Estimation of Surface Water Susceptibility to Pollution Index of Natural Wetlands of North-East India using Multi-Criteria Decision Model.

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Rajendra Jena, Sanjeevi Ramakrishnan, Arun Sarma, Vinay Shankar Prasad Sinha, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7162838/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract The study's primary goal was to develop the Surface Water Susceptibility to Pollution (SWSP) index to assess the health and quality of the wetland. This landscape is the best indicator of ecological and environmental conditions and serves as blue infrastructure for climate change adaptation. The study was conducted in the wetland-dominated area of the northeast region of India to demonstrate the scalability and replicability of the model. Eight independent watershed characteristics and fifty-five subfactors are included in the index for better performance at a larger scale. The water quality index (WQI) was measured through in situ and laboratory tests of the physicochemical parameters of surface water in three natural wetlands, namely Deepor Beel, Chandubi Lake, and Digholi Bil. WQI was used to validate the Susceptibility to Pollution (SWSP) index. The result revealed that Deepor Beel (Ramsar site, 2002) is highly turbid (73.6 NTU), and 96% of the geographical area of the lake has WQI values above 200, leading to the water being completely unsuitable for any usage. High and very highly SWSP regions of the catchment fall under built-up, agricultural land and hilly forest areas in Deepor Beel (72%), Digholi Bil (63%), and Chandubi Lake (62%). Linear regression between SWSP Index and WQI is significantly highly correlated in all three wetlands: Deepor Beel (R2 = 0.72), Chandubi Lake (R2 = 0.85) and Digholi Bil (R2 = 0.68) with p < 0.05. The SWSP index benefits water resource managers by assessing surface water quality and pollution status and adopting remedial measures to control pollution from non-point sources. Susceptibility Wetland Water Quality Index Pollution Land use land cover Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Wetlands are unique natural resources in the landscape that serve a variety of roles, including water resource conservation, aquatic and wildlife habitat ecosystems, and blue infrastructure for climate change adaptation (Kumar et al., 2021 ; EPA, 1990). In addition to those mentioned above, it maintains ecosystem services by controlling water quality and quantity, nutrient cycling, sedimentation control, and increasing biodiversity (Kaynor, 1998 ) The first and most crucial stage in developing a watershed characterization and vulnerability assessment for water resource managers and decision-makers with effective methods is to manage these wetlands sustainably (Khawlie et al., 2005 ). The world has lost a significant portion of its wetlands (about 87%), and they are disappearing three times faster than forests, despite the World Wildlife Fund (WWF) 's tremendous efforts to conserve and maintain these priceless natural areas (Gillespie, 2023 ). Natural wetlands make up about 3.66% of India's total land area. Still, throughout the past 40 years, watershed degradation in upstream water resources, pollution, invasive species, aggressive agriculture, industry, and urban expansion have severely impacted roughly one-third of these areas (Chakraborty, 2021 ). According to the Ministry of Environment, Forests & Climate Change (MoEF&CC), Government of India, the loss has numerous adverse effects on the ecosystem and has faced countless existential risks. According to the Environmental Protection Agency (EPA), non-point Sources (NPS) pollute 85% of streams and rivers and 80% of surface water bodies. The most significant concern is non-point source contamination, which results from careless land management that fails to adequately consider the watershed's physical characteristics, including slope, soils, hydrology, and meteorological characteristics (Ritter et al., 2002 ). Therefore, even if the areas are tiny, it is essential to determine the characteristics of land activities under a specific watershed with a wetland. It is crucial to comprehend the following important contributing factors when estimating non-point source pollution-based watershed characteristics: stream network, soil properties, urbanisation process, industrial development, surface transport, agriculture, vegetation growth, watershed slope, and precipitation intensity and amount. A fair and reasonable way to understand the phenomenon of surface water pollution susceptibility is to link the water quality of any surface water body with the aforementioned watershed parameters. Any wetland must identify toxins and pollutants and prohibit their dispersion before the source of the pollution can be identified. The concentration of many water quality parameters, including pH, turbidity (TUR), total dissolved solids (TDS), total hardness (TH), total alkalinity (TA), dissolved oxygen (DO), electric conductivity (EC), and others, determines the quality of the water. Linking the above factors within the watershed with the water quality of any surface water body provides good and reasonable insights into the phenomenon of susceptibility to surface water pollution. Before attribution of the source of pollution, identifying contaminants and pollutants and their prevention against dispersion is very much needed for any wetland. The water quality depends on the concentration of water quality parameters such as pH, Electric Conductivity (EC), Dissolved Oxygen (DO), Total Dissolved Solids (TDS), Total Hardness (TH), Total Alkalinity (TA), Turbidity (TUR), and others. All aquatic living things in freshwater are always harmed by concentrations beyond the permissible limit set by different agencies, including the Indian Council of Medical Research (ICMR), the World Health Organization (WHO), and the Bureau of Indian Standards (BIS). Numerous case studies on water quality indicate that the primary cause of surface water pollution and degradation in its natural state is the inflow of agricultural and urban discharges, which contain organic compounds, heavy metals, and nitrogen and phosphorus compounds that lead to eutrophication. Land use and land cover in catchment areas significantly impact the hydrological cycle, surface runoff, and ecological processes of surface water quality. Water Quality Index (WQI), a numerical descriptor (values 0-100) of water quality, effectively expresses water quality based on suitability for human consumption using physical, chemical, and biological variables. It combines the influence of individual contaminants on the overall water quality. After that, it is ranked based on its suitability for human consumption and use for different reasons (Chidiac et al., 2023 ). Due to the combined exposure of major ions, hazardous metals, and mineral dissociation from household and industrial waste effluents, vehicle leaks, and effluents from the city's high-traffic regions (Dash et al., 2021 ). Given its importance, the wetland must take all feasible measures to detect the sources of anthropogenic and geogenic contaminants before their toxicity levels become catastrophic and impact the aquatic life and surrounding population (Dash et al., 2020a ; Dash et al., 2020b ; Kleinschroth et al., 2021 ; Singh et al., 2012; Roy & Majumder, 2022 ). When polluted surface water seeps into aquifers through seepage and permeable soil medium, serious water problems result. Therefore, improper surface water treatment poses a similar risk to human health. Thus, the quality of drinking water must be adopted for freshwater bodies. Drinking, irrigation, and industrial uses are the primary uses of freshwater from wetlands (Chidiac et al., 2023 ). Geographic Information Systems (GIS) are more accurate and scientific for inventorying, monitoring, and managing wetlands through satellite imagery and spatial analysis. It is beneficial to monitor the dynamics and patterns of the landscape. Additionally, it locates and measures the origins of water contamination. This technology is widely used for spatial and temporal data on wetland ecosystems and associated quality concerns (Alparslan et al., 2007 ; Ramadas & Samantaray, 2017 ). Using multilayer information of watershed characteristics, the Multi Criterion Decision Model (MCDM) and Analytical Hierarchy Process (AHP) method are gradually utilized and enhanced to map the actual hazard-management issues, such as flood susceptibility zonation (FSZ), Gully Vulnerability Index (GVI), forest fire and infrastructure vulnerability, landslide susceptibility zone, ground water potential zone, site suitability analysis (solar farms, dump site, habitat suitability, etc. More than 85% model prediction accuracy is possible through MCDM (Mitra et al., 2022 ). A successful model always depends on the unbiased selection of relevant variables for susceptibility/suitability mapping. In addition, numerous researchers, including the United States Environmental Protection Agency (EPA, 1990), have created various techniques and strategies to evaluate a watershed's vulnerability to surface water contamination. The water quality index (WQI) and vulnerability index had a significant connection (more than 75%), according to the results, which suggests that it might be implemented without any doubt (Rigina, 1998 ; Jabbar et al., 2019 ). These techniques can overcome the ambiguity of data from different sources, the discrepancy in resolution, and the variance in coordinate system platforms, including data interoperability challenges, by utilizing remote sensing-based information and GIS technology. Scientists are attempting various methods to identify risk factors and indicators, considering all potential dependent factors. They also assess watersheds' health and spatiotemporal changes (Jabbar, 2019). A scientific method must be developed to identify the boundaries of the catchment's most vulnerable areas and the contributing elements of pollutants. To maintain a sustainable environment, estimating the quantum of the vulnerable regions in watersheds susceptible to NPS pollution is crucial to determining the extent and severity of susceptibility. 2 Study Area Approximately 3500 freshwater lake (Beels) wetland ecosystems in Assam's Brahmaputra and Barak valleys are rapidly declining, endangering aquatic life and vegetation cover, lowering the health of numerous mega-herbivores like rhinos, elephants, and Asiatic water buffalo, and upsetting biodiversity. It has been stated that ten ancient lakes in Guwahati city have poor water quality and that Borsola Beel, Sarusola Beel, Silsako Beel, and Bondajan are on the verge of dying and going extinct (Dutta et al., 2024 ). Numerous other studies have documented that the water quality of Satajaan wetland in Lakhimpur, four wetlands (Itila, Dhankhuli, Jiong, and Hahchara) in the Dimoria Block of Kamrup District, and the Jatinga River in Dima Hasao has deteriorated due to various other reasons Dutta et al, 2016 ; Das et al., 2024 ; Gohain & Bordoloi, 2021 ). The impact of the surrounding LULC is sufficient to understand this situation. The freshwater ecosystem of Deepor Beel (Ramsar Lake) is threatened by both natural and anthropogenic factors, including urbanization, nearby industrial establishment, unplanned settlements (Singh et al., 2021 ), and, more specifically, invasive species like water hyacinth, according to satellite research and field observations (Mozumder et al., 2014 ; Sharma et al., 2024 ). Decadal land use and land cover changes near Chandubi Lake occurred between 1980 and 2017. A thorough examination of several physicochemical parameters that affect water quality and Landsat imagery was used to study the lake. The results showed that the DO level was relatively low (less than 5.0 mg/L) in Zones, the TDS level appeared very low, and the pH values were somewhat acidic in certain places (Sharma et al., 2016). Physicochemical measurements and estimates of the lake system's primary productivity reveal poor productivity but excellent WQI status due to the absence of arsenic. The lake's environment is suffering from losing its link to flowing water, and as a result, it is becoming shallower due to the proliferation of aquatic macrophytes (Sharma et al., 2019 ). With a focus on heavy metal and physicochemical parameter contamination analysis, most research on water quality analysis for Assam wetlands (Beels) emphasizes the water quality index and its source allocation using statistical methods. Nevertheless, none of the studies emphasize identifying a particular spatial extent susceptible to surface water contamination (Non-Point Source) in Assam wetlands. Despite Deepor Beel being the most significant lake, no scientific assessment or study of land use, land cover, or other contributory watershed issues has been conducted on degraded wetlands. The three wetlands were chosen through field visits to evaluate the water quality, the lake's size, and the catchment. To examine the variations in water quality according to the water source, it is determined that these wetlands have three distinct water accumulation environments: urban, rural habitat, agricultural land, and natural forest areas. Based on land use/land cover patterns, three wetlands—Chandubi Lake, Deepor Beel, and Digholi Bil in the Kamrup district of Assam—were selected for this study (Fig. 1 ). Deepor Beel Situated around 20 kilometers from the Guwahati International Airport and confined by latitudes 26°6'6.37"N to 26°7'53.78"N and longitudes 91°36'51.81"E to 91°40'28.49"E, this is the only Ramsar Site in the state of Assam. According to reports, the lake is heavily infected with water hyacinth all year round. According to locals, the lake teemed with several kinds of freshwater aquatic plants twenty years ago, including lotuses, lilies, and others. Biologically, ecologically, and environmentally, the lake is one of the Brahmaputra basin's biggest and most significant riverfront wetlands. Chandubi Lake At the base of the Garo hills, which stretch over the boundary between Assam and Meghalaya, this wetland is roughly 65 kilometers from the central city and 40 kilometers southwest of Guwahati City Airport. The lake is bounded by latitudes 25°52'19.91"N to 25°53'9.62"N and longitudes 91°24'45.44"E to 91°26'5.53"E. It was created by the 1897 earthquake and is encircled by tea gardens, tiny settlements, and reserve woods. In addition to a wide diversity of floating and submerged aquatic vegetation, it has a very minor water hyacinth infestation. Digholi Bil A large permanent freshwater body is situated on the Brahmaputra River's north bank, roughly 20 kilometers from Guwahati City. The water body is delimited by longitude (91°39'22.54"E to 91°39'47.05"E) and latitude (26°12'42.25"N to 26°13'53.61"N). Although many other floating aquatic plants are in the beel, nearby people have told us that the water hyacinth is only there temporarily and dries out as soon as the lake gets shallow in dry season. According to the locals, there is considerable natural fish production and plenty of fishing opportunities, although the water is somewhat brownish. 