Estimation of precipitation using PERSIANN-CDR and CMC-based satellite productions (Case study: upstream of the Zayandehroud dam)

Document Type : Research Article


1 Department of Civil Engineering, Faculty of civil engineering and transportation, University of Isfahan

2 Surveying and Geomatics Engineering Department , Faculty of civil engineering and transportation, University of

3 Department of Civil Engineering, Faculty of civil engineering and transportation, University of Isfahan, Isfahan


Precipitation and its measurement by rain and snow gauges are mostly important data for water resources management. However, measuring these data has limitations due to both temporal and spatial resolutions. By developing the SRE (Satellite-based Rainfall Estimates) datasets, assessment, evaluation, and comparison of SREs datasets against in-situ observations in different parts of Glob, also Iran is developed in various studies. In this study, by selecting the upstream area of the Zayandehroud Dam as the case study, two datasets, PERSIANN-CDR (PERSIANN-Climate Data Record) and Canadian Meteorological Center (CMC) Daily Snow Depth Analysis Data, are selected to evaluate their performance for the estimation of two variables of rainfall and snow water equivalent (SWE) in the period 1999 to 2019, respectively. The performance of these two SREs using in-situ gauges (a total of 16 rain gauges and 14 SWE gauges) in the upstream area of the Zayandehroud Dam on a monthly time scale is evaluated. For this purpose, different evaluation indices of the correlation coefficient, mean square root of error, and relative error are used. In addition, the performance of SREs in precipitation detection is examined using the classification statistics. Based on the obtained results, the correlation coefficient of 0.48 and relative error of 54.55% indicates that the estimated rainfall amounts by PERSIANN-CDR are in the upper-estimation condition over the study area. Also, the evaluation CMC-based SWE estimations show that the best Amounts SWE is estimated with an error of 4.22% and the highest correlation (CC) is 0.34.


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Volume 9, Issue 1
April 2022
Pages 157-172
  • Receive Date: 29 August 2021
  • Revise Date: 03 October 2021
  • Accept Date: 26 February 2022
  • First Publish Date: 21 March 2022
  • Publish Date: 21 March 2022