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Volumn 81, Issue 1, 2016, Pages 203-225

Toward more robust extreme flood prediction by Bayesian hierarchical and multimodeling

Author keywords

BMA; Extreme flood; Spatial Bayesian hierarchical; Uncertainty

Indexed keywords

BAYESIAN ANALYSIS; FLOOD DAMAGE; FLOODING; NUMERICAL MODEL; PREDICTION; RISK ASSESSMENT; UNCERTAINTY ANALYSIS;

EID: 84957849948     PISSN: 0921030X     EISSN: 15730840     Source Type: Journal    
DOI: 10.1007/s11069-015-2070-6     Document Type: Article
Times cited : (43)

References (83)
  • 1
    • 1642291794 scopus 로고    scopus 로고
    • Alternative futures for the Willamette River Basin, Oregon
    • Baker JP, Hulse DW, Gregory SV et al (2004) Alternative futures for the Willamette River Basin, Oregon. Ecol Appl 14:313–324. doi:10.1890/02-5011
    • (2004) Ecol Appl , vol.14 , pp. 313-324
    • Baker, J.P.1    Hulse, D.W.2    Gregory, S.V.3
  • 3
    • 84923374970 scopus 로고    scopus 로고
    • Nonstationary precipitation intensity–duration–frequency curves for infrastructure design in a changing climate
    • Cheng L, AghaKouchak A (2014) Nonstationary precipitation intensity–duration–frequency curves for infrastructure design in a changing climate. Sci Rep 4:7093. doi:10.1038/srep07093
    • (2014) Sci Rep , vol.4 , pp. 7093
    • Cheng, L.1    AghaKouchak, A.2
  • 4
    • 84939932917 scopus 로고    scopus 로고
    • Non-stationary extreme value analysis in a changing climate
    • Cheng L, AghaKouchak A, Gilleland E, Katz RW (2014) Non-stationary extreme value analysis in a changing climate. Clim Change 127:353–369. doi:10.1007/s10584-014-1254-5
    • (2014) Clim Change , vol.127 , pp. 353-369
    • Cheng, L.1    AghaKouchak, A.2    Gilleland, E.3    Katz, R.W.4
  • 5
    • 84885153769 scopus 로고    scopus 로고
    • A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series
    • Cohn TA, England JF, Berenbrock CE et al (2013) A generalized Grubbs-Beck test statistic for detecting multiple potentially influential low outliers in flood series. Water Resour Res 49:5047–5058
    • (2013) Water Resour Res , vol.49 , pp. 5047-5058
    • Cohn, T.A.1    England, J.F.2    Berenbrock, C.E.3
  • 6
    • 0142219342 scopus 로고    scopus 로고
    • Anticipating catastrophes through extreme value modelling
    • Coles S, Pericchi L (2003) Anticipating catastrophes through extreme value modelling. J R Stat Soc Ser C Appl Stat 52:405–416. doi:10.1111/1467-9876.00413
    • (2003) J R Stat Soc Ser C Appl Stat , vol.52 , pp. 405-416
    • Coles, S.1    Pericchi, L.2
  • 7
    • 80055070051 scopus 로고    scopus 로고
    • Spatial hierarchical modeling of precipitation extremes from a regional climate model
    • Cooley D, Sain SR (2010) Spatial hierarchical modeling of precipitation extremes from a regional climate model. J Agric Biol Environ Stat 15:381–402. doi:10.1007/s13253-010-0023-9
    • (2010) J Agric Biol Environ Stat , vol.15 , pp. 381-402
    • Cooley, D.1    Sain, S.R.2
  • 8
    • 34548761070 scopus 로고    scopus 로고
    • Bayesian spatial modeling of extreme precipitation return levels
    • Cooley D, Nychka D, Naveau P (2007) Bayesian spatial modeling of extreme precipitation return levels. J Am Stat As 102:824–840
    • (2007) J Am Stat As , vol.102 , pp. 824-840
