Advertisement

Synthetic design hydrographs for ungauged catchments: a comparison of regionalization methods

  • Manuela I. BrunnerEmail author
  • Reinhard Furrer
  • Anna E. Sikorska
  • Daniel Viviroli
  • Jan Seibert
  • Anne-Catherine Favre
Original Paper

Abstract

Design flood estimates for a given return period are required in both gauged and ungauged catchments for hydraulic design and risk assessments. Contrary to classical design estimates, synthetic design hydrographs provide not only information on the peak magnitude of events but also on the corresponding hydrograph volumes together with the hydrograph shapes. In this study, we tested different regionalization approaches to transfer parameters of synthetic design hydrographs from gauged to ungauged catchments. These approaches include classical regionalization methods such as linear regression techniques, spatial methods, and methods based on the formation of homogeneous regions. In addition to these classical approaches, we tested nonlinear regression models not commonly used in hydrological regionalization studies, such as random forest, bagging, and boosting. We found that parameters related to the magnitude of the design event can be regionalized well using both linear and nonlinear regression techniques using catchment area, length of the main channel, maximum precipitation intensity, and relief energy as explanatory variables. The hydrograph shape, however, was found to be more difficult to regionalize due to its high variability within a catchment. Such variability might be better represented by looking at flood-type specific synthetic design hydrographs.

Keywords

Regionalization Ungauged catchments Design hydrographs Flood estimation Regression trees 

Notes

Acknowledgements

We thank the Federal Office for the Environment (FOEN) for funding the project (contract 13.0028.KP/M285-0623) and for providing runoff measurement data. We also thank MeteoSwiss for providing precipitation data. The data used in this study is available upon order from the FOEN and MeteoSwiss. For the hydrological data of the federal stations, the order form under http://www.bafu.admin.ch/wasser/13462/13494/15076/index.html?lang=de can be used. The hydrological data of the cantonal stations can be ordered from the respective cantons. The meteorological data can be ordered via https://shop.meteoswiss.ch/index.html. We thank the associate editor and the four reviewers for their constructive and detailed comments.

References

  1. Abrahart RJ, See LM (2007) Neural network modelling of non-linear hydrological relationships. Hydrol Earth Syst Sci 11:1563–1579.  https://doi.org/10.5194/hess-11-1563-2007 CrossRefGoogle Scholar
  2. Acreman MC, Sinclair CD (1986) Classification of drainage basins acording to their physical characteristics; an application for flood frequency analysis in Scotland. J Hydrol 84:365–380.  https://doi.org/10.1016/0022-1694(86)90134-4 CrossRefGoogle Scholar
  3. Ahn KH, Palmer R (2016) Regional flood frequency analysis using spatial proximity and basin characteristics: quantile regression versus parameter regression technique. J Hydrol 540:515–526.  https://doi.org/10.1016/j.jhydrol.2016.06.047 CrossRefGoogle Scholar
  4. Ali G, Tetzlaff D, Soulsby C, McDonnell JJ, Capell R (2012) A comparison of similarity indices for catchment classification using a cross-regional dataset. Adv Water Resour 40:11–22.  https://doi.org/10.1016/j.advwatres.2012.01.008 CrossRefGoogle Scholar
  5. Archfield SA, Pugliese A, Castellarin A, Skøien JO, Kiang JE (2013) Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach? Hydrol Earth Syst Sci 17(4):1575–1588.  https://doi.org/10.5194/hess-17-1575-2013 CrossRefGoogle Scholar
  6. Aziz K, Rai S, Rahmen A (2015) Design flood estimation in ungauged catchments using genetic algorithm-based artificial neural network (GAANN) technique for Australia. Nat Hazards 77:805–821.  https://doi.org/10.1007/s11069-015-1625-x CrossRefGoogle Scholar
