Significance of Exchanging SSURGO and STATSGO Data When Modeling Hydrology in Diverse Physiographic Terranes

The Water Availability Tool for Environmental Resources (WATER) is a TOPMODEL‐based hydrologic model that depends on spatially accurate soils data to function in diverse terranes. In Kentucky, this includes mountainous regions, karstic plateau, and alluvial plains. Soils data are critical because th...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Soil Science Society of America journal 2013-05, Vol.77 (3), p.877-889
Hauptverfasser: Williamson, Tanja N., Taylor, Charles J., Newson, Jeremy K.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 889
container_issue 3
container_start_page 877
container_title Soil Science Society of America journal
container_volume 77
creator Williamson, Tanja N.
Taylor, Charles J.
Newson, Jeremy K.
description The Water Availability Tool for Environmental Resources (WATER) is a TOPMODEL‐based hydrologic model that depends on spatially accurate soils data to function in diverse terranes. In Kentucky, this includes mountainous regions, karstic plateau, and alluvial plains. Soils data are critical because they quantify the space to store water, as well as how water moves through the soil to the stream during storm events. We compared how the model performs using two different sources of soils data—Soil Survey Geographic Database (SSURGO) and State Soil Geographic Database laboratory data (STATSGO)—for 21 basins ranging in size from 17 to 1564 km2. Model results were consistently better when SSURGO data were used, likely due to the higher field capacity, porosity, and available‐water holding capacity, which cause the model to store more soil‐water in the landscape and improve streamflow estimates for both low‐ and high‐flow conditions. In addition, there were significant differences in the conductivity multiplier and scaling parameter values that describe how water moves vertically and laterally, respectively, as quantified by TOPMODEL. We also evaluated whether partitioning areas that drain to streams via sinkholes in karstic basins as separate hydrologic modeling units (HMUs) improved model performance. There were significant differences between HMUs in properties that control soil‐water storage in the model, although the effect of partitioning these HMUs on streamflow simulation was inconclusive.
doi_str_mv 10.2136/sssaj2012.0069
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1642244030</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3043058391</sourcerecordid><originalsourceid>FETCH-LOGICAL-a4109-d8409478ea60786657573c66bac7db97e5fd79c67e9acd2776e16e6c9ae0b6283</originalsourceid><addsrcrecordid>eNqNkTFv2zAQhYmiAeomXTsT6JJFLkmJR3PIYCRu0iBBgspBR4KmTjINhXTJuK3-feS46NClWe4Oh-8d3uER8pGzqeAlfM45241gXEwZA_2GTHhVyoIB8LdkwkrghdRaviPvc94wxqVmbEL62nfBt97Z4JDGli5-u7UNnQ8dreuHb5d31IaG1sv5sh7nC_tk6fc1BnobG-z31NXQpNjHbqA-0Av_E1NGer8eso9dstu1d3SJKdmA-YQctbbP-OFPPyYPXxbL86vi5u7y6_n8prAVZ7poZhXTlZqhBaZmAFJJVTqAlXWqWWmFsm2UdqBQW9cIpQA5IDhtka1AzMpjcnq4u03xxw7zk3n02WHfjybiLhsOlRBVxUr2ClQorXgpYUQ__YNu4i6F8RHDK66VUqXkIzU9UC7FnBO2Zpv8o02D4czsczJ_czL7nEbB2UHwy_c4_Ic29fxa1PW-jqsX_TOgZ5dp</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1419777351</pqid></control><display><type>article</type><title>Significance of Exchanging SSURGO and STATSGO Data When Modeling Hydrology in Diverse Physiographic Terranes</title><source>Access via Wiley Online Library</source><creator>Williamson, Tanja N. ; Taylor, Charles J. ; Newson, Jeremy K.</creator><creatorcontrib>Williamson, Tanja N. ; Taylor, Charles J. ; Newson, Jeremy K.