    • Cooley, D.1    Nychka, D.2    Naveau, P.3
  • 11
    • 85001842799 scopus 로고    scopus 로고
    • Regional flood-frequency analysis: how we got here and where we are going
    • Dawdy DR, Griffis VW, Gupta VK (2012) Regional flood-frequency analysis: how we got here and where we are going. J Hydrol Eng 17:953–959
    • (2012) J Hydrol Eng , vol.17 , pp. 953-959
    • Dawdy, D.R.1    Griffis, V.W.2    Gupta, V.K.3
  • 13
    • 84918841653 scopus 로고    scopus 로고
    • Toward a reliable prediction of seasonal forecast uncertainty: addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination
    • DeChant CM, Moradkhani H (2014b) Toward a reliable prediction of seasonal forecast uncertainty: addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination. J Hydrol 519:2967–2977
    • (2014) J Hydrol , vol.519 , pp. 2967-2977
    • DeChant, C.M.1    Moradkhani, H.2
  • 14
    • 33847274843 scopus 로고    scopus 로고
    • Multi-model ensemble hydrologic prediction using Bayesian model averaging
    • Duan Q, Ajami N, Gao X, Sorooshian S (2007) Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv Water Resour 30:1371–1386. doi:10.1016/j.advwatres.2006.11.014
    • (2007) Adv Water Resour , vol.30 , pp. 1371-1386
    • Duan, Q.1    Ajami, N.2    Gao, X.3    Sorooshian, S.4
  • 15
    • 0002344794 scopus 로고
    • Bootstrap methods: another look at the jackknife
    • Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7:1–26
    • (1979) Ann Stat , vol.7 , pp. 1-26
    • Efron, B.1
  • 16
    • 33750224092 scopus 로고    scopus 로고
    • A hierarchical model for extreme wind speeds
    • Fawcett L, Walshaw D (2006) A hierarchical model for extreme wind speeds. J R Stat Soc Ser C Appl Stat 55:631–646. doi:10.1111/j.1467-9876.2006.00557.x
    • (2006) J R Stat Soc Ser C Appl Stat , vol.55 , pp. 631-646
    • Fawcett, L.1    Walshaw, D.2
  • 17
    • 84950453304 scopus 로고
    • Sampling-based approaches to calculating marginal densities
    • Gelfand AE, Smith AFM (1990) Sampling-based approaches to calculating marginal densities. J Am Stat As 85:398–409. doi:10.1080/01621459.1990.10476213
    • (1990) J Am Stat As , vol.85 , pp. 398-409
    • Gelfand, A.E.1    Smith, A.F.M.2
  • 18
    • 84972492387 scopus 로고
    • Inference from iterative simulation using multiple sequences
    • Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7:457–472
    • (1992) Stat Sci , vol.7 , pp. 457-472
    • Gelman, A.1    Rubin, D.B.2
  • 20
    • 84855201150 scopus 로고    scopus 로고
    • A nonstationary flood frequency analysis method to adjust for future climate change and urbanization
    • Gilroy KL, McCuen RH (2012) A nonstationary flood frequency analysis method to adjust for future climate change and urbanization. J Hydrol 414–415:40–48. doi:10.1016/j.jhydrol.2011.10.009
    • (2012) J Hydrol , vol.414-415 , pp. 40-48
    • Gilroy, K.L.1    McCuen, R.H.2
  • 21
    • 34548436221 scopus 로고    scopus 로고
    • The use of GLS regression in regional hydrologic analyses
    • Griffis VW, Stedinger JR (2007) The use of GLS regression in regional hydrologic analyses. J Hydrol 344:82–95. doi:10.1016/j.jhydrol.2007.06.023
    • (2007) J Hydrol , vol.344 , pp. 82-95
    • Griffis, V.W.1    Stedinger, J.R.2
  • 22
    • 58549116730 scopus 로고    scopus 로고
