Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6421-2021
© Author(s) 2021. 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-25-6421-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2021
Research article |
| 20 Dec 2021
| 20 Dec 2021
Possibilistic response surfaces: incorporating fuzzy thresholds into bottom-up flood vulnerability analysis
Thibaut Lachaut
CORRESPONDING AUTHOR
Département de Génie Civil et de Génie des Eaux, Laval University, Quebec, QC G1V 0A6, Canada
Amaury Tilmant
Département de Génie Civil et de Génie des Eaux, Laval University, Quebec, QC G1V 0A6, Canada
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Thibaut Lachaut and Amaury Tilmant
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-214, https://doi.org/10.5194/hess-2020-214, 2020
Manuscript not accepted for further review
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Response surfaces are increasingly used to identify the hydro-climatic conditions leading to water resources system's failure. Partitioning the surface usually requires performance thresholds that are not necessarily crisp. We propose a methodology that combines the inherent uncertainty of response surfaces with the ambiguity of performance thresholds. The proposed methodology is illustrated with a multireservoir system in Canada for which some performance thresholds are imprecise.
Etienne Guilpart, Vahid Espanmanesh, Amaury Tilmant, and François Anctil
Hydrol. Earth Syst. Sci., 25, 4611–4629, https://doi.org/10.5194/hess-25-4611-2021, https://doi.org/10.5194/hess-25-4611-2021, 2021
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The stationary assumption in hydrology has become obsolete because of climate changes. In that context, it is crucial to assess the performance of a hydrologic model over a wide range of climates and their corresponding hydrologic conditions. In this paper, numerous, contrasted, climate sequences identified by a hidden Markov model (HMM) are used in a differential split-sample testing framework to assess the robustness of a hydrologic model. We illustrate the method on the Senegal River.
Thibaut Lachaut and Amaury Tilmant
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Manuscript not accepted for further review
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Response surfaces are increasingly used to identify the hydro-climatic conditions leading to water resources system's failure. Partitioning the surface usually requires performance thresholds that are not necessarily crisp. We propose a methodology that combines the inherent uncertainty of response surfaces with the ambiguity of performance thresholds. The proposed methodology is illustrated with a multireservoir system in Canada for which some performance thresholds are imprecise.
Nicolas Avisse, Amaury Tilmant, Marc François Müller, and Hua Zhang
Hydrol. Earth Syst. Sci., 21, 6445–6459, https://doi.org/10.5194/hess-21-6445-2017, https://doi.org/10.5194/hess-21-6445-2017, 2017
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Information on small reservoir storage is crucial for water management in a river basin. However, it is most of the time not freely available in remote, ungauged, or conflict-torn areas. We propose a novel approach using satellite imagery information only to quantitatively estimate storage variations in such inaccessible areas. We apply the method to southern Syria, where ground monitoring is impeded by the ongoing civil war, and validate it against in situ measurements in neighbouring Jordan.
Diane Arjoon, Amaury Tilmant, and Markus Herrmann
Hydrol. Earth Syst. Sci., 20, 2135–2150, https://doi.org/10.5194/hess-20-2135-2016, https://doi.org/10.5194/hess-20-2135-2016, 2016
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An institutional arrangement that distributes welfare in a transboundary river basin is described. A hydro-economic model is used to allocate water resources, water users pay for water and the totals of these water charges are equitably redistributed through a sharing rule developed using an axiomatic approach. The technique may ensure economic efficiency and lead to more equitable solutions in the sharing of benefits in transboundary river basins.
H. Macian-Sorribes, M. Pulido-Velazquez, and A. Tilmant
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One of the most promising alternatives to improve the efficiency in water usage is the implementation of scarcity-based pricing policies based on the opportunity cost of water at the basin scale. Time series of the marginal value of water at selected locations (reservoirs) are obtained using a stochastic hydro-economic model and then post-processed to define step water pricing policies.
