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...
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Veröffentlicht in: | Soil Science Society of America journal 2013-05, Vol.77 (3), p.877-889 |
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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. |
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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 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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 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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> |
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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 |
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