3 Materials and Methodology The surface water quality of the selected water bodies is examined, and an effort has been made to create a WQI for each of the three wetlands. To evaluate the negative aspects affecting Surface Water Susceptibility to Pollution (SWSP), a Multi-Criterion Decision Model (MCDM) based on the Analytical Hierarchy Process (AHP) technique index has been developed. Eight independent factors have been selected, and their weights and rankings are determined by expert judgment obtained through a thorough literature review and online survey. Land use and land cover, soil type, average annual precipitation, slope gradient, drainage density, road and stream distances, and bedrock type are among these elements, which have 55 sub-components. A hierarchical structure is assigned to the independent components, each sub-factor's weight is determined, the relative weights of the main factors and sub-factors are ranked, and the final score is measured. LULC data was extracted from remote sensing datasets using Sentinel 2A/B Optical on November 2, 2023. A supervised classification technique that used 13 spectral bands for training sets was used to prepare LULC. Surface slope is determined using the CartoSat digital elevation model (DEM) with a 10 m resolution. The Government of India's National Soil Survey and Land Use Board provided the map of soil taxonomy and texture classes. DEM rectification, planimetric data, watershed delineation, slope, and drainage ordering are all accomplished using a GIS spatial analyst and hydro tool. Google Earth was used to estimate the area of Deepor Beel (1437.59 ha), Chandubi Lake (129.58 ha), and Digholi Bil (220.3 ha). Hydro tools in a GIS environment were used to calculate the catchment area of Deepor Beel (246.01 sq. km), Chandubi Lake (25.87 sq. km), and Digholi Bil (91.67 sq. km). These three catchments primarily contain first- to second-order streams (Fig. 2 ). The drainage network was used to calculate the drainage density. The hydrological features were verified using a 1:50,000 scale Survey of India (SoI) topographical sheet. The road network was updated using visual interpretation of the most recent high-resolution satellite datasets after being retrieved from the OpenStreetMap Global Road database. The distance to the road and the distance to the stream were calculated in GIS environments. The Indian Meteorological Department's station data was used to construct the average annual precipitation data. The Inverse Distance Weighted (IDW) interpolated method created a 5-inch rainfall interval for the average yearly precipitation sub-factor. The Geological Survey of India's (GSI) District Resource map was utilized in the study to identify the type of bedrock and its permeability. 3.1 Water sampling and water quality index (WQI) In-situ and laboratory analysis of water sampling of 12 physico-chemical parameters was conducted during the immediate post-monsoon period at their maximum water capacity, laden with dissolved and suspended sediments from the catchment on October 22 and 23, 2023, in Deepor Beel, and on October 26 in Chandubi Lake and Digholi Bil. Spectral characteristics of Waterbodies were used in selecting the sampling sites through the Earth observation satellite product Sentinel 2A/B. Diverse aquatic flora, water hyacinth mat coverage, silt presence, and lake depth are sensitive to the spectral signatures of waterbodies. It also assists sampling locations because it leads to restricted access and limited open water areas for selecting a water sample. The Digholi Bil's perennial water restriction is significantly lower than that of the other two waterbodies. A total of 109 water sampling locations were recorded using GPS coordinates in the following waterbodies: Digholi Bil (14), Chandubi Lake (27), and Deepor Beel (68). Temperature (Temp), pH, Electrical Conductivity (EC), Turbidity (TUR), Dissolved Oxygen (DO), and Oxygen Redox Potential (ORP) were among the six physicochemical parameters that were measured on-site right away using the NABL-certified Digital Water and Soil Analysis Kit-7P. However, few parameters such as Total Dissolved Solid (TDS), Salinity (SAL), Total Hardness (TH), Total Alkalinity (TA), Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Phosphorus (P) and Nitrate ion (NO 3 ) are analyzed in laboratory. WQI (Brown et al., 1970 ; Katyal, 2011 ; Sinha et al., 2022 ) was calculated using the weighted arithmetic index using 12 physico-chemical factors (Table 1 ). WHO guidelines and IS: 10500 (2012) [Second Revision] are considered while determining the standard and allowable limit. Table 1 Water quality status using WQI range WQI Range Water Quality Status Possible Usage 0–25 Excellent Drinking, Irrigation, and Industrial 26–50 Good Drinking, Irrigation, and Industrial 51–75 Poor Irrigation and Industrial 76–100 Very poor Irrigation > 100 Unsuitable Proper treatment is required before use The spatial distribution of lake water quality is represented by the WQI of the three wetlands, which is calculated at each site. To estimate WQI at an unknown location, the Inverse Distance Weighted (IDW) interpolated approach was employed by assuming that places closest to one another are more similar than those farther apart. It uses the known (observed) values to surround the prediction location. The calibration and validation of the non-point source pollution region with WQI zones were done using the results of the interpolated surface. 3.2 Surface water susceptibility to pollution (SWSP) index The study's purpose is to critically examine water quality and identify all non-point sources of contamination in the watershed with a degree of vulnerability to surface water pollution using simple and easy scientific methods. MCDM-based AHP methods (Gohari et al., 2022 ) are used to identify the most probable non-point sources of pollution. The water quality index of each of the three catchments is compared with an AHP index on watershed susceptibility to pollution. Eight independent components and fifty-five sub-factors were chosen for susceptibility modeling. The research area is more prone to runoff, hence there is a higher likelihood of suspended sediments/contaminants being transported to water bodies, resulting in significant water pollution. Fifty-five sub-factors linked with runoff potential and vulnerability were examined to determine the degree of vulnerability to pollution in watersheds. The components and sub-factors were then synthesized into appropriate weights, ranks, and ratings (Table 2 ). These were further investigated to determine the degree of impact on surface water quality utilizing previous literature research and an expert survey (Emovon et al., 2018 ). As a result, these parameters were incorporated into the vulnerable mapping in the GIS environment. Table 2 The criteria for selection of factors, sub-factors with ranks and orders, and data collection methodology adopted for MCDM Factors/Rank Rank 8 (high) to 1(low) Criteria, Sub-factors, and Data collection source & methodology Land use Land cover (LULC) Rank 8/8 Agriculture and urban lands considerably affect surface water quality, carrying a significant load of contaminants from various point and nonpoint sources (Yan et al., 2023 ; Wubie & Assen, 2020 ). The cropland and urban areas have a direct and highly positive correlation with the water quality pollution indicators such as ammonia, nitrogen, phosphorus, and heavy metals. In contrast, it has a negative relationship with forests, scrublands, and grasslands (Jordan et al., 2014 ). Sub-factors weightage given to sub-classes of LULC with higher to lower ranks under the following order: agriculture land, built-up area, barren land, wetland, grassland, shrubland, forest land, and water bodies on vulnerability to pollution in this study. Distance to Road (DTR) Rank 7/8 In the urban scenario, the significant sources of contamination are highways, roadways, and parking lots, which might pollute surface water with considerable vehicular transport load (Gjessing et al., 1984 ; Uliasz-Misiak et al., 2022 ). The road network is interpolated with the distance from the road, and a raster derivative layer is generated as the distance from the road. The weightage was assigned with the lower value as the distance from the road increased. Soil type (ST) Rank 6/8 The chemical & biological soluble materials and suspended sediments in soil are the most effective sources of pollutants (Novotny & Chesters, 1989 ; Förstner, 2004 ), which are governed by soil texture and permeability. Based on the higher permeability and porosity of soils, lower weightage is assigned to the sub-factors of soils. Slope (SLP) Rank 5/8 Surface water flow is controlled by the slope of the watershed, which directly affects soil erosion and sedimentation rate, carrying several pollutants in the form of nutrients, pesticides, and pathogens to nearby rivers/water bodies. The high runoff potential relates to steeper slopes, making them more susceptible to pollution (Yan et al., 2023 ; Wubie & Assen, 2020 ). Drainage Density (DD) Rank 4/8 The drainage density is the property of the watershed carrying the stream network and is calculated per unit area of the catchment. The nature of the watershed regions, such as weak and impermeable subsurface materials, sparse vegetation, and augmented high slope gradients, leads to higher drainage lengths. An area with high drainage density has high potential surface runoff and is prone to a high sediment yield entrapped through streams/rivers (Dragičević et al., 2019 ). Annual Average Precipitation (AAP) Rank 3/8 Annual precipitation carries a high value due to its magnitude, intensity, and frequency of rainfall, leading to high sedimentation, affecting pollutant concentrations (Xie et al., 2019 ), and hence becoming more vulnerable to pollution. The average annual precipitation for 2022 and 2023 was consider in this study. Distance to Stream (DTS) Rank 2/8 The rivers/ streams carry pollution from urban and agricultural areas and continue to threaten water quality, depending on their distance from the pollution point. Streams with significantly shorter distances have a high probability of surface runoff, contributing to water pollution. Hence, distance to stream (DTS) is essential in determining the areas susceptible to surface water pollution (Hatt et al., 2004 ; Frey et al., 2015 ). Bed Rock Type (BRT) Rank 1/8 The lateral flow of catchment water over the bedrock is controlled by its Geological formation, rock type, and permeability character. In combination with the physical properties of the soil layer, the bedrock type and infiltration decide the nature of interaction with water and its contamination for quality analysis (Kosugi et al., 2006 ; Dong et al., 2023 ). The geological map revealed that the small catchments have almost a single type of bedrock, and it has minimal impact or role on the MCDM modeling process. With the analogy explained, factors, sub-factors, and their importance to surface water pollution, a normalized pairwise matrix (Table 3 ) and relative weight with rating scores (Table 4 ) are formulated based on AHP. The required independent variables were obtained in appropriate compatible formats, such as Raster or Vector, in a GIS environment, and then converted into a derived layer in Raster format under a 10 m spatial resolution. The raster layer of each variable is further reclassified into sub-categories based on the rankings. GIS operations such as vector to raster conversion, scale, resolution, projection, raster calculator, reclassify, and overlay were used to complete the steps (Fig. 3 ). Later, MCDM was performed on three wetland catchments to obtain Surface Water Susceptible to Pollution (SWSP) with an index value. Table 3 Normalized pairwise matrix of the factors susceptible to pollution. Factor LULC DTR DTS ST SLP DD AAP BRT Weights Wt (%) LULC 8 7 6 5 4 3 2 1 0.367 36.8 Distance to Road (DTR) 8/2 7/2 6/2 5/2 4/2 3/2 2/2 1/2 0.183 18.4 Distance to Stream (DTS) 8/3 7/3 6/3 5/3 4/3 3/3 2/3 1/3 0.122 12.3 Soil type (ST) 8/4 7/4 6/4 5/4 4/4 3/4 2/4 1/4 0.091 9.2 Gradient Slope (SLP) 8/5 7/5 6/5 5/5 4/5 3/5 2/5 1/5 0.073 7.4 Drainage Density (DD) 8/6 7/6 6/6 5/6 4/6 3/6 2/6 1/6 0.061 6.1 Annual average Precipitation (AAP) 8/7 7/7 6/7 5/7 4/7 3/7 2/7 1/7 0.052 5.3 Bedrock type (BRT) 8/8 7/8 6/8 5/8 4/8 3/8 2/8 1/8 0.045 4.6 Table 4 The relative weights and rating scores of the factors and sub-factors susceptible to pollution Factors Weight% Sub-factors (Ratings) at the scale of 10 − 1, high to low susceptibility Land Use Land Cover 36.8 Agriculture (10); Built-up area (9); Barren land (7); Wetland (6); Grass land (5); Shrubland (4); Forest area (3); and Water bodies (1). Distance to Road (in m) 18.4 ≤ 500 m (10); 500–1000 m (7); 1000–1500 m (5); 1500–2500 m (3) and > 2500 (1). Distance to Stream (in m) 12.3 ≤ 50 m (10); 50–100 m (7); 100–200 m (5); 200–500 m (3) and > 500 m (1). Soil type 9.2 Clay loam (10); Silt loam (8); Silt clay loam (7); Clay (6); Silt (5); Sandy loam (4); Peat (3), and Sandy (2). Slope (in degrees) 7.4 > 35° (10); 35–20° (8); 20–15° (6); 15–10° (4); 10–5° (2) and 2500 (10); 2500–2000 (8); 2000–1500 (6); 1500–1000 (5); 1000–500 (4); 500–100 (3) and 75ʺ (10); 75–70ʺ (9); 70–65ʺ (8); 65–60ʺ (7); 60–55ʺ (6); 55–50ʺ (5); 50–45ʺ (4); 45–40ʺ (3); 40–35ʺ (2) and < 35ʺ (1). Bedrock type 4.6 Limestone (10); Dolomite (9); Shale (7); Claystone (5); Sandstone (3), and Metamorphous (1). 