, pH, and turbidity, with strong associations along both axes. In the dry season (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), the inimitability of the Debir sites was primarily influenced by nutrient parameters (NH₄-N, NO₃-N, T-N, SPR, and T-P), along with pH, TDS, temperature, and turbidity, which were mainly aligned with the horizontal axis. Meanwhile, in the dry season, variation in the Adiminda and Adigolo sites was driven predominantly by SiO₃-Si along the vertical axis and Chl-a along the horizontal axis. This indicates that the ecological dynamics of the Debir sites are significantly affected by nutrient availability during the dry season, whereas the Adiminda and Adigolo sites exhibit a different set of influences. Understanding these variations is crucial for developing targeted conservation strategies and managing the water quality effectively in these regions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eCluster analysis\u003c/h2\u003e\u003cp\u003eHierarchical cluster analysis was performed to assess the spatial variation of water quality parameters across different sampling sites in LH during both dry and wet seasons. The resulting dendrogram revealed three major clusters for each season, indicating consistent spatial groupings but with seasonal shifts in cluster membership. A dendrogram of sampling sites were obtained using Ward\u0026rsquo;s method (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Fifteen sampling sites were divided into three groups.\u003c/p\u003e\u003cp\u003eIn the dry season, the dendrogram identified three distinct cluster sites, grouped according to similarities in physico-chemical and nutrient parameters. These clusters reflect relative homogeneity within each group and pronounced differences between groups, suggesting the influence of localized pollution sources, internal nutrient dynamics, and hydrological isolation during the dry months.\u003c/p\u003e\u003cp\u003eDuring the wet season, the structure of the clusters changed. While three main groupings still emerged, the composition of sites within each cluster varied compared to the dry season. This indicates the role of seasonal hydrological processes such as runoff, sediment transport, and nutrient influx in altering water quality conditions and the spatial relationships among sites.\u003c/p\u003e\u003cp\u003eCluster 1 corresponded to the site riverine (D1, Ab1, E1, and Ag1), which was located on the lake shore of LH. Cluster 2 included site littoral part of the lake (D2, Ab2, E2, and Am1), which were located in the peripheral part of the lake. Cluster 3 contained sites in the profundal part of the lake in Debir, Endedo, Abakiros, Adigolo, and Adiminda.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSpatial and temporal patterns of LH physico-chemical parameters\u003c/h2\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003ePhysico-chemical patterns of LH\u003c/h2\u003e\u003cp\u003eLH showed minimal seasonal variation in temperature (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), averaging 20.7 C in the wet season and 21.5 C in the dry, consistent with other Ethiopian crater lakes (Tibebe et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Its depth (16 m) stabilizes thermal conditions, with only slight warming during prolonged dry-season sunshine.\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\u003eComparative analysis of Lake Hashenge's physico-chemical and nutrient characteristics (mgL) with other tropical lakes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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=\"char\" char=\".\" 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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLakes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTemp(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSRP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO3-N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSiO2-Si\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eReferences\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHawassa\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\u003e8.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(Tilahun \u0026amp; Ahlgren, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e)\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\u003e8.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(Tilahun \u0026amp; Ahlgren, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e)\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\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(Fetahi, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\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\u003e20\u0026ndash;27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.9-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.3\u0026ndash;8.