Skill of large-scale seasonal drought impact forecasts
<p>Forecasting of drought impacts is still lacking in drought early-warning systems (DEWSs), which presently do not go beyond hazard forecasting. Therefore, we developed drought impact functions using machine learning approaches (logistic regression and random forest) to predict drought impact...
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2020-06-01
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doaj-579203e9cce440488fea245089450bfd2020-11-25T03:30:30ZengCopernicus PublicationsNatural Hazards and Earth System Sciences1561-86331684-99812020-06-01201595160810.5194/nhess-20-1595-2020Skill of large-scale seasonal drought impact forecastsS. J. Sutanto0M. van der Weert1V. Blauhut2H. A. J. Van Lanen3Hydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a,6708 PB Wageningen, the NetherlandsHydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a,6708 PB Wageningen, the NetherlandsHydrological Environmental Systems, University of Freiburg, Fahnenbergplatz, 79098 Freiburg, GermanyHydrology and Quantitative Water Management Group, Environmental Sciences Department, Wageningen University and Research, Droevendaalsesteeg 3a,6708 PB Wageningen, the Netherlands<p>Forecasting of drought impacts is still lacking in drought early-warning systems (DEWSs), which presently do not go beyond hazard forecasting. Therefore, we developed drought impact functions using machine learning approaches (logistic regression and random forest) to predict drought impacts with lead times up to 7 months ahead. The observed and forecasted hydrometeorological drought hazards – such as the standardized precipitation index (SPI), standardized precipitation evaporation index (SPEI), and standardized runoff index (SRI) – were obtained from the The EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) DEWS. Reported drought impact data, taken from the European Drought Impact Report Inventory (EDII), were used to develop and validate drought impact functions. The skill of the drought impact functions in forecasting drought impacts was evaluated using the Brier skill score and relative operating characteristic metrics for five cases representing different spatial aggregation and lumping of impacted sectors. Results show that hydrological drought hazard represented by SRI has higher skill than meteorological drought represented by SPI and SPEI. For German regions, impact functions developed using random forests indicate a higher discriminative ability to forecast drought impacts than logistic regression. Moreover, skill is higher for cases with higher spatial resolution and less lumped impacted sectors (cases 4 and 5), with considerable skill up to 3–4 months ahead. The forecasting skill of drought impacts using machine learning greatly depends on the availability of impact data. This study demonstrates that the drought impact functions could not be developed for certain regions and impacted sectors, owing to the lack of reported impacts.</p>https://www.nat-hazards-earth-syst-sci.net/20/1595/2020/nhess-20-1595-2020.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
S. J. Sutanto M. van der Weert V. Blauhut H. A. J. Van Lanen |
spellingShingle |
S. J. Sutanto M. van der Weert V. Blauhut H. A. J. Van Lanen Skill of large-scale seasonal drought impact forecasts Natural Hazards and Earth System Sciences |
author_facet |
S. J. Sutanto M. van der Weert V. Blauhut H. A. J. Van Lanen |
author_sort |
S. J. Sutanto |
title |
Skill of large-scale seasonal drought impact forecasts |
title_short |
Skill of large-scale seasonal drought impact forecasts |
title_full |
Skill of large-scale seasonal drought impact forecasts |
title_fullStr |
Skill of large-scale seasonal drought impact forecasts |
title_full_unstemmed |
Skill of large-scale seasonal drought impact forecasts |
title_sort |
skill of large-scale seasonal drought impact forecasts |
publisher |
Copernicus Publications |
series |
Natural Hazards and Earth System Sciences |
issn |
1561-8633 1684-9981 |
publishDate |
2020-06-01 |
description |
<p>Forecasting of drought impacts is still lacking in drought early-warning systems (DEWSs), which presently do not go beyond hazard forecasting. Therefore, we developed drought impact functions using machine learning approaches (logistic regression and random forest) to predict drought impacts with lead times up to 7 months ahead. The observed and forecasted hydrometeorological drought hazards – such as the standardized precipitation index (SPI), standardized precipitation evaporation index (SPEI), and standardized runoff index (SRI) – were obtained from the The EU-funded Enhancing Emergency Management and Response to Extreme Weather and Climate Events (ANYWHERE) DEWS. Reported drought impact data, taken from the European Drought Impact Report Inventory (EDII), were used to develop and validate drought impact functions. The skill of the drought impact functions in forecasting drought impacts was evaluated using the Brier skill score and relative operating characteristic metrics for five cases representing different spatial aggregation and lumping of impacted sectors. Results show that hydrological drought hazard represented by SRI has higher skill than meteorological drought represented by SPI and SPEI. For German regions, impact functions developed using random forests indicate a higher discriminative ability to forecast drought impacts than logistic regression. Moreover, skill is higher for cases with higher spatial resolution and less lumped impacted sectors (cases 4 and 5), with considerable skill up to 3–4 months ahead. The forecasting skill of drought impacts using machine learning greatly depends on the availability of impact data. This study demonstrates that the drought impact functions could not be developed for certain regions and impacted sectors, owing to the lack of reported impacts.</p> |
url |
https://www.nat-hazards-earth-syst-sci.net/20/1595/2020/nhess-20-1595-2020.pdf |
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