Nonstationary-Probabilistic Framework to Assess the Water Resources System Vulnerability: Long-Term Robust Planning and Timing
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 8
Abstract
Multiobjective water resources systems require long-term operational plans that consider vulnerability to failure under nonstationary conditions due to climate change. The paper presents a probabilistic decision-making framework under hydroclimatic nonstationary assumptions to evaluate the robustness of the preselected plans and identify the optimal plan using a genetic algorithm approach. The framework incorporates a new metric for maximum allowable time to apply the adaptations and maintaining operational targets without penalties. The framework has four stages for climate exposure identification, production of water supply scenarios, generation of water demand scenarios, and evaluation of system performance. Hydrologic variables considered include precipitation, temperature, and wind speed. The Diyala River Basin in Iraq was used as a case study to test the effectiveness of the framework. Three synthetic preselected plans were developed by reducing the demand ratios of the system. Results indicate that current operational rules are robust for flood protection but vulnerable in drought periods. Precipitation changes were dominant in flood and drought management, and temperature and wind speed change effects were significant during drought. Results demonstrated the framework’s effectiveness to quantify detrimental climate change effects, provide long-term guides for operational planning, and identify the upper limit of application time of the adaptation strategies in the system to avert the climate change impact. Framework application suggests an optimal adaptation strategy, robustness examination of the presuggested plans, and identification of the maximum allowable time for the robust plans.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct request for these materials may be made to the provider as indicated in the Acknowledgments. The data implemented in this study can be shared conjugated with an approval from MoWR.
Acknowledgments
This study was funded by the Higher Committee for Education Development in Iraq (HCED). The authors are grateful to the Iraqi Ministry of Water Resources (MoWR) for assistance in this study.
References
Abbas, N., S. A. Wasimi, and N. Al-Ansari. 2016. “Impacts of climate change on water resources in Diyala River Basin, Iraq.” J. Civ. Eng. Archit. 10 (9): 1059–1074. https://doi.org/10.17265/1934-7359/2016.09.009.
Al-Faraj, F. A. M., and M. Scholz. 2014. “Assessment of temporal hydrologic anomalies coupled with drought impact for a transboundary river flow regime: The Diyala watershed case study.” J. Hydrol. 517 (Sep): 64–73. https://doi.org/10.1016/j.jhydrol.2014.05.021.
Al-Faraj, F. A. M., M. Scholz, and D. Tigkas. 2014. “Sensitivity of surface runoff to drought and climate change: Application for shared river basins.” Water 6 (10): 3033–3048. https://doi.org/10.3390/w6103033.
Al-Jawad, J. Y., H. M. Alsaffar, D. Bertram, and R. M. Kalin. 2019. “A comprehensive optimum integrated water resources management approach for multidisciplinary water resources management problems.” J. Environ. Manage. 239 (2): 211–224. https://doi.org/10.1016/j.jenvman.2019.03.045.
Al-Khafaji, M. S., and R. D. Al-Chalabi. 2019. “Assessment and mitigation of streamflow and sediment yield under climate change conditions in Diyala River Basin, Iraq.” Hydrology 6 (3): 63. https://doi.org/10.3390/hydrology6030063.
Azhoni, A., S. Jude, and I. Holman. 2018. “Adapting to climate change by water management organisations: Enablers and barriers.” J. Hydrol. 559 (Apr): 736–748. https://doi.org/10.1016/j.jhydrol.2018.02.047.
Beh, E. H. Y., H. R. Maier, and G. C. Dandy. 2015. “Scenario driven optimal sequencing under deep uncertainty.” Environ. Modell. Software 68 (Jun): 181–195. https://doi.org/10.1016/j.envsoft.2015.02.006.
Brown, C., W. Werick, W. Leger, and D. Fay. 2011. “A decision‐analytic approach to managing climate risks: Application to the upper great lakes 1.” J. Am. Water Resour. Assoc. 47 (3): 524–534. https://doi.org/10.1111/j.1752-1688.2011.00552.x.
Brown, C., and R. L. Wilby. 2012. “An alternate approach to assessing climate risks.” Trans. Am. Geophys. Union 93 (41): 401–402. https://doi.org/10.1029/2012EO410001.
