Showing papers by "Thomas Kjeldsen published in 2009"

A formal statistical model for pooled analysis of extreme floods

Abstract: This paper describes a formal statistical model underlying the region-of-influence method routinely used in regional frequency analysis of hydrological extremes, and is an improvement to the existing Flood Estimation Handbook (FEH) method for pooled frequency analysis of annual maximum flows in the UK. Specification of a pooling-group method requires three issues to be resolved: how to define hydrological similarity, the size of pooling groups and calculation of the pooled L-moment ratios. Because these issues are interrelated, an exploratory and iterative study has been undertaken before arriving at the final version of the method. Improvements provided by the model are: (1) that it allows an increased weight to be given to a gauged catchment when it is itself the target location and (2) it does not require identification of a homogeneous region, since the expected differences between the L-moment ratios within a pooling group are explicitly accounted for. Using annual maximum series from 602 gauged rural catchments, a comparison of candidate methods shows that the new method performs better than these others, including the FEH method. While the numerical comparison suggests that the improvement is 4%, and thus only minor, arguments are given for why this is a misleading conclusion.

An exploratory analysis of error components in hydrological regression modeling

Abstract: [1] The use of the generalized least squares (GLS) technique for estimation of hydrological regression models has become good practice in hydrology. Through a regression model, a simple link between a particular hydrological variable and a set of catchment descriptors can be established. The regression residuals can be treated as the sum of sampling errors in the hydrological variable and errors in the regression model. This article presents a method for exploring and developing a parameterized form for the cross-correlation between the regression model errors. Given an initial GLS analysis, a reweighted set of regression residuals is defined such that the covariance of these residuals is essentially similar to that of the model errors. The cross-products of the reweighted regression residuals, pooled within bins, are then used to identify a structure and to fit a parameterized form for the cross-correlations of the regression errors. These estimated cross-correlations are then used to inform improved GLS and reweighting steps, leading to a recursive procedure. The main advantage of the recursive GLS procedure is that it allows for a simple demonstration of the actual existence of model error correlation as well as for exploring suitable models for the correlation. However, while the procedure for identifying a parametric description for the model error correlation is reasonable, estimation of the parameters within the recursive procedure is affected by the data-binning step. Thus it is suggested that once a structure for the correlation has been decided, further data analysis—such as final decisions about variables included in the regression model and final estimation of parameters—should be undertaken within a maximum likelihood framework. The procedure has been tested on annual maximum flow data from 602 catchments located throughout the United Kingdom. A set of Monte Carlo experiments further confirmed the ability of the recursive GLS procedure to correctly identify and estimate the true model error correlation.

Modelling the impact of urbanisation on flood runoff volume

Abstract: In this study, a relatively simple and intuitive extension to an event-based lumped conceptual rainfall–runoff model, the revitalised flood hydrograph model is introduced and shown to improve model performance on a heavily urbanised catchment. The extension does not affect model performance on a rural catchment. The hydrological losses in the model are calculated based on a percentage runoff type model, initially developed for use in rural catchments and relating losses to soil moisture content, soil moisture capacity and rainfall. By assuming a fixed percentage runoff of 70% from the urban (impervious) parts of the catchment, an overall improvement in predictive ability of the extended urban model is observed on the heavily urbanised catchment. Model performance is generally found to be improved more when considering events occurring with low initial soil moisture content than for events associated with higher initial soil moisture content. A sensitivity analysis indicates that the urban loss-model is re...