4 Results and Discussion 4.1 Water quality analysis The water quality of wetlands is analyzed based on mean values of all the parameters sampled in various locations ( Table 5 ) . The average water temperature ( ° C) of the wetlands during October 2023 is found to be 28.37, 29.81, and 26.48 with standard deviations of 1.22, 0.24, and 1.41 for Deepor Beel, Chandubi Lake, and Digholi Bil, respectively, indicating a steady temperature in the wetlands. The average pH values show 7.90, 6.76, and 7.10 for Deepor Beel, Chandubi Lake, and Digholi Bil, indicating the water is within the standard-permissible pH value of 6.5–8.5. Other parameters revealed values within the permissible/acceptable values, except for a very high value in turbidity, with a mean of 73.60 NTU and ranging from a minimum of 52.68 to a maximum of 95.45 in the Deepor Beel. The Potassium concentration in the Deepor Beel is higher (mean 7 mg/l) than the standard quantity's prescribed limit (2.7 mg/l). Chandubi Lake revealed moderately to high values of turbidity with a mean of 14.82 NTU (13.14–17.20 NTU), whereas the Digholi Bil revealed mean turbidity values of 32.32 NTU and a range of 28.55–36.74 NTU. The high turbidity in all the wetlands justifies that water quality is affected by sediment loading through runoff from the catchments. The high average rainfall over the region with moderate slope and scanty vegetation cover in the study catchments leads to stormwater runoff. It helps to carry unprotected sediments, in the form of topsoil particles, which contribute high turbidity in the water if analyzed immediately post-monsoon. Table 5 The average value of various water quality parameters in selected wetlands Parameters Units of measurement Mean IS:10500 (2012)* Deepor Beel Chandubi Lake Digholi Bil pH pH units 7.90 6.76 7.10 6.5 a – 8.5 b ORP mv 56.37 23.22 19.07 - EC µS/cm 278.85 296.03 156.7 1000 a – 3000 b TDS mg/L 186.66 199.18 104.71 500 a – 2000 b Salinity mg/l 254 280.48 149.14 - Dissolved Oxygen mg/L 7.82 7.82 9.93 6 a – 8 b TH as CaCO 3 mg/L 81.26 11.37 65.14 200 a – 600 b Turbidity NTU 73.60 14.82 32.22 1 a – 5 b TA (as CaCO 3 ) mg/L 78.57 12.18 52.28 200 a – 600 b Calcium mg/L 30.32 4.28 16.37 75 a – 200 b Magnesium mg/L 12.38 1.72 24.35 30 a – 100 b Sodium mg/L 19.83 3.55 12.63 200** Potassium mg/L 7.00 0.619 1.16 2.7** Phosphorus mg/L 0.43 0.366 0.3 - Nitrate mg/L 12.83 8.77 8.78 45** a. Acceptable limit, b. Permissible limit in the absence of any alternate source. *Standards prescribed by the Indian Standard of Drinking Water having specification [IS: 10500 (2012)] (Second Revision), ** WHO guideline. For overall WQI for the three wetlands based on mean concentration of all observation sites against their benchmark values prescribed by IS/WHO guidelines was assessed and it was observed that the mean concentration of all observation sites, the WQI of Deepor Beel is 238.12 which is exceeding the limit of 100 (unsuitable) unit (Table 3 ). Deepor Beel will be highly turbid in October 2023 and significantly contribute to the deteriorated water quality. The wetland is unsuitable for Drinking, Irrigation, and Industrial use, and proper treatment is required for any use during the post-monsoon period. Similarly, based on the mean concentration of all observation sites, the WQI of Digholi Bil is 73.34, which is greater than 50 units, and Chandubi Lake is 48.45, less than 50 units, which shows that overall, the status of Digholi Bil is poor, and Chandubi Lake is in good status. The WQI values of 68 locations in Deepor Beel fall under unsuitable for different uses. WQI values of 45 locations (66%) lie between 200 and 250, and 21 locations (30%) have WQI values above 250 (Fig. 4 ). Out of 27 locations sampled in Chandubi Lake, 19 locations (70%) have WQI values less than 50 (Good), whereas the remaining 30% locations have WQI values between 50 and 64 (Poor). Out of 14 locations sampled in Digholi Bil, 7 locations (50%) have WQI values ranging from 64 to 75 (Poor), and the remaining 50% locations have WQI values between 75 and 88 (Very poor). However, water quality at different sample locations was analyzed to explore the spatial distribution of water quality index during October, which helps to identify various environmental conditions in three wetlands. The three wetlands' interpolated surface of water quality index value was generated (Fig. 5 ). It helps to identify the potential contaminated zones. The WQI interpolated surface of the lake shows varied contamination zones in the open water areas. The Deepor Beel shows that the south and southeast part is highly contaminated. The affected zones might be due to water runoff from two major streams/rivers, namely Morabharalu Nala and Basistha River, which pass through highly populated urban areas of Guwahati city situated on the northeast side of the Beel. The possible reason for contamination might be anthropogenic causes, as point and non-point sources in the catchment. The water quality index surface of Chandubi Lake and Digholi Bil shows relatively low values, and a significant part of the open water is under good water quality zones. 4.2 Susceptibility indicators Eight independent factors and their fifty-five sub-factors were selected for susceptibility modeling, providing the three lakes' existing geophysical and environmental conditions). The Deepor Beel catchment is dominated by densely populated urban Guwahati city, and 24% of the area is covered by built-up land. The catchment also covers 23% of scrubland with 8% of forest land. 68% of the catchment is surrounded by less than 500 m of road distance, which is a significant threat to the environmental pollution of the lakes. The catchment shows more than 60% of the area lies within 200 m of the streams, contributing significantly to the lake's pollution load. Catchment receiving maximum infiltration due to its sandy nature since the area is close to the Brahmaputra. Fifty per cent of the catchment area is below a 5-degree slope, enhancing infiltration and reducing surface runoff. A significant part of the area covered with high drainage density, with annual average rainfall above 70 inches, clearly favours a higher contribution of water runoff, leading to a high contribution to pollution (Table 6 ). Table 6 Coverage of catchment area under different factors and sub-factors Factors Sub-factors Deepor (%) Chandubi (%) Digholi (%) Land Use Land Cover Agriculture 9.30 12.16 32.20 Built-up area 24.57 8.29 10.81 Barren land 7.67 1.35 13.81 Wetland 6.25 3.45 12.39 Grass land 8.30 7.85 7.30 Shrubland 23.63 11.71 12.25 Forest area 16.75 50.59 8.49 Water bodies 3.53 4.56 2.71 Distance to Road ≤ 500 m 68.84 49.13 93.82 500–1000 m 10.97 16.86 5.58 1000–1500 m 6.58 7.19 0.90 1500–2500 m 7.18 12.21 - > 2500 m 6.43 14.60 - Distance to Stream ≤ 50 m 16.48 30.65 12.86 50–100 m 25.53 27.15 27.61 100–200 m 20.16 33.16 32.55 200–500 m 36.35 9.03 26.46 > 500 m 1.48 - 0.52 Soil type Clay loam 23.42 83.25 - Silt loam 11.82 10.15 0.28 Silt clay loam 0.13 83.24 7.78 Silt 1.33 - 28.38 Sandy loam 28.38 6.59 19.17 Peat 23.42 - 36.88 Sandy 34.89 - 7.47 Slope (in degrees) > 35° 4.53 4.93 1.63 35–20° 16.48 18.29 4.68 20–15° 7.80 8.96 2.11 15–10° 8.96 11.57 3.04 10–5° 12.70 16.51 10.77 2500 - 25.58 - 2500–2000 - 34.50 - 2000–1500 - 20.35 - 1500–1000 2.79 9.84 - 1000–500 22.67 5.24 - 500–100 74.54 4.49 - 75ʺ 46.44 100.00 100.00 75–70ʺ 37.83 - - 70–65ʺ 14.89 - - 65–60ʺ 0.84 - - 60–55ʺ - - - 55–50ʺ - - - 50–45ʺ - - - 45–40ʺ - - - 40–35ʺ - - - < 35ʺ - - - Bedrock type Limestone - - - Dolomite - - - Shale - - - Claystone - - - Sandstone - - - Metamorphous 100.00 100.00 100.00 On the other hand, Chandubi Lake is dominated by forest cover (50.59%), but 49.13% of the catchment areas lie within 500 m of road distance and contribute to high vulnerability. 91% of the area contributes to vulnerability within 200 m of the stream's distance. This area has very low infiltration because 84% of the soil is clayey, which supports more runoff. Only 40% of the area falls under less than 5 degrees of slope. Drainage density (above 500) and average annual rainfall are relatively high (above 85 inches) compared to Deepor Lake. The rainfall seems to be very high, which leads to more chances of surface runoff and high vulnerability to pollution in the Chandubi catchment. Digholi catchment dominates the agricultural area (32%), where the expected contamination source is mainly agriculture, other than any point source in the catchment. Digholi Bil entire catchment area, with a very close proximity distance, gives the highest vulnerability for pollution. Distance to stream indicates a relatively low contribution to pollution. A significant part of catchments has a low percolation texture of soil, and low percolation is high vulnerability to pollution and vice versa. 77.7% of the Digholi catchment lies within less than 5 degrees of slope. The drainage density of the Digholi catchment is very high compared to the other two lakes, leading to a high probability of surface pollution. The average annual precipitation of the Digholi catchment has the highest percentage of rainfall (> 75 inches). It signifies that rainfall is a significant contributing parameter for the high vulnerability of surface water pollution. The entire bedrock type of Deepor Beel and Chandubi Lake Catchment area is Gneiss and high-grade schist, and it has low permeability, making it highly vulnerable to water runoff and leading to high chances of surface pollution. However, the Digholi catchment area is covered with unconsolidated sand, without clay or silt, has high permeability, leads to low surface runoff and has minimum surface pollution contribution. However, the percolated contaminants may affect groundwater potential zones and indirectly affect surface water pollution. 4.3 Surface water susceptibility to pollution index A susceptibility resulted from each catchment obtained through the MCDM process by considering eight criteria and fifty-five sub-criteria depicting vulnerability (SWSP) index rated 1 to 4, where one (1) indicates low, two (2) as moderate, three (3) as high, and four (4) as very high (Fig. 6 ). The SWSP index of the three wetland catchments shows Very High Susceptible area to pollution, estimated at Deepor Beel (53.33%), Digholi Bil (33.91%), and Chandubi Lake (26.94%) of the total area of the catchment. Highly susceptible area to pollution is estimated in Deeper Beel as 36.88%, Digholi Bil as 31.74% and Chandubi Lake as 26.2% (Fig. 7 ). The above high vulnerability area was further superimposed on LULC and analyzed that the Deepor Beel SWSP was contributed mainly by built up (49.03%), Agriculture (24.38%), and Barren land (21.46%). However, the SWSP contribution in Chandubi catchment shows Agriculture (42.27%), followed by built up (28.60%), wetland (10.50%), and grassland (7.25%). The SWSP of Digholi catchment is significantly contributed to by agricultural land (55.37%), built-up (18.3%), and Barren land (17.15%). The result shows that the SWSP index is high under urban and agricultural land in the lake environment (Fig. 8 ). Despite giving lower weight and ranks in the MCDM criteria for forest area, it was observed that high vulnerability area falls under forest land in the lake environment of Chandubi Lake (74.42%) and Digholi Bil (27.48%). It could be because these forest lands fall under steep slope and the clay nature of the dominant soils, and thereby carry higher weight and rank for susceptibility that might have come into play as a significant factor contributing to surface water runoff carrying sediments/contaminants to the wetlands. 4.4 Relationship between WQIs and SWSP index To establish the relationship between water quality index (WQI) by observed water quality test and Surface Water Susceptibility to Pollution (SWSP) index computed by various environmental factors using MCDM, the entire catchment was first divided into sub-catchments, and then the mean zonal WQI was calculated for each sub-catchment (Fig. 9 ). Further, a linear regression was established on the mean zonal WQI of the part of the respective sub-catchments and the % of high (H) and very high (VH) susceptibility areas (SWSP index 3 and 4) in the same sub-catchments (Fig. 10 ). The results show that the relationship between WQI and SWSP index (Table 7 ) has a significant positive correlation in Deeper Beel (R 2 = 0.72 with p = 0.02), Chandubi Lake (R 2 = 0.85 with p = 0.04) and Digholi Bil (R 2 = 0.77 with p = 0.03). The result reveals that water quality is directly proportional to the high & very highly vulnerable areas in the SWSP Index. It indicates that the considered drivers/factor and sub-factors and their weightage and rank are highly reliable in predicting the SWSP index for any wetland water quality issues. Table 7 Relationship between WQI and Susceptibility Wetland Equation R² p value Deepor Bil WQI = 1.7982× % of Susceptibility (H + VH) + 104.42 0.720 0.020 Chandubi Lake WQI = 0.1144× % of Susceptibility (H + VH) + 42.616 0.856 0.004 Digholi Bil WQI = 0.1168× % of Susceptibility (H + VH) + 63.893 0.689 0.004 The proposed model will support civic authorities in monitoring and managing the pollutant/contamination by controlling all anthropogenic activities in vulnerable areas of the catchment, specifically the pour points in the periphery, which must be identified for suitable treatment of contaminants. Also, it will provide permanent remedial measures/approaches for controlling the pollutants and maintaining the quality of the lakes, especially Deepor Beel, which is a Ramsar Site. Other rural lakes will give the local community cleaner and safer water for various purposes. 