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e115\u0026ndash;148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.8\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\u003e0.1-1\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\u003e0.51\u0026ndash;1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(Wondie \u0026amp; Mengistou, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2006\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\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e623\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.04\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\u003e40\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(Wood \u0026amp; Talling, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\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\u003e9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\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\u003e48\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(Wood \u0026amp; Talling, \u003cspan citationid=\"CR82\" 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\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\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\u003e38\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(Wood \u0026amp; Talling, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbjata\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\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05\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\u003e128\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(Wood \u0026amp; Talling, \u003cspan citationid=\"CR82\" 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\u003e9.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.76\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\u003e112\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(Wood \u0026amp; Talling, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1988\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\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.7\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\u003e320\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(Wood \u0026amp; Talling, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1988\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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\u003e8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e40.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHashenge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u0026ndash;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.9\u0026ndash;7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.9\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e207.3-475.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1\u0026ndash;9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2\u0026ndash;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u0026ndash;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.3-5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\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\u003eWater pH is an important measure because it shapes both chemical toxicity and biological activity in aquatic systems. In Lake Hashenge, the pH stayed alkaline in all seasons, ranging from 9.0\u0026ndash;10.0 during the dry season and 8.9\u0026ndash;9.8 in the wet season (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Although slightly on the higher side, these values generally fit within the WHO\u0026rsquo;s safe guideline of 6.5\u0026ndash;9.5 (Tilahun \u0026amp; Ahlgren, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e; WHO, 2007)(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The consistent alkalinity can be traced to the lake\u0026rsquo;s geochemical setting (Ghaemi \u0026amp; Noshadi, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and strong photosynthetic activity, as algae absorb CO₂ and push pH upward (Hamdhani, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Talling J.F., 2009). This suggests that natural biogeochemical processes play the dominant role in controlling pH year-round. While the usual freshwater range is between 6.0 and 8.5, the lake\u0026rsquo;s levels still remain within acceptable ecological limits (Herschy, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDO showed clear seasonal and site-based differences across the lake. The average concentration (6.3 mg/L) was consistent with earlier findings (Teame et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Lower DO values were recorded in the dry season, likely influenced by human activities such as fishing and washing (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). In contrast, sites like Debir and Adiminda had higher DO, probably linked to greater growth of macrophytes and phytoplankton (Goshu, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). During the wet season, rainfall and dilution appeared to boost oxygen levels. Overall, DO remained within Ethiopia\u0026rsquo;s guideline range for aquatic life (5.0\u0026ndash;9.0 mg/L) (Environmental et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), unlike heavily polluted lakes such as Ziway, where levels can drop to 1.4 mg/L near floriculture effluent (Tadele, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTDS showed seasonal variation, averaging 531.5 mg/L in the dry season and rising to 830.2 mg/L in the wet season (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). These values remain below the WHO guideline of 960 mg/L (Environmental et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) but are still elevated compared to ideal conditions. The increase is largely linked to runoff from degraded farmlands (Gebreslase, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e), and concentration effects from evaporation. Although lower than previously reported values (Park et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; T et al., 2016), such levels may still pose long-term ecological risks (Dutta et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEarlier studies characterized Lake Hashenge as highly turbid and eutrophic, with low water transparency of about 0.7 m due to catchment degradation and siltation (Gebreslase, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e; Teame et al., 2016, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, the present study recorded much lower turbidity, ranging from 0.3 to 2.5 NTU (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), is well within the recommended limit of 5 NTU (Bhavan et al., 1991). This suggests some improvement in water clarity compared to past conditions.