National Aeronautics and Space Administration


Date of this Version



Nie, W., Zaitchik, B. F., Rodell, M., Kumar, S. V., 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, 54, 5282–5299. https://doi. org/10.1029/2US govt work017WR022178


US gov't work


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 multiyear 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) data set 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 ALEXI ET data, 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 weaknesses in representing subsurface hydrology in this region. This study highlights the value of GRACE data sets for model evaluation and development and the potential to advance the dynamic representations of the interactions between human water use and the hydrological cycle.