Near surface roughness estimation: a parameterization derived from artificial rainfall experiments and two-dimensional hydrodynamic modelling for multiple vegetation coverages

  • Roughness is the key parameter for surface runoff simulations. This study aims to determine robust Manning resistance coefficients on the basis of consecutive artificial rainfall experiments on natural hillslopes available in literature, obtained at 22 different sites with different degrees of vegetation cover and type. The Manning resistance coefficient is particularly important in the context of two-dimensional (2D) hydraulic heavy rainfall simulations. Since there is a wide range of possible resistance values available leading to significantly different results regarding the accumulation of surface runoff, especially for shallow water depths. The planning of flood protection structures is directly affected by these uncertainties. This work also improves the knowledge between roughness and the shape of the hydrograph allowing a better calibration of infiltration models. As flow velocity, water depth, and infiltration rate were not observed during the rainfall experiments, only theRoughness is the key parameter for surface runoff simulations. This study aims to determine robust Manning resistance coefficients on the basis of consecutive artificial rainfall experiments on natural hillslopes available in literature, obtained at 22 different sites with different degrees of vegetation cover and type. The Manning resistance coefficient is particularly important in the context of two-dimensional (2D) hydraulic heavy rainfall simulations. Since there is a wide range of possible resistance values available leading to significantly different results regarding the accumulation of surface runoff, especially for shallow water depths. The planning of flood protection structures is directly affected by these uncertainties. This work also improves the knowledge between roughness and the shape of the hydrograph allowing a better calibration of infiltration models. As flow velocity, water depth, and infiltration rate were not observed during the rainfall experiments, only the outflow of the test field and rain intensity are known. For this purpose, a framework was developed to parameterize shallow water depth ( cm) -dependent roughness coefficients. To test the robustness of the framework, three different formulations of depth-dependent roughness and a constant Manning coefficient are used by comparing the measured discharge under different rainfall intensities with simulations in a 2D-hydraulic model. We identified a strong dependency of Manning’s on the degree of vegetation cover and -type as well as an influence of consecutive rainfall events. This finally leads to a more robust parameterization of near surface roughness for hydrodynamic modelling, which is particularly important for the simulation of heavy rainfall events.show moreshow less

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Metadaten
Author:David Feldmann, Patrick LauxORCiDGND, Andreas Heckl, Manfred Schindler, Harald KunstmannORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1006052
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/100605
ISSN:0022-1694OPAC
Parent Title (English):Journal of Hydrology
Publisher:Elsevier BV
Place of publication:Amsterdam
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/01/09
Tag:Water Science and Technology
Volume:617
Issue:Part A
First Page:128786
DOI:https://doi.org/10.1016/j.jhydrol.2022.128786
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Geographie
Fakultät für Angewandte Informatik / Institut für Geographie / Lehrstuhl für Regionales Klima und Hydrologie
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)