Soil moisture plays a crucial role in surface hydrological processes and land–atmosphere interactions. It can influence vegetation growth directly, serving as a significant indicator for monitoring agricultural drought. However, spatially continuous datasets of root zone soil moisture rely on model simulations, introducing numerous uncertainties associated with model parameters and input data. Currently, multiple soil moisture products derived from model simulations exist, but their representation at spatial scales remains unclear. Moreover, their abilities to express soil–atmosphere and soil–vegetation interactions within land–atmosphere coupling are not understood, leading to divergent inclinations toward drought. This study investigates the performance of five soil moisture products, European Centre for Medium-Range Weather Forecasts Reanalysis v5-Land (ERA5-Land), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and SoMo.ml, under drought conditions. The bias, correlation, and difference of standard deviation (STDD) were calculated between these products and the observations from International Soil Moisture Network stations. The causal probability of soil, meteorological, and agricultural drought was calculated using the causal-effect Peter and Clark (PC) Momentary Conditional Independence (MCI) method to evaluate the data propensity of these products. ERA5-Land and SoMo.ml gave a similar performance with the highest accuracy, which was attributed to the use of the same meteorological forcing data. The biases of soil moisture from these two products at surface, middle and deep depths against station observations are below 0.1 m3/m3, and the STDD is within 0.05 m3/m3. The accuracy of GLDAS is comparatively lower, characterized by lower correlations (below 0.2 for deeper layers) and high bias (above 0.15 and 0.2 for middle and deep layers, respectively). This discrepancy could be attributed to substantial biases in the precipitation forcing data. ERA5-Land shows higher spatial resolution and greater spatial heterogeneity, whereas MERRA-2 underperformed in this area. MERRA-2 had the strongest connection to agricultural drought, with a propensity probability of 0.477. Conversely, SoMo.ml demonstrates the strongest connection to meteorological drought, with a propensity probability of 0.234. Due to the errors in simulated and observational data during the MERRA data assimilation, substantial biases in the soil moisture data, and low accuracy in meteorological forcing of GLDAS, there was no clear causal relationship between soil moisture drought and meteorological drought between these two products. These findings provide recommendations for the use of soil moisture products in drought research.

中文翻译：

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干旱中根区土壤水分表现的因果推断


土壤水分在地表水文过程和陆地-大气相互作用中起着至关重要的作用。它可以直接影响植被生长，是监测农业干旱的重要指标。然而，根区土壤水分的空间连续数据集依赖于模型模拟，引入了许多与模型参数和输入数据相关的不确定性。目前，存在多种来自模型模拟的土壤水分产品，但它们在空间尺度上的表示仍不清楚。此外，它们在陆地-大气耦合中表达土壤-大气和土壤-植被相互作用的能力尚不清楚，导致对干旱的倾向不同。本研究调查了五种土壤水分产品的性能，即欧洲中期天气预报中心再分析 v5-Land （ERA5-Land）、全球土地数据同化系统 （GLDAS）、全球陆地蒸发阿姆斯特丹模型 （GLEAM）、现代研究和应用回顾性分析，第 2 版 （MERRA-2） 和 SoMo.ml，在干旱条件下。计算这些产品与国际土壤水分网络站观测值之间的偏差、相关性和标准差 （STDD） 差异。使用因果效应 Peter and Clark （PC） 瞬时条件独立性 （MCI） 方法计算土壤、气象和农业干旱的因果概率，以评估这些产品的数据倾向。ERA5-Land 和 SoMo.ml 给出了相似的性能和最高的准确性，这归因于使用相同的气象强迫数据。这两种产品在地表、中、深深度土壤水分与站观测值的偏差均小于 0。1 m3/m3，STDD 在 0.05 m3/m3 以内。GLDAS 的准确率相对较低，其特点是相关性较低（较深层低于 0.2）和高偏置（中间层和深层分别高于 0.15 和 0.2）。这种差异可归因于降水强迫数据中存在重大偏差。ERA5-Land 显示出更高的空间分辨率和更大的空间异质性，而 MERRA-2 在该区域表现不佳。MERRA-2 与农业干旱的关联性最强，倾向概率为 0.477。相反，SoMo.ml 与气象干旱的联系最强，倾向概率为 0.234。由于 MERRA 数据同化过程中模拟和观测数据存在误差，土壤水分数据存在很大偏差，以及 GLDAS 的气象强迫准确性低，这两种产品之间土壤水分干旱与气象干旱之间没有明确的因果关系。这些发现为土壤水分产品在干旱研究中的应用提供了建议。