Detection Time for Nonstationary Reservoir System Performance Driven by Climate and Land-Use Change
Publication: Journal of Water Resources Planning and Management
Volume 150, Issue 4
Abstract
Dynamic planning of water infrastructure requires identifying signals for adaptation, including measures of system performance linked to vulnerabilities. However, it remains a challenge to detect projected changes in performance outside the envelope of natural variability, and to identify whether such detections can be attributed to one or more uncertain drivers. This study investigated these questions using a combination of ensemble simulation, nonparametric tests, and variance decomposition, which were demonstrated for a case study of the Sacramento–San Joaquin River Basin, California. We trained a logistic regression classifier to predict future detections given observed trends in performance over time. The scenario ensemble includes coupled climate and land-use change through the end of the century, evaluated using a multireservoir simulation model to determine changes in water supply reliability and flooding metrics relative to the historical period (1951–2000). The results show that the reliability metric is far more likely to exhibit a significant change within the century, with the most severe scenarios tending to be detected earlier, reflecting long-term trends. Changes in flooding often are not detected due to natural variability despite severe events in some scenarios. We found that the variance in detection times is attributable largely to the choice of climate model, and also to the emissions scenario and its interaction with the choice of climate model. Finally, in the prediction model for both cases, reliability and flooding, the model learns to associate more-recent observations of system performance with nonstationarity detection. These findings underscore the importance of differentiating between long-term change and natural variability in identifying signals for adaptation.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All code corresponding to methods and figure generation are available at the repository https://github.com/Matt2371/climate_detection.
Acknowledgments
This work was supported by the US National Science Foundation grant CBET-2041826. We further acknowledge the World Climate Research Program’s Working Group on Coupled Modeling and the climate modeling groups listed in the Supplemental Materials for producing and making available their model output.
References
Anghileri, D., M. Botter, A. Castelletti, H. Weigt, and P. Burlando. 2018. “A comparative assessment of the impact of climate change and energy policies on Alpine hydropower.” Water Resour. Res. 54 (11): 9144–9161. https://doi.org/10.1029/2017WR022289.
Bass, B., J. Norris, C. Thackeray, and A. Hall. 2022. “Natural variability has concealed increases in Western US flood hazard since the 1970s.” Geophys. Res. Lett. 49 (7): 1–10. https://doi.org/10.1029/2021GL097706.
Borgomeo, E., M. Mortazavi-Naeini, J. W. Hall, and B. P. Guillod. 2018. “Risk, robustness and water resources planning under uncertainty.” Earth’s Future 6 (3): 468–487. https://doi.org/10.1002/2017EF000730.
Brekke, L., A. Wood, and T. Pruitt. 2014. “Downscaled CMIP3 and CMIP5 hydrology projections release of hydrology projections, comparison with preceding information, and summary of user needs.” Accessed September 9, 2022. https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/.
Broderick, C., C. Murphy, R. L. Wilby, T. Matthews, C. Prudhomme, and M. Adamson. 2019. “Using a scenario-neutral framework to avoid potential maladaptation to future flood risk.” Water Resour. Res. 55 (2): 1079–1104. https://doi.org/10.1029/2018WR023623.
Bryant, B. P., and R. J. Lempert. 2010. “Thinking inside the box: A participatory, computer-assisted approach to scenario discovery.” Technol. Forecasting Social Change 77 (1): 34–49. https://doi.org/10.1016/j.techfore.2009.08.002.
Ceres, R. L., C. E. Forest, and K. Keller. 2017. “Understanding the detectability of potential changes to the 100-year peak storm surge.” Clim. Change 145 (1–2): 221–235. https://doi.org/10.1007/s10584-017-2075-0.
Christian-Smith, J., H. Matthew, and L. Allen. 2012. “Urban water demand in California to 2100: Incorporating climate change.” Accessed September 14, 2022. https://www.pacinst.org.
Cohen, J. S., and J. D. Herman. 2021. “Dynamic adaptation of water resources systems under uncertainty by learning policy structure and indicators.” Water Resour. Res. 57 (11): 1–24. https://doi.org/10.1029/2021WR030433.
de Neufville, R., and K. Smet. 2019. “Engineering options analysis (EOA).” In Decision making under deep uncertainty, 117–132. Berlin: Springer.
