Getirana Augusto, Rodell Matthew, Kumar Sujay, Beaudoing Hiroko Kato, Arsenault Kristi, Zaitchik Benjamin, Save Himanshu, Bettadpur Srinivas
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD.
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD.
J Hydrometeorol. 2020 Jan;21(1):59-71. doi: 10.1175/jhm-d-19-0096.1. Epub 2020 Jan 16.
We evaluate the impact of Gravity Recovery and Climate Experiment data assimilation (GRACE-DA) on seasonal hydrological forecast initialization over the U.S., focusing on groundwater storage. GRACE-based terrestrial water storage (TWS) estimates are assimilated into a land surface model for the 2003-2016 period. Three-month hindcast (i.e., forecast of past events) simulations are initialized using states from the reference (no data assimilation) and GRACE-DA runs. Differences between the two initial hydrological condition (IHC) sets are evaluated for two forecast techniques at 305 wells where depth-to-water-table measurements are available. Results show that using GRACE-DA-based IHC improves seasonal groundwater forecast performance in terms of both RMSE and correlation. While most regions show improvement, degradation is common in the High Plains, where withdrawals for irrigation practices affect groundwater variability more strongly than the weather variability, which demonstrates the need for simulating such activities. These findings contribute to recent efforts towards an improved U.S. drought monitor and forecast system.
我们评估了重力恢复与气候实验数据同化（GRACE-DA）对美国季节性水文预报初始化的影响，重点关注地下蓄水情况。基于GRACE的陆地水储量（TWS）估算值被同化到2003年至2016年期间的陆面模型中。利用参考（无数据同化）和GRACE-DA运行的状态对三个月的后报（即对过去事件的预报）模拟进行初始化。在305口有地下水位测量数据的井处，针对两种预报技术评估了两组初始水文条件（IHC）之间的差异。结果表明，使用基于GRACE-DA的IHC在均方根误差（RMSE）和相关性方面均能改善季节性地下水预报性能。虽然大多数地区都有改善，但在高平原地区退化情况很常见，在该地区，灌溉用水抽取对地下水变异性的影响比天气变异性更强，这表明需要对这类活动进行模拟。这些发现有助于近期为改进美国干旱监测和预报系统所做的努力。
