Accuracy of groundwater storage (GWS) estimates from the Gravity Recovery and Climate Experiment (GRACE) mission usually has certain relations with hydrological models. This study develops a statistical selection approach to optimally estimate GWS from GRACE using two hydrological models: the Global Land Data Assimilation System (GLDAS) and the WaterGAP Global Hydrology Model (WGHM), over Tasmania, Australia. This approach involves three variables: the long-term trend, Pearson correlation coefficient (PR), and root mean square error (RMSE). The results show that in-situ observations are highly correlated with GRACE-GLDAS (PR from 0.64 to 0.85) and GRACE-WGHM (PR from 0.69 to 0.88) in eastern and northern regions of Tasmania, respectively. The interannual trends of GRACE-GLDAS estimates are generally ~1.8 times larger than those from GRACE-WGHM solutions. With regard to the standard method, the statistical selection approach can effectively improve the PR and Nash-Sutcliffe efficiency index (NSE) by 3.80 and 1.38%, respectively, over the northern region, while it decreases the RMSE by 1.07%. Similar improvements can also be detected in the eastern region. In terms of spatial distribution, the statistical approach benefits from advantages of the different models, especially to preserve the characteristics of Central Highland. Overall, according to the models, Tasmania experienced a pronounced GWS decline during the Millennium Drought (2003–2010), at a depletion rate of –2.57 mm/year, mainly due to decreasing precipitation. The increasing precipitation infiltration after 2010 lead to the GWS recovery by 3.94 mm/year. The limitation of the method is that it depends on the availability of in-situ groundwater level data.

重力恢复和气候实验（GRACE）任务估计的地下水储量（GWS）的准确性通常与水文模型有一定关系。这项研究开发了一种统计选择方法，可以使用两种水文模型从GRACE最佳估算GWS：澳大利亚塔斯马尼亚州的全球土地数据同化系统（GLDAS）和WaterGAP全球水文模型（WGHM）。该方法涉及三个变量：长期趋势，皮尔逊相关系数（PR）和均方根误差（RMSE）。结果表明，在塔斯马尼亚州东部和北部地区，原位观测分别与GRACE-GLDAS（PR从0.64至0.85）和GRACE-WGHM（PR从0.69至0.88）高度相关。GRACE-GLDAS估算值的年际趋势通常约为1。比GRACE-WGHM解决方案大8倍。对于标准方法，统计选择方法可以有效地将北部地区的PR和Nash-Sutcliffe效率指数（NSE）分别提高3.80和1.38％，而将RMSE降低1.07％。在东部地区也可以发现类似的改善。在空间分布方面，统计方法得益于不同模型的优势，尤其是保留了中部高原的特征。总体而言，根据这些模型，塔斯马尼亚州在千年干旱（2003-2010年）期间经历了明显的GWS下降，损耗速率为–2.57 mm /年，这主要是由于降水减少所致。2010年以后，降水渗透增加，导致GWS恢复3.94毫米/年。