Digital terrain analysis of the Haute-Mentue catchment an scale effect for hydrological modelling with TOPMODEL

Higy, Christophe; Musy, A.

doi:10.5194/hess-4-225-2000

Cited by 12 publications

(4 citation statements)

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“…[30] TOPMODEL relies on the notion of saturation deficit, defined as the quantity of water to be added to the soil profile in order to bring it to complete saturation [Beven and Kirkby, 1979;Higy, 2000]. A field experiment was conducted on the Haute-Mentue catchment to monitor the temporal evolution of the soil moisture between 0 and 75 cm depth [Balin, 2004] within a plot of about 1 m 2 .…”

Section: Local Saturation Deficit As Topmodel Internal Variablementioning

confidence: 99%

Bayesian multiresponse calibration of TOPMODEL: Application to the Haute‐Mentue catchment, Switzerland

Talamba

Parent

Musy

2010

Water Resources Research

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[1] This paper introduces a general framework that evaluates a numerical Bayesian multiresponse calibration approach based on a Gibbs within Metropolis searching algorithm and a statistical likelihood function. The methodology has been applied with two versions of TOPMODEL on the Haute-Mentue experimental basin in Switzerland. The approach computes the following: the parameter's uncertainty, the parametric uncertainty of the output responses stemming from parameter uncertainty, and the predictive uncertainty of the output responses stemming from an error term including, indiscriminately in a lumped way, model structure and input and output errors. Two case studies are presented: The first one applies this methodology with the classical TOPMODEL to assess the role of two-response calibration (observed discharge and soil saturation deficits) on model parameters and output uncertainty. The second one uses a three-response calibration (observed discharge, silica, and calcium stream water concentrations) with a modified version of TOPMODEL to study the uncertainty of the parameters and of the simulated responses. Despite its limitations, the present multiresponse Bayesian approach proved a valuable tool in uncertainty analyses, and it contributed to a better understanding of the role of the internal variables and the value of additional information for enhancing model structure robustness and for checking the performance of conceptual models.

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Section: Local Saturation Deficit As Topmodel Internal Variablementioning

confidence: 99%

Bayesian multiresponse calibration of TOPMODEL: Application to the Haute‐Mentue catchment, Switzerland

Talamba

Parent

Musy

2010

Water Resources Research

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“…with coarser cell sizes (e.g. Zhang and Montgomery, 1994;Franchini et al, 1996;Saulnier et al, 1997b;Brasington and Richards, 1998;Higy and Musy, 2000;Valeo and Moin, 2000;Wu et al, 2007). This shift in x leads to larger calibrated transmissivities and saturated hydraulic conductivities when the DEM resolution decreases, as detailed by Franchini et al (1996).…”

Section: Interplay Between Dem Resolution Ti Distribution and Topmodel's Parametersmentioning

confidence: 99%

Explicitation of an important scale dependence in TOPMODEL using a dimensionless topographic index

Ducharne¹

2009

Preprint

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Abstract. This paper stems from the fact that the topographic index used in TOPMODEL is not dimensionless. In each pixel i in a catchment, it is defined as xi=ln (ai /Si), where ai is the specific contributing area per unit contour length and Si is the topographic slope. In the SI unit system, ai /Si is in meters, and the unit of xi is problematic. Even if all the equations in TOPMODEL are homogeneous, it is confusing to use the logarithm of a non-dimensionless quantity as an index, and we propose a simple solution to this issue in the nowadays widespread cases where the topographic index is computed from a regular raster digital elevation model (DEM). The pixel length C being constant, we can define a dimensionless topographic index yi=xi−ln C. Reformulating TOPMODEL's equations to use yi instead of xi helps giving the units of all the terms in TOPMODEL's equations. Another advantage is to raise awareness about the scale dependence of these equations via the explicit use of the DEM cell size C outside from the topographic index, in what what can be defined as the transmissivity at saturation per unit contour length T0/C. We eventually demonstrate, based on various examples from the literature, that both T0/C and the spatial mean y of the proposed dimensionless topographic index are very stable with respect to DEM resolution. This markedly reduces the recalibration necessity when changing DEM resolution, thus offering an efficient rescaling framework.

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“…There seem to be more examples in the domain of hydrological modelling, perhaps due to the importance of terrain characteristics on hydrologic flows, and the sensitivity of terrain measures such as slope and aspect to the models and resolutions selected to represent the terrain (e.g. Band 1993, Band et al 1995, Higy and Musy 2000, Kirkby 1997. Results from these primarily hydrologic models, however, have implications for coupled models that examine feedbacks between hydrology and vegetation growth and biogeochemical cycling (i.e.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Spatial Partitioning in Hydroecological Models: Predicting Grassland Productivity with RHESSys

Mitchell

Csillag²,

Tague³

2005

Transactions in GIS

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Environmental models constructed with a spatial domain require choices about the representation of space. Decisions in the adaptation of a spatial data model can have significant consequences on the ability to predict environmental function as a result of changes to levels of aggregation of input parameters and scaling issues in the processes being modelled. In some cases, it is possible to construct a systematic framework to evaluate the uncertainty in predictions using different spatial models; in other cases, the realm of possibilities plus the complexity of the environmental model in question may inhibit numeric uncertainty estimates. We demonstrate a range of potential spatial data models to parameterize a landscape-level hydroecological model (RHESSys). The effects of data model choice are illustrated, both in terms of input parameter distributions and resulting ecophysiological predictions. Predicted productivity varied widely, as a function of both the number of modelling units, and of arbitrary decisions such as the origin of a raster grid. It is therefore important to use as much information about the modelled environment as possible. Combinations of adaptive methods to evaluate distributions of input data, plus knowledge of dominant controls of ecosystem processes, can help evaluate potential representations. In this case, variance-based delineation of vegetation patches is shown to improve the ability to intelligently choose a patch distribution that minimizes the number of patches, while maintaining a degree of aggregation that does not overly bias the predictions.

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Digital terrain analysis of the Haute-Mentue catchment an scale effect for hydrological modelling with TOPMODEL

Cited by 12 publications

References 0 publications

Bayesian multiresponse calibration of TOPMODEL: Application to the Haute‐Mentue catchment, Switzerland

Bayesian multiresponse calibration of TOPMODEL: Application to the Haute‐Mentue catchment, Switzerland

Explicitation of an important scale dependence in TOPMODEL using a dimensionless topographic index

Impacts of Spatial Partitioning in Hydroecological Models: Predicting Grassland Productivity with RHESSys

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