A. Tilmant, G. Marques, and Y. Mohamed
Hydrol. Earth Syst. Sci., 19, 1457–1467, https://doi.org/10.5194/hess-19-1457-2015, https://doi.org/10.5194/hess-19-1457-2015, 2015
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As water resources are increasingly used for various purposes, there is a need for a unified framework to describe, quantify and classify water use in a region, be it a catchment, a river basin or a country. This paper presents a novel water accounting framework whereby the contribution of traditional water uses but also storage services are properly considered.
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Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System
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Describing the interannual variability of precipitation with the derived distribution approach: effects of record length and resolution
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Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data
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Jitao Zhang, Dimitri Solomatine, and Zengchuan Dong
Hydrol. Earth Syst. Sci., 28, 3739–3753, https://doi.org/10.5194/hess-28-3739-2024, https://doi.org/10.5194/hess-28-3739-2024, 2024
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Faced with the problem of uncertainty in the field of water resources management, this paper proposes the Copula Multi-objective Robust Optimization and Probabilistic Analysis of Robustness (CM-ROPAR) approach to obtain robust water allocation schemes based on the uncertainty of drought and wet encounters and the uncertainty of inflow. We believe that this research article not only highlights the significance of the CM-ROPAR approach but also provides a new concept for uncertainty analysis.
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This paper presents an interdisciplinary model for quantifying uncertainties in water allocation under climate change. It combines climate, hydrological, and microeconomic experiments with a decision support system. Multi-model analyses reveal potential futures for water management policies, emphasizing nonlinear climate responses. As illustrated in the Douro River Basin, minor water allocation changes have significant economic impacts, stresssing the need for adaptation strategies.
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, https://doi.org/10.5194/hess-26-2939-2022, 2022
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The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used does not represent the flow in the rivers correctly. We found that, by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected, thus becoming more useful.
Marina R. L. Mautner, Laura Foglia, and Jonathan D. Herman
Hydrol. Earth Syst. Sci., 26, 1319–1340, https://doi.org/10.5194/hess-26-1319-2022, https://doi.org/10.5194/hess-26-1319-2022, 2022
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Jessica E. Cherry, Corrie Knapp, Sarah Trainor, Andrea J. Ray, Molly Tedesche, and Susan Walker
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Claudio I. Meier, Jorge Sebastián Moraga, Geri Pranzini, and Peter Molnar
Hydrol. Earth Syst. Sci., 20, 4177–4190, https://doi.org/10.5194/hess-20-4177-2016, https://doi.org/10.5194/hess-20-4177-2016, 2016
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Usman T. Khan and Caterina Valeo
Hydrol. Earth Syst. Sci., 20, 2267–2293, https://doi.org/10.5194/hess-20-2267-2016, https://doi.org/10.5194/hess-20-2267-2016, 2016
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M. Biasutti and R. Seager
Hydrol. Earth Syst. Sci., 19, 2945–2961, https://doi.org/10.5194/hess-19-2945-2015, https://doi.org/10.5194/hess-19-2945-2015, 2015
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We estimate future changes in US erosivity from the most recent ensemble projections of daily and monthly rainfall accumulation. The expectation of overall increase in erosivity is confirmed by these calculations, but a quantitative assessment is marred by large uncertainties. Specifically, the uncertainty in the method of estimation of erosivity is more consequential than that deriving from the spread in climate simulations, and leads to changes of uncertain sign in parts of the south.
M. C. Peel, R. Srikanthan, T. A. McMahon, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 1615–1639, https://doi.org/10.5194/hess-19-1615-2015, https://doi.org/10.5194/hess-19-1615-2015, 2015
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T. A. McMahon, M. C. Peel, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 361–377, https://doi.org/10.5194/hess-19-361-2015, https://doi.org/10.5194/hess-19-361-2015, 2015
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Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.
