资源环境科技发展态势分析平台

Large-scale co-variations 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 hydro-climatic hazards. Such large-scale co-variation 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 hydro-climatic data time series over the period 1980-2010 for 6405 catchments around the world. From observation-based data, we identify dominant large-scale linear co-variation patterns between monthly freshwater fluxes and soil moisture (SM) for different world parts and climates. These co-variation patterns are also compared with those obtained from reanalysis products and Earth System Models (ESMs). The observation-based datasets 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 co-variation 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 co-variations 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.

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