Investigation of the impact of climate change on the trend and temperature distribution of precipitation phase in snow-rainy basin: Beheshtabad and Koohrang

Document Type : Complete scientific research article

Authors

1 Dept. of Civil Engineering-Water Resources Management, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran.

2 Corresponding Author, Dept. of Civil Engineering-Water Resources Management, Shoushtar Branch, Islamic Azad University, Shoushtar, Iran.

3 Dept. of Water Resources Engineering, Ahwaz Branch, Islamic Azad University, Ahwaz, Iran.

4 Dept. of Civil Engineering, Ahwaz Branch, Islamic Azad University, Ahwaz, Iran.

Abstract

Abstract
Background and objectives :When it comes to climate change, the first emphasis should be detecting these changes in high mountainous regions, since they will have a direct impact on water supplies in the major stems. In the Behesht Abad and Koohrang zones of Iran's central Zagros mountains, one of the country's highest mountain ranges, the influence of climate change on snow-rain phase separation in the future is investigated. Because of the Karun River's varied exploitation, understanding how it will evolve in the future and under the effect of climate change is critical. The aim of the research is to determine the consequences of climate change on precipitation in the region, particularly in terms of future changes in snow and rain phases.
Materials and methods: For this, the research region's precipitation, temperature, and precipitation type data from 1985 to 2018 were used. For the three scenarios RCP2.6, RCP4.5, and RCP8.5, the National Center for Environmental Protection's (NCEP) atmospheric reanalysis data and the CanESM2 model were used to forecast future climate change. Furthermore, the downscaling was done using the SDSM5.3 model. To find data patterns, the classic and modified Mann-Kendall tests were performed. Fixed temperature approaches, the UBC watershed model, the USCE model, and Kienzel's suggested method were utilized to separate the precipitation phase.
Results: To separate the precipitation phase throughout the fundamental period, observational reports were examined, and the approaches Kienzle and USCE gave satisfactory results. Climate change will also produce major changes in the precipitation temperature distribution in the examined mountain region in the future, according to the findings of this study. Also, a significant portion of the influence of climate change on the snow and rain phases. The modifications are done in such a way that rainfall will rise at higher temperatures and decrease at lower temperatures in the prediction period (2026-2060) compared to the observation period (1985-2018). The greatest total rainfall recorded at Shahrekord station during the observation period was 5.7 ° C, which has fallen to 0 ° C in the projected period. The temperature range of precipitation at this station was -10 to +18 degrees Celsius during the observation period, and will climb to an average of -10 to +24 degrees Celsius for all three scenarios over the forecast period. The range of precipitation in the future and measurements at Koohrang station is essentially the same, but climate change has produced a rapid shift in the amount of precipitation in this temperature range. Over example, during the 34-year observation period, the greatest rainfall that occurred at a temperature of 1.6 ° C was a total of 5700 mm, which was reduced to -1.6 ° C and a value of 3700 mm owing to climate change for the next 34 years.
The highest limit of the precipitation range at Boroojen station has increased from +18 ° C in the historical era to +24 °C in the anticipated period as a result of the modifications.
Conclusion: The results of the trend test on the predicted data demonstrate that it is present in the monthly rainfall in the study stations in a substantial way. The temperature distribution of precipitation varies as a result of these changes, which are caused by the impacts of climate change on the study region.

