شبیه سازی رواناب حوضه ای در شرایط تغییر اقلیم بر اساس مدل SWAT
محورهای موضوعی : مدیریت منابع آبمحمد غریب تاوسی 1 , محسن نجارچی 2 , محمد رضا جلالی 3 , حسین مظاهری 4 , سعید شعبانلو 5
1 - دانشجوي دکتري عمران، گروه مهندسي عمران، واحد اراک، دانشگاه آزاد اسلامي، اراک ، ايران.
2 - گروه مهندسي عمران، واحد اراک، دانشگاه آزاد اسلامي ، اراک ، ايران.
3 - گروه مهندسي عمران، واحد اراک، دانشگاه آزاد اسلامي، اراک ، ايران.
4 - گروه مهندسي شيمي، واحد اراک، دانشگاه آزاد اسلامي، اراک ، ايران.
5 - گروه مهندسي آب، واحد کرمانشاه، دانشگاه آزاد اسلامي، کرمانشاه، ايران.
کلید واژه: حوضه پل شاه, شبيه‌سازي دبي, تغيير اقليم, LARS-WG6,
چکیده مقاله :
زمينه و هدف: ارزيابي پديده تغيير اقليم و پيامدهاي احتمالي آن بر فرآيندهاي هيدرولوژيکي حوضه کمک فراواني به مديران و برنامه‌ريزان منابع آب در دوره‌هاي آتي خواهد کرد. اثر تغيير اقليم به‌وسيله شبيه‌سازي فرآيندهاي هيدرولوژيکي با مدل فيزيکي بارش رواناب مورد بررسي قرار مي‌گيرد. مدل‌هاي هيدرولوژي چارچوبي را براي بررسي رابطه بين هواشناسي و منابع آب فراهم مي‌کنند. هدف از اين پژوهش شبيه‌سازي توليد رواناب در شرايط تغيير اقليم بر اساس سناريوهاي اقليمي و مدل SWAT مي‌باشد.
روش پژوهش: تغيير اقليم و پيامدهاي ناشي از آن يکي از مشکلات اساسي در مديريت منابع آب است و برآورد آثار و تبعات آن در دوره آتي ضروري است. منطقه مورد مطالعه در اين تحقيق، حوضه آبريز پل شاه با مساحت 721 کيلومتر مربع است که يکي از زيرحوضه‌هاي حوضه الوند در استان کرمانشاه محسوب مي گردد. اين حوضه سالانه داراي سيلاب‌هاي متعددي بوده که گاهي باعث خسارات و آب‌گرفتگي زمين‌هاي کشاورزي مي‌گردد. رودخانه ديره واقع در اين حوضه منبع تامين آب بخشي از اراضي کشاورزي مجاور و پايين‌دست رودخانه است. لذا بررسي اثر تغيير اقليم بر آورد اين رودخانه از اهميت زيادي برخوردار است. در اين پژوهش براي برآورد ماهيانه دما و بارش در دوره آتي از مدل‌هاي گردش عمومي AOGCM استفاده شد. جهت اعتبارسنجي و ارزيابي دقت برآورد مدل‌هاي گردش عمومي و برازش داده‌ها از شاخص‌هاي RMSE، MAE و NS بهره گرفته شد. در اين پژوهش ابتدا به بررسي رواناب در ايستگاه هيدرومتري پل شاه پرداخته شد. با استفاده از نرم افزار SWAT CUP بر اساس آمار ايستگاه هيدرومتري و بکارگيري الگوريتم بهينه‌سازي SUFI2، پارامترهاي موثر بر دبي جريان براي دوره 1994 تا 2011 واسنجي و براي دوره 2015-2012 صحت‌سنجي شدند. سپس در جهت بررسي شاخص‌هاي آماري بارش و دما تحت تاثير تغيير اقليم با بهره‌گيري از نرم افزار LARS-WG6 و استفاده از مدل‌هاي اقليمي HADGEM2 و MIROC5 تحت سناريوهاي انتشار RCP2.6، RCP4.5 و RCP8.5 ريزمقياس نمايي و استخراج داده‌هاي بارش و دما براي طول آماري 2020 تا 2080 انجام شد. در نهايت براي شبيه‌سازي تاثير تغيير اقليم بر رواناب، نرم افزار SWAT تحت هريک از سناريوهاي اقليمي در دوره‌هاي آماري مختلف اجرا گرديد. سپس نتايج حاصل از شبيه‌سازي رواناب ماهيانه تحت سناريوهاي اقليمي با داده هاي مشاهداتي ثبت شده مقايسه گرديد.
