In situ rainfall data observed by gauges is the most important data in water resources management. However, these data have some limitations both spatially and temporally. With the advancements in satellite rainfall products, it is now possible to evaluate whether these products can capture the climatology of known rainfall characteristics. In this study, five satellite rainfall estimates (SREs) were evaluated against gauge data based on different rainfall regimes over Iran. The evaluated SREs are Climate Prediction Center Morphing Technique, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM), PERSIANN Climate Data Record (PERSIANN-CDR) and the most recently available Multi-Source Weighted-Ensemble Precipitation (MSWEP) data. The performance of these five SREs is evaluated with respect to gauge data (total: 958 stations) in eight different climatic zones at daily, monthly, and wet/dry spells during a ten-year period (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). Performance of SREs was evaluated using metrics of comparison based on correlation coefficient (CC), root mean square error, and relative error. The study shows that MSWEP has the highest CC (0.72) followed by TRMM (0.46) and PERSIANN-CDR (0.43) at daily time scale. The performance of SREs varies with respect to climatic regimes, for example, the best correlation was observed in the south, the shore of Persian Gulf with 'very hot and humid' climate with CC values of 0.72, 0.70, and 0.82 for MSWEP, TRMM and PERSIANN-CDR, respectively. Further, the performance of SREs was evaluated using the categorical statistics to capture the rainfall pattern based on different groups (e.g. light, moderate and heavy rainfall events). Results show that MSWEP, PERSIANN-CDR, and TRMM performed well to distinguish rain from no-rain condition, whereas for higher rainfall rates, PERSIANN-CDR outperforms the other SREs.

This study provides a comprehensive evaluation of a great variety of state-of-theart precipitation datasets against gauge observations over the Karun basin in southwestern Iran. In particular, we consider (a) gauge-interpolated datasets (GPCCv8, CRU TS4.01, PREC/L, and CPC-Unified), (b) multi-source products (PERSIANN-CDR, CHIRPS2.0, MSWEP V2, HydroGFD2.0, and SM2RAIN-CCI), and (c) reanalyses (ERA-Interim, ERA5, CFSR, and JRA-55). The spatiotemporal performance of each product is evaluated against monthly precipitation observations from 155 gauges distributed across the basin during the period 2000-2015. This way, we find that overall the GPCCv8 dataset agrees best with the measurements. Most datasets show significant underestimations, which are largest for the interpolated datasets. These underestimations are usually smallest at low altitudes and increase towards more mountainous areas, although there is large spread across the products. Interestingly, no overall performance difference can be found between precipitation datasets for which gauge observations from Karun basin were used, versus products that were derived without these measurements, except in the case of GPCCv8. In general, our findings highlight remarkable differences between state-of-the-art precipitation products over regions with comparatively sparse gauge density, such as Iran. Revealing the best-performing datasets and their remaining weaknesses, we provide guidance for monitoring and modelling applications which rely on high-quality precipitation input.

No abstract

No abstract

No abstract