توسعه مدل پویایی سیستم برای مدیریت پایدار منابع آب با استفاده از رویکرد پایتون در سطح حوضه آبریز تجن

نوع مقاله : مقاله پژوهشی

نویسندگان

1 پژوهشگر پسادکتری، دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 استاد دانشگاه علوم کشاورزی و منابع طبیعی ساری

3 دانشجوی کارشناسی ارشد کامپیوتر- نرم افزار، دانشگاه آزاد واحد لاهیجان.

4 پژوهشگر پسادکتری، دانشگاه علوم کشاورزی و منابع طبیعی ساری.

چکیده

در این تحقیق به‏منظور تخصیص منابع آب به محصولات حوضه آبریز تجن، مدل سیستم پویایی VENSIM ساخته و مورد اعتبارسنجی قرار گرفته است. سپس مدل با استفاده از رویکرد کدنویسی توسعه و مدلی به نام PySD ساخته شد. هدف اولیه شبیه‏سازی مدل PySD، پیش‏بینی میزان آب تخصیص یافته از منابع حوضه به تقاضاهای شرب، صنعت، کشاورزی و زیست‏محیطی است و برآورد کمبود احتمالی است. نتایج حاصل از اعتبارسنجی مدل PySD نشان داد که مقادیر RMSE، nRMSE و Ttest در همه موارد پیشنهادی به‌جز ضرایب پیشنهادی برای مخزن سد زارم و تقاضای کشاورزی در این منطقه (Zr0 و agri coe1.5) بهترین تطابق را بین مقادیر شبیه‏سازی و مشاهده‌ای نشان داده است که میزان RMSE و nRMSE آن از یک سو به‌ترتیب 08/0 متر و 12 درصد و از سویی دیگر میزان Ttest آن 051/0 محاسبه شد. بررسی نتایج نشان داد گندم در اکثر ماه‏های فصل زراعی با کمبود مواجه بوده به‌طوری که از مجموع 56/1 میلیون‏مترمکعب تقاضای آبی آن تنها 3/0 میلیون‏متر مکعب تأمین شده است. از طرفی براساس اصل قابلیت تأمین‏پذیری نیازهای کشاورزی، تنها برای سه محصول سیاه‏ریشه، شالی و مرکبات بیش از 85 درصد نیازها تأمین شده و باقی محصولات قابلیت تامین پذیری بسیار اندکی دارند. نتایج ارزیابی معیارهای اعتمادپذیری، برگشت‏پذیری، آسیب‏پذیری و پایداری در تخمین کارایی سیستم منابع آب در تأمین مصارف کشاورزی حوضه آبریز تجن، نشان می‏دهد که مرکبات، جالیز و دانه‏های روغنی در منطقه شهید رجایی، زارم و فینسک در محدوده قابل قبولی قرار دارند و این پارامتر برای محصولات سیاه‏ریشه کم‌ترین میزان پایداری را دارا هستند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Development of system dynamics model for sustainable management of water resources by python approach in Tajan watershed level

نویسندگان [English]

  • Seyedeh Fatemeh Hashemi 1
  • Ali Shahnazari 2
  • Roozbeh Mustafavi Eshkelak 3
  • Sonia Sadeghi 4
1 Postdoc Researcher, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
2 Professor, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
3 MSc. Student Computer-Software, Lahijan Azad University.
4 ostdoc Researcher, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
چکیده [English]

The study of supply and shortage related to rice also has a similar trend with other products in this sector, out of the total 17 million cubic meters of water demand, 7 million cubic meters have been supplied and 42% of it has not been supplied. Of course, checking the numbers of supply and shortage in different months of this product shows that the shortage is mainly in the hot months of the year when the base flow and release of the reservoir in this region is reduced and it is between the months of May to September and according to Due to the priority of less supply compared to the other two products on the one hand and the relatively lower demand of this product with black root and citrus fruits, its supply has had such a trend. In the case of wheat, grain corn, and oilseeds, there is a more or less different trend, so that the availability of oilseeds was almost 50%. The results of the balance of resources and water consumption in the Tajan catchment basin in the existing conditions by the VENSIM model during the years 2013-2017 showed that the agricultural demand in Finesk, Shahid Rajaee, Zarem and the Tajan diversion dam was 18, 146, 93 and It is 478 million cubic meters, respectively, 6 million cubic meters (33%), 93 million cubic meters (63%), 100 million cubic meters (59%) and 380 million cubic meters (80%) have been supplied, which is only in the Band area.

کلیدواژه‌ها [English]

  • Model development
  • Open source
  • system dynamics modeling

مراجع

  • Allen, R.G., Pereira, L.S., Smith, M., Raes, D. and Wright, J.L., 2005. FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions. Journal of irrigation and drainage engineering, 131(1), pp.2-13. Doi: 1061/(ASCE)0733-9437(2005)131:1(2.

