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. Here we study responses of monthly evaporation and runoff to soil moisture droughts occurring between 2001 and 2015 using datasets based on machine learning-assisted upscaling of satellite and in situ observations. We find that runoff and evaporation show generally contrasting drought responses across climate regimes. In wet regions, runoff is strongly reduced while evaporation is decoupled from soil moisture decreases and enhanced by sunny and warm weather typically accompanying soil moisture droughts. In drier regions, evaporation 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 evaporation where the evaporation deficit (surplus) is overestimated (underestimated). In summary, our study illustrates how the joint analysis of machine learning-enhanced Earth observations can advance the understanding of global eco-hydrological processes

LI Wantong;  REICHSTEIN Markus;  O Sungmin;  MAY Carla;  DESTOUNI Georgia;  MIGLIAVACCA Mirco;  KRAFT Basil;  WEBER Ulrich;  ORTH Rene; 

2023-07-10

AMER GEOPHYSICAL UNION

JRC133488

2328-4277 (online),   

https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2022EF003441,    https://publications.jrc.ec.europa.eu/repository/handle/JRC133488,   

doi.org/10.1029/2022EF003441 (online),