  7. Aziz K, Haque MM, Rahman A, Shamseldin AY, Shoaib M (2016) Flood estimation in ungauged catchments: application of artificial intelligence based methods for Eastern Australia. Stoch Env Res Risk Assess.  https://doi.org/10.1007/s00477-016-1272-0 CrossRefGoogle Scholar
  8. Bardossy A (2007) Calibration of hydrological model parameters for ungauged catchments. Hydrol Earth Syst Sci 11:703–710CrossRefGoogle Scholar
  9. Bardossy A, Lehmann W (1997) Spatial distribution of soil moisture in a small catchment. Part 1: geostatistical analysis. J Hydrol 206:1–15.  https://doi.org/10.1016/S0022-1694(97)00152-2 CrossRefGoogle Scholar
  10. Beygelzimer A, Kakadet S, Langford J, Arya S, Mount D, Li S (2013) Package ’FNN’: fast nearest neighbor search algorithms and applications. http://cran.r-project.org/package=FNN
  11. Bhunya PK, Panda SN, Goel MK (2011) Synthetic unit hydrograph methods: a critical review. Open Hydrol J 5:1–8.  https://doi.org/10.2174/1874378101105010001 CrossRefGoogle Scholar
  12. Bitterli T, Aviolat P, Brändli R, Christe R, Fracheboud S, Frey D, George M, Matousek F, Tripet JP (2007) Groundwater resources. In: Hydrological Atlas of Switzerland, Bern, p 8.6Google Scholar
  13. Blöschl G (2006) Geostatistische Methoden bei der hydrologischen Regionalisierung. In: Godina R, Blöschl G (eds) Methoden der hydrologischen Regionalisierung, vol 197. Wiener Mitteilungen, Wien, pp 21–40Google Scholar
  14. Blöschl G, Sivapalan M, Wagener T, Viglione A, Savenije H (2013) Runoff prediction in ungauged basins. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  15. Boscarello L, Ravazzani G, Cislaghi A, Mancini M (2016) Regionalization of flow-duration curves through catchment classification with streamflow signatures and physiographic-climate indices. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001307
  16. Breiman L (1996) Bagging predictors. Mach Learn 24(421):123–140.  https://doi.org/10.1007/BF00058655 CrossRefGoogle Scholar
  17. Breiman L (2001) Random Forests. Mach Learn 45(1):5–32.  https://doi.org/10.1023/A:1010933404324 CrossRefGoogle Scholar
  18. Brunner MI, Seibert J, Favre AC (2016) Bivariate return periods and their importance for flood peak and volume estimation. Wire’s Water 3:819–833.  https://doi.org/10.1002/wat2.1173 CrossRefGoogle Scholar
  19. Brunner MI, Sikorska AE, Furrer R, Favre AC (2017a) Uncertainty assessment of synthetic design hydrographs for gauged and ungauged catchments. Water Resour Res (accepted) Google Scholar
  20. Brunner MI, Viviroli D, Sikorska AE, Vannier O, Favre AC, Seibert J (2017b) Flood type specific construction of synthetic design hydrographs. Water Resour Res. https://doi.org/10.1002/2016WR019535
  21. Bundesamt für Statistik (2003) Geodaten der Bundesstatistik. https://www.bfs.admin.ch/bfs/de/home/dienstleistungen/geostat/geodaten-bundesstatistik.html
  22. Burn DH (1989) Cluster analysis as applied to regional flood frequency. J Water Resour Plan Manag 115:567–582CrossRefGoogle Scholar
  23. Burn DH (1990) Evaluation of regional flood frequency analysis with a region of influence approach. Water Resour Res 26(10):2257–2265.  https://doi.org/10.1029/WR026i010p02257 CrossRefGoogle Scholar
  24. Burn DH, Boorman DB (1992) Catchment classification applied to the estimation of hydrological parameters at ungauged catchments. Tech. rep, Institute of Hydrology, Wallingford, OxfordshireGoogle Scholar
  25. Burn DH, Boorman DB (1993) Estimation of hydrological parameters at ungauged catchments. J Hydrol 143:429–454.  https://doi.org/10.1016/0022-1694(93)90203-L CrossRefGoogle Scholar
  26. Camezind-Wildi R (2005) Empfehlung Raumplanung und Naturgefahren. Tech. rep., Bundesamt für Raumentwicklung, Bundesamt für Wasser und Geologie, Bundesamt für Umwelt, Wald und Landschaft, BernGoogle Scholar
  27. Castellarin A, Burn DH, Brath A (2001) Assessing the effectiveness of hydrological similarity measures for flood frequency analysis. J Hydrol 241(3):270–285.  https://doi.org/10.1016/S0022-1694(00)00383-8 CrossRefGoogle Scholar
  28. Castiglioni S, Castellarin A, Montanari A, Skøien JO, Laaha G, Blöschl G (2011) Smooth regional estimation of low-flow indices: physiographical space based interpolation and top-kriging. Hydrol Earth Syst Sci 15(3):715–727.  https://doi.org/10.5194/hess-15-715-2011 CrossRefGoogle Scholar
