Assessing soil water content variation in a small mountainous catchment over different time scales and land covers using geographical variables

•Soil water content (SWC) variations are linked to geomorphology in badlands.•SWC dynamics at the catchment scale were captured by a small number of SWC probes.•Rainfall intensity and antecedent soil moisture impact outflows.•Subsurface flow processes depend on the catchment dryness/wetness. Soil Wa...

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Veröffentlicht in:Journal of hydrology (Amsterdam) 2020-12, Vol.591, p.125593, Article 125593
Hauptverfasser: Mallet, Florian, Marc, Vincent, Douvinet, Johnny, Rossello, Philippe, Joly, Daniel, Ruy, Stéphane
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creator Mallet, Florian
Marc, Vincent
Douvinet, Johnny
Rossello, Philippe
Joly, Daniel
Ruy, Stéphane
description •Soil water content (SWC) variations are linked to geomorphology in badlands.•SWC dynamics at the catchment scale were captured by a small number of SWC probes.•Rainfall intensity and antecedent soil moisture impact outflows.•Subsurface flow processes depend on the catchment dryness/wetness. Soil Water Content (SWC) plays a key role in hydrological processes at the catchment scale. It is highly dependent on local variables (such as soil properties, vegetation type, and topography) and rainfall, which makes it vary in space and time. To characterize its complexity, scientists need SWC monitoring at short time-steps and at different spatial scales (plot, hillslope, sub-catchment). Today such monitoring is possible due to sensors based on geophysical methods that measure SWC at the plot scale, but assessing the space–time variation of SWC remains a challenge in mountainous catchments. This study focuses on a small headwater catchment (Laval, 0.86 km2, Draix-Bléone observatory) composed primarily of black marls in which soil water content variations are assumed to occur primarily in the subsurface (0 cm–50 cm). To characterize the catchment inner hydrological response and to identify parameters controlling hydrological processes, 14 capacitance sensors were set up to monitor SWC at a 15 min time-step at depths of 10 cm to 20 cm; this network complements classic measurements (rainfall, outflows, erosion) in various soil-landscape-hydrological units. The model LISDQS (Interpolation of Quantitative and Spatial Data) based on local topographic variables (from a 1 m resolution Digital Elevation Model) (DEM), Normalized Difference Vegetation Index (NDVI), and soil depth made it possible to estimate SWC at the plot scale and at different time scales. Estimated SWC were compared to SWC time-series collected since May 2016. Finally, this method points out how local parameters that control SWC affect this variable according to rainfall and the catchment initial soil hydric status. At the hourly and daily time scales, SWC variations were highly dependent on curvature index and NDVI during wetting period, and primarily on soil depth and NDVI during drying periods. At 15 min time steps, 7 rainfall-runoff events have been investigated. SWC variations were highly dependent on rainfall intensity and initial SWC. At the same time step, matrix flow was characterized as the main subsurface flow type during fall flood events in every monitored environment while preferential flow
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To characterize the catchment inner hydrological response and to identify parameters controlling hydrological processes, 14 capacitance sensors were set up to monitor SWC at a 15 min time-step at depths of 10 cm to 20 cm; this network complements classic measurements (rainfall, outflows, erosion) in various soil-landscape-hydrological units. The model LISDQS (Interpolation of Quantitative and Spatial Data) based on local topographic variables (from a 1 m resolution Digital Elevation Model) (DEM), Normalized Difference Vegetation Index (NDVI), and soil depth made it possible to estimate SWC at the plot scale and at different time scales. Estimated SWC were compared to SWC time-series collected since May 2016. Finally, this method points out how local parameters that control SWC affect this variable according to rainfall and the catchment initial soil hydric status. 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To characterize the catchment inner hydrological response and to identify parameters controlling hydrological processes, 14 capacitance sensors were set up to monitor SWC at a 15 min time-step at depths of 10 cm to 20 cm; this network complements classic measurements (rainfall, outflows, erosion) in various soil-landscape-hydrological units. The model LISDQS (Interpolation of Quantitative and Spatial Data) based on local topographic variables (from a 1 m resolution Digital Elevation Model) (DEM), Normalized Difference Vegetation Index (NDVI), and soil depth made it possible to estimate SWC at the plot scale and at different time scales. Estimated SWC were compared to SWC time-series collected since May 2016. Finally, this method points out how local parameters that control SWC affect this variable according to rainfall and the catchment initial soil hydric status. 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To characterize the catchment inner hydrological response and to identify parameters controlling hydrological processes, 14 capacitance sensors were set up to monitor SWC at a 15 min time-step at depths of 10 cm to 20 cm; this network complements classic measurements (rainfall, outflows, erosion) in various soil-landscape-hydrological units. The model LISDQS (Interpolation of Quantitative and Spatial Data) based on local topographic variables (from a 1 m resolution Digital Elevation Model) (DEM), Normalized Difference Vegetation Index (NDVI), and soil depth made it possible to estimate SWC at the plot scale and at different time scales. Estimated SWC were compared to SWC time-series collected since May 2016. Finally, this method points out how local parameters that control SWC affect this variable according to rainfall and the catchment initial soil hydric status. At the hourly and daily time scales, SWC variations were highly dependent on curvature index and NDVI during wetting period, and primarily on soil depth and NDVI during drying periods. At 15 min time steps, 7 rainfall-runoff events have been investigated. SWC variations were highly dependent on rainfall intensity and initial SWC. At the same time step, matrix flow was characterized as the main subsurface flow type during fall flood events in every monitored environment while preferential flow appeared during summer flood events and mainly in grassland.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.jhydrol.2020.125593</doi><orcidid>https://orcid.org/0000-0003-0385-3239</orcidid><orcidid>https://orcid.org/0000-0002-4817-1779</orcidid><orcidid>https://orcid.org/0000-0002-4198-1955</orcidid><orcidid>https://orcid.org/0000-0002-2434-1999</orcidid><orcidid>https://orcid.org/0000-0002-9474-4857</orcidid><oa>free_for_read</oa></addata></record>
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subjects Earth Sciences
Flash flood
Headwater catchment
Hydrology
Monitoring
Sciences of the Universe
Soil water content
title Assessing soil water content variation in a small mountainous catchment over different time scales and land covers using geographical variables
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