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Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions

Numerous satellite‐based precipitation datasets have been successively made available. Their precipitation estimates rely on clouds properties derived from microwave and thermal sensors in a so‐named ‘top‐down’ approach. Recently, a ‘bottom‐up’ approach to infer precipitation from soil moisture (SM) estimates has resulted in the release of two new precipitation datasets (P‐datasets). One uses satellite‐based SM estimates from the European Spatial Agency (ESA) Climate Change Initiative (CCI) (SM2RAIN‐CCI) while the other uses satellite‐based SM from European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Advanced SCATterometer (ASCAT) (SM2RAIN‐ASCAT). This study assesses SM2RAIN‐ASCAT and ‐CCI reliability over two arid regions: Bolivian and Peruvian Altiplano and Pakistan (South Asia) using (a) direct comparisons with rain gauges and (b) testing the sensitivity of streamflow modelling to the P‐datasets. Selecting two different regions and different indicators helps to assess whether the P‐dataset reliability varies depending on the assessment method and location. For comparison purposes, the most reliable P‐datasets from the literature are also considered (IMERG‐E v.6, IMERG‐L v.6, IMERG‐F v.6, CHIRPS v.2 and MSWEP v.2.2). Compared to rain gauge observations and based on the modified Kling–Gupta Efficiency (KGE) values, the SM2RAIN‐ASCAT and ‐CCI are more accurate in the Altiplano than in Pakistan. This difference is explained by a more favourable physical context for satellite‐based SM estimates in the Altiplano. Over the Altiplano and despite an overall positive bias, SM2RAIN‐ASCAT describes rain gauges temporal dynamics as well as IMERG‐F v.6, CHIRPS v.2 and MSWEP v.2.2 and provides streamflow simulations very close to those obtained when using IMERG‐F v.6, CHIRPS v.2 and MSWEP v.2.2 as forcing data.