Estimating flood quantiles at ungauged sites using nonparametric regression methods with spatial components

References

• Acreman, M. and Sinclair, C., 1986. Classification of drainage basins according to their physical characteristics; an application for flood frequency analysis in Scotland. Journal of Hydrology, 84, 365–380. doi:10.1016/0022-1694(86)90134-4
   Web of Science ®Google Scholar
• Ahn, K.-H. and Palmer, R., 2016. Regional flood frequency analysis using spatial proximity and basin characteristics: quantile regression vs. parameter regression technique. Journal of Hydrology, 540, 515–526. doi:10.1016/j.jhydrol.2016.06.047
   Web of Science ®Google Scholar
• Altman, N.S., 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46, 175–185. doi:10.1080/00031305.1992.10475879
   Web of Science ®Google Scholar
• Archfield, S.A., et al., 2013. Topological and canonical kriging for design flood prediction in ungauged catchments: an improvement over a traditional regional regression approach? Hydrology and Earth System Sciences, 17, 1575–1588. doi:10.5194/hess-17-1575-2013
   Web of Science ®Google Scholar
• Aziz, K., et al., 2014. Application of artificial neural networks in regional flood frequency analysis: A case study for Australia. Stochastic Environmental Research and Risk Assessmen, 28, 541–554. doi:10.1007/s00477-013-0771-5
   Web of Science ®Google Scholar
• Bárdossy, A., 2006. Copula-based geostatistical models for groundwater quality parameters. Water Resources Research, 42. doi:10.1029/2005WR004754
   Web of Science ®Google Scholar
• Basu, B. and Srinivas, V.V., 2015. A recursive multi-scaling approach to regional flood frequency analysis. Journal of Hydrology, 529, 373–383. doi:10.1016/j.jhydrol.2015.07.037
   Web of Science ®Google Scholar
• Bishop, C.M., 1995. Neural networks for pattern recognition. New York, NY: Oxford university press.
   Google Scholar
• Blöschl, G. and Sivapalan, M., 1997. Process controls on regional flood frequency: coefficient of variation and basin scale. Water Resources Research, 33, 2967–2980. doi:10.1029/97WR00568
   Web of Science ®Google Scholar
• Burn, D.H., 1990. An appraisal of the “region of influence” approach to flood frequency analysis. Hydrological Sciences Journal, 35, 149–165. doi:10.1080/02626669009492415
   Web of Science ®Google Scholar
• Burn, D.H., 1997. Catchment similarity for regional flood frequency analysis using seasonality measures. Journal of Hydrology, 202, 212–230. doi:10.1016/S0022-1694(97)00068-1
   Web of Science ®Google Scholar
• Burn, D.H., Whitfield, P.H., and Sharif, M., 2016. Identification of changes in floods and flood regimes in Canada using a peaks over threshold approach. Hydrological Processes, 30, 3303–3314. doi:10.1002/hyp.10861
   Web of Science ®Google Scholar
• Burn, D.H., Zrinji, Z., and Kowalchuk, M., 1997. Regionalization of catchments for regional flood frequency analysis. Journal of Hydrologic Engineering, 2, 76–82. doi:10.1061/(ASCE)1084-0699(1997)2:2(76)
   Web of Science ®Google Scholar
• Burnham, K.P. and Anderson, D.R., 2002. Model selection and multimodel inference: a practical information-theoretic approach. 2nd. New York: Springer-Verlag.
