ارزیابی اثر تغییر کاربری زمین بر اندازه‌ی روان‌آب با زنجیره‌ی مارکوف و سلول‌های خودکار در آبخیز بیدگل، استان فارس

نوع مقاله : پژوهشی

نویسندگان

1 دانشجو دکتری تخصصی علوم و مهندسی آبخیزداری، گروه احیا مناطق خشک و کوهستانی، دانشکده منابع طبیعی دانشگاه تهران

2 دانشیار گروه احیا مناطق خشک و کوهستانی، دانشکده منابع طبیعی دانشگاه تهران

3 استاد گروه احیا مناطق خشک و کوهستانی، دانشکده منابع طبیعی دانشگاه تهران

چکیده

این پژوهش برای پیش‌بینی‌کردن تغییر کاربری زمین و تاثیر آن بر اندازه‌ی روان‌آب آبخیز بیدگل استان فارس در چشم‌انداز سال 1411 انجام ‌شد. مدل آب‌شناسی سوات (ابزار ارزیابی خاک و آب) با داده‌های آبی-اقلیمی سال‌های 1383 تا 1392 برمبنای الگوریتم SUFI-2 واسنجی، و با داده‌های 1393 تا 1397 اعتبار‌سنجی کرده‌شد. سنجه‌‌های آماری ضریب نش– ساتکلیف، ضریب تبیین، ضریب تغییر مکان جانبی و ضریب رفتار در مرحله­ی واسنجی به‌ترتیب 0/74، 0/77، 0/8، 0/83 و برای مرحله­ی اعتبار‌سنجی به‌ترتیب  ‌0/68، 0/66، 0/72 و 1/3 به­دست ‌آورده شد. تصویرهای سال‌های 1383 و 1397 ماهواره‌ی لندست ‌پردازش‌‌ و به شش رده‌ی کاربری رده‌بندی شد. با روش زنجیره‌ی مارکوف و سلول‌های خودکار و برپایه‌ی نقشه‌ی کاربری زمین سال‌های 1383 و 1397، نقشه‌ی پیش‌بینی کاربری زمین سال 1411 تهیه‌شد. نقشه‌های کاربری زمین به مدل واسنجی‌شده‌ی‌ سوات وارد و تاثیر تغییر‌ کاربری زمین در دوره‌ی‌ 1397 تا 1411 بر اندازه‌ی روان‌آب حوزه پیش‌بینی کرده شد. نتایج نشان‌داد که بیش‌ترین تغییر کاربری در تبدیل مرتع به زمین کشاورزی، و بیش‌ترین درصد تغییر در کاربری مسکونی بود. در پاسخ به این تغییر، در ﺳـﺎل 1411 ﻣﻘﺪار ﻣﺘﻮﺳﻂ ﺳﺎﻻﻧﻪ‌ی روان‌آب ﺳﻄﺤﻲ 19% کاهش نسبت به ﺳـﺎل 1397 نشان‌ می‌دهد.

کلیدواژه‌ها


عنوان مقاله [English]

An Assessment of the Effect of Land Use Change on the Runoff Using the Markov Chain and Cellular Automata in the Bidgol Watershed, the Province of Fars

نویسندگان [English]

  • Hadi Masoomi 1
  • Arash Malekian 2
  • Ali Salajegheh 3
  • Aliakbar Nazari Samani 2
1 PhD., Student, Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Iran
2 Associate Professor, Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Iran
3 Professor, Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Iran
چکیده [English]

The present study was conducted to predict land use change and its impact on runoff of the Bidgol Watershed in the Fars province in 2032. The SWAT hydrological model was first calibrated using the hydrometeorological collected data during the 2004 to 2013 period based on the SUFI-2 algorithm and validated using the 2014 to 2018 data. The values of the statistical parameters of the Nash – Sutcliffe model efficiency coefficient (NS), coefficient of determination (R2), deflection amplification factor (P-factor) and the response modification factor (R-factor) at the calibration stage were 0.74, 0.77, 0.8, 0.83, respectively while for the validation stage, the obtained values were 0.68, 0.66, 0.72 and 1.3, respectively. Then Landsat satellite images of 2004 and 2018 were used and following the necessary steps, the images were classified into six main land use classes. Using the Markov Chain method and Cellular Automata based on the land use maps of 2004 and 2018, the land use forecasting map of 2032 was prepared. The land use maps were imported into the SWAT calibrated model and the impact of land use change from 2018 to 2032 on the basin runoff was predicted. The results showed that the highest change of land use will be related to the conversion of rangelands into agricultural land, and the highest percentage of change will be related to the residential land use. In response to these changes, the annual average runoff value of 2032 represents a 19% decrease as compared to that of 2018.