  29. Cavadias GS, Ouarda TBMJ, Bobée B, Girard C (2001) A canonical correlation approach to the determination of homogeneous regions for regional flood estimation of ungauged basins. Hydrol Sci J 46(4):499–512.  https://doi.org/10.1080/02626660109492846 CrossRefGoogle Scholar
  30. Centre for Ecology and Hydrology (1999) Flood estimation handbook. Centre for Ecology and Hydrology, Wallingford, OxfordshireGoogle Scholar
  31. Chapman TG, Maxwell AI (1996) Baseflow separation–comparison of numerical methods with tracer experiments. 23rd hydrology and water resources symposium. Hobart, Australia, pp 539–545Google Scholar
  32. Chebana F, Ouarda T (2009) Multivariate quantiles in hydrological frequency analysis. Environmetrics 22:63–78.  https://doi.org/10.1002/env.1027 CrossRefGoogle Scholar
  33. Cheng L, Yaeger M, Viglione A, Coopersmith E, Ye S, Sivapalan M (2012) Exploring the physical controls of regional patterns of flow duration curves—Part 1: insights from statistical analyses. Hydrol Earth Syst Sci 16:4435–4446.  https://doi.org/10.5194/hess-16-4435-2012 CrossRefGoogle Scholar
  34. Chokmani K, Ouarda TBMJ (2004) Physiographical space-based kriging for regional flood frequency estimation at ungauged sites. Water Resour Res 40(12):W12,514. https://doi.org/10.1029/2003WR002983
  35. Cipriani T, Toilliez T, Sauquet E (2012) Estimation régionale des débits décennaux et durées caractéristiques de crue en France. La Houille Blanche 4–5:5–13.  https://doi.org/10.1051/lhb/2012024 CrossRefGoogle Scholar
  36. Coles S (2001) An introduction to statistical modeling of extreme values. Springer, LondonCrossRefGoogle Scholar
  37. Cuevas A, Febrero M, Fraiman R (2007) Robust estimation and classification for functional data via projection-based depth notions. Comput Stat 22(3):481–496.  https://doi.org/10.1007/s00180-007-0053-0 CrossRefGoogle Scholar
  38. Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319:391–409.  https://doi.org/10.1016/j.jhydrol.2005.07.032 CrossRefGoogle Scholar
  39. Deutsche Vereinigung für Wasserwirtschaft Abwasser und Abfall (2012) Merkblatt DWA-M 552. Tech. rep, DWA, Hennef, GermanyGoogle Scholar
  40. Diggle PJ, Ribeiro PJ Jr (2007) Model-based geostatistics. Springer series in statistics. Springer, New YorkGoogle Scholar
  41. Eckhardt K (2005) How to construct recursive digital filters for baseflow separation. Hydrol Process 19:507–515.  https://doi.org/10.1002/hyp.5675 CrossRefGoogle Scholar
  42. Eidgenössische Forschungsanstalt für Wald Schnee und Landschaft (WSL) (1999) Schweizerisches Landesforstinventar. Ergebnisse der Zwietaufnahme 1993-1995. BUWAL, BernGoogle Scholar
  43. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813.  https://doi.org/10.1111/j.1365-2656.2008.01390.x CrossRefGoogle Scholar
  44. Farmer WH (2016) Ordinary kriging as a tool to estimate historical daily streamflow records. Hydrol Earth Syst Sci 20(7):2721–2735.  https://doi.org/10.5194/hess-20-2721-2016 CrossRefGoogle Scholar
  45. Freund Y, Schapire RRE (1996) Experiments with a new boosting algorithm. In: International conference on machine learning, pp 148–156, https://doi.org/10.1.1.133.1040, http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.6252
  46. Friedman AJ, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordiante descent. J Stat Softw 33(1):1–22CrossRefGoogle Scholar
  47. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232CrossRefGoogle Scholar
  48. Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378CrossRefGoogle Scholar
  49. Gaál L, Kysel J, Szolgay J (2008) Region-of-influence approach to a frequency analysis of heavy precipitation in Slovakia. Hydrol Earth Syst Sci 12:825–839.  https://doi.org/10.5194/hess-12-825-2008 CrossRefGoogle Scholar
  50. Ganora D, Claps P, Laio F, Viglione A (2009) An approach to estimate nonparametric flow duration curves in ungauged basins. Water Resour Res 45(10):1–10.  https://doi.org/10.1029/2008WR007472 CrossRefGoogle Scholar