L. J. M. Peeters, G. M. Podger, T. Smith, T. Pickett, R. H. Bark, and S. M. Cuddy
Hydrol. Earth Syst. Sci., 18, 3777–3785, https://doi.org/10.5194/hess-18-3777-2014, https://doi.org/10.5194/hess-18-3777-2014, 2014
L. Zhuo, M. M. Mekonnen, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, https://doi.org/10.5194/hess-18-2219-2014, 2014
C. A. Scott, S. Vicuña, I. Blanco-Gutiérrez, F. Meza, and C. Varela-Ortega
Hydrol. Earth Syst. Sci., 18, 1339–1348, https://doi.org/10.5194/hess-18-1339-2014, https://doi.org/10.5194/hess-18-1339-2014, 2014
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
D. Zhu, D. Z. Peng, and I. D. Cluckie
Hydrol. Earth Syst. Sci., 17, 1445–1453, https://doi.org/10.5194/hess-17-1445-2013, https://doi.org/10.5194/hess-17-1445-2013, 2013
B. L. Harding, A. W. Wood, and J. R. Prairie
Hydrol. Earth Syst. Sci., 16, 3989–4007, https://doi.org/10.5194/hess-16-3989-2012, https://doi.org/10.5194/hess-16-3989-2012, 2012
J.-S. Yang, E.-S. Chung, S.-U. Kim, and T.-W. Kim
Hydrol. Earth Syst. Sci., 16, 801–814, https://doi.org/10.5194/hess-16-801-2012, https://doi.org/10.5194/hess-16-801-2012, 2012
S. Quiroga, Z. Fernández-Haddad, and A. Iglesias
Hydrol. Earth Syst. Sci., 15, 505–518, https://doi.org/10.5194/hess-15-505-2011, https://doi.org/10.5194/hess-15-505-2011, 2011
Cited articles
Afshar, A., Mariño, M. A., Saadatpour, M., and Afshar, A.: Fuzzy TOPSIS
Multi-Criteria Decision Analysis Applied to Karun Reservoirs System, Water Resour. Manage., 25, 545–563, https://doi.org/10.1007/s11269-010-9713-x, 2011.
Andrade, R. A. E., González, E., Fernández, E., and Gutiérrez, S. M.: A Fuzzy Approach to Prospect Theory, in: Soft Computing for Business Intelligence, in: Studies in Computational Intelligence, edited by: Espin, R., Pérez, R. B., Cobo, A., Marx, J., and Valdés, A. R., Springer, Berlin, Heidelberg, 45–66, https://doi.org/10.1007/978-3-642-53737-0_3, 2014.
Ben-Haim, Y.: Info-Gap Decision Theory: Decisions Under Severe Uncertainty, Academic Press, London, https://doi.org/10.1016/B978-0-12-373552-2.X5000-0, 2006.
Borgomeo, E., Farmer, C. L., and Hall, J. W.: Numerical rivers: A synthetic
streamflow generator for water resources vulnerability assessments, Water
Resour. Res., 51, 5382–5405, https://doi.org/10.1002/2014WR016827, 2015.
Broderick, C., Murphy, C., Wilby, R. L., Matthews, T., Prudhomme, C., and
Adamson, M.: Using a scenario‐neutral framework to avoid potential
maladaptation to future flood risk, Water Resour. Res., 55, 1079–1104,
https://doi.org/10.1029/2018WR023623, 2019.
Brown, C. and Wilby, R. L.: An alternate approach to assessing climate risks,
Eos Trans. Am. Geophys. Union, 93, 401–402, https://doi.org/10.1029/2012EO410001, 2012.
Brown, C., Werick, W., Leger, W., and Fay, D.: A decision-analytic approach to managing climate risks: Application to the upper Great Lakes, J. Am. Water Resour. Assoc., 47, 524–534, https://doi.org/10.1111/j.1752-1688.2011.00552.x, 2011.
Brown, C., Ghile, Y., Laverty, M., and Li, K.: Decision scaling: Linking
bottom-up vulnerability analysis with climate projections in the water sector, Water Resour. Res., 48, W09537, https://doi.org/10.1029/2011WR011212, 2012.
Brown, C., Steinschneider, S., Ray, P., Wi, S., Basdekas, L., and Yates, D.:
Decision Scaling (DS): decision support for climate change, in: Decision
Making under Deep Uncertainty: From Theory to Practice, edited by: Marchau, V. A. W. J., Walker, W. E., Bloemen, P. J. T. M., and Popper, S. W., Springer International Publishing, Cham, 255–287, https://doi.org/10.1007/978-3-030-05252-2_12, 2019.
