Quantifying storage changes in regional Great Lakes watersheds using a coupled subsurface-land surface process model and GRACE, MODIS products

Abstract

As a direct measure of watershed resilience, watershed storage is important for understanding climate change impacts on water resources. In this paper we quantify water budget components and storage changes for two of the largest watersheds in the State of Michigan, USA (the Grand River and the Saginaw Bay watersheds) using remotely sensed data and a process-based hydrologic model (PAWS) that includes detailed representations of subsurface and land surface processes. Model performance is evaluated using ground-based observations (streamflows, groundwater heads, soil moisture, and soil temperature) as well as satellite-based estimates of evapotranspiration (Moderate-resolution Imaging Spectroradiometer, MODIS) and watershed storage changes (Gravity Recovery and Climate Experiment, GRACE). We use the model to compute annual-average fluxes due to evapotranspiration, surface runoff, recharge and groundwater contribution to streams and analyze the impacts of land use and land cover (LULC) and soil types on annual hydrologic budgets using correlation analysis. Watershed storage changes based on GRACE data and model results showed similar patterns. Storage was dominated by subsurface components and showed an increasing trend over the past decade. This work provides new estimates of water budgets and storage changes in Great Lakes watersheds and the results are expected to aid in the analysis and interpretation of the current trend of declining lake levels, in understanding projected future impacts of climate change as well as in identifying appropriate climate adaptation strategies.