</creatorcontrib><description>The Water Availability Tool for Environmental Resources (WATER) is a TOPMODEL‐based hydrologic model that depends on spatially accurate soils data to function in diverse terranes. In Kentucky, this includes mountainous regions, karstic plateau, and alluvial plains. Soils data are critical because they quantify the space to store water, as well as how water moves through the soil to the stream during storm events. We compared how the model performs using two different sources of soils data—Soil Survey Geographic Database (SSURGO) and State Soil Geographic Database laboratory data (STATSGO)—for 21 basins ranging in size from 17 to 1564 km2. Model results were consistently better when SSURGO data were used, likely due to the higher field capacity, porosity, and available‐water holding capacity, which cause the model to store more soil‐water in the landscape and improve streamflow estimates for both low‐ and high‐flow conditions. In addition, there were significant differences in the conductivity multiplier and scaling parameter values that describe how water moves vertically and laterally, respectively, as quantified by TOPMODEL. We also evaluated whether partitioning areas that drain to streams via sinkholes in karstic basins as separate hydrologic modeling units (HMUs) improved model performance. There were significant differences between HMUs in properties that control soil‐water storage in the model, although the effect of partitioning these HMUs on streamflow simulation was inconclusive.</description><identifier>ISSN: 0361-5995</identifier><identifier>EISSN: 1435-0661</identifier><identifier>DOI: 10.2136/sssaj2012.0069</identifier><identifier>CODEN: SSSJD4</identifier><language>eng</language><publisher>Madison: The Soil Science Society of America, Inc</publisher><subject>Alluvial plains ; Basins ; Climate change ; Drought ; Field capacity ; Floods ; High flow ; Hydrologic models ; Hydrology ; Karstic areas ; Mathematical models ; Moisture content ; Mountain regions ; Partitioning ; Porosity ; Precipitation ; Programming languages ; Sinkholes ; Soil (material) ; Soil surveys ; Soil water ; Stores ; Stream discharge ; Stream flow ; Streams ; Studies ; Water availability ; Water quality ; Water storage</subject><ispartof>Soil Science Society of America journal, 2013-05, Vol.77 (3), p.877-889</ispartof><rights>Copyright © by the Soil Science Society of America, Inc.</rights><rights>Copyright American Society of Agronomy May 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a4109-d8409478ea60786657573c66bac7db97e5fd79c67e9acd2776e16e6c9ae0b6283</citedby><cites>FETCH-LOGICAL-a4109-d8409478ea60786657573c66bac7db97e5fd79c67e9acd2776e16e6c9ae0b6283</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.2136%2Fsssaj2012.0069$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.2136%2Fsssaj2012.0069$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Williamson, Tanja N.</creatorcontrib><creatorcontrib>Taylor, Charles J.</creatorcontrib><creatorcontrib>Newson, Jeremy K.</creatorcontrib><title>Significance of Exchanging SSURGO and STATSGO Data When Modeling Hydrology in Diverse Physiographic Terranes</title><title>Soil Science Society of America journal</title><description>The Water Availability Tool for Environmental Resources (WATER) is a TOPMODEL‐based hydrologic model that depends on spatially accurate soils data to function in diverse terranes. In Kentucky, this includes mountainous regions, karstic plateau, and alluvial plains. Soils data are critical because they quantify the space to store water, as well as how water moves through the soil to the stream during storm events. We compared how the model performs using two different sources of soils data—Soil Survey Geographic Database (SSURGO) and State Soil Geographic Database laboratory data (STATSGO)—for 21 basins ranging in size from 17 to 1564 km2. Model results were consistently better when SSURGO data were used, likely due to the higher field capacity, porosity, and available‐water holding capacity, which cause the model to store more soil‐water in the landscape and improve streamflow estimates for both low‐ and high‐flow conditions. In addition, there were significant differences in the conductivity multiplier and scaling parameter values that describe how water moves vertically and laterally, respectively, as quantified by TOPMODEL. We also evaluated whether partitioning areas that drain to streams via sinkholes in karstic basins as separate hydrologic modeling units (HMUs) improved model performance. There were significant differences between HMUs in properties that control soil‐water storage in the model, although the effect of partitioning these HMUs on streamflow simulation was inconclusive.