Abstract
We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA’s Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE’s coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information.
Highlights
Hydrologic predictability depends on knowledge of initial hydrologic conditions
Correlation results show that data assimilation improved Catchment Land Surface Model (CLSM) terrestrial water storage anomaly (TWSA) simulations in the study region
CLSM-DA is a viable dataset for hydrologic analyses based on several potential benefits
Summary
Hydrologic predictability depends on knowledge of initial hydrologic conditions. While current precipitation may be the most visible hydrological driver of streamflow, antecedent states of groundwater and soil moisture are less visible, but of critical importance. Data assimilation is the process of merging measurements with model predictions to maximize spatial and temporal coverage, consistency, resolution, and accuracy It addresses weaknesses in the representation of land-surface model states at the grid scale level, and can create more realistic predictions of hydrological components like runoff, river flow, and groundwater [26,27]. Houborg et al [15] suggest that data assimilation may be the key to realizing the full potential of intrinsically-coarse GRACE TWSA observations The outcomes of these studies included moderate, but statistically significant, improvements to the hydrological modeling skill of the CLSM across major parts of the United States.
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