    • Log-Pearson Type 3 distribution and its application in flood frequency analysis. III: Sample skew and weighted skew estimators
    • Griffis VW, Stedinger JR (2009) Log-Pearson Type 3 distribution and its application in flood frequency analysis. III: Sample skew and weighted skew estimators. J Hydrol Eng 14:121–130
    • (2009) J Hydrol Eng , vol.14 , pp. 121-130
    • Griffis, V.W.1    Stedinger, J.R.2
  • 23
    • 4143112347 scopus 로고    scopus 로고
    • Log Pearson type 3 quantile estimators with regional skew information and low outlier adjustments
    • Griffis VW, Stedinger JR, Cohn TA (2004) Log Pearson type 3 quantile estimators with regional skew information and low outlier adjustments. Water Resour Res 40. doi:10.1029/2003WR002697
    • (2004) Water Resour Res , pp. 40
    • Griffis, V.W.1    Stedinger, J.R.2    Cohn, T.A.3
  • 25
    • 0028666441 scopus 로고
    • Multiscaling theory of flood peaks: regional quantile analysis
    • Gupta VK, Mesa OJ, Dawdy DR (1994) Multiscaling theory of flood peaks: regional quantile analysis. Water Resour Res 30:3405
    • (1994) Water Resour Res , vol.30 , pp. 3405
    • Gupta, V.K.1    Mesa, O.J.2    Dawdy, D.R.3
  • 26
    • 77956890234 scopus 로고
    • Monte carlo sampling methods using Markov chains and their applications
    • Hastings WK (1970) Monte carlo sampling methods using Markov chains and their applications. Biometrika 57:97–109. doi:10.1093/biomet/57.1.97
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.K.1
  • 27
    • 0348088382 scopus 로고
    • Discussion on “Flood flows” by WE Fuller
    • Hazen A (1914) Discussion on “Flood flows” by WE Fuller. Trans ASCE 77:526–563
    • (1914) Trans ASCE , vol.77 , pp. 526-563
    • Hazen, A.1
  • 28
    • 0000310360 scopus 로고
    • L-moments: analysis and estimation of distributions using linear combinations of order statistics
    • Hosking JRM (1990) L-moments: analysis and estimation of distributions using linear combinations of order statistics. J R Stat Soc Ser C Appl Stat 52:105–124. doi:10.2307/2345653
    • (1990) J R Stat Soc Ser C Appl Stat , vol.52 , pp. 105-124
    • Hosking, J.R.M.1
  • 29
    • 0023794202 scopus 로고
    • The effect of intersite dependence on regional flood frequency analysis
    • Hosking JRM, Wallis JR (1988) The effect of intersite dependence on regional flood frequency analysis. Water Resour Res 24:588–600
    • (1988) Water Resour Res , vol.24 , pp. 588-600
    • Hosking, J.R.M.1    Wallis, J.R.2
  • 31
    • 68349099316 scopus 로고    scopus 로고
    • A sequential Bayesian approach for hydrologic model selection and prediction
    • Hsu KL, Moradkhani H, Sorooshian S (2009) A sequential Bayesian approach for hydrologic model selection and prediction. Water Resour Res. doi:10.1029/2008WR006824
    • (2009) Water Resour Res
    • Hsu, K.L.1    Moradkhani, H.2    Sorooshian, S.3
  • 32
    • 0036704796 scopus 로고    scopus 로고
    • Statistics of extremes in hydrology
    • Katz RW, Parlange MB, Naveau P (2002) Statistics of extremes in hydrology. Adv Water Resour 25:1287–1304. doi:10.1016/S0309-1708(02)00056-8
    • (2002) Adv Water Resour , vol.25 , pp. 1287-1304
    • Katz, R.W.1    Parlange, M.B.2    Naveau, P.3
  • 33
    • 0036501518 scopus 로고    scopus 로고