   Google Scholar
• Buttle, J.M., et al., 2016. Flood processes in Canada: Regional and special aspects. Canadian Water Resources Journal/Revue Canadienne des Ressources Hydriques, 41, 7–30. doi:10.1080/07011784.2015.1131629
   Web of Science ®Google Scholar
• Castellarin, A., Burn, D.H., and Brath, A., 2008. Homogeneity testing: how homogeneous do heterogeneous cross-correlated regions seem? Journal of Hydrology, 360, 67–76. doi:10.1016/j.jhydrol.2008.07.014
   Web of Science ®Google Scholar
• Castiglioni, S., Castellarin, A., and Montanari, A., 2009. Prediction of low-flow indices in ungauged basins through physiographical space-based interpolation. Journal of Hydrology, 378, 272–280. doi:10.1016/j.jhydrol.2009.09.032
   Web of Science ®Google Scholar
• Chebana, F., et al., 2014. Regional frequency analysis at ungauged sites with the generalized additive model. Journal of Hydrometeorology, 15, 2418–2428. doi:10.1175/JHM-D-14-0060.1
   Web of Science ®Google Scholar
• Chokmani, K. and Ouarda, T.B.M.J., 2004. Physiographical space-based kriging for regional flood frequency estimation at ungauged sites. Water Resources Research, 40. doi:10.1029/2003WR002983
   Web of Science ®Google Scholar
• Cleveland, W.S. and Devlin, S.J., 1988. Locally Weighted Regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association, 83, 596–610. doi:10.2307/2289282
   Web of Science ®Google Scholar
• Craven, P. and Wahba, G., 1978. Smoothing noisy data with spline functions. Numerische Mathematik, 31, 377–403. doi:10.1007/BF01404567
   Web of Science ®Google Scholar
• Dawson, C.W., et al., 2006. Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology, 319, 391–409. doi:10.1016/j.jhydrol.2005.07.032
   Web of Science ®Google Scholar
• DelSole, T. and Tippett, M.K., 2014. Comparing forecast skill. Monthly Weather Review, 142, 4658–4678. doi:10.1175/MWR-D-14-00045.1
   Web of Science ®Google Scholar
• Di Baldassarre, G., Laio, F., and Montanari, A., 2009. Design flood estimation using model selection criteria. Physics and Chemistry of the Earth, Parts A/B/C, Recent developments of Statistical Tools for Hydrological Application, 34, 606–611. doi:10.1016/j.pce.2008.10.066
   Web of Science ®Google Scholar
• Durocher, M., Burn, D.H., and Mostofi Zadeh, S., 2018. A nationwide regional flood frequency analysis at ungauged sites using ROI/GLS with copulas and super regions. Journal of Hydrology, 567, 191–202. doi:10.1016/j.jhydrol.2018.10.011
   Web of Science ®Google Scholar
• El-Jabi, N., Caissie, D., and Turkkan, N., 2016. Flood analysis and flood projections under climate change in New Brunswick. Canadian Water Resources Journal, 41, 319–330. doi:10.1080/07011784.2015.1071205
   Web of Science ®Google Scholar
• Eng, K., Milly, P.C., and Tasker, G.D., 2007. Flood regionalization: A hybrid geographic and predictor-variable region-of-influence regression method. Journal of Hydrologic Engineering, 12, 585–591. doi:10.1061/(ASCE)1084-0699(2007)12:6(585)
   Web of Science ®Google Scholar
• Favre, A., Quessy, J., and Toupin, M., 2018. The new family of Fisher copulas to model upper tail dependence and radial asymmetry: properties and application to high‐dimensional rainfall data. Environmetrics, e2494. doi:10.1002/env.2494
   Web of Science ®Google Scholar
• Gizaw, M.S. and Gan, T.Y., 2016. Regional flood frequency analysis using support vector regression under historical and future climate. Journal of Hydrology, 538, 387–398. doi:10.1016/j.jhydrol.2016.04.041
   Web of Science ®Google Scholar
• Green, P.J. and Silverman, B.W., 1993. Nonparametric regression and generalized linear models: a roughness penalty approach. Boca Raton, FL: Chapman & Hall/CRC.
   Google Scholar
• GREHYS, 1996. Presentation and review of some methods for regional flood frequency analysis. Journal of Hydrology, 186, 63–84. doi:10.1016/S0022-1694(96)03042-9
   Web of Science ®Google Scholar
• Haddad, K. and 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. Journal of Hydrology, 430–431, 142–161. doi:10.1016/j.jhydrol.2012.02.012
   Web of Science ®Google Scholar
• Hamill, T.M., 1999. Hypothesis Tests for Evaluating Numerical Precipitation Forecasts. Weather Forecasting, 14, 155–167. doi:10.1175/1520-0434(1999)014<0155:HTFENP>2.0.CO;2
   Web of Science ®Google Scholar
• Hastie, T. and Tibshirani, R., 1986. Generalized additive models. Statistical Science, 297–310. doi:10.1214/ss/1177013604
   Google Scholar
• Hastie, T., Tibshirani, R., and Friedman, J.H., 2009. The elements of statistical learning: data mining, inference, and prediction. Springer series in statistics. New York, NY: Springer.
