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محاسبه میزان رواناب در گوگل ارث انجین با استفاده از روش شماره منحنی (مطالعه موردی: دشت بیرجند)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه محیط زیست دانشگاه بیرجند

2 کارشناسی ارشد هیدرو انفرماتیک، گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه بیرجند

3 کارشناسی ارشد ارزیابی و آمایش سرزمین، گروه محیط زیست، دانشکده منابع طبیعی و محیط زیست دانشگاه بیرجند

10.22077/jaaq.2025.9003.1101

چکیده

افت سطح آبخوان‌ها به دلیل برداشت بی‌رویه و کاهش بارندگی، منابع آبی مناطق خشک و نیمه‌خشک را تهدید می‌کند. رواناب، عامل کلیدی در تغذیه آبخوان‌ها، مدیریت منابع آب و پیش‌بینی سیلاب‌هاست. در این مناطق، تغییرات بارندگی و ویژگی‌های سطحی خاک تأثیر بسزایی بر رواناب و منابع آب دارند. ارزیابی دقیق رواناب برای مدیریت سیلاب، بهینه‌سازی استفاده از اراضی و برنامه‌ریزی منابع آب ضروری است. در این تحقیق، از مدل SCS-CN برای ارزیابی رواناب با استفاده از گوگل ارث انجین در حوزه آبخیز بیرجند استفاده شده است. مدل SCS-CN با استفاده از داده‌های بافت خاک، کاربری اراضی، گروه‌های هیدرولوژیکی خاک و بارش ماهواره‌ای، میزان رواناب دشت بیرجند را برآورد کرده است. نتایج نشان می‌دهد که چهار نوع بافت خاک در منطقه وجود دارد که مستقیماً بر مقدار رواناب تأثیر می‌گذارند. تحلیل داده‌ها نشان داد که 27٪ منطقه رواناب بسیار کم (0-6 میلی‌متر) و 28٪ رواناب کم (6-12 میلی‌متر) دارد، درحالی‌که 25٪ دارای رواناب متوسط (12-20 میلی‌متر)، 13٪ رواناب زیاد (20-30 میلی‌متر) و 7٪ رواناب بسیار زیاد (30-51 میلی‌متر) است. بیشترین رواناب در نواحی شمال شرقی و مرکزی حوضه مشاهده شد که به بارش‌های شدیدتر و نفوذپذیری کمتر خاک مرتبط است. در مقابل، نواحی جنوب غربی، به دلیل نفوذپذیری بالای خاک و پوشش گیاهی بیشتر، کمترین رواناب را دارد. این تحقیق می‌تواند مبنای علمی مؤثری برای مدیریت منابع آب و پیش‌بینی سیلاب‌ها در مناطق مشابه باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Calculating runoff in Google Earth Engine using the curve number method (Case study: Birjand Plain)

نویسندگان [English]

  • Elham Yousefi 1
  • Amir Khazai 2
  • , Fateme sahragard 3

1 Assistant professor, Department of Environmental Engineering ,Faculty of Natural Resources and Environment,, University of Birjand, Birjand, Iran.

2 MSc in Hydroinformatics, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand

3 MSc in Land use planning, Department of Environment, Faculty of Natural Resources and Environment, University of Birjand

چکیده [English]

Aquifer depletion due to excessive extraction and reduced rainfall threatens water resources in arid and semi-arid regions. Runoff is a key factor in aquifer recharge, water resource management, and flood prediction. In these regions, changes in rainfall and soil surface characteristics have a significant impact on runoff and water resources. Accurate runoff assessment is essential for flood management, land use optimization, and water resource planning. In this study, the SCS-CN model was used to assess runoff using Google Earth Engine in the Birjand watershed. The SCS-CN model estimated the runoff amount of the Birjand plain using soil texture, land use, soil hydrological groups, and satellite precipitation data. The results show that there are four types of soil textures in the region that directly affect the amount of runoff. Data analysis showed that 27% of the area has very low runoff (0-6 mm) and 28% has low runoff (6-12 mm), while 25% has moderate runoff (12-20 mm), 13% has high runoff (20-30 mm) and 7% has very high runoff (30-51 mm). The highest runoff was observed in the northeastern and central areas of the basin, which is related to more intense rainfall and lower soil permeability. In contrast, the southwestern areas, due to high soil permeability and more vegetation, have the lowest runoff. This research can be an effective scientific basis for water resources management and flood forecasting in similar areas.