    • Probability distribution of low streamflow series in the United States
    • Kroll CN, Vogel RM (2002) Probability distribution of low streamflow series in the United States. J Hydrol Eng 7:137–146
    • (2002) J Hydrol Eng , vol.7 , pp. 137-146
    • Kroll, C.N.1    Vogel, R.M.2
  • 34
    • 43549117060 scopus 로고    scopus 로고
    • Climate informed flood frequency analysis and prediction in Montana using hierarchical Bayesian modeling
    • Kwon H-H, Brown C, Lall U (2008) Climate informed flood frequency analysis and prediction in Montana using hierarchical Bayesian modeling. Geophys Res Lett 35. doi:10.1029/2007GL032220
    • (2008) Geophys Res Lett , pp. 35
    • Kwon, H.-H.1    Brown, C.2    Lall, U.3
  • 35
    • 84868007919 scopus 로고    scopus 로고
    • The detection of atmospheric rivers in atmospheric reanalyses and their links to British winter floods and the large-scale climatic circulation
    • Lavers DA, Villarini G, Allan RP, Wood EF, Wade AJ (2012) The detection of atmospheric rivers in atmospheric reanalyses and their links to British winter floods and the large-scale climatic circulation. J Geophys Res: Atmos (1984–2012) 117. doi:10.1029/2012JD018027
    • (2012) J Geophys Res: Atmos (1984–2012) , pp. 117
    • Lavers, D.A.1    Villarini, G.2    Allan, R.P.3    Wood, E.F.4    Wade, A.J.5
  • 36
    • 0023524221 scopus 로고
    • Effect of regional heterogeneity on flood frequency estimation
    • Lettenmaier DP, Wallis JR, Wood EF (1987) Effect of regional heterogeneity on flood frequency estimation. Water Resour Res 23:313–323
    • (1987) Water Resour Res , vol.23 , pp. 313-323
    • Lettenmaier, D.P.1    Wallis, J.R.2    Wood, E.F.3
  • 37
    • 77649193501 scopus 로고    scopus 로고
    • Spatial scaling in a changing climate: a hierarchical bayesian model for non-stationary multi-site annual maximum and monthly streamflow
    • Lima CHR, Lall U (2010) Spatial scaling in a changing climate: a hierarchical bayesian model for non-stationary multi-site annual maximum and monthly streamflow. J Hydrol 383:307–318. doi:10.1016/j.jhydrol.2009.12.045
    • (2010) J Hydrol , vol.383 , pp. 307-318
    • Lima, C.H.R.1    Lall, U.2
  • 38
    • 85027932033 scopus 로고    scopus 로고
    • Improved Bayesian multimodeling: integration of copulas and Bayesian model averaging
    • Madadgar S, Moradkhani H (2014) Improved Bayesian multimodeling: integration of copulas and Bayesian model averaging. Water Resour Res 50:9586–9603
    • (2014) Water Resour Res , vol.50 , pp. 9586-9603
    • Madadgar, S.1    Moradkhani, H.2
  • 39
    • 0034019933 scopus 로고    scopus 로고
    • Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data
    • Martins ES, Stedinger JR (2000) Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data. Water Resour Res 36:737–744. doi:10.1029/1999WR900330
    • (2000) Water Resour Res , vol.36 , pp. 737-744
    • Martins, E.S.1    Stedinger, J.R.2
  • 41
    • 0035396765 scopus 로고    scopus 로고
    • Generalized flood skew: map versus watershed skew
    • McCuen RH (2001) Generalized flood skew: map versus watershed skew. J Hydrol Eng 6:293–299
    • (2001) J Hydrol Eng , vol.6 , pp. 293-299
    • McCuen, R.H.1
  • 42
    • 38849090083 scopus 로고    scopus 로고
    • Climate change. Stationarity is dead: whither water management?