5 Conclusions The present study was conducted on three different lake environments of Assam state in northeastern India, commonly known as ‘Beel’. Because of the natural topography and floodplain of the Brahmaputra valley, this area has a large number of small and large natural beels. Therefore, the present study was conducted in a critical region of a natural wetland. These wetlands significantly contribute to blue infrastructure to combat the impact of climate change. It has significantly contributed to climate resilience and the achievement of several SDG targets. This study is based on field-based observation, which includes water quality testing of 14 parameters at 309 locations in three different lakes. Further, these qualities of water are converted into the WQI index to help policymakers understand the quality status. However, the estimation of WQI in the unsampled area was also computed using geospatial tools. The water sample test and interpolated surface of the water quality index concluded that the Deepor Beel surface water quality is unsuitable for any use, and the Digholi Bil water quality is poor to very poor. However, Chandubi Lake surface water quality is suitable, where some samples test falls under good and a few samples test falls under poor. This study is not limited to water quality index preparation. The study aims to identify the cause of poor quality and prepare a model for the Surface Water Susceptibility to Pollution (SWSP) index. It requires the selection of criteria based on the MCDA approach. Eight environmental factor and their fifty-five sub-factors were created based on field observation. The AHP approach was applied to the SWSP index, and its weight was given based on a literature survey and expert judgment. Finally, the Surface Water Susceptibility to Pollution (SWSP) index was prepared for all three lake catchments. The study's main objective is to identify the causes of susceptibility to pollution in the lake environment. It was recognized that the significant region of pollution in the lake environment is urban development because of a poor sewerage system, presence of chemicals due to agricultural practices and use of chemical fertilizer and pesticides. These two are the primary non-point sources of anthropogenic pollution. However, some forest areas are also highly susceptible to pollution due to natural factors such as high runoff from steep slopes and clay soil texture with low percolation. It led to a heavy load of debris and sediments. The ultimate goal of the study is to validate the Surface Water Susceptibility to Pollution (SWSP) index for replication and rescale to other similar kinds of natural wetland. Therefore, a regression analysis was performed on the SWSP index concerning the WQI index. The highly significant correlation between the SWSP index and WQI indicated that selected factors and sub-factors significantly govern the lake environment's water quality. It suggests that the method is highly reliable for water quality assessment. However, choosing other drivers may enhance the model quality based on the area's local environmental conditions and human ecology function. The present study concludes that the SWSP index is replicable and scalable to other areas of similar ecological conditions, which is required to achieve SDG goals and climate change targets. Declarations Ethical Approval All authors are aware of the submission of the manuscript in this journal, and the manuscript in full is original, and the manuscript in part or in full has not been published or simultaneously communicated in any other journal. Consent to Participate All authors know that the manuscript submission in this journal is associated with wetland water quality assessment, and “consent to participate” is not applicable in this study. This paper does not include human subjects and/or animal trials. Consent to Publish All authors are aware of the manuscript submission to this journal and the associated data. The data is original and has been created and analyzed by all authors. The consent to publish does not apply to this manuscript. Authors Contributions Conceptualization – Mr Rajendra Jena and Dr Arun Sarma; Methodology – Mr Rajendra Jena; Data collection, Investigation & Analysis – Mr Rajendra Jena; Validation – Dr R. Sanjeevi and Dr Arun Sarma; Writing draft manuscript – Mr Rajendra Jena; Review and editing – Prof Vinay SP Sinha, Dr Arun Sarma, Dr R. Sanjeevi and Dr J. Anuradha; Supervision – Dr R. Sanjeevi and Dr Arun Sarma; All authors have read and agreed to the published version of the manuscript. Funding This work was supported by NECTAR. Department of Science and Technology, Government of India (Grant numbers Ref No. B-11/1/2024) and the Author, Mr Rajendra Jena, have received research support from NECTAR, GoI for minor equipment. Competing Interests Authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Financial interests: Author Dr Arun Sarma, Dr R. Sanjeevi, Dr J. Anuradha and Prof Vinay SP Sinha declare they have no financial interests. Author Mr Rajendra Jena has received minor laboratory equipment from NECTAR, GoI Availability of data and materials The data sources are authenticated, collected from corresponding sources, and used only for research purposes. The datasets generated during and/or analyzed during the current study are not publicly available due to a State Agency Confidentiality request. 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10:55:38","extension":"html","order_by":63,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":232986,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/816d46ed87680d66317d2f82.html"},{"id":92165667,"identity":"06901f1b-51e2-4d4f-bcc1-31969fce682b","added_by":"auto","created_at":"2025-09-25 10:55:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2394181,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area of the selected three wetlands\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/2bfe47b0f267ab0247122b79.jpg"},{"id":92165993,"identity":"65681723-fc5d-4b26-b007-a839e184dc60","added_by":"auto","created_at":"2025-09-25 11:03:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2259122,"visible":true,"origin":"","legend":"\u003cp\u003eHydrological features and surface elevation of the watersheds of the respective wetland\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/bf4b9ddba7f672fbe821b1d7.jpg"},{"id":92165655,"identity":"36c092a4-7751-4af7-bba6-925b9c31cfd5","added_by":"auto","created_at":"2025-09-25 10:55:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3815427,"visible":true,"origin":"","legend":"\u003cp\u003eEight spatial susceptibility indicators for Deepor Beel, Chandubi Lake, and Digholi Bil\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/c3a3c1a3167619a16489745b.jpg"},{"id":92165651,"identity":"136d12a7-b34f-4958-af6b-4eda76305068","added_by":"auto","created_at":"2025-09-25 10:55:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2285471,"visible":true,"origin":"","legend":"\u003cp\u003eWQIs for all sampling locations in three wetlands\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/345b83c8b27b367071d3b62a.jpg"},{"id":92165652,"identity":"9124f15b-0ff6-4c63-b432-a53ca2990c5e","added_by":"auto","created_at":"2025-09-25 10:55:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":826261,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of sampled water quality index: a) Deepor Beel, b) Chandubi Lake, and c) Digholi Bil and its interpolated quality zone\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/e2a04db3ce877f502aeae65f.jpg"},{"id":92165991,"identity":"6cd1787d-fe16-4601-b9bc-22dc0d918125","added_by":"auto","created_at":"2025-09-25 11:03:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3885193,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the SWSP index in three catchments of the respective lakes\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/63e90b053b559f9eccb3b04d.jpg"},{"id":92165989,"identity":"fcf02a30-ee01-40f8-9039-a814e0a36852","added_by":"auto","created_at":"2025-09-25 11:03:36","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1578291,"visible":true,"origin":"","legend":"\u003cp\u003eVulnerability under various SWSP index zones of selected lake catchments\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/5fc060e179ee0b7f65841cc6.jpg"},{"id":92165656,"identity":"9ea088d8-abe0-4e8e-90db-8694b90a9107","added_by":"auto","created_at":"2025-09-25 10:55:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2103309,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of land practices with Surface Water Susceptibility to Pollution (SWSP) index\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/1891ee3245ca0ba12734c47a.jpg"},{"id":92165992,"identity":"82d5d0c6-49e9-4b96-8577-d328c207f714","added_by":"auto","created_at":"2025-09-25 11:03:36","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2840224,"visible":true,"origin":"","legend":"\u003cp\u003eWater quality index in the associated sub-catchment of the selected three lakes\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/ab3884a45efd72b3b4c75f2d.jpg"},{"id":92165665,"identity":"75131adc-41f0-4ae5-bce9-c05e7d8b69dd","added_by":"auto","created_at":"2025-09-25 10:55:36","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2092424,"visible":true,"origin":"","legend":"\u003cp\u003eRegression analysis on sub-catchment level vulnerability index with corresponding water quality zones in Deepor Beel\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/c882fc4b44be557ca109c569.jpg"},{"id":102785463,"identity":"ac695a04-deb2-44b5-a755-6a792cb9f994","added_by":"auto","created_at":"2026-02-16 16:07:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25322150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/9b9b4ed8-7e24-45ca-8b32-337e64a6dd32.pdf"},{"id":92165669,"identity":"28caab49-07d4-4242-bfe5-47f1742b5315","added_by":"auto","created_at":"2025-09-25 10:55:36","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":175603,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7162838/v1/195a131bf5ccea31cf5f426f.jpg"}],"financialInterests":"","formattedTitle":"Estimation of Surface Water Susceptibility to Pollution Index of Natural Wetlands of North-East India using Multi-Criteria Decision Model.","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWetlands are unique natural resources in the landscape that serve a variety of roles, including water resource conservation, aquatic and wildlife habitat ecosystems, and blue infrastructure for climate change adaptation (Kumar et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; EPA, 1990). In addition to those mentioned above, it maintains ecosystem services by controlling water quality and quantity, nutrient cycling, sedimentation control, and increasing biodiversity (Kaynor, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) The first and most crucial stage in developing a watershed characterization and vulnerability assessment for water resource managers and decision-makers with effective methods is to manage these wetlands sustainably (Khawlie et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe world has lost a significant portion of its wetlands (about 87%), and they are disappearing three times faster than forests, despite the World Wildlife Fund (WWF) 's tremendous efforts to conserve and maintain these priceless natural areas (Gillespie, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Natural wetlands make up about 3.66% of India's total land area. Still, throughout the past 40 years, watershed degradation in upstream water resources, pollution, invasive species, aggressive agriculture, industry, and urban expansion have severely impacted roughly one-third of these areas (Chakraborty, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to the Ministry of Environment, Forests \u0026amp; Climate Change (MoEF\u0026amp;CC), Government of India, the loss has numerous adverse effects on the ecosystem and has faced countless existential risks.\u003c/p\u003e\u003cp\u003eAccording to the Environmental Protection Agency (EPA), non-point Sources (NPS) pollute 85% of streams and rivers and 80% of surface water bodies. The most significant concern is non-point source contamination, which results from careless land management that fails to adequately consider the watershed's physical characteristics, including slope, soils, hydrology, and meteorological characteristics (Ritter et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Therefore, even if the areas are tiny, it is essential to determine the characteristics of land activities under a specific watershed with a wetland. It is crucial to comprehend the following important contributing factors when estimating non-point source pollution-based watershed characteristics: stream network, soil properties, urbanisation process, industrial development, surface transport, agriculture, vegetation growth, watershed slope, and precipitation intensity and amount.\u003c/p\u003e\u003cp\u003eA fair and reasonable way to understand the phenomenon of surface water pollution susceptibility is to link the water quality of any surface water body with the aforementioned watershed parameters. Any wetland must identify toxins and pollutants and prohibit their dispersion before the source of the pollution can be identified. The concentration of many water quality parameters, including pH, turbidity (TUR), total dissolved solids (TDS), total hardness (TH), total alkalinity (TA), dissolved oxygen (DO), electric conductivity (EC), and others, determines the quality of the water.\u003c/p\u003e\u003cp\u003eLinking the above factors within the watershed with the water quality of any surface water body provides good and reasonable insights into the phenomenon of susceptibility to surface water pollution. Before attribution of the source of pollution, identifying contaminants and pollutants and their prevention against dispersion is very much needed for any wetland. The water quality depends on the concentration of water quality parameters such as pH, Electric Conductivity (EC), Dissolved Oxygen (DO), Total Dissolved Solids (TDS), Total Hardness (TH), Total Alkalinity (TA), Turbidity (TUR), and others. All aquatic living things in freshwater are always harmed by concentrations beyond the permissible limit set by different agencies, including the Indian Council of Medical Research (ICMR), the World Health Organization (WHO), and the Bureau of Indian Standards (BIS).\u003c/p\u003e\u003cp\u003eNumerous case studies on water quality indicate that the primary cause of surface water pollution and degradation in its natural state is the inflow of agricultural and urban discharges, which contain organic compounds, heavy metals, and nitrogen and phosphorus compounds that lead to eutrophication. Land use and land cover in catchment areas significantly impact the hydrological cycle, surface runoff, and ecological processes of surface water quality. Water Quality Index (WQI), a numerical descriptor (values 0-100) of water quality, effectively expresses water quality based on suitability for human consumption using physical, chemical, and biological variables. It combines the influence of individual contaminants on the overall water quality. After that, it is ranked based on its suitability for human consumption and use for different reasons (Chidiac et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDue to the combined exposure of major ions, hazardous metals, and mineral dissociation from household and industrial waste effluents, vehicle leaks, and effluents from the city's high-traffic regions (Dash et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Given its importance, the wetland must take all feasible measures to detect the sources of anthropogenic and geogenic contaminants before their toxicity levels become catastrophic and impact the aquatic life and surrounding population (Dash et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e; Dash et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e; Kleinschroth et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Singh et al., 2012; Roy \u0026amp; Majumder, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When polluted surface water seeps into aquifers through seepage and permeable soil medium, serious water problems result. Therefore, improper surface water treatment poses a similar risk to human health. Thus, the quality of drinking water must be adopted for freshwater bodies. Drinking, irrigation, and industrial uses are the primary uses of freshwater from wetlands (Chidiac et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGeographic Information Systems (GIS) are more accurate and scientific for inventorying, monitoring, and managing wetlands through satellite imagery and spatial analysis. It is beneficial to monitor the dynamics and patterns of the landscape. Additionally, it locates and measures the origins of water contamination. This technology is widely used for spatial and temporal data on wetland ecosystems and associated quality concerns (Alparslan et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Ramadas \u0026amp; Samantaray, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUsing multilayer information of watershed characteristics, the Multi Criterion Decision Model (MCDM) and Analytical Hierarchy Process (AHP) method are gradually utilized and enhanced to map the actual hazard-management issues, such as flood susceptibility zonation (FSZ), Gully Vulnerability Index (GVI), forest fire and infrastructure vulnerability, landslide susceptibility zone, ground water potential zone, site suitability analysis (solar farms, dump site, habitat suitability, etc. More than 85% model prediction accuracy is possible through MCDM (Mitra et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A successful model always depends on the unbiased selection of relevant variables for susceptibility/suitability mapping.