Articles | Volume 28, issue 13
https://doi.org/10.5194/hess-28-2831-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2831-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jul 2024
Research article |
| 03 Jul 2024
| 03 Jul 2024
Using the classical model for structured expert judgment to estimate extremes: a case study of discharges in the Meuse River
Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Pattle Delamore Partners Ltd., Ōtautahi / Christchurch, New Zealand
Oswaldo Morales-Nápoles
Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
Matthijs Kok
Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
HKV consultants, Delft, the Netherlands
Related authors
Fumihiko Uemura, Guus Rongen, Shigekazu Masuya, Takatoshi Yoshida, and Tomohito J. Yamada
Proc. IAHS, 386, 69–74, https://doi.org/10.5194/piahs-386-69-2024, https://doi.org/10.5194/piahs-386-69-2024, 2024
Short summary
Short summary
To accurately assess flood risk, it is necessary to evaluate whether a dike will fail. The internal structure and slope conditions of dikes are different from place to place, and it is difficult to survey all of them. Thus, we proposed a method to define the heterogeneity of levees as uncertainty and to calculate the dike failure as probability. Our method can set conditions of dike failure that are closer to reality, and will contribute to improving the accuracy of flood risk assessment.
Bart Strijker and Matthijs Kok
Nat. Hazards Earth Syst. Sci., 25, 3355–3379, https://doi.org/10.5194/nhess-25-3355-2025, https://doi.org/10.5194/nhess-25-3355-2025, 2025
Short summary
Short summary
This study examines how hydraulic head levels in canal dikes respond to heavy rainfall, potentially causing instabilities and flooding. Using time series models and simulating long-term head levels, we identified clusters of dikes where head peaks are driven by similar rainfall events. Statistical analyses show that extreme and yearly conditions are close. However, extreme conditions are expected to become more frequent due to climate change, though some dikes will be less affected than others.
Cees Oerlemans, Martine van den Boomen, Ties Rijcken, and Matthijs Kok
EGUsphere, https://doi.org/10.5194/egusphere-2024-2910, https://doi.org/10.5194/egusphere-2024-2910, 2025
Short summary
Short summary
This study analyzes flood exposure in Rotterdam's unembanked areas from 1970 to 2150, exploring the interplay between rising sea levels, urban development, and flood protection measures. Without measures, flood exposure will increase, especially after 2100. The Maeslant storm surge barrier had the most impact on flood exposure, followed by urban development and sea level rise. Varied exposure levels across neighborhoods suggest the need for localized adaptation strategies.
Fumihiko Uemura, Guus Rongen, Shigekazu Masuya, Takatoshi Yoshida, and Tomohito J. Yamada
Proc. IAHS, 386, 69–74, https://doi.org/10.5194/piahs-386-69-2024, https://doi.org/10.5194/piahs-386-69-2024, 2024
Short summary
Short summary
To accurately assess flood risk, it is necessary to evaluate whether a dike will fail. The internal structure and slope conditions of dikes are different from place to place, and it is difficult to survey all of them. Thus, we proposed a method to define the heterogeneity of levees as uncertainty and to calculate the dike failure as probability. Our method can set conditions of dike failure that are closer to reality, and will contribute to improving the accuracy of flood risk assessment.
Elisa Ragno, Markus Hrachowitz, and Oswaldo Morales-Nápoles
Hydrol. Earth Syst. Sci., 26, 1695–1711, https://doi.org/10.5194/hess-26-1695-2022, https://doi.org/10.5194/hess-26-1695-2022, 2022
Short summary
Short summary
We explore the ability of non-parametric Bayesian networks to reproduce maximum daily discharge in a given month in a catchment when the remaining hydro-meteorological and catchment attributes are known. We show that a saturated network evaluated in an individual catchment can reproduce statistical characteristics of discharge in about ~ 40 % of the cases, while challenges remain when a saturated network considering all the catchments together is evaluated.