    • Milly PCD, Betancourt J, Falkenmark M et al (2008) Climate change. Stationarity is dead: whither water management? Science 319:573–574. doi:10.1126/science.1151915
    • (2008) Science , vol.319 , pp. 573-574
    • Milly, P.C.D.1    Betancourt, J.2    Falkenmark, M.3
  • 43
    • 20844449766 scopus 로고    scopus 로고
    • Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter
    • Moradkhani H, Hsu K-L, Gupta H, Sorooshian S (2005) Uncertainty assessment of hydrologic model states and parameters: sequential data assimilation using the particle filter. Water Resour Res 41:W05012. doi:10.1029/2004WR003604
    • (2005) Water Resour Res , vol.41 , pp. 05012
    • Moradkhani, H.1    Hsu, K.-L.2    Gupta, H.3    Sorooshian, S.4
  • 44
    • 84871364784 scopus 로고    scopus 로고
    • Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method
    • Moradkhani H, Dechant CM, Sorooshian S (2012) Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method. Water Resour Res 48:W12520. doi:10.1029/2012WR012144
    • (2012) Water Resour Res , vol.48 , pp. 12520
    • Moradkhani, H.1    Dechant, C.M.2    Sorooshian, S.3
  • 45
    • 84885533219 scopus 로고    scopus 로고
    • Analysis of runoff extremes using spatial hierarchical Bayesian modeling
    • Najafi MR, Moradkhani H (2013) Analysis of runoff extremes using spatial hierarchical Bayesian modeling. Water Resour Res 49:6656–6670. doi:10.1002/wrcr.20381
    • (2013) Water Resour Res , vol.49 , pp. 6656-6670
    • Najafi, M.R.1    Moradkhani, H.2
  • 46
    • 84916908812 scopus 로고    scopus 로고
    • A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes
    • Najafi MR, Moradkhani H (2014) A hierarchical Bayesian approach for the analysis of climate change impact on runoff extremes. Hydrol Process 28:6292–6308
    • (2014) Hydrol Process , vol.28 , pp. 6292-6308
    • Najafi, M.R.1    Moradkhani, H.2
  • 47
    • 84951320469 scopus 로고    scopus 로고
    • Ensemble combination of seasonal streamflow forecasts
    • Najafi MR, Moradkhani H (2015a) Ensemble combination of seasonal streamflow forecasts. J Hydrol Eng. doi:10.1061/(ASCE)HE.1943-5584.0001250
    • (2015) J Hydrol Eng
    • Najafi, M.R.1    Moradkhani, H.2
  • 48
    • 84927640926 scopus 로고    scopus 로고
    • Multi-model ensemble analysis of runoff extremes for climate change impact assessments
    • Najafi MR, Moradkhani H (2015b) Multi-model ensemble analysis of runoff extremes for climate change impact assessments. J Hydrol 525:352–361. doi:10.1016/j.jhydrol.2015.03.045
    • (2015) J Hydrol , vol.525 , pp. 352-361
    • Najafi, M.R.1    Moradkhani, H.2
  • 49
    • 80051577111 scopus 로고    scopus 로고
    • Assessing the uncertainties of hydrologic model selection in climate change impact studies
    • Najafi MR, Moradkhani H, Jung IW (2011) Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol Process 25:2814–2826. doi:10.1002/hyp.8043
    • (2011) Hydrol Process , vol.25 , pp. 2814-2826
    • Najafi, M.R.1    Moradkhani, H.2    Jung, I.W.3
  • 50
    • 84876708297 scopus 로고    scopus 로고
    • Dynamical structure of extreme floods in the US Midwest and the United Kingdom
    • Nakamura J, Lall U, Kushnir Y, Robertson AW, Seager R (2013) Dynamical structure of extreme floods in the US Midwest and the United Kingdom. J Hydrometeorol 14:485–504
    • (2013) J Hydrometeorol , vol.14 , pp. 485-504
    • Nakamura, J.1    Lall, U.2    Kushnir, Y.3    Robertson, A.W.4    Seager, R.5
  • 51
    • 77951978159 scopus 로고    scopus 로고
    • Likelihood-based inference for max-stable processes
    • Padoan SA, Ribatet M, Sisson SA (2010) Likelihood-based inference for max-stable processes. J Am Stat As 105:263–277
    • (2010) J Am Stat As , vol.105 , pp. 263-277
    • Padoan, S.A.1    Ribatet, M.2    Sisson, S.A.3
  • 52
    • 84858823339 scopus 로고    scopus 로고
    • Toward reduction of model uncertainty: integration of Bayesian model averaging and data assimilation
    • Parrish MA, Moradkhani H, Dechant CM (2012) Toward reduction of model uncertainty: integration of Bayesian model averaging and data assimilation. Water Resour Res. doi:10.1029/2011WR011116
    • (2012) Water Resour Res
    • Parrish, M.A.1    Moradkhani, H.2    Dechant, C.M.3
  • 53
    • 79952687006 scopus 로고    scopus 로고
    • Can atmospheric circulation be linked to flooding in Europe?
