Monitoring and predicting the outstanding 2017 drought in Spain

The increase in societal exposure and vulnerability to drought, call to move from post-crisis to pre-impact drought risk management. Accurate and timely information of evolving drought conditions is crucial to take early actions to avoid devastating long-term impacts. A previous study indicated that a statistical empirical method, the ensemble streamflow prediction system (ESP; an ensemble based on reordering historical data), represents a computationally fast alternative to dynamical prediction applications for drought prediction (Turco et al. 2017). Extending this work, here we present an assessment of the ability of the ESP method in predicting the drought of 2017 in Spain considering also the uncertainties coming from the observations. For this, four different datasets are used: that cover a period of 36 years (1981-2017) and with a spatial resolution of 0.25 x 0.25º based on observations of interpolated stations (E-OBS, AEMET), on reanalysis data (ERA5), and on combining stations and satellite data (CHIRPS). Meteorological droughts are defined using the Standardized Precipitation Index aggregated over the months April–September. All the datasets show a similar spatial pattern, with most of the domain suffering extreme drought conditions. In addition, the ESP system achieves reasonable skill in predicting this drought event 2 months in advance with, again, similar pattern among the different datasets. These results suggest the feasibility of the development of an operational early warning system, also considering that the data of CHIRPS and ERA5 are updated every month, i.e., that are available for near-real time applications.

 

References

Turco, M., et al. (2017). Summer drought predictability over Europe: empirical versus dynamical forecasts. Environmental Research Letters, 12(8), 084006.

 

Acknowledgments

The authors acknowledge the ACEX project (CGL2017-87921-R) of the Ministerio de Economía y Competitividad of Spain. AHM thanks his predoctoral contract FPU18/00824 to the Ministerio de Ciencia, Innovación y Universidades of Spain. M.T. has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00).