</description><subject>Alluvial plains</subject><subject>Basins</subject><subject>Climate change</subject><subject>Drought</subject><subject>Field capacity</subject><subject>Floods</subject><subject>High flow</subject><subject>Hydrologic models</subject><subject>Hydrology</subject><subject>Karstic areas</subject><subject>Mathematical models</subject><subject>Moisture content</subject><subject>Mountain regions</subject><subject>Partitioning</subject><subject>Porosity</subject><subject>Precipitation</subject><subject>Programming languages</subject><subject>Sinkholes</subject><subject>Soil (material)</subject><subject>Soil surveys</subject><subject>Soil water</subject><subject>Stores</subject><subject>Stream discharge</subject><subject>Stream flow</subject><subject>Streams</subject><subject>Studies</subject><subject>Water availability</subject><subject>Water quality</subject><subject>Water storage</subject><issn>0361-5995</issn><issn>1435-0661</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkTFv2zAQhYmiAeomXTsT6JJFLkmJR3PIYCRu0iBBgspBR4KmTjINhXTJuK3-feS46NClWe4Oh-8d3uER8pGzqeAlfM45241gXEwZA_2GTHhVyoIB8LdkwkrghdRaviPvc94wxqVmbEL62nfBt97Z4JDGli5-u7UNnQ8dreuHb5d31IaG1sv5sh7nC_tk6fc1BnobG-z31NXQpNjHbqA-0Av_E1NGer8eso9dstu1d3SJKdmA-YQctbbP-OFPPyYPXxbL86vi5u7y6_n8prAVZ7poZhXTlZqhBaZmAFJJVTqAlXWqWWmFsm2UdqBQW9cIpQA5IDhtka1AzMpjcnq4u03xxw7zk3n02WHfjybiLhsOlRBVxUr2ClQorXgpYUQ__YNu4i6F8RHDK66VUqXkIzU9UC7FnBO2Zpv8o02D4czsczJ_czL7nEbB2UHwy_c4_Ic29fxa1PW-jqsX_TOgZ5dp</recordid><startdate>201305</startdate><enddate>201305</enddate><creator>Williamson, Tanja N.</creator><creator>Taylor, Charles J.</creator><creator>Newson, Jeremy K.</creator><general>The Soil Science Society of America, Inc</general><general>American Society of Agronomy</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7T7</scope><scope>7X2</scope><scope>7XB</scope><scope>88I</scope><scope>8AF</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FK</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M0K</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P64</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>R05</scope><scope>S0X</scope><scope>SOI</scope><scope>7QH</scope><scope>7UA</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>KR7</scope></search><sort><creationdate>201305</creationdate><title>Significance of Exchanging SSURGO and STATSGO Data When Modeling Hydrology in Diverse Physiographic Terranes</title><author>Williamson, Tanja N. ; Taylor, Charles J. ; Newson, Jeremy K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a4109-d8409478ea60786657573c66bac7db97e5fd79c67e9acd2776e16e6c9ae0b6283</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Alluvial plains</topic><topic>Basins</topic><topic>Climate change</topic><topic>Drought</topic><topic>Field capacity</topic><topic>Floods</topic><topic>High flow</topic><topic>Hydrologic models</topic><topic>Hydrology</topic><topic>Karstic areas</topic><topic>Mathematical models</topic><topic>Moisture content</topic><topic>Mountain regions</topic><topic>Partitioning</topic><topic>Porosity</topic><topic>Precipitation</topic><topic>Programming languages</topic><topic>Sinkholes</topic><topic>Soil (material)</topic><topic>Soil surveys</topic><topic>Soil water</topic><topic>Stores</topic><topic>Stream discharge</topic><topic>Stream flow</topic><topic>Streams</topic><topic>Studies</topic><topic>Water availability</topic><topic>Water quality</topic><topic>Water storage</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Williamson, Tanja N.</creatorcontrib><creatorcontrib>Taylor, Charles J.</creatorcontrib><creatorcontrib>Newson, Jeremy K.