Cited articles
Al-Awadhi, S. A. and Garthwaite, P. H.: An elicitation method for multivariate normal distributions, Commun. Stat. Theory 27, 1123–1142, 1998. a
Bamber, J. L., Oppenheimer, M., Kopp, R. E., Aspinall, W. P., and Cooke, R. M.: Ice sheet contributions to future sea-level rise from structured expert judgment, P. Natl. Acad. Sci. USA, 116, 11195–11200, 2019. a
Benito, G. and Thorndycraft, V.: Palaeoflood hydrology and its role in applied hydrological sciences, J. Hydrol., 313, 3–15, 2005. a
Bernard, A. and Bos-Levenbach, E.: The plotting of observations on probability-paper, Stichting Mathematisch Centrum, Statistische Afdeling, https://ir.cwi.nl/pub/8241 (last access: 21 June 2024), 1955. a
Bouaziz, L. J. E., Fenicia, F., Thirel, G., de Boer-Euser, T., Buitink, J., Brauer, C. C., De Niel, J., Dewals, B. J., Drogue, G., Grelier, B., Melsen, L. A., Moustakas, S., Nossent, J., Pereira, F., Sprokkereef, E., Stam, J., Weerts, A. H., Willems, P., Savenije, H. H. G., and Hrachowitz, M.: Behind the scenes of streamflow model performance, Hydrol. Earth Syst. Sci., 25, 1069–1095, https://doi.org/10.5194/hess-25-1069-2021, 2021. a
Brázdil, R., Kundzewicz, Z. W., Benito, G., Demarée, G., Macdonald, N., and Roald, L. A.: Historical floods in Europe in the past millennium, in: Changes in Flood Risk in Europe, edited by: Kundzewicz, Z. W., IAHS Press, Wallingford, 121–166, ISBN 978-1-907161-28-5, 2012. a
Colson, A. R. and Cooke, R. M.: Cross validation for the classical model of structured expert judgment, Reliab. Eng. Syst. Safe., 163, 109–120, 2017. a
Cooke, R. M. and Goossens, L. L.: TU Delft expert judgment data base, Reliab. Eng. Syst. Safe., 93, 657–674, https://doi.org/10.1016/j.ress.2007.03.005, 2008. a, b, c
Cooke, R. M., Marti, D., and Mazzuchi, T.: Expert forecasting with and without uncertainty quantification and weighting: What do the data say?, Int. J. Forecasting, 37, 378–387, 2021. a
Copernicus Land Monitoring Service: EU-DEM, ©European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA), https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1/view (last access: 12 October 2021), 2017. a
Copernicus Land Monitoring Service: CORINE Land Cover, © European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA), https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download (last access: 16 March 2022), 2018. a
Copernicus Land Monitoring Service: E-OBS, ©European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA), https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview (last access: 19 May 2022), 2020. a
de Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue, G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H., Thirel, G., and Willems, P.: Looking beyond general metrics for model comparison – lessons from an international model intercomparison study, Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017, 2017. a
De Niel, J., Demarée, G., and Willems, P.: Weather Typing-Based Flood Frequency Analysis Verified for Exceptional Historical Events of Past 500 Years Along the Meuse River, Water Resour. Res., 53, 8459–8474, https://doi.org/10.1002/2017WR020803, 2017. a
Dewals, B., Erpicum, S., Pirotton, M., and Archambeau, P.: Extreme floods in Belgium. The July 2021 extreme floods in the Belgian part of the Meuse basin, Hydrolink, 104–107, 2021. a
Dimitriadis, P., Koutsoyiannis, D., Iliopoulou, T., and Papanicolaou, P.: A Global-Scale Investigation of Stochastic Similarities in Marginal Distribution and Dependence Structure of Key Hydrological-Cycle Processes, Hydrology, 8, 59, https://doi.org/10.3390/hydrology8020059, 2021. a
Dion, P., Galbraith, N., and Sirag, E.: Using expert elicitation to build long-term projection assumptions, in: Developments in demographic forecasting, Springer, Cham, 43–62, https://doi.org/10.1007/978-3-030-42472-5, 2020. a
Eggstaff, J. W., Mazzuchi, T. A., and Sarkani, S.: The effect of the number of seed variables on the performance of Cooke's classical model, Reliab. Eng. Syst. Safe., 121, 72–82, https://doi.org/10.1016/j.ress.2013.07.015, 2014. a
