Scale dependent parameterization of soil hydraulic conductivity in 3D simulation of hydrological processes in a forested headwater catchment

Fang, Zhufeng; Bogena, Heye; Kollet, Stefan; Vereecken, Harry

doi:10.1016/j.jhydrol.2016.03.020

Cited by 25 publications

(21 citation statements)

References 31 publications

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“…Soil characteristics were represented using van Genuchten functions for different soil types in the model, with parameter values obtained from Schaap and Leij (). In order to alleviate a wet bias in soil moisture due to the coarse lateral discretization (Shrestha et al, ), the horizontal hydraulic conductivity was increased by 2 orders of magnitude, as recently suggested by Fang et al () following the upscaling approach of Niedda (). The global database of Gleeson et al () was used for the deeper (lower 20 layers) subsurface characterization.…”

Section: Methodsmentioning

confidence: 99%

Quantifying the Impact of Subsurface‐Land Surface Physical Processes on the Predictive Skill of Subseasonal Mesoscale Atmospheric Simulations

et al. 2018

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Integrated terrestrial system modeling platforms, which simulate the 3‐D flow of water both in the subsurface and the atmosphere, are expected to improve the realism of predictions through a more detailed physics‐based representation of hydrometeorological processes and feedbacks. We test this expectation by evaluating simulation results from different configurations of an atmospheric model with increasing complexity in the representation of land surface and subsurface physical processes. The evaluation is performed using observations during the (HD(CP)2) Observational Prototype Experiment field campaign in April–May 2013 over western Germany. The augmented model physics do not improve the prediction of daily cumulative precipitation and minimum temperature during this period. Moreover, a cold bias is introduced in the simulated daily maximum temperature, which decreases the performance of the atmospheric model with respect to its standard configuration. The decreased performance in the maximum temperature is traced in part to a higher simulated soil moisture, which shifts surface flux partitioning toward higher latent and lower sensible heat fluxes. The better reproduced air temperature profiles simulated by the standard atmospheric model comes, however, with an overestimated heat flux at the land surface caused by a warm bias in the simulated soil temperature. Simulated atmospheric states do not correlate significantly with differences in soil moisture and temperature; thus, different turbulent flux parameterizations dominate the propagation of the subsurface signal into the atmosphere. The strong influence of the lateral synoptic forcings on the results suggests, however, the need for further investigations encompassing different weather situations or regions with stronger land‐atmosphere coupling conditions.

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Section: Methodsmentioning

confidence: 99%

Quantifying the Impact of Subsurface‐Land Surface Physical Processes on the Predictive Skill of Subseasonal Mesoscale Atmospheric Simulations

et al. 2018

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“…Vegetation was grouped into three classes based on vegetation height, as vegetation species are relatively homogenous across the catchment (Farnes et al, 1995;Mincemoyer and Birdsall, 2007) and height is an important determinant of aerodynamic resistance, used within both the snow and evapotranspiration modules to estimate catchment response on a cell-by-cell basis (Wigmosta et al, 2002). Vegetation classes included tall trees (> 10 m), trees (2-10 m), and undergrowth (< 2 m; Fig.…”

Section: Spatial Forcing Datamentioning

confidence: 99%

“…While our approach is demonstrated for a single catchment for a relatively short time period, it has broader implications for the use of alternative data sources in the parameter estimation process as well as the application of distributed models to simulate internal catchment behavior. (Farnes et al, 1995). Snowmelt, occurring in May or June, is the primary driver of the hydrologic cycle.…”

Section: Introductionmentioning

confidence: 99%

“…Bedrock under Stringer Creek includes biotite hornblende quartz monzonite overlaying a layer of shale in the upper parts of the catchment and flathead sandstone underlain by granite gneiss in the lower portions of the catchment (Reynolds, 1995). Lodgepole pine dominates the forest vegetation types, with shrubs and grasses occurring in riparian areas (Farnes et al, 1995;Mincemoyer and Birdsall, 2006). Stringer Creek, the most well-studied catchment in Tenderfoot Creek Experimental Forest, is a second-order catchment that drains an area of 5.5 km 2 .…”

Section: Introductionmentioning

confidence: 99%

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Response to Reviewer 2

Kelleher¹

2017

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Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding. General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/userguides/explore-bristol-research/ebr-terms/

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“…Schalge et al (2017) show that without any scaling, the grid-scale routing scheme at a resolution of 400 m delays the discharge peak at the mouth of the Neckar catchment (south-western Germany) by 3 days, whereas peaks are underestimated compared with observations. Sub-scale parameterizations have been developed based on the subgrid scale topographic index, for example, by Niedda (2004), which has been refined, for example, by Fang, Bogena, Kollet, and Vereecken (2016), and shown to improve river discharge. Other methods employ probabilistic approaches (Piccolroaz et al, 2016) to account for uncertainty related to overland flow.…”

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confidence: 99%

Improvement of surface run‐off in the hydrological model ParFlow by a scale‐consistent river parameterization

Schalge

Haefliger

Kollet

et al. 2019

Hydrological Processes

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We propose an improvement of the overland‐flow parameterization in a distributed hydrological model, which uses a constant horizontal grid resolution and employs the kinematic wave approximation for both hillslope and river channel flow. The standard parameterization lacks any channel flow characteristics for rivers, which results in reduced river flow velocities for streams narrower than the horizontal grid resolution. Moreover, the surface areas, through which these wider model rivers may exchange water with the subsurface, are larger than the real river channels potentially leading to unrealistic vertical flows. We propose an approximation of the subscale channel flow by scaling Manning's roughness in the kinematic wave formulation via a relationship between river width and grid cell size, following a simplified version of the Barré de Saint‐Venant equations (Manning–Strickler equations). The too large exchange areas between model rivers and the subsurface are compensated by a grid resolution‐dependent scaling of the infiltration/exfiltration rate across river beds. We test both scaling approaches in the integrated hydrological model ParFlow. An empirical relation is used for estimating the true river width from the mean annual discharge. Our simulations show that the scaling of the roughness coefficient and the hydraulic conductivity effectively corrects overland flow velocities calculated on the coarse grid leading to a better representation of flood waves in the river channels.

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Scale dependent parameterization of soil hydraulic conductivity in 3D simulation of hydrological processes in a forested headwater catchment

Cited by 25 publications

References 31 publications

Quantifying the Impact of Subsurface‐Land Surface Physical Processes on the Predictive Skill of Subseasonal Mesoscale Atmospheric Simulations

Quantifying the Impact of Subsurface‐Land Surface Physical Processes on the Predictive Skill of Subseasonal Mesoscale Atmospheric Simulations

Response to Reviewer 2

Improvement of surface run‐off in the hydrological model ParFlow by a scale‐consistent river parameterization

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