کلیدواژه‌ها [English]

  • Curve Number (SCS-CN)
  • Flood
  • Water Resources
  • Satellite Data
  • Birjand Plain
مراجع
Adhikari, P., Hong, Y., Douglas, K. R., Kirschbaum, D. B., Gourley, J., Adler, R., & Brakenridge, G. (2021). Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. Journal of Hydrology, 603, 127046. https://doi.org/10.1016/j.jhydrol.2021.127046
Adhikari, P., Hong, Y., Douglas, K. R., Kirschbaum, D. B., Gourley, J., Adler, R., & Brakenridge, G. (2021). Artificial neural networks for flood susceptibility mapping in data-scarce urban areas. Journal of Hydrology, 603, 127046. https://doi.org/10.1016/j.jhydrol.2021.127046
Adhikari, P., Hong, Y., Douglas, K. R., Kirschbaum, D. B., Gourley, J., Adler, R., & Brakenridge, G. (2010). A digitized global flood inventory (1998–2008): Compilation and preliminary results. Natural Hazards, 55, 405-422. https://doi.org/10.1007/s11069-010-9712-8
Ahern, M., Kovats, R. S., Wilkinson, P., Few, R., & Matthies, F. (2005). Global health impacts of floods: Epidemiologic evidence. Epidemiology Reviews, 27(1), 36-45. https://doi.org/10.1093/epirev/mxi033
Ahmadi, M., & Sohrabi, A. (2013). Analysis of hydrological characteristics of the Birjand watershed with emphasis on water resource management. Journal of Regional Geography and Development, (12), 45–60.
Alam, H., Fallahi, M., & Nahas Faramaniyeh, S. (2019). Runoff estimation using the SCS-CN method based on GIS, case study (Shirvan, Bojnourd, Farooj, Safiabad, and Meshkan counties). Innovative Findings in Applied Geology, 13(26), 156-166.
Alonso, A., Muñoz-Carpena, R., Kennedy, R. E., & Murcia, C. (2016). Wetland landscape spatio-temporal degradation dynamics using the new Google Earth Engine cloud-based platform: Opportunities for non-specialists in remote sensing. Transactions of the ASABE, 59(5), 1331-1342. https://doi.org/10.13031/trans.59.11870
Asante, K. O., Macuacua, R. D., Artan, G. A., Lietzow, R., & Verdin, J. P. (2007). Developing a flood monitoring system from remotely sensed data for the Limpopo Basin. IEEE Transactions on Geoscience and Remote Sensing, 45(6), 1709–1714. https://doi.org/10.1109/TGRS.2007.899364
Askar, M. K. (2013). Rainfall-runoff model using the SCS-CN method and geographic information systems: A case study of Gomal River watershed. WIT Transactions on Ecology and the Environment, 178, 159–170.
Aziz, M. T., Islam, M. R., Kader, Z., Imran, H. M., Miah, M., Islam, M. R., & Salehin, M. (2023). Runoff assessment in the Padma River Basin, Bangladesh: A GIS and RS platform in the SCS-CN approach. Journal of Sedimentary Environments, 8(2), 247-260. https://doi.org/10.2136/jse.2023.0892
Bansode, A., & Patil, K. A. (2014). Estimation of runoff by using the SCS curve number method and ArcGIS. International Journal of Science and Engineering Research, 5(7), 1283–1287.
Bansode, M., et al. (2020). Hydrological modeling using the SCS-CN method in the Jajrood watershed, Iran. Journal of Hydrology, 578, 124-136. https://doi.org/10.1016/j.jhydrol.2019.124136
Bhura, C. S., Singh, N. P., Mori, P. R., Prakash, I., & Mehmood, K. (2015). Estimation of surface runoff for the Ahmedabad urban area using the SCS-CN method and GIS. International Journal of Science, Technology and Engineering, 1(11), 411–416.
Caletka, M. (2021). Surface runoff modelling using the SCS-CN method and delineation of flood extents by a non-hydrodynamic GIS tool.
Das, G. K. (2024). Google Earth Engine-based rainfall-runoff modelling for hydrological assessment in the Rasulpur River basin. Knowledge-Based Engineering and Sciences, 5(1), 81-97. https://doi.org/10.1016/j.jkes.2024.01.001
Doocy, S., Daniels, A., Packer, C., Dick, A., & Kirsch, T. D. (2013). The human impact of earthquakes: A historical review of events 1980-2009 and systematic literature review. PLoS Currents, 5. https://doi.org/10.1371/currents.dis.02e7b417a0eafc859bdf9c5c07cf9f50