Food and Agriculture Organization of the United Nations: Digital Soil Map of the World, Land and Water Development Division, FAO, Rome, https://data.apps.fao.org/map/catalog/srv/eng/catalog.search?id=14116#/metadata/446ed430-8383-11db-b9b2-000d939bc5d8 (last access: 20 June 2022, 2003. a
Foreman-Mackey, D., Hogg, D. W., Lang, D., and Goodman, J.: emcee: The MCMC Hammer, Publ. Astron. Soc. Pac., 125, 306, https://doi.org/10.1086/670067, 2013. a
Goodman, J. and Weare, J.: Ensemble samplers with affine invariance, Comm. App. Math. Com. Sc., 5, 65–80, 2010. a
Hanea, A., Morales Napoles, O., and Ababei, D.: Non-parametric Bayesian networks: Improving theory and reviewing applications, Reliab. Eng. Syst. Safe., 144, 265–284, https://doi.org/10.1016/j.ress.2015.07.027, 2015. a
Hegnauer, M. and Van den Boogaard, H.: GPD verdeling in de GRADE onzekerheidsanalyse voor de Maas, Tech. rep., Deltares, Delft, 2016. a
Hegnauer, M., Beersma, J., Van den Boogaard, H., Buishand, T., and Passchier, R.: Generator of Rainfall and Discharge Extremes (GRADE) for the Rhine and Meuse basins. Final report of GRADE 2.0, Tech. rep., Deltares, Delft, https://edepot.wur.nl/505208 (last access: 21 June 2024), 2014. a
Jenkinson, A. F.: The frequency distribution of the annual maximum (or minimum) values of meteorological elements, Q. J. Roy. Meteor. Soc., 81, 158–171, https://doi.org/10.1002/qj.49708134804, 1955. a
Keelin, T. W.: The metalog distributions, Decis. Anal., 13, 243–277, 2016. a
Kindermann, P. E., Brouwer, W. S., van Hamel, A., van Haren, M., Verboeket, R. P., Nane, G. F., Lakhe, H., Prajapati, R., and Davids, J. C.: Return level analysis of the hanumante river using structured expert judgment: a reconstruction of historical water levels, Water, 12, 3229, https://doi.org/10.3390/w12113229, 2020. a
Koutsoyiannis, D.: Statistics of extremes and estimation of extreme rainfall: I. Theoretical investigation/Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: I. Recherche théorique, Hydrolog. Sci. J., 49, 575–590, https://doi.org/10.1623/hysj.49.4.575.54430, 2004a. a
Koutsoyiannis, D.: Statistics of extremes and estimation of extreme rainfall: II. Empirical investigation of long rainfall records/Statistiques de valeurs extrêmes et estimation de précipitations extrêmes: II. Recherche empirique sur de longues séries de précipitations, Hydrolog. Sci. J., 49, 591–610, https://doi.org/10.1623/hysj.49.4.591.54424, 2004b. a
Land NRW: ELWAS-WEB, https://www.elwasweb.nrw.de (last access: 21 June 2024), 2022. a
Leander, R., Buishand, A., Aalders, P., and Wit, M. D.: Estimation of extreme floods of the River Meuse using a stochastic weather generator and a rainfall, Hydrolog. Sci. J., 50, 1089–1103, https://doi.org/10.1623/hysj.2005.50.6.1089, 2005. a, b
Leontaris, G. and Morales-Nápoles, O.: ANDURIL – A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments, SoftwareX, 7, 313–317, https://doi.org/10.1016/j.softx.2018.07.001, 2018. a
Marti, D., Mazzuchi, T. A., and Cooke, R. M.: Are Performance Weights Beneficial? Investigating the Random Expert Hypothesis, Expert Judgement in Risk and Decision Analysis, 293, 53–82, https://doi.org/10.1007/978-3-030-46474-5_3, 2021. a
Martins, E. S. and Stedinger, J. R.: Generalized maximum-likelihood generalized extreme-value quantile estimators for hydrologic data, Water Resour. Res., 36, 737–744, 2000. a
Ministry of Infrastructure and Environment: Regeling veiligheid primaire waterkeringen 2017 no IENM/BSK-2016/283517, https://wetten.overheid.nl/BWBR0039040/2017-01-01 (last access: 21 June 2024), 2016. a
Mohr, S., Ehret, U., Kunz, M., Ludwig, P., Caldas-Alvarez, A., Daniell, J. E., Ehmele, F., Feldmann, H., Franca, M. J., Gattke, C., Hundhausen, M., Knippertz, P., Küpfer, K., Mühr, B., Pinto, J. G., Quinting, J., Schäfer, A. M., Scheibel, M., Seidel, F., and Wisotzky, C.: A multi-disciplinary analysis of the exceptional flood event of July 2021 in central Europe – Part 1: Event description and analysis, Nat. Hazards Earth Syst. Sci., 23, 525–551, https://doi.org/10.5194/nhess-23-525-2023, 2023. a
