Discovering the links between land use change, climate factors, and environmental flows (e-flows) for long-term water security

Abstract

Muger River watershed, located in the Upper Blue Nile Basin in the central highlands of Ethiopia, covering 731,000 ha. This study investigates the impacts of land use change on e-flows by analyzing long-term changes in hydro-climatic and environmental parameters. The Google Earth Engine platform was used to assess land surface temperature (LST), soil moisture content (SMC), evapotranspiration (ET), standardized precipitation evapotranspiration index (SPEI), and climate extremes including consecutive dry days (CDD), warm spell duration index (WSDI), and TX90p for 1984 and 2024. Land-use-land-cover classification was conducted using Landsat imagery and a Random Forest model. Results reveal significant landscape transformation, with forest cover declining from 3031.07 km² to 2106.64 km² and grassland from 1855.96 km² to 831.68 km², replaced by cultivated land and settlements. These changes correspond with increased LST (28°C to 35°C), declining SMC (0.819–0.457), and intensified drought conditions (more negative SPEI). Climate extremes also increased, with WSDI rising from 7 to 36 days, TX90p from 7.86 to 30.25 days, and CDD reaching up to 135 days. These combined effects have significantly reduced water balance and e-flow availability. The findings highlight the growing vulnerability of river systems to coupled land use and climate pressures and demonstrate the utility of Google Earth Engine for integrated, long-term hydrological assessments supporting adaptive water resource management. • GEE offers access to vast satellite data and powerful computing for tracking LULCC and environmental parameters. • Linking LULCC with environmental parameters provides key insights for sustainable land use decision-making. • Advanced machine learning and the inclusion of socioeconomic factors can improve future land use change prediction.