Abstract
In this study, a water balance model, namely, the RAK model, is developed, which links a tank groundwater model and a surface water balance model. The RAK model optimizes the parameters of the two linked models simultaneously by minimizing errors in the groundwater table and surface outflow estimations. It is expected that by this model, more accurate estimates of water exchanges between surface and groundwater sources can be obtained compared to previously developed monthly water balance models. A Genetic Algorithm (GA) was used to optimize the parameters of the proposed model. To evaluate the efficiency of the proposed model, it was calibrated and validated for the Ghorve-Deh-Gholan basin located in northwestern Iran. The results of this study indicated the potential of the proposed approach for rapid water balance modeling of basins, specifically those located in areas with limited data availability.
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The datasets are available on request.
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Acknowledgements
Mohammadreza Moeini would like to thank the National Science Foundation (NSF), the University of Illinois Chicago, Department of Civil, Materials, and Environmental Engineering, and Dr. Ahmed Abokifa for their support while he is continuing his Ph.D. studies. Data and information used in this study were provided by the Iran Water Resources Management Company and Water Institute of the University of Tehran. Technical contributions of Dr. Hamed Poorsepahy-Samian are hereby acknowledged.
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Mohammadreza Moeini contributed to the writing—original draft, conceptualization, investigation, review, editing, and supervision. Banafsheh Zahraie contributed to the conceptualization, review, and advising. Farnaz Sadeghi contributed to review, editing, and technical application.
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Moeini, M., Zahraie, B. & Sadeghi, F. Developing a new lumped monthly water balance model for estimating groundwater level and runoff volume. Sustain. Water Resour. Manag. 10, 113 (2024). https://doi.org/10.1007/s40899-024-01087-2
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DOI: https://doi.org/10.1007/s40899-024-01087-2