\u003c/p\u003e\u003cp\u003eIn addition, numerous researchers, including the United States Environmental Protection Agency (EPA, 1990), have created various techniques and strategies to evaluate a watershed's vulnerability to surface water contamination. The water quality index (WQI) and vulnerability index had a significant connection (more than 75%), according to the results, which suggests that it might be implemented without any doubt (Rigina, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Jabbar et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These techniques can overcome the ambiguity of data from different sources, the discrepancy in resolution, and the variance in coordinate system platforms, including data interoperability challenges, by utilizing remote sensing-based information and GIS technology.\u003c/p\u003e\u003cp\u003eScientists are attempting various methods to identify risk factors and indicators, considering all potential dependent factors. They also assess watersheds' health and spatiotemporal changes (Jabbar, 2019). A scientific method must be developed to identify the boundaries of the catchment's most vulnerable areas and the contributing elements of pollutants. To maintain a sustainable environment, estimating the quantum of the vulnerable regions in watersheds susceptible to NPS pollution is crucial to determining the extent and severity of susceptibility.\u003c/p\u003e"},{"header":"2 Study Area","content":"\u003cp\u003eApproximately 3500 freshwater lake (Beels) wetland ecosystems in Assam's Brahmaputra and Barak valleys are rapidly declining, endangering aquatic life and vegetation cover, lowering the health of numerous mega-herbivores like rhinos, elephants, and Asiatic water buffalo, and upsetting biodiversity. It has been stated that ten ancient lakes in Guwahati city have poor water quality and that Borsola Beel, Sarusola Beel, Silsako Beel, and Bondajan are on the verge of dying and going extinct (Dutta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Numerous other studies have documented that the water quality of Satajaan wetland in Lakhimpur, four wetlands (Itila, Dhankhuli, Jiong, and Hahchara) in the Dimoria Block of Kamrup District, and the Jatinga River in Dima Hasao has deteriorated due to various other reasons Dutta et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Das et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gohain \u0026amp; Bordoloi, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The impact of the surrounding LULC is sufficient to understand this situation. The freshwater ecosystem of Deepor Beel (Ramsar Lake) is threatened by both natural and anthropogenic factors, including urbanization, nearby industrial establishment, unplanned settlements (Singh et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and, more specifically, invasive species like water hyacinth, according to satellite research and field observations (Mozumder et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDecadal land use and land cover changes near Chandubi Lake occurred between 1980 and 2017. A thorough examination of several physicochemical parameters that affect water quality and Landsat imagery was used to study the lake. The results showed that the DO level was relatively low (less than 5.0 mg/L) in Zones, the TDS level appeared very low, and the pH values were somewhat acidic in certain places (Sharma et al., 2016). Physicochemical measurements and estimates of the lake system's primary productivity reveal poor productivity but excellent WQI status due to the absence of arsenic. The lake's environment is suffering from losing its link to flowing water, and as a result, it is becoming shallower due to the proliferation of aquatic macrophytes (Sharma et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith a focus on heavy metal and physicochemical parameter contamination analysis, most research on water quality analysis for Assam wetlands (Beels) emphasizes the water quality index and its source allocation using statistical methods. Nevertheless, none of the studies emphasize identifying a particular spatial extent susceptible to surface water contamination (Non-Point Source) in Assam wetlands. Despite Deepor Beel being the most significant lake, no scientific assessment or study of land use, land cover, or other contributory watershed issues has been conducted on degraded wetlands.\u003c/p\u003e\u003cp\u003eThe three wetlands were chosen through field visits to evaluate the water quality, the lake's size, and the catchment. To examine the variations in water quality according to the water source, it is determined that these wetlands have three distinct water accumulation environments: urban, rural habitat, agricultural land, and natural forest areas. Based on land use/land cover patterns, three wetlands\u0026mdash;Chandubi Lake, Deepor Beel, and Digholi Bil in the Kamrup district of Assam\u0026mdash;were selected for this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDeepor Beel\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSituated around 20 kilometers from the Guwahati International Airport and confined by latitudes 26\u0026deg;6'6.37\"N to 26\u0026deg;7'53.78\"N and longitudes 91\u0026deg;36'51.81\"E to 91\u0026deg;40'28.49\"E, this is the only Ramsar Site in the state of Assam. According to reports, the lake is heavily infected with water hyacinth all year round. According to locals, the lake teemed with several kinds of freshwater aquatic plants twenty years ago, including lotuses, lilies, and others. Biologically, ecologically, and environmentally, the lake is one of the Brahmaputra basin's biggest and most significant riverfront wetlands.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eChandubi Lake\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eAt the base of the Garo hills, which stretch over the boundary between Assam and Meghalaya, this wetland is roughly 65 kilometers from the central city and 40 kilometers southwest of Guwahati City Airport. The lake is bounded by latitudes 25\u0026deg;52'19.91\"N to 25\u0026deg;53'9.62\"N and longitudes 91\u0026deg;24'45.44\"E to 91\u0026deg;26'5.53\"E. It was created by the 1897 earthquake and is encircled by tea gardens, tiny settlements, and reserve woods. In addition to a wide diversity of floating and submerged aquatic vegetation, it has a very minor water hyacinth infestation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDigholi Bil\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eA large permanent freshwater body is situated on the Brahmaputra River's north bank, roughly 20 kilometers from Guwahati City. The water body is delimited by longitude (91\u0026deg;39'22.54\"E to 91\u0026deg;39'47.05\"E) and latitude (26\u0026deg;12'42.25\"N to 26\u0026deg;13'53.61\"N). Although many other floating aquatic plants are in the beel, nearby people have told us that the water hyacinth is only there temporarily and dries out as soon as the lake gets shallow in dry season. According to the locals, there is considerable natural fish production and plenty of fishing opportunities, although the water is somewhat brownish.\u003c/p\u003e"},{"header":"3 Materials and Methodology","content":"\u003cp\u003eThe surface water quality of the selected water bodies is examined, and an effort has been made to create a WQI for each of the three wetlands. To evaluate the negative aspects affecting Surface Water Susceptibility to Pollution (SWSP), a Multi-Criterion Decision Model (MCDM) based on the Analytical Hierarchy Process (AHP) technique index has been developed. Eight independent factors have been selected, and their weights and rankings are determined by expert judgment obtained through a thorough literature review and online survey. Land use and land cover, soil type, average annual precipitation, slope gradient, drainage density, road and stream distances, and bedrock type are among these elements, which have 55 sub-components. A hierarchical structure is assigned to the independent components, each sub-factor's weight is determined, the relative weights of the main factors and sub-factors are ranked, and the final score is measured.\u003c/p\u003e\u003cp\u003eLULC data was extracted from remote sensing datasets using Sentinel 2A/B Optical on November 2, 2023. A supervised classification technique that used 13 spectral bands for training sets was used to prepare LULC. Surface slope is determined using the CartoSat digital elevation model (DEM) with a 10 m resolution. The Government of India's National Soil Survey and Land Use Board provided the map of soil taxonomy and texture classes. DEM rectification, planimetric data, watershed delineation, slope, and drainage ordering are all accomplished using a GIS spatial analyst and hydro tool.\u003c/p\u003e\u003cp\u003eGoogle Earth was used to estimate the area of Deepor Beel (1437.59 ha), Chandubi Lake (129.58 ha), and Digholi Bil (220.3 ha). Hydro tools in a GIS environment were used to calculate the catchment area of Deepor Beel (246.01 sq. km), Chandubi Lake (25.87 sq. km), and Digholi Bil (91.67 sq. km). These three catchments primarily contain first- to second-order streams (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The drainage network was used to calculate the drainage density. The hydrological features were verified using a 1:50,000 scale Survey of India (SoI) topographical sheet. The road network was updated using visual interpretation of the most recent high-resolution satellite datasets after being retrieved from the OpenStreetMap Global Road database. The distance to the road and the distance to the stream were calculated in GIS environments.\u003c/p\u003e\u003cp\u003eThe Indian Meteorological Department's station data was used to construct the average annual precipitation data. The Inverse Distance Weighted (IDW) interpolated method created a 5-inch rainfall interval for the average yearly precipitation sub-factor. The Geological Survey of India's (GSI) District Resource map was utilized in the study to identify the type of bedrock and its permeability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Water sampling and water quality index (WQI)\u003c/h2\u003e\u003cp\u003eIn-situ and laboratory analysis of water sampling of 12 physico-chemical parameters was conducted during the immediate post-monsoon period at their maximum water capacity, laden with dissolved and suspended sediments from the catchment on October 22 and 23, 2023, in Deepor Beel, and on October 26 in Chandubi Lake and Digholi Bil. Spectral characteristics of Waterbodies were used in selecting the sampling sites through the Earth observation satellite product Sentinel 2A/B. Diverse aquatic flora, water hyacinth mat coverage, silt presence, and lake depth are sensitive to the spectral signatures of waterbodies. It also assists sampling locations because it leads to restricted access and limited open water areas for selecting a water sample. The Digholi Bil's perennial water restriction is significantly lower than that of the other two waterbodies. A total of 109 water sampling locations were recorded using GPS coordinates in the following waterbodies: Digholi Bil (14), Chandubi Lake (27), and Deepor Beel (68). Temperature (Temp), pH, Electrical Conductivity (EC), Turbidity (TUR), Dissolved Oxygen (DO), and Oxygen Redox Potential (ORP) were among the six physicochemical parameters that were measured on-site right away using the NABL-certified Digital Water and Soil Analysis Kit-7P. However, few parameters such as Total Dissolved Solid (TDS), Salinity (SAL), Total Hardness (TH), Total Alkalinity (TA), Calcium (Ca), Magnesium (Mg), Sodium (Na), Potassium (K), Phosphorus (P) and Nitrate ion (NO\u003csub\u003e3\u003c/sub\u003e) are analyzed in laboratory. WQI (Brown et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; Katyal, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sinha et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was calculated using the weighted arithmetic index using 12 physico-chemical factors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). WHO guidelines and IS: 10500 (2012) [Second Revision] are considered while determining the standard and allowable limit.\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 status using WQI range\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\u003eWQI 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\u003ePossible Usage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExcellent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDrinking, Irrigation, and Industrial\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDrinking, Irrigation, and Industrial\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51\u0026ndash;75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIrrigation and Industrial\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e76\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery poor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIrrigation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnsuitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProper treatment is required before use\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 spatial distribution of lake water quality is represented by the WQI of the three wetlands, which is calculated at each site. To estimate WQI at an unknown location, the Inverse Distance Weighted (IDW) interpolated approach was employed by assuming that places closest to one another are more similar than those farther apart. It uses the known (observed) values to surround the prediction location. The calibration and validation of the non-point source pollution region with WQI zones were done using the results of the interpolated surface.