Bucchignani, E., L. Cattaneo, H. J. Panitz, and P. Mercogliano. 2016. “Sensitivity analysis with the regional climate model COSMO-CLM over the CORDEX-MENA domain.” Meteorol. Atmos. Phys. 128 (1): 73–95. https://doi.org/10.1007/s00703-015-0403-3.
Carter, T. R., M. Hulme, and M. Lal. 1999. Guidelines on the use of scenario data for climate impact and adaptation assessment v1. Geneva: Intergovernmental Panel on Climate Change.
Culley, S., S. Noble, A. Yates, M. Timbs, S. Westra, H. R. Maier, M. Giuliani, and A. Castelletti. 2016. “A bottom-up approach to identifying the maximum operational adaptive capacity of water resource systems to a changing climate.” Water Resour. Res. 52 (9): 6751–6768. https://doi.org/10.1002/2015WR018253.
Deb, K., A. Pratap, S. Agarwal, and T. A. M. T. Meyarivan. 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput. 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
Delgarm, N., B. Sajadi, S. Delgarm, and F. Kowsary. 2016. “A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-II: Case study in Iran.” Energy Build. 127 (6): 552–560. https://doi.org/10.1016/j.enbuild.2016.05.052.
Ehret, U., E. Zehe, V. Wulfmeyer, K. Warrach-Sagi, and J. Liebert. 2012. “HESS Opinions” Should we apply bias correction to global and regional climate model data?” Hydrol. Earth Syst. Sci. 16 (9): 3391–3404. https://doi.org/10.5194/hess-16-3391-2012.
Fletcher, S., M. Lickley, and K. Strzepek. 2019. “Learning about climate change uncertainty enables flexible water infrastructure planning.” Nat. Commun. 10 (1): 1–11. https://doi.org/10.1038/s41467-019-09677-x.
Fletcher, S. M., et al. 2017. “Water supply infrastructure planning: Decision-making framework to classify multiple uncertainties and evaluate flexible design.” J. Water Resour. Plann. Manage. 143 (10): 04017061. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000823.
Gao, J., A. Y. Sheshukov, H. Yen, K. R. Douglas-Mankin, M. J. White, and J. G. Arnold. 2019. “Uncertainty of hydrologic processes caused by bias-corrected CMIP5 climate change projections with alternative historical data sources.” J. Hydrol. 568 (2): 551–561. https://doi.org/10.1016/j.jhydrol.2018.10.041.
Giuliani, M., D. Anghileri, A. Castelletti, P. Nam Vu, and R. Soncini-Sessa. 2016. “Large storage operations under climate change: Expanding uncertainties and evolving tradeoffs.” Environ. Res. Lett. 11 (3): 035009. https://doi.org/10.1088/1748-9326/11/3/035009.
Golfam, P., P. S. Ashofteh, T. Rajaee, and X. Chu. 2019. “Prioritization of water allocation for adaptation to climate change using multi-criteria decision making (MCDM).” Water Resour. Manage. 33 (10): 3401–3416. https://doi.org/10.1007/s11269-019-02307-7.
Haasnoot, M., et al. 2013. “Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world.” Global Environ. Change 23 (2): 485–498. https://doi.org/10.1016/j.gloenvcha.2012.12.006.
Hallegatte, S., A. Shah, R. Lempert, C. Brown, and S. Gill. 2012. “Investment decision making under deep uncertainty.” Policy Res. 6193 (1): 1–41.
Hamza, N. H. 2012. “Evaluation of water quality of Diyala River for irrigation purposes.” Diyala J. Eng. Sci. 5 (2): 82–98.
Herman, J. D., P. M. Reed, H. B. Zeff, and G. W. Characklis. 2015. “How should robustness be defined for water systems planning under change?” J. Water Resour. Plann. Manage. 141 (10): 04015012. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000509.
Jiang, Y., Y. Luo, Z. Zhao, Y. Shi, Y. Xu, and J. Zhu. 2010. “Projections of wind changes for 21st century in China by three regional climate models.” Chin. Geogr. Sci. 20 (3): 226–235. https://doi.org/10.1007/s11769-010-0226-6.
