Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots
The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (...
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| Veröffentlicht in: | Hydrogeology journal 2017-02, Vol.25 (1), p.151-167 |
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| description | The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (
P
) and runoff (
Q
) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed
P
and
Q
datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall–runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (
λ
) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median
λ
values were, respectively, 0.030 and 0 for the natural rainfall–runoff dataset and 0.108 and 0 for the ordered rainfall–runoff dataset. Runoff estimation was very sensitive to
λ
and it improved consistently as
λ
changed from 0.2 to 0.03. |
| doi_str_mv | 10.1007/s10040-016-1460-5 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1884122515</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1884122515</sourcerecordid><originalsourceid>FETCH-LOGICAL-c382t-ab408d0aa98e88dd840c86651ab2369f089b7b130c07817896ead9c3ef50334e3</originalsourceid><addsrcrecordid>eNqNkU1LxDAQhoMoqKs_wFvAi5dqpmma9CiLXyB4WD2HNE27XdJmTZoF_71Z60EEwcvMMPPMMDMvQhdAroEQfhOSLUhGoMygKEnGDtAJFJSlDOOHXzFkOfDiGJ2GsCGJBk5PUHe3UzaqqXcjdi2e1gavXG_x0o3B-N1cWKWo1wbr6HcGj3GojceDmdaucdZ1HziGfuxwoyaFW-8GrDrf62in6JXFW-umcIaOWmWDOf_2C_R2f_e6fMyeXx6elrfPmaYinzJVF0Q0RKlKGCGaRhREi7JkoOqcllVLRFXzGijRhAvgoiqNaipNTcsIpYWhC3Q1z9169x5NmOTQB22sVaNxMUgQooA8Z8D-gXIuGIH0ugW6_IVuXPRjOiRRJa8oUFYmCmZKexeCN63c-n5Q_kMCkXuV5KySTCrJvUpyv0Q-94TEjp3xPyb_2fQJNxWUlA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1867931356</pqid></control><display><type>article</type><title>Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots</title><source>SpringerLink Journals - AutoHoldings</source><creator>Lal, Mohan ; Mishra, S. K. ; Pandey, Ashish ; Pandey, R. P. ; Meena, P. K. ; Chaudhary, Anubhav ; Jha, Ranjit Kumar ; Shreevastava, Ajit Kumar ; Kumar, Yogendra</creator><creatorcontrib>Lal, Mohan ; Mishra, S. K. ; Pandey, Ashish ; Pandey, R. P. ; Meena, P. K. ; Chaudhary, Anubhav ; Jha, Ranjit Kumar ; Shreevastava, Ajit Kumar ; Kumar, Yogendra</creatorcontrib><description>The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (
P
) and runoff (
Q
) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed
P
and
Q
datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall–runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (
λ
) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median
λ
values were, respectively, 0.030 and 0 for the natural rainfall–runoff dataset and 0.108 and 0 for the ordered rainfall–runoff dataset. Runoff estimation was very sensitive to
λ
and it improved consistently as
λ
changed from 0.2 to 0.03.</description><identifier>ISSN: 1431-2174</identifier><identifier>EISSN: 1435-0157</identifier><identifier>DOI: 10.1007/s10040-016-1460-5</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Agricultural practices ; Agriculture ; Aquatic Pollution ; Earth and Environmental Science ; Earth Sciences ; Geoengineering ; Geology ; Geophysics/Geodesy ; Hydrogeology ; Hydrology ; Hydrology/Water Resources ; Infiltration capacity ; Natural resources ; Rainfall ; Rainfall runoff relationships ; Resource conservation ; Runoff ; Soil conservation ; Surface runoff ; Waste Water Technology ; Water Management ; Water Pollution Control ; Water Quality/Water Pollution ; Watersheds</subject><ispartof>Hydrogeology journal, 2017-02, Vol.25 (1), p.151-167</ispartof><rights>Springer-Verlag Berlin Heidelberg 2016</rights><rights>Hydrogeology Journal is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c382t-ab408d0aa98e88dd840c86651ab2369f089b7b130c07817896ead9c3ef50334e3</citedby><cites>FETCH-LOGICAL-c382t-ab408d0aa98e88dd840c86651ab2369f089b7b130c07817896ead9c3ef50334e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10040-016-1460-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10040-016-1460-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Lal, Mohan</creatorcontrib><creatorcontrib>Mishra, S. K.</creatorcontrib><creatorcontrib>Pandey, Ashish</creatorcontrib><creatorcontrib>Pandey, R. P.</creatorcontrib><creatorcontrib>Meena, P. K.</creatorcontrib><creatorcontrib>Chaudhary, Anubhav</creatorcontrib><creatorcontrib>Jha, Ranjit Kumar</creatorcontrib><creatorcontrib>Shreevastava, Ajit Kumar</creatorcontrib><creatorcontrib>Kumar, Yogendra</creatorcontrib><title>Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots</title><title>Hydrogeology journal</title><addtitle>Hydrogeol J</addtitle><description>The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (
P
) and runoff (
Q
) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed
P
and
Q
datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall–runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (
λ
) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median
λ
values were, respectively, 0.030 and 0 for the natural rainfall–runoff dataset and 0.108 and 0 for the ordered rainfall–runoff dataset. Runoff estimation was very sensitive to
λ
and it improved consistently as
λ
