Performace Evaluation of PERSIAN PDIR-Now and PERSIANN CCS Products for Precipitation leading to the Most Severe Floods in Iran between 2017 and 2019

Precipitation is one of the main components of the hydrological cycle. Rainfall distribution plays a significant role in the Earth’s energy balance and human access to water resources, but rainfall is not always promising for humans. Floods cause a lot of human and financial losses to people and infrastructure all over the world annually. Planning for the damage reduction must be done with some measurements. It is necessary to record the amount of rainfall in high resolution, but measuring precipitation in high spatial and temporal resolution is a costly process. Scientists have developed many models to estimate precipitation values. It is necessary to evaluate the output data of these models and their performance before using them. In this study, the performance of two precipitation estimation products, PERSIANN-CCS and PDIR-Now, was evaluated against precipitation recorded in all synoptic stations of Iran for the devastating flood events from 2017 to 2019. For comparing precipitation estimations with the ground data, 9 indices (PC, BIAS, FAR, POD, HSS, , ME, MAE & RMSE) were used. The results showed high variability of indicators in different events. On average, PERSIANN-CCS and PDIR-Now products have a coefficient of determination of 0.16 and 0.19 and RMSE of 9.38 and 12.78, respectively. The values are not desirable, even though the average of PC is high. Moreover, the results showed that the PERSIANN-CCS and PDIR-Now products do not perform well due to the statistical indexes for all stations, and further researches for better heavy rainfall estimation are needed.