- -

Hydrological post-processing based on approximate Bayesian computation (ABC)

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by


Hydrological post-processing based on approximate Bayesian computation (ABC)

Show full item record

Romero-Cuellar, J.; Abbruzzo, A.; Adelfio, G.; Francés, F. (2019). Hydrological post-processing based on approximate Bayesian computation (ABC). Stochastic Environmental Research and Risk Assessment. 33(7):1361-1373. https://doi.org/10.1007/s00477-019-01694-y

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/128735

Files in this item

Item Metadata

Title: Hydrological post-processing based on approximate Bayesian computation (ABC)
Author: Romero-Cuellar, Jonathan Abbruzzo, Antonino Adelfio, Giada Francés, F.
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería Hidráulica y Medio Ambiente - Departament d'Enginyeria Hidràulica i Medi Ambient
Issued date:
[EN] This study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can ...[+]
Subjects: Free-likelihood approach , Probabilistic modelling , Uncertainty analysis , Hydrological forecasting , Summary statistics
Copyrigths: Reserva de todos los derechos
Stochastic Environmental Research and Risk Assessment. (issn: 1436-3240 )
DOI: 10.1007/s00477-019-01694-y
Publisher version: http://doi.org/10.1007/s00477-019-01694-y
Project ID:
This study was partially supported by the Departamento del Huila Scholarship Program No. 677 (Colombia) and Colciencias, by the Spanish Research Project TETIS-MED (ref. CGL2014-58127-C3-3-R) and TETIS-CHANGE (ref.RTI2018 ...[+]
Type: Artículo


Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162(4):2025–2035

Blackwell D, Dubins L (1962) Merging of opinions with increasing information. Ann Math Stat 33(3):882–886

Bogner K, Liechti K, Zappa M (2016) Post-processing of stream flows in Switzerland with an emphasis on low flows and floods. Water 8(4):115 [+]
Beaumont MA, Zhang W, Balding DJ (2002) Approximate Bayesian computation in population genetics. Genetics 162(4):2025–2035

Blackwell D, Dubins L (1962) Merging of opinions with increasing information. Ann Math Stat 33(3):882–886

Bogner K, Liechti K, Zappa M (2016) Post-processing of stream flows in Switzerland with an emphasis on low flows and floods. Water 8(4):115

Brown JD, Seo D-J (2010) A nonparametric postprocessor for bias correction of hydrometeorological and hydrologic ensemble forecasts. J Hydrometeorol 11(3):642–665

Butts MB, Payne JT, Kristensen M, Madsen H (2004) An evaluation of the impact of model structure on hydrological modelling uncertainty for streamflow simulation. J Hydrol 298(1):242–266

Coccia G, Todini E (2011) Recent developments in predictive uncertainty assessment based on the model conditional processor approach. Hydrol Earth Syst Sci 15:3253–3274

Csillery K, Francois O, Blum MGB (2012) abc: an R package for approximate Bayesian computation (abc). Methods Ecol Evol 3:475–479

Diaconis P, Freedman D (1986) On the consistency of bayes estimates. Ann Stat 14(1):1–26

Diks CGH, Vrugt JA (2010) Comparison of point forecast accuracy of model averaging methods in hydrologic applications. Stoch Environ Res Risk Assess 24(6):809–820

Drovandi CC, Pettitt AN (2011) Likelihood-free Bayesian estimation of multivariate quantile distributions. Comput Stat Data Anal 55(9):2541–2556

Evin G, Thyer M, Kavetski D, McInerney D, Kuczera G (2014) Comparison of joint versus postprocessor approaches for hydrological uncertainty estimation accounting for error autocorrelation and heteroscedasticity. Water Resour Res 50(3):2350–2375

Fearnhead P, Prangle D (2012) Constructing summary statistics for approximate bayesian computation: semi-automatic approximate Bayesian computation. J R Stat Soc Ser B Stat Methodol 74(3):419–474

Fenicia F, Kavetski D, Reichert P, Albert C (2018) Signature-domain calibration of hydrological models using approximate Bayesian computation: empirical analysis of fundamental properties. Water Resour Res 54:3958–3987

Francés F, Vélez JI, Vélez JJ (2007) Split-parameter structure for the automatic calibration of distributed hydrological models. J Hydrol 332(1):226–240

Frazier DT, Maneesoonthorn W, Martin GM, McCabe BP (2019) Approximate Bayesian forecasting. Int J Forecast 35(2):521–539

Gelman A, Rubin DB (1992) Inference from iterative simulation using multiple sequences. Stat Sci 7(4):457–472

Gelman A, Stern HS, Carlin JB, Dunson DB, Vehtari A, Rubin DB (2013) Bayesian data analysis. Chapman and Hall/CRC, Boca Raton

Glahn HR, Lowry DA (1972) The use of model output statistics (mos) in objective weather forecasting. J Appl Meteorol 11(8):1203–1211

