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

Frédéric Satgé, Yawar Hussain, Jorge Molina-Carpio, Ramiro Pillco, Coralie Laugner, Gulraiz Akhter, Marie Paule Bonnet

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

9 Citas (Scopus)


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.

Idioma originalInglés
Páginas (desde-hasta)E517-E536
PublicaciónInternational Journal of Climatology
EstadoPublicada - ene. 2021

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© 2020 Royal Meteorological Society


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