651
Views
6
CrossRef citations to date
0
Altmetric
Research Article

Bayesian regional flood frequency analysis with GEV hierarchical models under spatial dependency structures

ORCID Icon & ORCID Icon
Pages 422-433 | Received 21 Apr 2020, Accepted 11 Nov 2020, Published online: 02 Feb 2021

References

  • Ahani, A., Nadoushani, S.M., and Moridi, A., 2020. Simultaneous regionalization of gauged and ungauged watersheds using a missing data clustering method. Journal of Hydrologic Engineering, 25 (5), 04020015. doi:10.1061/(ASCE)HE.1943-5584.0001916
  • Ahn, K. 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
  • Ahn, K., Palmer, R., and Steinschneider, S., 2017. A hierarchical Bayesian model for regionalized seasonal forecasts: application to low flows in the northeastern United States. Water Resources Research, 53 (1), 503–521. doi:10.1002/2016WR019605
  • Ahn, K. and Steinschneider, S., 2019. Hierarchical Bayesian model for streamflow estimation at ungauged sites via spatial scaling in the Great Lakes basin. Journal of Water Resources Planning and Management, 145 (8), 04019030. doi:10.1061/(ASCE)WR.1943-5452.0001091
  • Alexander, R.B., Schwarz, G.E., and Boyer, E.W., 2019. Advances in quantifying streamflow variability across continental scales: 2. Improved model regionalization and prediction uncertainties using hierarchical Bayesian methods. Water Resources Research, 55 (12), 11061–11087. doi:10.1029/2019WR025037
  • ANA (Agência Nacional de Águas), 2003. Projeto de Gerenciamento Integrado das Atividades Desenvolvidas em Terra na Bacia do São Francisco, Sub-projeto 4.5.A – diagnóstico Analítico da Bacia e sua Zona Costeira. Brasília: ANA.
  • Baker, V.R., et al., 2003. A bright future for old flows: origin, status and future paleoflood hydrology. In: V.R. Thorndycraft, ed. Proceedings of the 2002 PHEFRA workshop – Paleofloods, historical floods and climatic variability: applications in flood risk assessment, Madri: Centro de Ciencias Medioambientales, 13–18.
  • Basu, B. and Srinivas, V.V., 2016. Evaluation of the index-flood approach related regional frequency analysis procedures. Journal of Hydrologic Engineering, 21 (1), 04015052. doi:10.1061/(ASCE)HE.1943-5584.0001264
  • Bezac, N., Brilly, M., and Sraj, M., 2014. Comparison between the peaks-over-threshold method and the annual maximum method for flood frequency analysis. Hydrological Sciences Journal, 59 (5), 595–977. doi:10.1080/02626667.2013.831174
  • Botero, B.A. and Frances, F., 2010. Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models. Hydrology and Earth System Sciences, 14, 2617–2628. doi:10.5194/hess-14-2617-2010
  • Bracken, C., et al., 2016. Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain. Water Resources Research, 52 (8), 6643–6655. doi:10.1002/2016WR018768
  • Coles, S.G., 2001. An introduction to statistical modeling of extreme events. London: Springer. doi:10.1007/978-1-4471-3675-0
  • Cooley, D., Nychka, D., and Naveau, P., 2007. Bayesian spatial modeling of extreme precipitation return levels. Journal of the American Statistical Association, 102 (479), 824–840. doi:10.1198/016214506000000780
  • Costa, V. and Fernandes, W., 2017. Bayesian estimation of extreme flood quantiles using a rainfall-runoff model and a stochastic daily rainfall generator. Journal of Hydrology, 554, 137–154. doi:10.1016/j.jhydrol.2017.09.003
  • CPRM, 2001. Regionalização de Vazões das Sub-bacias 40 e 41 – Alto São Francisco. Belo Horizonte: ANEEL/CPRM.
  • Dalrymple, T., 1960. Flood frequency analyses. Washignton, DC: US Geological Survey Water-Supply Paper Bi. 1543-A, 80.
  • Davison, A.C., Huser, R., and Thibaud, E., 2013. Geostatistics of dependent and asymptotically independent extremes. Mathematical Geosciences, 45 (5), 511–529. doi:10.1007/s11004-013-9469-y
  • Dyrrdal, A.V., et al. 2015. Bayesian hierarchical modeling of extreme hourly precipitation in Norway. Environmetrics, 26 (2), 89–106. doi:10.1002/env.2301
  • Fernandes, W., Naghettini, M., and Loschi, R., 2010. A Bayesian approach for estimating extreme flood probabilities with upper-bounded distribution functions. Stochastic Environmental Research and Risk Assessment, 24 (8), 1127–1143. doi:10.1007/s00477-010-0365-4
  • Gupta, V.K. and Dawdy, D.R., 1995. Physical interpretations of regional variations in the scaling exponents of flood quantiles. Hydrological Processes, 9 (3–4), 347–361. doi:10.1002/hyp.3360090309