Castagnoli, E. and Li Calzi, M. L.: Expected utility without utility, Theor.
Decis., 41, 281–301, https://doi.org/10.1007/BF00136129, 1996.
CEHQ – Centre d'Expertise Hydrique du Québec: Atlas hydroclimatique du
Québec méridional – Impact des changements climatiques sur les
régimes de crue, d'étiage et d'hydraulicité à l'horizon 2050,
Québec, 2015.
Chukhrova, N. and Johannssen, A.: Fuzzy regression analysis: Systematic review and bibliography, Appl. Soft Comput., 84, 105708,
https://doi.org/10.1016/j.asoc.2019.105708, 2019.
Culley, S., Noble, S., Yates, A., Timbs, M., Westra, S., Maier, H. R.,
Giuliani, M., and Castelletti, A.: A bottom-up approach to identifying the
maximum operational adaptive capacity of water resource systems to a changing
climate: identifying the maximum operational adaptive capacity, Water Resour. Res., 52, 6751–6768, https://doi.org/10.1002/2015WR018253, 2016.
DiFrancesco, K., Gitelman, A., and Purkey, D.: Bottom-Up Assessment of Climate Risk and the Robustness of Proposed Flood Management Strategies in the American River, CA, Water, 12, 907, https://doi.org/10.3390/w12030907, 2020.
Dubois, D. and Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum, New York, ISBN 9780306425202, 1988.
Dubois, D., Foulloy, L., Mauris, G., and Prade, H.: Probability-possibility
transformations, triangular fuzzy sets, and probabilistic inequalities,
Reliable Comput., 10, 273–297, https://doi.org/10.1023/B:REOM.0000032115.22510.b5,
2004.
El-Baroudy, I. and Simonovic, S. P.: Fuzzy criteria for the evaluation of water resource systems performance, Water Resour. Res., 40, W10503, https://doi.org/10.1029/2003WR002828, 2004.
Feng, M., Liu, P., Guo, S., Gui, Z., Zhang, X., Zhang, W., and Xiong, L.:
Identifying changing patterns of reservoir operating rules under various
inflow alteration scenarios, Adv. Water Resour., 104, 23–36,
https://doi.org/10.1016/j.advwatres.2017.03.003, 2017.
Fortin, J.-P., Turcotte, R., Massicotte, S., Moussa, R., Fitzback, J., and Villeneuve, J.-P.: Distributed Watershed Model Compatible with Remote Sensing and GIS Data. I: Description of Model, J. Hydrol. Eng., 6, 91–99, https://doi.org/10.1061/(ASCE)1084-0699(2001)6:2(91), 2001.
Fortin, L.-G., Turcotte, R., Pugin, S., Cyr, J.-F., and Picard, F.: Impact des changements climatiques sur les plans de gestion des lacs Saint-François et Aylmer au sud du Québec, Can. J. Civ. Eng., 34,
934–945, https://doi.org/10.1139/l07-030, 2007.
Garibaldi, J. and John, R.: Choosing membership functions of linguistic terms, in: vol. 1, The 12th IEEE International Conference on Fuzzy Systems,
FUZZ'03, 25–28 May 2003, St. Louis, MO, USA, 578–583, https://doi.org/10.1109/FUZZ.2003.1209428, 2003.
Gu, J., Zheng, Y., Tian, X., and Xu, Z.: A decision-making framework based on
prospect theory with probabilistic linguistic term sets, J. Operat. Res. Soc., 72, 879–888, https://doi.org/10.1080/01605682.2019.1701957, 2020.
Hadjimichael, A., Quinn, J., Wilson, E., Reed, P., Basdekas, L., Yates, D., and Garrison, M.: Defining Robustness, Vulnerabilities, and Consequential
Scenarios for Diverse Stakeholder Interests in Institutionally Complex River Basins, Earth's Future, 8, e2020EF001503, https://doi.org/10.1029/2020EF001503, 2020.