Fletcher, S., M. Lickley, and K. Strzepek. 2019. “Learning about climate change uncertainty enables flexible water infrastructure planning.” Nat. Commun. 10 (1): 1782. https://doi.org/10.1038/s41467-019-09677-x.
Greve, P., L. Gudmundsson, and S. I. Seneviratne. 2018. “Regional scaling of annual mean precipitation and water availability with global temperature change.” Earth Syst. Dyn. 9 (1): 227–240. https://doi.org/10.5194/esd-9-227-2018.
Groves, D. G., E. Bloom, R. J. Lempert, J. R. Fischbach, J. Nevills, and B. Goshi. 2015. “Developing key indicators for adaptive water planning.” J. Water Resour. Plann. Manage. 141 (7): 05014008. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000471.
Haasnoot, M., J. H. Kwakkel, W. E. Walker, and J. ter Maat. 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.
Haasnoot, M., S. van’t Klooster, and J. van Alphen. 2018. “Designing a monitoring system to detect signals to adapt to uncertain climate change.” Global Environ. Change 52 (Sep): 273–285. https://doi.org/10.1016/j.gloenvcha.2018.08.003.
Hamarat, C., J. H. Kwakkel, E. Pruyt, and E. T. Loonen. 2014. “An exploratory approach for adaptive policymaking by using multi-objective robust optimization.” Simul. Modell. Pract. Theory 46 (Aug): 25–39. https://doi.org/10.1016/j.simpat.2014.02.008.
Hawkins, E., and R. Sutton. 2009. “The potential to narrow uncertainty in regional climate predictions.” Bull. Am. Meteorol. Soc. 90 (8): 1095–1108. https://doi.org/10.1175/2009BAMS2607.1.
Hecht, J. S., and R. M. Vogel. 2020. “Updating urban design floods for changes in central tendency and variability using regression.” Adv. Water Resour. 136 (Feb): 103484. https://doi.org/10.1016/j.advwatres.2019.103484.
Hegerl, G., and F. Zwiers. 2011. “Use of models in detection and attribution of climate change.” In Wiley interdisciplinary reviews: Climate change. Hoboken, NJ: Wiley. https://doi.org/10.1002/wcc.121.
Herman, J., and W. Usher. 2017. “SALib: An open-source Python library for sensitivity analysis.” J. Open Source Software 2 (9): 1–2. https://doi.org/10.21105/joss.00097.
Herman, J. D., J. D. Quinn, S. Steinschneider, M. Giuliani, and S. Fletcher. 2020. “Climate adaptation as a control problem: Review and perspectives on dynamic water resources planning under uncertainty.” Water Resour. Res. 56 (2): e24389. https://doi.org/10.1029/2019WR025502.
Hinkel, J., and A. Bisaro. 2016. “Methodological choices in solution-oriented adaptation research: A diagnostic framework.” Reg. Environ. Change 16 (1): 7–20. https://doi.org/10.1007/s10113-014-0682-0.
Hui, R., J. Herman, J. Lund, and K. Madani. 2018. “Adaptive water infrastructure planning for nonstationary hydrology.” Adv. Water Resour. 118 (Aug): 83–94. https://doi.org/10.1016/j.advwatres.2018.05.009.
Jafino, B. A., M. Haasnoot, and J. H. Kwakkel. 2019. “What are the merits of endogenising land-use change dynamics into model-based climate adaptation planning?” Socio-Environ. Syst. Modell. 1 (Feb): 16126. https://doi.org/10.18174/sesmo.2019a16126.
Katz, R. W. 2013. “Statistical methods for nonstationary extremes.” In Extremes in a changing climate, edited by A. AghaKouchak, D. Easterling, K. Hsu, S. Schubert, and S. Sorooshian, 15–37. Dordrecht, Netherlands: Springer.
Kwakkel, J. H., M. Haasnoot, 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.
Lee, B. S., M. Haran, and K. Keller. 2017. “Multidecadal scale detection time for potentially increasing Atlantic storm surges in a warming climate.” Geophys. Res. Lett. 44 (20): 10–617. https://doi.org/10.1002/2017GL074606.
Lehner, F., C. Deser, N. Maher, J. Marotzke, E. M. Fischer, L. Brunner, R. Knutti, and E. Hawkins. 2020. “Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6.” Earth Syst. Dyn. 11 (2): 491–508. https://doi.org/10.5194/esd-11-491-2020.
