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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #348616

Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Groundwater withdrawals under drought: reconciling GRACE and land surface models in the United States High Plains Aquifer

Author
item Nie, W. - Johns Hopkins University
item Zaitchik, B.f. - Johns Hopkins University
item Rodell, M. - Goddard Space Flight Center
item Kumar, S. - Goddard Space Flight Center
item Anderson, Martha
item Hain, C. - Goddard Space Flight Center

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/2/2018
Publication Date: 5/7/2018
Citation: Nie, W., Zaitchik, B., Rodell, M., Kumar, S., Anderson, M.C., Hain, C. 2018. Groundwater withdrawals under drought: reconciling GRACE and land surface models in the United States High Plains Aquifer. Water Resources Research. https://doi.org/10.1029/2017WR022178.

Interpretive Summary: Groundwater extraction in support of irrigated agriculture can have a significant impact on both water table levels and the amount of water lost to the atmosphere through evapotranspiration (ET). State of the art land-surface models attempt to explicitly model groundwater extraction to better track impacts on the local and regional hydrology; however, these models may miss important processes and fail to accurately capture response to climatic extremes. This study investigates the performance of the Noah-Multiparameterization (MP) land-surface model in reproducing groundwater declines and evaporative losses over the High Plains Aquifer (HPA) in comparison with estimates generated from satellite remote sensing. The results show that improvements in the representation of the greenness vegetation fraction assumed in the model, as well as how this greenness fraction is used to identify actively irrigated areas, significantly improves model agreement with remotely sensed ET and terrestrial water changes and their response to drought over the HPA. These improved models can facilitate assessments of human impacts on water resources in irrigated agricultural areas both within the United States and globally.

Technical Abstract: Advanced Land Surface Models (LSM) offer a powerful tool for studying hydrological variability. Highly managed systems, however, present a challenge for these models, which typically have simplified or incomplete representations of human water use. Here we examine recent groundwater declines in the US High Plains Aquifer (HPA), a region that is heavily utilized for irrigation and that is also affected by episodic drought. To understand observed decline in groundwater and terrestrial water storage during a recent multi-year drought, we modify the Noah-MP LSM to include a groundwater irrigation scheme. To account for seasonal and interannual variability in active irrigated area, we apply a monthly time-varying greenness vegetation fraction (GVF) dataset within the model. A set of five experiments were performed to study the impact of groundwater irrigation on the simulated hydrological cycle of the HPA and to assess the importance of time-varying GVF when simulating drought conditions. The results show that including the groundwater irrigation scheme improves model agreement with remotely sensed ET data from the ALEXI energy balance model, mascon-based GRACE TWS data and depth-to-groundwater measurements in the southern HPA, including Texas and Kansas, and that accounting for time-varying GVF is important for model realism under drought. Results for the HPA in Nebraska are mixed, likely due to the model’s failure to capture major groundwater recharge in winter 2011 and 2012. This study highlights the value of GRACE datasets for model evaluation and development and the potential to advance the dynamic representations of the interactions between human water use and the hydrological cycle.