Theoretical and Applied Climatology

, Volume 122, Issue 3–4, pp 497–516 | Cite as

A modified regionalization weighting approach for climate change impact assessment at watershed scale

  • Mohammad Javad ZareianEmail author
  • Saeid Eslamian
  • Hamid Reza Safavi
Original Paper


This study is conducted to investigate the regional effects of climate change on Zayandeh-Rud River Basin located in the central part of Iran for the both near and far future scenarios. A combination of various general circulation models (GCMs) is used through a weighting approach to generate different climate change patterns including the ideal, medium, and critical patterns. Each of the GCMs has different ability to simulate the baseline climatic parameters in various months and regions of the basin. A new method, namely “modified weighting method based on the actual values” (MWM-AV), also is applied to convert the local effects of climate change to the regional effects. The results showed that the annual temperature of Zayandeh-Rud River Basin would increase by 0.59–1.34 and 1.02–2.53 °C, respectively, in the near and far futures in which maximum increase in seasonal temperature is expected to happen in the summer. Annual precipitation would change by +1.78 to −20.78 % in the near future and −14.35 to −32.82 % in the far future. The maximum decrease in precipitation is observed to be in the winter. The results of temperature and precipitation regionalization showed that the applied method has a good precision in estimating temperature and precipitation in different regions of the desired basin. The eastern part of the basin would have the maximum increase in temperature, while the western part would experience the maximum decrease in precipitation. Overall, the results demonstrated that due to the centralization of the main water uses in the east and the water resources in the west, the Zayandeh-Rud River Basin will face an intensive water shortage under climate change.


Weather Station Precipitation Data Meteorological Parameter Precipitation Change Inverse Distance Weighting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank Isfahan Regional Water Company for financial support and providing data for this study.


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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Mohammad Javad Zareian
    • 1
    Email author
  • Saeid Eslamian
    • 1
  • Hamid Reza Safavi
    • 2
  1. 1.Department of Water Engineering, College of AgricultureIsfahan University of TechnologyIsfahanIran
  2. 2.Department of Civil EngineeringIsfahan University of TechnologyIsfahanIran

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