Satellite-based precipitation products can be a better alternative of rain gauges for hydro-meteorological studies in data-poor regions. This study aimed to evaluate how regional and seasonal precipitation and drought patterns had changed in the Ganga–Brahmaputra Basin between 1983 and 2020 with PERSIANN-CDR precipitation data. The spatial pattern of winter drought, monsoon drought, and Standardized Precipitation Index (SPI) calculated for different time scales were evaluated using principal component analysis. Ganga–Brahmaputra is one of the most populated river basins that flows through different geographical regions. Rain gauges are heterogeneously distributed in the basin due to its complex orography, highlighting the significance of gridded precipitation products over gauge observations for climate studies. Annual and monthly precipitation trends between 1983 and 2020 were evaluated using the original and modified Mann–Kendall trend test, and annual precipitation in the basin was found to be declining at a rate of 5.8 mm/year. An increasing trend was observed in pre-monsoon rainfall, whereas precipitation exhibited a decreasing trend for other months. Results of the Pettitt test showed precipitation time series was inhomogeneous and changepoint occurred around 2000. Decreasing trends of SPI indicated increasing frequency and intensity of drought events. Winter drought showed a clear spatial pattern in the basin; however, SPIs calculated for different time scales and monsoon drought had complex spatial patterns. This study demonstrates the applicability of satellite-based PERSIANN-CDR precipitation data in climate research in the Ganga–Brahmaputra Basin.

Mobile weather radars have been routinely deployed to acquire high-quality meteorological data for research purposes, particularly for monitoring rapidly evolving weather phenomena at low altitudes. However, identifying an optimal location for mobile weather radar deployment is a complex challenge, as it requires consideration of operational safety, data quality, and environmental constraints. In this study, we introduce a framework using a coverage-based location problem to solve the strategic deployment of a single mobile weather radar. This approach aims to enhance weather observation while accounting for the deployment space’s safety constraints and geospatial characteristics. The proposed location problem is solved optimally using the geometric branch-and-bound algorithm and heuristically using swarm-based optimization algorithms. The implementation relies entirely on open-source Python packages, allowing the work to be verified, replicated, and expanded upon by the broader scientific community. Results demonstrate that exact solution methods are ideal when ample time is available for decision-making and optimal deployment locations are desired. In contrast, heuristic algorithms can efficiently identify multiple near-optimal deployment locations, making them highly suitable for rapid decision-making and evaluating alternative deployment options. Moreover, the findings highlight the potential of quantitative decision-making techniques in improving the effectiveness of mobile radar positioning, thereby contributing to efficient weather observation, forecasting, and better-informed emergency response strategies.

Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development.