کلیدواژه‌ها [English]

  • Bidgol watershed
  • Land use
  • Markov chain
  • SWAT model
Abbaspour KC, Yang J, Maximov I, Siber R. 2007. Modelling of hydrology and water quality in the Pre-Alpine/Alpine Thur Watershed using SWAT. Journal of Hydrology. 333(2-4): 413–430.
Abbasspour K. 2009. SWAT-CUP2; SWAT Calibration and Uncertainty Programs user manual.
Abbaspour KC. 2011. SWAT-CUP4: SWAT Calibration and Uncertainty Programs –A User Manual. Swiss Federal Institute of Aquatic Science and Technology, Eawag.
Alimohammadisarab A, Mottakan AA, Mirbagheri B. 2009. Evaluation of the efficiency of cellular automata model in simulating urban development in the southwestern suburbs of Tehran. Journal of Planning and space planning. 14(2): 81–102. )In Persian).
Anaba LA, Banadda N, Kiggundu N, Wanyama J, Engel B,  Moriasi D. 2017. Application of SWAT to assess the eEffects of land use change in the Murchison Bay Catchment in Uganda. Computational Water, Energy, and Environmental Engineering. 6)1(: 24–40. 
Anand J, Gosain AK, Khosa R, 2018. Prediction of land use changes based on land change modeler and attribution of changes in the water balance of Ganga Basin to land use change using the SWAT Model. Science of the Total Environment. 644: 503–519.
Arnold JG, Srinivasan R, Muttiah RS, Williams JR. 1998. Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resource Association. 34(1): 73–89.
Azizi Ghalati S, Rangzan K, Sadidi J, Heydarian P, Taghizadeh A. 2016. Predicting locational trend of land use changes using CA-Markov model (Case study: Kohmare Sorkhi, Fars Province). RS & GIS Techniques for Natural Resources. 7(1): 59–71. (In Persian).
Bosch JM, Hewlett JD. 1982. A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration. Journal of Hydrology. 55(1-4): 3–23.
Chang CL, Chang JC. 2006. Markov model and cellular automata for vegetation. Journal of Geographical Research. 45(1): 45–57.
Estman JR. 2006. IDRISI Andes Tutorial. ClarkLabs, Clark University, Worcester, Ma, 284 p.
Fars Meteorogical Bureau. 2018. Personal Communication.
Fathizad H, Zare M, Karimi H, Khanamani A. 2018. Spatio-temporal modeling of landscape changes using Markov Chain compilation model and automated cells (Case Study: Arid and Semi-Arid Area Dehloran). Arid Biome Scientific and Research Journa. 8(1): 11–26. (In Persian).
Fazeli Farsani A, Ghazavi R. 2019. Investigating the effect of land use changes on surface runoff using SWAT model. Journal of Water and Soil Conservation. 25(6): 191–206. (In Persian).
Hathout S. 2002. The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. Journal of Environmental Management. 66(3): 229–238.
Iran National Cartographic Center. 2018. Personal Communication.
Jenerette Darrel G, Wu J. 2001. Analysis and simulation of land use change in the central Arizona-Phonix region, USA. Landscape Ecology. 16(7): 611–626.
Jensen JR. 2007. Remote Sensing of the Environment: An Earth Resource Perspective. Pearson Prentice Hall, 592 p.
Khoshgoftar M, Talei M, Malekpour M. 2010. Spatiotemporal Modeling of urban Sprawl: An approach based on integration Cellular Automata and Markov Chains. Proceeding of Geomatics 89, National Conference and Exhibition, 9 p.
Kult J, Choi W, Choi J. 2014. Sensitivity of the snowmelt runoff model to snow covered and temperature inputs. Applied Geography. 55: 30–38.
Lambin EF. 1997. Modeling and monitoring land cover changes processes in tropical regions. Progress in physical geography. 21(3): 357–393.

Li F, Zhang G, Li H,Lu W. 2019. Land Use Change Impacts on Hydrology in the Nenjiang River Basin, northeast China. Forests. 10(6): 1–18.

