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موسوی, وحید, حیات زاده, مهدی. (1401). مدل‌سازی میزان تغذیه سفره آب زیرزمینی با استفاده از مدل نیمه‌توزیعی SWAT، مطالعه موردی: دشت مروست. سامانه مدیریت نشریات علمی, 14(3), 282-298. doi: 10.22092/ijwmse.2021.128733.1747
وحید موسوی; مهدی حیات زاده. "مدل‌سازی میزان تغذیه سفره آب زیرزمینی با استفاده از مدل نیمه‌توزیعی SWAT، مطالعه موردی: دشت مروست". سامانه مدیریت نشریات علمی, 14, 3, 1401, 282-298. doi: 10.22092/ijwmse.2021.128733.1747
موسوی, وحید, حیات زاده, مهدی. (1401). 'مدل‌سازی میزان تغذیه سفره آب زیرزمینی با استفاده از مدل نیمه‌توزیعی SWAT، مطالعه موردی: دشت مروست', سامانه مدیریت نشریات علمی, 14(3), pp. 282-298. doi: 10.22092/ijwmse.2021.128733.1747
موسوی, وحید, حیات زاده, مهدی. مدل‌سازی میزان تغذیه سفره آب زیرزمینی با استفاده از مدل نیمه‌توزیعی SWAT، مطالعه موردی: دشت مروست. سامانه مدیریت نشریات علمی, 1401; 14(3): 282-298. doi: 10.22092/ijwmse.2021.128733.1747

مدل‌سازی میزان تغذیه سفره آب زیرزمینی با استفاده از مدل نیمه‌توزیعی SWAT، مطالعه موردی: دشت مروست

مهندسی و مدیریت آبخیز
مقاله 1، دوره 14، شماره 3، مهر 1401، صفحه 282-298    اصل مقاله (1.87 M)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22092/ijwmse.2021.128733.1747
نویسندگان
وحید موسوی1؛ مهدی حیات زاده* 2
1استادیار، دانشکده منابع طبیعی و علوم دریایی، دانشگاه تربیت مدرس
2استادیار گروه مهندسی طبیعت، دانشکده کشاورزی و منابع طبیعی، دانشگاه اردکان
چکیده
تغذیه به سفره‌­های آب زیرزمینی به‌ویژه در مناطق نیمه‌خشک از اهمیت بالایی برخوردار است. تعیین میزان تغذیه سفره­‌های آب زیرزمینی می‌تواند کمک شایانی به مدیران برای مدیریت منابع آب زیرزمینی کند. در مطالعه حاضر، به‌منظور تعیین میزان تغذیه سفره­ آب زیرزمینی در دشت مروست، فرایند هیدرولوژیک حوزه آبخیز مروست به‌وسیله مدل SWAT شبیه­‌سازی شد. برای این منظور ابتدا، نقشه­‌های پایه مورد نیاز یعنی نقشه شیب، خاک و کاربری اراضی تهیه شد. داده­‌های اقلیمی مورد نیاز با مقیاس روزانه به مدل وارد شدند. با توجه به اهمیت بحث آبیاری و تاثیر آن در میزان تبخیر و تعرق و نیز تغذیه سفره آب زیرزمینی، مقادیر آبیاری نیز با تعیین برنامه آبیاری در مدل SWAT در فرایند مدل‌سازی در نظر گرفته شد. پس از ساخت مدل به‌منظور واسنجی مدل، از بسته نرم‌افزاری SWAT CUP و الگوریتم SUFI-2 استفاده شد. نتایج نشان داد که متوسط سالانه میزان تغذیه به سفره آب زیرزمینی از منطقه مورد مطالعه در دوره مدل‌سازی (2015-2005) معادل 27.08 میلیون متر مکعب و ضریب آب برگشتی از کشاورزی حدود 34 درصد می­‌باشد. بهبود الگوی کشت، جلوگیری از حفر چاه‌­های غیرمجاز و برداشت بیش از حد از سفره و نیز سامانه­‌های آبیاری مناسب می‌تواند در کاهش کسری مخزن و جلوگیری از افزایش بحران منابع آب موثر باشد. به علاوه، پژوهش حاضر نشان داد که مدل برای کوتاه‌مدت (مثلا دوره یک‌ساله) نتایج مناسبی را به‌دست نمی­‌دهد، اما برای دوره‌های بلندمدت نتایج انطباق بهتری با واقعیت خواهد داشت. پیشنهاد می­‌شود تا به‌منظور مطالعه همزمان جریانات سطحی و زیرزمینی از ترکیب مدل SWAT و MODFLOW استفاده شود. همچنین، می‌توان برای تعیین میزان آب برگشتی و تغذیه از لایسی‌متر یا مدل SWAP نیز استفاده کرد.