Eniyew, S., Meshesha, D. T., Zeleke, G. A., & Wassie, S. B. (2024). Combining geospatial information and SCS-CN for surface runoff estimation in the Rib watershed, upper Blue Nile Basin, Ethiopia. Geomatics, Natural Hazards and Risk, 15(1), 2338533. https://doi.org/10.1080/19475705.2024.2338533
Eskandari, M., et al. (2022). Integration of the SCS-CN model with remote sensing data for runoff estimation in Khorasan Razavi. Remote Sensing Applications: Society and Environment, 22, 100383. https://doi.org/10.1016/j.rsase.2022.100383
Eftekhari, M., Hajialiassi, A., & Islami Nejad, S. A. (2024). Evaluation of machine learning models in GIS for groundwater prediction in semi-arid areas of Eastern Iran. Aquifer and Qanat, 4(2), 49-66. https://doi.org/10.22077/jaaq.2024.7282.1062
Gharshasbi, F., & Jokar Serhangi, I. (2024). Investigating the impact of land use changes and precipitation on soil erosion in the Ovard Nkarood basin. Environmental Erosion Research Quarterly, 14(1), 102-124
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.03
Hasami, N., Asakereh, H., & Raispour, K. (2024). Assessment of runoff response to decadal changes in land cover and climatic factors such as rainfall and snow in the Zayanderoud basin. Iranian Water Resources Research Journal, 20(1), 17-36.
Hawkins, R. H., Theurer, F. D., & Rezaeianzadeh, M. (2019). Understanding the basis of the curve number method for watershed models and TMDLs. Journal of Hydrologic Engineering, 24(7), 06019003. https://doi.org/10.1061/(ASCE)HE.19435584.0001710
Haynes, K., Coates, L., van den Honert, R., Gissing, A., Bird, D., de Oliveira, F. D., … & Radford. (In Spatial Modeling in GIS and R for Earth and Environmental Sciences, pp. 323-336).
Hjelmfelt, A. T., Kramer, L. A., & Burwell, R. E. (1982). Curve numbers as random variables. In Rainfall-Runoff Relationships (pp. 365–370). Water Resources Publications.
Hosseinzadeh, M. M., & Eimani, S. (2018). Estimation of runoff height using the curve number method and the Arc-CN Runoff tool in the Afjeh watershed. Spatial Analysis of Environmental Hazards Journal, 5(2), 91-106.
Intergovernmental Panel on Climate Change (IPCC). (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.
Jahangir, M. H., Reineh, S. M. M., & Abolghasemi, M. (2019). Spatial prediction of flood zonation mapping in Kan River Watershed, Iran, using an artificial neural network algorithm. Journal of Natural Hazards, 55(2), 405–422. https://doi.org/10.1007/s11069-010-9615-7
Jahani, M., Talaghani, S., & Akbari, M. (2024). Quantitative evaluation of soil erosion potential using the SLEMSA model (case study: Karakh watershed in Lorestan province, Iran). Environmental Erosion Research Quarterly, 14(2), 161-179.
Javad, M. R., Mir Dareh Herijani, F., & Chatarsimab, Z. (2011). Estimation of runoff height using the curve number method in the ArcGIS software environment with the Arc CN-Runoff tool. Remote Sensing and GIS Applications in Planning, 2(3), 55-62.
Kadam, A. K., Kale, S. S., Pande, N. N., Pawar, N. J., & Sankhua, R. N. (2012). Identifying potential rainwater harvesting sites of a semi-arid, basaltic region of Western India, using the SCS-CN method. Water Resources Management, 26, 2537–2554. https://doi.org/10.1007/s11269-012-0025-6
Khayaat, A., Amirabadizadeh, M., Poorreza Bilandi, M., & Khazimehnajad, H. (2021). Prediction of groundwater levels in the Birjand plain using a fuzzy neural network under climate change impact. Aquifer and Qanat, 2(1), 38-51. https://doi.org/10.22077/jaaq.2021.1863
Khosravi, K., Panahi, M., Golkarian, A., Keesstra, S. D., Saco, P. M., Bui, D. T., & Lee, S. (2020).
Kirch, W., Menne, B., & Bertollini, R. (Eds.) (2005). Extreme weather events and public health. Springer Science & Business Media.
