Estimating runoff and baseflow is crucial for understanding water flow dynamics, resource availability, and usage patterns in water resource management. Traditional large-scale modeling demands extensive data, complex parameterization, and high computational resources. Recent advancements in satellite datasets for water cycle modeling offer a promising alternative, requiring fewer parameters and enabling efficient computations over broader spatial and temporal scales. This study explores the potential of satellite-derived data, particularly the Gravity Recovery and Climate Experiment (GRACE) mission datasets, for estimating runoff and baseflow in the Conterminous United States (CONUS). The study employs a combination of forward regression analysis and a water balance method to predict runoff and baseflow based on evapotranspiration estimates. The results will be evaluated against United States Geological Survey (USGS) runoff data, utilizing rain gauge station boundaries for validation. Additionally, the model results will be compared to land surface models from the Global Land Data Assimilation System (GLDAS) database and a groundwater recharge product derived from 1 km-resolution monthly data developed by the USGS. The scope of the study includes 1) the development of a data driven hydrological model to predict the volumetric flow budget, 2) evaluate improvements in annual predictions of stream flows and groundwater recharge fluxes by developing a novel hydrological model. The study results will help address water security issues catalyzed by environmental changes in watersheds by assessing surface water and groundwater fluxes.
.jpg "Pooja Preetha (she/her/hers) photo")