    • Prudhomme C, Genevier M (2011) Can atmospheric circulation be linked to flooding in Europe? Hydrol Process 25:1180–1190. doi:10.1002/hyp.7879
    • (2011) Hydrol Process , vol.25 , pp. 1180-1190
    • Prudhomme, C.1    Genevier, M.2
  • 55
    • 26944484410 scopus 로고    scopus 로고
    • Bayesian MCMC flood frequency analysis with historical information
    • Reis DS, Stedinger JR (2005). Bayesian MCMC flood frequency analysis with historical information. J Hydrol 313:97–116
    • (2005) J Hydrol , vol.313 , pp. 97-116
    • Reis, D.S.1    Stedinger, J.R.2
  • 56
    • 28244446739 scopus 로고    scopus 로고
    • Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation
    • Reis DS, Stedinger JR, Martins ES (2005) Bayesian generalized least squares regression with application to log Pearson type 3 regional skew estimation. Water Resour Res. doi:10.1029/2004WR003445
    • (2005) Water Resour Res
    • Reis, D.S.1    Stedinger, J.R.2    Martins, E.S.3
  • 57
    • 81155138235 scopus 로고    scopus 로고
    • A Bayesian hierarchical approach to regional frequency analysis
    • Renard B (2011) A Bayesian hierarchical approach to regional frequency analysis. Water Resour Res. doi:10.1029/2010WR010089
    • (2011) Water Resour Res
    • Renard, B.1
  • 58
    • 85027937787 scopus 로고    scopus 로고
    • Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields
    • Renard B, Lall U (2014) Regional frequency analysis conditioned on large-scale atmospheric or oceanic fields. Water Resour Res 50:9536–9554
    • (2014) Water Resour Res , vol.50 , pp. 9536-9554
    • Renard, B.1    Lall, U.2
  • 59
    • 84889095242 scopus 로고    scopus 로고
    • Bayesian methods for non-stationary extreme value analysis
    • AghaKouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S, (eds), Water science and technology library, Springer, Netherlands
    • Renard B, Sun X, Lang M (2013) Bayesian methods for non-stationary extreme value analysis. In: AghaKouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S (eds) Extremes in a changing climate, water science and technology library, vol 65. Springer, Netherlands, pp 39–95
    • (2013) Extremes in a changing climate , pp. 39-95
    • Renard, B.1    Sun, X.2    Lang, M.3
  • 61
    • 0030619030 scopus 로고    scopus 로고
    • An investigation into the physical causes of scaling and heterogeneity of regional flood frequency
    • Robinson JS, Sivapalan M (1997) An investigation into the physical causes of scaling and heterogeneity of regional flood frequency. Water Resour Res 33:1045
    • (1997) Water Resour Res , vol.33 , pp. 1045
    • Robinson, J.S.1    Sivapalan, M.2
  • 62
    • 0025223964 scopus 로고
    • Regional analyses of precipitation annual maxima in Washington State
    • Schaefer MG (1990) Regional analyses of precipitation annual maxima in Washington State. Water Resour Res 26:119–131
    • (1990) Water Resour Res , vol.26 , pp. 119-131
    • Schaefer, M.G.1
  • 63
    • 77954727893 scopus 로고    scopus 로고
    • Schoups G, Vrugt JA (2010) A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors. Water Resour Res 46:W10531
    • Schoups G, Vrugt JA (2010) A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non-Gaussian errors. Water Resour Res 46:W10531. doi:10.1029/2009WR008933
  • 64
    • 0020971814 scopus 로고
    • Estimating a regional flood frequency distribution
    • Stedinger JR (1983) Estimating a regional flood frequency distribution. Water Resour Res 19:503–510
    • (1983) Water Resour Res , vol.19 , pp. 503-510
    • Stedinger, J.R.1
  • 65
    • 41049107629 scopus 로고    scopus 로고
    • Flood frequency analysis in the United States: time to update
