Validation of CHIRPS satellite-based precipitation data against the in situ observations using the Copula method: a case study of Kosar Dam basin, Iran.

The present study compares the daily and monthly precipitation estimates of the CHIRPS satellite data with the in situ measurements at four stations scattered over the Kosar Dam basin in southwestern Iran. The uncertainty of the satellite precipitation estimates was calculated through simulation with the Copula functions. For this purpose, 55% of the stations, daily and monthly rainfall data relative to the 1987–2012 period were used for training (simulation), and the other 45% were used for testing (validation) the performance of the Copula model. First, the daily, monthly, and annual satellite precipitation estimates were statistically compared with precipitation observed at the stations and the whole basin using the Pearson correlation coefficient (CC), root mean square error (RMSE), and Bias statistics. The computed CC between the areal average of observed and satellite precipitation estimation at the basin is 0.49, 0.82, and 0.33 for daily, monthly, and annual time scales, respectively. The difference (biases) between the satellite estimates and in situ measurements was then calculated for daily, monthly, and annual time scales over the training period. The obtained biases were subsequently fitted with the General Extreme Value distribution function coupled with the Gaussian Copula model to generate a series of similar random biases for all precipitation events. Then, the generated random biases were summed with the original satellite estimates to correct the associated biases. The bias-corrected precipitation for the training period was then compared to the original estimates of the satellite at the stations and the whole basin using the P-factor, R-factor, Bias, RMSE, and CC statistics. The statistics show that the random biases generated by the Copula method for the monthly CHIRPS satellite data relative to the 14-year training period have reduced the error rate of the satellite data by 74 to 95 percent when compared to observations. The satellite precipitation estimates of the 11-year test period were also corrected using the generated random biases in the training period. The results show that the bias correction considerably improved the monthly estimates and reduced the error rate of the satellite estimation by about 76 percent. In general, the simulation of the satellite precipitation with the Gaussian Copula model was performed satisfactorily at the monthly time scale, but it was less efficient at the daily time scale. [ABSTRACT FROM AUTHOR]