Spatially distributed data for erosion model calibration and validation: The Ganspoel and Kinderveld datasets

Recent experience in distributed modelling has revealed that the performance of process-based erosion and hydrological models are extremely sensitive to parameter estimations and that predictions are often poor. It is also observed that quite different parameter sets may lead to very similar results...

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Vydáno v:Catena (Giessen) Ročník 61; číslo 2; s. 105 - 121
Hlavní autoři: Van Oost, K., Govers, G., Cerdan, O., Thauré, D., Van Rompaey, A., Steegen, A., Nachtergaele, J., Takken, I., Poesen, J.
Médium: Journal Article Konferenční příspěvek
Jazyk:angličtina
Vydáno: Cremlingen-Destedt Elsevier B.V 30.06.2005
Catena
Elsevier
Témata:
Bgi / Prodig
Catchments. Hydrological cycle
Earth Sciences
Hydrometeorology
Model validation
Physical geography
Runoff
Sciences of the Universe
Soil erosion
Uncertainty
WATEM
Belgium
Uncertainty
WATEM
Soil erosion
Runoff
Model validation
Loess
Precipitation
Spatial distribution
Agricultural land use
Watershed
Model
Gully erosion
ISSN:0341-8162, 1872-6887
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Popis
Shrnutí:Recent experience in distributed modelling has revealed that the performance of process-based erosion and hydrological models are extremely sensitive to parameter estimations and that predictions are often poor. It is also observed that quite different parameter sets may lead to very similar results and that no ‘best’ parameter set can be identified. In this study, we describe a dataset that offers possibilities for improved evaluation and parameterisation of spatially distributed soil erosion models. The dataset combines rainfall, runoff and sediment discharge data collected at the outlet and field surveys within the catchments that describe soil surface characteristics and soil erosion features. This offers clear advantages over traditional model evaluation as not only the simulated overall system response, integrated over time and space, but also the simulated internal system dynamics can be compared with measured data. The paper discusses and illustrates the use of the dataset to narrow uncertainties associated with model predictions.
ISSN:0341-8162
1872-6887
DOI:10.1016/j.catena.2005.03.001