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Spatio-temporal variation of hydro-climatic variables and extreme indices over Iran based on reanalysis data

Malaekeh, S. M ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1007/s00477-022-02223-0
  3. Publisher: Springer Science and Business Media Deutschland GmbH , 2022
  4. Abstract:
  5. A comprehensive investigation of historical hydro-climatic changes at a county level is an essential prerequisite of developing any adaptation or mitigation strategies to deal with the destructive impacts of climate change. In this study, spatial distributions and trends in thirty-seven hydro-climatic mean and extreme indices across Iran were analyzed based on the state-of-the-art reanalysis datasets (ERA5-Land and AgERA5) at the county level from 1986 to 2015 using several nonparametric approaches such as multiple modified Mann–Kendall statistical tests and Sen’s Slope estimator. Their interannual oscillations were also examined using continuous wavelet transform to portray a complete picture of the temporal variability of the variables. The micro-scale of the reanalysis datasets enabled us to deal with the sparse distribution of weather stations in Iran, study various hydro-climatic variables that have received less attention, and subsequently provide a comprehensive, all-in-one reference for later policy actions and further studies. Analyzing diverse, intercorrelated variables provides a great opportunity to explore the driving forces of the temporal patterns. The results showed that trends in hydro-climatic mean and extreme indices highly depended on their geographical location and climate zones. The snow and the warm temperate climates encountered larger hydro-climatic changes compared to the arid and semi-arid climates zones. The most noticeable trends were found for temperature which significantly increased over all counties while drying trends were observed in precipitation, surface reservoir content, and runoff. Wind speed and surface albedo in most counties experienced upward and downward trends, respectively, whereas solar radiation, surface air pressure, and evaporation exhibited a high spatial variability. In terms of extreme events, Iran was faced with an increase in hot climate extremes and a decrease in cold and precipitation extremes. At last, the wavelet power spectrum analysis demonstrated the annual and seasonal dominant periods for precipitation and an annual dominant period for temperature, and there were significant periodic variations at 2–3 years for hot and cold spell durations and intensity-based precipitation index and 4–6 years for consecutive dry and wet days. The annual fluctuation of precipitation reduced over the study period, but that of temperature remained almost constant. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature
  6. Keywords:
  7. Climate change ; Continuous wavelet transform ; Extreme indices ; Hyrdo-climatic variables ; Reanalysis ; Solar radiation ; Spectrum analysis ; Wavelet transforms ; Wind ; Climatic changes ; Climatic variables ; County level ; Dominant periods ; Extreme index ; Hyrdo-climatic variable ; Reanalysis ; Spatio-temporal variation ; Climate change ; Annual variation ; climate change ; Extreme event ; Numerical model ; Spatiotemporal analysis ; Stochasticity ; Wavelet analysis ; Iran
  8. Source: Stochastic Environmental Research and Risk Assessment ; Volume 36, Issue 11 , 2022 , Pages 3725-3752 ; 14363240 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s00477-022-02223-0