Team Location: Ames Research Center
Authors: Amber Kuss, William Brandt, Michelle Newcomer, Andrew Nguyen, Wei-Chen Hsu
Science Advisors/Mentors: Cindy Schmidt
Past Authors/Contributors: Joshua Randall, Bridget Floyd, Abdelwahab Bourai, Dr. Joseph Skiles
Abstract: California’s Central Valley is one of the most agriculturally productive regions in the world, and supplies nearly 7 percent of the food supply of the United States, with an estimated annual value of $21 billion dollars. Groundwater is an important resource in this region, although it is difficult to manage. Therefore, the California Department of Water Resources (DWR) would like to improve groundwater management techniques through the use of current modeled, in-situ, and remotely sensed data. This project focused on the refinement of hydrologic estimates of soil moisture and snowpack used in groundwater storage calculations in the Central Valley (including separate estimates for the Sacramento Hydrologic Region and the San Joaquin Hydrologic Region). These hydrologic variables were subtracted from total water storage (TWS) anomalies obtained from the GRACE mission to estimate groundwater storage. This work used techniques developed during the summer of 2011 and new directives such as the incorporation of climate models and daily hydrologic parameters. This project also incorporated a different GRACE dataset, obtained from the GRACE Tellus website. The additional improvements for soil moisture estimates (initially from AMSR-E) were obtained using a climate model, the Global Land Data Assimilation System (GLDAS) and a hydrologic reanalysis dataset, the North American Regional Reanalysis (NARR). Additionally, snowpack estimates were improved using daily snow water equivalent (SWE) estimates from the Snow Data Assimilation System (SNODAS). Groundwater storage estimates were compared with the DWR’s C2VSim hydrological model. Both C2VSim and the GRACE method produced comparable results for the Central Valley, and significant improvements provided greater confidence for the DWR to use remotely sensed data for management decisions.
Video transcript available here.