  • Gupta, V.K., Mesa, O.J., and Dawdy, D.R., 1994. Multiscaling theory of flood peaks: regional quantile analysis. Water Resources Research, 30 (12), 3405–3421. doi:10.1029/94WR01791
  • Gupta, V.K., Troutman, B.M., and Dawdy, D.R., 2007. Towards a nonlinear geophysical theory of floods in river networks: an overview of 20 years of progress. In: A. Tsonis and J. Elsner, eds. Nonlinear dynamics in geosciences. New York: Springer, 121–151.
  • Gupta, V.K. and Waymire, E., 1990. Multiscaling properties of spatial rainfall and river flow distributions. Journal of Geophysiscal Research, 95 (D3), 1999–2009. doi:10.1029/JD095iD03p01999
  • Hailegeorgis, T.T. and Alfredsen, K., 2017. Regional flood frequency analysis and prediction in ungauged basins including estimation of major uncertainties for mid-Norway. Journal of Hydrology: Regional Studies, 9, 104–126. doi:10.1016/j.ejrh.2016.11.004
  • Hall, J. and Solomatine, D., 2008. A framework for uncertainty analysis in flood risk management decisions. International Journal of River Basin Management, 6 (2), 85–98. doi:10.1080/15715124.2008.9635339
  • He, J., Anderson., A., and Valeo, C., 2015. Bias compensation in flood frequency analysis. Hydrological Sciences Journal, 60 (3), 381–401. doi:10.1080/02626667.2014.885651
  • Hoffman, M.D. and Gelman, A., 2014. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15, 1593–1623.
  • Hosking, J.R.M. and Wallis, J.R., 1997. Regional frequency analysis: an approach based on L-moments. Cambridge: Cambridge University Press, 224. doi:10.1017/CBO9780511529443
  • Ishak, E., et al., 2011. Scaling property of regional floods in New South Wales Australia. Natural Hazards, 58 (3), 1155–1167. doi:10.1007/s11069-011-9719-6
  • Jenkinson, A.F., 1955. The frequency distribution of the annual maximum (or minimum) of meteorological elements. Quarterly Journal of the Royal Meteorological Society, 81 (348), 158–171. doi:10.1002/qj.49708134804
  • Jesus, L.F.L., Costa, V., and Fernandes, W., 2020. Evaluating the influence of extending hydrologic time series in extreme quantile estimation. Water and Environment Journal, (S1), in press. doi:10.1111/wej.12579
  • Khali, B. and Adamowski, J., 2014. Evaluation of the performance of eight record-extension techniques under different levels of association, presence of outliers and different sizes of concurrent records: A Monte Carlo study. Water Resources Management, 28 (14), 5139–5155. doi:10.1007/s11269-014-0799-4
  • Khalil, B., et al., 2016. A novel record-extension technique for water quality variables based on L-moments. Water Air Soil Poll, 227 (6), 1–20. doi:10.1007/s11270-016-2852-9
  • Kim, T., Kwon, H., and Lima, C., 2018. A Bayesian partial pooling approach to mean field bias correction of weather radar rainfall estimates: application to Osungsan weather radar in South Korea. Journal of Hydrology, 565, 14–26. doi:10.1016/j.jhydrol.2018.07.082
  • Kjeldsen, T.R. and Jones, D., 2007. Estimation of an index flood using data transfer in the UK. Hydrological Sciences Journal, 52 (1), 86–98. doi:10.1623/hysj.52.1.86
  • Kjeldsen, T.R. and Jones, D.A., 2009. An exploratory analysis of error components in hydrological regression modeling. Water Resources Research, 45 (2), W02407. doi:10.1029/2007WR006283
  • Kwon, H., Brown, C., and Lall, U., 2008. Climate informed flood frequency analysis and prediction in Montana using hierarchical Bayesian modeling. Geophysical Research Letters, 35 (5), L05404. doi:10.1029/2007GL032220
  • Lima, C.H.R., et al., 2016. A hierarchical Bayesian GEV model for improving local and regional flood quantile estimates. Journal of Hydrology, 541, 816–823. doi:10.1016/j.jhydrol.2016.07.042
  • Lima, C.H.R. and Lall, U., 2010. Spatial scaling in a changing climate: A hierarchical Bayesian model for non-stationary multi-site annual maximum and monthly streamflow. Journal of Hydrology, 383 (3–4), 307–318. doi:10.1016/j.jhydrol.2009.12.045
  • Martins, E. and Stedinger, J., 2000. Generalized maximum‐likelihood generalized extreme‐value quantile estimators for hydrologic data. Water Resources Research, 36 (3), 737–744. doi:10.1029/1999WR900330
  • McClymont, K., et al., 2019. Flood resilience: a systematic review. Journal of Environment Planning and Management. doi:10.1080/09640568.2019.1641474
