An approach for the determination of precipitation input for worst-case flood modelling

References

• Beauchamp, J.R., et al., 2013. Estimation of the summer-fall PMP and PMF of a northern watershed under a changed climate. Water Resources Research, 49 (6), 3852–3862. doi:10.1002/wrcr.v49.6
   Web of Science ®Google Scholar
• Boughton, W.C., 1986. Linear and curvilinear baseflow recessions. Journal of Hydrology (N.Z.), 25 (1), 41–48.
   Google Scholar
• Brandsma, T. and Buishand, A., 1998. Simulation of extreme precipitation in the Rhine basin by nearest-neighbour resampling. Hydrology and Earth System Sciences, 2 (2/3), 195–209. doi:10.5194/hess-2-195-1998
   Web of Science ®Google Scholar
• Cerdan, O., et al., 2004. Scale effect on runoff from experimental plots to catchments in agricultural areas in Normandy. Journal of Hydrology, 299, 4–14. doi:10.1016/j.jhydrol.2004.02.017
   Web of Science ®Google Scholar
• Chapman, T., 1999. A comparison of algorithms for stream flow recession and baseflow separation. Hydrological Processes, 13, 701–714. doi:10.1002/(ISSN)1099-1085
   Web of Science ®Google Scholar
• Coles, S., 2004. An introduction to statistical modelling of extreme values. London, UK: Springer Press.
   Google Scholar
• Dodov, B. and Foufoula-Georgiou, E., 2005. Incorporating the spatio-temporal distribution of rainfall and basin geomorphology into nonlinear analyses of streamflow dynamics. Advances in Water Resources, 28, 711–728. doi:10.1016/j.advwatres.2004.12.013
   Web of Science ®Google Scholar
• Dooge, J.C.I., 1959. A general theory of the unit hydrograph. Journal of Geophysical Research, 64 (2), 241–256. doi:10.1029/JZ064i002p00241
   Web of Science ®Google Scholar
• FOEN (Federal Office for the Environment), 1991. Event-analysis of the 1987 flood. Report Nr. LHG-14-D. Bern, Switzerland.
   Google Scholar
• FOEN (Federal Office for the Environment), 2000. Flood 1999. Data Analysis and statistical classification. Report Nr. LHG-28-D. Bern, Switzerland.
   Google Scholar
• FOEN (Federal Office for the Environment), 2008. Event-analysis of the 2005 Flood. Part 2: Analysis of processes, measures and hazards. Report Nr. UW-0825-D. Bern, Switzerland.
   Google Scholar
• FOEN (Federal Office for the Environment), 2009. Event-analysis of the August 2007 Flood. Report Nr. UW-0927-D. Bern, Switzerland.
   Google Scholar
• Foufoula-Georgiou, E., 1989. A probabilistic storm transposition approach for estimating exceedance probabilities of extreme precipitation depths. Water Resources Research, 25 (5), 799–815. doi:10.1029/WR025i005p00799
   Web of Science ®Google Scholar
• Franchini, M., et al., 1996. Stochastic storm transposition coupled with rainfall-runoff modeling for estimation of exceedance probabilities of design floods. Journal of Hydrology, 175, 511–532. doi:10.1016/S0022-1694(96)80022-9
   Web of Science ®Google Scholar
• Furrer, E.M. and Katz, R.W., 2008. Improving the simulation of extreme precipitation events by stochastic weather generators. Water Resources Research, 44 (12). doi:10.1029/2008WR007316
   Web of Science ®Google Scholar
• Grebner, D. and Roesch, T., 1998. Flächen-Mengen-Dauer-Beziehungen von Spitzenniederschlägen und mögliche Niederschlagsgrenzwerte in der Schweiz. Projektschlussbericht im Rahmen des Nationalen Forschungsprogrammes „Klimaänderungen und Naturkatastrophen“, NFP 31. Zurich: vdf Hochschulverlag.
