Evaluation of the applicability of four global grid precipitation datasets in Iran and analysis of Iran’s modern precipitation features

Publish Year: 1400
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

IQA04_018

تاریخ نمایه سازی: 27 آبان 1400

Abstract:

Abstract: The arid central Asia (ACA) is the largest non-zonal arid zone on earth, also the core area of the"Belt and Road". Iran is located in the southwest of the ACA. The precipitation is mainly winter and spring,which is different from the northern part of the ACA. The study found that the precipitation in Iran hasgradually decreased in the past ۸۰ years, mainly due to the decrease in winter precipitation. However, thereliability of the data used in previous studies has yet to be tested and is not suitable for long-term climatechange research in Iran. Therefore, this paper uses statistical indicators (RMSE, ME, R) to analyze andevaluate the applicability of four global grid precipitation datasets suitable for long-term climate changeresearch in Iran from both time and space. And discuss the influence of altitude on the accuracy of differentdatasets. The results found that the GPCC has the smallest deviation from the station data, also has thehighest correlation. The trend is also closest to the station data. The other three datasets have their ownadvantages and disadvantages. Therefore, the GPCC datasets is most suitable for modern climate changeresearch in Iran. All datasets can better reflect the precipitation value when the precipitation is small, andwhen the precipitation is large, the precipitation value is underestimated. The CRU dataset will seriouslyunderestimate the precipitation value of precipitation exceeding ۶۰۰ mm. Altitude and slope have a certaininfluence on the average deviation, root mean square error and correlation coefficient. The aspect has littleeffect on the accuracy of the datasets. When the altitude exceeds ۱۵۰۰ m, the deviation of the precipitationdataset will increase and the correlation coefficient will decrease. When the slope is greater than ۴°, thedeviation will increase and the correlation coefficient will decrease significantly. The four datasets areaffected by altitude and slope to different degrees. Using the GPCC dataset to analyze the temporal andspatial characteristics of precipitation in Iran from ۱۹۶۰ to ۲۰۱۶, it is found that the annual, winter andsummer precipitation in Iran has the consistent characteristics. The regional average precipitation resultsshow that the precipitation in Iran has gradually decreased in the past ۶۰ years, especially since ۲۰۰۰. Theresults of correlation between the regional average precipitation in Iran and mid-latitude Asia show thatthe annual precipitation and summer precipitation in Iran on the interdecadal scale change inversely withthe precipitation in Xinjiang and the arid regions of Central Asia, which are consistent with theprecipitation in the Mongolian Plateau and North China. The precipitation in mid-latitude Asia presents a"- + -" spatial pattern. This study clarified the modern precipitation pattern in mid-latitude Asia andexpanded the theory of "westerlies-dominated climatic regime" (WDCR).

Keywords:

Iran , precipitation dataset evaluation , interdecadal precipitation changes , westerliesdominated climatic regime (WDCR)

Authors

S Ma

Lanzhou University, Lanzhou, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

C Li

Lanzhou University, Lanzhou, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

T Xie

Lanzhou University, Lanzhou, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

J Chen

Lanzhou University, Lanzhou, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

F Chen

Lanzhou University, Lanzhou, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

W Huang

Lanzhou University, Lanzhou, China; Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China