Skip to main content
SpringerLink
Log in

Reservoir operation under influence of the joint uncertainty of inflow and evaporation

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Reservoirs play a major role as an essential source of surface water, especially in arid and semi-arid regions. To optimize the operation of a reservoir and determine its storage, which varies in time, the uncertainties of major influencing factors such as its inflow and evaporation should be considered. The objective of this study is to examine the effects of joint uncertainties of the inflow and evaporation of Durudzan reservoir on its performance for the first time. The Monte Carlo simulation is used for uncertainty assessment. Specifically, the monthly time series of inflow and evaporation were generated by using artificial neural networks and the standard operation policy was used for reservoir operation. Furthermore, the probabilistic distributions of four performance indices, including time-based reliability, volumetric reliability, vulnerability, and resiliency were calculated to assess the effects of the joint uncertainties of inflow and evaporation as well as the physical parameters on the reservoir variables (e.g., water release, storage, and spill). The results showed that the highest and lowest uncertainties of the reservoir water release occurred in July and May, respectively. In addition, the highest and lowest uncertainties were, respectively, observed in March and October for the reservoir storage, and in March and May for the water spill. The results also showed that the volumetric reliability had the highest uncertainty with a coefficient of variation (CV) of 0.158, while the resiliency had the lowest uncertainty with a CV of 0.020.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data Availability Statement

All of the required data have been presented in our article.

References

  • Akbari-Alashti, H., Bozorg-Haddad, O., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Multi-reservoir real-time operation rules: A new genetic programming approach. Proceedings of the Institution of Civil Engineers: Water Management, 167(10), 561–576. https://doi.org/10.1680/wama.13.00021

    Article  Google Scholar 

  • Araghinejad, S. (2013). Data-driven modeling: Using MATLAB® in water resources and environmental engineering. Springer Science and Business Media.

    Google Scholar 

  • Asgari, H.-R., Bozorg-Haddad, O., Pazoki, M., & Loáiciga, H. A. (2016). Weed optimization algorithm for optimal reservoir operation. Journal of Irrigation and Drainage Engineering, 142(2), 04015055. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000963

    Article  Google Scholar 

  • Ashofteh, P.-S., Bozorg-Haddad, O., & Loáiciga, H. A. (2019). Application of bi-objective genetic programming for optimizing irrigation rules using two reservoir performance criteria. International Journal of River Basin Management. https://doi.org/10.1080/15715124.2019.1613415 in Press.

    Article  Google Scholar 

  • Bai, Y., and Jin, W.-L. (2016). Random variables and uncertainty analysis. Marine Structural Design, 615–625.

  • Bozorg-Haddad, O., (2018). “Water Resources Systems Optimization.” Tehran university Publication, No.3, Tehran, Iran.

  • Bozorg-Haddad, O., Athari, E., Fallah-Mehdipour, E., Bahrami, M., & Loáiciga, H. A. (2019). Allocation of reservoir releases under drought conditions: A conflict-resolution approach. Proceedings of the Institution of Civil Engineers: Water Management, 172(5), 218–228.

    Google Scholar 

  • Bozorg-Haddad, O., Azad, M., Fallah-Mehdipour, E., Delpasand, M., & Chu, X. (2020). Verification of FPA and PSO algorithms for rule curve extraction and optimization of single- and multi-reservoir systems’ operations considering their specific purposes. Water Supply. https://doi.org/10.2166/ws.2020.274

    Article  Google Scholar 

  • Bozorg-Haddad, O., Farhangi, M., Fallah-Mehdipour, E., & Mariño, M. A. (2014). Effects of inflow uncertainty on the performance of multireservoir systems. Journal of Irrigation and Drainage Engineering, 140(11), 04014035.

    Article  Google Scholar 

  • Bozorg-Haddad, O., Moradi-Jalal, M., Mirmomeni, M., Kholghi, M. K. H., & Mariño, M. A. (2009). Optimal cultivation rules in multi-crop irrigation areas. Irrigation and Drainage, 58(1), 38–49. https://doi.org/10.1002/ird.381

    Article  Google Scholar 

  • Bozorg-Haddad, O., Zarezadeh-Mehrizi, M., Abdi-Dehkordi, M., Loáiciga, H. A., & Mariño, M. A. (2016). A self-tuning ANN model for simulation and forecasting of surface flows. Water Resources Management, 30(9), 2907–2929. https://doi.org/10.1007/s11269-016-1301-2

    Article  Google Scholar 

  • Chang, L. C., & Chang, F. J. (2009). Multi-objective evolutionary algorithm for operating parallel reservoir system. Journal of Hydrology, 377(1–2), 12–20.

    Article  Google Scholar 

  • Cigizoglu, H. K. (2008). Artificial neural networks in water resources. In H. G. Coskun, H. K. Cigizoglu, & M. D. Maktav (Eds.), NATO Science for peace and security series C: Environmental security (pp. 115–148). Springer. https://doi.org/10.1007/978-1-4020-6575-0_8

    Chapter  Google Scholar 

  • Cremon, M. A., Christie, M. A., and Gerritsen, M. G. (2018). “Monte Carlo Simulation for Uncertainty Quantification in Reservoir Simulation: A Convergence Study. In: Conference proceedings, ECMOR XVI - 16th european conference on the mathematics of oil, pp.1 - 18.

