TY - JOUR
T1 - A framework for comparing permanent and forecast-based flood risk-reduction strategies
AU - Bischiniotis, Konstantinos
AU - de Moel, Hans
AU - van den Homberg, Marc
AU - Couasnon, Anaïs
AU - Aerts, Jeroen
AU - Guimarães Nobre, Gabriela
AU - Zsoter, Ervin
AU - van den Hurk, Bart
PY - 2020/6/10
Y1 - 2020/6/10
N2 - Flood risk can be reduced at various stages of the disaster management cycle. Traditionally, permanent infrastructure is used for flood prevention, while residual risk is managed with emergency measures that are triggered by forecasts. Advances in flood forecasting hold promise for a more prominent role to forecast-based measures. In this study, we present a methodology that compares permanent with forecast-based flood-prevention measures. On the basis of this methodology, we demonstrate how operational decision-makers can select between acting against frequent low-impact, and rare high-impact events. Through a hypothetical example, we describe a number of decision scenarios using flood risk indicators for Chikwawa, Malawi, and modelled and forecasted discharge data from 1997 to 2018. The results indicate that the choice between permanent and temporary measures is affected by the cost of measures, climatological flood risk, and forecast ability to produce accurate flood warnings. Temporary measures are likely to be more cost-effective than permanent measures when the probability of flooding is low. Furthermore, a combination of the two types of measures can be the most cost-effective solution, particularly when the forecast is more skillful in capturing low-frequency events. Finally, we show that action against frequent low-impact events could more cost-effective than action against rare high-impact ones. We conclude that forecast-based measures could be used as an alternative to some of the permanent measures rather than being used only to cover the residual risk, and thus, should be taken into consideration when identifying the optimal flood risk strategy.
AB - Flood risk can be reduced at various stages of the disaster management cycle. Traditionally, permanent infrastructure is used for flood prevention, while residual risk is managed with emergency measures that are triggered by forecasts. Advances in flood forecasting hold promise for a more prominent role to forecast-based measures. In this study, we present a methodology that compares permanent with forecast-based flood-prevention measures. On the basis of this methodology, we demonstrate how operational decision-makers can select between acting against frequent low-impact, and rare high-impact events. Through a hypothetical example, we describe a number of decision scenarios using flood risk indicators for Chikwawa, Malawi, and modelled and forecasted discharge data from 1997 to 2018. The results indicate that the choice between permanent and temporary measures is affected by the cost of measures, climatological flood risk, and forecast ability to produce accurate flood warnings. Temporary measures are likely to be more cost-effective than permanent measures when the probability of flooding is low. Furthermore, a combination of the two types of measures can be the most cost-effective solution, particularly when the forecast is more skillful in capturing low-frequency events. Finally, we show that action against frequent low-impact events could more cost-effective than action against rare high-impact ones. We conclude that forecast-based measures could be used as an alternative to some of the permanent measures rather than being used only to cover the residual risk, and thus, should be taken into consideration when identifying the optimal flood risk strategy.
KW - And early action
KW - Early warning
KW - Flood prevention
KW - Flood risk management
KW - Forecast quality
KW - Forecast-based financing
UR - http://www.scopus.com/inward/record.url?scp=85080945760&partnerID=8YFLogxK
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U2 - 10.1016/j.scitotenv.2020.137572
DO - 10.1016/j.scitotenv.2020.137572
M3 - Article
AN - SCOPUS:85080945760
SN - 0048-9697
VL - 720
SP - 1
EP - 16
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 137572
ER -