Predicting near-saturated hydraulic conductivity in urban soils
[Display omitted] •We built pedotransfer functions for hydraulic conductivity with data from 11 cities.•Models predicted near-saturated hydraulic conductivity (Kn) with moderate accuracy.•Predicted Kn values were mostly the same order-of-magnitude as measured Kn values.•Sand and clay percentages wer...
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Veröffentlicht in: | Journal of hydrology (Amsterdam) 2021-04, Vol.595 (C), p.126051, Article 126051 |
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creator | Jian, Jinshi Shiklomanov, Alexey Shuster, William D. Stewart, Ryan D. |
description | [Display omitted]
•We built pedotransfer functions for hydraulic conductivity with data from 11 cities.•Models predicted near-saturated hydraulic conductivity (Kn) with moderate accuracy.•Predicted Kn values were mostly the same order-of-magnitude as measured Kn values.•Sand and clay percentages were most important input parameters for predicting Kn.•The models have been made available as an open-source software package.
Pedotransfer functions (PTFs) provide point predictions of soil hydraulic properties from more readily measured soil characteristics, yet uncertainties and biases in measurement methods, sampling distributions, and boundary conditions can limit accuracy when estimating near-saturated hydraulic conductivity (Kn). These limitations may be particularly problematic in understudied urban landscapes that often contain altered hydraulic properties. To better treat deficiencies in PTF performance, we addressed three objectives, which were to: 1) develop PTFs to predict urban Kn, 2) assess bulk density and coarse fragments as explanatory variables; and 3) evaluate the predictive capability of these PTFs by comparing their output to measured hydraulic conductivity values from three other studies of urban soil hydraulics. We used artificial neural networks (ANN) and random forest (RF) approaches to predict urban Kn, with the training dataset including 307 tension infiltrometer tests and other measurements drawn from urban soil assessments in 11 U.S. cities. The PTFs utilized a hierarchy of inputs, starting with percentage sand, silt, clay, and then adding percentage coarse fragments and bulk density. The ANN models performed similar to the RF models, and all models exhibited similar or better predictive performance as models results collected from published articles. The inclusion of bulk density or coarse fragments did not improve accuracy over soil texture alone. Possible reasons for this result include low correlation between Kn and bulk density and the exclusion of large voids during flow measurements with tension infiltrometers. The models have been made available as an open-source software package to encourage adoption by users working in urban systems. |
doi_str_mv | 10.1016/j.jhydrol.2021.126051 |
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•We built pedotransfer functions for hydraulic conductivity with data from 11 cities.•Models predicted near-saturated hydraulic conductivity (Kn) with moderate accuracy.•Predicted Kn values were mostly the same order-of-magnitude as measured Kn values.•Sand and clay percentages were most important input parameters for predicting Kn.•The models have been made available as an open-source software package.
Pedotransfer functions (PTFs) provide point predictions of soil hydraulic properties from more readily measured soil characteristics, yet uncertainties and biases in measurement methods, sampling distributions, and boundary conditions can limit accuracy when estimating near-saturated hydraulic conductivity (Kn). These limitations may be particularly problematic in understudied urban landscapes that often contain altered hydraulic properties. To better treat deficiencies in PTF performance, we addressed three objectives, which were to: 1) develop PTFs to predict urban Kn, 2) assess bulk density and coarse fragments as explanatory variables; and 3) evaluate the predictive capability of these PTFs by comparing their output to measured hydraulic conductivity values from three other studies of urban soil hydraulics. We used artificial neural networks (ANN) and random forest (RF) approaches to predict urban Kn, with the training dataset including 307 tension infiltrometer tests and other measurements drawn from urban soil assessments in 11 U.S. cities. The PTFs utilized a hierarchy of inputs, starting with percentage sand, silt, clay, and then adding percentage coarse fragments and bulk density. The ANN models performed similar to the RF models, and all models exhibited similar or better predictive performance as models results collected from published articles. The inclusion of bulk density or coarse fragments did not improve accuracy over soil texture alone. Possible reasons for this result include low correlation between Kn and bulk density and the exclusion of large voids during flow measurements with tension infiltrometers. The models have been made available as an open-source software package to encourage adoption by users working in urban systems.