Month: January 2025

Abstract

Climate change impact studies have been significantly applied in the past few decades, where understanding and implementing the available global datasets at regional and even station levels is inevitable. In the highly complex hydrological phenomena happening globally, the various predictors and their complex relationships with local predictand (precipitation) play a crucial role. Towards this step, numerous predictor selection techniques such as maximum relevance minimum redundancy (MRMR), least absolute shrinkage and selection operator (LASSO), step-wise regression (SWR), and whale optimization technique (WOA) are adapted. Twenty-two predictors measured during a period of 30 years (from 1985 to 2014) from 14 stations distributed throughout the lower Krishna River basin are used as the data set in this study. The selected predictors from each method are tested on simulated historical data from 1985 to 2014 by CMIP6 MPI-ESM1-2-HR global climate model at every station using artificial neural networks (ANN). Applying the best set of predictors and choosing them according to the site, can boost downscaling of precipitation by the ANN model, based on the evaluation results of screening methods. WOA outperformed in finding the optimal predictors over other methods, SWR, MRMR, and LASSO. The most effective predictors found for WOA for all sites are relative humidity at 850 and 1000 hPa, geo-potential height at 500 hPa, u-wind at 850 hPa, v-wind at 10 m and 500 hPa, minimum temperature, mean sea level pressure and mean zonal gravity wave stress.

Abstract

As a result of the burgeoning population, rapid urbanization, and climate change, water resources are under tremendous pressure. Water scarcity, secondary soil salination, waterlogging, and lowered groundwater levels are alarming situations that require immediate intervention to control their effects and plan sustainable water resources. Thus, the purpose of this study is to model and assess the hydrological impacts of land-use change and land-cover loss on the Pawana basin in the state of Maharashtra. The efficient hydrological model must be used to achieve the goals, which is why SWAT (Soil and Water Assessment Tool) was used in this investigation. Data from metrology was analysed for the SWAT model between 2000 and 2020. The India Meteorological Department in Pune provided the meteorological data, while the HDUG group in Nasik provided the hydrological data. The results of the SWAT model were calibrated and validated using the SWAT-CUP tool and the SUFI II algorithm. Following processing, it was found that there is a good degree of fitness between the simulated and observed data, indicating that the SWAT model is accepted. The Statistical parameters like NSE and R² were used to assess the sensitivity of the work. The observed R² value was 0.80, while the NSE was 0.78. In order to simulate the water balance components for the Pawana basin, downscaled Global Climate Models (GCMs) data were used, along with hypothetical LULC change and management scenarios, to compare future scenarios (2030–2100) to a baseline period (1974–2004) under RCP-4.5 and RCP-8.5. Lastly, it was shown that while precipitation over the study basin exhibits substantial variety, average temperature over the basin is increasing across all GCMs. These findings will help organizations and stakeholders working in the water resources area to adapt and implement the correctives action for alleviating the negative effects of LULC and climate change on water supplies.