SPEI-based Projection and Analysis of Drought's Spatiotemporal Characteristics Using GCM (CanESM2)

Authors

‎10.22052/deej.2021.10.33.61

Abstract

Introduction
Climate change conditions have been deteriorated in recent years due to increasing emissions of greenhouse gases, whose negative effects on human societies are one of the major concerns in 21st century, leading to introduction of several scenarios for predicting the climate parameters affected by increasing emissions of greenhouse gases. Therefore, this study sought to investigate the effects of climate change on prospective drought in Tehran province using the Standardized Precipitation and Evapotranspiration Index (SPEI). To this end, daily climate parameters (T-min, T-max, T-mean, and precipitation) of eight synoptic stations were predicted in for the study period (1996-2017), using GCM-based emission Scenarios (RCP2.6, RCP4.5, and RCP8.5) extracted from the IPCC's Fifth report until 2112. Then, the drought's SPEI was calculated based on the predicted parameters, followed by the evaluation of the spatiotemporal characteristics of the drought. A general review of the results showed that the most severe drought would occur in Abali station in July 2073, which would be almost unprecedented in its kind. Moreover, Tehran city would experience more drought stress than other parts of the Tehran province in the coming years. It should be noted that according to the analysis of future drought's time series, "Very dry" months in future would have a 4-month displacement to the backward and would be shifted from September to May than what had been recorded in terms of time period.
Materials and Methods: This study attempted to predict the precipitation and temperature data at the synoptic station level based on climate change scenarios using SDSM exponential microscopy technique. The section 2 of the article introduces the study area and the stations concerned, the climate change scenarios, the SDSM microscopy model, SPEI drought index, and regional zoning model. In Section 3, the regional drought will be calculated and spatially analyzed based on SPEI index using the predicted data. Finally, the last section of the study is devoted to the summary and general conclusions. Based on the monthly average observational charts and forecasts at each station based on each scenario, it can be claimed that the drought phenomenon is moving backwards in the coming years. In other words, most of the stations are predicted to experience their driest year from September to October. However, according to climate change scenarios, May, June, and July are symbols of high drought months in the coming years.

Results
Temporal Analysis
As one of the dimensions of drought characteristics, the detailed drought analysis offers very useful information regarding the intensity, duration, and frequency of drought. According to average monthly observation charts and forecasts prepared for each station in each scenario, it can be argued that the drought phenomenon is moving backwards in the coming years, according to which most stations are predicted to experience their driest years, especially in September and October of each year. However, the climate change scenarios revealed that May, June, and July would be the symbols of high-drought months in the years to come.
Spatial analysis based on scenario 2.6
At first glance, it could be said that in all months of the year, the Tehran city would suffer water stress and drought crisis. On the other hand, according to the images obtained, the drought would have a moving trend from January to July, shifting from the west to the east of the province. However, the trend would be concentrated in the west of Tehran province from August to January, except for the December.
Spatial analysis based on scenario 4.5 per month
Scenario 4.5 reported more severe climate change than Scenario 2.6. The remarkable point in the obtained images was the frequent continuation of drought in the center of Tehran province, i.e., Tehran city.
Spatial analysis based on Scenario 8.5 per month
Scenario 8.5 shows more different changes in the distribution of drought-prone areas in the coming years than previous climate change scenarios. One of the points to consider in this regard is the significant reduction in the frequency of droughts in the west of Tehran province, which is even lower than those of the center and east parts of the province, being almost the opposite of what was found in the 2.6 scenario.

Discussion and Conclusion
The comparison of data found for the observation years and the what was predicted for the upcoming years based on different scenarios shows that the frequency of droughts in the coming period. Therefore, if looked more closely, it could be found that the most severe and frequent droughts have occurred throughout the 7th decade of the 21st century, for which proper measures should be devised. The study's results also indicate that the probability of drought in the observation months will change more than what is anticipated, suggesting a seasonal retreat both in drought and wet season. Finally, according to the spatial analysis, it could be said that Tehran city will have higher temperature and precipitation stress (drought) than other parts of Tehran province. On the other hand, with the increase in altitude and the decrease in temperature, the severity of drought will decrease, whose effect on high altitude stations in this study was totally evident.