Kwakkel, J. H., H. Marjolijn, and W. E. Walker. 2015. “Developing dynamic adaptive policy pathways: A computer-assisted approach for developing adaptive strategies for a deeply uncertain world.” Clim. Change 132 (3): 373–386. https://doi.org/10.1007/s10584-014-1210-4.
Lafon, T., S. Dadson, G. Buys, and C. Prudhomme. 2013. “Bias correction of daily precipitation simulated by a regional climate model: A comparison of methods.” Int. J. Climatol. 33 (6): 1367–1381. https://doi.org/10.1002/joc.3518.
Milly, P. C. D., et al. 2008. “Stationarity is dead: Whither water management?” Science 319 (5863): 573–574. https://doi.org/10.1126/science.1151915.
Moemken, J., M. Reyers, H. Feldmann, and J. G. Pinto. 2017. “Wind speed and wind energy potentials in EURO-CORDEX ensemble simulations: evaluation, bias-correction and future changes.” In Proc., EGU General Assembly Conf. Abstracts. Munich, Germany: European Geosciences Union.
Moemken, J., M. Reyers, H. Feldmann, and J. G. Pinto. 2018. “Future changes of wind speed and wind energy potentials in EURO-CORDEX ensemble simulations.” J. Geophys. Res. Atmos. 123 (12): 6373–6389. https://doi.org/10.1029/2018JD028473.
Moody, P., and C. Brown. 2013. “Robustness indicators for evaluation under climate change: Application to the upper Great Lakes.” Water Resour. Res. 49 (6): 3576–3588. https://doi.org/10.1002/wrcr.20228.
Najac, J., C. Lac, and L. Terray. 2011. “Impact of climate change on surface winds in France using a statistical-dynamical downscaling method with mesoscale modelling.” Int. J. Climatol. 31 (3): 415–430. https://doi.org/10.1002/joc.2075.
Ongoma, V., H. Chen, and C. Gao. 2019. “Evaluation of CMIP5 twentieth century rainfall simulation over the equatorial East Africa.” Theor. Appl. Climatol. 135 (3–4): 893–910. https://doi.org/10.1007/s00704-018-2392-x.
Opalinski, N. 2018. “Response of municipal water use to weather across the contiguous US.” Ph.D. dissertation, Dept. of Civil and Environmental Engineering, Colorado State Univ.
PaiMazumder, D., and J. M. Done. 2015. “The roles of bias-correction and resolution in regional climate simulations of summer extremes.” Clim. Dyn. 45 (5–6): 1565–1581. https://doi.org/10.1007/s00382-014-2413-0.
Paton, F. L., H. R. Maier, and G. C. Dandy. 2013. “Relative magnitudes of sources of uncertainty in assessing climate change impacts on water supply security for the southern Adelaide water supply system.” Water Resour. Res. 49 (3): 1643–1667. https://doi.org/10.1002/wrcr.20153.
Paton, F. L., H. R. Maier, and G. C. Dandy. 2014. “Including adaptation and mitigation responses to climate change in a multiobjective evolutionary algorithm framework for urban water supply systems incorporating GHG emissions.” Water Resour. Res. 50 (8): 6285–6304. https://doi.org/10.1002/2013WR015195.
Pryor, S. C., and R. J. Barthelmie. 2010. “Climate change impacts on wind energy: A review.” Renewable Sustainable Energy Rev. 14 (1): 430–437. https://doi.org/10.1016/j.rser.2009.07.028.
Roach, T., Z. Kapelan, and R. Ledbetter. 2018. “A resilience-based methodology for improved water resources adaptation planning under deep uncertainty with real world application.” Water Resour. Manage. 32 (6): 2013–2031. https://doi.org/10.1007/s11269-018-1914-8.
Smitha, P. S., B. Narasimhan, K. P. Sudheer, and H. Annamalai. 2018. “An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment.” J. Hydrol. 556 (Jan): 100–118. https://doi.org/10.1016/j.jhydrol.2017.11.010.
