Articles | Volume 20, issue 6
Nat. Hazards Earth Syst. Sci., 20, 1595–1608, 2020

Special issue: Recent advances in drought and water scarcity monitoring,...

Nat. Hazards Earth Syst. Sci., 20, 1595–1608, 2020

Research article 04 Jun 2020

Research article | 04 Jun 2020

Skill of large-scale seasonal drought impact forecasts

Samuel J. Sutanto et al.

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Cited articles

Arnal, L., Cloke, H. L., Stephens, E., Wetterhall, F., Prudhomme, C., Neumann, J., Krzeminski, B., and Pappenberger, F.: Skilful seasonal forecasts of streamflow over europe?, Hydrol. Earth Syst. Sci., 22, 2057–2072,, 2018. a, b
Bachmair, S., Svensson, C., Hannaford, J., Barker, L. J., and Stahl, K.: A quantitative analysis to objectively appraise drought indicators and model drought impacts, Hydrol. Earth Syst. Sci., 20, 2589–2609,, 2016. a, b, c, d
Bachmair, S., Svensson, C., Prosdocimi, I., Hannaford, J., and Stahl, K.: Developing drought impact functions for drought risk management, Nat. Hazards Earth Syst. Sci., 17, 1947–1960,, 2017. a, b, c, d, e, f
Bartholmes, J., Thielen, J., and Kalas, M.: Forecasting medium-range flood hazard on European scale, Georisk, 2, 181–186,, 2008. a
Bett, P., Thornton, H., and Troccoli, A.: Skill assessment of energy-relevant climate variables in a selection of seasonal forecast models, Report using final data sets, ECEM Deliverable D2.2.1 v2, ECMWF Copernicus Report, ECMWF, Reading, UK, 2018. a
Short summary
Present-day drought early warning systems only provide information on drought hazard forecasts. Here, we have developed drought impact functions to forecast drought impacts up to 7 months ahead using machine learning techniques, logistic regression, and random forest. Our results show that random forest produces a higher-impact forecasting skill than logistic regression. For German county levels, drought impacts can be forecasted up to 4 months ahead using random forest.
Final-revised paper