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Research Article

A new cost-performance grid to compare different flood modelling approaches

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 434-449 | Received 26 Feb 2020, Accepted 19 Oct 2020, Published online: 09 Feb 2021

References

  • Abdallah, C., et al., 2013. Flood hazard mapping assessment for Lebanon- UNDP/CNRS-2382, 110pp. Lebanon.
  • Arkesteijn, L. and Pande, S., 2013. On hydrological model complexity, its geometrical interpretations and prediction uncertainty. Water Resources Research, 49 (10), 7048–7063. doi:10.1002/wrcr.20529.
  • Aronica, G.T., Bates, P.D., and Horritt, M.S., 2002. Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE. Hydrological Processes, 16 (10), 2001–2016. doi:10.1002/hyp.398.
  • Bates, P.D., 2004. Remote sensing and flood inundation modelling. Hydrological Processes, 18 (13), 2593–2597. doi:10.1002/hyp.5649.
  • Bates, P.D. and Roo, A.P.J.D., 2000. A simple raster-based model for flood inundation simulation. Journal of Hydrology, 236 (1–2), 54–77. doi:10.1016/S0022-1694(00)00278-X.
  • Bergström, S., Lindström, G., and Pettersson, A., 2002. Multi-variable parameter estimation to increase confidence in hydrological modelling. Hydrological Processes, 16 (2), 413–421. doi:10.1002/hyp.332.
  • Beven, K.J. and Kirkby, M.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin, 24 (1), 43–69. doi:10.1080/02626667909491834.
  • Bleichrodt, H. and Quiggin, J. 1999. Life-cycle preferences over consumption and health: When is cost-effectiveness analysis equivalent to cost-benefit analysis? Journal of Health Economics, 18 (6), 681–708. doi:10.1016/S0167-6296(99)00014-4
  • Chatterjee, C., F‎orster, S., and Bronstert, A., 2008. Comparison of hydrodynamic models of different complexities to model floods with emergency storage areas. Hydrological Processes, 22 (24), 4695–4709. doi:10.1002/hyp.7079.
  • Chien, H. and Mackay, D.S., 2014. How much complexity is needed to simulate watershed streamflow and water quality? A test combining time series and hydrological models. Hydrological Processes, 28 (22), 5624–5636. doi:10.1002/hyp.10066.
  • Ciarapica, L. and Todini, E., 2002. TOPKAPI: A model for the representation of the rainfall-runoff process at different scales. Hydrological Processes, 16 (2), 207–229. doi:10.1002/hyp.342.
  • Coustau, M., et al., 2012. Flood modelling with a distributed event-based parsimonious rainfall-runoff model: case of the karstic Lez river catchment. Natural Hazards and Earth System Sciences, 12 (4), 1119–1133. doi:10.5194/nhess-12-1119-2012.
  • Crout, N.M.J., Tarsitano, D., and Wood, A.T., 2009. Is my model too complex? Evaluating model formulation using model reduction. Environmental Modelling & Software, 24 (1), 1–7. doi:10.1016/j.envsoft.2008.06.004.
  • Dhami, B.S. and Pandey, A., 2013. Comparative review of recently developed hydrologic models. Journal of Indian Water Resources Society, 33, 34–42.
  • Dimitriadis, P., et al., 2016. Comparative evaluation of 1D and quasi-2D hydraulic models based on benchmark and real-world applications for uncertainty assessment in flood mapping. Journal of Hydrology, 534, 478–492. doi:10.1016/j.jhydrol.2016.01.020.
  • Dolan, R.J. and Shapiro, B.P., 1989. Performance curves: costs, prices, and value. Harvard Business School Background Note 590-010. Boston, Massachusetts: Harvard Business Publishing.
  • Dooge, J.C.I., 1981. IIASA Proceedings series: logistics and benefits of using methematical models of hydrologic and water resource systems (1978, Pisa) general report on model structure and classification. A.J. Askew, F. Greco, and J. Kindler, eds. International Institute for Applied Systems Analysis, Pergamon Press. A. Wheaton & Co. Ltd., Exeter.
  • Finger, D., et al., 2015. The value of multiple data set calibration vs. model complexity for improving the performance of hydrological models in mountain catchments. Water Resources Research, 51 (4), 1939–1958. doi:10.1002/2014WR015712.
  • Fleischmann, A., Paiva, R., and Collischonn, W., 2019. Can regional to continental river hydrodynamic models be locally relevant? A cross-scale comparison. Journal of Hydrology X, 3, 100027. The Authors. doi:10.1016/j.hydroa.2019.100027.
  • Fuentes-Andino, D., et al., 2017. Reproducing an extreme flood with uncertain post-event information. Hydrology and Earth System Sciences, 21 (7), 3597–3618. doi:10.5194/hess-21-3597-2017.