   Google Scholar
• Gutknecht, D., et al., 2006. A „Multi-Pillar“-approach to the estimation of low probability design floods. Österreichische Wasser- und Abfallwirtschaft, 58 (3–4), 44–50. doi:10.1007/BF03165683
   Google Scholar
• Institute of Hydrology, 1999. Flood estimation handbook. Wallingford, UK: Institute of Hydrology.
   Google Scholar
• Koutsoyiannis, D., 1999. A probabilistic view of Hershfield’s method for estimating probable maximum precipitation. Water Resources Research, 35 (4), 1313–1322. doi:10.1029/1999WR900002
   Web of Science ®Google Scholar
• Langousis, A., Carsteanu, A.A., and Deidda, R., 2013. A simple approximation to multifractal rainfall maxima using a generalized extreme value distribution model. Stochastic Environmental Research and Risk Assessment, 27, 1525–1531. doi:10.1007/s00477-013-0687-0
   Web of Science ®Google Scholar
• Leander, R., et al., 2005. Estimation of extreme floods of the River Meuse using a stochastic weather generator and a rainfall-runoff model. Hydrological Sciences Journal, 50 (6), 1089–1103. doi:10.1623/hysj.2005.50.6.1089
   Web of Science ®Google Scholar
• Matsumoto, M. and Nishimura, T., 1998. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. Transactions on Modeling and Computer Simulation, 8 (1), 3–30. doi:10.1145/272991.272995
   Google Scholar
• McCuen, R.H., 2005. Hydrologic analysis and design. 3rd ed. Upper Saddle River, NJ: Pearson.
   Google Scholar
• Merz, R. and Blöschl, G., 2008. Flood frequency hydrology: 1. Temporal, spatial and causal expansion of information. Water Resources Research, 44 (8). doi:10.1029/2007WR006744
   Web of Science ®Google Scholar
• Merz, R., Blöschl, G., and Parajka, J., 2006. Spatio-temporal variability of event runoff coefficients. Journal of Hydrology, 331, 591–604. doi:10.1016/j.jhydrol.2006.06.008
   Web of Science ®Google Scholar
• MeteoSwiss, 2011. Documentation of MeteoSwiss Grid-Data Products. Daily Precipitation (final analysis): RhiresD. Available from: http://www.ifu.ethz.ch/hydrologie/research/research_data/proddocrhiresd.pdf [ Accessed 21 Apr 2015].
   Google Scholar
• Micovic, Z., Schaefer, M., and Taylor, G., 2015. Uncertainty analysis for Probable Maximum Precipitation estimates. Journal of Hydrology, 521, 360–373. doi:10.1016/j.jhydrol.2014.12.033
   Web of Science ®Google Scholar
• Naef, F., Zuidema, P., and Kölla, E., 1986. Abschätzung von Hochwassern in kleinen Einzugsgebieten. In: M. Spreafico, ed.Beiträge zur Geologie der Schweiz – Hydrologie, Vol. 31. Bern, Switzerland: Swiss Geotechnical Commission, 195–233.