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eNutrient Dynamics and Spatial Heterogeneity of LH\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003eNutrient temporal dynamics of LH\u003c/h2\u003e\u003cp\u003eNutrient levels in LH changed significantly by season (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The dry season exhibited a rise in most parameters, including NH₄-N, NO₃-N, T-N, SRP, T-P, and Fe. This was attributed to lower water volume, limited flushing, and increased evaporation (Alemayehu et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tadesse et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wetzel, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Agricultural runoff and animal activities, particularly in Debir, Endedo, and Abakiros, contributed to the enrichment. Under intense sun radiation, these factors increase the risk of eutrophication (Bhateria \u0026amp; Jain, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bhattarai et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn contrast, nutrient concentrations declined in the wet season due to dilution and enhanced flushing (Pant et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, SiO₂-Si levels were consistently higher during this period, likely due to increased runoff and sediment re-suspension rather than internal cycling (Sharpley et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Sites such as Endedo, Adigolo, and Adiminda recorded silica concentrations exceeding 5 mg/L, reflecting strong catchment influence. Notably, Adiminda maintained lower levels of most nutrients in both seasons, suggesting limited external inputs and better ecological stability.\u003c/p\u003e\u003cp\u003eIn the wet season, most nutrient concentrations decreased due to dilution and flushing (Pant et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). SiO₂-Si, however, rose with runoff and sediment re-suspension (Sharpley et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), exceeding 5 mg/L at Endedo, Adigolo, and Adiminda. Adiminda consistently showed lower nutrient levels in both seasons, indicating limited external inputs and greater ecological stability. These results highlight the need for continuous seasonal monitoring, particularly during rainy periods when runoff shifts nutrient balance and influences phytoplankton and diatom communities. Maintaining water quality will depend on stronger watershed management and better land-use practices to reduce nutrient loading (Nafeza et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zeinalzadeh, K. \u0026amp; Rezaei, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eProductivity and Geochemical Influences\u003c/h2\u003e\u003cp\u003eSilica, iron, and chlorophyll-a levels showed evident seasonal variations related to hydrology and internal lake dynamics (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e9\u003c/span\u003e). SiO₂-Si levels were highest in the wet season at Adigolo and Abakiros due to runoff from silicate-rich soils, but decreased in the dry season due to lower inflow and diatom uptake. Elevated silica levels (\u0026gt;\u0026thinsp;10 mg/L) in African lakes may promote diatom productivity and disrupt ecological equilibrium (Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Iron levels remained largely low, with only minor increases at Endedo and Adiminda throughout the dry season, most likely due to sediment release under low-oxygen circumstances. Chlorophyll-a levels climbed throughout the dry season, indicating nutrient enrichment and algae development.\u003c/p\u003e\u003cp\u003eChl-a concentrations, a proxy for phytoplankton biomass, remained consistently high across seasons, with peak levels recorded at Adiminda. This sustained elevation indicates ongoing productivity, likely driven by internal nutrient recycling and favorable climatic conditions that may promote non-native phytoplankton growth (Flores-Moreno et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Justic et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, the weak correlation between Chl-a and nutrient levels suggests that factors such as light availability, grazing pressure, or micronutrient limitation may exert stronger control on algal biomass (Ayele, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eSpatial Heterogeneity of nutrients in LH\u003c/h2\u003e\u003cp\u003eSeasonal water quality patterns in LH reflect both catchment inputs and internal processes. At Debir, persistently high N and P indicate continuous external loading from agriculture and grazing, with wet-season rainfall intensifying N transport (Molla et al., 2024). Endodo showed seasonal contrasts, with elevated total nitrogen in the dry season and higher SRP and turbidity in the wet season, pointing to sediment interactions and phosphorus release under anoxic conditions (Alemayehu et al., 2023). At Abakiros, wet-season peaks in turbidity, silica, and TDS suggest runoff-driven erosion and mineral inputs, consistent with patterns in other Ethiopian lakes (Gebremedhin et al., 2022; Molla et al., 2024). Adigolo remained stable across seasons, likely due to its small, well-buffered catchment (Fenta \u0026amp; Belete, 2022). In contrast, Adiminda consistently recorded the highest chl-a levels, with slightly higher values in the dry season, reflecting sustained algal productivity supported by internal nutrient recycling and favorable post-rainfall conditions (Alemayehu et al., 2023; Gebremedhin et al., 2022).