Simulation-Optimization of Reservoir Water Quality under Climate Change
Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 9
Abstract
Increasing population and water use, rising pollution of water resources, and climate change affect the quantity and quality of water resources. Reservoir operation is an important tool for water supply that can be optimized by simulation-optimization considering the impact of climate change on water quality. This study presents a simulation-optimization approach linking the CE-QUAL-W2 hydrodynamic model with the firefly algorithm -nearest neighbor (FA-KNN) model to obtain optimal reservoir discharges to achieve water quality objectives under climate change conditions. The developed algorithm overcomes the computational burden of CE-QUAL-W2. The FA-KNN hybrid algorithm is employed to optimize the total dissolved solids (TDS) while achieving computational efficiently beyond what could be achieved with CE-QUAL-W2 simulations alone. This paper’s approach is evaluated with the Aidoghmoush Reservoir (East Azerbaijan, Iran). Overall, 36 simulation-optimization scenarios for dry and wet years under baseline and climate change conditions are evaluated by considering three initial water levels for the reservoir (minimum, average, and normal) and three thresholds for assessing the hybrid algorithm. The TDS released from the reservoir in wet years would be acceptable for agricultural use; in dry years, on average, the TDS would not be acceptable for 24 days per year under climate change. The reservoir in winter undergoes complete mixing; it becomes stratified in spring and summer, and it is close to complete mixing in the autumn. The highest TDS in the reservoir would occur during the summer in dry years under climate change, reaching TDS of approximately .
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Data Availability Statement
All data, models, or code that supports the findings of this study are available from the corresponding author upon reasonable request.
References
Alizadeh, M. J., M. R. Kavianpour, M. Danesh, J. Adolf, S. Shamshiband, and K.-W. Chau. 2018. “Effect of river flow on the quality of estuarine and coastal waters using machine learning models.” Eng. Appl. Comput. Fluid Mech. 12 (1): 810–823. https://doi.org/10.1080/19942060.2018.1528480.
Amirkhani, M., O. Bozorg-Haddad, E. Fallah-Mehdipour, and H. A. Loáiciga. 2016. “Multiobjective reservoir operation for water quality optimization.” J. Irrig. Drain. Eng. 142 (12): 04016065. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001105.
Ashofteh, P.-S., O. Bozorg-Haddad, and H. A. Loáiciga. 2015. “Evaluation of climatic-change impacts on multiobjective reservoir operation with multiobjective genetic programming.” J. Water Resour. Plann. Manage. 141 (11): 04015030. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000540.
Ashofteh, P.-S., O. Bozorg-Haddad, and H. A. Loáiciga. 2017a. “Development of adaptive strategies for irrigation water demand management under climate change.” J. Irrig. Drain. Eng. 143 (2): 04016077. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001123.
Ashofteh, P.-S., O. Bozorg-Haddad, and H. A. Loáiciga. 2017b. “Logical genetic programming (LGP) development for irrigation water supply hedging under climate-change conditions.” Irrig. Drain. 66 (4): 530–541. https://doi.org/10.1002/ird.2144.
Ashofteh, P.-S., O. Bozorg-Haddad, and M. A. Mariño. 2013. “Climate change impact on reservoir performance indexes in agricultural water supply.” J. Irrig. Drain. Eng. 139 (2): 85–97. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000496.
Azadi, F., P.-S. Ashofteh, and X. Chu. 2021. “Evaluation of the effects of climate change on thermal stratification of reservoirs.” Sustainable Cities Soc. 66 (Mar): 102531. https://doi.org/10.1016/j.scs.2020.102531.
Azadi, F., P.-S. Ashofteh, and H. A. Loáiciga. 2019. “Reservoir water-quality projections under climate-change conditions.” Water Resour. Manage. 33 (1): 401–421. https://doi.org/10.1007/s11269-018-2109-z.
Bansal, S., and G. Ganesan. 2018. “Advanced evaluation methodology for water quality assessment using artificial neural network approach.” Water Resour. Manage. 32 (7): 2561–2568. https://doi.org/10.1007/s11269-019-02289-6.
Bayesteh, M., and A. Azari. 2021. “Stochastic optimization of reservoir operation by applying hedging rules.” J. Water Resour. Plann. Manage. 147 (2): 04020099. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001312.