  • Galland, J.C., Goutal, N., and Hervouet, J.M., 1991. TELEMAC: A new numerical model for solving shallow water equations. Advances in Water Resources, 14 (3), 138–148. doi:10.1016/0309-1708(91)90006-A.
  • Grayson, R. and Blöschl, G., 2001. Spatial patterns in catchment hydrology: observations and modelling. R. Grayson and G. Blöschl, eds. Cambridge: Cambridge University Press. Available from: http://www.cup.cam.ac.uk
  • Hdeib, R., et al., 2018. Constraining coupled hydrological-hydraulic flood model by past storm events and post-event measurements in data-sparse regions. Journal of Hydrology, 565, 160–176. doi:10.1016/j.jhydrol.2018.08.008
  • Hoch, J.M. and Trigg, M.A., 2019. Advancing global flood hazard simulations by improving comparability, benchmarking, and integration of global flood models. Environmental Research Letters, 14 (3), 034001. doi:10.1088/1748-9326/aaf3d3.
  • Horritt, M.S. and Bates, P.D., 2002. Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of Hydrology, 268 (1–4), 87–99. doi:10.1016/S0022-1694(02)00121-X.
  • Hunt, R. and Zheng, C., 1999. Debating complexity in modeling. Eos (Washington. DC), 80 (3), 29. doi:10.1029/99EO00025.
  • Kampf, S.K. and Burges, S.J., 2007. A framework for classifying and comparing distributed hillslope and catchment hydrologic models. Water Resources Research, 43 (5). doi:10.1029/2006WR005370.
  • Knebl, M.R., et al., 2005. Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: A case study for the San Antonio River Basin Summer 2002 storm event. Journal of Environmental Management, 75 (4), 325–336. doi:10.1016/j.jenvman.2004.11.024.
  • Koutroulis, A.G. and Tsanis, I.K., 2010. A method for estimating flash flood peak discharge in a poorly gauged basin: case study for the 13–14 January 1994 flood, Giofiros basin, Crete, Greece. Journal of Hydrology, 385 (1–4), 150–164. doi:10.1016/j.jhydrol.2010.02.012.
  • Lei, X., et al., 2011. Development of efficient and cost-effective distributed hydrological modeling tool MWEasyDHM based on open-source MapWindow GIS. Computers & Geosciences, 37 (9), 1476–1489. doi:10.1016/j.cageo.2011.03.016.
  • Liu, Z., Martina, M.L.V., and Todini, E., 2005. Flood forecasting using a fully distributed model: application of the TOPKAPI model to the Upper Xixian Catchment. Hydrology and Earth System Sciences, 9 (4), 347–364. doi:10.5194/hess-9-347-2005.
  • Liu, Z., Merwade, V., and Jafarzadegan, K., 2019. Investigating the role of model structure and surface roughness in generating flood inundation extents using one- and two-dimensional hydraulic models. Journal of Flood Risk Management, 12 (1), e12347(1),1–19. doi:10.1111/jfr3.12347.
  • Marks, K. and Bates, P., 2000. Integration of high‐resolution topographic data with floodplain flow models. Hydrological Processes, 14 (11–12), 2109–2122. doi:10.1002/1099-1085(20000815/30)14:11/12<2109::aid-hyp58><2109::AID-HYP58>3.0.CO;2-1.
  • Mason, D.C., Schumann, G.J.P., and Bates, P.D., 2010. Data utilization in flood inundation modelling. In: G. Pender and H. Faulkner, eds. Flood risk science and management. Hoboken, New Jersey, United States: Blackwell Publishing Ltd, 209–233. doi:10.1002/9781444324846.ch11.
  • McCabe, M.F., et al., 2017. The future of earth observation in hydrology. Hydrology and Earth System Sciences, 21 (7), 3879–3914. doi:10.5194/hess-21-3879-2017.
  • Melsen, L.A., et al., 2019. Subjective modeling decisions can significantly impact the simulation of flood and drought events. Journal of Hydrology, 568, 1093–1104. doi:10.1016/j.jhydrol.2018.11.046.
  • Montanari, M., et al., 2009. Calibration and sequential updating of a coupled hydrologic-hydraulic model using remote sensing-derived water stages. Hydrology and Earth System Sciences, 13 (3), 367–380. doi:10.5194/hess-13-367-2009.
  • Moussa, R., 1991. Variabilité spatio-temporelle et modélisation hydrologique: application au bassin du Gardon d’Anduze. Thèse de Doctorat. Montpellier 2.
  • Moussa, R. and Chahinian, N., 2009. Comparison of different multi-objective calibration criteria using a conceptual rainfall-runoff model of flood events. Hydrology and Earth System Sciences, 13 (4), 519–535. doi:10.5194/hess-13-519-2009.