Gupta HV, Kling H, Yilmaz KK, Martinez GF (2009) Decomposition of the mean squared error and nse performance criteria: implications for improving hydrological modelling. J Hydrol 377(1):80–91

Haario H, Saksman E, Tamminen J (2001) An adaptive metropolis algorithm. Bernoulli 7(2):223–242

Kavetski D, Fenicia F, Reichert P, Albert C (2018) Signature-domain calibration of hydrological models using approximate Bayesian computation: theory and comparison to existing applications. Water Resour Res 54:4059–4083

Khajehei S, Moradkhani H (2017) Towards an improved ensemble precipitation forecast: a probabilistic post-processing approach. J Hydrol 546:476–489

Klein B, Meissner D, Kobialka H-U, Reggiani P (2016) Predictive uncertainty estimation of hydrological multi-model ensembles using pair-copula construction. Water 8(4):125

Krzysztofowicz R, Kelly KS (2000) Hydrologic uncertainty processor for probabilistic river stage forecasting. Water Resour Res 36(11):3265–3277

Laio F, Tamea S (2007) Verification tools for probabilistic forecasts of continuous hydrological variables. Hydrol Earth Syst Sci 11(4):1267–1277

Li B, Liang Z, He Y, Hu L, Zhao W, Acharya K (2017) Comparison of parameter uncertainty analysis techniques for a topmodel application. Stoch Environ Res Risk Assess 31(5):1045–1059

Liang Z, Chang W, Li B (2012) Bayesian flood frequency analysis in the light of model and parameter uncertainties. Stoch Environ Res Risk Assess 26(5):721–730

Lindley DV, Smith AFM (1972) Bayes estimates for the linear model. J R Stat Soc Ser B Methodol 34(1):1–41

Liu Y, Gupta HV (2007) Uncertainty in hydrologic modeling: toward an integrated data assimilation framework. Water Resour Res 43(7):W07401

Madadgar S, Moradkhani H (2014) Improved Bayesian multimodeling: integration of copulas and Bayesian model averaging. Water Resour Res 50(12):9586–9603

Marin J-M, Pudlo P, Robert CP, Ryder RJ (2012) Approximate Bayesian computational methods. Stat Comput 22(6):1167–1180

Marjoram P, Molitor J, Plagnol V, Tavaré S (2003) Markov chain monte carlo without likelihoods. Proc Natl Acad Sci 100(26):15324–15328

Marshall L, Nott D, Sharma A (2004) A comparative study of Markov chain Monte Carlo methods for conceptual rainfall-runoff modeling. Water Resour Res 40(2):W02501

Mengersen KL, Pudlo P, Robert CP (2013) Bayesian computation via empirical likelihood. Proc Natl Acad Sci 110(4):1321–1326

Montanari A, Brath A (2004) A stochastic approach for assessing the uncertainty of rainfall-runoff simulations. Water Resour Res 40:W01106. https://doi.org/10.1029/2003WR002540

Montanari A, Grossi G (2008) Estimating the uncertainty of hydrological forecasts: a statistical approach. Water Resour Res 44:W00B08. https://doi.org/10.1029/2008WR006897

Montanari A, Koutsoyiannis D (2012) A blueprint for process-based modeling of uncertain hydrological systems. Water Resour Res 48(9):W09555

Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900

Nott DJ, Marshall L, Brown J (2011) Generalized likelihood uncertainty estimation (glue) and approximate Bayesian computation: what’s the connection? Water Resour Res 48(12):W12602

Price LF, Drovandi CC, Lee A, Nott DJ (2018) Bayesian synthetic likelihood. J Comput Graph Stat 27(1):1–11

Pritchard JK, Seielstad MT, Perez-Lezaun A, Feldman MW (1999) Population growth of human y chromosomes: a study of y chromosome microsatellites. Mol Biol Evol 16(12):1791–1798

R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria

Raftery AE, Gneiting T, Balabdaoui F, Polakowski M (2005) Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 133(5):1155–1174

Reichert P, Langhans SD, Lienert J, Schuwirth N (2015) The conceptual foundation of environmental decision support. J Environ Manag 154:316–332

Robert CP (2016) Approximate bayesian computation: A survey on recent results. In: Cools R, Nuyens D (eds) Monte Carlo and Quasi-Monte Carlo Methods. Springer, Cham, pp 185–205

Romero-Cuéllar J, Buitrago-Vargas A, Quintero-Ruiz T, Francés F (2018) Modelling the potential impacts of climate change on the hydrology of the Aipe river basin in Huila, Colombia. Ribagua 5(1):63–78

Schefzik R, Thorarinsdottir TL, Gneiting T (2013) Uncertainty quantification in complex simulation models using ensemble copula coupling. Stat Sci 28(4):616–640