 

  • Al-Zahrani, M.A. and Abo-Monasar, A., 2015. Urban residential water demand prediction based on artificial neural networks and time series models. Water resources management, 29, pp.3651-3662.

 

  • Cosgrove, W.J. and Loucks, D.P., 2015. Water management: Current and future challenges and research directions. Water Resources Research, 51(6), pp.4823-4839. Doi: 1002/2014WR016869.

 

  • Flinn, J.C. and Guise, J.W., 1970. An application of spatial equilibrium analysis to water resource allocation. Water Resources Research, 6(2), pp.398-409. Doi: 1029/WR006i002p00398.

 

  • Fu, Z.H., Zhao, H.J., Wang, H., Lu, W.T., Wang, J. and Guo, H.C., 2017. Integrated planning for regional development planning and water resources management under uncertainty: A case study of Xining, China. Journal of Hydrology, 554, pp.623-634. Doi: 1016/j.jhydrol.2017.08.022.

 

  • Gregersen, J.B., Gijsbers, P.J.A. and Westen, S.J.P., 2007. OpenMI: Open modelling interface. Journal of hydroinformatics, 9(3), pp.175-191. Doi: 2166/hydro.2007.023.

 

  • Habibi Davijani, M., Banihabib, M. E., Nadjafzadeh Anvar, A. and Hashemi, S. R. 2016. Multi-objective optimization model for the allocation of water resources in arid regions based on the maximization of socioeconomic efficiency. Water resources management, 30, 927-946.

 

  • Hashemi, S.F. and Shahnazari, A., 2015. Evaluating Reliability Index and Determining the Allocation Levels of Water Resources in Water User Association of Alborz Scheme. Water and Soil, 29(5), pp.1232-1246. Doi: 22067/jsw.v29i5.34561 .(In Persian)

 

  • Hashemi, F., Shahnazari, A., Rayini, M., Shahbazbegian, M. and Adamowski, J. F. 2020. Water Resource Allocation and Crop Yield Simulation in Tajan Plain Watershed by Coupling of WOFOST and VENSIM Models. Iranian Journal of Irrigation and Drainage. No. 3, Vol. 14. (In Persian)

 

  • Hashemi, S Rayeni, M. and Shahbazbegian, M., 2021. Evaluation of Optimal Cropping Pattern in Tajan Watershed with Systematic Modeling. Journal of Watershed Management Research, 12(23), pp.155-168. Doi: 52547/jwmr.12.23.155 .(In Persian)

 

  • Hashimoto, T., Stedinger, J.R. and Loucks, D.P., 1982. Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water resources research, 18(1), pp.14-20. Doi: 1029/WR018i001p00014.

 

  • He, L., Bao, J., Daccache, A., Wang, S., and Guo, P. 2020. Optimize the spatial distribution of crop water consumption based on a cellular automata model: a case study of the middle Heihe River basin, China. Science of the Total Environment, 720, 137569. Doi: 1016/j.scitotenv.2020.137569.

 

  • Honti, G., Dörgő, G. and Abonyi, J., 2019. Review and structural analysis of system dynamics models in sustainability science. Journal of Cleaner Production, 240, p.118015. Doi: 1016/j.jclepro.2019.118015.

 

  • Horlemann, L. and Jafari Berenji, P., 2017. Participation in water Management in Iran. Reviving the Dying Giant: Integrated Water Resource Management in the Zayandeh Rud Catchment, Iran, pp.51-62.

 

  • Hosseinpur, Z., Menhaj, M.H. and Kavoosi-Kalashami, M., 2016. Assessment Improving and organizing mechanism of water users cooperatives using AHP method in Esfarayen County, Iran. International Journal of Agricultural Management and Development (IJAMAD), 6(1047-2017-1689), pp.339-351. Doi: 22004/ag.econ.262600.

 

  • Hou, C., Wen, Y., Liu, X. and Dong, M., 2021. Impacts of regional water shortage information disclosure on public acceptance of recycled water—evidences from China’s urban residents. Journal of Cleaner Production, 278, p.123965. Doi: 1016/j.jclepro.2020.123965.

 

  • Houghton, J., 2018. PySD Documentation Release 0.9. 0. Read the Docs

 

  • Houghton, J. and Siegel, M., 2015. Advanced data analytics for system dynamics models using PySD. revolution, 3(4).

 

  • Houghton, J., Siegel, M., Wirsch, A., Moulton, A., Madnick, S.E. and Goldsmith, D., 2014. A survey of methods for data inclusion in System Dynamics models: methods, tools and applications.

 

  • Inam, A., Adamowski, J., Prasher, S., Halbe, J., Malard, J. and Albano, R., 2017a. Coupling of a distributed stakeholder-built system dynamics socio-economic model with SAHYSMOD for sustainable soil salinity management–Part 1: model development. Journal of Hydrology, 551, pp.596-618. Doi: 1016/j.jhydrol.2017.03.039.