يافته‌ها: نتايج ارزيابي کارايي مدل SWAT حاکي از عملکرد مناسب اين مدل در دوره واسنجي و صحت سنجي بود. طوري که مقادير ضريب همبستگي و ضريب ناش-ساتکليف براي مرحله واسنجي به ترتيب 75/0 و 79/0 و براي مرحله صحت‌سنجي 71/0 و61/0 بدست آمد. نتايج اجراي مدل SWAT نشان داد در تمامي سناريوهاي اقليمي، الگوي توليد رواناب ماهيانه با الگوي تغييرات بارندگي در ماه‌هاي مختلف در اين سناريوها مطابقت دارد. لذا در تمامي سناريوهاي آتي، توزيع رواناب ماهيانه در ماه‌هاي مختلف نسبت به سناريوي پايه بهم ريخته است. طوري که در برخي از ماه‌ها کاهش رواناب و در برخي ماه‌ها افزايش رواناب مشاهده شد. اين امر لزوم استفاده از سد و سازه‌هاي کنترل جريان براي ذخيره آب در ماه‌هاي پرآب مانند زمستان و بهار و استفاده از آن در ماه‌هاي کم آب را نشان مي‌دهد. نتايج حاکي از آن است که تغييرات حجم رواناب توليدي ساليانه تحت سناريوهاي اقليمي RCP2.6، RCP4.5 و RCP8.5 براي دوره‌هاي 2018-2045 و 2046-2072 نسبت به دوره 1991-2018 به‌طور ميانگين بين 60 تا 87 ميليون متر مکعب متغير است. ميزان تغييرات حجم رواناب ساليانه در دوره 2018-2045 در بيشتر سناريوها ناچيز است. در دوره 2046-2072 حجم رواناب ساليانه در اکثر سناريوها بين 3 تا 10 درصد کاهش يافته است.
نتايج: نتايج نشان داد کاهش بارش و افزايش دما و به موجب آن افزايش تبخير، باعث تغييراتي در چرخه آب و هوايي موجود مي‌گردد که کاهش رواناب را به دنبال دارد. لذا لازم است براي سازگاري و کاهش تبعات منفي ناشي از تغيير اقليم بر منابع آب منطقه، با بکارگيري مديريت صحيح منابع آب و در نظر گرفتن نياز کشاورزي، مصارف شرب، صنعت و زيست‌محيطي در سال‌هاي آتي، از آثار سوء تغيير اقليم بر منابع آب منطقه کاست، تا به حفظ هر چه بهتر اين منابع منجر گردد. نتايج نشان داد در اکثر سناريوهاي اقليمي جابجايي بارندگي اتفاق افتاده است. لذا بايد متناسب با تغييرات ميزان بارش و تغييرات دما در ماه‌هاي مختلف نسبت به تغيير الگوي کشت يا تغيير تاريخ کشت محصولات مختلف اقدام نمود.
Background and Aim: Evaluation of the climate change phenomenon and its probable consequences on basin hydrological processes will effectively help managers and planners of water resources in upcoming periods. The impact of climate change is evaluated through the simulation of hydrological processes via the runoff-precipitation physical model. Hydrological models provide a framework for evaluating the relationship between meteorology and water resources. The objective of this study is to simulate the runoff production in climate change condition based on climate scenarios and the SWAT model.