\u003c/p\u003e\u003cp\u003eThese findings highlight the need for targeted lake management - reducing nutrient input at Debir, controlling sediment and P at Endodo and Abakiros, and managing algal blooms at Adiminda. Preserving natural buffers and adapting to climate and land use changes are crucial for maintaining ecosystem resilience (Houghton et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)(IPCC, 2021).\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eMultivariate analysis of nutrient pollution in LH\u003c/h2\u003e\u003cdiv id=\"Sec24\" class=\"Section4\"\u003e\u003ch2\u003ePrincipal Component Analysis (PCA)\u003c/h2\u003e\u003cp\u003eWe used Principal Component Analysis (PCA) to pinpoint the main drivers of eutrophication in Lake Hashenge (LH). The dataset was suitable for PCA, with a Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) value of 0.683 and a highly significant Bartlett\u0026rsquo;s test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating strong relationships among variables. Variables with low communalities (\u0026lt;\u0026thinsp;0.5) were removed, and only those with strong factor loadings (\u0026ge;\u0026thinsp;0.6) were kept (Dharmarathna \u0026amp; Galagedara, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). PCA revealed five key components during the dry season and four during the wet season, with Varimax rotation applied to make the patterns clearer and easier to interpret.\u003c/p\u003e\u003cp\u003eIn the wet season, four principal components explained 81.2% of the variation in Lake Hashenge. The first component, influenced by nutrients such as T-N, NH₄-N, NO₃-N, and T-P, along with pH, TDS, and DO, was most evident at Debir, Ag1, and E1. These patterns suggest strong human impacts\u0026mdash;agricultural runoff, grazing, fertilizer use, and domestic wastewater\u0026mdash;with phosphorus likely from soils and nitrogen from fertilizers and organic matter, reflecting the lake\u0026rsquo;s overall eutrophic state (Barnard et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hamdhani, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Magdoff, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Ndungu et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe rotated component matrix (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) highlights how different water quality parameters are grouped across the PCs. In the wet season, PC1 emphasizes nutrient inputs, whereas PC2, with high loadings for TDS, SRP, SiO₂-Si, and Fe, points to a combination of domestic sources (e.g., detergents) and geological contributions. Silica likely comes from bedrock weathering, and iron may be released through redox processes or sediment disturbance, indicating additional non-point sources (Hamdhani, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). PC3 was dominated by temperature and DO, reflecting seasonal thermal dynamics, while PC4 had negative loadings for Chl-a at Adiminda (Am1\u0026ndash;Am2), Abakiros (Ab1\u0026ndash;Ab2), and E1, possibly due to algal nutrient uptake or senescence.\u003c/p\u003e\u003cp\u003eThe PCA results and biplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) for the wet season highlight how human activities and natural processes shape water quality in LH. Nutrient enrichment was most pronounced at Debir, Ag1, and E1, where high levels of T-N, NH₄-N, NO₃-N, and T-P point to strong influences from agriculture, grazing, fertilizer application, and domestic wastewater (Barnard et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hamdhani, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Magdoff, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Ndungu et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, PC2 suggested that some water quality patterns are linked to natural geological sources, with silica originating from bedrock weathering and iron mobilized through sediment disturbance or redox processes (Hamdhani, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTemperature and dissolved oxygen, represented by PC3, reflected the seasonal thermal dynamics of the lake, while negative Chl-a loadings in PC4 at sites like Adiminda and Abakiros likely indicate periods of algal nutrient uptake or senescence. The wet-season biplot further emphasized these trends, showing nutrient hotspots at Debir, Ag1, and E1, geological influences at Am1 and Ab2, and localized algal activity at Am2 and Ag2. Profundal sites such as Am3, Ab3, and E3 appeared largely insulated from surface pollutants, except for iron, underscoring the spatial variability in nutrient and contaminant dynamics across the lake.\u003c/p\u003e\u003cp\u003eOverall, these findings suggest that LH\u0026rsquo;s water quality is shaped by a complex interplay of human-induced nutrient loading and natural geological processes, with clear spatial patterns that reflect both catchment activities and in-lake ecological responses.