Study the Long-Term Effects of Industrial and Agricultural Uses on the Fluctuations of the Groundwater Level of Shazand Plain

Subject Areas : Water resources management

Siamak  Amiri ¹ (Ph.D. Candidate, Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.)
Ahmad  Rajabi ² (Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.)
Saeid  Shabanlou ³ (Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.)
Fariborz  Yosefvand ⁴ (Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.)
Mohammad ali  Izadbakhsh ⁵ (Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.)

Received: 2023-02-20 Accepted : 2023-11-01 Published : 2023-11-22

Keywords: Groundwater level, Hydraulic Conductivity, irrigation efficiency, GMS Numerical model,

Abstract :

Background and Aim: Nowadays, by increasing the water demand in different sectors, the withdrawal amount from groundwater resources is increasing leading to more drawdown of Markazi province aquifers. One of the most suitable methods for the optimal management of groundwater resources is the analysis of the behavior of aquifers in various conditions using mathematical models. The objective of this paper is to investigate the effects of withdrawal for agricultural and industrial consumptions on the groundwater level of the Shazand plain located in Markazi Province and the impact of a 20% increase in irrigation efficiency of farms in the case of the development of under pressure and low-consumption systems using the GMS numerical model. Method: First, the conceptual and numerical model of the Shazand aquifer was executed in the GMS software and calibrated in the steady state. Then, the model was recalibrated in a transient state for the statistical period from October 2015 to September 2019. To examine the reactions of the model to the changes of important and effective parameters, the sensitivity analysis of the model was performed and the model was verified for the statistical period of October 2019 to September 2021. Then, the changes in the groundwater level in the aquifer under two reference management scenarios and increasing irrigation efficiency were investigated and compared. In the reference scenario assuming the continuation of the current conditions and in the efficiency increase scenario assuming a 20% increase in irrigation efficiency, the simulation of changes in the groundwater level in the entire Shazand plain for the upcoming 20 years from October 2021 to September 2041 was carried out. Results: Based on the obtained results, the RMSE error value related to the steady state recalibration is about 0.7 meters and the average RMSE error value in the transient state in all months of simulation in two recalibration and validation periods is less than 0.6 meters, which shows the high accuracy of the model in simulating the groundwater level in the whole plain. The sensitivity analysis showed that the changes in specific yield and hydraulic conductivity parameters have the greatest effect on the fluctuations of groundwater in the whole plain. The results showed that in the reference scenario, the drop in the groundwater level at the end of the 20-year operation period is 3.95 meters. In the scenario of a 20% increase in efficiency, with the reduction of extraction from wells due to the increase in irrigation efficiency, the amount of drop will reach 2.76 meters, in which case the amount of drop will be mitigated by 1.2 meters. Conclusion: According to the results, the highest drop in the groundwater level in both reference and increase in efficiency scenarios in the central areas of the plain is 9.2 and 6.9 meters, respectively, and the lowest drop in the western areas of the plain is 1 and 0.5 meters, respectively. Considering that the agricultural sector has the greatest impact on the level drop in the aquifer in the central areas of the plain, it is better to focus management plans to control withdrawal from the aquifer, such as increasing efficiency or modifying the cultivation pattern, on this sector. In case of the implementation of systems under pressure and increasing efficiency in the plain, the amount of drawdown in the region will be mitigated to some extent, but the problem will not be solved and it is necessary to implement supplementary programs to cultivate high consumption plants instead of high consumption crops and in the industry sector instead of extracting groundwater, treated municipal wastewater should be used.

References:

Amiri, S., Rajabi, A., Shabanlou, S., Yosefvand, F., izadbakhsh, MA. (2023). Prediction of groundwater level variations using deep learning methods and GMS numerical model. Earth Science Informatic. https://doi.org/10.1007/s12145-023-01052-1

Azizi, E., Yosefvand, F., Yaghoubi, B., Izadbakhsh, MA., Shabanlou, S. (2023). Modelling and prediction of groundwater level using wavelet transform and machine learning methods: A case study for the Sahneh Plain, Iran. Irrigation and Drainage, 72(3), 747–762.

Azizpour, A., Izadbakhsh, MA., Shabanlou, S., Yosefvand, F., Rajabi, A. (2021). Estimation of water level fluctuations in groundwater through a hybrid learning machine, Groundwater for Sustainable Development, 15, 100687.

Azizpour, A., Izadbakhsh, MA., Shabanlou, S., Yosefvand, F., Rajabi, A. (2022). Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model. Environment Science Pollution Research, 29, 28414–28430.SA

Bayesteh, M., Azari, A. (2021). Stochastic Optimization of Reservoir Operation by Applying Hedging Rules. Journal of Water Resources Planning Management, 147(2), 04020099.

Esmaeili, F., Shabanlou, S., Saadat, MA. (2021). Wavelet-outlier robust extreme learning machine for rainfall forecasting in Ardabil City. Iran. Earth Sci Inform, 14, 2087–2100.

Fallahi, MM., Shabanlou, S., Rajabi, A., Yosefvand, F., izadbakhsh, MA. (2023). Effects of climate change on groundwater level variations affected by uncertainty (case study: Razan aquifer). Applied Water Science, 13, 143.