   Google Scholar
• Hosking, J.R.M. and Wallis, J.R., 1997. Regional frequency analysis: an approach based on L-moments. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Kammann, E.E. and Wand, M.P., 2003. Geoadditive models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 52, 1–18. doi:10.1111/1467-9876.00385
   Web of Science ®Google Scholar
• Kjeldsen, T.R. and Jones, D.A., 2009. An exploratory analysis of error components in hydrological regression modeling. Water Resources Research, 45, W02407. doi:10.1029/2007WR006283
   Web of Science ®Google Scholar
• Laaha, G. and Blöschl, G., 2006. A comparison of low flow regionalisation methods—catchment grouping. Journal of Hydrology, 323, 193–214. doi:10.1016/j.jhydrol.2005.09.001
   Web of Science ®Google Scholar
• Laio, F., et al., 2011. Spatially smooth regional estimation of the flood frequency curve (with uncertainty). Journal of Hydrology, 408, 67–77. doi:10.1016/j.jhydrol.2011.07.022
   Web of Science ®Google Scholar
• Latraverse, M., Rasmussen, P.F., and Bobée, B., 2002. Regional estimation of flood quantiles: parametric versus nonparametric regression models. Water Resources Research, 38. doi:10.1029/2001WR000677
   Web of Science ®Google Scholar
• López, J. and Francés, F., 2013. Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates. Hydrology and Earth System Sciences, 17, 3189–3203. doi:10.5194/hess-17-3189-2013
   Web of Science ®Google Scholar
• Meigh, J.R., Farquharson, F.A.K., and Sutcliffe, J.V., 1997. A worldwide comparison of regional flood estimation methods and climate. Hydrological Sciences Journal, 42, 225–244. doi:10.1080/02626669709492022
   Web of Science ®Google Scholar
• Oudin, L., et al., 2008. Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments. Water Resources Research, 44. doi:10.1029/2007WR006240
   Web of Science ®Google Scholar
• Oudin, L., et al., 2010. Are seemingly physically similar catchments truly hydrologically similar? Water Resources Research. 46. doi:10.1029/2009WR008887
   PubMed Web of Science ®Google Scholar
• Pandey, G.R. and Nguyen, -V.-T.-V., 1999. A comparative study of regression based methods in regional flood frequency analysis. Journal of Hydrology, 225, 92–101. doi:10.1016/S0022-1694(99)00135-3
   Web of Science ®Google Scholar
• Rahman, A., et al., 2018. Development of regional flood frequency analysis techniques using generalized additive models for Australia. Stochastic Environmental Research and Risk Assessment, 32, 123–139. doi:10.1007/s00477-017-1384-1
   Web of Science ®Google Scholar
• Requena, A.I., Chebana, F., and Ouarda, T.B.M.J., 2017. Heterogeneity measures in hydrological frequency analysis: review and new developments. Hydrology and Earth System Sciences, 21, 1651–1668. doi:10.5194/hess-21-1651-2017
   Web of Science ®Google Scholar
• Rigby, R.A. and Stasinopoulos, D.M., 2005. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54, 507–554. doi:10.1111/j.1467-9876.2005.00510.x
   Web of Science ®Google Scholar
• Salinas, J.L., et al., 2014. Regional parent flood frequency distributions in Europe – part 1: is the GEV model suitable as a pan-European parent? Hydrology and Earth System Sciences, 18, 4381–4389. doi:10.5194/hess-18-4381-2014
   Web of Science ®Google Scholar
• Sandrock, G., Viraraghavan, T., and Fuller, G.A., 1992. Estimation of peak flows for natural ungauged watersheds in southern saskatchewan. Canadian Water Resources Journal/Revue Canadienne des Ressources Hydriques, 17, 21–31. doi:10.4296/cwrj1701021
   Google Scholar
• Sauquet, E., Gottschalk, L., and Leblois, E., 2000. Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45, 799–815. doi:10.1080/02626660009492385
   Web of Science ®Google Scholar
• Schabenberger, O. and Gotway, C.A., 2004. Statistical methods for spatial data analysis. Boca Raton, FL: CRC Press.