  51. Genest C, Favre AC (2007) Everything you always wanted to know about copula modeling but were afraid to ask. J Hydrol Eng 12(4):347–368.  https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(347) CrossRefGoogle Scholar
  52. Gottschalk L (1993) Correlation and covariance of runoff. Stoch Hydrol Hydraul 7:85–101CrossRefGoogle Scholar
  53. Gottschalk L, Leblois E, Skoien JO (2011) Correlation and covariance of runoff revisited. J Hydrol 398:76–90.  https://doi.org/10.1016/j.jhydrol.2010.12.011 CrossRefGoogle Scholar
  54. Green IRA, Stephenson D (1986) Criteria for comparison of single event models. Hydrol Sci J 31(3):395–411.  https://doi.org/10.1080/02626668609491056 CrossRefGoogle Scholar
  55. Greene W (2002) Econometric analysis, 5th edn. Prentice Hall, New JerseyGoogle Scholar
  56. GREHYS (1996) Presentation and review of some methods for regional flood frequency analysis. J Hydrol 186:63–84.  https://doi.org/10.1016/S0022-1694(96)03042-9
  57. Grimaldi S, Petroselli A (2015) Do we still need the Rational Formula? An alternative empirical procedure for peak discharge estimation in small and ungauged basins. Hydrol Sci J 60(1):67–77.  https://doi.org/10.1080/02626667.2014.880546 CrossRefGoogle Scholar
  58. Haberlandt U, Klöcking B, Krysanova V, Becker A (2001) Regionalisation of the base flow index from dynamically simulated flow components—a case study in the Elbe River Basin. J Hydrol 248:35–53.  https://doi.org/10.1016/S0022-1694(01)00391-2 CrossRefGoogle Scholar
  59. Haddad K, Rahman A (2012) Regional flood frequency analysis in eastern Australia: Bayesian GLS regression-based methods within fixed region and ROI framework—quantile regression vs. parameter regression technique. J Hydrol 430–431:142–161.  https://doi.org/10.1016/j.jhydrol.2012.02.012 CrossRefGoogle Scholar
  60. Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation techniques. J Intell Inf Syst 17(2/3):107–145.  https://doi.org/10.1023/A:1012801612483 CrossRefGoogle Scholar
  61. Harrell FE (2015) Regression modeling strategies. With applications to linear models, logistic and ordinal regression, and survival analysis. Springer, ChamGoogle Scholar
  62. Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning. Springer series in statistics. Springer, StanfordGoogle Scholar
  63. He Y, Bardossy A, Zehe E (2011) A review of regionalisation for continuous streamflow simulation. Hydrol Earth Syst Sci 15:3539–3553.  https://doi.org/10.5194/hess-15-3539-2011 CrossRefGoogle Scholar
  64. Hechenbichler K, Schliep K (2004) Weighted k-nearest-neighbor techniques and ordinal classification. Mol Ecol 399:17Google Scholar
  65. Hofner B, Mayr A, Robinzonov N, Schmid M (2009) Model-based Boosting in R. A Hands-on Tutorial Using the R Package mboost. Tech. rep., Department of statistics. University of Munich, MunichGoogle Scholar
  66. Hosking JRM, Wallis JR (1993) Some statistics useful in regional frequency analysis. Water Resour Res 29(92):271–281CrossRefGoogle Scholar
  67. Hrachowitz M, Savenije HHG, Blöschl G, McDonnell JJ, Sivapalan M, Pomeroy JW, Arheimer B, Blume T, Clark MP, Ehret U, Fenicia F, Freer JE, Gelfan A, Gupta HV, Hughes DA, Hut RW, Montanari A, Pande S, Tetzlaff D, Troch PA, Uhlenbrook S, Wagener T, Winsemius HC, Woods RA, Zehe E, Cudennec C (2013) A decade of Predictions in Ungauged Basins (PUB)—a review. Hydrol Sci J 58(6):1198–1255.  https://doi.org/10.1080/02626667.2013.803183 CrossRefGoogle Scholar
  68. Hundecha Y, Ouarda TBMJ, Bardossy A (2008) Regional estimation of parameters of a rainfall-runoff model at ungauged watersheds using the spatial structures of the parameters within a canonical physiographic-climatic space. Water Resour Res 44(W01):427.  https://doi.org/10.1029/2006WR005439 CrossRefGoogle Scholar
  69. Ilorme F, Griffis VW (2013) A novel procedure for delineation of hydrologically homogeneous regions and the classification of ungauged sites for design flood estimation. J Hydrol 492:151–162.  https://doi.org/10.1016/j.jhydrol.2013.03.045 CrossRefGoogle Scholar
