A comparison of three approaches to spatial generalization of rainfall–runoff models

Abstract

Abstract:The generalization of the parameters of rainfall-runoff models, to enable application at ungauged sites, is an important and ongoing area of research. This paper compares the performance of three alternative methods of generalization, for two parameter-sparse conceptual models (PDM and TATE), specifically for use in flood frequency estimation using continuous simulation. Two of the methods are based on fitting regression relationships between catchment properties and calibrated parameter values, using weighted or sequential regression (with weights based on estimates of calibration uncertainty), and the third is based on the use of pooling groups, defined through measures of site-similarity based on catchment properties. The study uses a relatively large sample of catchments in Britain.For the PDM, the site-similarity method performs best, but not greatly better than either regression method, so there may be cases where the use of regression would be preferable. For the TATE model, weighted regression performs best (with a very similar level of performance to that of the PDM with site-similarity), whereas site-similarity performs worst (due to poor performance for catchments with higher baseflow), indicating that the choice of model and generalization method should not be separated. The use of sequential regression, which was developed to try to allow for parameter interdependence, shows no clear advantage for either model. Other than the poor performance of the TATE model with site-similarity for catchments with a higher baseflow index, there are no clear relationships between performance of any model/method and catchment type.

Parameter regionalization for a process-oriented distributed model dedicated to flash floods

“…Even for large datasets, modellers' choices, rainfall to runoff model's structure and parameterization (Bárdossy, 2007;Kay et al, 2006), or physical descriptors availability (Merz et al, 2006) influence regionalization performances and the possible physical interpretations.…”

HESS Opinions "Crash tests for a standardized evaluation of hydrological models"

“…But we are convinced that large catchment sets are the only possible way to learn from the variety of catchments, simply because they make it easier to falsify (refute) the models we wish to test (Popper, 1959). A few modellers seem to share this point of view: they also have published model tests based on large catchment sets, particularly in the perspective of modelling ungauged catchments (see among others Nathan and McMahon, 1990;Vandewiele et al, 1992;Merz and Blöschl, 2004;McIntyre et al, 2005;Kay et al, 2006;Young, 2006;Boughton and Chiew, 2007).…”

Catchment Variability and Parameter Estimation in Multi-Objective Regionalisation of a Rainfall–Runoff Model

“…In Wagener and Wheater (2006) the focus is much more on issues that relate to model identification. Our work more resembles the works by Kay et al (2006), Young (2006) and Oudin et al (2008) that generally aim to evaluate effectiveness of regionalisation by considering multiple objectives and/or effects of parsimonious model structures.…”

“…Concluding that PCCs are inappropriate or that optimal parameter sets are wrongly defined cannot be simply reasoned for. It must be noted that the base flow index as effective PCC in Young (2006), Kay et al (2006), McIntyre et al (2005) and Wagener and Wheater (2006) was not used in this study, but obviously has (very) positively affected the outcomes of these works. The latter work considered only 10 catchments in south east England with, presumably, much less variability.…”