Hydrological Model Hypothesis Testing Using Imprecise Spatial Flux Measurements

Tobias Krueger1, Jim Freer2, John N. Quinton3, Philip M. Haygarth3, Christopher J. A. Macleod4, Jane M. B. Hawkins4, Gary S. Bilotta5, Richard E. Brazier6
1. School of Environmental Sciences University of East Anglia Norwich, UK

2. School of Geographical Sciences University of Bristol Bristol, UK
3. Lancaster Environment Centre Lancaster University Lancaster, UK
4. Cross Institute Programme for Sustainable Soil Function North Wyke Research Okehampton, UK
5. School of Environment and Technology University of Brighton Brighton, UK

6. School of Geography University of Exeter, Exeter, UK
1.t.krueger@uea.ac.uk; 2. jim.freer@bristol.ac.uk; 3. {j.quinton, p.haygarth}@lancaster.ac.uk;
4. g.s.bilotta@brighton.ac.uk; 5. r.e.brazier@ex.ac.uk; 6. {kit.macleod, jane.hawkins}@bbsrc.ac.uk

Abstract: The semi-distributed conceptual Dynamic Topmodel is set up to model runoff generation in the headwater catchment Den Brook in Devon, UK. The model is tested for its spatial consistency using discharge measurements made at four nested locations within the extended Generalised Likelihood Uncertainty Estimation (GLUE) framework accounting explicitly for parameter and observational uncertainties. It is shown how model simulations deemed behavioural at the catchment outlet can be composed of highly unrealistic internal fluxes. It follows that a catchment model's spatial predictions should not be assumed reasonable unless tested against observations. A close integration of model development and field experimentation is advocated as part of an iterative learning framework of catchment hydrology.

Keywords: data uncertainty; catchment topology; Dynamic Topmodel; extended GLUE

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