Improved water storage estimates within the North China Plain by assimilating GRACE data into the CABLE model (SCI一区收录, IF=5.722)
Improved water storage estimates within the North China Plain by assimilating GRACE data into the CABLE model (SCI一区收录, IF=5.722)
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DOI：
10.1016/j.jhydrol.2020.125348
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发表时间：
2020
影响因子：
6.4
通讯作者：
Ghobadi-Far Khosro
中科院分区：
文献类型：
--
作者：
Yin Wenjie;Han Shin-Chan;Zheng Wei;Yeo In-Young;Hu Litang;Tangdamrongsub Natthachet;Ghobadi-Far Khosro
华北平原作为中国重要的粮食生产基地之一，由于气候干燥和人类活动频繁，地下水资源严重枯竭。准确估算地下水库存量对当地社会和区域经济的可持续发展具有重要意义。本文研究了一种基于重力恢复和气候实验（GRACE）数据的陆地水储存（TWS）数据同化到社区大气生物圈土地交换（CABLE）模型的方法，以确定整个NCP的GWS变化。我们发现CABLE模型不能计算出主要城市所在的西南地区GWS变化的长期下降（且自2013年以来加速）。由于降水强迫数据存在误差，该模型对库水量年际和年际变化的空间格局描述不准确。TWS的年际变化反映了GWS的变化，而季节变化主要是根区土壤水分的变化。GRACE数据同化是提高GWS计算效率的最有效方法。利用NCP地区156个实测地下水位资料对GWS同化结果进行了验证。与模型计算相比，在相互相关方面有显著改善，平均从0.12（同化前）到0.54（同化后）。本研究证明了GRACE数据同化在可靠估计NCP地下水储量变化方面的有效性，并有望量化南水北调工程在NCP内供水的潜在影响。
As one of the most important crop-producing bases, the North China Plain (NCP) has suffered serious groundwater depletion due to drying climate and intensive human activities. An accurate estimation of groundwater storage (GWS) is of great importance for the sustainable development of local society and region’s economy. This study investigates a methodology to determine improved GWS variation throughout the NCP based on assimilation of terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) data into the Community Atmosphere Biosphere Land Exchange (CABLE) model. We found that the CABLE model is not able to compute the prolonged decrease (and accelerated since 2013) in the GWS change around the southwestern region of the NCP where major cities are located. The model incorrectly characterizes the spatial patterns of interannual and annual changes of water storage variation, mainly caused by errors in the precipitation forcing data. The interannual TWS change is indicative of GWS, while the observed seasonal variation is primarily that of root-zone soil moisture. The GRACE data assimilation most effectively improves GWS computation. The GWS assimilation results were validated against a total of 156in-situgroundwater level data in the NCP. Compared to the model computation, there was a significant improvement in terms of cross correlation, on average, from 0.12 (before assimilation) to 0.54 (after assimilation). This study demonstrates the effectiveness of GRACE data assimilation toward reliable estimation of ground water storage variation in the NCP, and its promise to quantify the potential implication of water supply from the South-to-North Water Transfer Project within the NCP.