Laishram, R. J., & Alam, W. (2024). Estimation of rainfall-runoff potential using SCS-CN and geospatial approach for Loktak Lake Watershed, India. International Journal of Hydrology Science and Technology, 18(1), 77-105.
Li, H., Zhang, Y., Vaze, J., & Wang, B. (2012). Separating effects of vegetation change and climate variability using hydrological modelling and sensitivity-based approaches. Journal of Hydrology, 420, 403–418. https://doi.org/10.1016/j.jhydrol.2011.12.044
 Miceli, R., Sotgiu, I., & Settanni, M. (2008). Disaster preparedness and perception of flood risk. PLoS Currents, 5.
Mir Dareh Herijani, F. (2011). Estimation and comparison of erosion potential with the EPM and MPSIAC models using GIS (Master's thesis, Islamic Azad University, Noor Branch, Watershed Management Department).
Mirmousavi, S. H., & Esmaeili, H. (2021). Mapping flood-prone areas using GIS and remote sensing (RS), case study: Darab County. Natural Environmental Hazards, 10(27), 21-46. https://doi.org/10.1007/s11069-021-04448-w
Mousavi, H., et al. (2021). Flood modeling using the SCS-CN method in the Dez River basin, Iran. Flood Risk Management, 14(3). https://doi.org/10.1002/frm.1632
Muneer, A. S., Afan, H. A., Kamel, A. H., & Sayl, K. N. (2022). Runoff mapping using the SCS-CN method and artificial neural network algorithm, Ratga Basin, Iraq. Arabian Journal of Geosciences, 15(7), 666. https://doi.org/10.1007/s12517-022-09191-4
Namati Mansour, A. R., Kardaan Moghadam, H., & Javad, S. (2024). Estimation of groundwater balance using a remote sensing approach: Case study of the Qomsha plain. Aquifer and Qanat, 4(2), 35-48. https://doi.org/10.22077/jaaq.2024.7258.1061
Rawat, K. S., & Singh, S. K. (2017). Estimation of surface runoff from semi-arid ungauged agricultural watershed using SCS-CN method and earth observation data sets. Water Conservation Science and Engineering, 1, 233–247. https://doi.org/10.1007/s41101-017-0014-4
Ray, D. K., & Pijanowski, B. C. (2010). A backcast land use change model to generate past land use maps for the Muskegon River Watershed. Land Use Policy, 27(3), 611-622. https://doi.org/10.1016/j.landusepol.2009.08.003
Ross, C. W., Prihodko, L., Anchang, J., Kumar, S., Ji, W., & Hanan, N. P. (2018). HYSOGs250m, global gridded hydrologic soil groups for curve-number-based runoff modeling. Scientific Data, 5(1), 1-9. https://doi.org/10.1038/sdata.2018.88
Safari, A., Ghavanati, I., Behishti Javid, E., & Hosseini, H. (2013). Estimation and mapping of runoff caused by maximum 24-hour rainfall using the SCS-CN method (Yamchi dam basin, Ardabil). Geography, 11(38), 201-217.
Sarvi, V. (2016). Locating rainwater harvesting potential areas for wildlife using GIS (Master's thesis, University of Guilan).
Siddi Raju, R., Sudarsana Raju, G., & Rajasekhar, M. (2018). Estimation of rainfall runoff using SCS-CN method with RS and GIS techniques for Mandavi Basin in YSR Kadapa District of Andhra Pradesh, India. Hydrospatial Analysis, 2(1), 1–15.
Study in an alpine valley in Italy. (2008). Journal of Environmental Psychology, 28(2), 164–173.
The implications for policy and practice. (2017). Journal of Environmental Science & Policy, 76, 165–176.
United Nations Office for Disaster Risk Reduction (UNDRR). (2020). The Human Cost of Disasters: An Overview of the Last 20 Years (2000-2019).
USDA, S. C. S. (1986). Urban hydrology for small watersheds (Technical Release No. 55, pp. 2–6). U.S. Department of Agriculture.
Vinithra, R., & Yeshodha, L. (2016). Rainfall–runoff modelling using SCS–CN method: A case study of Krishnagiri District, Tamil Nadu. International Journal of Scientific Research, 5(3), 2319–7064.
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دوره 5، شماره 2 - شماره پیاپی 9
اسفند 1403
صفحه 155-174
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