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(10):W10531

Schoups G, van de Giesen NC, Savenije HHG (2008) Model complexity control for hydrologic prediction. Water Resour Res 44(12):W00B03

Shafii M, Tolson B, Matott LS (2014) Uncertainty-based multi-criteria calibration of rainfall-runoff models: a comparative study. Stoch Environ Res Risk Assess 28(6):1493–1510

Sikorska AE, Montanari A, Koutsoyiannis D (2015) Estimating the uncertainty of hydrological predictions through data-driven resampling techniques. J Hydrol Eng 20(1):A4014009

Sisson SA, Fan Y, Tanaka MM (2007) Sequential Monte Carlo without likelihoods. Proc Natl Acad Sci 104(6):1760–1765

Solomatine DP, Shrestha DL (2009) A novel method to estimate model uncertainty using machine learning techniques. Water Resour Res 45:W00B11. https://doi.org/10.1029/2008WR006839

Tavaré S, Balding DJ, Griffiths RC, Donnelly P (1997) Inferring coalescence times from DNA sequence data. Genetics 145(2):505–518

Thomas H (1981) Improved methods for national water assessment, water resources contract: WR15249270. Technical report, Harvard University, Cambridge

Thyer M, Renard B, Kavetski D, Kuczera G, Franks SW, Srikanthan S (2009) Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: a case study using Bayesian total error analysis. Water Resour Res 45:W00B14. https://doi.org/10.1029/2008WR006825

Tian Y, Nearing GS, Peters-Lidard CD, Harrison KW, Tang L (2016) Performance metrics, error modeling, and uncertainty quantification. Mon Weather Rev 144(2):607–613

Todini E (2008) A model conditional processor to assess predictive uncertainty in flood forecasting. Int J River Basin Manag 6(2):123–137

Tran M-N, Nott DJ, Kohn R (2017) Variational bayes with intractable likelihood. J Comput Graph Stat 26(4):873–882

Turner BM, Van Zandt T (2012) A tutorial on approximate Bayesian computation. J Math Psychol 56(2):69–85

van Oijen M (2017) Bayesian methods for quantifying and reducing uncertainty and error in forest models. Curr For Rep 3(4):269–280

Vélez JJ, Puricelli M, López Unzu F, Francés F (2009) Parameter extrapolation to ungauged basins with a hydrological distributed model in a regional framework. Hydrol Earth Syst Sci 13(2):229–246

Vrugt JA, Robinson BA (2007) Treatment of uncertainty using ensemble methods: comparison of sequential data assimilation and Bayesian model averaging. Water Resour Res 43(1):W01411

Vrugt JA, Sadegh M (2013) Toward diagnostic model calibration and evaluation: approximate Bayesian computation. Water Resour Res 49:4335–4345

Waerden BVD (1953) Order tests for the two-sample problem and their power. Indag Math Proc 56:80

Wagener T, Gupta HV (2005) Model identification for hydrological forecasting under uncertainty. Stoch Environ Res Risk Assess 19(6):378–387

Wang Q, Robertson D, Chiew FS (2009) A bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour Res 45(5):W05407

Weerts AH, Winsemius HC, Verkade JS (2011) Estimation of predictive hydrological uncertainty using quantile regression: examples from the national flood forecasting system (england and wales). Hydrol Earth Syst Sci 15(1):255–265

Wentao L, Qingyun D, Chiyuan M, Aizhong Y, Wei G, Zhenhua D (2017) A review on statistical postprocessing methods for hydrometeorological ensemble forecasting. Wiley Interdiscip Rev Water 4(6):e1246

Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the river thames, UK. Water Resour Res 42(2):W02419

Woldemeskel F, McInerney D, Lerat J, Thyer M, Kavetski D, Shin D, Tuteja N, Kuczera G (2018) Evaluating post-processing approaches for monthly and seasonal streamflow forecasts. Hydrol Earth Syst Sci 22:6257–6278. https://doi.org/10.5194/hess-22-6257-2018

Ye A, Duan Q, Yuan X, Wood EF, Schaake J (2014) Hydrologic post-processing of MOPEX streamflow simulations. J Hydrol 508:147–156

Yoon S, Cho W, Heo J-H, Kim CE (2010) A full bayesian approach to generalized maximum likelihood estimation of generalized extreme value distribution. Stoch Environ Res Risk Assess 24(5):761–770

Zhang X, Zhao K (2012) Bayesian neural networks for uncertainty analysis of hydrologic modeling: a comparison of two schemes. Water Resour Manag 26(8):2365–2382

Zhao L, Duan Q, Schaake J, Ye A, Xia J (2011) A hydrologic post-processor for ensemble streamflow predictions. Adv Geosci 29:51–59

Zhu W, Marin JM, Leisen F (2016) A bootstrap likelihood approach to Bayesian computation. Aust N Z J Stat 58(2):227–244


This item appears in the following Collection(s)

Show full item record