Hashimoto, T., Stedinger, J. R., and Loucks, D. P.: Reliability, resiliency,
and vulnerability criteria for water resource system performance evaluation,
Water Resour. Res., 18, 14–20, https://doi.org/10.1029/WR018i001p00014, 1982.
Herman, J. D., Zeff, H. B., Reed, P. M., and Characklis, G. W.: Beyond
optimality: Multistakeholder robustness tradeoffs for regional water portfolio planning under deep uncertainty, Water Resour. Res., 50, 7692–7713, https://doi.org/10.1002/2014WR015338, 2014.
Huynh, V.-N., Nakamori, Y., Ryoke, M., and Ho, T.-B.: Decision making under
uncertainty with fuzzy targets, Fuzzy Optimiz. Decis. Mak., 6, 255–278, https://doi.org/10.1007/s10700-007-9011-0, 2007.
Jun, K.-S., Chung, E.-S., Kim, Y.-G., and Kim, Y.: A fuzzy multi-criteria
approach to flood risk vulnerability in South Korea by considering climate change impacts, Exp. Syst. Appl., 40, 1003–1013, https://doi.org/10.1016/j.eswa.2012.08.013, 2013.
Kahneman, D. and Tversky, A.: Prospect theory: an analysis of decision under
risk, Econometrica, 47, 263–291, https://doi.org/10.2307/1914185, 1979.
Kay, A. L., Crooks, S. M., and Reynard, N. S.: Using response surfaces to
estimate impacts of climate change on flood peaks: assessment of uncertainty,
Hydrol. Process., 28, 5273–5287, https://doi.org/10.1002/hyp.10000, 2014.
Keller, L., Rössler, O., Martius, O., and Weingartner, R.: Comparison of
scenario‐neutral approaches for estimation of climate change impacts on flood characteristics, Hydrol. Process., 33, 535–550, https://doi.org/10.1002/hyp.13341, 2019.
Khazaeni, G., Khanzadi, M., and Afshar, A.: Fuzzy adaptive decision making
model for selection balanced risk allocation, Int. J. Project Manage., 30, 511–522, https://doi.org/10.1016/j.ijproman.2011.10.003, 2012.
Kim, D., Chun, J. A., and Aikins, C. M.: An hourly-scale scenario-neutral flood risk assessment in a mesoscale catchment under climate change, Hydrol.
Process., 32, 3416–3430, https://doi.org/10.1002/hyp.13273, 2018.
Kim, D., Chun, J. A., and Choi, S. J.: Incorporating the logistic regression
into a decision-centric assessment of climate change impacts on a complex
river system, Hydrol. Earth Syst. Sci., 23, 1145–1162,
https://doi.org/10.5194/hess-23-1145-2019, 2019.
Kirsch, B. R., Characklis, G. W., and Zeff, H. B.: Evaluating the impact of
alternative hydro-climate scenarios on transfer agreements: practical
improvement for generating synthetic streamflows, J. Water Resour. Pl. Manage., 139, 396–406, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000287, 2013.
Klipsch, J. and Hurst, M.: HEC-ResSim: reservoir system simulation, User's manual version 3.0 CPD-82, USACE, Hydrologic Engineering Center, Davis, CA, 2007.
Knighton, J., Steinschneider, S., and Walter, M. T.: A vulnerability-based,
bottom-up assessment of future riverine flood risk using a modified
peaks-over-threshold approach and a physically based hydrologic model, Water
Resour. Res., 53, 10043–10064, https://doi.org/10.1002/2017WR021036, 2017.
Lamontagne, J. R., Reed, P. M., Marangoni, G., Keller, K., and Garner, G. G.:
Robust abatement pathways to tolerable climate futures require immediate
global action, Nat. Clim. Change, 9, 290–294, https://doi.org/10.1038/s41558-019-0426-8, 2019.
Le Cozannet, G., Manceau, J.-C., and Rohmer, J.: Bounding probabilistic
sea-level projections within the framework of the possibility theory,
Environ. Res. Lett., 12, 014012, https://doi.org/10.1088/1748-9326/aa5528, 2017.