Solomon, S., M. Manning, M. Marquis, and D. Qin. 2007. Climate change 2007-the physical science basis: Working group I contribution to the fourth assessment report of the IPCC. Cambridge, UK: Cambridge University Press.
Spence, C. M., and M. B. Casey. 2016. “Nonstationary decision model for flood risk decision scaling.” Water Resour. Res. 52 (11): 8650–8667. https://doi.org/10.1002/2016WR018981.
Steinschneider, S., R. McCrary, S. Wi, K. Mulligan, L. O. Mearns, and C. Brown. 2015a. “Expanded decision-scaling framework to select robust long-term water-system plans under hydroclimatic uncertainties.” J. Water Resour. Plann. Manage. 141 (11): 04015023. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000536.
Steinschneider, S., S. Wi, and C. Brown. 2015b. “The integrated effects of climate and hydrologic uncertainty on future flood risk assessments.” Hydrol. Process. 29 (12): 2823–2839. https://doi.org/10.1002/hyp.10409.
Stephenson, D. B., C. Matthew, J. C. Rougier, and R. E. Chandler. 2012. “Statistical problems in the probabilistic prediction of climate change.” Environmetrics 23 (5): 364–372. https://doi.org/10.1002/env.2153.
Stocker, T. F., et al. 2013. Climate change 2013: The physical science basis. Geneva: Intergovernmental Panel on Climate Change. https://doi.org/10.1016/j.crm.2017.08.002.
Taner, M. Ü., P. Ray, and C. Brown. 2017. “Robustness-based evaluation of hydropower infrastructure design under climate change.” Clim. Risk Manage. 18 (7): 34–50. https://doi.org/10.1016/j.crm.2017.08.002.
Taner, M. Ü., P. Ray, and C. Brown. 2019. “Incorporating multidimensional probabilistic information into robustness-based water systems planning.” Water Resour. Res. 55 (5): 3659–3679. https://doi.org/10.1029/2018WR022909.
Tennant, D. L. 1976. “Instream flow regimens for fish, wildlife, recreation and related environmental resources.” Fisheries 1 (4): 6–10. https://doi.org/10.1577/1548-8446(1976)001%3C0006:IFRFFW%3E2.0.CO;2.
Tian, J., S. Guo, D. Liu, Z. Pan, and X. Hong. 2019. “A fair approach for multi-objective water resources allocation.” Water Resour. Manage. 33 (10): 3633–3653. https://doi.org/10.1007/s11269-019-02325-5.
Tofiq, F. A., and A. Guven. 2014. “Prediction of design flood discharge by statistical downscaling and general circulation models.” J. Hydrol. 517 (Sep): 1145–1153. https://doi.org/10.1016/j.jhydrol.2014.06.028.
Trindade, B. C., P. M. Reed, and G. W. Characklis. 2019. “Deeply uncertain pathways: Integrated multi-city regional water supply infrastructure investment and portfolio management.” Adv. Water Resour. 134 (Dec): 103442. https://doi.org/10.1016/j.advwatres.2019.103442.
Turner, S. W. D., D. Marlow, M. Ekström, B. G. Rhodes, U. Kularathna, and P. J. Jeffrey. 2014. “Linking climate projections to performance: A yield-based decision scaling assessment of a large urban water resources system.” Water Resour. Res. 50 (4): 3553–3567. https://doi.org/10.1002/2013WR015156.
Ummenhofer, C. C., and A. M. Gerald. 2017. “Extreme weather and climate events with ecological relevance: A review.” Philos. Trans. R. Soc. 372 (1723): 20160135. https://doi.org/10.1098/rstb.2016.0135.
Van Tra, T., N. X. Thinh, and S. Greiving. 2018. “Combined top-down and bottom-up climate change impact assessment for the hydrological system in the Vu Gia-Thu Bon River Basin.” Sci. Total Environ. 630 (8): 718–727. https://doi.org/10.1016/j.scitotenv.2018.02.250.
Villarini, G., F. Serinaldi, J. A. Smith, and W. F. Krajewski. 2009. “On the stationarity of annual flood peaks in the continental United States during the 20th century.” Water Resour. Res. 45 (8). https://doi.org/10.1029/2008WR007645.
