اثرات اجرای اقدامات سازه ای و تغذیه مصنوعی بر نوسانات تراز آب زیرزمینی دشت فامنین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم آب و مهندسی، دانشگاه بوعلی سینا، همدان، ایران

2 گروه مهندسی آب، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه، ایران

3 دانشیار گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه،ایران

چکیده

افزایش بیش از حد برداشت از منابع آب‌های زیرزمینی دشت فامنین باعث افت شدید تراز آب و ایجاد فروچاله هایی در این دشت شده است. یکی از روشهای مدیریت منابع آب زیرزمینی، تجزیه و تحلیل رفتار آبخوان ها تحت اجرای سناریوهای مختلف بهره برداری با استفاده از مدلهای ریاضی است. هدف از این تحقیق بررسی اثرات اجرای اقدامات سازه ای مانند بندهای خاکی و حوضچه های تغذیه مصنوعی بر ترمیم تراز آب زیرزمینی دشت فامنین در استان همدان و ارایه راهکارهای مدیریتی برای بهره برداری بهتر با استفاده از مدل عددی GMS می باشد که در سال 1401 به انجام رسیده است. ابتدا مدل در حالت غیرماندگار واسنجی و صحت سنجی شد. همچنین آنالیز حساسیت پارامترهایی تاثیرگذار در مدل انجام شد. با فرض ادامه وضع موجود، شبیه سازی عملکرد سیستم از مهر 1402 تا شهریور 1417 به مدت 15 سال انجام شد. پس از آن در سناریوی دوم (اجرای اقدامات سازه ای) برای 15 سال آینده تراز آب زیرزمینی در دشت با فرض بهره برداری از سازه های ذخیره و یا حوضچه های تغذیه مصنوعیپیش بینی شد. نتایج نشان داد افت تراز آب زیرزمینی در شرایط ادامه وضع موجود 6/11 متر می‌باشد. با انجام اقدامات سازه ای و بهره برداری از آن  در طول 15 سال مقدار افت به 2/11 متر خواهد رسید. لذا میزان افت حدود 4/0 متر تعدیل خواهد یافت.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Impacts of the implementation of structural measures and artificial intelligence on groundwater level fluctuations of Famenin Plain

نویسندگان [English]

  • babak sanahmadi 1
  • majeid heydari 1
  • arash azari 2
  • saeid shabanlou 3
1 Department of Water Science and Engineering, Bu-Ali Sina University, Hamadan, Iran
2 Department of Water Engineering, College of Agriculture, Razi university, , Kermanshah, Iran
3 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
چکیده [English]

 
The excessive increase in extraction from the groundwater resources of Famenin-Plain has caused a sharp drop in the water level and created sinkholes in this plain. One of the methods of managing groundwater water resources is to analyze the behavior of aquifers under the implementation of different exploitation scenarios using mathematical models. The purpose of this research is to investigate the effects of implementing structural measures such as levees and artificial recharge ponds on restoring the groundwater level of Famenin-Plain in hamedan province and providing management strategies for better exploitation using GMS-numerical model that was done in 2022.First, the model was calibrated and validated in transient mode. Also, the sensitivity analysis of influential parameters in the model was done. Assuming the continuation of the current situation, the simulation of system performance was carried out from October-2023 to September-2038 for 15-years. After that, in the second scenario (implementation of structural measures),the groundwater level in the plain was predicted for the next 15-years, assuming the use of storage structures or artificial recharge ponds. The results showed that if the aquifer is operated according to the existing pattern, the water level in the aquifer will drop by an average of 11.6-meters at the end of the 15-year period. The results of the exploitation scenario of the structures showed that the average drop of the groundwater level in the entire aquifer at the end of the period will be 11.2-meters. Therefore, compared to the reference scenario, this scenario indicates an adjustment of the groundwater drop by-0.4-meters. 

کلیدواژه‌ها [English]

  • structural measures
  • artificial recharge
  • GMS model
  • groundwater level
  • Famenin Plain

Impacts of the implementation of structural measures and artificial intelligence on groundwater level fluctuations of Famenin Plain

 

EXTENDED ABSTRACT

Introduction

The excessive increase in extraction from the groundwater resources of Famenin Plain has caused a sharp drop in the water level and created sinkholes in this plain. One of the methods of managing groundwater water resources is to analyze the behavior of aquifers under the implementation of different exploitation scenarios using mathematical models. The purpose of this research is to investigate the effects of implementing structural measures such as levees and artificial recharge ponds on restoring the groundwater level of Famenin Plain in hamedan province and providing management strategies for better exploitation using GMS numerical model.

Materials and Methods

Due to the subsidences that has occurred in this plain and its surroundings, in this research, the simulation of the groundwater level of this plain was done using the GMS model under different management scenarios. This research was done in 2022. The conceptual and numerical model of Famenin Plain was prepared based on information about wells and piezometers, geological map and bedrock, information on hydraulic conductivity and specific yield, rivers and recharge of the plain. First, the model was calibrated and validated in transient mode. Also, the sensitivity analysis of influential parameters in the model was done. Assuming the continuation of the current situation, the simulation of system performance was carried out from October 2023 to September 2038 for 15 years. After that, in the second scenario (implementation of structural measures), the groundwater level in the plain was predicted for the next 15 years, assuming the use of storage structures or artificial recharge ponds.

