Multi-Scale Water Availability Modeling through Data Integration, Remote Sensing, and Machine Learning

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Presenter

Dr. Fitsume T. Wolkeba
Postdoctoral Fellow
Bureau of Economic Geology
Jackson School of Geosciences
The University of Texas at Austin

Description

Effective water-resource planning requires connecting available water with climate dynamics and sector-specific water demand. This presentation presents application of satellite remote sensing, hydrological modeling, data analytics and machine learning in estimating water availability across scales. Texas applications examine groundwater depletion, drought, TX municipal water-demand projections, and energy generation water use. Continental and global studies benchmark water-availability estimates, develop satellite-based indicators, and integrate GRACE satellite-based observations into hydrological models using machine learning. Daily evapotranspiration mapping further supports assessment of water use and drivers of droughts. Together, these studies demonstrate how complementary observations and modeling approaches can strengthen water-resource assessment and planning.