Lempert, R. J.: Robust Decision Making (RDM), in: Decision Making under Deep Uncertainty: From Theory to Practice, edited by: Marchau, V. A. W. J., Walker, W. E., Bloemen, P. J. T. M., and Popper, S. W., Springer International Publishing, Cham, 23–51, https://doi.org/10.1007/978-3-030-05252-2_2,
2019.
Lempert, R. J., Groves, D. G., Popper, S. W., and Bankes, S. C.: A general,
analytic method for generating robust strategies and narrative scenarios,
Manage. Sci., 52, 514–528, https://doi.org/10.1287/mnsc.1050.0472, 2006.
Liu, Y., Fan, Z.-P., and Zhang, Y.: Risk decision analysis in emergency
response: A method based on cumulative prospect theory, Comput. Operat. Res., 42, 75–82, https://doi.org/10.1016/j.cor.2012.08.008, 2014.
Loucks, D. P. and van Beek, E.: Water Resource Systems Planning and Management: An Introduction to Methods, Models, and Applications, google-Books-ID: stlCDwAAQBAJ, Springer, Cham, https://doi.org/10.1007/978-3-319-44234-1, 2017.
Maier, H., Guillaume, J., van Delden, H., Riddell, G., Haasnoot, M., and
Kwakkel, J.: An uncertain future, deep uncertainty, scenarios, robustness and
adaptation: How do they fit together?, Environ. Model. Softw., 81, 154–164, https://doi.org/10.1016/j.envsoft.2016.03.014, 2016.
Marchau, V. A. W. J., Walker, W. E., Bloemen, P. J. T. M., and Popper, S. W. (Eds.): Decision Making under Deep Uncertainty: from theory to practice,
Springer International Publishing, Cham, https://doi.org/10.1007/978-3-030-05252-2, 2019.
Marcos-Garcia, P., Brown, C., and Pulido-Velazquez, M.: Development of
Climate Impact Response Functions for highly regulated water resource systems, J. Hydrol., 590, 125251, https://doi.org/10.1016/j.jhydrol.2020.125251, 2020.
Masaki, Y., Hanasaki, N., Takahashi, K., and Hijioka, Y.: Global-scale analysis on future changes in flow regimes using Gini and Lorenz asymmetry
coefficients, Water Resour. Res., 50, 4054–4078, https://doi.org/10.1002/2013WR014266, 2014.
Mastrandrea, M. D., Heller, N. E., Root, T. L., and Schneider, S. H.: Bridging the gap: linking climate-impacts research with adaptation planning and management, Climatic Change, 100, 87–101, https://doi.org/10.1007/s10584-010-9827-4, 2010.
MELCC – Ministère de l'Environnement et de la Lutte contre les Changements Climatiques: Données du Programme de surveillance du climat, Direction générale du suivi de l'état de l'environnement, Québec, 2018.
Milly, P. C. D., Betancourt, J., Falkenmark, M., Hirsch, R. M., Kundzewicz,
Z. W., Lettenmaier, D. P., and Stouffer, R. J.: Stationarity is dead: whither
water management?, Science, 319, 573–574, https://doi.org/10.1126/science.1151915,
2008.
Moody, P. and Brown, C.: Robustness indicators for evaluation under climate
change: Application to the upper Great Lakes, Water Resour. Res., 49, 3576–3588, https://doi.org/10.1002/wrcr.20228, 2013.
Mpelasoka, F. S. and Chiew, F. H. S.: Influence of rainfall scenario
construction methods on runoff projections, J. Hydrometeorol., 10, 1168–1183, https://doi.org/10.1175/2009JHM1045.1, 2009.
Namdari, M., Taheri, S. M., Abadi, A., Rezaei, M., and Kalantari, N.:
Possibilistic logistic regression for fuzzy categorical response data, in:
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 7–10 July 2013, Hyderabad, India, 1–6, https://doi.org/10.1109/FUZZ-IEEE.2013.6622457, 2013.