   Google Scholar
• Schnier, S. and Cai, X., 2014. Prediction of regional streamflow frequency using model tree ensembles. Journal of Hydrology, 517, 298–309. doi:10.1016/j.jhydrol.2014.05.029
   Web of Science ®Google Scholar
• Shu, C. and Burn, D.H., 2004. Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resources Research, 40, W09301. doi:10.1029/2003WR002816
   Web of Science ®Google Scholar
• Shu, C. and Ouarda, T.B.M.J., 2007. Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space. Water Resources Research, 43. doi:10.1029/2006WR005142
   Web of Science ®Google Scholar
• Skøien, J.O. and Blöschl, G., 2007. Spatiotemporal topological kriging of runoff time series. Water Resources Research. 43. doi:10.1029/2006WR005760
   PubMed Web of Science ®Google Scholar
• Smith, A., Sampson, C., and Bates, P., 2015. Regional flood frequency analysis at the global scale. Water Resources Research, 51, 539–553. doi:10.1002/2014WR015814
   Web of Science ®Google Scholar
• Steinschneider, S., Yang, Y.-C.E., and Brown, C., 2015. Combining regression and spatial proximity for catchment model regionalization: a comparative study. Hydrological Sciences Journal, 60, 1026–1043. doi:10.1080/02626667.2014.899701
   Web of Science ®Google Scholar
• Tasker, G. and Stedinger, J., 1989. An operational GLS model for hydrologic regression. Journal of Hydrology, 111, 361–375. doi:10.1016/0022-1694(89)90268-0
   Web of Science ®Google Scholar
• Tasker, G.D., Hodge, S.A., and Barks, C.S., 1996. Region of influence regression for estimating the 50-year flood at ungaged sites. JAWRA Journal of the American Water Resources Association, 32, 163–170. doi:10.1111/j.1752-1688.1996.tb03444.x
   Google Scholar
• Thomas, D. and Benson, M., 1975. Generalization of streamflow characteristics from drainage-basin characteristics. US Geological Survey Water-Supply Paper.
   Google Scholar
• Viglione, A., Laio, F., and Claps, P., 2007. A comparison of homogeneity tests for regional frequency analysis. Water Resources Research, 43, W03428. doi:10.1029/2006WR005095
   Web of Science ®Google Scholar
• Villarini, G., et al., 2009. On the stationarity of annual flood peaks in the continental United States during the 20th century. Water Resources Research, 45, W08417. doi:10.1029/2008WR007645
   Web of Science ®Google Scholar
• Ward, J.H., 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244. doi:10.1080/01621459.1963.10500845
   Web of Science ®Google Scholar
• Wittenberg, H., 1999. Baseflow recession and recharge as nonlinear storage processes. Hydrological Processes, 13, 715–726. doi:10.1002/(SICI)1099-1085(19990415)13:5<715::AID-HYP775>3.0.CO;2-N
   Web of Science ®Google Scholar
• Wood, S., 2006. Generalized additive models: an introduction with R. Boca Raton, FL: Chapman & Hall/CRC.
   Google Scholar
• Wood, S.N., 2003. Thin plate regression splines. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65, 95–114. doi:10.1111/1467-9868.00374
   Web of Science ®Google Scholar
• Zhang, Z., Stadnyk, T.A., and Burn, D.H., 2018. Identification of a broadly accepted statistical distribution for at-site flood frequency analysis in Canada. Presented at the 71rst CWRA National Conference, May 28 – June 1, Victoria, BC, Canada.
   Google Scholar
• Zrinji, Z. and Burn, D.H., 1994. Flood frequency analysis for ungauged sites using a region of influence approach. Journal of Hydrology, 153, 1–21. doi:10.1016/0022-1694(94)90184-8
   Web of Science ®Google Scholar