{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102508"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102508","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Using ensemble precipitation forecasts to improve hydrologic risk assessment at river crossings","abstract":"Current National Weather Service operations forecast hydrologic conditions probabilistically at most of the United States Geological Survey (USGS) stream gauges across the continental United States. The successful implementation of such an approach, operationally, suggests the hypothesis that a probabilistic approach would reduce uncertainty of hydrologic forecasts in ungauged basins. Forecast improvement in ungauged basins is of great interest to the United States Army due to a combination of the remoteness and hydrologic safety risk associated with low-water crossings (LWXs) commonly used as river infrastructure on military training lands. In this work, two historical deadly flooding events were hindcasted at three LWXs at Fort Hood, Texas. A probabilistic precipitation forcing cascades uncertainty through hydrologic and hydraulic models. Each precipitation ensemble member corresponds to an independent model run, resulting in ensembles of streamflow at a 24-hour lead time. The forecast is expanded to predict river hydraulics, through flow velocity and depth, at specific river LWXs. Analysis of the hindcast of two events indicates that cascading probabilistic precipitation through hydrologic and hydraulic models adds robustness to river forecasts compared to deterministic methods. The approach provides a means to communicate the uncertainty of predictions through model member agreement. Analysis of different methods for conveying hydrologic risk from model output leads to our recommendation that a hydraulic safety threshold, calculated as the multiplication of flow velocity and depth, is the best approach for U.S. Army stakeholders in terms of communicating hydrologic risk, as well as associated model uncertainty in the simplest manner possible.","abstract_html":"Current National Weather Service operations forecast hydrologic conditions probabilistically at most of the United States Geological Survey (USGS) stream gauges across the continental United States. The successful implementation of such an approach, operationally, suggests the hypothesis that a probabilistic approach would reduce uncertainty of hydrologic forecasts in ungauged basins. Forecast improvement in ungauged basins is of great interest to the United States Army due to a combination of the remoteness and hydrologic safety risk associated with low-water crossings (LWXs) commonly used as river infrastructure on military training lands. In this work, two historical deadly flooding events were hindcasted at three LWXs at Fort Hood, Texas. A probabilistic precipitation forcing cascades uncertainty through hydrologic and hydraulic models. Each precipitation ensemble member corresponds to an independent model run, resulting in ensembles of streamflow at a 24-hour lead time. The forecast is expanded to predict river hydraulics, through flow velocity and depth, at specific river LWXs. Analysis of the hindcast of two events indicates that cascading probabilistic precipitation through hydrologic and hydraulic models adds robustness to river forecasts compared to deterministic methods. The approach provides a means to communicate the uncertainty of predictions through model member agreement. Analysis of different methods for conveying hydrologic risk from model output leads to our recommendation that a hydraulic safety threshold, calculated as the multiplication of flow velocity and depth, is the best approach for U.S. Army stakeholders in terms of communicating hydrologic risk, as well as associated model uncertainty in the simplest manner possible.","abstract_has_math":false,"creators":["Matus, Sean Alan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Environ Engr in Civil Engr","degree_department":null,"school":null,"contributors":["Kumar, Praveen","Dominguez, Francina"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02-06T19:36:42Z","date_published":"2019-02-06T19:36:42Z","updated_at":"2026-07-22T22:24:42Z","subjects":["probabilistic","hydrology","hydraulics","risk assessment","uncertainty","ungauged","flooding"],"languages":["en"],"rights":["Copyright 2018 Sean Matus"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102508","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kumar, Praveen","Dominguez, Francina"]},{"key":"dc:creator","label":"Author","values":["Matus, Sean Alan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-06T19:36:42Z","2018-12-11","2018-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Environ Engr in Civil Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["probabilistic","hydrology","hydraulics","risk assessment","uncertainty","ungauged","flooding"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Sean Matus"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102508"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Current National Weather Service operations forecast hydrologic conditions probabilistically at most of the United States Geological Survey (USGS) stream gauges across the continental United States. The successful implementation of such an approach, operationally, suggests