Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation
The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface‐only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This st...
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| Veröffentlicht in: | Water resources research 2018-03, Vol.54 (3), p.1476-1492 |
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| creator | Akbar, Ruzbeh Short Gianotti, Daniel McColl, Kaighin A. Haghighi, Erfan Salvucci, Guido D. Entekhabi, Dara |
| description | The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface‐only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface‐only soil moisture observations. To proceed, first an observation‐based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry‐downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root‐mean‐squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation‐driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge‐corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east‐west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.
Key Points
Surface soil moisture dynamic often reflects deeper soil water content characteristics
By performing water balance closure using only soil moisture and precipitation a length scale can be obtained
Estimates of the length scale show an east‐west gradient across US with larger values in wetter regions |
| doi_str_mv | 10.1002/2017WR021508 |
| format | Article |
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Key Points
Surface soil moisture dynamic often reflects deeper soil water content characteristics
By performing water balance closure using only soil moisture and precipitation a length scale can be obtained
Estimates of the length scale show an east‐west gradient across US with larger values in wetter regions</description><identifier>ISSN: 0043-1397</identifier><identifier>EISSN: 1944-7973</identifier><identifier>DOI: 10.1002/2017WR021508</identifier><language>eng</language><publisher>Washington: John Wiley & Sons, Inc</publisher><subject>Active control ; Climate prediction ; Dynamics ; Fluxes ; Global precipitation ; Hydrology ; Length ; Mean precipitation ; Moisture content ; Moisture loss ; Precipitation ; Remote sensing ; SMAP ; Soil ; Soil analysis ; Soil dynamics ; Soil erosion ; Soil moisture ; Soil properties ; Soil surfaces ; Soil water ; Soil water storage ; Variance analysis ; Water balance ; Water content</subject><ispartof>Water resources research, 2018-03, Vol.54 (3), p.1476-1492</ispartof><rights>2018. American Geophysical Union. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a3685-6bde9097abba4d0f50f835f3b50d7793b23f06c90865ef9aefc523f57fea82b03</citedby><cites>FETCH-LOGICAL-a3685-6bde9097abba4d0f50f835f3b50d7793b23f06c90865ef9aefc523f57fea82b03</cites><orcidid>0000-0002-8362-4761 ; 0000-0002-8358-5619 ; 0000-0003-4891-2474 ; 0000-0001-9201-6760 ; 0000-0003-3609-5972 ; 0000-0002-9963-0488</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%2F2017WR021508$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2017WR021508$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,11514,27924,27925,45574,45575,46468,46892</link.rule.ids></links><search><creatorcontrib>Akbar, Ruzbeh</creatorcontrib><creatorcontrib>Short Gianotti, Daniel</creatorcontrib><creatorcontrib>McColl, Kaighin A.</creatorcontrib><creatorcontrib>Haghighi, Erfan</creatorcontrib><creatorcontrib>Salvucci, Guido D.</creatorcontrib><creatorcontrib>Entekhabi, Dara</creatorcontrib><title>Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation</title><title>Water resources research</title><description>The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface‐only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface‐only soil moisture observations. To proceed, first an observation‐based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry‐downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root‐mean‐squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation‐driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge‐corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east‐west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.
Key Points
Surface soil moisture dynamic often reflects deeper soil water content characteristics
By performing water balance closure using only soil moisture and precipitation a length scale can be obtained
Estimates of the length scale show an east‐west gradient across US with larger values in wetter regions</description><subject>Active control</subject><subject>Climate prediction</subject><subject>Dynamics</subject><subject>Fluxes</subject><subject>Global precipitation</subject><subject>Hydrology</subject><subject>Length</subject><subject>Mean precipitation</subject><subject>Moisture content</subject><subject>Moisture loss</subject><subject>Precipitation</subject><subject>Remote sensing</subject><subject>SMAP</subject><subject>Soil</subject><subject>Soil analysis</subject><subject>Soil dynamics</subject><subject>Soil erosion</subject><subject>Soil moisture</subject><subject>Soil properties</subject><subject>Soil surfaces</subject><subject>Soil water</subject><subject>Soil water storage</subject><subject>Variance analysis</subject><subject>Water balance</subject><subject>Water content</subject><issn>0043-1397</issn><issn>1944-7973</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp90MFKAzEQBuAgCtbqzQcIeHV1kmw2m6MUtUJFaZUel-zupKZsNzVJkb69K_XgydMww8cM8xNyyeCGAfBbDkwt58CZhPKIjJjO80xpJY7JCCAXGRNanZKzGNcALJeFGpHNdN8G3_mVa0xHF8kHs0I6w36VPuhimGGkc9wGjNgnbGm9H9qNT0gX2EfXr-h9TG5j0uC8pQvvOvrsXUy7gNT0LX0N2LitSyY535-TE2u6iBe_dUzeH-7fJtNs9vL4NLmbZUYUpcyKukUNWpm6NnkLVoIthbSiltAqpUXNhYWi0VAWEq02aBs5jKSyaEpegxiTq8PebfCfO4ypWvtd6IeTFQdeCMk0iEFdH1QTfIwBbbUNwythXzGofgKt_gY6cHHgX67D_b-2Ws4ncy4Yl-IbWgl4Ww</recordid><startdate>201803</startdate><enddate>201803</enddate><creator>Akbar, Ruzbeh</creator><creator>Short Gianotti, Daniel</creator><creator>McColl, Kaighin A.