Bari Doab in the Indus Basin, Pakistan. A new methodology was proposed for assessing depleted groundwater aquifer by integrating Gravity Recovery and Climate Experiment (GRACE) data, Global Land Data Assimilation System, and monitoring wells data along with hydro-climatic variables and aquifer characteristics through groundwater modelling across Bari Doab, Punjab, Pakistan. The GRACE-derived Groundwater Storage Anomalies (GWSA) was spatially downscaled from 0.25°× 0.25° to 0.01°× 0.01° using the Boosted Regression Tree (BRT), and further temporally disaggregated to a daily time step using the Quadratic Match Sum approach. The spatiotemporally downscaled GWSA was validated against monitoring data, and integrated into Groundwater Modeling system to simulate the groundwater dynamics from 2007 to 2023. GRACE downscaling using BRT performed well during training and testing periods. Simulation results indicated that interannual climate fluctuations significantly impact groundwater recharge, resulting in substantially decreased recharge during drought years. Recharge to the aquifer is about 1/100 of the precipitation and irrigation infiltration into the soil. Assessment results of groundwater depletion using monitoring wells and GRACE downscaled data agreed well with daily sensor data. Significant lateral flow was observed across eight canal command areas in the study region. The results highlight the potential of assessing regional groundwater depletion using spatiotemporal downscaled GRACE data and groundwater modelling, and support informed policy decisions for sustainable groundwater management. • We assessed groundwater depletion by combining GRACE data with well observation. • GRACE data was spatiotemporally downscaled. • Groundwater Modeling System was used to simulate groundwater depletion. • The model's results matched well with sensor observations. • IoT sensor data and modeling outputs support data-driven groundwater management.
Khan et al. (Wed,) studied this question.