FD-VIEWS: A new operational global flash drought early-warning system based on evaporative stress forecasts

Early warning of flash droughts is crucial to mitigate their adverse impacts on agriculture, ecosystems, and water resources. In recent years, advances in weather forecasting have been significant, paving the way for the development of reliable flash drought early-warning systems. Based on these recent developments, we present the operational, global-scale Flash Drought Viewer, Index, and Early Warning System (FD-VIEWS), which combines a deep learning hybrid version of the Global Land Evaporation Amsterdam Model (GLEAM, Koppa et al. 2022) with high-resolution ensemble meteorological forecasts from the Multi-Source Weather product (MSWX, Beck et al. 2022). Based on probabilistic forecasts of evaporative stress, FD-VIEWS diagnoses flash droughts using the Standardized Evaporation Stress Ratio (SESR) proposed by Christian et al. (2019) and further developed by Gou et al. (2022). The early-warning system predicts not only onset, continuation, and termination, but also estimates intensification rate and drought severity. FD-VIEWS is evaluated on its ability to predict flash droughts globally over a 10-day forecast horizon. The evaluation of FD-VIEWS reveals a high skill in predicting flash drought onset and termination; the onset forecast skill is higher in arid regions, whereas the termination forecast skill is higher in humid areas. Overall, FD-VIEWS shows potential in improving our understanding of flash drought predictability and its drivers, and enables more effective water management.

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Beck, H. E., van Dijk, A. I., Larraondo, P. R., McVicar, T. R., Pan, M., Dutra, E., Miralles, D. G., 2022: MSWX: Global 3-Hourly 0.1° Bias-Corrected Meteorological Data Including Near-Real-Time Updates and Forecast Ensembles. Bulletin of the American Meteorological Society, 103 (3), E710-E732.

Christian, J. I., Basara, J. B., Otkin, J. A., Hunt, E. D., Wakefield, R. A., Flanagan, P. X., Xiao, X., 2019: A Methodology for Flash Drought Identification: Application of Flash Drought Frequency across the United States. Journal of Hydrometeorology, 20 (5), 833-846.

Gou, Q., Zhu, Y., Lü, H., Horton, R., Yu, X., Zhang, H., Wang, X., Su, J., Liu, E., Ding, Z., Wang, Z., Yuan, F., 2022: Application of an improved spatio-temporal identification method of flash droughts. Journal of Hydrology, 604, 127224.

Koppa, A., Rains, D., Hulsman, P., Poyatos, R., Miralles, D. G., 2022: A deep learning-based hybrid model of global terrestrial evaporation. Nature Communications, 13 (1), 1912.