HR: 12:05h
AN: H42D-08    [Abstracts]
TI: Improving environmental model diagnostic techniques – Development of a timestep-based performance measure for hydrological models
AU: * Freer, J
EM: j.freer@lancaster.ac.uk
AF: Institute of Environmental and Natural Sciences, Lancaster University, Lancaster, LA1 4YQ, United Kingdom
AU: Liu, Y
EM: liuyl@student.dlut.edu.cn
AF: Water Resources and Hydrology Insititute, School of Civil and Hydraulic Engineering, Da Lian University of Technology, Da Lian, 116024, China
AU: Younger, P
EM: p.younger@lancaster.ac.uk
AF: Institute of Environmental and Natural Sciences, Lancaster University, Lancaster, LA1 4YQ, United Kingdom
AU: Beven, K
EM: k.j.beven@lancaster.ac.uk
AF: Institute of Environmental and Natural Sciences, Lancaster University, Lancaster, LA1 4YQ, United Kingdom
AB: The idea that a number of different models may model the observed data equally well is not a new one (see discussion in Beven, 2001 and Beven and Freer, 2001). This is the concept known as equifinality. This idea was developed into the Generalised Likelihood Uncertainty Estimation (GLUE) of Beven and Binley, (1992). Beven, (2006) commented on the need to improve the equifinality technique by defining levels of acceptability for model predictions; this issue is addressed in the present study. Traditionally models have been analysed using a performance measures, such as Root Mean Squared Error (RMSE). These measures only take into account the modelled output against the observed output for the entire data range. Whilst these measures can be very useful in giving an overall picture of modelled performance, they cannot give information about model performance at individual time steps or acknowledge observational error in a meaningful way. By contrast, a performance measure based on individual time steps will give information allowing the unknown observed values to be reconstructed from an ensemble of different model estimates. In this paper a model is used to give predictions of flow at a catchment gauge using real data. The results are then analysed using the method created in this paper, an extension of the GLUE procedure. The study area, data and model are presented as is an outline of the methodology created. The paper suggests that we need to be more thoughtful about errors in our observations and be inclusive to these when we evaluate our models. K. Beven. A manifesto for the equifinality thesis. Journal of Hydrology, 320(1-2):18-36, 2006. K.J. Beven. Rainfall-Runoff Modelling: The Primer. John Wiley & Sons, Chichester, 2001 K.J. Beven and A.M. Binley. The future of distributed models - model calibration and uncertainty prediction. Hydrological Processes, 6(3):279-298, 1992. K. Beven and J. Freer. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology, 249(1-4):11-29, 2001.
DE: 1816 Estimation and forecasting
DE: 1846 Model calibration (3333)
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting