{"id":{"repo_id":"wvu","oai_identifier":"oai:researchrepository.wvu.edu:etd-2155"},"canonical_url":"https://search.dev.ndltd.org/etd/wvu/oai:researchrepository.wvu.edu:etd-2155","repository":{"repo_id":"wvu","name":"West Virginia University","base_url":"https://researchrepository.wvu.edu/do/oai/"},"display":{"title":"Green-Ampt infiltration model parameter determination using SCS curve number (CN) and soil texture class, and application to the SCS runoff model","abstract":"The U.S. Department of Agriculture (USDA) Soil Conservation Service Curve Number (SCS CN) method is a simple and widely popular technique of estimation of direct runoff volume for design and natural rainfall events in small watersheds. The SCS CN procedure is incorporated into such computer programs as TR-20 and TR-55. Although the method reliably predicts 24-hr runoff volume, the predicted distribution of runoff during a storm event is not realistic. This shortcoming of the SCS CN method is due to weakness in the infiltration concept of the method. Use of a physically realistic and distributed Green-Ampt infiltration model can significantly improve the SCS CN method.;The purposes of the present investigation were to incorporate the Green-Ampt infiltration model into the SCS CN method and to explore its advantages over the Curve Number infiltration model. As a result, a procedure of evaluating the Green-Ampt parameters from the SCS Curve Numbers for various soil texture classes was developed. The comparison of peak discharge estimation by both models for various rainfall depths and time of concentrations showed a divergence in predictions of up to 77 percent for low runoff potential soils.","abstract_html":"The U.S. Department of Agriculture (USDA) Soil Conservation Service Curve Number (SCS CN) method is a simple and widely popular technique of estimation of direct runoff volume for design and natural rainfall events in small watersheds. The SCS CN procedure is incorporated into such computer programs as TR-20 and TR-55. Although the method reliably predicts 24-hr runoff volume, the predicted distribution of runoff during a storm event is not realistic. This shortcoming of the SCS CN method is due to weakness in the infiltration concept of the method. Use of a physically realistic and distributed Green-Ampt infiltration model can significantly improve the SCS CN method.;The purposes of the present investigation were to incorporate the Green-Ampt infiltration model into the SCS CN method and to explore its advantages over the Curve Number infiltration model. As a result, a procedure of evaluating the Green-Ampt parameters from the SCS Curve Numbers for various soil texture classes was developed. The comparison of peak discharge estimation by both models for various rainfall depths and time of concentrations showed a divergence in predictions of up to 77 percent for low runoff potential soils.","abstract_has_math":false,"creators":["Brevnova, Elena Viktorovna"],"institution":null,"degree_name":"MS","degree_level":"Thesis","degree_discipline":"Civil and Environmental Engineering","degree_department":null,"school":null,"contributors":["Robert Eli."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2001,"date_issued":"2001-08-01T07:00:00Z","date_published":"2001-08-01T07:00:00Z","updated_at":"2026-07-24T06:15:23Z","subjects":["Hydrologic sciences","Civil engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://researchrepository.wvu.edu/etd/1152"],"render_values":[{"text":"https://researchrepository.wvu.edu/etd/1152","href":"https://researchrepository.wvu.edu/etd/1152","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.33915/etd.1152","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Robert Eli."]},{"key":"dc:creator","label":"Author","values":["Brevnova, Elena Viktorovna"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-01-17T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil and Environmental Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Hydrologic sciences","Civil engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.33915/etd.1152","https://researchrepository.wvu.edu/etd/1152"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The U.S. Department of Agriculture (USDA) Soil Conservation Service Curve Number (SCS CN) method is a simple and widely popular technique of estimation of direct runoff volume for design and natural rainfall events in small watersheds. The SCS CN procedure is incorporated into such computer programs as TR-20 and TR-55. Although the method reliably predicts 24-hr runoff volume, the predicted distribution of runoff during a storm event is not realistic. This shortcoming of the SCS CN method is due to weakness in the infiltration concept of the method. Use of a physically realistic and distributed Green-Ampt infiltration model can significantly improve the SCS CN method.;The purposes of the present investigation were to incorporate the Green-Ampt infiltration model into the SCS CN method and to explore its advantages over the Curve Number infiltration model. As a result, a procedure of evaluating the Green-Ampt parameters from the SCS Curve Numbers for various soil texture classes was developed. The comparison of peak discharge estimation by both models for various rainfall depths and time of concentrations showed a divergence in predictions of up to 77 percent for low runoff potential soils."]},{"key":"dc:title","label":"Title","values":["Green-Ampt infiltration model parameter determination using SCS curve number (CN) and soil texture class, and application to the SCS runoff model"]}]}],"canonical_facts":{"dc:contributor":["Robert Eli."],"dc:creator":["Brevnova, Elena Viktorovna"],"dc:date.available":["2019-01-17T08:00:00Z"],"dc:description.abstract":["The U.S. Department of Agriculture (USDA) Soil Conservation Service Curve Number (SCS CN) method is a simple and widely popular technique of estimation of direct runoff volume for design and natural rainfall events in small watersheds. The SCS CN procedure is incorporated into such computer programs as TR-20 and TR-55. Although the method reliably predicts 24-hr runoff volume, the predicted distribution of runoff during a storm event is not realistic. This shortcoming of the SCS CN method is due to weakness in the infiltration concept of the method. Use of a physically realistic and distributed Green-Ampt infiltration model can significantly improve the SCS CN method.;The purposes of the present investigation were to incorporate the Green-Ampt infiltration model into the SCS CN method and to explore its advantages over the Curve Number infiltration model. As a result, a procedure of evaluating the Green-Ampt parameters from the SCS Curve Numbers for various soil texture classes was developed. The comparison of peak discharge estimation by both models for various rainfall depths and time of concentrations showed a divergence in predictions of up to 77 percent for low runoff potential soils."],"dc:identifier":["https://doi.org/10.33915/etd.1152","https://researchrepository.wvu.edu/etd/1152"],"dc:subject":["Hydrologic sciences","Civil engineering"],"dc:title":["Green-Ampt infiltration model parameter determination using SCS curve number (CN) and soil texture class, and application to the SCS runoff model"],"thesis:degree_discipline":["Civil and Environmental Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T06:15:23Z"}