Advanced Water Availability Analysis in the Amu Darya River Basin through Integrated Remote Sensing and Modeling Techniques

Effective management of water resources is crucial for sustainable development, particularly in regions like the Amu Darya River Basin, where water availability directly influences ecological and human systems. This study leverages integrated remote sensing data and model outputs to enhance water budget and availability analyses within the basin. Utilizing the GLDAS CLSM 2.1 model alongside satellite-derived precipitation and evapotranspiration data, this research provides a comprehensive assessment of water budget components, including precipitation, evapotranspiration, terrestrial water storage changes, and runoff, during the period 2001-2023. Moreover, by utilizing the NASA GLDAS model, a novel global dataset of the water budget components measured in millimeters per month was developed, streamlining the estimation process across different scales. Our results reveal significant variations in the precipitation estimates between the satellite observations and model predictions, with the satellite data generally indicating lower precipitation rates. The evapotranspiration analysis showed that remote sensing data tend to underestimate the values compared to model outputs, emphasizing the necessity of multi-source data integration for accurate water budget estimations. Furthermore, this study highlights discrepancies in the runoff estimation between the modeled outcomes and the gauge observations, illustrating the challenges in capturing the actual streamflow dynamics without streamflow routing in the models. This analysis underscores the importance of using advanced remote sensing and modeling techniques to improve water availability assessments, facilitating better management and conservation practices in the basin. These findings contribute to a deeper understanding of the hydrological processes in the Amu Darya River Basin, supporting efforts towards more resilient water resource management.