  • McMillan, H., et al., 2017. How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications. Water Resources Research, 53 (7), 5220–5228. doi:10.1002/2016WR020328
  • Morrison, J.E. and Smith, J.A., 2002. Stochastic modeling of flood peaks using the generalized extreme value distribution. Water Resources Research, 38 (12), 1–12. doi:10.1029/2001WR000502
  • Müller, M.F. and Thompson, S.E., 2015. TopREML: a topological restricted maximum likelihood approach to regionalize trended runoff signatures in stream networks. Hydrology and Earth System Sciences, 19 (6), 2925–2942. doi:10.5194/hess-19-2925-2015
  • Naghettini, M., 2017. Fundamentals of statistical hydrology. Cham: Springer Internation Publishing.
  • Najafi, M.R. and Moradkhani, H., 2013. Analysis of runoff extremes using spatial hierarchical Bayesian modeling. Water Resources Research, 49 (10), 6656–6670. doi:10.1002/wrcr.20381
  • Northrop, P.J., 2004. Likelihood-based approaches to flood frequency estimation. Journal of Hydrology, 292 (1–4), 96–113. doi:10.1016/j.jhydrol.2003.12.031
  • O’Brien, N.L. and Burn, D.H., 2014. A nonstationary index-flood technique for estimating extreme quantiles for annual maximum streamflow. Journal of Hydrology, 519, 2040–2048. doi:10.1016/j.jhydrol.2014.09.041
  • Renard, B., 2011. A Bayesian hierarchical approach to regional frequency analysis. Water Resources Research, 47 (11), 1–21. doi:10.1029/2010WR010089
  • Sene, K.J., Houghton-Carr, H.A., and Hachache, A., 2001. Preliminary flood frequency estimates for Lebanon. Hydrological Sciences Journal, 46 (5), 659–676. doi:10.1080/02626660109492863
  • Silva, A., et al., 2017. A Bayesian peaks-over-threshold analysis of floods in the Itajaí-açu River under stationarity and nonstationarity. Stochastic Environmental Research and Risk Assessment (Print), 31 (1), 185–204. doi:10.1007/s00477-015-1184-4
  • Silva, A.T., Naghettini, M., and Portela, M.M., 2016. On some aspects of peaks-over-threshold modeling of floods under nonstationarity using climate covariates. Stochastic Environmental Research and Risk Assessment (Print), 30 (1), 207–224. doi:10.1007/s00477-015-1072-y
  • Silva, A.T., Portela, M.M., and Naghettini, M., 2014. On peaks-over-threshold modeling of floods with zero-inflated poisson arrivals under stationarity and nonstationarity. Stochastic Environmental Research and Risk Assessment (Print), 28 (6), 1587–1599. doi:10.1007/s00477-013-0813-z
  • Stan Development Team, 2020. RStan: the R interface to Stan. R package version 2.19.3. Retrieved from http://mc-stan.org/.
  • Thorarinsdottir, T.L., et al., 2018. Bayesian regional flood frequency analysis for large catchments. Water Resources Research, 54 (9), 6929–6947. doi:10.1029/2017WR022460
  • Tyralis, H., Papacharalampous, G., and Sarintip, T., 2019. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset. Journal of Hydrology, 574, 628–645. doi:10.20944/preprints201811.0265.v1
  • Vehtari, A., et al., 2019. Rank-normalization, folding, and localization: an improved R for assessing convergence of MCMC [online]. Available from: http://arxiv.org/abs/1903.08008v2 [Accessed 3 March 2020].
  • Villarini, G., et al., 2011. Analyses of seasonal and annual maximum daily discharge records for central Europe. Journal of Hydrology, 399 (3), 299–312. doi:10.1016/j.jhydrol.2011.01.007
  • Villarini, G., Smith, J.A., and Napolitano, F., 2010. Nonstationary modeling of a long record of rainfall and temperature over Rome. Advances in Water Resources, 33 (10), 1256–1267. doi:10.1016/j.advwatres.2010.03.013
  • Wu, Y., et al., 2018. Local and regional flood frequency analysis based on hierarchical Bayesian model: application to annual maximum streamflow for the Huaihe River basin. Hydrology and Earth System Sciences Discussions, 1–21. doi:10.5194/hess-2018-22
  • Wu, Y., Xue, L., and Liu, Y., 2019. Local and regional flood frequency analysis based on hierarchical Bayesian model in Dongting Lake Basin, China. Water Science and Engineering, 12 (4), 253–262. doi:10.1016/j.wse.2019.12.001
  • Yan, H. and Moradkhani, H., 2015. A regional Bayesian hierarchical model for flood frequency analysis. Stochastic Environmental Research and Risk Assessment, 29 (3), 1019–1036. doi:10.1007/s00477-014-0975-3
  • Yan, H. and Moradkhani, H., 2016. Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling. Natural Hazards, 81 (1), 203–225. doi:10.1007/s11069-015-2070-6

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.