  70. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. With applications in R. Springer, New York.  https://doi.org/10.1007/978-1-4614-7138-7 CrossRefGoogle Scholar
  71. Jensen H, Lang H, Rinderknecht J (1997) Extreme point rainfall of varying duration and return period 1901–1970. In: Hydrological Atlas of Switzerland, FOEN, Bern, chap 2.4Google Scholar
  72. Ji Z, Li N, Xie W, Wu J, Zhou Y (2013) Comprehensive assessment of flood risk using the classification and regression tree method. Stoch Env Res Risk Assess 27(8):1815–1828.  https://doi.org/10.1007/s00477-013-0716-z CrossRefGoogle Scholar
  73. Joe H (1997) Multivariate models and dependence concepts. Chapman and Hall/CRC, LondonCrossRefGoogle Scholar
  74. Kiers HAL, Smilde AK (2007) A comparison of various methods for multivariate regression with highly collinear variables. Stat Methods Appl 16:193–228.  https://doi.org/10.1007/s10260-006-0025-5 CrossRefGoogle Scholar
  75. Kjeldsen TR, Jones DA (2010) Predicting the index flood in ungauged UK catchments: on the link between data-transfer and spatial model error structure. J Hydrol 387(1–2):1–9.  https://doi.org/10.1016/j.jhydrol.2010.03.024 CrossRefGoogle Scholar
  76. Kokkonen TS, Jakeman AJ, Young PC, Koivusalo HJ (2003) Predicting daily flows in ungauged catchments: model regionalization from catchment descriptors at the Coweeta Hydrologic Laboratory, North Carolina. Hydrol Process 17(11):2219–2238.  https://doi.org/10.1002/hyp.1329 CrossRefGoogle Scholar
  77. Laaha G, Blöschl G (2006) A comparison of low flow regionalisation methods-catchment grouping. J Hydrol 323(1–4):193–214.  https://doi.org/10.1016/j.jhydrol.2005.09.001 CrossRefGoogle Scholar
  78. Laaha G, Skoien JO, Blöschl G (2014) Spatial prediction on river networks: comparison of top-kriging with regional regression. Hydrol Process 28:315–324.  https://doi.org/10.1002/hyp.9578 CrossRefGoogle Scholar
  79. Lang M, Ouarda T, Bobée B (1999) Towards operational guidelines for over-threshold modeling. J Hydrol 225:103–117CrossRefGoogle Scholar
  80. Le Cessie S, van Houwelingen JC (1992) Ridge estimators in logistic regression. J Appl Stat 41(1):191–201.  https://doi.org/10.2307/2347628 CrossRefGoogle Scholar
  81. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
  82. Longobardi A, Villani P (2008) Baseflow index regionalization analysis in a Mediterranean area and data scarcity context: role of the catchment permeability index. J Hydrol 355:63–75.  https://doi.org/10.1016/j.jhydrol.2008.03.011 CrossRefGoogle Scholar
  83. Lu GY, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34(9):1044–1055.  https://doi.org/10.1016/j.cageo.2007.07.010 CrossRefGoogle Scholar
  84. Matheron G (1971) The theory of regionalized variables and its applications, vol 5. École nationale supérieure des Mines, ParisGoogle Scholar
  85. McIntyre N, Lee H, Wheater H, Young A, Wagener T (2005) Ensemble predictions of runoff in ungauged catchments. Water Resour Res 41(12):1–14.  https://doi.org/10.1029/2005WR004289 CrossRefGoogle Scholar
  86. Mediero L, Jiménez-Alvarez A, Garrote L (2010) Design flood hydrographs from the relationship between flood peak and volume. Hydrol Earth Syst Sci 14:2495–2505.  https://doi.org/10.5194/hess-14-2495-2010 CrossRefGoogle Scholar
  87. Merz R (2006) Regionalisierung von statistischen Hochwasserkenngrössen. In: Godina R, Blöschl G (eds) Methoden der hydrologischen Regionalisierung, vol 197. Wiener Mitteilungen, Wien, pp 109–130Google Scholar
  88. Merz R, Blöschl G (2003) A process typology of regional floods. Water Resour Res 39(12):1340.  https://doi.org/10.1029/2002WR001952 CrossRefGoogle Scholar
  89. Merz R, Blöschl G (2004) Regionalisation of catchment model parameters. J Hydrol 287(1):95–123.  https://doi.org/10.1016/j.jhydrol.2003.09.028 CrossRefGoogle Scholar
  90. MeteoSwiss (2013) Documentation of MeteoSwiss grid-data products: Daily precipitation (final analysis): RhiresD. Tech. rep., MeteoSwiss, http://www.meteoschweiz.admin.ch/web/de/services/datenportal/gitterdaten/precip/rhiresd.Par.0007.DownloadFile.tmp/proddocrhiresd.pdf