Lempert, R. J., and D. G. Groves. 2010. “Identifying and evaluating robust adaptive policy responses to climate change for water management agencies in the American west.” Technol. Forecasting Social Change 77 (6): 960–974. https://doi.org/10.1016/j.techfore.2010.04.007.
Mall, N. K., and J. D. Herman. 2019. “Water shortage risks from perennial crop expansion in California’s Central Valley.” Environ. Res. Lett. 14 (10): 104014. https://doi.org/10.1088/1748-9326/ab4035.
Mortazavi-Naeini, M., G. Kuczera, A. S. Kiem, L. Cui, B. Henley, B. Berghout, and E. Turner. 2015. “Robust optimization to secure urban bulk water supply against extreme drought and uncertain climate change.” Environ. Modell. Software 69 (Jul): 437–451. https://doi.org/10.1016/j.envsoft.2015.02.021.
Orlowsky, B., and S. I. Seneviratne. 2013. “Elusive drought: Uncertainty in observed trends and short- and long-term CMIP5 projections.” Hydrol. Earth Syst. Sci. 17 (5): 1765–1781. https://doi.org/10.5194/hess-17-1765-2013.
Papalexiou, S. M., and A. Montanari. 2019. “Global and regional increase of precipitation extremes under global warming.” Water Resour. Res. 55 (6): 4901–4914. https://doi.org/10.1029/2018WR024067.
Pedregosa, F., et al. 2011. “Scikit-learn: Machine learning in python.” J. Mach. Learn. Res. 12 (85): 2825–2830.
Prosdocimi, I., T. R. Kjeldsen, and J. D. Miller. 2015. “Detection and attribution of urbanization effect on flood extremes using nonstationary flood-frequency models.” Water Resour. Res. 51 (6): 4244–4262. https://doi.org/10.1002/2015WR017065.
Quinn, J. D., P. M. Reed, and K. Keller. 2017. “Direct policy search for robust multi-objective management of deeply uncertain socio-ecological tipping points.” Environ. Modell. Software 92 (Jun): 125–141. https://doi.org/10.1016/j.envsoft.2017.02.017.
Raso, L., J. Kwakkel, and J. Timmermans. 2019. “Assessing the capacity of adaptive policy pathways to adapt on time by mapping trigger values to their outcomes.” Sustainability 11 (6): 1–16. https://doi.org/10.3390/su11061716.
Robinson, B., J. S. Cohen, and J. D. Herman. 2020. “Detecting early warning signals of long-term water supply vulnerability using machine learning.” Environ. Modell. Software 131 (Sep): 104781. https://doi.org/10.1016/j.envsoft.2020.104781.
Robinson, B., and J. D. Herman. 2019. “A framework for testing dynamic classification of vulnerable scenarios in ensemble water supply projections.” Clim. Change 152 (3–4): 431–448. https://doi.org/10.1007/s10584-018-2347-3.
Siler, N., C. Proistosescu, and S. Po-Chedley. 2019. “Natural variability has slowed the decline in western US snowpack since the 1980s.” Geophys. Res. Lett. 46 (1): 346–355. https://doi.org/10.1029/2018GL081080.
Slater, L. J., et al. 2021. “Nonstationary weather and water extremes: A review of methods for their detection, attribution, and management.” Hydrol. Earth Syst. Sci. 25 (7): 3897–3935. https://doi.org/10.5194/hess-25-3897-2021.
Sleeter, B. M., and T. T. Wilson. 2017. Land-use and land-cover projections for California’s 4th climate assessment. Reston, VA: USGS.
Smith, R. L., C. Tebaldi, D. Nychka, and L. O. Mearns. 2009. “Bayesian modeling of uncertainty in ensembles of climate models.” J. Am. Stat. Assoc. 104 (485): 97–116. https://doi.org/10.1198/jasa.2009.0007.
Sobol, I. M. 2001. “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.” Math. Comput. Simul. 55 (1–3): 271–280. https://doi.org/10.1016/S0378-4754(00)00270-6.
Sohl, T. L., et al. 2014. “Spatially explicit modeling of 1992–2100 land cover and forest stand age for the conterminous United States.” Ecol. Appl. 24 (5): 1015–1036. https://doi.org/10.1890/13-1245.1.