    • Stedinger JR, Griffis VW (2008) Flood frequency analysis in the United States: time to update. J Hydrol Eng 13:199–204
    • (2008) J Hydrol Eng , vol.13 , pp. 199-204
    • Stedinger, J.R.1    Griffis, V.W.2
  • 66
    • 0022264492 scopus 로고
    • Regional hydrologic analysis. 1. Ordinary, weighted, and generalized least-squares compared
    • Stedinger JR, Tasker GD (1985) Regional hydrologic analysis. 1. Ordinary, weighted, and generalized least-squares compared. Water Resour Res 21:1421–1432. doi:10.1029/WR022i005p00844
    • (1985) Water Resour Res , vol.21 , pp. 1421-1432
    • Stedinger, J.R.1    Tasker, G.D.2
  • 67
    • 0022787981 scopus 로고
    • Regional hydrologic analysis, 2, model-error estimators, estimation of sigma and log-Pearson Type 3 distributions
    • Stedinger JR, Tasker GD (1986) Regional hydrologic analysis, 2, model-error estimators, estimation of sigma and log-Pearson Type 3 distributions. Water Resour Res 22:1487–1499. doi:10.1029/WR022i010p01487
    • (1986) Water Resour Res , vol.22 , pp. 1487-1499
    • Stedinger, J.R.1    Tasker, G.D.2
  • 69
    • 0024856194 scopus 로고
    • An operational GLS model for hydrologic regression
    • Tasker GD, Stedinger JR (1989) An operational GLS model for hydrologic regression. J Hydrol 111:361–375
    • (1989) J Hydrol , vol.111 , pp. 361-375
    • Tasker, G.D.1    Stedinger, J.R.2
  • 70
    • 78349280388 scopus 로고    scopus 로고
    • Modeling hydrologic and water quality extremes in a changing climate: a statistical approach based on extreme value theory
    • Towler E, Rajagopalan B, Gilleland E et al (2010) Modeling hydrologic and water quality extremes in a changing climate: a statistical approach based on extreme value theory. Water Resour Res. doi:10.1029/2009WR008876
    • (2010) Water Resour Res
    • Towler, E.1    Rajagopalan, B.2    Gilleland, E.3
  • 71
    • 84957847709 scopus 로고
    • Guidelines for determining flood flow frequency. Bulletin 17B, Hydrology Subcommittee, Office of Water Data Coordination
    • Reston, Virginia
    • U.S. Water Resources Council (1982) Guidelines for determining flood flow frequency. Bulletin 17B, Hydrology Subcommittee, Office of Water Data Coordination, US Geological Survey, Reston, Virginia
    • (1982) US Geological Survey
  • 73
    • 0027796194 scopus 로고
    • L moment diagrams should replace product moment diagrams
    • Vogel RM, Fennessey NM (1993) L moment diagrams should replace product moment diagrams. Water Resour Res 29:1745–1752
    • (1993) Water Resour Res , vol.29 , pp. 1745-1752
    • Vogel, R.M.1    Fennessey, N.M.2
  • 74
    • 0030126025 scopus 로고    scopus 로고
    • Probability distribution of annual maximum, mean, and minimum streamflows in the United States
    • Vogel RM, Wilson I (1996) Probability distribution of annual maximum, mean, and minimum streamflows in the United States. J Hydrol Eng 1:69–76
    • (1996) J Hydrol Eng , vol.1 , pp. 69-76
    • Vogel, R.M.1    Wilson, I.2
  • 75
    • 0027788447 scopus 로고
    • Floodflow frequency model selection in Australia
    • Vogel RM, McMahon TA, Chiew FHS (1993) Floodflow frequency model selection in Australia. J Hydrol 146:421–449
    • (1993) J Hydrol , vol.146 , pp. 421-449
    • Vogel, R.M.1    McMahon, T.A.2    Chiew, F.H.S.3
  • 76
    • 85027933298 scopus 로고    scopus 로고
    • Incorporating spatial dependence in regional frequency analysis
    • Wang Z, Yan J, Zhang X (2014) Incorporating spatial dependence in regional frequency analysis. Water Resour Res 50:9570–9585
    • (2014) Water Resour Res , vol.50 , pp. 9570-9585
    • Wang, Z.1    Yan, J.2    Zhang, X.3
  • 77
    • 41949124639 scopus 로고    scopus 로고
    • Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts?