Boehlert, F., K. M. Strzepek, S. C. Chapra, C. Fant, Y. Gebretsadik, M. Lickley, R. Swanson, A. McCluskey, J. E. Neumann, and J. Martinich. 2015. “Climate change impacts and greenhouse gas mitigation effects on US water quality.” J. Adv. Model. Earth Syst. 7 (3): 1326–1338. https://doi.org/10.1002/2014MS000400.
Castelletti, A., H. Yajima, M. Giuliani, R. Soncini-Sessa, and E. Weber. 2014. “Planning the optimal operation of a multioutlet water reservoir with water quality and quantity targets.” J. Water Resour. Plann. Manage. 140 (4): 496–510. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000348.
Chen, X.-Y., and K.-W. Chau. 2019. “Uncertainty analysis on hybrid double feedforward neural network model for sediment load estimation with LUBE method.” Water Resour. Manage. 33 (10): 3563–3577. https://doi.org/10.1007/s11269-019-02318-4.
Ehteram, M., S. F. Mousavi, H. Karami, S. Farzin, V. P. Singh, K.-W. Chau, and A. El-Shafie. 2018. “Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty.” J. Hydroinf. 20 (2): 332–355. https://doi.org/10.2166/hydro.2018.094.
Guo, D., A. Lintern, J. A. Webb, D. Ryu, S. Liu, U. Bende-Michl, P. Leahy, P. Wilson, and A. W. Western. 2019. “Key factors affecting temporal variability in stream water quality.” Water Resour. Res. 55 (1): 112–129. https://doi.org/10.1029/2018WR023370.
He, Y., X. Jiang, N. Wang, S. Zhang, T. Ning, Y. Zhao, and Y. Hu. 2019a. “Changes in mountainous runoff in three inland river basins in the arid Hexi Corridor, China, and its influencing factors.” Sustainable Cities Soc. 50 (Oct): 101703. https://doi.org/10.1016/j.scs.2019.101703.
He, Y., H. Qiu, J. Song, Y. Zhao, L. Zhang, S. Hu, and Y. Hu. 2019b. “Quantitative contribution of climate change and human activities to runoff changes in the Bahe River watershed of the Qinling Mountains, China.” Sustainable Cities Soc. 51 (Nov): 101729. https://doi.org/10.1016/j.scs.2019.101729.
Heidarzadeh, N., and O. Motiey Nejad. 2017. “Evaluating sedimentation effect on reservoirs water quality: A case study, Shahriyar Reservoir.” J. Environ. Sci. Technol. 14 (12): 2623–2636. https://doi.org/10.1007/s13762-017-1317-z.
Hutchins, M. G., and M. J. Bowes. 2018. “Balancing water demand needs with protection of river water quality by minimizing stream residence time: An example from the Thames, UK.” Water Resour. Manage. 32 (7): 2561–2568. https://doi.org/10.1007/s11269-018-1946-0.
IPCC-DDC (Intergovernmental Panel on Climate Change, Data Distribution Centre). 1988. “Data distribution portal.” Accessed March 4, 2020. https://www.ipcc-data.org/.
IPCC-TGCIA (Intergovernmental Panel on Climate Change, Task Group on Scenarios for Climate Impact Assessment). 1999. Guideline on the use of scenario date for climate impact and adaptation assessment: Version 1. Edited by T. R. Carter, M. Hullme, and M. Lal. Geneva: IPCC-TGCIA.
Jaramillo, P., and A. Nazemi. 2018. “Assessing urban water security under changing climate: Challenges and ways forward.” Sustainable Cities Soc. 41 (Aug): 907–918. https://doi.org/10.1016/j.scs.2017.04.005.
Jiang, T., M. Zhong, Y. Cao, L. Zou, B. Lin, and A. Zhu. 2016. “Simulation of water quality under different reservoir regulation scenarios in the tidal river.” Water Resour. Manage. 30 (10): 3593–3607. https://doi.org/10.1007/s11269-016-1375-x.
Lee, H., E. Kim, S. Park, and J. Choi. 2015. “Effects of climate change on the movement of turbidity flow in a stratified reservoir.” Water Resour. Manage. 29 (11): 4095–4110. https://doi.org/10.1007/s11269-015-1047-2.
Li, H., T. Huang, Z. Zhou, S. Long, and H. Zhang. 2018. “Effects of reservoir operation and climate change on thermal stratification of a canyon-shaped reservoir, in northwest China.” Water Supply 33 (9): 3127–3141. https://doi.org/10.2166/ws.2017.068.
