A framework for testing large-scale distributed soil erosion and sediment delivery models: dealing with uncertainty in models and the observational data

  • Evaluating distributed soil erosion models is challenging because of the uncertainty in models and measurements of system responses. Here, we present an approach to evaluate soil erosion and sediment delivery models, which incorporates sediment source fingerprinting and sediment-rating curve uncertainty into model testing. We applied the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to the Sediment Delivery Distributed model (SEDD) for a large catchment in Southeast Brazil. The model was not rejected, as 23.4% of model realizations were considered behavioral. Fingerprinting results and SEDD simulations showed a partial agreement regarding the identification of the main sediment sources in the catchment. However, grid-based estimates of soil erosion and sediment delivery rates were highly uncertain, which restricted the model's usefulness for quantifying sediment dynamics. Although our results are case-specific, similar levels of error might be expected in erosionEvaluating distributed soil erosion models is challenging because of the uncertainty in models and measurements of system responses. Here, we present an approach to evaluate soil erosion and sediment delivery models, which incorporates sediment source fingerprinting and sediment-rating curve uncertainty into model testing. We applied the Generalized Likelihood Uncertainty Estimation (GLUE) methodology to the Sediment Delivery Distributed model (SEDD) for a large catchment in Southeast Brazil. The model was not rejected, as 23.4% of model realizations were considered behavioral. Fingerprinting results and SEDD simulations showed a partial agreement regarding the identification of the main sediment sources in the catchment. However, grid-based estimates of soil erosion and sediment delivery rates were highly uncertain, which restricted the model's usefulness for quantifying sediment dynamics. Although our results are case-specific, similar levels of error might be expected in erosion models elsewhere. The representation of such errors should be standard practice.show moreshow less

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Metadaten
Author:Pedro V. G. BatistaORCiDGND, J. Patrick Laceby, Jessica Davies, Teotônio S. Carvalho, Diego Tassinari, Marx L. N. Silva, Nilton Curi, John N. Quinton
URN:urn:nbn:de:bvb:384-opus4-1010355
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/101035
ISSN:1364-8152OPAC
Parent Title (English):Environmental Modelling & Software
Publisher:Elsevier BV
Type:Article
Language:English
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2023/01/16
Tag:Ecological Modeling; Environmental Engineering; Software
Volume:137
First Page:104961
DOI:https://doi.org/10.1016/j.envsoft.2021.104961
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Geographie
Fakultät für Angewandte Informatik / Institut für Geographie / Professur für Wasser- und Bodenressourcenforschung
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)