Hydrological parameters like overland flow, soil loss and nutrient losses can be studied by using different watershed models. However, all these models vary significantly in their analysis of parameters, input and output flexibility, scale accountability, processing ability, computational efficiency and capability of modeling the changes in catchments. This paper reviews different watershed models used for analyzing overland flow, soil loss and sediment yield with their shortcomings and strengths. These watershed models are described briefly along with their capabilities and shortcomings with their examples of applications, results and comparisons. An outcome of these discussions is presented in tabular format as a screening tool to allow the researchers and decision makers to choose the appropriate watershed model for the specific purpose.

The conjunction of heavy snowfall during winters and intensive rainfall during monsoons along with the mountainous topography expose the Lidder watershed to serious erosion and flood aggravation issues. Barely any attempts have been made for an in-depth examination of the Lidder watershed for precise estimation of sub-basin level runoff and erosion. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated using the Sequential Uncertainty Fitting algorithm (SUFI-2) for modelling streamflow and sediment yield of the Lidder watershed. Daily runoff and sediment event data from 2003–2013 were used in this study; data from 2003–2008 was used for calibration and 2009–2013 for validation. Model performance was evaluated using various statistical tools, which showed good results revealing excellent potential of the SWAT model to simulate streamflow and sediment yield for both calibration and validation periods. The annual rate of average upland sediment drawn from the watershed was approximately 853.96 Mg/ha for an average surface runoff of 394.15 mm/year. This study identifies the vulnerable areas of the Lidder watershed, which can be thoroughly examined by decision-makers for effective management and planning. Further, the calibrated model can be applied to other watersheds with similar characterization to influence strategies in the management of watershed processes.

Land use changes in the Kashmir Himalayas must be mapped and monitored for long-term development and efficient planning. This work uses geospatial technologies such as remote sensing and geographic information system to track changes in land cover trends in four main watersheds in the Kashmir Valley's north-eastern Himalayas from 2003 to 2013. Land cover maps were created using images from the Landsat-5 Thematic Mapper. The watershed's land use/land cover (LULC) maps were created using supervised classification utilizing the maximum likelihood classifier (MLC). Over the previous decade, the LULC in the study watersheds has undergone a series of intricate changes as a result of deforestation, climate change, and agroforestry growth. A total of 11 major LULC classifications were discovered, indicating that forests are the most common land use in all four watersheds. Forest cover, river beds, water bodies, non-perennial snow, and glaciers have all decreased significantly, whereas scrubland, horticulture, rock mass, built-up areas, barren land, and agriculture have all increased significantly, except for Sindh watershed, where the scrubland class has decreased by 5.97% from 2003 to 2013. The study's methodology and conclusions point to crucial policy implications for long-term LULC management in the Kashmir Himalayas' Madhumati, Arin, Sindh, and Lidder watersheds.

Surface waterbodies, on which the growing population of Kashmir Valley is reliant in a variety of ways, are increasingly deteriorated due to anthropogenic pollution from rapid economic development. This research aims to assess the quality of the surface waterbodies in the north-eastern region of Kashmir Valley. Standard analytical procedures were used to analyze the water samples taken from 11 distinct sampling stations for 14 physicochemical parameters. The results were compared with the standard permissible levels which showed that the water quality of rivers and lakes in the north-east Himalayan region has steadily declined. Furthermore, multivariate statistical techniques were used with the goal of identifying key variables that influence seasonal and sectional water quality variations. The analysis of variance (ANOVA) analysis revealed that there is substantial spatio-temporal variability in the water quality parameters. According to principal component analysis (PCA) results, four primary components, which together accounted for 79.23% of the total variance, could be used to evaluate all data. Chemical, organic, and conventional pollutants were found to be significant latent factors influencing the water quality of rivers in the study region. The results indicate that PCA and ANOVA may be used as vital tools to identify crucial surface water quality indices and the most contaminated river sections.

In this study, the Soil and Water Assessment Tool (SWAT) model was used to examine the spatial variability of sediment yield, quantify runoff, and soil loss at the sub-basin level and prioritize sub-basins in the Sindh watershed due to its computational efficiency in complex watersheds. The Sequential Uncertainty Fitting-2 approach was used to determine the sensitivity and uncertainty of model parameters. The parameter sensitivity analysis showed that Soil Conservation Services Curve Number II is the most sensitive model parameter for streamflow simulation, whereas linear parameters for sediment re-entrainment is the most significant parameter for sediment yield simulation. This study used daily runoff and sediment event data from 2003 to 2013; data from 2003 to 2008 were utilized for calibration and data from 2009 to 2013 were used for validation. In general, the model performance statistics showed good agreement between observed and simulated values of streamflow and sediment yield for both calibration and validation periods. The noticed insights of this research show the ability of the SWAT model in simulating the hydrology of the Sindh watershed and its reliability to be utilized as a decision-making tool by decision-makers and researchers to influence strategies in the management of watershed processes.