  91. Mevik BH, Wehrens R (2007) The pls package: principal component and partial least squares regression in R. J Stat Softw 18(2):1–23.  https://doi.org/10.18637/jss.v018.i02 CrossRefGoogle Scholar
  92. Meylan P, Favre AC, Musy A (2012) Predictive hydrology. A frequency analysis approach. Science Publishers, St. Helier, Jersey, British Channel IslandsGoogle Scholar
  93. Myers RH, Montgomery DC, Vining GG, Robinson TJ (2010) Generalized Linear Models, vol 4. Wiley, HobokenCrossRefGoogle Scholar
  94. Nathan RJ, McMahon TA (1990) Identification of homogeneous regions for the purposes of regionalisation. J Hydrol 121:217–238CrossRefGoogle Scholar
  95. Nied M, Pardowitz T, Nissen K, Ulbrich U, Hundecha Y, Merz B (2014) On the relationship between hydro-meteorological patterns and flood types. J Hydrol 519:3249–3262.  https://doi.org/10.1016/j.jhydrol.2014.09.089 CrossRefGoogle Scholar
  96. Osborne JW (2010) Improving your data transformations: applying the Box-Cox transformation. Pract Assess Res Eval 15(12):1–9Google Scholar
  97. Ouarda T, Cunderlik JM, St-Hilaire A, Barbet M, Bruneau P, Bobée B (2006) Data-based comparison of seasonality-based regional flood frequency methods. J Hydrol 330(1):329–339.  https://doi.org/10.1016/j.jhydrol.2006.03.023 CrossRefGoogle Scholar
  98. Ouarda TBMJ, Haché M, Bruneau P, Bobée B (2000) Regional flood peak and volume estimation in northern Canadian basin. J Cold Reg Eng 14:176–191.  https://doi.org/10.1061/(ASCE)0887-381X(2000)14:4(176) CrossRefGoogle Scholar
  99. Ouarda TBMJ, Girard C, Cavadias GS, Bobée B (2001) Regional flood frequency estimation with canonical correlation analysis. J Hydrol 254:157–173CrossRefGoogle Scholar
  100. Oudin L, Andréassian V, Perrin C, Michel C, Moine NL (2008) Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments. Water Resour Res 44:W03, 413. https://doi.org/10.1029/2007WR006240
  101. Oudin L, Kay A, Andréassian V, Perrin C (2010) Are seemingly physically similar catchments truly hydrologically similar? Water Resour Res 46(W11):558.  https://doi.org/10.1029/2009WR008887 CrossRefGoogle Scholar
  102. Parajka J, Merz R, Blöschl G (2005) A comparison of regionalisation methods for catchment model parameters. Hydrol Earth Syst Sci 9:157–171.  https://doi.org/10.5194/hess-9-157-2005 CrossRefGoogle Scholar
  103. Parajka J, Andréassian V, Archfield SA, Bardossy A, Blöschl G, Chiew F, Duan Q, Gelfan A, Hlavconva K, Merz R, McIntyre N, Oudin L, Perrin C, Rogger M, Salinas JL, Savenije HG, Skoien JO, Wagener T, Zehe E, Zhang Y (2013) Prediction of runoff hydrographs in ungauged basins. In: Blöschl G, Sivapalan M, Wagener T, Viglione A, Savenije H (eds) Predictions in ungauged basins. A synthesis across processes, places and scales, Cambridge University Press, Cambridge, pp 227–269Google Scholar
  104. Pebesma E (2004) Multivariable geostatistics in S: the gstat package. Comput Geosci 30:683–691.  https://doi.org/10.1016/j.cageo.2004.03.012 CrossRefGoogle Scholar
  105. Peters A, Hothorn T, Ripley BD, Therneau T, Atkinson B (2015) Package ‘ ipred ’ : improved predictors. http://cran.r-project.org/package=ipred
  106. Petroselli A, Grimaldi S (2015) Design hydrograph estimation in small and fully ungauged basins: a preliminary assessment of the EBA4SUB framework. J Flood Risk Manag. https://doi.org/10.1111/jfr3.12193
  107. Pilgrim DH (1986) Bridging the gap between flood research and design practice. Water Resour Res 22(9):165–176CrossRefGoogle Scholar
  108. Prinzio MD, Castellarin A, Toth E (2011) Data-driven catchment classification: application to the pub problem. Hydrol Earth Syst Sci 15:1921–1935.  https://doi.org/10.5194/hess-15-1921-2011 CrossRefGoogle Scholar
  109. R Core Team (2015) R: a language and environment for statistical computing. http://www.r-project.org/
  110. Rahman A, Charron C, Ouarda TBMJ, Chebana F (2017) Development of regional flood frequency analysis techniques using generalized additive models for Australia. Stoch Environ Res Risk Assess pp 1–17, https://doi.org/10.1007/s00477-017-1384-1
  111. Rai RK, Sarkar S, Singh VP (2009) Evaluation of the adequacy of statistical distribution functions for deriving unit hydrograph. Water Resour Manage 23:899–929.  https://doi.org/10.1007/s11269-008-9306-0 CrossRefGoogle Scholar