changed from 0.2 to 0.03.</description><subject>Agricultural practices</subject><subject>Agriculture</subject><subject>Aquatic Pollution</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Geoengineering</subject><subject>Geology</subject><subject>Geophysics/Geodesy</subject><subject>Hydrogeology</subject><subject>Hydrology</subject><subject>Hydrology/Water Resources</subject><subject>Infiltration capacity</subject><subject>Natural resources</subject><subject>Rainfall</subject><subject>Rainfall runoff relationships</subject><subject>Resource conservation</subject><subject>Runoff</subject><subject>Soil conservation</subject><subject>Surface runoff</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><subject>Water Quality/Water Pollution</subject><subject>Watersheds</subject><issn>1431-2174</issn><issn>1435-0157</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNqNkU1LxDAQhoMoqKs_wFvAi5dqpmma9CiLXyB4WD2HNE27XdJmTZoF_71Z60EEwcvMMPPMMDMvQhdAroEQfhOSLUhGoMygKEnGDtAJFJSlDOOHXzFkOfDiGJ2GsCGJBk5PUHe3UzaqqXcjdi2e1gavXG_x0o3B-N1cWKWo1wbr6HcGj3GojceDmdaucdZ1HziGfuxwoyaFW-8GrDrf62in6JXFW-umcIaOWmWDOf_2C_R2f_e6fMyeXx6elrfPmaYinzJVF0Q0RKlKGCGaRhREi7JkoOqcllVLRFXzGijRhAvgoiqNaipNTcsIpYWhC3Q1z9169x5NmOTQB22sVaNxMUgQooA8Z8D-gXIuGIH0ugW6_IVuXPRjOiRRJa8oUFYmCmZKexeCN63c-n5Q_kMCkXuV5KySTCrJvUpyv0Q-94TEjp3xPyb_2fQJNxWUlA</recordid><startdate>20170201</startdate><enddate>20170201</enddate><creator>Lal, Mohan</creator><creator>Mishra, S. 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K. ; Pandey, Ashish ; Pandey, R. P. ; Meena, P. K. ; Chaudhary, Anubhav ; Jha, Ranjit Kumar ; Shreevastava, Ajit Kumar ; Kumar, Yogendra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c382t-ab408d0aa98e88dd840c86651ab2369f089b7b130c07817896ead9c3ef50334e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Agricultural practices</topic><topic>Agriculture</topic><topic>Aquatic Pollution</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Geoengineering</topic><topic>Geology</topic><topic>Geophysics/Geodesy</topic><topic>Hydrogeology</topic><topic>Hydrology</topic><topic>Hydrology/Water Resources</topic><topic>Infiltration capacity</topic><topic>Natural resources</topic><topic>Rainfall</topic><topic>Rainfall runoff relationships</topic><topic>Resource conservation</topic><topic>Runoff</topic><topic>Soil conservation</topic><topic>Surface runoff</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><topic>Water Quality/Water Pollution</topic><topic>Watersheds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lal, Mohan</creatorcontrib><creatorcontrib>Mishra, S. K.</creatorcontrib><creatorcontrib>Pandey, Ashish</creatorcontrib><creatorcontrib>Pandey, R. P.</creatorcontrib><creatorcontrib>Meena, P. 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K.</au><au>Pandey, Ashish</au><au>Pandey, R. P.</au><au>Meena, P. K.</au><au>Chaudhary, Anubhav</au><au>Jha, Ranjit Kumar</au><au>Shreevastava, Ajit Kumar</au><au>Kumar, Yogendra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots</atitle><jtitle>Hydrogeology journal</jtitle><stitle>Hydrogeol J</stitle><date>2017-02-01</date><risdate>2017</risdate><volume>25</volume><issue>1</issue><spage>151</spage><epage>167</epage><pages>151-167</pages><issn>1431-2174</issn><eissn>1435-0157</eissn><abstract>The Soil Conservation Service curve number (SCS-CN) method, also known as the Natural Resources Conservation Service curve number (NRCS-CN) method, is popular for computing the volume of direct surface runoff for a given rainfall event. The performance of the SCS-CN method, based on large rainfall (
P
) and runoff (
Q
) datasets of United States watersheds, is evaluated using a large dataset of natural storm events from 27 agricultural plots in India. On the whole, the CN estimates from the National Engineering Handbook (chapter 4) tables do not match those derived from the observed
P
and
Q
datasets. As a result, the runoff prediction using former CNs was poor for the data of 22 (out of 24) plots. However, the match was little better for higher CN values, consistent with the general notion that the existing SCS-CN method performs better for high rainfall–runoff (high CN) events. Infiltration capacity (fc) was the main explanatory variable for runoff (or CN) production in study plots as it exhibited the expected inverse relationship between CN and fc. The plot-data optimization yielded initial abstraction coefficient (
λ
) values from 0 to 0.659 for the ordered dataset and 0 to 0.208 for the natural dataset (with 0 as the most frequent value). Mean and median
λ
values were, respectively, 0.030 and 0 for the natural rainfall–runoff dataset and 0.108 and 0 for the ordered rainfall–runoff dataset. Runoff estimation was very sensitive to
λ
and it improved consistently as
λ
changed from 0.2 to 0.03.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s10040-016-1460-5</doi><tpages>17</tpages></addata></record> |
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| subjects | Agricultural practices Agriculture Aquatic Pollution Earth and Environmental Science Earth Sciences Geoengineering Geology Geophysics/Geodesy Hydrogeology Hydrology Hydrology/Water Resources Infiltration capacity Natural resources Rainfall Rainfall runoff relationships Resource conservation Runoff Soil conservation Surface runoff Waste Water Technology Water Management Water Pollution Control Water Quality/Water Pollution Watersheds |
| title | Evaluation of the Soil Conservation Service curve number methodology using data from agricultural plots |
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