</description><identifier>ISSN: 0022-1694</identifier><identifier>EISSN: 1879-2707</identifier><identifier>DOI: 10.1016/j.jhydrol.2021.126051</identifier><language>eng</language><publisher>Goddard Space Flight Center: Elsevier B.V</publisher><subject>Artificial neural network ; bulk density ; clay ; computer software ; data collection ; fluid mechanics ; hydraulic conductivity ; Infiltration ; infiltrometers ; Meteorology And Climatology ; Pedotransfer function ; Random forest ; sand ; silt ; Soil science ; soil texture ; Urban hydrology ; urban soils</subject><ispartof>Journal of hydrology (Amsterdam), 2021-04, Vol.595 (C), p.126051, Article 126051</ispartof><rights>2021 Elsevier B.V.</rights><rights>Copyright Determination: MAY_INCLUDE_COPYRIGHT_MATERIAL</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c437t-3251e54a050253678455b6554380509f52cffa38a70b336395b263203bfc017e3</citedby><cites>FETCH-LOGICAL-c437t-3251e54a050253678455b6554380509f52cffa38a70b336395b263203bfc017e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0022169421000986$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,796,881,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttps://www.osti.gov/biblio/1775961$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Jian, Jinshi</creatorcontrib><creatorcontrib>Shiklomanov, Alexey</creatorcontrib><creatorcontrib>Shuster, William D.</creatorcontrib><creatorcontrib>Stewart, Ryan D.</creatorcontrib><title>Predicting near-saturated hydraulic conductivity in urban soils</title><title>Journal of hydrology (Amsterdam)</title><description>[Display omitted]
•We built pedotransfer functions for hydraulic conductivity with data from 11 cities.•Models predicted near-saturated hydraulic conductivity (Kn) with moderate accuracy.•Predicted Kn values were mostly the same order-of-magnitude as measured Kn values.•Sand and clay percentages were most important input parameters for predicting Kn.•The models have been made available as an open-source software package.
Pedotransfer functions (PTFs) provide point predictions of soil hydraulic properties from more readily measured soil characteristics, yet uncertainties and biases in measurement methods, sampling distributions, and boundary conditions can limit accuracy when estimating near-saturated hydraulic conductivity (Kn). These limitations may be particularly problematic in understudied urban landscapes that often contain altered hydraulic properties. To better treat deficiencies in PTF performance, we addressed three objectives, which were to: 1) develop PTFs to predict urban Kn, 2) assess bulk density and coarse fragments as explanatory variables; and 3) evaluate the predictive capability of these PTFs by comparing their output to measured hydraulic conductivity values from three other studies of urban soil hydraulics. We used artificial neural networks (ANN) and random forest (RF) approaches to predict urban Kn, with the training dataset including 307 tension infiltrometer tests and other measurements drawn from urban soil assessments in 11 U.S. cities. The PTFs utilized a hierarchy of inputs, starting with percentage sand, silt, clay, and then adding percentage coarse fragments and bulk density. The ANN models performed similar to the RF models, and all models exhibited similar or better predictive performance as models results collected from published articles. The inclusion of bulk density or coarse fragments did not improve accuracy over soil texture alone. Possible reasons for this result include low correlation between Kn and bulk density and the exclusion of large voids during flow measurements with tension infiltrometers. The models have been made available as an open-source software package to encourage adoption by users working in urban systems.</description><subject>Artificial neural network</subject><subject>bulk density</subject><subject>clay</subject><subject>computer software</subject><subject>data collection</subject><subject>fluid mechanics</subject><subject>hydraulic conductivity</subject><subject>Infiltration</subject><subject>infiltrometers</subject><subject>Meteorology And Climatology</subject><subject>Pedotransfer function</subject><subject>Random forest</subject><subject>sand</subject><subject>silt</subject><subject>Soil science</subject><subject>soil texture</subject><subject>Urban hydrology</subject><subject>urban soils</subject><issn>0022-1694</issn><issn>1879-2707</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>CYI</sourceid><recordid>eNqFkEtLxDAUhYMoOI7-A4Xiyk1rHk3SrgYRXzCgC12HNE2dlE4yJunA_HtTO3vvJnDPOTeHD4AbBAsEEbvvi35zaL0bCgwxKhBmkKITsEAVr3PMIT8FCwgxzhGry3NwEUIP0xBSLsDqw-vWqGjsd2a19HmQcfQy6jabbspxMCpTzrZj8uxNPGTGZqNvpM2CM0O4BGedHIK-Or5L8PX89Pn4mq_fX94eH9a5KgmPOcEUaVpKSCGmhPGqpLRhlJakSqu6o1h1nSSV5LAhhJGaNpgRDEnTKYi4JktwO991IRoRlIlabVIvq1UUiHNaM5RMd7Np593PqEMUWxOUHgZptRuDwBSXhFSYV8lKZ6vyLgSvO7HzZiv9QSAoJqqiF0eqYqIqZqopdz3nrAxS2OjDnwwhSqFJXs2yTiz2RvupqrYqMfZT09aZfz74Bep7iVE</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Jian, Jinshi</creator><creator>Shiklomanov, Alexey</creator><creator>Shuster, William D.