Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran
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ORIGINAL PAPER
Long-term spatiotemporal evaluation of CHIRPS satellite precipitation product over different climatic regions of Iran Ali Ghozat 1 & Ahmad Sharafati 1
&
Seyed Abbas Hosseini 1
Received: 16 June 2020 / Accepted: 7 October 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract Satellite precipitation products are important data sources in different spatial resolutions, time scales, and spatio-temporal coverage. In this study, the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product with a high spatial resolution (0.05°) is evaluated in the period of 1987 to 2017 over different climate regions of Iran. The accuracy of the satellite product is compared with the 68 ground-based meteorological stations over different time scales (i.e., daily, monthly, and annual) and precipitation classes. Results show that the performance of CHIRPS depends on the time scale, precipitation depth, and climate type. The best performance of the product (CC = 0.80, FRMSE = 0.57, NSE = 0.63) across the country is observed in the annual time scale, while the monthly product offers the best performance in the regional scale. The product provides inadequate performance (CC = 0.34, FRMSE = 5.72, NSE = − 0.2) in daily time scale across the country and most of the climatic regions. The product is found to be most accurate in the south and southwest of the country, while the lowest performance is observed over the Caspian coast. The CHIRPS satellite provides the best performance in detection of no/tiny precipitation (POD > 0.90) and the worst performance in light and low, moderate precipitation (POD < 0.10). It is expected that the findings of the current study can be used to manage the water resources and mitigate the disaster at the national level. Keywords CHIRPS . Climatic regions . Precipitation . Satellite product
1 Introduction Lack of rain-gauge stations is a crucial concern for hydrological analysis, especially in arid and semi-arid regions (Nabaei et al. 2019; Sharafati and Pezeshki 2020; Sharafati et al. 2020). The radar- and satellite-based precipitation data are the possible alternatives for providing the required data. However, the radar data would be distorted due to the surrounding environment impact (Li et al. 2013), while the high-resolution satellite-based precipitation products are more practical in meteorological and hydrological modeling. To provide the precipitation data required in hydrological modeling, a number of satellite products with high spatiotemporal resolutions have been developed, including Tropical Rainfall Measuring Mission (TRMM; Kummerow et al. 1998), Precipitation Estimation from
* Ahmad Sharafati [email protected] 1
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Remotely Sensed Information using Artificial Neural Networks (PERSIANN; Hsu et al. 1997; Sorooshian et al. 2011), Climate Prediction Center Morphing Technique (CMORPH; Joyce et al. 2004), the TRMM Multi-s
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