Nazemi, A., Wheater, H. S., Chun, K. P., and Elshorbagy, A.: A stochastic
reconstruction framework for analysis of water resource system vulnerability
to climate-induced changes in river flow regime, Water Resour. Res., 49, 291–305, https://doi.org/10.1029/2012WR012755, 2013.
Nazemi, A., Masoud, Z., and Elmira, H.: Uncertainty in bottom-up vulnerability assessments of water supply systems due to regional streamflow generation under changing conditions, J. Water Resour. Pl. Manage., 146, 04019071, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001149, 2020.
Nowak, K., Prairie, J., Rajagopalan, B., and Lall, U.: A nonparametric
stochastic approach for multisite disaggregation of annual to daily streamflow, Water Resour. Res., 46, W08529, https://doi.org/10.1029/2009WR008530, 2010.
Pirttioja, N., Palosuo, T., Fronzek, S., Räisänen, J., Rötter, R. P., and Carter, T. R.: Using impact response surfaces to analyse the likelihood of impacts on crop yield under probabilistic climate change, Agr. Forest Meteorol., 264, 213–224, https://doi.org/10.1016/j.agrformet.2018.10.006, 2019.
Poff, N. L., Brown, C. M., Grantham, T. E., Matthews, J. H., Palmer, M. A.,
Spence, C. M., Wilby, R. L., Haasnoot, M., Mendoza, G. F., Dominique, K. C.,
and Baeza-Castro, A.: Sustainable water management under future uncertainty
with eco-engineering decision scaling, Nat. Clim. Change, 6, 25–34,
https://doi.org/10.1038/nclimate2765, 2016.
Pourahmad, S., Ayatollahi, S. M. T., Taheri, S. M., and Agahi, Z. H.: Fuzzy
logistic regression based on the least squares approach with application in
clinical studies, Comput. Math. Appl., 62, 3353–3365,
https://doi.org/10.1016/j.camwa.2011.08.050, 2011.
Prudhomme, C., Wilby, R., Crooks, S., Kay, A., and Reynard, N.: Scenario-neutral approach to climate change impact studies: Application to
flood risk, J. Hydrol., 390, 198–209, https://doi.org/10.1016/j.jhydrol.2010.06.043, 2010.
Qiu, Q., Liu, J., Li, C., Yu, X., and Wang, Y.: The use of an integrated
variable fuzzy sets in water resources management, Proc. IAHS, 379, 249–253, https://doi.org/10.5194/piahs-379-249-2018, 2018.
Quinn, J. D., Reed, P. M., Giuliani, M., and Castelletti, A.: Rival framings:
A framework for discovering how problem formulation uncertainties shape risk management trade-offs in water resources systems, Water Resour. Res., 53, 7208–7233, https://doi.org/10.1002/2017WR020524, 2017a.
Quinn, J., Giuliani, M., and Herman, J.: Kirsch–Nowak Streamflow Generator, Github, https://github.com/julianneq/Kirsch-Nowak_Streamflow_Generator (last access: 24 February 2020), 2017b.
Quinn, J. D., Reed, P. M., Giuliani, M., Castelletti, A., Oyler, J. W., and
Nicholas, R. E.: Exploring How Changing Monsoonal Dynamics and Human Pressures Challenge Multireservoir Management for Flood Protection, Hydropower Production, and Agricultural Water Supply, Water Resour. Res., 54, 4638–4662, https://doi.org/10.1029/2018WR022743, 2018.
Ray, P., Wi, S., Schwarz, A., Correa, M., He, M., and Brown, C.: Vulnerability and risk: climate change and water supply from California's Central Valley water system, Climatic Change, 161, 177–199, https://doi.org/10.1007/s10584-020-02655-z, 2020.
Sadollah, A.: Introductory Chapter: Which Membership Function is Appropriate in Fuzzy System?, Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications, Intechopen, London, https://doi.org/10.5772/intechopen.79552, 2018.
Simon, H. A.: A behavioral model of rational choice, Quart. J. Econ., 69, 99–118, https://doi.org/10.2307/1884852, 1955.