MiSadeghian, C. H., A. Sadeghian, K.-E. Lindenschmidt, and K. Rinke. 2019. “Variable withdrawal elevations as a management tool to counter the effects of climate warming in Germany’s largest drinking water reservoir.” J. Irrig. Drain. Eng. 31 (1): 1–15. https://doi.org/10.1186/s12302-019-0202-4.
Moreno-Rodenas, A. M., V. Bellos, J. G. Langeveld, and F. H. Clemens. 2018. “A dynamic emulator for physically based flow simulators under varying rainfall and parametric condition.” Water Res. 142 (Oct): 512–527. https://doi.org/10.1016/j.watres.2018.06.011.
Orabona, F., C. Castellini, B. Caputo, L. Jie, and C. Sandini. 2010. “On-line independent support vector machines.” Pattern Recognit. 43 (4): 1402–1412. https://doi.org/10.1016/j.patcog.2009.09.021.
Saadatpour, M., A. Afshar, and J. Edinger. 2018. “Meta-model assisted 2D hydrodynamic and thermal simulation model (CE-QUAL-W2) in deriving optimal reservoir operational strategy in selective withdrawal scheme.” Water Resour. Manage. 31 (9): 2729–2744. https://doi.org/10.1007/s11269-017-1658-x.
Shamshirband, S., E. Jafari Nodoushan, J. E. Adolf, A. A. Manaf, A. Mosavi, and K.-W. Chau. 2019. “Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters.” Eng. Appl. Comput. Fluid Mech. 13 (1): 91–101. https://doi.org/10.1080/19942060.2018.1553742.
Shokri, A., O. Bozorg-Haddad, and M. A. Mariño. 2013. “Algorithm for increasing the speed of evolutionary optimization and its accuracy in multi-objective problems.” Water Resour. Manage. 27 (7): 2231–2249. https://doi.org/10.1007/s11269-013-0285-4.
Shokri, A., O. Bozorg-Haddad, and M. A. Mariño. 2014. “Multi-objective quantity–quality reservoir operation in sudden pollution.” Water Resour. Manage. 28 (2): 567–586. https://doi.org/10.1007/s11269-013-0504-z.
Shokri, A., J. P. Walker, A. I. J. M. Dijk, A. J. Wright, and V. R. N. Pauwels. 2018. “Application of the patient rule induction method to detect hydrologic model behavioural parameters and quantify uncertainty.” Hydrol. Process. 32 (8): 1005–1025. https://doi.org/10.1002/hyp.11464.
Tchobanoglous, G., and E. D. Schroeder. 1985. Water quality characteristics, modeling, and modification. Boston: Addison-Wesley.
Tung, T. M., and Z. M. Yaseen. 2020. “A survey on river water quality modelling using artificial intelligence models.” J. Hydrol. 565 (Jun): 124670.
Wang, D., H. Qiao, B. Zhang, and M. Wang. 2013. “Online support vector machine based on convex hull vertices selection.” IEEE Trans. Neural Networks Learn. Syst. 24 (4): 593–609. https://doi.org/10.1109/TNNLS.2013.2238556.
Wu, C. L., and K.-W. Chau. 2013. “Prediction of rainfall time series using modular soft computing methods.” Eng. Appl. Artif. Intell. 26 (3): 997–1007. https://doi.org/10.1016/j.engappai.2012.05.023.
Zhang, C., M. Brett, S. Brattebo, and E. Welch. 2018. “How well dose the mechanistic water quality model CE-QUAL-W2 represent biogeochemical responses to climatic and hydrologic forcing?” Water Resour. Res. 54 (9): 6609–6624. https://doi.org/10.1029/2018WR022580.
Zheng, J., F. Shen, H. Fan, and J. Zhao. 2013. “An online incremental learning support vector machine for large-scale data.” Neural Comput. Appl. 22 (5): 1023–1035. https://doi.org/10.1007/s00521-011-0793-1.
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Published In
Journal of Water Resources Planning and Management
Volume 147 • Issue 9 • September 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 26, 2020
Accepted: Apr 6, 2021
Published online: Jun 28, 2021
Published in print: Sep 1, 2021
Discussion open until: Nov 28, 2021
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