Dynamic Models for Floodplain Management
Publication: Journal of Water Resources Planning and Management
Volume 126, Issue 3
Abstract
A dynamic model of floodplain management is shown to address nonstationary conditions, including land-use changes, channel modifications, economic development, and climate change and variability. The dynamic approach permits zoning, levee construction, and other decisions to be made sequentially, rather than only at the present. The dynamic model is formulated as a Markov decision process. A single-floodplain, single-objective, stationary model is extended to include multiple floodplains, nonstationarity, and multiple objectives. Linear programming is used for solution, though the problem may be large. The model is applied to a problem at Chester Creek, Pa. The optimal policy for levee building or replacement is found to depend on if a flood has just occurred and on the costs of buying out property owners and rebuilding homes and levees. Two cases of nonstationarity are examined, future bridge construction and future hydrologic changes. With nonstationarity, buyout of property owners following levee overtopping is an optimal policy since increased future flooding reduces the expected benefits of structural flood measures. When economic development is included with management costs and flood damages in a multiobjective formulation, the optimal policies include building larger levees and increasing floodplain development.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Arumkumar, S., and Chon, K. (1978). “On optimal regulation policies for certain multi-reservoir systems.” Operations Res., 26, 551–562.
2.
Committee on American River Flood Frequencies, National Research Council. (1999). Improving American River flood frequency analyses. National Academy Press, Washington, D.C.
3.
Committee on Flood Control Alternatives in the American River Basin, National Research Council. (1995). Flood risk management and the American River basin an evaluation. National Academy Press, Washington, D.C.
4.
Economic and Environmental Principles and Guidelines for Water and Related Land Resources Implementation Studies. (1983).
5.
Haimes, Y. Y., Tarvainen, K., Shima, T., and Thadathil, J. (1990). Hierarchical multiobjective analysis of large-scale systems. Hemisphere Publishing Corp., New York.
6.
Haimes, Y. Y., Li, D., Tulsiani, V., Lambert, J. H., and Krzysztofowicz, R. (1996). “Risk-based evaluation of flood warning and preparedness systems.” Inst. for Water Resour. Rep. 96-R-25, U.S. Army Corps of Engineers, Alexandria, Va.
7.
Howard, R. A. (1960). Dynamic programming and Markov processes. Wiley, London.
8.
Intergovernmental Panel on Climate Change. (1996). Climate change 1995 economic and social dimensions of climate change. Cambridge University Press, Cambridge, U.K.
9.
Kamien, M. I., and Schwartz, N. L. (1991). Dynamic optimization. North-Holland, New York.
10.
Krzysztofowicz, R. (1986). “Optimal water supply planning based on seasonal runoff forecasts.” Water Resour. Res., 22, 313–321.
11.
Olsen, J. R., Beling, P. A., Lambert, J. H., and Haimes, Y. Y. (1998). “Input-output economic evaluation of system of levees.”J. Water Resour. Plng. and Mgmt., ASCE, 124(5), 237–245.
12.
Olsen, J. R., Lambert, J. H., and Haimes, Y. Y. (1999). “Risk of extreme events under non-stationary conditions.” Risk Anal., 18(4), 497–510.
13.
Philippi, N. (1994). “Plugging the gaps in flood-control policy.” Issues in Sci. and Technol., Winter, 1994–1995, 71–78.
14.
Russel, C. B. (1972). “An optimal policy for operating a multi-purpose reservoir.” Operations Res., 20, 1181–1189.
15.
Stedinger, J. R., Sule, B. F., and Loucks, D. P. (1984). “Stochastic dynamic programming models for reservoir operation optimisation.” Water Resour. Res., 20, 1499–1505.
16.
Tauxe, B. W., Inman, R. R., and Mades, D. M. (1979a). “Multi-objective dynamic programming: A classic problem redressed.” Water Resour. Res., 15, 1398–1402.
17.
Tauxe, G. W., Inman, R. R., and Mades, D. M. (1979b). “Multi-objective dynamic programming with application to a reservoir.” Water Resour. Res., 15, 1403–1408.
18.
Turgeon, A. (1980). “Optimal operation of multireservoir power systems with stochastic inflows.” Water Resour. Res., 16, 275–283.
19.
U.S. Army Corps of Engineers (USACE). (1990). “Guidance for conducting civil works planning studies.” ER 1105-2-100.
20.
U.S. Army Corps of Engineers (USACE). (1991). National economic development procedures manual—Overview manual for conducting national economic development analysis. Water Resour. Support Ctr., Institute for Water Resources, Fort Belvoir, Va.
21.
U.S. Army Corps of Engineers (USACE). (1996). “Risk-based analysis for flood damage reduction studies.” Engineering and design manual EM 1110-2-1619, Washington, D.C.
22.
U.S. Army Corps of Engineers (USACE). ( 1998). HEC-FDA flood damage reduction analysis, user's manual v. 1.0, Hydrologic Engineering Center, Davis, Calif., CPD-72.
23.
White, D. J. (1985). “Real applications of Markov decision processes.” Interfaces, 15(6), 73–83.
24.
White, D. J. (1993). Markov decision processes. Wiley, Chichester, U.K.
25.
Yakowitz, S. (1982). “Dynamic programming applications in water resources.” Water Resour. Res., 18(4), 673–696.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 126 • Issue 3 • May 2000
Pages: 167 - 175
History
Received: Apr 1, 1999
Published online: May 1, 2000
Published in print: May 2000
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.