  112. Rasmussen PF, Bobée B, Bernier J (1993) Une méthodologie générale de comparaison de modèles d’estimation régionale de crue. Revue des sciences de l’eau 7:23–41.  https://doi.org/10.7202/705187ar CrossRefGoogle Scholar
  113. Razavi T, Coulibaly P (2013) Streamflow prediction in ungauged basins: review of regionalization methods. J Hydrol Eng 18(8):958–975.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000690 CrossRefGoogle Scholar
  114. Requena AI, Mediero L, Garrote L (2013) A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation. Hydrol Earth Syst Sci 17:3023–3038.  https://doi.org/10.5194/hess-17-3023-2013 CrossRefGoogle Scholar
  115. Ridgeway G (2007) Generalized boosted models: a guide to the gbm package. Compute 1(4):1–12.  https://doi.org/10.1111/j.1467-9752.1996.tb00390.x CrossRefGoogle Scholar
  116. Rosbjerg D, Blöschl G, Burn DH, Castellarin A, Croke B, Baldassarre GD, Iacobellis V, Kjeldsen TR, Kuczera G, Merz R, Montanari A, Morris D, Ouarda T, Ren L, Rogger M, Salinas JL, Toth E, Viglione A (2013) Prediction of floods in ungauged basins. In: Blöschl G, Sivapalan M, Wagener T, Viglione A, Savenije H (eds) Runoff prediction in ungauged basins. A synthesis across processes, places and scales, Cambridge University Press, Cambridge, chap 9, pp 189–226Google Scholar
  117. Salinas JL, Laaha G, Rogger M, Parajka J, Viglione A, Sivapalan M, Blöschl G (2013) Comparative assessment of predictions in ungauged basins—Part 2: flood and low flow studies. Hydrol Earth Syst Sci 17:2637–2652.  https://doi.org/10.5194/hess-17-2637-2013 CrossRefGoogle Scholar
  118. Samuel J, Coulibaly P, Metcalfe RA (2011) Estimation of continuous streamflow in ontario ungauged basins: comparison of regionalization methods. J Hydrol Eng 16(5):447–459.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000338 CrossRefGoogle Scholar
  119. Sauquet E (2006) Mapping mean annual river discharges: geostatistical developments for incorporating river network dependencies. J Hydrol 331:300–314.  https://doi.org/10.1016/j.jhydrol.2006.05.018 CrossRefGoogle Scholar
  120. Sauquet E, Catalogne C (2011) Comparison of catchment grouping methods for flow duration curve estimation at ungauged sites in France. Hydrol Earth Syst Sci 15:2421–2435.  https://doi.org/10.5194/hess-15-2421-2011 CrossRefGoogle Scholar
  121. Sefton CEM, Howarth SM (1998) Relationships between dynamic response characteristics and physical descriptors of catchments in England and wales. J Hydrol 211(1–4):1–16.  https://doi.org/10.1016/S0022-1694(98)00163-2 CrossRefGoogle Scholar
  122. Serinaldi F, Grimaldi S (2011) Synthetic design hydrographs based on distribution functions with finite support. J Hydrol Eng 16:434–446.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000339 CrossRefGoogle Scholar
  123. Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52(34):591–611CrossRefGoogle Scholar
  124. Shiau J, Wang HY, Tsai CT (2006) Bivariate Frequency Analysis of floods using copulas. J Am Water Resour Assoc pp 1549–1564, https://doi.org/10.1111/j.1752-1688.2006.tb06020.x
  125. Shu C, Burn DH (2004) Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resour Res 40(9):1–10.  https://doi.org/10.1029/2003WR002816 CrossRefGoogle Scholar
  126. Shu C, Ouarda T (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349:31–43.  https://doi.org/10.1016/j.jhydrol.2007.10.050 CrossRefGoogle Scholar
  127. Sikorska AE, Viviroli D, Seibert J (2015) Flood type classification in mountainous catchments using crisp and fuzzy decision trees. Water Resour Res 51(10):7959–7976.  https://doi.org/10.1002/2015WR017326 CrossRefGoogle Scholar
  128. Singh PK, Mishra SK, Jain MK (2014) A review of the synthetic unit hydrograph: from the empirical UH to advanced geomorphological methods. Hydrol Sci J. https://doi.org/10.1080/02626667.2013.870664
  129. Sivapalan M (2003) Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrol Process 17:3163–3170.  https://doi.org/10.1002/hyp.5155 CrossRefGoogle Scholar
