Spatial and Temporal Analysis of Climate Change in the Future and Comparison of SDSM, LARS-WG and Artificial Neural Network Downscaling Methods (Case Study: Khuzestan Province)

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RAHIMI RAZIEH; RAHIMI MAHDI

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Journal: IRANIAN JOURNAL OF ECOHYDROLOGY Year:2019 | Volume:5 | Issue:4 Page(s): 1161-1174

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Title

Spatial and Temporal Analysis of Climate Change in the Future and Comparison of SDSM, LARS-WG and Artificial Neural Network Downscaling Methods (Case Study: Khuzestan Province)

Author(s)

RAHIMI RAZIEH | RAHIMI MAHDI | Issue Writer Certificate

Keywords

Climate ChangeQ2

SPEIQ2

LARS-WGQ1

SDSMQ2

Artificial Neural NetworkQ2

Abstract

Drought is a complex danger. Therefore, the study and prediction of Climate Change could have a significant role in the management and planning. In this study, at first rainfall and temperature data were obtained daily from the period of 1985-2010 from eight selected stations in the region. The indices during the observation period with SPEI and SPI indexes was calculated using statistical methods and their zoning map was drawn. In this study, GCM data and HADCM3 model under two scenarios A2 and B1 were used for prediction of drought. Then, the GCM large-scale data were downscaled using three methods including SDSM, LARS-WG and Artificial Neural Networks. The results of statistical measures of performance evaluation showed that the ability the ANN model to simulate rainfall is relatively more acceptable than other models. The results of the Man-Kendall statistics for drought indexes show that the predicted values by the LARS-WG model for the SPI and SPEI indexes are consistently more negatively correlated. This can be deduced by observing the zoning map of drought indicators in Khuzestan province that in the coming periods, the mean values of the two indices have always dropped but did not change significantly in terms of spatial.

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APA: Copy

RAHIMI, RAZIEH, & RAHIMI, MAHDI. (2019). Spatial and Temporal Analysis of Climate Change in the Future and Comparison of SDSM, LARS-WG and Artificial Neural Network Downscaling Methods (Case Study: Khuzestan Province). IRANIAN JOURNAL OF ECOHYDROLOGY, 5(4 ), 1161-1174. SID. https://sid.ir/paper/254124/en

Vancouver: Copy

RAHIMI RAZIEH, RAHIMI MAHDI. Spatial and Temporal Analysis of Climate Change in the Future and Comparison of SDSM, LARS-WG and Artificial Neural Network Downscaling Methods (Case Study: Khuzestan Province). IRANIAN JOURNAL OF ECOHYDROLOGY[Internet]. 2019;5(4 ):1161-1174. Available from: https://sid.ir/paper/254124/en

IEEE: Copy

RAZIEH RAHIMI, and MAHDI RAHIMI, “Spatial and Temporal Analysis of Climate Change in the Future and Comparison of SDSM, LARS-WG and Artificial Neural Network Downscaling Methods (Case Study: Khuzestan Province),” IRANIAN JOURNAL OF ECOHYDROLOGY, vol. 5, no. 4 , pp. 1161–1174, 2019, [Online]. Available: https://sid.ir/paper/254124/en

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