\u003c/p\u003e\u003cp\u003eDuring the dry season, five principal components explained 79.12% of total variance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with PC1 accounting for 43.3%. PC1 measured a variety of variables, including NH₃-N, NO₃-N, SRP, T-P, TDS, pH, DO, and Chl-a, indicating persistent nutrient pollution at Debir, Ag1, and E1 during low inflow. The reduced dilution in this season likely amplified nutrient concentrations from livestock waste, domestic uses, and shoreline activities. PC2, with high loadings of SRP and TDS, particularly at Debir D1, indicated pollution from fertilizers and pesticides at the lake\u0026rsquo;s edge (Hamdhani, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). PC3 and PC4 isolated the influence of T-N and Fe, respectively, pointing to localized nitrogen enrichment and possible internal loading or sediment interaction. The fifth component had minor influence but added nuance to site-specific variation\u003c/p\u003e\u003cp\u003eIn the dry season, Chl-a loaded positively with key nutrient indicators, reflecting an algal response to nutrient-rich yet relatively stable water conditions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The consistent influence of TDS and SRP across principal components further indicates the sustained impact of both natural processes and human activities. This pattern is clearly illustrated in the PCA biplot for the dry season (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eSimilar to the wet season, Debir and E1 were influenced by several nutrient vectors\u0026mdash;particularly TDS, NH₃-N, and SRP. However, the shorter vector lengths suggest seasonally weaker correlations, likely resulting from reduced runoff or limited internal nutrient recycling during the dry period. Meanwhile, Am2 and Ag2 again aligned closely with Chl-a, highlighting potential zones of algal proliferation, whereas profundal sites remained largely detached from the major pollution vectors\u0026mdash;except for Fe, which continued to exert some influence.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eCluster Analysis (CA)\u003c/h2\u003e\u003cp\u003eCluster analysis (CA) using Ward\u0026rsquo;s method grouped the fifteen sampling sites in LH into three statistically significant clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e5\u003c/span\u003e), reflecting distinct spatial variations in water quality. These groupings were influenced by factors such as natural background features, land use/land cover, and anthropogenic activities (Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCluster I primarily included sites such as Ab1, Ag1, D1, and El (excluding Am1 during the wet season) and Ab1, Ag1, Am1, D1, and El (excluding Ab2 and D2 in the dry season). These sites are mostly located along river inlets and the lake shore, where runoff from surrounding agricultural fields and grazing areas is prevalent. As a result, Cluster I sites represent highly polluted (HP) zones, influenced heavily by agrochemical inputs and livestock activities (Abay et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e; Gebreslase, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCluster II comprised sites like Ab2, D2, and E2 in the wet season (excluding Am1) and only D3 in the dry season. These sites are positioned around the littoral zone of the lake and showed moderate pollution (MP) levels. The placement of only one site (D3) under Cluster II during the dry season may be attributed to the lake\u0026rsquo;s flat bathymetric and contour profile, which results in hydrological similarities between the littoral and surrounding zones (Yazew, Mesfin, GebreSamueal, et al., 2013).\u003c/p\u003e\u003cp\u003eCluster III included Ab3, Ag3, Am3, D3, and E3 in the wet season (excluding Ag2 and Am2) and similar sites in the dry season. These sites, generally situated in the deeper (profundal) part of the lake, represent relatively less polluted (LP) areas, with limited direct anthropogenic disturbance. The exclusion of more impacted sites suggests these central zones are hydrologically more stable, as supported by similar findings from other Ethiopian lakes (Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSpatial variations were evident, with the grouping of E1 in Cluster II and Ab2 and D2 in Cluster I during the wet season, indicating some overlap in water quality characteristics between clusters. Nevertheless, the consistent placement of central sites (e.g., Ab3, Ag3, Am3, and E3) in Cluster III across both seasons confirms better water quality in the lake\u0026rsquo;s profundal region. The CA results suggest that the technique provides a reliable spatial classification of water quality, enabling a more targeted and cost-effective monitoring strategy. This approach can guide the selection of representative sites, reducing redundancy without compromising data quality. Similar utility of CA for spatial optimization has been reported in other studies (Bhattarai et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Khattree \u0026amp; Naik, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, the integration of CA and PCA proved valuable in source apportionment and understanding parameter associations, as noted in comparable studies. For instance, Zhao et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) used these techniques to assess nutrient sources in Baiyangdian Lake, highlighting runoff-driven pollution during the wet season and point-source inputs in the dry season. Ndungu et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) applied PCA and CA to Lake Naivasha and found that river-influenced regions displayed distinct water quality patterns. Their observations align with the current study, particularly the seasonal shifts in parameter concentrations\u0026mdash;higher in the dry season due to evaporation and lower during the wet season due to dilution by rainfall (Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eSynthesis of Seasonal Trends\u003c/h2\u003e\u003cp\u003eAcross both seasons, Debir (D1\u0026ndash;D3), Ag1, and E1 consistently emerged as nutrient pollution hotspots, driven largely by runoff from agriculture and household activities. The spatial clustering (Figs.