Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential

Remote Sensing

John T. Reager³  Alys C. Thomas²  Eric A. Sproles⁴  Matthew Rodell¹  Hiroko K. Beaudoing¹  Bailing Li¹  James S. Famiglietti³  Guy J-P. Schumann⁵  Magaly Koch⁵ 

[1] NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA; E-Mails:;Department of Earth System Science, University of California, Irvine, CA 92697, USA; E-Mail:;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA; E-Mail:;Centro de Estudios Avanzados en Zonas Áridas, La Serena Región de Coquimbo, Chile; E-Mail:;Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91125, USA; E-Mail

关键词: GRACE;    gravity;    flood;    assimilation;   

DOI  :  10.3390/rs71114663

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.