کلیدواژه‌ها
آب زیرزمینی؛ بیلان آب؛ تغذیه؛ مدل‌سازی؛ مدل SWAT
عنوان مقاله [English]
Groundwater recharge modeling using semi-distributed SWAT Model, case study: Marvast Plain
نویسندگان [English]
Vahid Moosavi1؛ Mehdi Hayatzadeh2
1Assistant Professor, Department of Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Tehran, Iran
2Assistant Professor, Department of Watershed Management Engineering, Faculty of Agriculture and Natural Resources, Ardakan University, Ardakan, Iran
چکیده [English]
Groundwater recharge or deep drainage or deep percolation is a hydrologic process where water moves downward from surface water to groundwater. Recharge is the primary method through which water enters an aquifer. Groundwater recharge depends on several factors such as infiltration capacity, stochastic characteristics of rainfall, and climate factors. Groundwater recharge is of great importance especially in semiarid regions. In arid and semi-arid regions of the world, groundwater serves as an essential alternative to surface water resources for water supply purposes. It plays a significant role in meeting the water demands of man and the ecosystem and is perceived as the panacea to the looming water scarcity scare. Determination of recharge quantity provides worthy help for managers in water resources management. Ground water recharge includes recharge as a natural part of the hydrologic cycle and human-induced recharge, either directly through spreading basins or injection wells, or as a consequence of human activities such as irrigation and waste disposal. The Soil and Water Assessment Tool (SWAT) is a river basin scale model developed to quantify the impact of land management practices in large, complex watersheds. SWAT is a public domain hydrology model with the following components: weather, surface runoff, return flow, percolation, evapotranspiration, transmission losses, pond and reservoir storage, crop growth and irrigation, groundwater flow, reach routing, nutrient and pesticide loading, and water transfer. SWAT is a continuous time model that operates on a daily time step at basin scale. Its objective is to predict the long-term impacts of management and of the timing of agricultural practices within a year (i.e., crop rotations, planting and harvest dates, irrigation, fertilizer, and pesticide application rates and timing). It can be used to simulate at the basin scale water and nutrients cycle in landscapes whose dominant land use is agriculture. It can also help in assessing the environmental efficiency of best management practices and alternative management policies. In this study, the hydrologic process of the Marvast basin was simulated using SWAT Model in order to determine the amount of groundwater recharge in Marvast plain. In this way, firstly, the required maps i.e. slope, soil and land use maps were produced. In order to produce land use maps, panchromatic and multi-spectral imagery were fused to enhance the spectral and spatial resolution of Landsat imagery. In the next step, the fused imagery was used to produce land use maps using pixel based and object oriented image processing techniques. The slope map was produced using digital elevation model. The soil map was also produced using soil profiles in the regions. The requisite climatic data were also imported to the model with a daily scale. According to the importance of irrigation and its effect on evapotranspiration and groundwater recharge, irrigation amounts ​​were also considered importing irrigation plan in SWAT Model. Afterwards, the model was calibrated using SWAT CUP software and the SUFI-2 algorithm. Finally, the verification showed that the model with Nash-Sutcliff of 0.59, coefficient of determination of 0.83 and the root mean square error of 0.05 has a relatively good performance. The results showed that object oriented image processing technique outperformed pixel based technique. It was shown that the amount of groundwater recharge was 27082602 cubic meters and the irrigation water return coefficient is 34%. It was confirmed that SWAT Model has a relatively good performance for groundwater recharge modeling. Improving the cropping pattern, preventing development of unauthorized wells and excessive groundwater withdrawals, as well as proper irrigation systems, can be effective in reducing the groundwater storage deficiency and preventing an increase in water resource crisis. This study showed that this model is not efficient for short term runs, however, its performance is better for long term runs. It is suggested that the SWAT and MODFLOW Model be used together to study both surface and underground currents. Also, lysimeters or SWAP Model can be used to better determine the amount of return flow and groundwater recharge.
کلیدواژه‌ها [English]
Groundwater, Modeling, Recharge, SWAT Model, Water balance
مراجع
  1. Acerbi-Junior, F.W., J.G.P. Clevers and M.E. Schaepman. 2006. The assessment of multi sensor image fusion using wavelet transforms for mapping the Brazilian Savanna. International Journal of Applied Earth Observation and Geoinformation, 8: 278-288.
  2. Adamowski, J. and H. Fung Chan. 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407: 28-40.
  3. Akhavan, S. and H. Jodi. 2015. Simulation of Inflow to Urmia Lake using SWAT Model. JWSS-Isfahan University of Technology, 19(72): 23-34 (in Persian).
  4. Arnold, J.G., R.S. Muttiah, R. Srinivasan and P.M. Allen. 2000. Regional estimation of base flow and groundwater recharge in the Upper Mississippi River Basin. Journal of Hydrology, 227: 21–40.