    • Weigel AP, Liniger MA, Appenzeller C (2008) Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Q J R Meteorol Soc 134:241–260
    • (2008) Q J R Meteorol Soc , vol.134 , pp. 241-260
    • Weigel, A.P.1    Liniger, M.A.2    Appenzeller, C.3
  • 79
    • 84888062752 scopus 로고    scopus 로고
    • Effects of land use change on hydrologic response at a watershed scale, Arkansas
    • Yan H, Edwards FG (2013) Effects of land use change on hydrologic response at a watershed scale, Arkansas. J Hydrol Eng 18:1779–1785. doi:10.1061/(ASCE)HE.1943-5584.0000743
    • (2013) J Hydrol Eng , vol.18 , pp. 1779-1785
    • Yan, H.1    Edwards, F.G.2
  • 80
    • 84925465263 scopus 로고    scopus 로고
    • A regional Bayesian hierarchical model for flood frequency analysis
    • Yan H, Moradkhani H (2015) A regional Bayesian hierarchical model for flood frequency analysis. Stoch Environ Res Risk Assess 29:1019–1036. doi:10.1007/s00477-014-0975-3
    • (2015) Stoch Environ Res Risk Assess , vol.29 , pp. 1019-1036
    • Yan, H.1    Moradkhani, H.2
  • 81
    • 85027941044 scopus 로고    scopus 로고
    • Improving soil moisture profile prediction with the particle filter-Markov chain Monte Carlo method
    • Yan H, DeChant CM, Moradkhani H (2015) Improving soil moisture profile prediction with the particle filter-Markov chain Monte Carlo method. IEEE Trans Geosci Remote Sens 53:6134–6147. doi:10.1109/TGRS.2015.2432067
    • (2015) IEEE Trans Geosci Remote Sens , vol.53 , pp. 6134-6147
    • Yan, H.1    DeChant, C.M.2    Moradkhani, H.3
  • 82
    • 9444277765 scopus 로고    scopus 로고
    • Possible regional probability distribution type of Canadian annual streamflow by L-moments
    • Yue S, Wang CY (2004) Possible regional probability distribution type of Canadian annual streamflow by L-moments. Water Resour Manag 18:425–438
    • (2004) Water Resour Manag , vol.18 , pp. 425-438
    • Yue, S.1    Wang, C.Y.2
  • 83
    • 84930207276 scopus 로고    scopus 로고
    • Evaluation of flood frequency under non-stationarity resulting from climate indices and reservoir indices in the East River basin, China
    • Zhang Q, Gu X, Singh VP et al (2015) Evaluation of flood frequency under non-stationarity resulting from climate indices and reservoir indices in the East River basin, China. J Hydrol 527:565–575. doi:10.1016/j.jhydrol.2015.05.029
    • (2015) J Hydrol , vol.527 , pp. 565-575
    • Zhang, Q.1    Gu, X.2    Singh, V.P.3


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