This study aims to develop a comprehensive and spatially explicit Surface Water Susceptibility to Pollution (SWSP) index using a Multi-Criteria Decision-Making (MCDM) framework, integrated with spatial environmental considerations. The proposed SWSP model employs a data-driven modelling approach, incorporating eight independent watershed characteristics and 55 sub-factors into the index to assess the health and quality of the wetland landscape. Unlike conventional single-parameter or empirical assessments, it includes a holistic evaluation of determinants contributing to surface water pollution within a catchment. The study was conducted in the wetland-dominated area of northeast India to demonstrate the scalability and replicability of the model for larger applications. Meanwhile, the Water Quality Index (WQI) is scientifically important because it consolidates multiple water quality parameters into a single, standardised score that reflects the overall health of a wetland, which depends on watershed characteristics. Therefore, the WQI can be used to cross-validate the SWSP index by comparing predicted pollution vulnerability with actual water quality measurements. In the present study, the WQI was measured through in situ and laboratory tests of the physicochemical parameters of surface water in three natural wetlands: Deepor Beel, Chandubi Lake, and Digholi Bil. This measurement was used to validate the SWSP index. The water quality analysis reveals that 96% of the total geographical area (TGA) of Deepor Beel has a WQI above 200, characterised by high turbidity (73.6 NTU), rendering the water unsuitable for any use. High and very high SWSP index areas within the catchment of Deepor Beel (72% TGA), Digholi Bil (63% TGA), and Chandubi Lake (62% TGA) are encompassed by built-up areas, agricultural land, and hilly forested regions. Linear regression shows a significant correlation between the SWSP Index and the WQI in all three wetlands: Deepor Beel ( R 2 = 0.72), Chandubi Lake ( R 2 = 0.85), and Digholi Bil ( R 2 = 0.68), with p < 0.05. A strong correlation between the two confirms the model’s reliability, while discrepancies suggest the need for refinement. This cross-validation enhances the scientific credibility of the SWSP index, supporting policymakers in sustainable watershed management, public health protection, and ecological conservation.
Jena et al. (Wed,) studied this question.