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doi:10.1016/j.envsoft.2003.12.006 
Copyright © 2004 Elsevier Ltd. All rights reserved.
On the value of experimental data to reduce the prediction uncertainty of a process-oriented catchment model
Received 9 July 2003;
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Abstract
Predicting hydrological response to rainfall or snowmelt including an estimation of prediction uncertainty is a major challenge in current hydrological research. The process-based catchment model TACD (tracer aided catchment model, distributed) was applied to the mountainous Brugga basin (40 km2), located in the Black Forest Mountains, southwest Germany. The Monte Carlo-based generalized likelihood uncertainty estimation (GLUE) framework was used to analyse the uncertainty of discharge predictions. The model input parameter sets were generated using the Latin Hypercube sampling method, which is an efficient way to sample the parameter space representatively. It was shown that the number of investigated parameters should exceed the number of varied parameters by at least a factor of 10. Even if the process basis and suitability of the model could be proven, relatively large uncertainty ranges of the discharge predictions still occurred during the simulation of floods. Prediction uncertainty varied both temporally and spatially. Incorporating additional data, i.e. sub-basin runoff and observed tracer concentrations, reduced the prediction uncertainty. However, the potential restriction of the uncertainty clearly depends on the goodness of the simulation of the additional data set. Knowledge of the uncertainty of model predictions and of the potential for experimental data to reduce it are crucial to sustainable environmental management, and should be considered more thoroughly during the planning of future field studies.
Author Keywords: Uncertainty; Catchment modelling; TACD model; GLUE; Flood modelling; Multi-response data; Model validation
Article Outline
- 1. Introduction
- 2. The Brugga basin
- 3. Model description and input data
- 3.1. Meteorological input data
- 3.2. Snow module
- 3.3. Soil module
- 3.4. Runoff generation and routing module
- 3.5. Hydrological and hydrochemical data
- 4. Investigating model uncertainty
- 4.1. Monte Carlo simulations using Latin Hypercube sampling
- 4.2. Objective functions and choice of acceptable parameter sets with the GLUE framework
- 5. Simulation results
- 6. Discussion and conclusions
- Acknowledgements
- References