Keywords


  1. Abramowitz, M., & Stegun, I. A. 1965. Handbook of mathematical functions with formulas, graphs, and mathematical table. US Department of Commerce; National Bureau of Standards Applied Mathematics Series, 55.‏
  2. Chaumont, D. 2014. A guidebook on climate scenarios: Using climate information to guide adaptation research and decisions.‏
  3. Coppola, E., Nogherotto, R., Ciarlo', J. M., Giorgi, F., van Meijgaard, E., Kadygrov, N. and Wulfmeyer, V. 2021. Assessment of the European climate projections as simulated by the large EURO‚ÄźCORDEX regional and global climate model ensemble. Journal of Geophysical Research: Atmospheres126(4), e2019JD032356.
  4. ‏ Dehghan, S., Salehnia, N., Sayari, N. and Bakhtiari, B. 2020. Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran. Journal of Arid Land, 12(2), 318-330.‏
  5. Dai, A. 2013. Increasing drought under global warming in observations and models. Nature climate change3(1), 52-58.‏
  6. Duggins, J., Williams, M., Kim, D. Y. and Smith, E. 2010. Changepoint detection in SPI transition probabilities. Journal of hydrology388(3-4), 456-463.‏
  7. Eskandari, H., Borji, M., Khosravi, H., & Mesbahzadeh, T. 2016. Desertification of forest, range and desert in Tehran province, affected by climate change. Solid Earth, 7(3), 905-915.‏
  8. Farajirad, A. Seyyednasiri, S.Z. winter (2009). Tehran Tourism Geography and the Role of Urbanism and Architecture in its Development, Journal of New attitudes in human geography (human geography) Volume 2, Issue 1; page 71 - 84. (in Farsi)
  9. Gebremedhin, M. A., Abraha, A. Z. and Fenta, A. A. 2018. Changes in future climate indices using Statistical Downscaling Model in the upper Baro basin of Ethiopia. Theoretical and applied climatology133(1-2), 39-46.‏
  10. Hasan Yazdani, M., Amininia, K., Safarianzengir, V. and Soltani, N. 2021. Analyzing climate change and its effects on drought and water scarcity (case study: Ardabil, Northwestern Province of Iran, Iran). Sustainable Water Resources Management7(2), 1-
  11. IPCC, 2007. Climate change: The Physical Scientific Basis. Contribution of working Group to the Fourth Assessment Report of the intergovernmental Panell on climate change. Cambridge Univ, Perss.1-18.
  12. Jahangir, M. and Abolghasemi, M. 2019. Determining the most appropriate probability distribution function for calculate and compare the SPEI and SPI drought index in Tehran, Desert Ecosystem Engineering Journal, 8(23), 1-16. magiran.com/p2005097
  13. Kim, B., Sung, J. H., Lee, B. H. and Kim, D. J. 2013. Evaluation on the impact of extreme droughts in South Korea using the SPEI and RCP8. 5 climate change scenario. Journal of Korean Society of Hazard Mitigation13(2), 97-109.‏
  14. Kim, T. W. and Jehanzaib, M. (2020). Drought risk analysis, forecasting and assessment under climate change.‏ Volume4, Issue 1; page 7 - 8.
  15. Lorenzo-Lacruz, J., Vicente-Serrano, S. M., López-Moreno, J. I., Beguería, S., García-Ruiz, J. M. and Cuadrat, J. M. 2010. The impact of droughts and water management on various hydrological systems in the headwaters of the Tagus River (central Spain). Journal of Hydrology386(1-4), 13-26
  16. Manzanas, R., Brands, S., San-Martín, D., Lucero, A., Limbo, C. and Gutiérrez, J. M. 2015. Statistical downscaling in the tropics can be sensitive to reanalysis choice: a case study for precipitation in the Philippines. Journal of Climate28(10), 4171-4184.‏
  17. Mavromatis, T. 2007. Drought index evaluation for assessing future wheat production in Greece. International Journal of Climatology: A Journal of the Royal Meteorological Society, 27(7), 911-924.‏
  18. ‏ Mostafazadeh, R. and Zabihi, M. 2016. Comparison of SPI and SPEI indices to meteorological drought assessment using R programming (Case study: Kurdistan Province). Journal of the Earth and Space Physics42(3), 633-643.‏
  19. Najafinejad A. Mirdashtvan ,M, Malekianband A. Sa’doddina, 2017, Downscaling the contribution to uncertainty in climate-change assessments: representative concentration pathway (RCP) scenarios for the South Alborz Range, Iran, Royal Meteorological Society.
  20. Nodeh Farahani, M.; Rasekhi, A.; And Keshvari, A., Fall 1397, Investigation of the effects of climate change on temperature, rainfall and droughts in the future Dashtegan Basin, Water Resources Science and Engineering Association, Volume 14, Number 3, pp. 125-139.
  21. Nosrati, K. Mohsenisaravi, M. Shahbazi, R. spting and summer 2014. Comparison and application of two standardized precipitation indexes and standardized precipitation, evapotranspiration and transpiration for assessing drought weather conditions in Tehran Province, Desert Management Journal, No. 3, page 77-90. (in Farsi)
  22. Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., ... and Dubash, N. K. 2014. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change(p. 151). Ipcc.
  23. Rasifaghihi, N., Li, S. S. and Haghighat, F. 2020. Forecast of urban water consumption under the impact of climate change. Sustainable Cities and Society52, 101848.‏
  24. Samadi, S., Ehteramian, K. and Sarraf, B. S. 2011. SDSM ability in simulate predictors for climate detecting over Khorasan province. Procedia-Social and Behavioral Sciences19, 741-749.‏
  25. Trenberth, K. E., Dai, A., Van Der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R. and Sheffield, J. 2014. Global warming and changes in drought. Nature Climate Change, 4(1), 17-22.‏
  26. Vano, J. A., Scott, M. J., Voisin, N., Stöckle, C. O., Hamlet, A. F., Mickelson, K. E., ... and Lettenmaier, D. P. 2010. Climate change impacts on water management and irrigated agriculture in the Yakima River Basin, Washington, USA. Climatic Change, 102(1-2), 287-317.‏
  27. Vicente-Serrano, S. M., López-Moreno, J. I., Drumond, A., Gimeno, L., Nieto, R., Morán-Tejeda, E., ... and Zabalza, J. 2011. Effects of warming processes on droughts and water resources in the NW Iberian Peninsula (1930− 2006). Climate Research, 48(2-3), 203-212.‏
  28. Wang, X., Zhuo, L., Li, C., Engel, B. A., Sun, S., & Wang, Y. (2020). Temporal and spatial evolution trends of drought in northern Shaanxi of China: 1960–2100. Theoretical and Applied Climatology139(3), 965-979.‏
  29. Wilby, R. L., Dawson, C. W. and Barrow, E. M. 2002. SDSM—a decision support tool for the assessment of regional climate change impacts. Environmental Modelling & Software, 17(2), 145-157.‏
  30. Wilby, R. L. and Dawson, C. W. 2013. The statistical downscaling model: insights from one decade of application. International Journal of Climatology, 33(7), 1707-1719.