Method: Climate change and its consequences are one of the basic problems in water resources management, and it is necessary to estimate its effects in the future period. The area studied in this research is the Po-e-Shah catchment area with an area of 721 square kilometers, which is one of the sub-basins of the Alvand basin in Kermanshah province. This basin has many floods every year, which sometimes causes damages and flooding of agricultural lands. The Dirah River located in this basin is the source of water supply for part of the agricultural lands nearby and downstream of the river. Therefore, investigating the effect of climate change on the yield of this river is very important. In this research, AOGCM general circulation models are used to estimate monthly temperature and precipitation in the future period. The RMSE, MAE and NS indices are used to validate and evaluate the accuracy of estimation of general circulation models and data fitting. In this research, the runoff at the Pol-e-Shah hydrometry station is investigated first. Using SWAT CUP software, based on hydrometric station statistics and using SUFI2 optimization algorithm, parameters affecting flow rate are recalibrated for the period 1994 to 2011 and validated for the period 2012-2015. Then, in order to investigate the statistical indices of precipitation and temperature under the influence of climate change, using the LARS-WG6 software and using HADGEM2 and MIROC5 climate models under RCP2.6, RCP4.5 and RCP8.5 emission scenarios, the micro-scaling and extraction of precipitation data and temperatures are performed for the statistical period from 2020 to 2080. Finally, to simulate the impact of climate change on runoff, the SWAT software is implemented under each of the climate scenarios in different statistical periods. Then, the results of monthly runoff simulation under climate scenarios are compared with recorded observational data.
Results: The results of the SWAT model efficiency evaluation indicate the proper performance of this model in the validation and verification period, so that the values of correlation coefficient and Nash-Sutcliffe coefficient are obtained for the calibration stage 0.75 and 0.79 respectively and for the validation stage 0.71 and 0.61 respectively. The results of the SWAT model implementation show that in all climate scenarios, the pattern of monthly runoff production is consistent with the pattern of precipitation changes in different months in these scenarios. Therefore, in all future scenarios, the distribution of monthly runoff in different months is messed up compared to the base scenario so that in some months there is a decrease in runoff and in some months an increase in runoff. This shows the need to use dams and flow control structures to store water in high water months such as winter and spring and use it in low water months. The results indicate that the changes in the volume of annual production runoff under the RCP2.6, RCP4.5 and RCP8.5 climate scenarios for the periods of 2045-2018 and 2072-2046 compared to the period of 2018-1991 vary between 60 and 87 million cubic meters on average. The amount of annual runoff volume changes in the period 2045-2018 is insignificant in most scenarios. In the period of 2072-2046, the volume of annual runoff is decreased between 3 and 10 percent in most scenarios.
Conclusion: The results showed that the decrease in precipitation and increase in temperature and thereby increase in evaporation cause changes in the current climate cycle, which leads to a decrease in runoff. Therefore, it is necessary to adapt and reduce the negative consequences of climate change on the water resources of the region to avoid from adverse effects of climate change on the water resources of the region by using the correct management of water resources and considering the needs of agriculture, drinking, industry and the environment in the coming years to lead to the best conservation of these resources. The results showed that in most of the climate scenarios, the precipitation displacement has happened. Therefore, it is necessary to change the cultivation pattern or change the date of cultivation of different crops according to the changes in precipitation and temperature changes in different months.
Amirabadizadeh, M., Ghazali, A. H., Huang, Y. F. and Wayayok, A. (2017). Assessment of impacts of future climate change on water resources of the Hulu Langat basin using the swat model. Water Harvesting Research, 2 (2), 13-29.
Amiri, S., Rajabi, A., Shabanlou, S. et al. Prediction of groundwater level variations using deep learning methods and GMS numerical model. Earth Sci Inform 16, 3227–3241 (2023). https://doi.org/10.1007/s12145-023-01052-1
Acharyya, A. (2014). Groundwater, Climate Change and Sustainable Well Being of the Poor: Policy options for South Asia, China and Africa. Procedia-Social and Behavioral Sciences 157, 226–235.
Alizadeh, A., Rajabi, A., Shabanlou, S. et al. Modeling long-term rainfall-runoff time series through wavelet-weighted regularization extreme learning machine. Earth Sci Inform 14, 1047–1063 (2021). https://doi.org/10.1007/s12145-021-00603-8
Alizadeh, A., Rajabi, A., Shabanlou, S., Yaghoubi, B., and Yosefvand, F. (2021) Simulation of rainfall and runoff time series using robust machine learning. Irrig and Drain., 70: 84–102. https://doi.org/10.1002/ird.2518.
