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1 Shyrokaya, A.; Pappenberger, F.; Pechlivanidis, I; Messori, G.; Khatami, S.; Mazzoleni, M.; Di Baldassarre, G. 2023. Advances and gaps in the science and practice of impact-based forecasting of droughts. WIREs WATER, e1698. (Online first) [doi: https://doi.org/10.1002/wat2.1698]
Drought ; Forecasting ; Indicators ; Socioeconomic aspects ; Early warning systems ; Models ; Vulnerability ; Risk management
(Location: IWMI HQ Call no: e-copy only Record No: H052366)
https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wat2.1698
https://vlibrary.iwmi.org/pdf/H052366.pdf
https://vlibrary.iwmi.org/pdf/H052366.pdf
(3.71 MB) (3.71 MB)
Advances in impact modeling and numerical weather forecasting have allowed accurate drought monitoring and skilful forecasts that can drive decisions at the regional scale. State-of-the-art drought early-warning systems are currently based on statistical drought indicators, which do not account for dynamic regional vulnerabilities, and hence neglect the socio-economic impact for initiating actions. The transition from conventional physical forecasts of droughts toward impact-based forecasting (IbF) is a recent paradigm shift in early warning services, to ultimately bridge the gap between science and action. The demand to generate predictions of “what the weather will do” underpins the rising interest in drought IbF across all weather-sensitive sectors. Despite the large expected socio-economic benefits, migrating to this new paradigm presents myriad challenges. In this article, we provide a comprehensive overview of drought IbF, outlining the progress made in the field. Additionally, we present a road map highlighting current challenges and limitations in the science and practice of drought IbF and possible ways forward. We identify seven scientific and practical challenges/limitations: the contextual challenge (inadequate accounting for the spatio-sectoral dynamics of vulnerability and exposure), the human-water feedbacks challenge (neglecting how human activities influence the propagation of drought), the typology challenge (oversimplifying drought typology to meteorological), the model challenge (reliance on mainstream machine learning models), and the data challenge (mainly textual) with the linked sectoral and geographical limitations. Our vision is to facilitate the progress of drought IbF and its use in making informed and timely decisions on mitigation measures, thus minimizing the drought impacts globally.
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