Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips

The most widely implemented mitigation measure to reduce transfer of surface runoff pesticides and other pollutants to surface water bodies are vegetative filter strips (VFS). The most commonly used dynamic model for quantifying the reduction by VFS of surface runoff, eroded sediment, pesticides and...

Descripción completa

Detalles Bibliográficos
Autores: Reichenberger, Stefan, Sur, Robin, Sittig, Stephan, Multsch, Sebastián, Carmona Cabrero, Álvaro, López Rodríguez, José Javier, Muñoz Carpena, Rafael
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/45007
Acceso en línea:https://hdl.handle.net/2454/45007
Access Level:acceso abierto
Palabra clave:Global Sensitivity Analysis
Machine learning
Median particle size
Pesticides
Sediment trapping
Vegetated filter strips
id ES_ea654444fa235b52b1e8f6c498dbde2a
oai_identifier_str oai:academica-e.unavarra.es:2454/45007
network_acronym_str ES
network_name_str España
repository_id_str
spelling Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter stripsReichenberger, StefanSur, RobinSittig, StephanMultsch, SebastiánCarmona Cabrero, ÁlvaroLópez Rodríguez, José JavierMuñoz Carpena, RafaelGlobal Sensitivity AnalysisMachine learningMedian particle sizePesticidesSediment trappingVegetated filter stripsThe most widely implemented mitigation measure to reduce transfer of surface runoff pesticides and other pollutants to surface water bodies are vegetative filter strips (VFS). The most commonly used dynamic model for quantifying the reduction by VFS of surface runoff, eroded sediment, pesticides and other pollutants is VFSMOD, which simulates reduction of total inflow (ΔQ) and of incoming eroded sediment load (ΔE) mechanistically during the rainfall-runoff event. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (ΔP). Since errors in ΔQ and ΔE propagate into ΔP, for strongly-sorbing compounds an accurate prediction of ΔE is crucial for a reliable prediction of ΔP. The most important incoming sediment characteristic for ΔE is the median particle diameter (d50). Current d50 estimation methods are simplistic, yielding fixed d50 based on soil properties and ignoring specific event characteristics and dynamics. We derive an improved dynamic d50 parameterization equation for use in regulatory VFS scenarios based on an extensive dataset of 93 d50 values and 17 candidate explanatory variables compiled from heterogeneous data sources and methods. The dataset was analysed first using machine learning techniques (Random Forest, Gradient Boosting) and Global Sensitivity Analysis (GSA) as a dimension reduction technique and to identify potential interactions between explanatory variables. Using the knowledge gained, a parsimonious multiple regression equation with 6 predictors was developed and thoroughly tested. Since three of the predictors are eventspecific (eroded sediment yield, rainfall intensity and peak runoff rate), predicted d50 vary dynamically across event magnitudes and intensities. Incorporation of the improved d50 parameterization equation in higher-tier pesticide assessment tools with VFSMOD provides more realistic quantitative mitigation in regulatory US-EPA and EU FOCUS pesticide risk assessment frameworks. The equation is also readily applicable to other erosion management problems.This research was funded by Bayer AG, Monheim, Germany. RMC also acknowledges support from the USDA National Institute of Food and Agriculture (USDA-NIFA; 2016-67019-26855 ) and USDA-NIFA Hatch projects 1024705 and 1024706 , the University of Florida (USA), and Universidad Pública de Navarra (Spain) for the support received during his sabbatical year when part of this work was developed.ElsevierIngenieríaIngeniaritzaUniversidad Pública de Navarra / Nafarroako Unibertstitate Publikoa2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/ziphttps://hdl.handle.net/2454/45007reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarrainstname:Universidad Pública de NavarraInglés© 2022 The Authors. This is an open access article under the CC BY license.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:academica-e.unavarra.es:2454/450072026-06-17T12:41:47Z
dc.title.none.fl_str_mv Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
title Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
spellingShingle Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
Reichenberger, Stefan
Global Sensitivity Analysis
Machine learning
Median particle size
Pesticides
Sediment trapping
Vegetated filter strips
title_short Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
title_full Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
title_fullStr Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
title_full_unstemmed Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
title_sort Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips
dc.creator.none.fl_str_mv Reichenberger, Stefan
Sur, Robin
Sittig, Stephan
Multsch, Sebastián
Carmona Cabrero, Álvaro
López Rodríguez, José Javier
Muñoz Carpena, Rafael
author Reichenberger, Stefan
author_facet Reichenberger, Stefan
Sur, Robin
Sittig, Stephan
Multsch, Sebastián
Carmona Cabrero, Álvaro
López Rodríguez, José Javier
Muñoz Carpena, Rafael
author_role author
author2 Sur, Robin
Sittig, Stephan
Multsch, Sebastián
Carmona Cabrero, Álvaro
López Rodríguez, José Javier
Muñoz Carpena, Rafael
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería
Ingeniaritza
Universidad Pública de Navarra / Nafarroako Unibertstitate Publikoa
dc.subject.none.fl_str_mv Global Sensitivity Analysis
Machine learning
Median particle size
Pesticides
Sediment trapping
Vegetated filter strips
topic Global Sensitivity Analysis
Machine learning
Median particle size
Pesticides
Sediment trapping
Vegetated filter strips
description The most widely implemented mitigation measure to reduce transfer of surface runoff pesticides and other pollutants to surface water bodies are vegetative filter strips (VFS). The most commonly used dynamic model for quantifying the reduction by VFS of surface runoff, eroded sediment, pesticides and other pollutants is VFSMOD, which simulates reduction of total inflow (ΔQ) and of incoming eroded sediment load (ΔE) mechanistically during the rainfall-runoff event. These variables are subsequently used to calculate the reduction of pesticide load by the VFS (ΔP). Since errors in ΔQ and ΔE propagate into ΔP, for strongly-sorbing compounds an accurate prediction of ΔE is crucial for a reliable prediction of ΔP. The most important incoming sediment characteristic for ΔE is the median particle diameter (d50). Current d50 estimation methods are simplistic, yielding fixed d50 based on soil properties and ignoring specific event characteristics and dynamics. We derive an improved dynamic d50 parameterization equation for use in regulatory VFS scenarios based on an extensive dataset of 93 d50 values and 17 candidate explanatory variables compiled from heterogeneous data sources and methods. The dataset was analysed first using machine learning techniques (Random Forest, Gradient Boosting) and Global Sensitivity Analysis (GSA) as a dimension reduction technique and to identify potential interactions between explanatory variables. Using the knowledge gained, a parsimonious multiple regression equation with 6 predictors was developed and thoroughly tested. Since three of the predictors are eventspecific (eroded sediment yield, rainfall intensity and peak runoff rate), predicted d50 vary dynamically across event magnitudes and intensities. Incorporation of the improved d50 parameterization equation in higher-tier pesticide assessment tools with VFSMOD provides more realistic quantitative mitigation in regulatory US-EPA and EU FOCUS pesticide risk assessment frameworks. The equation is also readily applicable to other erosion management problems.
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2454/45007
url https://hdl.handle.net/2454/45007
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv © 2022 The Authors. This is an open access article under the CC BY license.
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv © 2022 The Authors. This is an open access article under the CC BY license.
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/zip
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
instname:Universidad Pública de Navarra
instname_str Universidad Pública de Navarra
reponame_str Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
collection Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869423137279442944
score 15,81155

Similares en LA Referencia