Ansari, H., Khadivi, M., Salehnia, N., and Babaeian, I. (2014). Evaluation of Uncertainty LARS Model under Scenarios A1B, A2 and B1 in Precipitation and Temperature Forecast (Case Study: Mashhad Synoptic Stations). Iranian Journal of Irrigation & Drainage, 8(4), 664-672. (In Persian)
Azizi, E., Yosefvand, F., Yaghoubi, B. et al. Prediction of groundwater level using GMDH artificial neural network based on climate change scenarios. Appl Water Sci 14, 77 (2024). https://doi.org/10.1007/s13201-024-02126-1
Busico, G., Colombani, N., Fronzi, D., Pellegrini, M., Tazioli, A. and Mastrocicco, M. (2020). Evaluating SWAT model performance, considering different soils data input, to quantify actual and future runoff susceptibility in a highly urbanized basin, Journal of Environmental Management, 266, 110625. https://doi.org/10.1016/j.jenvman.2020.110625
Ebrahimi, P., Salimi Kochi, J. and Mohseni Saravi, M. (2018). Calibration and validation of SWAT Model in runoff simulation, case study: Neka Watershed. Watershed Engineering and Management. 10 (3), 266-279. (In Persian)
Fallahi, M.M., Shabanlou, S., Rajabi, A. et al. Effects of climate change on groundwater level variations affected by uncertainty (case study: Razan aquifer). Appl Water Sci 13, 143 (2023). https://doi.org/10.1007/s13201-023-01949-
Farsi, N. and Mahjouri, N. (2019), Evaluating the contribution of the climate change and human activities to runoff change under uncertainty. Journal of Hydrology, 574, 872-891. https://doi.org/10.1016/j.jhydrol.2019.04.028
Fatehi, Z., and Shahoei, S. V. (2020). Application of SWAT Model for Simulating Monthly Runoff, Lake Urmia Watershed in Kurdistan Province, Iran. Environment and Water Engineering. 6 (3), 294-304. (In Persian)
Gord, S., Hafezparast Mavaddat, M. and Ghobadian, R. (2024). Flood impact assessment on agricultural and municipal areas using Sentinel-1 and 2 satellite images (case study: Kermanshah province). Natural Hazards 120, 8437–8457. https://doi.org/10.1007/s11069-024-06514-3
Gulacha, M.M., and Mulungu, D.M.M. (2017). Generation of climate change scenarios for precipitation and temperature at local scales using SDSM in Wami-Ruvu River Basin Tanzania. Physics and Chemistry of the Earth, 100, 62-72.
Hosseinikhah, M., Zeinivand, H., Haghizadeh, A., and Tahmasebipour, N. (2014). Validation of Global Climate Models (GCMS) Temperature and Rainfall Simulation in Kermanshah, Ravansar and West Islamabad Stations. Iranian journal of Ecohydrology, 1(3), 195-206. (In Persian)
IPCC, (2014). Summary for policmarkers. In:Climate Change. 2014: Impacts, Adaptation, and Vulnerability. Part a: Global and Sectoral Aspect. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change camberidge University Press,cambridge, United Kingdom and New York, NY,USA, pp. 1-132.
Javadinejad, S., Dara, R., Jafary, F. (2020). Climate Change Scenarios and Effects on Snow-Melt Runoff. Civil Engineering Journal, 6(9), 1715-1730. https://doi.org/10.28991/cej-2020-03091577
Jiang, R., Li, Y., Wang, Q., Kuramochi, K., Hayakawa, A., Woli, K. P. and Hatano, R. (2011). Modeling the water balance processes for understanding the components of river discharge in a non‐conservative watershed. American Society of Agricultural and Biological Engineers, 54 (6), 2171-2180. https://doi.org/10.13031/2013.40656
Jimeno-Sáez, P., Senent-Aparicio, J., Pérez-Sánchez, J. and Pulido-Velazquez, D. (2018). A comparison of SWAT and ANN models for daily runoff simulation in different climatic zones of peninsular spain. Water, 10, 192.
Kamal, A. and Massahbavani, A. (2012). The uncertainty assessment of AOGCM & hydrological models for estimating gharesu basin temperature, priciitation, and runoff under climate change impact. Iranian Water Research Journal, 5(9), 39-49. (In Persian)
Kumar, C. P. and Singh, S. (2015). Climate change effects on groundwater resources. Octa Journal of Environmental Research, 3(4), 264-271.
