Manipur River basin lies in the northeastern part of India in the lesser Himalayan ranges and it is under extreme pressure from natural and anthropogenic factors. This study aims to monitor and predict the future land use land cover (LULC) for the region using land change modeller (LCM) in TerrSet. Landsat satellite images were used to produce LULC map for three different years 2007, 2014 and 2017. Based on these past LULC map, future LULC map of the study area was developed using Markov Chain and artificial neural network (ANN) analysis in LCM. ANN was trained with the driver variable, namely, distance from roads, distance from settlement, elevation and slope. Results indicate that there was an increase in area under waterbodies, agriculture and built-up area by 15.93%, 2.42% and 11.58%, respectively, in 2017 with respect to initial LULC condition in 2007. It can also be observed that there was decrease of 6.08%, 28.65% and 0.55% in wetlands, herbaceous wetlands and forest, respectively. Furthermore, similar trends were observed in the predicted LULC map of 2030 with an increase of 16.4%, 3.06% and 20.99% in waterbodies, agriculture and built-up area, and decrease of 6.48%, 41.56% and 1.4% in wetlands, herbaceous wetlands and forest. Based on the result of predicted LULC of 2030 which indicates drastic change in built-up area and herbaceous wetlands, there is a need for formulating proper urban planning and environmental preservation policies.

Calibration of a hydrological model is a challenging task, especially in basins that are data scarce. With the incorporation of regional information and integration with satellite data, the parameters of hydrological models can be estimated for a basin with scant or no discharge records. The main objective of this study is to calibrate and validate a hydrological model based on a limited amount of in‐situ measured and remote sensing satellite datasets in a data‐sparse region. Multiple techniques were applied for the model calibration: (1) stage‐discharge curves using a spatial proximity approach, (2) Simplified Surface Energy Balance actual evapotranspiration, (3) river discharge using a physical similarity regionalization approach, and (4) a new hybrid approach by integrating remote sensing datasets along with field measured river bathymetry data to estimate the river discharge. To demonstrate the methodology, we employed the widely used Soil and Water Assessment Tool (SWAT) hydrological model in Manipur River Basin, India. The sensitivity, calibration, and validation of the SWAT model were carried out by using the Sequential Uncertainty Fitting Technique. During calibration, the coefficient of determination (R²) and the Kling Gupta Efficiency (KGE) were found to be in the range of 0.46–0.81 and 0.41–0.83, whereas during validation R² and KGE were found to be in the range of 0.40–0.79 and 0.53–0.77 for the four different techniques. Among all the four techniques applied in this study, calibration based on (i) stage‐discharge curve using spatial proximity approach and (ii) new hybrid approach by integrating remote sensing datasets and river bathymetry were found as the better approaches as indicated by the statistical indices. The performance evaluation of the model through a new hybrid approach by integrating remote sensing and in‐situ measured datasets for rivers with narrow width represents a promising technique for use in a data sparse region.

Manipur River basin is seriously affected by soil erosion. Assessment of soil erosion in such watersheds is useful in identifying causes and associated risks. This study aims to assess spatio-temporal change in soil erosion/deposition in the past, and project it for the future using the Unit Stream Power Erosion/Deposition model based on rainfall erosivity, soil erodobility, soil cover, slope length and steepness factor as the input parameter. From the model results, the area under extreme to slight erosion intensity was found to be 3.82%, 5.54%, 5.44%, 5.28% and 6.74% for the year 2007, 2011, 2014, 2017 and 2030, whereas for the deposition it was found to be 3.72%, 4.9%, 5.21%, 5.3% and 6.3% for the year 2007, 2011, 2014, 2017 and 2030 respectively. This study also indicates that the study area is suffering from moderate soil erosion but it is most likely to transit into severe in the future.

Planning and managing water resources necessitate the utilization of hydrologic models. However, the calibration of these models poses a significant challenge due to the high degree of parameter uncertainty, which has global implications. Moreover, regions with high seasonal variation in precipitation exhibit strong heteroskedasticity, further complicating the calibration process. This study aims to determine an optimized calibration approach and parameter optimizations by integrating two sensitivity methods with two optimization techniques. The methodology was demonstrated in the sub-basins of the Bay of Plenty, New Zealand, using the SWAT hydrological model. The performance indices of the calibrated models were analyzed, revealing striking similarities between GLUE and SUFI-2 approaches. Although GLUE marginally outperformed SUFI-2 in terms of the fit indices, indicating better model accuracy, SUFI-2 demonstrated less uncertainty, making it a more stable option. Among the parameters, the initial SCS curve number for moisture condition II, baseflow factor, groundwater delay, groundwater revap coefficient, soil evaporation compensation factor, Manning's coefficient value for the main channel, and effective hydraulic conductivity in the main channel were identified as the most sensitive. These findings provide critical insights into hydrological modeling, supporting the development of policies not only in the Bay of Plenty but also in similar regions.