  • Moussa, R. and Cheviron, B., 2015. Chapter 7: modeling of floods - state of the art and research challenges. In: P.M. Rowiński and A. Radecki-Pawlik, Springer, eds. Rivers - physical, fluvial and environmental processes. Midtown Manhattan, New York City, United States: Within the series: GeoPlanet: Earth and planetary sciences, 169–193. Available from: http://link.springer.com/10.1007/978-3-319-17719-9
  • Neal, J., Schumann, G., and Bates, P., 2012. A subgrid channel model for simulating river hydraulics and floodplain inundation over large and data sparse areas. Water Resources Research, 48 (11), 1–16. doi:10.1029/2012WR012514.
  • Nguyen, S. and Bouvier, C., 2019. Flood modelling using the distributed event-based SCS-LR model in the Mediterranean Real Collobrier catchment. Hydrological Sciences Journal, 64 (11), 1351–1369. doi:10.1080/02626667.2019.1639715.
  • Papaioannou, G., et al., 2017. Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling. Advances in Geosciences, 44, 23–34. doi:10.5194/adgeo-44-23-2017
  • Rajib, A., et al., 2020. Towards a large-scale locally relevant flood inundation modeling framework using SWAT and LISFLOOD-FP. Journal of Hydrology, 581, 124406. Elsevier B.V. doi:10.1016/j.jhydrol.2019.124406.
  • Refsgaard, J.C., 1996. Terminology, modelling protocol and classification of hydrological model codes. In: M.B. Abbot and J.C. Refsgaard, eds. Distributed hydrological modelling. Midtown Manhattan, New York City, United States: kluwer Academic Publishers, 17–39.
  • Samela, C., et al., 2018. A GIS tool for cost-effective delineation of flood-prone areas. Computers, Environment and Urban Systems, 70, 43–52. doi:10.1016/j.compenvurbsys.2018.01.013
  • Samela, C., Troy, T.J., and Manfreda, S., 2017. Geomorphic classifiers for flood-prone areas delineation for data-scarce environments. Advances in Water Resources, 102, 13–28. doi:10.1016/j.advwatres.2017.01.007
  • Sayama, T., et al., 2012. Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin. Hydrological Sciences Journal, 57 (2), 298–312. doi:10.1080/02626667.2011.644245.
  • Shustikova, I., et al., 2019. Comparing 2D capabilities of HEC-RAS and LISFLOOD-FP on complex topography. Hydrological Sciences Journal, 64 (14), 1769–1782. doi:10.1080/02626667.2019.1671982.
  • Siddiqui, M.J., et al., 2018. Rainfall–runoff, flood inundation and sensitivity analysis of the 2014 Pakistan flood in the Jhelum and Chenab river basin. Hydrological Sciences Journal, 63 (13–14), 1976–1997. doi:10.1080/02626667.2018.1546049.
  • Singh, V.P., 2018. Hydrologic modeling: progress and future directions. Geoscience Letters, 5 (1). Springer International Publishing. doi: 10.1186/s40562-018-0113-z.
  • Singh, V.P. and Woolhiser, D.A., 2002. Mathematical Modeling of Watershed Hydrology. Journal of Hydrologic Engineering, 7 (4), 270–292. doi:10.1061/(ASCE)1084-0699(2002)7:4(270).
  • Smith, M.J., et al., 2006. Exploitation of new data types to create digital surface models for flood inundation modelling. FRMRC Research Rep. UR3. Accessed 20 May 2018. Available from: www.floodrisk.org.uk
  • Spickova, M. and Myskova, R., 2015. Costs efficiency evaluation using life cycle costing as strategic method. Procedia Economics and Finance, 34 (15), 337–343. doi:10.1016/S2212-5671(15)01638-X.
  • Teng, J., et al., 2017. Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environmental Modelling & Software, 90, 201–216. doi:10.1016/j.envsoft.2017.01.006
  • Towner, J., et al., 2019. Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin. Hydrology and Earth System Sciences, 23 (7), 3057–3080. doi:10.5194/hess-23-3057-2019.
  • Tuominen, P., et al., 2015. Economic appraisal of energy efficiency in buildings using cost-effectiveness assessment. Procedia Economics and Finance, 21 (15), 422–430. doi:10.1016/S2212-5671(15)00195-1.
  • USACE, 2000. Hydrologic modeling system HEC-HMS:technical referance manual. Davis, CA: US Army Corps of Engineers Hydrologic Engineering Center.
  • USACE, 2016. HEC-RAS river analysis system - hydraulic reference manual, version 5.0. Davis, CA: US Army Corps of Engineers Hydrologic Engineering Center (HEC). Available from: http://www.hec.usace.army.mil/software/hec-ras/documentation.aspx
  • Ward, P.J., et al., 2015. Usefulness and limitations of global flood risk models. Nature Climate Change, 5 (8), 712–715. doi:10.1038/nclimate2742.
  • Zilberberg, M. D. and Shorr, A. F. 2010. Understanding cost-effectiveness. Clinical Microbiology and Infection, 16 (12), 1707–1712. doi:10.1111/j.1469-0691.2010.03331.x

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