Melaku, N. D., Renschler, C. S., Holzmann, H., Strohmeier, S., Bayu, W., Zucca, C., Ziadat, F. and Klik, A. (2018). Prediction of soil and water conservation structure impacts on runoff and erosion processes using SWAT model in the northern Ethiopian highlands. Journal of Soils and Sediments, 18, 1743–1755. https://doi.org/10.1007/s11368-017-1901-3
Naqash, T. B., Ahanger, M. A. and Maity, R. (2023). Impacts of hydrometeorological factors on discharge simulation in the North West Himalayas: a SUFI-2 algorithm-driven investigation using the SWAT model. Environmental Monitoring and Assessment. 195, 1366. https://doi.org/10.1007/s10661-023-11916-0
New, M. and Hulme, M. (2000). Representing uncertainty in climate change scenarios: a Monte-Carlo approach. Integrated Assessment, 1, 203–213.
Rafiei Emam, A., Kappas, M., Linh, N. H. K. and Renchin, T. (2017). Hydrological Modeling and Runoff Mitigation in an Ungauged Basin of Central Vietnam Using SWAT Model. Hydrology, 4 (1), 16. https://doi.org/10.3390/hydrology4010016
Rasoolzadeh darzi, N., Ahmadi, H., Moeini, A. and Motamedvaziri, B. (2022). The Use of the SWAT Model in the Simulation and Analysis of Hydrological Uncertainty Analysis. Journal of Geography and Environmental Hazards. 11 (2), 77-95. (In Persian)
Sadat Ashofte, P. and Bozorg Hadad, O. (2014). A New Probabilistic Approach for Evaluation of the Effects of Climate Change on Water Resources. Water Resources Engineering, 6(19), 51-66. (In Persian)
Shahoei, S. V., Porhemmat, J., Sedghi, H., Hosseini, M. and Saremi, A. (2018). Monthly runoff simulation through SWAT hydrological model and evaluation of model in calibration and validation periods, case study: Ravansar Sanjabi Basin in Kermanshah Province, Iran. Watershed Engineering and Management, 10(3), 464-477. (In Persian)
Sokolowski, J. and Banks, C. (2011). Principles of modeling and simulation: a multidisciplinary approach. John Wiley and Sons, Hoboken, New Jersey, USA.
Soltani, K.; Ebtehaj, I.; Amiri, A.; Azari, A.; Gharabaghi, B.; Bonakdari, H. (2021). Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of The Total Environment. 770, 145288. https://doi.org/10.1016/j.scitotenv.2021.145288
Tasdighi, A., Arabi, M., Harmel, H. (2018). A probabilistic appraisal of rainfall-runoff modeling approaches within SWAT in mixed land use watersheds, Journal of Hydrology, 564, 476-489. https://doi.org/10.1016/j.jhydrol.2018.07.035
Teutschbein, C. and Seibert, J. 2012. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology. 456-458, 12-29. https://doi.org/10.1016/j.jhydrol.2012.05.052
Vilaysane, B., Takara, K., Luo, P., Akkharath, I. and Duan, W. (2015). Hydrological stream flow modelling for calibration and uncertainty analysis using SWAT model in the Xedone river basin, Lao PDR. Procedia Environmental Sciences, 28, 380–390. https://doi.org/10.1016/j.proenv.2015.07.047
Wang, Y., Jiang, R., Xie, J., Zhao, Y., Yan. D. and Yang, S. (2019). Soil and Water Assessment Tool (SWAT) Model: A Systemic Review. Journal of Coastal Research. 93(sp1): 22. https://doi.org/10.2112/SI93-004.1
Wilby, R. and Harris, I. (2006). A framework for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames. UK, Water Resources Research, 42(2), 1-10.
Wootten, A. W., Dixon, K. W., Adams-Smith, D. J. and McPherson, R. A. (2021). Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique. International Journal of Climatology. 41 (2), 980-1001. https://doi.org/10.1002/joc.6716
Zhihua, LV., Junjie, Z., Rodriguez, D. (2020). Predicting of Runoff Using an Optimized SWAT-ANN: A Case Study, Journal of Hydrology: Regional Studies, 29, 100688. https://doi.org/10.1016/j.ejrh.2020.100688
