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

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dc.contributor.authorReichenberger, Stefan
dc.contributor.authorSur, Robin
dc.contributor.authorSittig, Stephan
dc.contributor.authorMultsch, Sebastián
dc.contributor.authorCarmona Cabrero, Álvaro
dc.contributor.authorLópez Rodríguez, José Javier
dc.contributor.authorMuñoz Carpena, Rafael
dc.contributor.departmentIngenieríaes_ES
dc.contributor.departmentIngeniaritzaeu
dc.contributor.funderUniversidad Pública de Navarra / Nafarroako Unibertstitate Publikoaes
dc.date.accessioned2023-03-30T11:49:19Z
dc.date.available2023-03-30T11:49:19Z
dc.date.issued2023
dc.date.updated2023-03-30T11:40:39Z
dc.description.abstractThe 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.en
dc.description.sponsorshipThis 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.en
dc.format.mimetypeapplication/pdfen
dc.format.mimetypeapplication/zipen
dc.identifier.citationReichenberger, S., Sur, R., Sittig, S., Multsch, S., Carmona-Cabrero, Á., López, J. J., & Muñoz-Carpena, R. (2023). Dynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter strips. Science of The Total Environment, 857, 159572. https://doi.org/10.1016/j.scitotenv.2022.159572en
dc.identifier.doi10.1016/j.scitotenv.2022.159572
dc.identifier.issn0048-9697
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/45007
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofScience of the Total Environment 587 (2023) 159572en
dc.relation.publisherversionhttps://doi.org/10.1016/j.scitotenv.2022.159572
dc.rights© 2022 The Authors. This is an open access article under the CC BY license.en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectGlobal Sensitivity Analysisen
dc.subjectMachine learningen
dc.subjectMedian particle sizeen
dc.subjectPesticidesen
dc.subjectSediment trappingen
dc.subjectVegetated filter stripsen
dc.titleDynamic prediction of effective runoff sediment particle size for improved assessment of erosion mitigation efficiency with vegetative filter stripsen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublicationd980d8ec-142b-4974-850b-1e9d7d0c478f
relation.isAuthorOfPublication152af0c2-c115-4074-9715-fdf5f8fa6837
relation.isAuthorOfPublication.latestForDiscoveryd980d8ec-142b-4974-850b-1e9d7d0c478f

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