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Surface water susceptibility to pollution (SWSP) index\u003c/h2\u003e\u003cp\u003eThe study's purpose is to critically examine water quality and identify all non-point sources of contamination in the watershed with a degree of vulnerability to surface water pollution using simple and easy scientific methods. MCDM-based AHP methods (Gohari et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) are used to identify the most probable non-point sources of pollution. The water quality index of each of the three catchments is compared with an AHP index on watershed susceptibility to pollution. Eight independent components and fifty-five sub-factors were chosen for susceptibility modeling. The research area is more prone to runoff, hence there is a higher likelihood of suspended sediments/contaminants being transported to water bodies, resulting in significant water pollution. Fifty-five sub-factors linked with runoff potential and vulnerability were examined to determine the degree of vulnerability to pollution in watersheds. The components and sub-factors were then synthesized into appropriate weights, ranks, and ratings (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These were further investigated to determine the degree of impact on surface water quality utilizing previous literature research and an expert survey (Emovon et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As a result, these parameters were incorporated into the vulnerable mapping in the GIS environment.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe criteria for selection of factors, sub-factors with ranks and orders, and data collection methodology adopted for MCDM\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors/Rank\u003c/p\u003e\u003cp\u003eRank 8 (high) to 1(low)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriteria, Sub-factors, and Data collection source \u0026amp; methodology\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand use Land cover (LULC)\u003c/p\u003e\u003cp\u003eRank 8/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgriculture and urban lands considerably affect surface water quality, carrying a significant load of contaminants from various point and nonpoint sources (Yan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wubie \u0026amp; Assen, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The cropland and urban areas have a direct and highly positive correlation with the water quality pollution indicators such as ammonia, nitrogen, phosphorus, and heavy metals. In contrast, it has a negative relationship with forests, scrublands, and grasslands (Jordan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Sub-factors weightage given to sub-classes of LULC with higher to lower ranks under the following order: agriculture land, built-up area, barren land, wetland, grassland, shrubland, forest land, and water bodies on vulnerability to pollution in this study.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Road (DTR)\u003c/p\u003e\u003cp\u003eRank 7/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIn the urban scenario, the significant sources of contamination are highways, roadways, and parking lots, which might pollute surface water with considerable vehicular transport load (Gjessing et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1984\u003c/span\u003e; Uliasz-Misiak et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The road network is interpolated with the distance from the road, and a raster derivative layer is generated as the distance from the road. The weightage was assigned with the lower value as the distance from the road increased.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil type (ST)\u003c/p\u003e\u003cp\u003eRank 6/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe chemical \u0026amp; biological soluble materials and suspended sediments in soil are the most effective sources of pollutants (Novotny \u0026amp; Chesters, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; F\u0026ouml;rstner, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which are governed by soil texture and permeability. Based on the higher permeability and porosity of soils, lower weightage is assigned to the sub-factors of soils.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope (SLP)\u003c/p\u003e\u003cp\u003eRank 5/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurface water flow is controlled by the slope of the watershed, which directly affects soil erosion and sedimentation rate, carrying several pollutants in the form of nutrients, pesticides, and pathogens to nearby rivers/water bodies. The high runoff potential relates to steeper slopes, making them more susceptible to pollution (Yan et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wubie \u0026amp; Assen, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrainage Density (DD)\u003c/p\u003e\u003cp\u003eRank 4/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe drainage density is the property of the watershed carrying the stream network and is calculated per unit area of the catchment. The nature of the watershed regions, such as weak and impermeable subsurface materials, sparse vegetation, and augmented high slope gradients, leads to higher drainage lengths. An area with high drainage density has high potential surface runoff and is prone to a high sediment yield entrapped through streams/rivers (Dragičević et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual Average Precipitation (AAP)\u003c/p\u003e\u003cp\u003eRank 3/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual precipitation carries a high value due to its magnitude, intensity, and frequency of rainfall, leading to high sedimentation, affecting pollutant concentrations (Xie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and hence becoming more vulnerable to pollution. The average annual precipitation for 2022 and 2023 was consider in this study.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Stream (DTS)\u003c/p\u003e\u003cp\u003eRank 2/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe rivers/ streams carry pollution from urban and agricultural areas and continue to threaten water quality, depending on their distance from the pollution point. Streams with significantly shorter distances have a high probability of surface runoff, contributing to water pollution. Hence, distance to stream (DTS) is essential in determining the areas susceptible to surface water pollution (Hatt et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Frey et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBed Rock Type (BRT)\u003c/p\u003e\u003cp\u003eRank 1/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe lateral flow of catchment water over the bedrock is controlled by its Geological formation, rock type, and permeability character. In combination with the physical properties of the soil layer, the bedrock type and infiltration decide the nature of interaction with water and its contamination for quality analysis (Kosugi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The geological map revealed that the small catchments have almost a single type of bedrock, and it has minimal impact or role on the MCDM modeling process.\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\u003eWith the analogy explained, factors, sub-factors, and their importance to surface water pollution, a normalized pairwise matrix (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and relative weight with rating scores (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) are formulated based on AHP. The required independent variables were obtained in appropriate compatible formats, such as Raster or Vector, in a GIS environment, and then converted into a derived layer in Raster format under a 10 m spatial resolution. The raster layer of each variable is further reclassified into sub-categories based on the rankings. GIS operations such as vector to raster conversion, scale, resolution, projection, raster calculator, reclassify, and overlay were used to complete the steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Later, MCDM was performed on three wetland catchments to obtain Surface Water Susceptible to Pollution (SWSP) with an index value.\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNormalized pairwise matrix of the factors susceptible to pollution.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDTR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eDTS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSLP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAAP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eBRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eWeights\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eWt (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Road (DTR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e6/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e18.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Stream (DTS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil type (ST)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGradient Slope (SLP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrainage Density (DD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1/6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual average Precipitation (AAP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4/7\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\u003e1/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBedrock type (BRT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e5/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe relative weights and rating scores of the factors and sub-factors susceptible to pollution\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\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWeight%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSub-factors (Ratings) at the scale of 10\u0026thinsp;\u0026minus;\u0026thinsp;1, high to low susceptibility\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Use Land Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAgriculture (10); Built-up area (9); Barren land (7); Wetland (6); Grass land (5); Shrubland (4); Forest area (3); and Water bodies (1).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Road (in m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;500 m (10); 500\u0026ndash;1000 m (7); 1000\u0026ndash;1500 m (5); 1500\u0026ndash;2500 m (3) and \u0026gt;\u0026thinsp;2500 (1).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Stream (in m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;50 m (10); 50\u0026ndash;100 m (7); 100\u0026ndash;200 m (5); 200\u0026ndash;500 m (3) and \u0026gt;\u0026thinsp;500 m (1).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eClay loam (10); Silt loam (8); Silt clay loam (7); Clay (6); Silt (5); Sandy loam (4); Peat (3), and Sandy (2).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope (in degrees)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;35\u0026deg; (10); 35\u0026ndash;20\u0026deg; (8); 20\u0026ndash;15\u0026deg; (6); 15\u0026ndash;10\u0026deg; (4); 10\u0026ndash;5\u0026deg; (2) and \u0026lt;\u0026thinsp;5\u0026deg; (1).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrainage Density (in m/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2500 (10); 2500\u0026ndash;2000 (8); 2000\u0026ndash;1500 (6); 1500\u0026ndash;1000 (5); 1000\u0026ndash;500 (4); 500\u0026ndash;100 (3) and \u0026lt;\u0026thinsp;100 (1).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual Average \u003c/p\u003e\u003cp\u003ePrecipitation (in inches)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;75ʺ (10); 75\u0026ndash;70ʺ (9); 70\u0026ndash;65ʺ (8); 65\u0026ndash;60ʺ (7); 60\u0026ndash;55ʺ (6); 55\u0026ndash;50ʺ (5); 50\u0026ndash;45ʺ (4); 45\u0026ndash;40ʺ (3); 40\u0026ndash;35ʺ (2) and \u0026lt;\u0026thinsp;35ʺ (1).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBedrock type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLimestone (10); Dolomite (9); Shale (7); Claystone (5); Sandstone (3), and Metamorphous (1).\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"},{"header":"4 Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Water quality analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe water quality of wetlands is analyzed based on mean values of all the parameters sampled in various locations \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. The average water temperature (\u003csup\u003e\u0026deg;\u003c/sup\u003eC) of the wetlands during October 2023 is found to be 28.37, 29.81, and 26.48 with standard deviations of 1.22, 0.24, and 1.41 for Deepor Beel, Chandubi Lake, and Digholi Bil, respectively, indicating a steady temperature in the wetlands. The average pH values show 7.90, 6.76, and 7.10 for Deepor Beel, Chandubi Lake, and Digholi Bil, indicating the water is within the standard-permissible pH value of 6.5\u0026ndash;8.5.\u003c/p\u003e\u003cp\u003eOther parameters revealed values within the permissible/acceptable values, except for a very high value in turbidity, with a mean of 73.60 NTU and ranging from a minimum of 52.68 to a maximum of 95.45 in the Deepor Beel. The Potassium concentration in the Deepor Beel is higher (mean 7 mg/l) than the standard quantity's prescribed limit (2.7 mg/l). Chandubi Lake revealed moderately to high values of turbidity with a mean of 14.82 NTU (13.14\u0026ndash;17.20 NTU), whereas the Digholi Bil revealed mean turbidity values of 32.32 NTU and a range of 28.55\u0026ndash;36.74 NTU.\u003c/p\u003e\u003cp\u003eThe high turbidity in all the wetlands justifies that water quality is affected by sediment loading through runoff from the catchments. The high average rainfall over the region with moderate slope and scanty vegetation cover in the study catchments leads to stormwater runoff. It helps to carry unprotected sediments, in the form of topsoil particles, which contribute high turbidity in the water if analyzed immediately post-monsoon.