Steinschneider, S., J. D. Herman, J. Kucharski, M. Abellera, and P. Ruggiero. 2023. “Uncertainty decomposition to understand the influence of water systems model error in climate vulnerability assessments.” Water Resour. Res. 59 (1): e2022WR032349. https://doi.org/10.1029/2022WR032349.
Storn, R., and K. Price. 1997. “Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces.” J. Global Optim. 11 (4): 341–359. https://doi.org/10.1023/A:1008202821328.
Tebaldi, C., R. L. Smith, D. Nychka, and L. O. Mearns. 2005. “Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multimodel ensembles.” J. Clim. 18 (10): 1524–1540. https://doi.org/10.1175/JCLI3363.1.
van Ginkel, K. C. H., M. Haasnoot, and W. J. Wouter Botzen. 2022. “A stepwise approach for identifying climate change induced socio-economic tipping points.” Clim. Risk Manage. 37 (Apr): 100445. https://doi.org/10.1016/j.crm.2022.100445.
Walker, W. E., M. Haasnoot, and J. H. Kwakkel. 2013. “Adapt or perish: A review of planning approaches for adaptation under deep uncertainty.” Sustainability 5 (3): 955–979. https://doi.org/10.3390/su5030955.
Weaver, C. P., R. J. Lempert, C. Brown, J. A. Hall, D. Revell, and D. Sarewitz. 2013. “Improving the contribution of climate model information to decision making: The value and demands of robust decision frameworks.” Wiley Interdiscip. Rev. Clim. Change 4 (1): 39–60. https://doi.org/10.1002/wcc.202.
West, T. O., and Y. Le Page. 2014. CMS: Land cover projections (5.6-km) from GCAM v3.1 for Conterminous USA, 2005–2095. Washington, DC: US DOE.
Westra, S., L. Alexander, and F. W. Zwiers. 2013. “Global increasing trends in annual maximum daily precipitation.” J. Clim. 26 (11): 3904–3918. https://doi.org/10.1175/JCLI-D-12-00502.1.
Whateley, S., and C. Brown. 2016. “Assessing the relative effects of emissions, climate means, and variability on large water supply systems.” Geophys. Res. Lett. 43 (21): 11–338. https://doi.org/10.1002/2016GL070241.
Wilby, R. L. 2006. “When and where might climate change be detectable in UK river flows?” Geophys. Res. Lett. 33 (19): 1–5. https://doi.org/10.1029/2006GL027552.
Wilby, R. L., and S. Dessai. 2010. “Robust adaptation to climate change.” Weather 65 (7): 180–185. https://doi.org/10.1002/wea.543.
Woodward, M., Z. Kapelan, and B. Gouldby. 2014. “Adaptive flood risk management under climate change uncertainty using real options and optimization.” Risk Anal. 34 (1): 75–92. https://doi.org/10.1111/risa.12088.
Yue, S., and C. Wang. 2002. “The influence of serial correlation on the Mann–Whitney test for detecting a shift in median.” Adv. Water Resour. 25 (3): 325–333. https://doi.org/10.1016/S0309-1708(01)00049-5.
Zeff, H. B., J. D. Herman, P. M. Reed, and G. W. Characklis. 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.
Ziegler, A. D., E. P. Maurer, J. Sheffield, B. Nijssen, E. F. Wood, and D. P. Lettenmaier. 2005. “Detection time for plausible changes in annual precipitation, evapotranspiration, and streamflow in three Mississippi River sub-basins.” Clim. Change 72 (1–2): 17–36. https://doi.org/10.1007/s10584-005-5379-4.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 150 • Issue 4 • April 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Apr 15, 2023
Accepted: Sep 21, 2023
Published online: Jan 29, 2024
Published in print: Apr 1, 2024
Discussion open until: Jun 29, 2024
ASCE Technical Topics:
- Case studies
- Climate change
- Climates
- Engineering fundamentals
- Environmental engineering
- Floods
- Hydraulic engineering
- Hydraulic structures
- Infrastructure
- Infrastructure vulnerability
- Land use
- Measurement (by type)
- Methodology (by type)
- Metric systems
- Research methods (by type)
- Reservoirs
- Urban and regional development
- Urban areas
- Water and water resources
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.