EGU22-12797, updated on 28 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Flash droughts early warning based on evaporative stress forecasts

Qiqi Gou1,2, Akash koppa1, Hylke E. Beck3, Petra Hulsman1, and Diego G. Miralles1
Qiqi Gou et al.
  • 1Hydro-Climate Extremes Lab (H-CEL), Ghent University, Ghent, Belgium
  • 2State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
  • 3GloH2O, Almere, the Netherlands

Flash droughts are regional phenomena that can manifest in region areas with a rapid intensification, and that often last for short periods of time. Flash droughts have received considerable scientific attention in recent years. However, their prediction is still a challenge, largely due to their abrupt onset and often unknown regional drivers. Here, we establish a forecast system to predict flash droughts at a medium-range weather scale. The system uses forcing data from the Multi-Source Weather (MSWX), an operational, high-resolution (3‑hourly, 0.1°), bias-corrected meteorological product with global coverage from 1979 to several months into the future (Beck et al. 2021). MSWX data are used as input to the Global Land Evaporation Amsterdam Model (GLEAM), more specifically its recent hybrid version (Koppa et al., 2021). This allows us to compute forecasts of actual and potential evaporation;  the ratio of both (also know as 'evaporative stress') is used here as flash drought diagnostic. This forecast system is evaluated on its ability to predict flash droughts globally and 2, 4, 7 and 10 days advance. The new tool shows potential to improve our understanding of flash droughts, and it serves as an early prediction system to enable more efficient agricultural and water management.



Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. M., van Dijk, A. I. J. M., McVicar, T. R., and Adler, R. F., MSWEP V2 global 3‑hourly 0.1° precipitation: methodology and quantitative assessment. Bulletin of the American Meteorological Society. 100(3), 473–500, 2019

Koppa, A., Rains, D., Hulsman, P., and Miralles, D. M., A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation. Preprint. 2021. 10.21203/

How to cite: Gou, Q., koppa, A., E. Beck, H., Hulsman, P., and G. Miralles, D.: Flash droughts early warning based on evaporative stress forecasts, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12797,, 2022.