</creator><creator>Stewart, Ryan D.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>CYE</scope><scope>CYI</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7S9</scope><scope>L.6</scope><scope>OTOTI</scope></search><sort><creationdate>20210401</creationdate><title>Predicting near-saturated hydraulic conductivity in urban soils</title><author>Jian, Jinshi ; Shiklomanov, Alexey ; Shuster, William D. ; Stewart, Ryan D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c437t-3251e54a050253678455b6554380509f52cffa38a70b336395b263203bfc017e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural network</topic><topic>bulk density</topic><topic>clay</topic><topic>computer software</topic><topic>data collection</topic><topic>fluid mechanics</topic><topic>hydraulic conductivity</topic><topic>Infiltration</topic><topic>infiltrometers</topic><topic>Meteorology And Climatology</topic><topic>Pedotransfer function</topic><topic>Random forest</topic><topic>sand</topic><topic>silt</topic><topic>Soil science</topic><topic>soil texture</topic><topic>Urban hydrology</topic><topic>urban soils</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jian, Jinshi</creatorcontrib><creatorcontrib>Shiklomanov, Alexey</creatorcontrib><creatorcontrib>Shuster, William D.</creatorcontrib><creatorcontrib>Stewart, Ryan D.</creatorcontrib><collection>NASA Scientific and Technical Information</collection><collection>NASA Technical Reports Server</collection><collection>CrossRef</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>OSTI.GOV</collection><jtitle>Journal of hydrology (Amsterdam)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jian, Jinshi</au><au>Shiklomanov, Alexey</au><au>Shuster, William D.</au><au>Stewart, Ryan D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predicting near-saturated hydraulic conductivity in urban soils</atitle><jtitle>Journal of hydrology (Amsterdam)</jtitle><date>2021-04-01</date><risdate>2021</risdate><volume>595</volume><issue>C</issue><spage>126051</spage><pages>126051-</pages><artnum>126051</artnum><issn>0022-1694</issn><eissn>1879-2707</eissn><abstract>[Display omitted]
•We built pedotransfer functions for hydraulic conductivity with data from 11 cities.•Models predicted near-saturated hydraulic conductivity (Kn) with moderate accuracy.•Predicted Kn values were mostly the same order-of-magnitude as measured Kn values.•Sand and clay percentages were most important input parameters for predicting Kn.•The models have been made available as an open-source software package.
Pedotransfer functions (PTFs) provide point predictions of soil hydraulic properties from more readily measured soil characteristics, yet uncertainties and biases in measurement methods, sampling distributions, and boundary conditions can limit accuracy when estimating near-saturated hydraulic conductivity (Kn). These limitations may be particularly problematic in understudied urban landscapes that often contain altered hydraulic properties. To better treat deficiencies in PTF performance, we addressed three objectives, which were to: 1) develop PTFs to predict urban Kn, 2) assess bulk density and coarse fragments as explanatory variables; and 3) evaluate the predictive capability of these PTFs by comparing their output to measured hydraulic conductivity values from three other studies of urban soil hydraulics. We used artificial neural networks (ANN) and random forest (RF) approaches to predict urban Kn, with the training dataset including 307 tension infiltrometer tests and other measurements drawn from urban soil assessments in 11 U.S. cities. The PTFs utilized a hierarchy of inputs, starting with percentage sand, silt, clay, and then adding percentage coarse fragments and bulk density. The ANN models performed similar to the RF models, and all models exhibited similar or better predictive performance as models results collected from published articles. The inclusion of bulk density or coarse fragments did not improve accuracy over soil texture alone. Possible reasons for this result include low correlation between Kn and bulk density and the exclusion of large voids during flow measurements with tension infiltrometers. The models have been made available as an open-source software package to encourage adoption by users working in urban systems.</abstract><cop>Goddard Space Flight Center</cop><pub>Elsevier B.V</pub><doi>10.1016/j.jhydrol.2021.126051</doi><oa>free_for_read</oa></addata></record> |
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subjects | Artificial neural network bulk density clay computer software data collection fluid mechanics hydraulic conductivity Infiltration infiltrometers Meteorology And Climatology Pedotransfer function Random forest sand silt Soil science soil texture Urban hydrology urban soils |
title | Predicting near-saturated hydraulic conductivity in urban soils |
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