Articles | Volume 20, issue 6
https://doi.org/10.5194/nhess-20-1595-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-20-1595-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jun 2020
Research article |
| 04 Jun 2020
| 04 Jun 2020
Skill of large-scale seasonal drought impact forecasts
Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a,6708 PB Wageningen, the Netherlands
Melati van der Weert
Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a,6708 PB Wageningen, the Netherlands
Veit Blauhut
Hydrological Environmental Systems, University of Freiburg, Fahnenbergplatz, 79098 Freiburg, Germany
Henny A. J. Van Lanen
Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a,6708 PB Wageningen, the Netherlands
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Cited
16 citations as recorded by crossref.
- Sustainability nexus analytics, informatics, and data (AID): Drought L. Huning et al. 10.1007/s00550-024-00546-w
- Farmers’ knowledge improves identification of drought impacts: A nationwide statistical analysis in Zambia M. Mauerman et al. 10.1016/j.cliser.2025.100543
- Drought impact prediction across time and space: limits and potentials of text reports R. Stephan et al. 10.1088/1748-9326/acd8da
- A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems I. Hanadé Houmma et al. 10.1080/19475705.2023.2223384
- Skill and lead time of vegetation drought impact forecasts based on soil moisture observations Y. Li et al. 10.1016/j.jhydrol.2023.129420
- Advances and gaps in the science and practice of impact‐based forecasting of droughts A. Shyrokaya et al. 10.1002/wat2.1698
- Preface: Recent advances in drought and water scarcity monitoring, modelling, and forecasting B. Bonaccorso et al. 10.5194/nhess-22-1857-2022
- Significant relationships between drought indicators and impacts for the 2018–2019 drought in Germany A. Shyrokaya et al. 10.1088/1748-9326/ad10d9
- Assessing the likelihood of drought impact occurrence with extreme gradient boosting: a case study on the public water supply in South Korea J. Seo & Y. Kim 10.2166/hydro.2023.064
- Seasonal drought predictions in the Mediterranean using the SPEI index: Paving the way for their operational applicability in climate services S. Brands et al. 10.1016/j.cliser.2025.100555
- Catchment memory explains hydrological drought forecast performance S. Sutanto & H. Van Lanen 10.1038/s41598-022-06553-5
- Modelling agricultural drought: a review of latest advances in big data technologies I. Hanadé Houmma et al. 10.1080/19475705.2022.2131471
- Seasonal climate predictions for marine risk assessment in the Barents Sea I. Polkova et al. 10.1016/j.cliser.2022.100291
- Long-range hydrological drought forecasting using multi-year cycles in the North Atlantic Oscillation W. Rust et al. 10.1016/j.jhydrol.2024.131831
- The role of spatial scale in drought monitoring and early warning systems: a review J. Mardian 10.1139/er-2021-0102
- Skill of large-scale seasonal drought impact forecasts S. Sutanto et al. 10.5194/nhess-20-1595-2020
15 citations as recorded by crossref.
- Sustainability nexus analytics, informatics, and data (AID): Drought L. Huning et al. 10.1007/s00550-024-00546-w
- Farmers’ knowledge improves identification of drought impacts: A nationwide statistical analysis in Zambia M. Mauerman et al. 10.1016/j.cliser.2025.100543
- Drought impact prediction across time and space: limits and potentials of text reports R. Stephan et al. 10.1088/1748-9326/acd8da
- A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems I. Hanadé Houmma et al. 10.1080/19475705.2023.2223384
- Skill and lead time of vegetation drought impact forecasts based on soil moisture observations Y. Li et al. 10.1016/j.jhydrol.2023.129420
- Advances and gaps in the science and practice of impact‐based forecasting of droughts A. Shyrokaya et al. 10.1002/wat2.1698
- Preface: Recent advances in drought and water scarcity monitoring, modelling, and forecasting B. Bonaccorso et al. 10.5194/nhess-22-1857-2022
- Significant relationships between drought indicators and impacts for the 2018–2019 drought in Germany A. Shyrokaya et al. 10.1088/1748-9326/ad10d9
- Assessing the likelihood of drought impact occurrence with extreme gradient boosting: a case study on the public water supply in South Korea J. Seo & Y. Kim 10.2166/hydro.2023.064
- Seasonal drought predictions in the Mediterranean using the SPEI index: Paving the way for their operational applicability in climate services S. Brands et al. 10.1016/j.cliser.2025.100555
- Catchment memory explains hydrological drought forecast performance S. Sutanto & H. Van Lanen 10.1038/s41598-022-06553-5
- Modelling agricultural drought: a review of latest advances in big data technologies I. Hanadé Houmma et al. 10.1080/19475705.2022.2131471
- Seasonal climate predictions for marine risk assessment in the Barents Sea I. Polkova et al. 10.1016/j.cliser.2022.100291
- Long-range hydrological drought forecasting using multi-year cycles in the North Atlantic Oscillation W. Rust et al. 10.1016/j.jhydrol.2024.131831
- The role of spatial scale in drought monitoring and early warning systems: a review J. Mardian 10.1139/er-2021-0102
1 citations as recorded by crossref.
Latest update: 12 Mar 2025
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.
Present-day drought early warning systems only provide information on drought hazard forecasts....
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