\u003c/p\u003e\u003c/div\u003e\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\u003eThe average value of various water quality parameters in selected wetlands\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\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\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eUnits of\u003c/p\u003e\u003cp\u003emeasurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIS:10500\u003c/p\u003e\u003cp\u003e(2012)*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeepor Beel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChandubi Lake\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDigholi Bil\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH units\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.5\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 8.5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eORP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026micro;S/cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e278.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e296.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e156.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1000\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 3000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e186.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e199.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e500\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 2000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSalinity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/l\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e280.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e149.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDissolved Oxygen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 8\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTH as CaCO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e200\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 600\u003csup\u003eb\u003c/sup\u003e\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=\"left\" colname=\"c2\"\u003e\u003cp\u003eNTU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 5\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA (as CaCO\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e200\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 600\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCalcium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e75\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 200\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagnesium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30\u003csup\u003ea\u003c/sup\u003e \u0026ndash; 100\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSodium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e200**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotassium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.7**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhosphorus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNitrate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emg/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e45**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ea. Acceptable limit, b. Permissible limit in the absence of any alternate source. *Standards prescribed by the Indian Standard of Drinking Water having specification [IS: 10500 (2012)] (Second Revision), ** WHO guideline.\u003c/p\u003e\u003cp\u003eFor overall WQI for the three wetlands based on mean concentration of all observation sites against their benchmark values prescribed by IS/WHO guidelines was assessed and it was observed that the mean concentration of all observation sites, the WQI of Deepor Beel is 238.12 which is exceeding the limit of 100 (unsuitable) unit (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Deepor Beel will be highly turbid in October 2023 and significantly contribute to the deteriorated water quality. The wetland is unsuitable for Drinking, Irrigation, and Industrial use, and proper treatment is required for any use during the post-monsoon period. Similarly, based on the mean concentration of all observation sites, the WQI of Digholi Bil is 73.34, which is greater than 50 units, and Chandubi Lake is 48.45, less than 50 units, which shows that overall, the status of Digholi Bil is poor, and Chandubi Lake is in good status.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe WQI values of 68 locations in Deepor Beel fall under unsuitable for different uses. WQI values of 45 locations (66%) lie between 200 and 250, and 21 locations (30%) have WQI values above 250 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Out of 27 locations sampled in Chandubi Lake, 19 locations (70%) have WQI values less than 50 (Good), whereas the remaining 30% locations have WQI values between 50 and 64 (Poor). Out of 14 locations sampled in Digholi Bil, 7 locations (50%) have WQI values ranging from 64 to 75 (Poor), and the remaining 50% locations have WQI values between 75 and 88 (Very poor).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHowever, water quality at different sample locations was analyzed to explore the spatial distribution of water quality index during October, which helps to identify various environmental conditions in three wetlands. The three wetlands' interpolated surface of water quality index value was generated (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It helps to identify the potential contaminated zones. The WQI interpolated surface of the lake shows varied contamination zones in the open water areas. The Deepor Beel shows that the south and southeast part is highly contaminated. The affected zones might be due to water runoff from two major streams/rivers, namely Morabharalu Nala and Basistha River, which pass through highly populated urban areas of Guwahati city situated on the northeast side of the Beel. The possible reason for contamination might be anthropogenic causes, as point and non-point sources in the catchment. The water quality index surface of Chandubi Lake and Digholi Bil shows relatively low values, and a significant part of the open water is under good water quality zones.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Susceptibility indicators\u003c/h2\u003e\u003cp\u003eEight independent factors and their fifty-five sub-factors were selected for susceptibility modeling, providing the three lakes' existing geophysical and environmental conditions). The Deepor Beel catchment is dominated by densely populated urban Guwahati city, and 24% of the area is covered by built-up land. The catchment also covers 23% of scrubland with 8% of forest land. 68% of the catchment is surrounded by less than 500 m of road distance, which is a significant threat to the environmental pollution of the lakes. The catchment shows more than 60% of the area lies within 200 m of the streams, contributing significantly to the lake's pollution load. Catchment receiving maximum infiltration due to its sandy nature since the area is close to the Brahmaputra. Fifty per cent of the catchment area is below a 5-degree slope, enhancing infiltration and reducing surface runoff. A significant part of the area covered with high drainage density, with annual average rainfall above 70 inches, clearly favours a higher contribution of water runoff, leading to a high contribution to pollution (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\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\u003eCoverage of catchment area under different factors and sub-factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSub-factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeepor (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChandubi (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDigholi (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Use Land Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBuilt-up area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBarren land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrass land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShrubland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForest area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater bodies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Road\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;500 m\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e93.82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500\u0026ndash;1000 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000\u0026ndash;1500 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u0026ndash;2500 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2500 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Stream\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;50 m\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u0026ndash;100 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100\u0026ndash;200 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e32.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200\u0026ndash;500 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;500 m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClay loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSilt loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSilt clay loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSilt\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSandy loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePeat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSandy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope (in degrees)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;35\u0026deg;\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u0026ndash;20\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;15\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u0026ndash;10\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;5\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDrainage Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2500\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\u003e25.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2500\u0026ndash;2000\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\u003e34.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u0026ndash;1500\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\u003e20.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1500\u0026ndash;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000\u0026ndash;500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e500\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;100\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual Average Precipitation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;75ʺ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75\u0026ndash;70ʺ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u0026ndash;65ʺ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u0026ndash;60ʺ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u0026ndash;55ʺ\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55\u0026ndash;50ʺ\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u0026ndash;45ʺ\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;40ʺ\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u0026ndash;35ʺ\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;35ʺ\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBedrock type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLimestone\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDolomite\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShale\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClaystone\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSandstone\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\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetamorphous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOn the other hand, Chandubi Lake is dominated by forest cover (50.59%), but 49.13% of the catchment areas lie within 500 m of road distance and contribute to high vulnerability. 91% of the area contributes to vulnerability within 200 m of the stream's distance. This area has very low infiltration because 84% of the soil is clayey, which supports more runoff. Only 40% of the area falls under less than 5 degrees of slope. Drainage density (above 500) and average annual rainfall are relatively high (above 85 inches) compared to Deepor Lake. The rainfall seems to be very high, which leads to more chances of surface runoff and high vulnerability to pollution in the Chandubi catchment.\u003c/p\u003e\u003cp\u003eDigholi catchment dominates the agricultural area (32%), where the expected contamination source is mainly agriculture, other than any point source in the catchment. Digholi Bil entire catchment area, with a very close proximity distance, gives the highest vulnerability for pollution. Distance to stream indicates a relatively low contribution to pollution. A significant part of catchments has a low percolation texture of soil, and low percolation is high vulnerability to pollution and vice versa. 77.7% of the Digholi catchment lies within less than 5 degrees of slope. The drainage density of the Digholi catchment is very high compared to the other two lakes, leading to a high probability of surface pollution. The average annual precipitation of the Digholi catchment has the highest percentage of rainfall (\u0026gt;\u0026thinsp;75 inches). It signifies that rainfall is a significant contributing parameter for the high vulnerability of surface water pollution.\u003c/p\u003e\u003cp\u003eThe entire bedrock type of Deepor Beel and Chandubi Lake Catchment area is Gneiss and high-grade schist, and it has low permeability, making it highly vulnerable to water runoff and leading to high chances of surface pollution. However, the Digholi catchment area is covered with unconsolidated sand, without clay or silt, has high permeability, leads to low surface runoff and has minimum surface pollution contribution. However, the percolated contaminants may affect groundwater potential zones and indirectly affect surface water pollution.