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Agricultural Science Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Agricultural Science Database</collection><collection>Research Library</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>University of Michigan</collection><collection>SIRS Editorial</collection><collection>Environment Abstracts</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Civil Engineering Abstracts</collection><jtitle>Soil Science Society of America journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Williamson, Tanja N.</au><au>Taylor, Charles J.</au><au>Newson, Jeremy K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Significance of Exchanging SSURGO and STATSGO Data When Modeling Hydrology in Diverse Physiographic Terranes</atitle><jtitle>Soil Science Society of America journal</jtitle><date>2013-05</date><risdate>2013</risdate><volume>77</volume><issue>3</issue><spage>877</spage><epage>889</epage><pages>877-889</pages><issn>0361-5995</issn><eissn>1435-0661</eissn><coden>SSSJD4</coden><abstract>The Water Availability Tool for Environmental Resources (WATER) is a TOPMODEL‐based hydrologic model that depends on spatially accurate soils data to function in diverse terranes. In Kentucky, this includes mountainous regions, karstic plateau, and alluvial plains. Soils data are critical because they quantify the space to store water, as well as how water moves through the soil to the stream during storm events. We compared how the model performs using two different sources of soils data—Soil Survey Geographic Database (SSURGO) and State Soil Geographic Database laboratory data (STATSGO)—for 21 basins ranging in size from 17 to 1564 km2. Model results were consistently better when SSURGO data were used, likely due to the higher field capacity, porosity, and available‐water holding capacity, which cause the model to store more soil‐water in the landscape and improve streamflow estimates for both low‐ and high‐flow conditions. In addition, there were significant differences in the conductivity multiplier and scaling parameter values that describe how water moves vertically and laterally, respectively, as quantified by TOPMODEL. We also evaluated whether partitioning areas that drain to streams via sinkholes in karstic basins as separate hydrologic modeling units (HMUs) improved model performance. There were significant differences between HMUs in properties that control soil‐water storage in the model, although the effect of partitioning these HMUs on streamflow simulation was inconclusive.</abstract><cop>Madison</cop><pub>The Soil Science Society of America, Inc</pub><doi>10.2136/sssaj2012.0069</doi><tpages>13</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0361-5995
ispartof Soil Science Society of America journal, 2013-05, Vol.77 (3), p.877-889
issn 0361-5995
1435-0661
language eng
recordid cdi_proquest_miscellaneous_1642244030
source Access via Wiley Online Library
subjects Alluvial plains
Basins
Climate change
Drought
Field capacity
Floods
High flow
Hydrologic models
Hydrology
Karstic areas
Mathematical models
Moisture content
Mountain regions
Partitioning
Porosity
Precipitation
Programming languages
Sinkholes
Soil (material)
Soil surveys
Soil water
Stores
Stream discharge
Stream flow
Streams
Studies
Water availability
Water quality
Water storage
title Significance of Exchanging SSURGO and STATSGO Data When Modeling Hydrology in Diverse Physiographic Terranes
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T16%3A26%3A55IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Significance%20of%20Exchanging%20SSURGO%20and%20STATSGO%20Data%20When%20Modeling%20Hydrology%20in%20Diverse%20Physiographic%20Terranes&rft.jtitle=Soil%20Science%20Society%20of%20America%20journal&rft.au=Williamson,%20Tanja%20N.&rft.date=2013-05&rft.volume=77&rft.issue=3&rft.spage=877&rft.epage=889&rft.pages=877-889&rft.issn=0361-5995&rft.eissn=1435-0661&rft.coden=SSSJD4&rft_id=info:doi/10.2136/sssaj2012.0069&rft_dat=%3Cproquest_cross%3E3043058391%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1419777351&rft_id=info:pmid/&rfr_iscdi=true