Evaluation of the performance of SDSM and LARSWG statistical downscaling models for quantitative screening in predicting climate scenarios (Case study: Dust center south and southeast of Ahvaz)

Document Type : پژوهشی

Authors

1 Department of Soil Science, Agricultural Sciences and Natural Resources University of Khuzestan, Iran.

2 Department of Soil Science, Agricultural Sciences and Natural Resources University of Khuzestan, Iran

3 Assistant Professor, Department of Nature Science, Agricultural Sciences and Natural Resources University of Khuzestan, Iran

Abstract

Abstract:
The climate change is a complex atmospheric-oceanic phenomenon on the global scale. Climate change and global warming is one of the most important factors affecting the degradation of water and soil resources in arid and semi-arid regions; Which increases the occurrence of dust phenomenon. The aim of this study was to evaluate the performance of two statistical downscaling models of SDSM and LARSWG for quantitative screening in predicting climate scenarios and also predicting climate change in the dust center of south and southeast of Ahvaz. In line with this goal, one of the three-dimensional paired oceanic models - AOGCM atmospheric general circulation called HadCM3, The results showed that the simulated data of both models, compared to the observed data, were significant compared to the long-term mean of the base period and had a high correlation with a high coefficient of determination (R2) for all parameters from 0.87 to 0.98. Finally, by confirming the existence of climate change in Khuzestan province , SDSM model due to direct use of HadCM3 models and large scale NCEP data and the type of simulation process and also Combined structure in data mining scale with RMSE, MAE and ME 0.97, 0.18 and 0.021, respectively; It has higher accuracy than LARSWG model in simulating climatic data in the dust center of southern Ahvaz. The SDSM model was also more successful in simulating daily temperature data and wind speed, and the LARSWG model had a better prediction of the daily precipitation parameter.

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References

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Hydrogeomorphology
Volume 8, Issue 28 - Serial Number 28
December 2021
Pages 107-126

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History

  • Receive Date: 25 April 2021
  • Revise Date: 20 August 2021
  • Accept Date: 20 September 2021

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