Volume 27, Issue 1 (Spring 2023)
jwss 2023, 27(1): 175-188 |
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siasar H, Salari A. Evaluation of Precipitation Data using CHIRPS and PERSIANN Models (Case Study: Bandar Abbas). jwss 2023; 27 (1) :175-188
URL: http://jstnar.iut.ac.ir/article-1-4266-en.html
URL: http://jstnar.iut.ac.ir/article-1-4266-en.html
Payame Noor University (PNU) , hadisiasar@pnu.ac.ir
Abstract: (886 Views)
Access to large precipitation data with appropriate accuracy can play an effective role in irrigation planning and water resources management. Satellite images generate high, wide, cheap, and up-to-date data is a good way to estimate precipitation. In this research, the Google Earth engine system and precipitation products from satellite images of PERSIANN and CHIRPS models in daily, monthly, and annual time intervals were used to evaluate and validate the amount of precipitation in Bandar Abbas station during the statistical period of 1983-2020. The results showed that the precipitation estimation by PERSIANN and CHIRPS satellites on a monthly and annual scale is more accurate than the daily scale. The highest correlation coefficient and the least RMSE belonged to the PERSIANN algorithm on monthly and annual scales. The value of the correlation coefficient in the PERSIANN algorithm on daily, monthly, and annual scales is equal to 0.32, 0.83, and 0.94, respectively. The correlation coefficient in the CHIRPS algorithm in daily, monthly, and annual scales is equal to 0.24, 0.71, and 0.90, respectively. The coefficient of determination (R2) of PERSIANN and Chrips algorithms on a monthly scale were 0.89 and 0.70, respectively, and for an annual scale were 0.88 and 0.80, respectively. The general conclusion of this study indicated that the accuracy of the two algorithms in determining the spatial pattern of rainfall on a monthly and annual scale is appropriate, and the PERSIANN algorithm had a higher accuracy on a monthly time scale.
Type of Study: Research |
Subject:
Ggeneral
Received: 2022/05/1 | Accepted: 2022/10/31 | Published: 2023/05/31
Received: 2022/05/1 | Accepted: 2022/10/31 | Published: 2023/05/31
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