Oppenheimer, M., Little, C. M., and Cooke, R. M.: Expert judgement and uncertainty quantification for climate change, Nat. Clim. Change, 6, 445–451, 2016. a
Parent, E. and Bernier, J.: Encoding prior experts judgments to improve risk analysis of extreme hydrological events via POT modeling, J. Hydrol., 283, 1–18, 2003. a
Python Software Foundation: Python Language Reference, version 3.10, http://www.python.org, last access: 24 June 2024. a
Rijkswaterstaat: Waterinfo, https://waterinfo.rws.nl/#!/kaart/Afvoer/Debiet___20Oppervlaktewater___20m3___2Fs/ (last access: 21 June 2024), Rijkswaterstaat Waterinfo, 2022. a
Rongen, G.: The effect of flooding along the Belgian Meuse on the discharge and hydrograph shape at Eijsden, MS thesis, Delft University of Technology, http://resolver.tudelft.nl/uuid:046c8e8e-34e8-4c92-a2d3-81e531997f0d (last access: 21 June 2024), 2016. a
Rongen, G.: Code and data underlying the thesis “Evidence based expert judgment in flood risk”, Version 1, 4TU.ResearchData [data set], https://doi.org/10.4121/a6333b17-bab2-476f-a636-61244b5c6f9e.v1, 2024. a
Rongen, G., 't Hart, C. M. P., Leontaris, G., and Morales-Nápoles, O.: Update (1.2) to ANDURIL and ANDURYL: Performance improvements and a graphical user interface, SoftwareX, 12, 100497, https://doi.org/10.1016/j.softx.2020.100497, 2020. a
Rongen, G., Morales-Nápoles, O., and Kok, M.: Extreme Discharge Uncertainty Estimates for the River Meuse Using a Hierarchical Non-Parametric Bayesian Network, in: Proceedings of the 32th European Safety and Reliability Conference (ESREL 2022), edited by: Leva, M. C., Patelli, E., Podofillini, L., and Wilson, S., Research Publishing, 2670–2677, https://doi.org/10.3850/978-981-18-5183-4_S17-04-622-cd, 2022a. a
Rongen, G., Morales-Nápoles, O., and Kok, M.: Expert judgment-based reliability analysis of the Dutch flood defense system, Reliab. Eng. Syst. Safe., 224, 108535, 2022b. a
Sebok, E., Henriksen, H. J., Pastén-Zapata, E., Berg, P., Thirel, G., Lemoine, A., Lira-Loarca, A., Photiadou, C., Pimentel, R., Royer-Gaspard, P., Kjellström, E., Christensen, J. H., Vidal, J. P., Lucas-Picher, P., Donat, M. G., Besio, G., Polo, M. J., Stisen, S., Caballero, Y., Pechlivanidis, I. G., Troldborg, L., and Refsgaard, J. C.: Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies, Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, 2022. a
Service public de Wallonie: Annuaires et statistiques, http://voies-hydrauliques.wallonie.be/opencms/opencms/fr/hydro/Archive/annuaires/index.html (last access: 21 June 2024), Voies Hydraulique Wallonie, 2022. a
't Hart, C. M. P., Leontaris, G., and Morales-Nápoles, O.: Update (1.1) to ANDURIL – A MATLAB toolbox for ANalysis and Decisions with UnceRtaInty: Learning from expert judgments: ANDURYL, SoftwareX, 10, 100295, https://doi.org/10.1016/j.softx.2019.100295, 2019. a
van de Langemheen, W. and Berger, H.: Hydraulische randvoorwaarden 2001: maatgevende afvoeren Rijn en Maas, Tech. rep., RIZA, https://repository.tudelft.nl/islandora/object/uuid:94cc1032-4115-43b9-b4e7-e01ee3195f50/datastream/OBJ/download (last access: 21 June 2024), ISBN 9036954355, 2001. a
Waterschap Limburg: Discharge Measurements, Historical time series from personal communication, Waterschap Limburg, https://www.waterstandlimburg.nl/Home) (last access: 16 August 2021), 2021. a
Short summary
This study proposes a new method for predicting extreme events such as floods on the river Meuse. The current method was shown to be unreliable as it did not predict a recent flood. We developed a model that includes information from experts and combines this with measurements. We found that this approach gives more accurate predictions, particularly for extreme events. The research is important for predictions of extreme flood levels that are necessary for protecting communities against floods.
This study proposes a new method for predicting extreme events such as floods on the river...