Mango LM, Melessel AM, McClain ME, Gann D, Setegn S.G. 2011. Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: Results of a modeling study to support better resource management. Hydrology and Earth System Sciences. 15(7): 2245–2258.
Marcos HC, Aurelie B, Jeffrey AC. 2003. Effects of large-scale changes in land cover on the discharge of the Rocantins River, Southeastern Amazonia. Journal of Hydrology. 283(1-4): 206–217.
Molina A, Vanacker V, Balthazar V, Mora D, Govers G. 2012. Complex land cover change, water and sediment yield in a degraded Andean environment. Journal of Hydrology. 472-473: 25–35.
Moriasi DN, Arnold JG, Van Liew MW, Binger RL, Harmel RD, Veith T. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the American Society of Agricultural Engineers. 50(3): 885–900.
Mustafa YM, Amin MSM, Lee TS, Shariff A.R.M. 2005. Evaluation of land development impact on a tropical watershed Hydrology Using Remote Sensing and GIS. Journal of Spatial Hydrology. 5(2):16–30.
Naderi M, Ilderomi A, Noori H, Agha Amin S, Zeinivand H. 2018. The impact of land use and climate change on watershed runoff using swat model (Case study: Garin watershed). Journal of Hydrogeomorphology. 4(14): 23–42. (In Persian).
Naef F, Simon S, Markus W. 2002. A process based assessment of the potential to reduce flood runoff by land use change. Journal of Hydrology. 267(1-2): 74–79.
Naserabadi F, Esmali Ouri A, Akbari H, Rostamian R. 2014. Sensitivity analysis of SWAT model in Ghareh Su watershed, Ardabil. Watershed Engineering and Management. 5)4(: 255 – 265. (In Persian).
Nash. JE, Sutcllife JV. 1970. River Flow Forecasting Through Conceptual Models Part I, A Discussion of Principles. Journal of Hydrology. 10(3):282–290.
Neitch SL, Arnold JG, Kiniry JR, Wiliams JR. 2005. Soil and water assessment tool theoretical documentation. Blackland Research Center, Temple, Texas, 494 p.
Norris JR.1997. Markov Chains. Cambridge University press, 237 p.
Omani N, Tajrishi M, Abrishamchi A. 2007. Simulation of river flow using gis and swat models. Seventh International Seminar on River Engineering, Ahvaz, Khuzestan Water and Power Organization, Shahid Chamran University of Ahvaz. )In persian(.
Palamuleni LG, Ndomba PM, Annegarn HJ. 2011. Evaluating land cover change and its impact on hydrological regime in Upper Shire river catchment, Malawi. Journal of Regional Environmental Change. 11(4): 845–855.
Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P. 2003. Multi agent systems for the simulation of land use and land cover change: A Review. Annals of the Association of American Geographers. 93(2): 314–337.
Peel MC, McMahon TA, Finlayson BL, Watson FGR. 2002. Implications of the relationship between catchment vegetation type and variability of annual runoff. Hydrological Processes. 16(15): 2995–3002.
Rahimzadeh Kivi M. 2017 Evaluation of the impacts of land use change on the amount of runoff in the watershed using a combination of SWAT hydrological model and remote sensing technique (Case study: Lorestan, Aleshtar basin). Master Thesis, Faculty of Agriculture, Malayer University, 108pp. (In Persian).
Regional Water Company of Fars. 2018. Personal communication.
Rezazadeh MS, Bakhtiari B, Abbaspour K, Ahmadi M. 2018. Simulation of Runoff, Sediment, and Evapotranspiration through Management Scenarios to Reduce Sediment Load Using SWAT Model. Iran-Watershed Management Science and Engineering. 12(40): 41–50. (In Persian).
Sakyeh Y, Dejhkam S. 2011. Cellular automata method in environmental modeling land use changes. Fifth National Conference and Exhibittion on environmental Engineering. Tehran, Iran. (In Persian).
Salmani H, Bardisheikh V, Salmanmahiny A, Onagh M, Fathabadi A. 2018. Evaluation of hydrological response in Tilabad watershed of Golestan for future periods as affected by the predicted land use change. Journal of Ecohydrology. 5(2): 399–418. (In Persian).
Salmani H, Mohseni Saravi M, Rouhani H, Salajegheh A. 2014. Evaluation of the efficiency of SWAT model and Parasol program in flow simulation (Gazaghli watershed in Golestan province). Journal of watershed management science. 7(22): 1 – 14. )In Persian.(
Salmanmahiny A, Kamyab H. 2012. Applied remote sensing and GIS with Idrisi. 2nd Edition Publication of Mehrmahdis. Tehran, Iran, 596. (In Persian).
Setegn SG, Srinivasan R, Dargahi B. 2008. Hydrological modelling in the Lake Tana Basin, Ethiopia Using SWAT Model. The Open Hydrology Journal. 2: 49–62.

Shanshan X,  Mingzhou Q, Shengyan D, Qinghe Z, Huimin L, Cangyu L, Xiaojie Y, Yanyan L, Jiaxin Y, Xiaoyu J. 2019. The impacts of climate variation and land use changes on streamflow in the Yihe River, China. Water. 11(5): 1–18.

Teklay A, Dile YT, Setegn SG, Demissie SS, Asfaw DH. 2019. Evaluation of static and dynamic land use data for watershed hydrologic process simulation: A case study in Gummara Watershed, Ethiopia. Catena. 172: 65–75.
Tolessa LO, El-Kadi AI, Dulai H, Ghazal KA. 2016. Assessment of climate change impacts on water balance components of Heeia Watershed in Hawaii. Journal of Hydrology, Regional Studies. 8(2016): 182–197.
Wang G, Liu J, Kubota J, Chen L. 2007. Effects of land-use changes on hydrological processes in the middle basin of the Heihe River, northwest China. Hydrological Processes. 21(10): 1370–1382.

Yi H, Jinxi S, Yiyi H, Xiang T, Yan Z. 2019. Impacts of different weather conditions and landuse change on runoff variations in the Beiluo River Watershed, China. Sustainable Cities and Society. 50(2019), https:// doi:10.1016/j.scs.2019.101674. 

Zare Garizi A, Sheikh V, Sadoddin A, Mahiny A. 2012. Simulating the spatiotemporal changes of forest extent for the Chehelchay Watershed (Golestan Province) using integrated CA-Markov Model. Iranian Journal of Forest and Poplar Research. 20(2): 273–285. (In Persian).
Zhan CS, Jiang SS, Sun FB, Jia YW, Niu CW, Yue WF. 2014. Quantitative contribution of climate change and human activities to runoff changes in the Wei River basin, China. Hydrology and Earth System Sciences. 18(8): 3069–3077.