  130. Skoien JO, Merz R, Blöschl G (2006) Top-kriging—geostatistics on stream networks. Hydrol Earth Syst Sci 10:277–287.  https://doi.org/10.5194/hess-10-277-2006 CrossRefGoogle Scholar
  131. Skoien JO, Blöschl G, Laaha G, Pebesma E, Parajka J, Viglione A (2014) An R package for interpolation of data with a variable spatial support, with an example from river networks. Comput Geosci 67:180–190CrossRefGoogle Scholar
  132. Smithers JC (2012) Methods for design flood estimation in South Africa. Water SA 38(4):633–646.  https://doi.org/10.4314/wsa.v38i4.19 CrossRefGoogle Scholar
  133. Steinschneider S, Yang YCE, Brown C (2014) Combining regression and spatial proximity for catchment model regionalization: a comparative study. Hydrol Sci J 6667:1–18. https://doi.org/10.1080/02626667.2014.899701
  134. Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychol Methods 14(4):323–348.  https://doi.org/10.1037/a0016973 CrossRefGoogle Scholar
  135. Takezawa K (2012) Introduction to nonparametric regression. Wiley, Hoboken, https://doi.org/10.1021/cr2001349
  136. Tibshirani R (1997) The lasso method for variable selection in the Cox model. Stat Med 16(4):385–395.  https://doi.org/10.1002/(SICI)1097-0258(19970228)16:4%3c385::AID-SIM380%3e3.0.CO;2-3 CrossRefGoogle Scholar
  137. Tung YK, Yeh KC, Yang JC (1997) Regionalization of unit hydrograph parameters: 1. Comparison of regression analysis techniques. Stoch Hydrol Hydraul 11:145–171CrossRefGoogle Scholar
  138. Viglione A, Merz R, Blöschl G (2009) On the role of the runoff coefficient in the mapping of rainfall to flood return periods. Hydrol Earth Syst Sci 6(1):627–665.  https://doi.org/10.5194/hessd-6-627-2009 CrossRefGoogle Scholar
  139. Viviroli D, Mittelbach H, Gurtz J, Weingartner R (2009a) Continuous simulation for flood estimation in ungauged mesoscale catchments of Switzerland—Part II: parameter regionalisation and flood estimation results. J Hydrol 377:208–225.  https://doi.org/10.1016/j.jhydrol.2009.08.022 CrossRefGoogle Scholar
  140. Viviroli D, Zappa M, Gurtz J, Weingartner R (2009b) An introduction to the hydrological modelling system PREVAH and its pre-and post-processing-tools. Environ Model Softw 24:1209–1222.  https://doi.org/10.1016/j.envsoft.2009.04.001 CrossRefGoogle Scholar
  141. Webster R, Oliver MA (2007) Geostatistics for environmental scientists. Statistics in practice. Wiley, ChichesterGoogle Scholar
  142. Weisberg S (2005) Applied Linear Regression, 3rd edn. Wiley, HobokenCrossRefGoogle Scholar
  143. Yamamoto JK (2007) On unbiased backtransform of lognormal kriging estimates. Comput Geosci 11:219–234.  https://doi.org/10.1007/s10596-007-9046-x CrossRefGoogle Scholar
  144. Yue S, Rasmussen P (2002) Bivariate frequency analysis: discussion of some useful concepts in hydrological application. Hydrol Process 16:2881–2898.  https://doi.org/10.1002/hyp.1185 CrossRefGoogle Scholar
  145. Yue S, Ouarda T, Bobée B, Legendre P, Bruneau P (2002) Approach for describing statistical properties of flood hydrograph. J Hydrol Eng 7(2):147–153.  https://doi.org/10.1061/(ASCE)1084-0699(2002)7:2(147) CrossRefGoogle Scholar
  146. Zhang Y, Chiew FHS (2009) Relative merits of different methods for runoff predictions in ungauged catchments. Water Resour Res 45(W07):412.  https://doi.org/10.1029/2008WR007504 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Manuela I. Brunner
    • 1
    • 2
    Email author
  • Reinhard Furrer
    • 3
    • 4
  • Anna E. Sikorska
    • 1
    • 5
  • Daniel Viviroli
    • 1
    • 6
  • Jan Seibert
    • 1
    • 7
  • Anne-Catherine Favre
    • 2
  1. 1.Department of GeographyUniversity of ZurichZurichSwitzerland
  2. 2.Université Grenoble-Alpes, CNRS, IRD, IGE, Grenoble INPGrenobleFrance
  3. 3.Department of MathematicsUniversity of ZurichZurichSwitzerland
  4. 4.Department of Computational ScienceUniversity of ZurichZurichSwitzerland
  5. 5.Department of Hydraulic EngineeringWarsaw University of Life Sciences, SGGWWarsawPoland
  6. 6.belop gmbhSarnenSwitzerland
  7. 7.Department of Earth SciencesUppsala UniversityUppsalaSweden

Personalised recommendations