Waheed, S. Q. 2013. “Effects of climate change on water availability in Diyala basin in Iraq.” In Proc., 2nd Int. Conf. on Hydrology & Groundwater Expo. Brussels, Belgium: Hilaris Publisher.
Waheed, S. Q., N. S. Grigg, and J. A. Ramirez. 2020b. “Variable infiltration capacity model sensitivity, parameter uncertainty, and data augmentation for the Diyala River Basin in Iraq.” J. Am. Soc. Civ. Eng. 25 (9): 04020040.
Waheed, S. Q., N. S. Grigg, and J. A. Ramirez. 2020a. “Development of a parametric regional multivariate statistical weather generator for risk assessment studies in areas with limited data availability.” J. Clim. MDPI 8 (8): 93. https://doi.org/10.3390/cli8080093.
Wang, K., H. Shi, J. Chen, and T. Li. 2019. “An improved operation-based reservoir scheme integrated with variable infiltration capacity model for multiyear and multipurpose reservoirs.” J. Hydrol. 571 (9): 365–375. https://doi.org/10.1016/j.jhydrol.2019.02.006.
Weaver, C. P., R. J. Lempert, B. Casey, J. A. Hall, R. David, and S. Daniel. 2013. “Improving the contribution of climate model information to decision making: The value and demands of robust decision frameworks.” Clim. Change 4 (1): 39–60. https://doi.org/10.1002/wcc.202.
Whateley, S., S. Steinschneider, and C. Brown. 2014. “A climate change range-based method for estimating robustness for water resources supply.” Water Resour. Res. 50 (11): 8944–8961. https://doi.org/10.1002/2014WR015956.
Wilby, R. L., and D. Suraje. 2010. “Robust adaptation to climate change.” Weather 65 (7): 180–185. https://doi.org/10.1002/wea.543.
Yu, W., B. Li, H. Jia, M. Zhang, and D. Wang. 2015. “Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design.” Energy Build. 88 (Feb): 135–143. https://doi.org/10.1016/j.enbuild.2014.11.063.
Yusoff, Y., M. S. Ngadiman, and A. M. Zain. 2011. “Overview of NSGA-II for optimizing machining process parameters.” Procedia Eng. 15 (Jan): 3978–3983. https://doi.org/10.1016/j.proeng.2011.08.745.
Zeff, H. B., et al. 2016. “Cooperative drought adaptation: Integrating infrastructure development, conservation, and water transfers into adaptive policy pathways.” Water Resour. Res. 52 (9): 7327–7346. https://doi.org/10.1002/2016WR018771.
Zhang, E., Z. Xu, and Z. Yang. 2018. “Bottom-up quantification of inter-basin water transfer vulnerability to climate change.” Ecol. Indic. 92 (2): 195–206. https://doi.org/10.1016/j.ecolind.2017.04.019.
Zheng, F., A. C. Zecchin, R. M. Holger, and A. R. Simpson. 2016. “Comparison of the searching behavior of NSGA-II, SAMODE, and Borg MOEAs applied to water distribution system design problems.” J. Water Resour. Plann. Manage. 142 (7): 04016017. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000650.
Zhu, F., P. Zhong, and Y. Sun. 2018. “Multi-criteria group decision making under uncertainty: Application in reservoir flood control operation.” Environ. Modell. Software 100 (8): 236–251. https://doi.org/10.1016/j.envsoft.2017.11.032.
Zolghadr-Asli, B., O. Bozorg-Haddad, P. Sarzaeim, and X. Chu. 2019. “Investigating the variability of GCMs’ simulations using time series analysis.” J. Water Clim. Change 10 (3): 449–463. https://doi.org/10.2166/wcc.2018.099.
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Journal of Water Resources Planning and Management
Volume 147 • Issue 8 • August 2021
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© 2021 American Society of Civil Engineers.
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Received: Jun 17, 2020
Accepted: Feb 9, 2021
Published online: May 28, 2021
Published in print: Aug 1, 2021
Discussion open until: Oct 28, 2021
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