Large-scale covariations of freshwater fluxes and storages on land can critically regulate the balance of green (evapotranspiration) and blue (runoff) water fluxes, and related land-atmosphere interactions and hydroclimatic hazards. Such large-scale covariation patterns are not evident from smaller-scale hydrological studies that have been most common so far, and remain largely unknown for various regions and climates around the world. To contribute to bridging the large-scale knowledge gaps, we synthesize and decipher hydroclimatic data time series over the period 1980–2010 for 6,405 catchments around the world. From observation-based data, we identify dominant large-scale linear covariation patterns between monthly freshwater fluxes and soil moisture (SM) for different world parts and climates. These covariation patterns are also compared with those obtained from reanalysis products and Earth System Models (ESMs). The observation-based data sets robustly show the strongest large-scale hydrological relationship to be that between SM and runoff (R), consistently across the study catchments and their different climate characteristics. This predominantly strongest covariation between monthly SM and R is also the most misrepresented by ESMs and reanalysis products, followed by that between monthly precipitation and R. Comparison of observation-based and ESM results also shows that an ESM may perform well for individual monthly variables, but fail in representing the patterns of large-scale linear covariations between them. Observation-based quantification of these patterns, and ESM and reanalysis improvements for their representation are essential for fundamental understanding, and more accurate and reliable modeling and projection of large-scale hydrological conditions and changes under ongoing global and regional change.

中文翻译：

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大尺度水文气候协变模式的数据驱动的全球量化以及与再分析和地球系统建模的比较

淡水通量和陆地储存量的大规模协变可以严格调节绿色（蒸散）和蓝色（径流）水通量的平衡，以及相关的陆地-大气相互作用和水文气候灾害。这种大规模的协变模式在迄今为止最常见的小规模水文研究中并不明显，并且在世界各地的不同地区和气候中仍然很大程度上未知。为了有助于弥合大规模的知识差距，我们合成和解读了 1980 年至 2010 年期间全球 6,405 个流域的水文气候数据时间序列。从基于观测的数据中，我们确定了世界不同地区和气候的月淡水通量和土壤水分 (SM) 之间主要的大规模线性协变模式。还将这些协变模式与从再分析产品和地球系统模型 (ESM) 中获得的模式进行比较。基于观测的数据集有力地显示了最强的大尺度水文关系是 SM 和径流 (R) 之间的关系，在研究流域及其不同的气候特征中始终如一。月度 SM 和 R 之间的这种主要最强的协变也最容易被 ESM 和再分析产品歪曲，其次是月降水量和 R 之间的协变。基于观测和 ESM 结果的比较还表明，ESM 可能对单个月变量表现良好，但未能表示它们之间大规模线性协变的模式。这些模式的基于观察的量化，