  5. Bouwer, H. 2002. Artificial recharge of groundwater: hydrogeology and engineering. Hydrogeology Journal, 10: 121-142.
  6. Cannas, B., A. Fanni, L. See and G. Sias. 2006. Data preprocessing for river flow forecasting using neural networks: wavelet transforms and data partitioning. Physics and Chemistry of the Earth, 31: 1164-1167.
  7. Cannas, B., A. Fanni, G. Sias, S. Tronei and M.K. Zedda. 2006. River flow forecasting using neural networks and wavelet analysis. In: Proceedings of the European Geosciences Union, 234–243.
  8. Chen, Z., S.E. Grasby and K.G. Osadetz. 2002. Predicting average annual groundwater levels form climatic variables: an empirical model. Journal of Hydrology, 260(1–4): 102–117.
  9. Dakhlalla, A.O. and B. Parajuli. 2016. Evaluation of the best management practices at the watershed scale to attenuate peak streamflow under climate change scenarios. Water Resources Management, 30(3): 963–982.
  10. Dashtakian, K., M. Pakparvar and M. Rad. 2011. Investigation of land use changes in relation to salinity, surface soil in Marvast region of Yazd. Iranian Journal of Range and Desert Research, 18(2): 292-306 (in Persian).
  11. Dastvareh, J., Z. Naserian, H. Hsanvaand and S. Amiri. 2020. Modeling groundwater level and investigating the aquifer status of Minab Plain. Geography and Human Relationships Journal, 3(2): 50-59 (in Persian).
  12. Debbarma, J., P.K. Roy, S. Halder, G. Banerjee and M. Pal. 2016. Estimating groundwater volumetric mass balance with hydraulic head using groundwater modeling system in Tripura, India. Asian Journal of Current Research, 1(1 ): 19-22.
  13. Dregne, H.E. 1991. Global status of desertification. Annals of Arid Zone, 30: 179–185.
  14. Faramarzi, M., K. Abbaspour, R. Schulin and H. Yang. 2009. Modelling blue and green water resources availability in Iran. Hydrological Processes, 23: 486–501.
  15. George, R.J., C.J. Clarke and T. Hatton. 2001. Computer-modelled groundwater response to recharge management for dryland 20, salinity control in Western Australia. Advances in Environmental Monitoring and Modelling, 2(1): 3–35.
  16. Hosseini, M. 2010. Effect of landuse changes on water balance and suspended sediment yield of Taleghan Catchment, Iran. PhD Thesis, Universiti Putra Malaysia, 169 pages.
  17. Jones, I.C. and J.L. Banner. 2003. Estimating recharge threshold in tropical karst island aquifers: Barbados, Puerto Rico and Guam. Journal of Hydrology, 278(1–4): 131–143.
  18. Kil, S.L. and C. Eun-Sung. 2006. Hydrological effects of climate change, groundwater withdrawal, and land use in a small Korean watershed. Hydrological Processes, 21: 3046–3056.
  19. Mohammadzadeh, H., M.A. Ddgar and H. Nassery. 2017. Prediction of the effect of water supplying from Shirindare Dam on the Bojnourd Aquifer using MODFLOW 2000. Water Resources, 44(2): 216-225.
  20. Moosavi, V., M. Vafakhah, B. Shirmohammadi and M. Ranjbar. 2013. Optimization of wavelet- ANFIS and wavelet-ANN hybrid models by Taguchi method for groundwater level forecasting. Arabian Journal for Science and Engineering, 39: 1785-1796.
  21. Nourani, V., Z. Kisi and K. Mehdi. 2011. Two hybrid artificial Intelligence approaches for modeling rainfall-runoff process. Journal of Hydrology, 402: 41-59.
  22. Piella, G. 2003. A general framework for multi-resolution image fusion: from pixels to regions. Information Fusion, 4(4): 259-280.
  23. Sedaghat, M. 2003. Earth and water resources (groundwaters). Payame Noor University Press, 4: 245-310 (in Persian).
  24. Singh, V.P. and D.A. Woolhiser. 2002. Mathematical modeling of watershed hydrology. Journal of Hydrologic Engineering, 7: 270-292.
  25. Sun, H., P.S. Cornish. 2005. Estimating shallow groundwater recharge in the headwaters of the Liverpool Plains using SWAT. Hydrological Processes, 19(3): 795–807.
  26. Tu, T.M., P.S. Huang, C.L. Hung and C.P. Chang. 2004. A fast intensity-hue-saturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geoscience and Remote Sensing Letters, 1(4): 309-312.
  27. Wang, X.P., R. Berndtsson, X.R. Li and R.S. Kang. 2004. Water balance change for a re-vegetated xerophyte shrub area. Hydrological Sciences Journal, 49(2): 283–295.
  28. 2003. Right to water. Health and Human Rights Publication Series No. 3, World Health Organization: Geneva, Switzerland.
آمار
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