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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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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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.</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.6704</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>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</subject><ispartof>International journal of climatology, 2021-01, Vol.41 (S1), p.E517-E536</ispartof><rights>2020 Royal Meteorological Society</rights><rights>2021 Royal Meteorological Society</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2764-25f39e57bfadb7839f751f0e0dc217d9538c08585c61a14154355518892531dd3</citedby><cites>FETCH-LOGICAL-c2764-25f39e57bfadb7839f751f0e0dc217d9538c08585c61a14154355518892531dd3</cites><orcidid>0000-0003-3662-6876 ; 0000-0002-4155-6764 ; 0000-0002-3950-4041</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjoc.6704$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjoc.6704$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://hal.science/hal-04375795$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Satgé, Frédéric</creatorcontrib><creatorcontrib>Hussain, Yawar</creatorcontrib><creatorcontrib>Molina‐Carpio, Jorge</creatorcontrib><creatorcontrib>Pillco, Ramiro</creatorcontrib><creatorcontrib>Laugner, Coralie</creatorcontrib><creatorcontrib>Akhter, Gulraiz</creatorcontrib><creatorcontrib>Bonnet, Marie‐Paule</creatorcontrib><title>Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions</title><title>International journal of climatology</title><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.</description><subject>arid region</subject><subject>Arid regions</subject><subject>Arid zones</subject><subject>assessment</subject><subject>Atmospheric precipitations</subject><subject>Climate change</subject><subject>Clouds</subject><subject>Datasets</subject><subject>Environmental Sciences</subject><subject>Estimates</subject><subject>Exploitation</subject><subject>Gauges</subject><subject>Hydrologic data</subject><subject>Hydrologic models</subject><subject>Hydrologic observations</subject><subject>hydrological modelling</subject><subject>Hydrology</subject><subject>Meteorological satellites</subject><subject>Modelling</subject><subject>Precipitation</subject><subject>Precipitation estimation</subject><subject>Rain</subject><subject>Rain gauges</subject><subject>Rainfall</subject><subject>Reliability</subject><subject>Reliability analysis</subject><subject>satellite precipitation</subject><subject>Satellites</subject><subject>Scatterometers</subject><subject>Sensitivity analysis</subject><subject>SM2RAIN</subject><subject>Soil</subject><subject>Soil moisture</subject><subject>Stream discharge</subject><subject>Stream flow</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp10U9LwzAYBvAgCs4p-BECXvTQmbRNkxzH8C_TwdRzyJK0ZmRNTbpJL352WyfePL3w8HtfXngAOMdoghFKr9deTQqK8gMwwojTBCHGDsEIMc4TlmN2DE5iXCOEOMfFCHwtjbNyZZ1tO-hL-PKULqcPz7AJRtnGtrK1voZatjKaNkJbQ-U3jQw29nHrYSW3lYF-FU3Y_dgIZa3he6eDd76ySjq48do4Z-sK-p0JsF_WMJhqwKfgqJQumrPfOQZvtzevs_tkvrh7mE3niUppkScpKTNuCF2VUq8oy3hJCS6RQVqlmGpOMqYQI4yoAkucY5JnhBDMGE9JhrXOxuBqf_ddOtEEu5GhE15acT-diyFDeUYJ5WSHe3uxt03wH1sTW7H221D374k0p4zwgqaDutwrFXyMwZR_ZzESQxP9lhJDEz1N9vTTOtP968TjYvbjvwG7v4m9</recordid><startdate>202101</startdate><enddate>202101</enddate><creator>Satgé, Frédéric</creator><creator>Hussain, Yawar</creator><creator>Molina‐Carpio, Jorge</creator><creator>Pillco, Ramiro</creator><creator>Laugner, Coralie</creator><creator>Akhter, Gulraiz</creator><creator>Bonnet, Marie‐Paule</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><general>Wiley</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>1XC</scope><scope>VOOES</scope><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></search><sort><creationdate>202101</creationdate><title>Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions</title><author>Satgé, Frédéric ; Hussain, Yawar ; Molina‐Carpio, Jorge ; Pillco, Ramiro ; Laugner, Coralie ; Akhter, Gulraiz ; Bonnet, Marie‐Paule</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2764-25f39e57bfadb7839f751f0e0dc217d9538c08585c61a14154355518892531dd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>arid region</topic><topic>Arid regions</topic><topic>Arid zones</topic><topic>assessment</topic><topic>Atmospheric precipitations</topic><topic>Climate change</topic><topic>Clouds</topic><topic>Datasets</topic><topic>Environmental Sciences</topic><topic>Estimates</topic><topic>Exploitation</topic><topic>Gauges</topic><topic>Hydrologic data</topic><topic>Hydrologic models</topic><topic>Hydrologic observations</topic><topic>hydrological modelling</topic><topic>Hydrology</topic><topic>Meteorological satellites</topic><topic>Modelling</topic><topic>Precipitation</topic><topic>Precipitation estimation</topic><topic>Rain</topic><topic>Rain gauges</topic><topic>Rainfall</topic><topic>Reliability</topic><topic>Reliability analysis</topic><topic>satellite precipitation</topic><topic>Satellites</topic><topic>Scatterometers</topic><topic>Sensitivity analysis</topic><topic>SM2RAIN</topic><topic>Soil</topic><topic>Soil moisture</topic><topic>Stream discharge</topic><topic>Stream flow</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Satgé, Frédéric</creatorcontrib><creatorcontrib>Hussain, Yawar</creatorcontrib><creatorcontrib>Molina‐Carpio, Jorge</creatorcontrib><creatorcontrib>Pillco, Ramiro</creatorcontrib><creatorcontrib>Laugner, Coralie</creatorcontrib><creatorcontrib>Akhter, Gulraiz</creatorcontrib><creatorcontrib>Bonnet, Marie‐Paule</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>International journal of climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Satgé, Frédéric</au><au>Hussain, Yawar</au><au>Molina‐Carpio, Jorge</au><au>Pillco, Ramiro</au><au>Laugner, Coralie</au><au>Akhter, Gulraiz</au><au>Bonnet, Marie‐Paule</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions</atitle><jtitle>International journal of climatology</jtitle><date>2021-01</date><risdate>2021</risdate><volume>41</volume><issue>S1</issue><spage>E517</spage><epage>E536</epage><pages>E517-E536</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>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.</abstract><cop>Chichester, UK</cop><pub>John Wiley & 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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