  • Delli Priscoli, J. (2000). Water and civilization: Using history to reframe water policy debates and to build a new ecological realism. Water Policy, 1(6), 623–636. https://doi.org/10.1016/s1366-7017(99)00019-7

    Article  Google Scholar 

  • Delpasand, M., Bozorg-Haddad, O., & Loáiciga, H. A. (2020). Integrated virtual water trade management considering self-sufficient production of strategic agricultural and industrial products. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2020.140797

    Article  Google Scholar 

  • Delpasand, M., Fallah-Mehdipour, E., Azizipour, M., Jalali, M., Safavi, H. R., Saghafian, B., Loáiciga, H. A., Babel, M. S., Savic, D., & Bozorg-Haddad, O. (2021). Forensic engineering analysis applied to flood control. Journal of Hydrology, 594, 125961.

    Article  Google Scholar 

  • Du, Y.-C., & Stephanus, A. (2018). Levenberg-Marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor. Sensors, 18(7), 2322. https://doi.org/10.3390/s18072322

    Article  Google Scholar 

  • Duckstein, L., & Plate, E. J. (Eds.). (1987). Engineering reliability and risk in water resources. Springer. https://doi.org/10.1007/978-94-009-3577-8

    Book  Google Scholar 

  • Ehsani, N., Fekete, B. M., Vörösmarty, C. J., & Tessler, Z. D. (2015). A neural network based general reservoir operation scheme. Stochastic Environmental Research and Risk Assessment, 30(4), 1151–1166. https://doi.org/10.1007/s00477-015-1147-9

    Article  Google Scholar 

  • Fallah-Mehdipour, E., Bozorg-Haddad, O., & Mariño, M. A. (2015). Evaluation of stakeholder utility risk caused by the objective functions in multipurpose multireservoir systems. Journal of Irrigation and Drainage Engineering, 141(2), 04014047. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000785

    Article  Google Scholar 

  • González-Manteiga, W., & Crujeiras, R. M. (2013). An updated review of goodness-of-fit tests for regression models. TEST, 22(3), 361–411. https://doi.org/10.1007/s11749-013-0327-5

    Article  Google Scholar 

  • Hashimoto, T., Stedinger, J. R., & Loucks, D. P. (1982). Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resources Research, 18(1), 14–20.

    Article  Google Scholar 

  • Iyer, M. S., & Rhinehart, R. R. (1999). A method to determine the required number of neural-network training repetitions. IEEE Transactions on Neural Networks, 10(2), 427–432. https://doi.org/10.1109/72.750573

    Article  CAS  Google Scholar 

  • Jain, S. K., Das, A., & Srivastava, D. K. (1999). Application of ANN for reservoir inflow prediction and operation. Journal of Water Resources Planning and Management, 125(5), 263–271. https://doi.org/10.1061/(asce)0733-9496(1999)125:5(263)

    Article  Google Scholar 

  • Jain, S. K., Reddy, N. S. R. K., & Chaube, U. C. (2005). Analysis of a large inter-basin water transfer system in India. Water and Energy Abstracts, 15(2), 32–32.

    Google Scholar 

  • Jothivenkatachalam, K., Nithya, A., & Mohan, S. C. (2010). Correlation analysis of drinking water quality in and around perur block of Coimbatore District, Tamil Nadu, India. Rasayan Journal of Chemistry, 3(4), 649–654.

    CAS  Google Scholar 

  • Karamouz, M., and Araghinejad, Sh. (2011), “Advanced Hydrology”, Industrial university of Amir Kabir publication, No. 2, Tehran, Iran.

  • King, L. M., & Simonovic, S. P. (2020). A deterministic Monte Carlo simulation framework for dam safety flow control assessment. Water, 12(2), 505.

    Article  Google Scholar 

  • Lee, J., Lee, M., Chun, Y.-Y., & Lee, K. (2018). Uncertainty analysis of the water scarcity footprint based on the AWARE model considering temporal variations. Water, 10(3), 341. https://doi.org/10.3390/w10030341

    Article  Google Scholar 

  • Liu, X., Lu, C., Zhu, Y., Singh, V. P., Qu, G., & Guo, X. (2017). Multi-objective reservoir operation during flood season considering spillway optimization. Journal of Hydrology, 552, 554–563.

    Article  Google Scholar 

  • Loucks, D. P., Stedinger, J. R., & Haith, D. A. (1981). Water resource systems planning and analysis Prentice-Hall Englewood Ciiffs NJ. Hydrological Science, 41(5), 697–713.

    Google Scholar 

  • Loucks, D. P., and van Beek, E. (2005). Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications. In: Studies and Reports in Hydrology. UNESCO Publishing, Paris.

  • Lowe, L. D., Webb, J. A., Nathan, R. J., Etchells, T., & Malano, H. M. (2009). Evaporation from water supply reservoirs: An assessment of uncertainty. Journal of Hydrology, 376(1–2), 261–274.