Results and discussion

The results showed that if the aquifer is operated according to the existing pattern, the water level in the aquifer will drop by an average of 11.6 meters at the end of the 15-year period. It should be noted that the maximum drop in the Famenin aquifer in this scenario was around 84 meters in the areas of the southwest of the aquifer. It should be noted that the largest concentration of sinkholes is in this section. The model was re-implemented for the operation conditions of the water storage structures and artificial recharge of the plain and the prediction of the groundwater level was made in these conditions for the next 15 years. The results of the exploitation scenario of the structures showed that the average drop of the groundwater level in the entire aquifer at the end of the period will be 11.2 meters. Therefore, compared to the reference scenario, this scenario indicates an adjustment of the groundwater drop by 0.4 meters. However, the largest amount of drop in this scenario will be 81.6 meters in the southwestern areas of the plain, which indicates a maximum drop adjustment of 2.4 meters. Also, the lowest amount of drop in the plain in both scenarios is related to the eastern and southeastern regions. So that in some of these areas, in the reference scenarios and exploitation of structures, the maximum amount of water level rise will be 3.1 and 3.2 meters, respectively.

Conclusion

It can be concluded that according to the state of exploitation of the groundwater resources of the Famenin aquifer, the biggest reason for the level drop in this plain is the withdrawal of agricultural wells due to their accumulation in the center of the plain. In case of implementation and exploitation of structural measures in the plain, the amount of level drop in the region will be adjusted to some extent, but the problem will not be solved. Therefore, it is suggested that considering the increasing trend of the population and the possibility of increasing the withdrawal from these resources in the coming years, in addition to exploiting the ponds and recharge wells and completing the structural measures by building new levees and increasing the number of recharge ponds, following the modification of the cultivation pattern of the area and replacement of low consumption crops instead of high consumption crops. However, it is better to take into account the current situation of the aquifer and the creation of numerous sinkholes that indicate excessive withdrawal from the aquifer, in addition to the above measures, a comprehensive plan should be implemented with the cooperation of the government and farmers and by spending a large budget to restore the aquifer and control settlement and in the first step, by persuading the farmers to fill the unauthorized wells or rent a part of the land that is harmful to the environment by the government and remove them from the groundwater withdrawal cycle.

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.
Azizi, K., Azari, A., & Farhadi Bansouleh, B. (2023). Simulation and determination of hydrodynamic coefficients and aquifer balance with Modflow mathematical model (Case study: Kermanshah Plain). Advanced Technologies in Water Efficiency, 2(4), 68-87. [in Persian]
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.
Bayesteh, M. & Azari, A. (2021). Stochastic Optimization of Reservoir Operation by Applying Hedging Rules. Journal of Water Resources Planning and 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 Ghaghabagi, A. & Zare, M. (2015). The effect of construction of irrigation and drainage network of Gavshan dam on underground water resources of Miandarband plain using GMS 6.5 model. Water research in agriculture (soil and water sciences), 28 (4), 759-772.
Guzman, SM., Paz, JO., 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. Hydrology, 543, 483–500.
Ivkovic, K. M. (2009). A top–down approach to characterise aquifer–river interaction processes. Hydrology, 365, 145–155.
Kamkar, V., Azari, A., & Fatemi, S. E. (2021). Estimation of Recharge and Flow Exchange between River and Aquifer Based on Coupled Surface Water-Groundwater Model. Iranian Journal of Soil and Water Research, 52(7), 1779-1793. [in Persian]
Lu, C., Chen, Y., Zhang, C., & Luo, J. (2013). Steady-state freshwater–seawater mixing zone in stratified coastal aquifers. Hydrology, 505, 24-34.
Mahdavi, M., Farokhzadeh, B., Salajegheh. A., Malakian, A & Soori, M. (2012). Simulation of Hamadan-Bahar plain aquifer and management scenarios analysis using PMWIN model. Watershed research (research and construction), 26 (1), 108-116.
mazandarani zadeh, H., & hoseini, M. (2023). Investigating the effect of agricultural product price forecasting on groundwater level using systems dynamics, in order to simultaneously maintain the welfare of farmers and groundwater resources. Iranian Journal of Soil and Water Research, 53(11), 2565-2582. [in Persian]
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]
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]
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.
Moradi, A., Akhtari, A. & Azari, A. (2023). Prediction of groundwater level fluctuation using methods based on machine learning and numerical model. Applied Research in Water and Wastewater, 10 (1), 20-28.
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.
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. S102-S116.
Pahar, G. & Dhar, A. (2014). A Dry Zone-Wet Zone Based Modeling of Surface Water and Groundwater Interaction for Generalized Ground Profile. Hydrology, 519(27), 2215-2223.
Pourhaghi, A., Akhondali. A. A., Radmanesh, F. & Mirzaee, S. Y. (2015). Manage the Groundwater Sources Exploration of the Nourabad Plain in the Drought Conditions with MODFLOW Modeling. Irrigation science and engineering, 37 (92), 71-82.
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.
Sgarifi, F. & ghafouri, A.R. (1997). Flood water spreading in Iran and Integrated Aproach. rain drope, series 2, vol 7: i-iii-1997
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]
Soltani, K., & Azari, A. (2022). Forecasting groundwater anomaly in the future using satellite information and machine learning. Hydrology, 612 (2), 128052.
Yosefvand, F. & Shabanlou, S. (2020). Forecasting of Groundwater Level Using Ensemble Hybrid Wavelet–Self-adaptive Extreme Learning Machine-Based Models. Natural Resource Research. 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). Multiobjective Optimization for Water Resource Management in Low-Flow Areas Based on a Coupled Surface Water–Groundwater Model. 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. Agricultural Economics Research, 5(1), 157-181.