Investigating the effect of climate change on snow cover with the approach of water resources management in the coming decades (Case study: Basin of watershed leading to Amir Kabir dam)

سال انتشار: 1396
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 211

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شناسه ملی سند علمی:

JR_JORAR-9-3_006

تاریخ نمایه سازی: 23 بهمن 1399

چکیده مقاله:

Background: Assessing the effects of climate change on changes in snow cover and melting behavior is very important in water management. These changes will have a direct impact on the hydrological regime and water resource management. The prediction of snow cover surface due to temperature changes in the future is applicable in a variety of fields including flood risk management, drought, etc. On the other hand, the advantages of using modern technologies and remote sensing in climatic studies and assessing the effects of climate change on snow cover have largely been neglected. The purpose of this research is to investigate changes in snow cover levels in future by integrating remote sensing science, new technologies, and climatic models for flood risk management. Method: In this study, the 8-day images of the MODIS satellite were extracted from 2010-2015 due to proper accuracy and reduction of cloud cover error; after receiving and storing satellite images of snow cover, software ERDAS was used to view and change the format of these images. Then, the study area was clipped and finally, the snow cover surface was calculated and extracted based on the number and the size of snow pixels using ArcGIS software. Snow surface area was controlled with 8-day intervals during 2010-2015. In the next step, temperature and precipitation variations were extracted using the latest CMIP5 climatic models and four scenarios RCP2.6, RCP4.5, RCP6.0 and RCP8.5 from 2020-2060. The snow cover was estimated for the years 2020-2060 with a 10-year interval using the relationship between snow cover, temperature, and precipitation. Findings: The observational data and satellite imagery showed that the snow cover density began in November in the study area and reached its largest area in January. In addition, since February, the snow cover has declined, and the snow cover had the smallest area in June. Investigation of temperature and precipitation changes using climate scenarios showed that the average temperature of the basin of Amir Kabir Dam in comparison with the base period (2015/1985) would be increased and the annual rainfall of the base period would be decreased. As a result, the surface of snow cover would be come down with a decreasing trend by considering the relationship between snow cover, temperature, and precipitation. Conclusion: The results show that snow level is important to study and measure as one of the main sources of water supply. Due to the hard physical conditions of mountainous terrain, there is no permanent ground measurement for estimating retrofitting resources and the formation of databases. Therefore, the use of satellite imagery is very important in identifying the snowfield areas and assessing its changes. In addition, the snow level in the area is also reduced due to the increasing temperature and decreasing rainfall, and the amount of water stored in the snow, which are the source of water supply in the warm seasons, will be reduced. So estimating the snow cover level in future can be a major step forward in managing water resources and risk management of water-related risks, including floods and droughts.

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نویسندگان

فاطمه فلاحتی

دانشجوی دکترای آب و هواشناسی، دانشکده جغرافیا، دانشگاه خوارزمی، تهران، ایران

بهلول علیجانی

استاد، دانشکده جغرافیا، دانشگاه خوارزمی، تهران، ایران

محمد سلیقه

دانشیار، دانشکده جغرافیا، دانشگاه خوارزمی، تهران، ایران

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  • northwestern of Iran. Master Thesis. GIS & remote sensing center ...
  • 2. Rango A, Shalaby A. Operational Applications of Remote Sensing in ...
  • 3. Metcalfe R A, Buttle J M. Semi distributed water balance ...
  • 4. Johansson B, Cves R, Fergusun R, et al. Using remote ...
  • 5. Avery T E, Berlin G L. Fundamentals of remote sensing ...
  • 6. Hall D K, Riggs G A, Salomonson V et al. ...
  • 7. Foppa N, Wunderle S, Hauser A. Spectral Unmixing of NOAA-AVHRR ...
  • 8. Malcher P, Heidinger M. Processing and data assimilation scheme for ...
  • 9. Miller N I, Bashford K E and Sterm E. Potential ...
  • 10. Bandyopadhyay A, Bhadra A, Maza M et al. Monthly variations ...
  • 11. Shafizade M H. Feasibility Promote the Power of MODIS Sensor ...
  • 12. Dadashi Kh S. Detection snow cover using image processing algorithms ...
  • 13. Taylor KE, Stouffer R. J, Meehl GA. an overview of ...
  • 14. Marengo JA, Chou SC, Torres RR, Giarolla A, Alves LM, ...
  • 15. Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson ...
  • 16. Khadka D, S. Babel M, Shrestha S, Nitin K. Tripathi. ...
  • 17. Akhtar M, Ahmad N, Booij MJ. The impact of climate ...
  • 18. Gan R, Luo Y, Zuo Q, Sun L. Effects of ...
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