    Article  Google Scholar 

  • Maity, R. (2018). Basic statistical properties of data. Springer transactions in civil and environmental engineering, 53–92. https://doi.org/10.1007/978-981-10-8779-0_3.

  • Marton, D., & Paseka, S. (2017). Uncertainty impact on water management analysis of open water reservoir. Environments, 4(1), 10.

    Article  Google Scholar 

  • McMillan, H., Seibert, J., Petersen-Overleir, A., Lang, M., White, P., Snelder, T., & Kiang, J. (2017). How uncertainty analysis of streamflow data can reduce costs and promote robust decisions in water management applications. Water Resources Research, 53(7), 5220–5228.

    Article  Google Scholar 

  • Okon, A. N., Adewole, S. E., & Uguma, E. M. (2020). Artificial neural network model for reservoir petrophysical properties: Porosity, permeability and water saturation prediction. Modeling Earth Systems and Environment. https://doi.org/10.1007/s40808-020-01012-4

    Article  Google Scholar 

  • Rolke, W., & Gongora, C. G. (2020). A chi-square goodness-of-fit test for continuous distributions against a known alternative. Computational Statistics. https://doi.org/10.1007/s00180-020-00997-x

    Article  Google Scholar 

  • Seifollahi-Aghmiuni, S., Bozorg-Haddad, O., & Loáiciga, H. A. (2016). Development of a sample multiattribute, multireservoir system for testing operational models. Journal Irrigation and Drainage Engineering, 142(1), 04015039.

    Article  Google Scholar 

  • Shokri, A., Bozorg-Haddad, O., & Mariño, M. A. (2013). Algorithm for increasing the speed of evolutionary optimization and its accuracy in multi-objective problems. Water Resources Management, 27(7), 2231–2249. https://doi.org/10.1007/s11269-013-0285-4

    Article  Google Scholar 

  • Soltanjalili, M., Bozorg-Haddad, O., & Mariño, M. A. (2011). Effect of breakage level one in design of water distribution networks. Water Resources Management, 25(1), 311–337. https://doi.org/10.1007/s11269-010-9701-1

    Article  Google Scholar 

  • Soundharajan, B.-S., Adeloye, A. J., & Remesan, R. (2016). Evaluating the variability in surface water reservoir planning characteristics during climate change impacts assessment. Journal of Hydrology, 538, 625–639.

    Article  Google Scholar 

  • Srdjevic, B., Medeiros, Y. D. P., & Faria, A. S. (2004). An objective multi-criteria evaluation of water management scenarios. Water Resources Management, 18(1), 35–54.

    Article  Google Scholar 

  • Tegegne, G., & Kim, Y.-O. (2020). Representing inflow uncertainty for the development of monthly reservoir operations using genetic algorithms. Journal of Hydrology, 586, 124876.

    Article  Google Scholar 

  • Tehrani, M., Samani, J., & Montaseri, M. (2008). Uncertainty analysis of reservoir sedimentation using latin hypercube sampling and Harr’s method: Shahar Chai Dam in Iran. Journal of Hydrology (New Zealand), 47(1), 25–42.

    Google Scholar 

  • Willis, R., Finney, B. A., & Chu, W.-S. (1984). Monte Carlo optimization for reservoir operation. Water Resources Research, 20(9), 1177–1182.

    Article  Google Scholar 

  • Xu, C.-Y., & Singh, V. P. (1998). A review on monthly water balance models for water resources investigations. Water Resources Management, 12(1), 20–50. https://doi.org/10.1023/a:1007916816469

    Article  Google Scholar 

  • Zhang, X., Liang, F., Srinivasan, R., & Van Liew, M. (2009). Estimating uncertainty of streamflow simulation using Bayesian neural networks. Water Resources Research. https://doi.org/10.1029/2008WR007030

    Article  Google Scholar 

  • Zhao, T., Cai, X., & Yang, D. (2011). Effect of streamflow forecast uncertainty on real-time reservoir operation. Advances in Water Resources, 34(4), 495–504.

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank Iran’s National Science Foundation (INSF) for the support for this research.

Author information

Authors and Affiliations

  1. Department of Irrigation & Reclamation Engineering, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

    Omid Bozorg-Haddad & Pouria Yari

  2. Department of Irrigation & Reclamation Engineering, Faculty of Agricultural Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Tehran, Iran

    Mohammad Delpasand

  3. Dept. of Civil & Environmental Engineering, North Dakota State Univ., Dept 2470, Fargo, ND, 58108-6050, USA

    Xuefeng Chu

Authors
  1. Omid Bozorg-Haddad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pouria Yari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohammad Delpasand
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xuefeng Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Omid Bozorg-Haddad.

Ethics declarations

Conflict of interest

The authors declared that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bozorg-Haddad, O., Yari, P., Delpasand, M. et al. Reservoir operation under influence of the joint uncertainty of inflow and evaporation. Environ Dev Sustain 24, 2914–2940 (2022). https://doi.org/10.1007/s10668-021-01560-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-021-01560-4

Keywords

Search

Navigation