the hypothesis that a probabilistic approach would reduce uncertainty of hydrologic forecasts in ungauged basins. Forecast improvement in ungauged basins is of great interest to the United States Army due to a combination of the remoteness and hydrologic safety risk associated with low-water crossings (LWXs) commonly used as river infrastructure on military training lands. In this work, two historical deadly flooding events were hindcasted at three LWXs at Fort Hood, Texas. A probabilistic precipitation forcing cascades uncertainty through hydrologic and hydraulic models. Each precipitation ensemble member corresponds to an independent model run, resulting in ensembles of streamflow at a 24-hour lead time. The forecast is expanded to predict river hydraulics, through flow velocity and depth, at specific river LWXs. Analysis of the hindcast of two events indicates that cascading probabilistic precipitation through hydrologic and hydraulic models adds robustness to river forecasts compared to deterministic methods. The approach provides a means to communicate the uncertainty of predictions through model member agreement. Analysis of different methods for conveying hydrologic risk from model output leads to our recommendation that a hydraulic safety threshold, calculated as the multiplication of flow velocity and depth, is the best approach for U.S. Army stakeholders in terms of communicating hydrologic risk, as well as associated model uncertainty in the simplest manner possible.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Sean Matus, accepted the attached license on 2018-12-10 at 16:39.","The student, Sean Matus, submitted this Thesis for approval on 2018-12-10 at 17:03.","This Thesis was approved for publication on 2018-12-11 at 08:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13274 on 2019-02-05 at 11:16:01","Made available in DSpace on 2019-02-06T19:36:42Z (GMT). No. of bitstreams: 2 MATUS-THESIS-2018.pdf: 3187454 bytes, checksum: 672193930675b5169f676ca16542716c (MD5) LICENSE.txt: 4207 bytes, checksum: dd6817b94dcf10bec17bbfc71e35d35a (MD5) Previous issue date: 2018-12-11"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Using ensemble precipitation forecasts to improve hydrologic risk assessment at river crossings"]}]}],"canonical_facts":{"dc:contributor":["Kumar, Praveen","Dominguez, Francina"],"dc:creator":["Matus, Sean Alan"],"dc:date":["2019-02-06T19:36:42Z","2018-12-11","2018-12"],"dc:description":["Current National Weather Service operations forecast hydrologic conditions probabilistically at most of the United States Geological Survey (USGS) stream gauges across the continental United States. The successful implementation of such an approach, operationally, suggests the hypothesis that a probabilistic approach would reduce uncertainty of hydrologic forecasts in ungauged basins. Forecast improvement in ungauged basins is of great interest to the United States Army due to a combination of the remoteness and hydrologic safety risk associated with low-water crossings (LWXs) commonly used as river infrastructure on military training lands. In this work, two historical deadly flooding events were hindcasted at three LWXs at Fort Hood, Texas. A probabilistic precipitation forcing cascades uncertainty through hydrologic and hydraulic models. Each precipitation ensemble member corresponds to an independent model run, resulting in ensembles of streamflow at a 24-hour lead time. The forecast is expanded to predict river hydraulics, through flow velocity and depth, at specific river LWXs. Analysis of the hindcast of two events indicates that cascading probabilistic precipitation through hydrologic and hydraulic models adds robustness to river forecasts compared to deterministic methods. The approach provides a means to communicate the uncertainty of predictions through model member agreement. Analysis of different methods for conveying hydrologic risk from model output leads to our recommendation that a hydraulic safety threshold, calculated as the multiplication of flow velocity and depth, is the best approach for U.S. Army stakeholders in terms of communicating hydrologic risk, as well as associated model uncertainty in the simplest manner possible.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Sean Matus, accepted the attached license on 2018-12-10 at 16:39.","The student, Sean Matus, submitted this Thesis for approval on 2018-12-10 at 17:03.","This Thesis was approved for publication on 2018-12-11 at 08:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13274 on 2019-02-05 at 11:16:01","Made available in DSpace on 2019-02-06T19:36:42Z (GMT). No. of bitstreams: 2 MATUS-THESIS-2018.pdf: 3187454 bytes, checksum: 672193930675b5169f676ca16542716c (MD5) LICENSE.txt: 4207 bytes, checksum: dd6817b94dcf10bec17bbfc71e35d35a (MD5) Previous issue date: 2018-12-11"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/102508"],"dc:language":["en"],"dc:rights":["Copyright 2018 Sean Matus"],"dc:subject":["probabilistic","hydrology","hydraulics","risk assessment","uncertainty","ungauged","flooding"],"dc:title":["Using ensemble precipitation forecasts to improve hydrologic risk assessment at river crossings"],"dc:type":["text"],"thesis:degree_discipline":["Environ Engr in Civil Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:42Z"}