Risk-Averse Economic Optimization in the Adaptation of River Dikes to Climate Change
- 319 Downloads
To guarantee a safe flood defence in a changing environment, the adaptation to climate change needs to be considered in the design of river dikes. However, the large uncertainty in the projections of future climate leads to varied estimations of future flood probability. How to cope with the uncertainties in future flood probability under climate change is an inevitable question in the adaptation. In this paper, the uncertainty introduced by climate projections was integrated into the ‘expected predictive flood probability’, and the risk-aversion attitude was introduced in the adaptation of river dikes. The uncertainty of climate change impact on flood probability was represented by the uncertainty in the parameters of the probabilistic model. This parameter uncertainty was estimated based on the outputs from the GCMs participated in IPCC AR4. The parameter uncertainty estimated from different GCMs under selected scenarios was integrated into the expected predictive probability of flooding, which was used in the risk-averse economic optimization. Different optimal results were obtained based on varied values of the risk-aversion index. A case of dike ring area in China was studied as an example using the proposed approach. The results show that the uncertainty of climate change increases the optimal dike height and decreases the optimal safety level. The proposed approach enables decision makers to cope with the climate change and the associated uncertainty by adjusting the risk-aversion level.
KeywordsClimate change Uncertainty Flood probability Risk-aversion Economic optimization
The work of the first author was supported by a fellowship program of the China Scholarship Council (CSC), China. The contribution of the fifth author to this work was partly supported by the AXA Research fund and the Deltares Harbour, Coastal and Offshore Engineering Research Programme 'Bouwen aan de Kust'. The authors are grateful to three anonymous reviewers for their valuable comments which greatly improved the manuscript.
- Golian S, Yazdi J, Martina MLV, Sheshangosht S (2014) “A deterministic framework for selecting a flood forecasting and warning system at watershed scale.” Journal of Flood Risk Management: n/a-n/aGoogle Scholar
- Kuijper B, Kallen M (2010) “The impact of risk aversion on optimal economic decisions”Google Scholar
- Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JF, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463(7282):747–756CrossRefGoogle Scholar
- Nakicenovic N, Alcamo J, Davis G, de Vries B, Fenhann J, Gaffin S, Gregory K, Grubler A, Jung TY, Kram T (2000) Special report on emissions scenarios: a special report of working group III of the intergovernmental panel on climate change. Pacific Northwest National Laboratory, Environmental Molecular Sciences Laboratory, Richland, WA (US)Google Scholar
- Slijkhuis KAH, Van Gelder PHAJM, Vrijling J (1997) Optimal dike height under statistical-construction-and damage uncertainty. Struct Saf and Reliab 7:1137–1140Google Scholar
- Van Dantzig D (1956) “Economic decision problems for flood prevention.” Econometrica: Journal of the Econometric Society: 276–287Google Scholar
- Van Gelder PHAJM (1996) How to deal with wave statistical and model uncertainties in the design of vertical breakwaters. Probabilistic Design Tools for Vertical Breakwaters; Proceedings Task 4 Meeting, Hannover, GermanyGoogle Scholar
- Van Gelder, PHAJM, Vrijling JK (1998) Sensitivity analysis of reliability-based optimization in sea dike designs. Sensitivity Analysis of Model Output: 313–315Google Scholar
- Xu Y, Chen P (1999) Economic assessment of flood defense system Bengbu City (In Chinese). J of Econ of Water Resour 5:30–33Google Scholar
- Zhao R-J (1992) The Xinanjiang model applied in China. J Hydrol 135(1–4):371–381Google Scholar