Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing.

Assessment of Terrestrial Water Storage (TWS) components is crucial for understanding regional climate and water resources, particularly in arid and semi-arid regions like Afghanistan. Given the scarcity of ground-based data, this study leverages remote sensing datasets to quantify water storage changes. We integrated Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (GRACE-FO) data with WaterGap, Global Land Water Storage (GWLS), Catchment Land Surface Model (CLSM), and climate variables (precipitation, temperature, potential evapotranspiration) using artificial neural networks (ANN) and random forests (RF).

Integrated hydrologic modeling as a key for sustainable urban water resources planning.

In this study, a coupling of surface water (SWAT), groundwater (MODFLOW) and solute transport (MT3DMS) models was performed to quantify surface-groundwater and quantity-quality interactions under urban area expansion. The responses of groundwater level, nitrate concentrations (related to human activities) and chloride concentrations (related to seawater intrusion) to urban area expansion and corresponding changes in the urban water budget were examined on a macro-scale level. The potentials of non-conventional water resources scenarios, namely desalination, stormwater harvesting and treated wastewater (TWW) reuse were investigated.