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) and the consistent loadings (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) provide strong evidence that eutrophication in LH is nutrient-driven and seasonally amplified. While physicochemical parameters such as pH, DO, and temperature influenced nutrient dynamics\u0026mdash;especially NH₄-N and NO₃-N\u0026mdash;TDS and SRP were critical in shaping the pollution profile, particularly at littoral and inflow-affected sites.\u003c/p\u003e\u003cp\u003eInterestingly, turbidity had little influence in the wet season, while temperature, EC, and DO had a more pronounced role in both biogeochemical cycling and algal behavior. Profundal sites, notably Am3, Ab3, and E3, remained minimally affected by surface pollution, suggesting a spatial buffer from anthropogenic inputs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eImplications for Management\u003c/h2\u003e\u003cp\u003eSeasonal PCA results confirm that both organic and inorganic agrochemicals are major contributors to water quality deterioration in LH, particularly in catchment-facing and riverine zones. The contamination patterns demand integrated watershed management, which focuses on reducing nutrient inputs from farming, improving sanitation, and regulating lakeshore activities. Seasonal monitoring of SRP, T-P, and Chl-a\u0026mdash;along with control of sediment mobilization\u0026mdash;will be crucial in mitigating eutrophication risks.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eWater Quality Index (WQI)\u003c/h2\u003e\u003cp\u003eThe nutrient-based Water Quality Index (WQI) and the Composite Pollution Index (CPI) for LH were assessed using key parameters, including NH₄⁺-N, NO₃⁻-N, T-N, SRP, T-P, and DO. These parameters exhibited notable seasonal and spatial variation across the sampling sites (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The dry season recorded significantly higher WQI values (CPI\u0026thinsp;=\u0026thinsp;0.67) compared to the wet season (CPI\u0026thinsp;=\u0026thinsp;0.36), indicating greater nutrient accumulation and pollutant concentration during periods of reduced water flow. This trend can be attributed to decreased dilution capacity due to lower water levels and intensified anthropogenic inputs such as runoff from agriculture and domestic waste discharge (Bhattaria, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wetzel, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2001\u003c/span\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\u003eSingle and comprehensive pollution index of five sampling sites in some selected water quality parameters in dry and wet seasons of Lake Hashenge.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e\u003cp\u003eDry season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c16\" namest=\"c10\"\u003e\u003cp\u003eWet season\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample pts.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003csub\u003eNH4N\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003csub\u003eNO3N\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003csub\u003eTN\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP\u003csub\u003eSRP\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003csub\u003eTP\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP\u003csub\u003eDO\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003ePCI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eP\u003csub\u003eNH4N\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c8\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e0.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c5\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAm2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e0.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAm3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.77\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cb\u003e0.16\u003c/b\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\u003eThe mean Secchi depth (SD) values observed in this study (0.21 m in the dry season and 0.16 m in the wet season) were comparable to those reported, 0.19 m for Lake Zeway (Tibebe et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the SD ranges recorded here (0.15\u0026ndash;0.25 m in the dry season and 0.08\u0026ndash;0.25 m in the wet season; Table SM5 and SM6) were narrower than those reported elsewhere, which varied between 0.20\u0026ndash;0.35 m and 0.40\u0026ndash;1.06 m (Assefa et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Teame et al., 2016). In contrast, Assefa et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) reported considerably higher mean SD values for LH, reaching 3.1 m in the dry period and 1.7 m in the wet period. The gradual increase in SD observed in recent years suggests declining turbidity, likely reflecting the positive effects of soil and water conservation measures implemented in the catchment.