Keywords

References

1.Ahmadi, F., Nazeri Tahroudi, M., Mirabbasi, R., Khalili, K., and Jhajharia, D. 2018. Spatiotemporal trend and abrupt change analysis of temperature in Iran. Meteorological Applications. 25: 2 . 314-323.
2.Baghanam, A.H., Nourani, V., Sheikhbabaei, A., and Jedari Seifi, A., 2019. Statistical downscaling and projection of future temperature change for Tabriz city, Iran. EasyChair Preprint 1813.
3.Bonsal, B., Peters, D., Seglenieks, F., Rivera, A., and Berg, A. 2019. Changes in freshwater availability across Canada. In Canada’s Changing, Government of Canada, Ottawa, Ontario, pp. 261-342.
4.Bonsal, B., Shrestha, R., Dibike, Y.L., Peters, D., Spence, C., Mudryk, L., and Yang, D. 2020. Western Canadian freshwater availability: current and future vulnerabilities. Environmental Reviews 28.
5.De Vries, H., Lenderink, G., and Meijgaard, E. 2014. Future snowfall in western and central Europe projected with a high-resolution regional climate model ensemble. Geophysical Research Letters. 41: 12. 4294-4299.
6.Derksen, C., Burgess, D., Duguay, C., Howell, S., Mudryk, L., Smith, S., Thackeray, C., and Kirchmeier-Young, M. 2019. Changes in snow,ice, and permafrost across Canada. In Canada’s Changing Climate Report, Government of Canada, Ottawa, Ontario, pp. 194-260.
7.Dini G., Zieaean Firouzabadi, R., Alimohammadi Sarab, P., Dadashi, A., and Khanghah, S. 2008. GIS-based snow mapping in Central Alborz Mountain chain using MODIS and AVHRRdata. Iran-Water Resources Research.3: 3. 87-94. (In Persian)
8.Dinpashoh, Y., Mirabbasi, R., Jhajharia, D., Abianeh, H.Z., and Mostafaeipour, A. 2014. Effect of short-term and long-
term persistence on identification of temporal trends. Journal of Hydrologic Engineering. 19: 3. 617-25. (In Persian)
9.Coppola, E., Raffaele, F., and Giorgi, F. 2018. Impact of climate change on snow melt driven runoff timing. Climate Dynamics. 51. 1259-1273.
10.Goodarzi, M., Jahanbakhsh, S., Razaee, M., Ghfrouri, A., and Mahdian, M.H. 2011. Assessment of Climate Change Statistical Downscaling Methods in a Single Site in Kermanshah, Iran. American-Eurasian Journal of Agricultural and Environmental Sciences. 6: 564-572.
11.Hamed, K.H., and Rao, A.R. 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology. 204: 182-196.
12.Hirsch, R.M., Slack, J.R., and Smith, R.A. 1982. Techniques of trend analysis for monthly water quality data. Water Resources Research. 18: 1. 107-121.
13.Khalili, K., Nazeri Tahoudi, M,. Mirabbasi, R., and Ahmadi, F. 2016. Investigation of spatial and temporal variability of precipitation in Iran over the last half century. Stochastic environmental research and risk assessment. 30: 4. 1205-1221.
14.Khan, M.S., Coulibaly, P., and Dibike, Y. 2006. Uncertainty analysis of statistical downscaling methods. Journal of Hydrology. 319: 357-382.
15.Khazaei, M., Sharafati, A., and Khazaei, H. 2018. Climate change impact assessment on maxima daily snowfalls, case study: Tehran. Watershed Engineering and Management. 10: 2. 204-213.(In Persian)
16.Kienzle, S.W. 2008. A new temperature based method to separate rain and snow. Hydrological Processes. 22: 5067-5085.
17.Kotlarski, S., Luthi, D., and Schar, C. 2015. The elevation dependency of21st century European climate change: an RCM ensemble perspective. International Journal of Climatology.35: 3902-3920.
18.Lotfi, M., Kamali, Gh.A., Meshkati, A., Varshavyan, V. 2020. Predicting maximum temperatures using global climate models under RCP scenariosand microscaling LARS-WG andSDSM models in the west of the country. Physical Geography Quarterly 14: 51. 115-130. (In Persian)
19.Lute, A.C., Abatzoglou, J.T., and Hegewisch, K.C. 2015. Projected changes in snowfall extremes and internal variability of snowfall in the western United States. Water Resources Research. 51: 2. 960-972.
20.Malcolm, R., Cawely, G.C., Harpham, C., Wilby, R.L., and Goodees, C.M. 2006. Downscaling heavy precipitaion over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarious. International journal of climatology. 26: 10. 1397-1415.
21.Newton, B.W., Farjad, B., and Orwin, J.F. 2021. Spatial and temporal shifts in historic and future temperature and precipitation patterns related to snow accumulation and melt regimes in Alberta, Canada. Water. 13: 8. 1013.
22.Partal, T., and Kahya, E. 2005. Trend analysis in Turkish precipitation data. Hydrological Processes. 20: 2011-2026.
23.Pipes, A., and Quick, M. 1977. UBS Watershed Model User. University of British Columbia, Vancouver, British Columbia, Canada.
24.Raziei, T., Jahanbakhsh Asl, S., Parandeh Khouzani, A., and Sari Saraf, B. 2018. Assesing the Accuracy of the Snow-Rain Phase Separation Models for Meteorological Weather Stations of the Mountainous Region of Zagros, Iran. Iran-Water Resources. 14: 3. 85-102.(In Persian)
25.Saghafian, B., and Davtalab, R.2007. Short Communication Mapping snow characteristics based on snow observation probability. International Journal Of Climatology. 27: 1277-1286.
26.Saghafian, B., Davtalab, R., and Kefayati, M. 2016. Comparison of threshold temperature determination methods and snowfall potential areas in Karkheh, Dez, Karun and Maroon catchments. Iranian Water Rrsource Journal. 9: 4. 31-9. (In Persian)
27.Shukla, S., Jain, S.K., and Kansal, M.L. 2021. Hydrological modelling of a snow/glacier-fed western Himalayan basin to simulate the current and future streamflows under changing climate scenarios. Science of the Total Environment. 795: 15. 148871.
28.USCE. 1956. Snow hydrology, Summary report of the snow investigation. US Army Corps of Engineers, North Pacific Division, Portland, Oregon.
29.Wilby, R.L., 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.
30.Willby, R.L., Dawson, C.W., and Barrow, E.M. 2002. SDSM- A decision support tool for the assessment of regional climate change impacts. Journal of Environmental Modeling and Software. 17: 147-159.
31.Yue, S., and Wang, C. 2004. TheMann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series. Water Resources Management. 18: 201-218.
32.Zamani, R., Mirabbasi, R., Nazeri, M., Meshram, S.G., and Ahmadi, F. 2018. Spatio-temporal analysis of daily, seasonal and annual precipitation concentration in Jharkhand state, India. Stochastic environmental research and risk assessment. 32: 4. 1085-1097.
33.Zhang, X., Flato, G., Kirchmeier-Young, M., Vincent, L., Wan, H., Wang, X., Rong, R., Fyfe, J., Li, G., and Kharin V. 2019. Changes in Temperature and Precipitation Across Canada. In Canada’s Changing Climate Report, Government of Canada, Ottawa, Ontario, pp. 112-193.
34.Zhang, X., Srinivasan, R., Debele, B., and Hao, F. 2008. runoff simulation of the headwaters of the Yellow  river using the swat model with tree snowmelt alguritme. American Water Resources Assocition. 44: 48-61.
35.Zoheyri, Z., Ghazavi, R., Omidvar, E., and Davudi-rad, A. 2020. Comparison of LARS-WG and SDSM Downscaling Models for Prediction Temperature and Precipitation Changes under RCP Scenarios. Arid Regions Geographic Studies. 10: 40. 39-52. (In Persian)
Journal of Water and Soil Conservation
Volume 28, Issue 4
March 2022
Pages 77-100

Files

Share

How to cite

Statistics