Steinschneider, S., Wi, S., and Brown, C.: The integrated effects of climate
and hydrologic uncertainty on future flood risk assessments, Hydrol. Process., 29, 2823–2839, https://doi.org/10.1002/hyp.10409, 2015.
Taner, M. U., Ray, P., and Brown, C.: Incorporating multidimensional
probabilistic information into robustness‐based water systems planning, Water Resour. Res., 55, 3659–3679, https://doi.org/10.1029/2018WR022909, 2019.
Tilmant, A., Vanclooster, M., Duckstein, L., and Persoons, E.: Comparison of
Fuzzy and Nonfuzzy Optimal Reservoir Operating Policies, J. Water Resour. Pl. Manage., 128, 390–398, https://doi.org/10.1061/(ASCE)0733-9496(2002)128:6(390), 2002.
Turner, S. W. D., Marlow, D., Ekström, M., Rhodes, B. G., Kularathna, U., and
Jeffrey, P. J.: Linking climate projections to performance: A yield-based
decision scaling assessment of a large urban water resources system, Water
Resour. Res., 50, 3553–3567, https://doi.org/10.1002/2013WR015156, 2014.
Von Neumann, J. and Morgenstern, O.: Theory of games and economic behavior,
Theory of games and economic behavior, Princeton University Press, Princeton,
NJ, USA, 1944.
Vormoor, K., Rössler, O., Bürger, G., Bronstert, A., and Weingartner, R.:
When timing matters-considering changing temporal structures in runoff
response surfaces, Climatic Change, 142, 213–226, https://doi.org/10.1007/s10584-017-1940-1, 2017.
Wang, W., Zhou, H., and Guo, L.: Emergency water supply decision-making of
transboundary river basin considering government – public perceived
satisfaction, J. Intell. Fuzzy Syst., 40, 381–401, https://doi.org/10.3233/JIFS-191828, 2021.
Weaver, C. P., Lempert, R. J., Brown, C., Hall, J. A., Revell, D., and
Sarewitz, D.: Improving the contribution of climate model information to
decision making: the value and demands of robust decision frameworks, Wiley
Interdisciplin. Rev.: Clim. Change, 4, 39–60, https://doi.org/10.1002/wcc.202, 2013.
Whateley, S. and Brown, C.: Assessing the relative effects of emissions,
climate means, and variability on large water supply systems, Geophys. Res. Lett., 43, 11329–11338, https://doi.org/10.1002/2016GL070241, 2016.
Whateley, S., Steinschneider, S., and Brown, C.: A climate change range-based
method for estimating robustness for water resources supply, Water Resour.
Res., 50, 8944–8961, https://doi.org/10.1002/2014WR015956, 2014.
Yu, C.-S.: A GP-AHP method for solving group decision-making fuzzy AHP problems, Comput. Operat. Res., 29, 1969–2001, https://doi.org/10.1016/S0305-0548(01)00068-5, 2002.
Zadeh, L. A.: Fuzzy Sets, Inform. Control, 8, 338–353, 1965.
Zadeh, L. A.: Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets
Syst., 1, 3–28, https://doi.org/10.1016/0165-0114(78)90029-5, 1978.
Zadeh, L. A.: Fuzzy probabilities, Inform. Process. Manage., 20, 363–372, https://doi.org/10.1016/0306-4573(84)90067-0, 1984.
Zimmermann, H.-J. (Ed.): Fuzzy Logic and Approximate Reasoning, in: Fuzzy Set
Theory – and Its Applications, Springer Netherlands, Dordrecht, 141–183, https://doi.org/10.1007/978-94-010-0646-0_9, 2001.
Short summary
Response surfaces are increasingly used to identify the hydroclimatic conditions leading to a water resources system's failure. Partitioning the surface usually requires performance thresholds that are not necessarily crisp. We propose a methodology that combines the inherent uncertainty of response surfaces with the ambiguity of performance thresholds. The proposed methodology is illustrated with a multireservoir system in Canada for which some performance thresholds are imprecise.
Response surfaces are increasingly used to identify the hydroclimatic conditions leading to a...