</creator><creator>Haghighi, Erfan</creator><creator>Salvucci, Guido D.</creator><creator>Entekhabi, Dara</creator><general>John Wiley & Sons, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7QL</scope><scope>7T7</scope><scope>7TG</scope><scope>7U9</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H94</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>M7N</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-8362-4761</orcidid><orcidid>https://orcid.org/0000-0002-8358-5619</orcidid><orcidid>https://orcid.org/0000-0003-4891-2474</orcidid><orcidid>https://orcid.org/0000-0001-9201-6760</orcidid><orcidid>https://orcid.org/0000-0003-3609-5972</orcidid><orcidid>https://orcid.org/0000-0002-9963-0488</orcidid></search><sort><creationdate>201803</creationdate><title>Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation</title><author>Akbar, Ruzbeh ; Short Gianotti, Daniel ; McColl, Kaighin A. ; Haghighi, Erfan ; Salvucci, Guido D. ; Entekhabi, Dara</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a3685-6bde9097abba4d0f50f835f3b50d7793b23f06c90865ef9aefc523f57fea82b03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Active control</topic><topic>Climate prediction</topic><topic>Dynamics</topic><topic>Fluxes</topic><topic>Global precipitation</topic><topic>Hydrology</topic><topic>Length</topic><topic>Mean precipitation</topic><topic>Moisture content</topic><topic>Moisture loss</topic><topic>Precipitation</topic><topic>Remote sensing</topic><topic>SMAP</topic><topic>Soil</topic><topic>Soil analysis</topic><topic>Soil dynamics</topic><topic>Soil erosion</topic><topic>Soil moisture</topic><topic>Soil properties</topic><topic>Soil surfaces</topic><topic>Soil water</topic><topic>Soil water storage</topic><topic>Variance analysis</topic><topic>Water balance</topic><topic>Water content</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Akbar, Ruzbeh</creatorcontrib><creatorcontrib>Short Gianotti, Daniel</creatorcontrib><creatorcontrib>McColl, Kaighin A.</creatorcontrib><creatorcontrib>Haghighi, Erfan</creatorcontrib><creatorcontrib>Salvucci, Guido D.</creatorcontrib><creatorcontrib>Entekhabi, Dara</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Water resources research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Akbar, Ruzbeh</au><au>Short Gianotti, Daniel</au><au>McColl, Kaighin A.</au><au>Haghighi, Erfan</au><au>Salvucci, Guido D.</au><au>Entekhabi, Dara</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation</atitle><jtitle>Water resources research</jtitle><date>2018-03</date><risdate>2018</risdate><volume>54</volume><issue>3</issue><spage>1476</spage><epage>1492</epage><pages>1476-1492</pages><issn>0043-1397</issn><eissn>1944-7973</eissn><abstract>The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface‐only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface‐only soil moisture observations. To proceed, first an observation‐based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry‐downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root‐mean‐squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation‐driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge‐corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east‐west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.
Key Points
Surface soil moisture dynamic often reflects deeper soil water content characteristics
By performing water balance closure using only soil moisture and precipitation a length scale can be obtained
Estimates of the length scale show an east‐west gradient across US with larger values in wetter regions</abstract><cop>Washington</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/2017WR021508</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-8362-4761</orcidid><orcidid>https://orcid.org/0000-0002-8358-5619</orcidid><orcidid>https://orcid.org/0000-0003-4891-2474</orcidid><orcidid>https://orcid.org/0000-0001-9201-6760</orcidid><orcidid>https://orcid.org/0000-0003-3609-5972</orcidid><orcidid>https://orcid.org/0000-0002-9963-0488</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Active control Climate prediction Dynamics Fluxes Global precipitation Hydrology Length Mean precipitation Moisture content Moisture loss Precipitation Remote sensing SMAP Soil Soil analysis Soil dynamics Soil erosion Soil moisture Soil properties Soil surfaces Soil water Soil water storage Variance analysis Water balance Water content |
| title | Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation |
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