\u003c/p\u003e\u003cp\u003eCPI values further revealed that most sites experienced moderate pollution (level III) during the dry season, ranging from 0.36 to 0.92. The highest CPI values were observed at sites E1 (0.92) and D3 (0.9), likely influenced by effluents from small-scale horticultural activities and livestock grazing around Debir and Endedo. During the wet season, CPI values ranged from 0.33 to 1.02, suggesting slightly polluted (level II) conditions across most sites except Ag3, which showed minimal contamination. The observed pollution sources include agricultural runoff, soil erosion, and waste from grazing by livestock, swimming, and fishing-related activities. Comparable findings were reported by Tibebe et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who applied the CPI model to Lake Ziway. Their results showed a broader pollution range - from low to severe - indicating that Lake Ziway is more polluted than LH. Nevertheless, the moderate pollution status of LH underscores growing anthropogenic pressures and early signs of ecological degradation that warrant preventive management interventions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLH exhibits clear seasonal and spatial variations in water quality. During the dry season, nutrients accumulate and dilution is limited, increasing eutrophication risks, especially at Debir and Endedo, while the wet season partially alleviates these effects but introduces phosphorus and silica through runoff. PCA and cluster analysis highlighted nitrogen and phosphorus as the main pollutants, with hotspots near inflows and along the shoreline, whereas deeper zones remained relatively stable. Persistent high chlorophyll-a levels indicate ongoing hypertrophic conditions fueled by both external inputs and internal nutrient recycling. Overall, the lake is moderately polluted, with localized hotspots that could disrupt its ecological balance. Implementing integrated watershed management\u0026mdash;reducing nutrient loading, controlling erosion, enhancing riparian buffers, and maintaining seasonal monitoring\u0026mdash;is critical to safeguard LH\u0026rsquo;s ecological integrity and socio-economic value amid growing human and climate pressures.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTitle\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSpatio-temporal variations of nutrient dynamics in Lake Hashenge\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eClinical trial number\u003c/h2\u003e\u003cp\u003eNot applicable (this is an environmental study).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003eAll authors have read, understood, and complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo;.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eResearch funds in this study were obtained from Phase IV of the Institutional Collaboration Program of Mekelle and Hawassa Universities (in Ethiopia) and the Norwegian University of Life Science (in Norway) (MU-HU-NMBU phase IV).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors took part in developing the study\u0026rsquo;s concept and design. Berhanu Menasbo and Professor Abraha Gebrekidan carried out the material preparation, data collection, and laboratory analyses. The first draft of the manuscript was prepared by Berhanu Menasbo and Emiru Birhane, with all authors providing feedback on earlier versions. St\u0026aring;le Haaland and Fasil Ejigu contributed through conceptualization, methodology, visualization, and critical review and editing. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003e1. Abadi Romha, Laboratory expert in the Earth Science department, Mekelle University\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eNo datasets were generated or analyzed during the current study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbay TG, Demissie B, Tesfamariam Z (2014a) Assessment of natural resources and ecotourism development. 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This study explored how nutrient levels change across space and time between 2022 and 2025 and how they affect water quality, using the Drivers-Pressures-State-Impact-Response (DPSIR) framework. We collected 180 water samples from 15 sites during both wet and dry seasons and analyzed key physico-chemical and nutrient parameters using standard methods. Statistical tools\u0026mdash;Principal Component Analysis (PCA) and Cluster Analysis (CA)\u0026mdash;helped identify pollution sources and group sites by pollution status, while the Water Quality Index (WQI) and Comprehensive Pollution Index (CPI) provided an overall picture of ecological health. Results showed clear seasonal differences: nutrients tended to accumulate more during the dry season when dilution was limited. PCA pointed to nutrient-driven eutrophication, particularly at Debir, Endedo, and Abakiros, linked to human activities. Chlorophyll-a levels confirmed hypertrophic conditions throughout the year. CA revealed three pollution categories, highlighting spatial variability. While overall pollution was moderate, local hotspots stress the need for targeted watershed management, including land-use planning, buffer zones, and community awareness programs.\u003c/p\u003e","manuscriptTitle":"Spatio‑temporal assessment of nutrient pollution and water quality in Lake Hashenge, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 07:24:31","doi":"10.21203/rs.3.rs-8035197/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":"866eafb6-a72a-4e5c-88c8-6f32cc2bf4d1","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-07T07:24:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-07 07:24:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8035197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8035197","identity":"rs-8035197","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}