This paper proposes a protocol to assess the space- time consistency of 12 satellite-based precipitation products (SPPs) according to various indicators, including (i) direct comparison of SPPs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPPs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPPs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000-2004, 2004- 2008, 2008-2012 and 2000-2012) to account for the space- time variability of the SPPs and to a large dataset composed of 12 SPPs (CMORPH-RAW v.1, CMORPH-CRT v.1, CMORPH-BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, MSWEP v.2.1, PERSIANN, PERSIANN-CDR, TMPA- RT v.7, TMPA-Adj v.7 and SM2Rain-CCI v.2), an unprecedented comparison. The aim of using different space scales and timescales and indicators was to evaluate whether the efficiency of SPPs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPPs. Compared to precipitation gauge observations, some SPPs (CMORPH-RAW v.1, CMORPH- CRT v.1, GSMaP v.6, PERSIANN, and TMPA-RT v.7) are unable to estimate regional precipitation, whereas the others (CHIRP v.2, CHIRPS v.2, CMORPH-BLD v.1, MSWEP v.2.1, PERSIANN-CDR, and TMPA-Adj v.7) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In 9 out of 10 of the cases studied, streamflow was more realistically simulated when SPPs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks, whereas the SPP's ability to reproduce the duration of MODIS-based snow cover resulted in poorer simulations than simulation using available precipitation gauges. Interestingly, the potential of the SPPs varied significantly when they were used to reproduce gauge precipitation estimates, streamflow observations or snow cover duration and depending on the time window considered. SPPs thus produce space-time errors that cannot be assessed when a single indicator and/or time window is used, underlining the importance of carefully considering their space-time consistency before using them for hydroclimatic studies. Among all the SPPs assessed, MSWEP v.2.1 showed the highest space-time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration.
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Acknowledgements. This work is part of a postdoctoral fellowship funded by the CNES (Centre National d’Etudes Spatiales, France). The authors are grateful to SPP and MODIS dataset providers and to the SENAMHI from Bolivia and Peru for providing in situ precipitation, temperature and streamflow observations. The authors are sincerely grateful to the two anonymous reviewers for the time and effort they spent in reading the manuscript and making numerous suggestions which contributed to the study’s enhancement.
© 2019 Author(s).