   Google Scholar
• Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I — a discussion of principles. Journal of Hydrology, 10, 282–290. doi:10.1016/0022-1694(70)90255-6
   Google Scholar
• Nikolopoulos, E.I., et al., 2014. Catchment-scale storm velocity: quantification, scale dependence and effect on flood response. Hydrological Sciences Journal, 59 (7), 1363–1376. doi:10.1080/02626667.2014.923889
   Web of Science ®Google Scholar
• Norbiato, D., et al., 2009. Controls on event runoff coefficients in the eastern Italian Alps. Journal of Hydrology, 375, 312–325. doi:10.1016/j.jhydrol.2009.06.044
   Web of Science ®Google Scholar
• Papalexiou, S.M. and Koutsoyiannis, D., 2006. A probabilistic approach to the concept of Probable Maximum Precipitation. Advances in Geosciences, 7, 51–54. doi:10.5194/adgeo-7-51-2006
   Google Scholar
• Papalexiou, S.M., Koutsoyiannis, D., and Makropoulos, C., 2013. How extreme is extreme? An assessment of daily rainfall distribution tails. Hydrology and Earth System Sciences, 17, 851–862. doi:10.5194/hess-17-851-2013
   Web of Science ®Google Scholar
• Paquet, E., et al., 2013. The SCHADEX method: A semi-continuous rainfall-runoff simulation for extreme flood estimation. Journal of Hydrology, 495, 23–37. doi:10.1016/j.jhydrol.2013.04.045
   Web of Science ®Google Scholar
• Rogger, M., et al., 2012a. Runoff models and flood frequency statistics for design flood estimation in Austria – do they tell a consistent story? Journal of Hydrology, 456–457, 30–43. doi:10.1016/j.jhydrol.2012.05.068
   Web of Science ®Google Scholar
• Rogger, M., et al., 2012b. Step changes in the flood frequency curve: process controls. Water Resources Research, 48 (5). doi:10.1029/2011WR011187
   Web of Science ®Google Scholar
• Rössler, O., et al., 2014. Retrospective analysis of a nonforecasted rain-on-snow flood in the Alps – a matter of model limitations or unpredictable nature? Hydrology and Earth System Sciences, 18, 2265–2285. doi:10.5194/hess-18-2265-2014
   Web of Science ®Google Scholar
• Salvadori, G. and De Michele, C., 2006. Statistical characterization of temporal structure of storms. Advances in Water Resources, 29, 827–842. doi:10.1016/j.advwatres.2005.07.013
   Web of Science ®Google Scholar
• Schumann, A., 2007. Application of partial probability weighted moments to consider historical events in statistics of extreme. Hydrology and Water Resources Management, 51 (2), 73–81.
   Google Scholar
• Schumann, A., 2012. What is the return period of an extreme flood event if it is estimated to be many times higher than the HQ100? Hydrology and Water Resources Management, 56 (2), 78–82.
   Google Scholar
• Semenov, M.A., 2008. Simulation of extreme weather events by a stochastic weather generator. Climate Research, 35, 203–212. doi:10.3354/cr00731
   Web of Science ®Google Scholar
• Seo, Y., Schmidt, A., and Sivapalan, M., 2012. Effect of storm movement on flood peaks: Analysis framework based on characteristic timescales. Water Resources Research, 48 (5). doi:10.1029/2011WR011761
   Web of Science ®Google Scholar
• Uboldi, F., et al., 2014. A spatial bootstrap technique for parameter estimation of rainfall annual maxima distribution. Hydrology and Earth System Sciences, 18, 981–995. doi:10.5194/hess-18-981-2014
   Web of Science ®Google Scholar
• Vandenberghe, S., et al., 2010. A stochastic design rainfall generator based on copulas and mass curves. Hydrology and Earth System Sciences, 14, 2429–2442. doi:10.5194/hess-14-2429-2010
   Web of Science ®Google Scholar
• Verhoest, N.E.C., et al., 2010. Are stochastic point rainfall models able to preserve extreme flood statistics? Hydrological Processes, 24, 3439–3445. doi:10.1002/hyp.7867
   Web of Science ®Google Scholar
• Wehren, B., 2010. The Hydrology of the Kander River – yesterday, today, tomorrow. Analysis and Modelling of the floods and their spatio-temporal dynamics. Thesis (PhD). University of Bern, Switzerland.
   Google Scholar
• Weingartner, R., 1999. Regionalhydrologische Analysen. Grundlagen und Anwendungen. Beiträge zur Hydrologie der Schweiz. Report Nr. 37. Bern, Switzerland.
   Google Scholar
• WMO (World Meteorological Organization), 1986. Manual for estimation of probable maximum precipitation. Operational Hydrology Reports 1, no. 332. 2nd ed. Geneva, Switzerland: WMO.
   Google Scholar
• WMO (World Meteorological Organization), 2009. Manual on Estimation of Probable Maximum Precipitation (PMP). Report no. 1045. WMO, Geneva, Switzerland.
   Google Scholar