| Autor: |
Wantong Li, Markus Reichstein, Sungmin O, Carla May, Georgia Destouni, Mirco Migliavacca, Basil Kraft, Ulrich Weber, Rene Orth |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Earth's Future, Vol 11, Iss 7, Pp n/a-n/a (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2328-4277 |
| DOI: |
10.1029/2022EF003441 |
| Popis: |
Abstract Drought's intensity and duration have increased in many regions over the last decades. However, the propagation of drought‐induced water deficits through the terrestrial water cycle is not fully understood at a global scale. Here we study responses of monthly evaporation (ET) and runoff to soil moisture droughts occurring between 2001 and 2015 using independent gridded datasets based on machine learning‐assisted upscaling of satellite and in‐situ observations. We find that runoff and ET show generally contrasting drought responses across climate regimes. In wet regions, runoff is strongly reduced while ET is decoupled from soil moisture decreases and enhanced by sunny and warm weather typically accompanying soil moisture droughts. In drier regions, ET is reduced during droughts due to vegetation water stress, while runoff is largely unchanged as precipitation deficits are typically low in these regions and ET decreases are buffering runoff reductions. While these water flux drought responses are controlled by the large‐scale climate regimes, they are additionally modulated by local vegetation characteristics. Land surface models capture the observed water cycle responses to drought in the case of runoff, but not for ET where the ET deficit (surplus) is overestimated (underestimated), related to a misrepresentation of the general soil moisture‐evaporation interplay. In summary, our study illustrates how the joint analysis of machine learning‐enhanced Earth observations can advance the understanding of global eco‐hydrological processes, as well as the validation of land surface models. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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