Evaluation of monthly gridded precipitation data products ERA-Interim, PERSIANN-CDR, PERSIANN-CCS and CRU over Khuzestan province

Journal of Water Science & Engineering

مقاله 6، دوره 7، شماره 18، اسفند 2017، صفحه 69-82   

نوع مقاله: Original Article

نویسندگان

reza koochaki^{* 1}؛ Ali shahbazi²؛ Kheyrolah khademi³

¹Water Engenieering,Civil Engineering,Jundishapur University of Technology-Dezful

²Ph.D. of Water resources, Khuzestan Water and Power Authority,Ahvaz,Iran.

³Assistant professor of Department of Civil engineering, Faculty of Water Engineering, jundishapur University of Technology-Dezful,Dezful,ahwaz.

چکیده

Deficiency and inappropriate distribution of reengage station is one of challenges faced by researchers in hydrology and climate science. In this research, evaluate the applicability of four gridded precipitation data products ERA-Interim, PERSIANN-CDR, PERSIANN-CCS and CRU as a supplement or substitute for ground data in a monthly time scales. This assessment was done by comparison with observational data and statistical methods. To evaluate the ability of precipitation estimation of these four product used Pearson correlation coefficient (CORR), bias (BIAS), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Normalized Mean Square Error (NRMSE) and model efficiency coefficient (EF). This statistical index extracted as zoning and map for the province. The result showed that ERA-Interim, PERSIANN-CDR and CRU products are in good agreement with observational data, and the monthly precipitation trend is estimated by little error, while PERSIANN-CCS was unsuccessful in estimating precipitation in the province and has poor reliability. In some parts of the province, the PERSIANN-CDR had a high reliability, but in total, the lowest error range and the highest reliability were obtained from the four satellite data sources for ERA-Interim precipitation and in case of shortage it can be used as an auxiliary or alternative data source.

کلیدواژه‌ها

Gridded Precipitation data؛ Estimation precipitation؛ statistical methods؛ Rain-Gage Station؛ Khuzestan province

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