Ghobadian, R., Fatahi, A., Zare, M. (2014). Studying the Effects of Gavoshan Dam's Irrigation and Drainage Network on Groundwater of Miandarband Plain Using GMS 6.5 Model. Journal of Water Research in Agriculture, 28(4), 759-772. [in Persian]

Guzman, S. M., Paz, J. O., Tagert, M. L. M., Mercer, A. E. (2019). Evaluation of Seasonally Classified Inputs for the Prediction of Daily Groundwater Levels: NARX Networks Vs Support Vector Machines. Environmental Modeling & Assessment, 24(2), 223-234.

Hu, L., Xu, Z., Huang, W. (2016). Development of a river-groundwater interaction model and its application to a catchment in Northwestern China. Journal of Hydrology, 543, 483–500.

Ivkovic, K.M. (2009). A top–down approach to characterize aquifer–river interaction processes. Journal of Hydrology, 365, 145–155.

Lu, C., Chen, Y., Zhang, C., Luo, J (2013). Steady-state freshwater–seawater mixing zone in stratified coastal aquifers. Journal of Hydrology, 505, 24-34.

Mahdavi, M., Farokhzadeh. B., Salajegheh, A., Malakian, A., Souri, M. (2013). Simulation of Hamadan-Bahar Aquifer and Investigation of Management Scenarios by using PMWIN. Watershed Management Researches (Pajouhesh-va-Sazandegi), 26(1 (98)), 108-116. [in Persian]

Malekzadeh, M., Kardar, S., Saeb, K., Shabanlou, S., Taghavi L. (2019a). A novel approach for prediction of monthly ground water level using a hybrid wavelet and non-tuned self-adaptive machine learning model. Water resources management, 33: 1609-1628.

Malekzadeh, M., Kardar, S., Shabanlou, S. (2019b). Simulation of groundwater level using MODFLOW, extreme learning machine and Wavelet-Extreme Learning Machine models, Groundwater for Sustainable Development, 9, 100279.

Mazraeh, A., Bagherifar, M., Shabanlou, S., Ekhlasmand, R. (2023). A Hybrid Machine Learning Model for Modeling Nitrate Concentration in Water Sources. Water, Air, & Soil Pollution, 234(11), 1-22.

Mohammed, KS., Shabanlou, S., Rajabi, A., Yosefvand, F., izadbakhsh, MA. (2023). Prediction of groundwater level fluctuations using artificial intelligence-based models and GMS. Applied Water Science, 13, 54.

Mohtsham, M., Dehghani, A.A., Akbarpour, A., Miftah Halaghi, M. (2011). Prediction of water level in aquifer using GMS software, case study: Birjand aquifer, 4th Iran Water Resources Management Conference, Tehran, Iran. [in Persian]

Nadiri, A. A., Naderi, K., Khatibi, R., Gharekhani, M. (2019). Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrological sciences journal, 64(2), 210-226.

Nagheli, S., Samani, N., Pasandi, M. (2011). Investigation of balance and sustainable development of Najaf Abad aquifer, 30th Earth Sciences Meeting, Tehran, Iran. [in Persian]

Narula, K.K., Gosian, A.K. (2013). Modeling hydrology, groundwater recharge and non-point nitrate loadings in the Himalayan Upper Yamuna basin, Science of the Total Environment, 468, S102-S116.

Pahar, G., Dhar, A. (2014). A Dry Zone-Wet Zone Based Modeling of Surface Water and Groundwater Interaction for Generalized Ground Profile. Journal of Hydrology, 519(27), 2215-2223.

Porhaghi, A., Akhondali, A., Radmanesh, F., Mirzaee, S. (2014). Manage the Groundwater Sources Exploration of the Nourabad Plain in the Drought Conditions with MODFLOW Modeling. Irrigation Sciences and Engineering, 37(2), 71-82. [in Persian]

Poursaeid, M., Mastouri, R., Shabanlou, S., Najarchi, M. (2020). Estimation of total dissolved solids, electrical conductivity, Salinity and groundwater levels using novel learning machines. Environment Earth Science, 79:1–25.

Poursaeid, M., Mastouri, R., Shabanlou, S., Najarchi, M. (2021). Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks. Water and Environment Journal, 35, 67–83.

Shamsai, A., Forghani, A. (2011). Conjunctive use of Surface and Ground Water Resources in Arid Regions. Iran-Water Resources Research, 7(2), 26-36. [in Persian]

Yosefvand, F., Shabanlou, S. (2020). Forecasting of Groundwater Level Using Ensemble Hybrid Wavelet–Self-adaptive Extreme Learning Machine-Based Models. Natural Resource Research, (2020); 29, 3215–3232.

Zeinali, M., Azari, A., Heidari, M. (2020a). Simulating Unsaturated Zone of Soil for Estimating the Recharge Rate and Flow Exchange Between a River and an Aquifer. Water Resources Management, 34, 425–443.

Zeinali, M., Azari, A., Heidari, M. (2020b). Multi-objective Optimization for Water Resource Management in Low-Flow Areas Based on a Coupled Surface Water–Groundwater Model. Journal of Water Resource Planning and Management (ASCE), 146(5), 04020020.

Zibaei, M.H., Zibaei, M., Ardokhani, K. (2013). Evaluation of scenarios of integrated use of surface and groundwater resources in Firoozabad plain of Fars. Journal of Agricultural Economics Research, 5(1), 157-181.