How do modeling choices and erosion zone locations impact the representation of connectivity and the dynamics of suspended sediments in a multi-source soil erosion model?

Abstract

Abstract. Soil erosion and suspended sediment transport understanding is an important issue in terms of soil and water resources management in the critical zone. In mesoscale watersheds (>10 km2) the spatial distribution of potential sediment sources within the catchment associated with rainfall dynamics is considered to be the main factor in the observed suspended sediment flux variability within and between runoff events. Given the high spatial heterogeneity that can exist for such scales of interest, distributed physically based models of soil erosion and sediment transport are powerful tools to distinguish the specific effect of structural and functional connectivity on suspended sediment flux dynamics. As the spatial discretization of a model and its parameterization can crucially influence how the structural connectivity of the catchment is represented in the model, this study analyzed the impact of modeling choices in terms of the contributing drainage area (CDA) threshold to define the river network and of Manning's roughness parameter (n) on the sediment flux variability at the outlet of two geomorphologically distinct watersheds. While the modeled liquid and solid discharges were found to be sensitive to these choices, the patterns of the modeled source contributions remained relatively similar when the CDA threshold was restricted to the range of 15 to 50 ha, with n restricted to the range 0.4–0.8 on the hillslopes and to 0.025–0.075 in the river. The comparison of the two catchments showed that the actual location of sediment sources was more important than the choices made during discretization and parameterization of the model. Among the various structural connectivity indicators used to describe the geological sources, the mean distance to the stream was the most relevant proxy for the temporal characteristics of the modeled sedigraphs.

Ötz-T: 3D-printed open-source turbidity sensor with Arduino shield for suspended sediment monitoring

“…hillslope erosion by rainfall events, glacier ice melt erosion, even hydropower storage in dams, as well as longer-term variability caused by ongoing climate change [33] , [21] , [8] , [9] . Measurements of SSC are also important for understanding how the impacts of hydroclimatic forcing on activating sediment sources may propagate through the river system in observations and also in physically-based hydrology-sediment models [5] , [6] , [9] , [25] , [41] . However, understanding such processes requires a temporal and spatial perspective on sediment pathways of production and storage within the catchment which cannot easily be achieved by current methods and sensing technology to measure SSCs.…”

Terrain analysis, erosion simulations, and sediment fingerprinting: a case study assessing the erosion sensitivity of agricultural catchments in the border of the volcanic plateau of Southern Brazil

“…Yet, while modeling outputs for stream channel and unpaved roads were not as significant, they are important sediment sources in S1 and S2, for instance. Sediment fingerprinting reflects the integration of all erosion processes occurring in a catchment and, although modelling added insights to the understanding of soil erosion, it may not be sufficient to reflect the connectivity of a catchment (Wohl et al 2019, Uber et al 2021.…”

“…For instance, to improve the understanding of erosion processes occurring from the source to the outlet of a catchment, sediment fingerprinting has been coupled to erosion modeling in target catchments (Palazón et al 2016;Battista et al 2020;Uber et al 2021).…”

Implementación de embalses en cálculos hidrológicos con Iber

“…La aplicación de modelos hidrológicos completamente distribuidos basados en las ecuaciones de aguas someras bidimensionales, también conocidas como 2D Shallow Water Equations (2D-SWE), es cada vez más habitual en las simulaciones lluvia-escorrentía a escala de cuenca (Costabile et al, 2012;Liang et al, 2015;Fernández-Pato et al, 2018;Bellos et al, 2020;Uber et al, 2021;Cea, Álvarez, et al, 2022;García-Alén et al, 2022). Estos modelos presentan varias ventajas con respecto a los modelos agregados o semi-distribuidos, como la capacidad de incluir en sus cálculos la variabilidad espacial de la lluvia y de las propiedades físicas de la cuenca (Refsgaard, 1997).…”