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)...

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Veröffentlicht in:International journal of climatology 2021-01, Vol.41 (S1), p.E517-E536
Hauptverfasser: Satgé, Frédéric, Hussain, Yawar, Molina‐Carpio, Jorge, Pillco, Ramiro, Laugner, Coralie, Akhter, Gulraiz, Bonnet, Marie‐Paule
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container_issue S1
container_start_page E517
container_title International journal of climatology
container_volume 41
creator Satgé, Frédéric
Hussain, Yawar
Molina‐Carpio, Jorge
Pillco, Ramiro
Laugner, Coralie
Akhter, Gulraiz
Bonnet, Marie‐Paule
description 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. This article outlines the reliability of recently released satellite precipitation datasets (P‐datasets) over the arid regions of the Altiplano and Pakistan. The considered P‐datasets (SM2Rain‐ASCAT and ‐CCI) estimate precipitation from satellite soil moisture observations according to a new method called ‘bottom‐up’ approach that differs from previous P‐datasets, which estimate precipitation from satellite‐based cloud properties according to a ‘top‐down’ approach. The bottom‐up approach offers great perspective to improve the precipitation monitoring over remote arid regions.
doi_str_mv 10.1002/joc.6704
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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. 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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. 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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. This article outlines the reliability of recently released satellite precipitation datasets (P‐datasets) over the arid regions of the Altiplano and Pakistan. The considered P‐datasets (SM2Rain‐ASCAT and ‐CCI) estimate precipitation from satellite soil moisture observations according to a new method called ‘bottom‐up’ approach that differs from previous P‐datasets, which estimate precipitation from satellite‐based cloud properties according to a ‘top‐down’ approach. The bottom‐up approach offers great perspective to improve the precipitation monitoring over remote arid regions.</abstract><cop>Chichester, UK</cop><pub>John Wiley &amp; Sons, Ltd</pub><doi>10.1002/joc.6704</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0003-3662-6876</orcidid><orcidid>https://orcid.org/0000-0002-4155-6764</orcidid><orcidid>https://orcid.org/0000-0002-3950-4041</orcidid><oa>free_for_read</oa></addata></record>
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subjects arid region
Arid regions
Arid zones
assessment
Atmospheric precipitations
Climate change
Clouds
Datasets
Environmental Sciences
Estimates
Exploitation
Gauges
Hydrologic data
Hydrologic models
Hydrologic observations
hydrological modelling
Hydrology
Meteorological satellites
Modelling
Precipitation
Precipitation estimation
Rain
Rain gauges
Rainfall
Reliability
Reliability analysis
satellite precipitation
Satellites
Scatterometers
Sensitivity analysis
SM2RAIN
Soil
Soil moisture
Stream discharge
Stream flow
title Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions
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