Investigating spatial-temporal changes in the average snowmelt of cold seasons in the northwestern region of Iran

Document Type : Original Article

Authors

1 Ph.D student of hydrology and meteorology, Zanjan University, Zanjan, Iran

2 Associate Professor, Faculty member of Zanjan University

3 Professor, Faculty member of Zanjan University, Zanjan, Iran

4 Assistant Professor, Faculty member of Zanjan University, Zanjan, Iran

Abstract

Examining temporal and spatial changes of snow melting is very important in various fields including water resource management. Therefore, the current research was conducted with the aim of investigating the temporal and spatial changes as well as the spatial autocorrelation of the amount of snow melting in the northwest of Iran for the months of the cold seasons. For this purpose, snowmelt data from the European Center for Medium-Range Weather Forecasts (ECMWF), version (ERA5) with a spatial resolution of 0.25 x 0.25 degrees for the cold season months during the statistical period from 1982 to 2022 received and then divided into four periods of ten years. In order to analyze spatial autocorrelation changes, global Moran indices and hot spot analysis (Gettis-ORDJ) were used at the significance level of 90, 95 and 99%. Also, in order to determine the effect of temperature on the amount of snow melting, the trend of changes in the average minimum monthly temperature of 20 synoptic stations in the northwest region was investigated. The results of the present research showed that in the studied area, snow melting has spatial autocorrelation and a strong cluster pattern. During the first decade to the end of the fourth decade, the amount of snow melting in the months of October, November and December was between 0  and 5.27 mm per day, and especially in the month of December, which is accompanied by a negative minimum temperature anomaly the area (number of pixels) and the amount of snowmelt in the northwest have been reduced. The results of the analysis of the amount of snow melting changes in the winter months also showed that both the amount and the area of snow melting have increased during the study period. Thus, the range of snow melting threshold changes in January, February and March has increased between 0.95 and 19.27 mm per day from the first decade to the end of the fourth decade. Among the months of the winter season, February is associated with a strong positive minimum temperature anomaly (significant increase in minimum temperature), and accordingly, the area (number of pixels) and the amount of snow melting in the northwest have been increased.

Keywords

Main Subjects

References

  1. Aalinejad, M.H., Dinpashoh, Y., & Jahanbakhsh ASL, S., 2016, Impact of Climate Change on Runoff from Snowmelt by Taking into Account the Uncertatinty of GCM Models (Case Study: Shahrchay Basin in Urmia), European Online Journal of Natural and Social Sciences, 1, pp. 200-211.
  2. Akhtar M., Ahmad N., & Booij M.J. 2008. The impact of climate change on the water resources of Hindu Kush Karakorum Himalaya region under different glacier coverage scenarios. J. Hydrol, 355 (1-4): 148- 163.
  3. Buttle, J.M. and McDonnell, J.J. 1987. Modelling the areal depletion of snowcover in a forested catchment, Hydrol, 90:43- 60.
  4. Calizaya, E., Mejía, A., Barboza, E., Calizaya, F., Corroto, F., Salas, R., Vásquez, H., & Turpo, E.2021. Modelling Snowmelt Runoff from Tropical Andean Glaciers under Climate Change Scenarios in the Santa River Sub-Basin (Peru). Water, 13, 3535, pp. 2- 16. https://doi.org/ 10.3390/ w13243535.
  5. Debeer, C., & Pomeroy, J.W. 2017. Influence of snowpack and melt energy heterogeneity on snow cover depletion and snowmelt runoff simulation in a cold mountain environment, Journal of Hydrology, 553: 199- 213.
  6. Gan, R., Luo, Y., Zuo, Q., & Sun, L. 2015.Effects of projected climate change on the glacier and runoff generation in the Naryn River Basin, Central Asia, Journal of Hydrology. 523: 240-251.
  7. Jain, S.K., Goswami, A., & Saraf, A. 2010. Assessment of Snowmelt Runoff Using Remote Sensing and Effect of Climate Change on Runoff, Water Resources Management, 24(9): 1763-1777.
  8. Marengo, J.A., Chou, S.C., Torres, R.R., Giarolla, A., Alves, L.M., & Lyra, A. 2014. Climate change in central and South America: Recent trends, future projections, and impacts on regional agriculture, Working Paper No.73
  9. Mohammadi, P., Ebrahimi, K., & Bazrafshan, J. 2022. Improving the precision of Snowmelt runoff simulation and sensitivity analysis of parameters and climatic variables in the Tamar basin, Iran. Iranian Journal of Soil and Water Research, 53(7): 1611- 1624
  10. Pavlovskii, I., hayashi, M., & Itenfisu, D. 2018. Effects of midwinter snowmelt on runoff generation and groundwater recharge in the Canadian prairies, Hydrology and Earth Sciences, pp. 1- 27. https://doi.org/10.5194/hess-2018-423
  11. Qiao, C., Huang, Q. Y., Chen, T., & Chen, Y. M. 2019. Study On Snowmelt Flood Disaster Model Based On Remote Sensing And Gis, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W13, pp. 709-713.
  12. Van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard K. 2011. The representative concentration pathways: An overview. Climatic change, 109: 5- 31.
  13. Wang, R., Kumar, M., & E.link, T. 2016. Potential trends in snowmelt- generated peak streamflows in a
    warming climate, Geophysical Research Letters, pp. 5052- 5059.
  14. Welty, J., & Zeng, X. 2021. Characteristics and Causes of Extreme Snowmelt over the Conterminous United States, Bams, pp. 1526- 1542. https://doi.org/10.1175/BAMS

 

Files

Share

How to cite

Statistics