 

  • Inam, A., Adamowski, J., Prasher, S., Halbe, J., Malard, J. and Albano, R., 2017b. Coupling of a distributed stakeholder-built system dynamics socio-economic model with SAHYSMOD for sustainable soil salinity management. Part 2: Model coupling and application. Journal of Hydrology, 551, pp.278-299. Doi: 1016/j.jhydrol.2017.03.039.

 

  • Khoshravesh, M. and Valizadeh, M., 2017. The Effects of Constructing Irrigation and Drainage Network of Rajaei Dam on Spatial and Temporal Changes of Groundwater Quality and Quantity. JWSS-Isfahan University of Technology, 21(2), pp.1-14. (In Persian)

 

  • Kiani, A.R., 2010. Optimal irrigation scheduling based on water-yield relations in soybean cultivars. Journal of Agricultural Engineering Research (Iran), 11(1), pp.85-102. (In Persian)

 

  • Loucks, D.P., 2000. Sustainable water resources management. Water international, 25(1), pp.3-10.

 

  • Ma, Y., Li, Y.P., Huang, G.H. and Liu, Y.R., 2020. Water-energy nexus under uncertainty: Development of a hierarchical decision-making model. Journal of Hydrology, 591, p.125297. Doi: 1016/j.jhydrol.2020.125297.

 

  • Madani, K., 2014. Water management in Iran: what is causing the looming crisis?. Journal of environmental studies and sciences, 4, pp.315-328.

 

  • Malard, J.J., Inam, A., Hassanzadeh, E., Adamowski, J., Tuy, H.A. and Melgar-Quiñonez, H., 2017. Development of a software tool for rapid, reproducible, and stakeholder-friendly dynamic coupling of system dynamics and physically-based models. Environmental modelling & software, 96, pp.410-420. Doi: 1016/j.envsoft.2017.06.053.

 

  • Marin, C.M. and Smith, M.G., 1988. Water resources assessment: A spatial equilibrium approach. Water Resources Research, 24(6), pp.793-801. Doi: 1029/WR024i006p00793.

 

  • Naghdi, S., Bozorg-Haddad, O., Khorsandi, M. and Chu, X., 2021. Multi-objective optimization for allocation of surface water and groundwater resources. Science of the Total Environment, 776, p.146026. Doi: 1016/j.scitotenv.2021.146026.

 

  • Peck, A., Neuwirth, C. and Simonovic, S.P., 2014. Coupling System Dynamics with Geographic Information Systems: CCaR Project Report. University of Western Ontario Department of Civil and Environmental Engineering(No. 086). report.

 

  • Prodanovic, P. and Simonovic, S.P., 2010. An operational model for support of integrated watershed management. Water resources management, 24, pp.1161-1194.

 

  • Read, L., Madani, K. and Inanloo, B., 2014. Optimality versus stability in water resource allocation. Journal of environmental management, 133, pp.343-354. Doi: 1016/j.jenvman.2013.11.045.

 

  • Shrestha, N.K., Leta, O.T., De Fraine, B., Van Griensven, A. and Bauwens, W., 2013. OpenMI-based integrated sediment transport modelling of the river Zenne, Belgium. Environmental Modelling & Software, 47, pp.193-206. Doi: 1016/j.envsoft.2013.05.004.

 

  • Sun, S., Wang, Y., Liu, J., Cai, H., Wu, P., Geng, Q. and Xu, L., 2016. Sustainability assessment of regional water resources under the DPSIR framework. Journal of Hydrology, 532, pp.140-148. Doi: 1016/j.jhydrol.2015.11.028

 

  • Tennant, D.L., 1976. Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries, 1(4), pp.6-10. Doi: 10.1577/1548-8446(1976)001<0006:IFRFFW>2.0.CO;2

 

  • Trieu T.T. 2005. Water Balance Analysis in Ea Knir Catchment, Dak Lak, Vietnam. Integrated Watershed Management: Studies and Experiences from Asia.

 

  • Van Rossum, G. and Drake Jr, F. L. 2014. The python language reference. Python Software Foundation: Wilmington, DE, USA.

 

  • Wei, F., Zhang, X., Xu, J., Bing, J. and Pan, G., 2020. Simulation of water resource allocation for sustainable urban development: An integrated optimization approach. Journal of cleaner production, 273, p.122537. Doi: 1016/j.jclepro.2020.122537.

 

  • Yu, S. and Lu, H. 2018. An integrated model of water resources optimization allocation based on projection pursuit model–Grey wolf optimization method in a transboundary river basin. Journal of Hydrology, 559, 156-165. Doi: 1016/j.jhydrol.2018.02.033.
علوم و مهندسی آبیاری
دوره 47، شماره 3
آبان 1403
صفحه 87-106

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سابقه مقاله

  • تاریخ دریافت: 17 بهمن 1401
  • تاریخ بازنگری: 15 مهر 1402
  • تاریخ پذیرش: 18 مهر 1402
  • تاریخ انتشار: 01 آبان 1403

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