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Surface water susceptibility to pollution index\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA susceptibility resulted from each catchment obtained through the MCDM process by considering eight criteria and fifty-five sub-criteria depicting vulnerability (SWSP) index rated 1 to 4, where one (1) indicates low, two (2) as moderate, three (3) as high, and four (4) as very high (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The SWSP index of the three wetland catchments shows Very High Susceptible area to pollution, estimated at Deepor Beel (53.33%), Digholi Bil (33.91%), and Chandubi Lake (26.94%) of the total area of the catchment. Highly susceptible area to pollution is estimated in Deeper Beel as 36.88%, Digholi Bil as 31.74% and Chandubi Lake as 26.2% (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe above high vulnerability area was further superimposed on LULC and analyzed that the Deepor Beel SWSP was contributed mainly by built up (49.03%), Agriculture (24.38%), and Barren land (21.46%). However, the SWSP contribution in Chandubi catchment shows Agriculture (42.27%), followed by built up (28.60%), wetland (10.50%), and grassland (7.25%). The SWSP of Digholi catchment is significantly contributed to by agricultural land (55.37%), built-up (18.3%), and Barren land (17.15%). The result shows that the SWSP index is high under urban and agricultural land in the lake environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Despite giving lower weight and ranks in the MCDM criteria for forest area, it was observed that high vulnerability area falls under forest land in the lake environment of Chandubi Lake (74.42%) and Digholi Bil (27.48%). It could be because these forest lands fall under steep slope and the clay nature of the dominant soils, and thereby carry higher weight and rank for susceptibility that might have come into play as a significant factor contributing to surface water runoff carrying sediments/contaminants to the wetlands.\u003c/p\u003e\u003ch2\u003e4.4 Relationship between WQIs and SWSP index\u003c/h2\u003e\u003cp\u003eTo establish the relationship between water quality index (WQI) by observed water quality test and Surface Water Susceptibility to Pollution (SWSP) index computed by various environmental factors using MCDM, the entire catchment was first divided into sub-catchments, and then the mean zonal WQI was calculated for each sub-catchment (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Further, a linear regression was established on the mean zonal WQI of the part of the respective sub-catchments and the % of high (H) and very high (VH) susceptibility areas (SWSP index 3 and 4) in the same sub-catchments (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe results show that the relationship between WQI and SWSP index (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) has a significant positive correlation in Deeper Beel (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.72 with p\u0026thinsp;=\u0026thinsp;0.02), Chandubi Lake (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.85 with p\u0026thinsp;=\u0026thinsp;0.04) and Digholi Bil (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.77 with p\u0026thinsp;=\u0026thinsp;0.03). The result reveals that water quality is directly proportional to the high \u0026amp; very highly vulnerable areas in the SWSP Index. It indicates that the considered drivers/factor and sub-factors and their weightage and rank are highly reliable in predicting the SWSP index for any wetland water quality issues.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between WQI and Susceptibility\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\u003eWetland\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEquation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeepor Bil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWQI\u0026thinsp;=\u0026thinsp;1.7982\u0026times; % of Susceptibility (H\u0026thinsp;+\u0026thinsp;VH)\u0026thinsp;+\u0026thinsp;104.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChandubi Lake\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWQI\u0026thinsp;=\u0026thinsp;0.1144\u0026times; % of Susceptibility (H\u0026thinsp;+\u0026thinsp;VH)\u0026thinsp;+\u0026thinsp;42.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigholi Bil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWQI\u0026thinsp;=\u0026thinsp;0.1168\u0026times; % of Susceptibility (H\u0026thinsp;+\u0026thinsp;VH)\u0026thinsp;+\u0026thinsp;63.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\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 proposed model will support civic authorities in monitoring and managing the pollutant/contamination by controlling all anthropogenic activities in vulnerable areas of the catchment, specifically the pour points in the periphery, which must be identified for suitable treatment of contaminants. Also, it will provide permanent remedial measures/approaches for controlling the pollutants and maintaining the quality of the lakes, especially Deepor Beel, which is a Ramsar Site. Other rural lakes will give the local community cleaner and safer water for various purposes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThe present study was conducted on three different lake environments of Assam state in northeastern India, commonly known as \u0026lsquo;Beel\u0026rsquo;. Because of the natural topography and floodplain of the Brahmaputra valley, this area has a large number of small and large natural beels. Therefore, the present study was conducted in a critical region of a natural wetland. These wetlands significantly contribute to blue infrastructure to combat the impact of climate change. It has significantly contributed to climate resilience and the achievement of several SDG targets.\u003c/p\u003e\u003cp\u003eThis study is based on field-based observation, which includes water quality testing of 14 parameters at 309 locations in three different lakes. Further, these qualities of water are converted into the WQI index to help policymakers understand the quality status. However, the estimation of WQI in the unsampled area was also computed using geospatial tools. The water sample test and interpolated surface of the water quality index concluded that the Deepor Beel surface water quality is unsuitable for any use, and the Digholi Bil water quality is poor to very poor. However, Chandubi Lake surface water quality is suitable, where some samples test falls under good and a few samples test falls under poor.\u003c/p\u003e\u003cp\u003eThis study is not limited to water quality index preparation. The study aims to identify the cause of poor quality and prepare a model for the Surface Water Susceptibility to Pollution (SWSP) index. It requires the selection of criteria based on the MCDA approach. Eight environmental factor and their fifty-five sub-factors were created based on field observation. The AHP approach was applied to the SWSP index, and its weight was given based on a literature survey and expert judgment. Finally, the Surface Water Susceptibility to Pollution (SWSP) index was prepared for all three lake catchments.\u003c/p\u003e\u003cp\u003eThe study's main objective is to identify the causes of susceptibility to pollution in the lake environment. It was recognized that the significant region of pollution in the lake environment is urban development because of a poor sewerage system, presence of chemicals due to agricultural practices and use of chemical fertilizer and pesticides. These two are the primary non-point sources of anthropogenic pollution. However, some forest areas are also highly susceptible to pollution due to natural factors such as high runoff from steep slopes and clay soil texture with low percolation. It led to a heavy load of debris and sediments.\u003c/p\u003e\u003cp\u003eThe ultimate goal of the study is to validate the Surface Water Susceptibility to Pollution (SWSP) index for replication and rescale to other similar kinds of natural wetland. Therefore, a regression analysis was performed on the SWSP index concerning the WQI index. The highly significant correlation between the SWSP index and WQI indicated that selected factors and sub-factors significantly govern the lake environment's water quality. It suggests that the method is highly reliable for water quality assessment. However, choosing other drivers may enhance the model quality based on the area's local environmental conditions and human ecology function. The present study concludes that the SWSP index is replicable and scalable to other areas of similar ecological conditions, which is required to achieve SDG goals and climate change targets.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Approval\u003c/p\u003e\n\u003cp\u003eAll authors are aware of the submission of the manuscript in this journal, and the manuscript in full is original, and the manuscript in part or in full has not been published or simultaneously communicated in any other journal.\u003c/p\u003e\n\u003cp\u003eConsent to Participate\u003c/p\u003e\n\u003cp\u003eAll authors know that the manuscript submission in this journal is associated with wetland water quality assessment, and \u0026ldquo;consent to participate\u0026rdquo; is not applicable in this study. This paper does not include human subjects and/or animal trials.\u003c/p\u003e\n\u003cp\u003eConsent to Publish\u003c/p\u003e\n\u003cp\u003eAll authors are aware of the manuscript submission to this journal and the associated data. The data is original and has been created and analyzed by all authors. The consent to publish does not apply to this manuscript.\u003c/p\u003e\n\u003cp\u003eAuthors Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization \u0026ndash; Mr Rajendra Jena and Dr Arun Sarma; Methodology \u0026ndash; Mr Rajendra Jena; Data collection, Investigation \u0026amp; Analysis \u0026ndash; Mr Rajendra Jena; Validation \u0026ndash; Dr R. Sanjeevi and Dr Arun Sarma; Writing draft manuscript \u0026ndash; Mr Rajendra Jena; Review and editing \u0026ndash; Prof Vinay SP Sinha, Dr Arun Sarma, Dr R. Sanjeevi and Dr J. Anuradha; Supervision \u0026ndash; Dr R. Sanjeevi and Dr Arun Sarma; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by NECTAR. Department of Science and Technology, Government of India (Grant numbers Ref No. B-11/1/2024) and the Author, Mr Rajendra Jena, have received research support from NECTAR, GoI for minor equipment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eAuthors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eFinancial interests: Author Dr Arun Sarma, Dr R. Sanjeevi, Dr J. Anuradha and Prof Vinay SP Sinha declare they have no financial interests. Author Mr Rajendra Jena has received minor laboratory equipment from NECTAR, GoI\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data sources are authenticated, collected from corresponding sources, and used only for research purposes. The datasets generated during and/or analyzed during the current study are not publicly available due to a State Agency Confidentiality request. The Analysis and testing were conducted through NABL-certified Instruments and National Accreditation Testing and Calibration Laboratories.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlparslan, E., Ayd\u0026ouml;ner, C., Tufekci, V., \u0026amp; T\u0026uuml;fekci, H. (2007). 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Effects of land use and slope on water quality at multi-spatial scales: A case study of the Weihe River Basin. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(20), 57599-57616.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Susceptibility, Wetland, Water Quality Index, Pollution, Land use land cover","lastPublishedDoi":"10.21203/rs.3.rs-7162838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7162838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study's primary goal was to develop the Surface Water Susceptibility to Pollution (SWSP) index to assess the health and quality of the wetland. This landscape is the best indicator of ecological and environmental conditions and serves as blue infrastructure for climate change adaptation.\u003c/p\u003e\u003cp\u003eThe study was conducted in the wetland-dominated area of the northeast region of India to demonstrate the scalability and replicability of the model. Eight independent watershed characteristics and fifty-five subfactors are included in the index for better performance at a larger scale. The water quality index (WQI) was measured through in situ and laboratory tests of the physicochemical parameters of surface water in three natural wetlands, namely Deepor Beel, Chandubi Lake, and Digholi Bil. WQI was used to validate the Susceptibility to Pollution (SWSP) index.\u003c/p\u003e\u003cp\u003eThe result revealed that Deepor Beel (Ramsar site, 2002) is highly turbid (73.6 NTU), and 96% of the geographical area of the lake has WQI values above 200, leading to the water being completely unsuitable for any usage. High and very highly SWSP regions of the catchment fall under built-up, agricultural land and hilly forest areas in Deepor Beel (72%), Digholi Bil (63%), and Chandubi Lake (62%). Linear regression between SWSP Index and WQI is significantly highly correlated in all three wetlands: Deepor Beel (R2\u0026thinsp;=\u0026thinsp;0.72), Chandubi Lake (R2\u0026thinsp;=\u0026thinsp;0.85) and Digholi Bil (R2\u0026thinsp;=\u0026thinsp;0.68) with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The SWSP index benefits water resource managers by assessing surface water quality and pollution status and adopting remedial measures to control pollution from non-point sources.\u003c/p\u003e","manuscriptTitle":"Estimation of Surface Water Susceptibility to Pollution Index of Natural Wetlands of North-East India using Multi-Criteria Decision Model.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 10:55:31","doi":"10.21203/rs.3.rs-7162838/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2025-10-21T01:47:11+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-16T20:02:12+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T20:00:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2025-08-29T12:33:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-25T04:33:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2025-08-21T07:10:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"42509da6-a2d6-4c8f-a49a-1cb355d90395","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:03:49+00:00","versionOfRecord":{"articleIdentity":"rs-7162838","link":"https://doi.org/10.1007/s11356-026-37442-3","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2026-02-11 15:59:29","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2025-09-25 10:55:31","video":"","vorDoi":"10.1007/s11356-026-37442-3","vorDoiUrl":"https://doi.org/10.1007/s11356-026-37442-3","workflowStages":[]},"version":"v1","identity":"rs-7162838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7162838","identity":"rs-7162838","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}