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Journal ArticleDOI

Predicting daily flows in ungauged catchments: model regionalization from catchment descriptors at the Coweeta Hydrologic Laboratory, North Carolina

TLDR
In this paper, the authors investigated regionalization approaches to daily streamflow prediction for 13 catchments in the Coweeta Hydrologic Laboratory using a conceptual rainfall-runoff model of low complexity (six parameters).
Abstract
Regionalization approaches to daily streamflow prediction are investigated for 13 catchments in the Coweeta Hydrologic Laboratory using a conceptual rainfall-runoff model of low complexity (six parameters). Model parameters are considered to represent the dynamic response characteristics (DRCs) of a catchment. It is demonstrated that all catchments within the region cannot be assumed to have a similar hydrological behaviour, and thence a regionalization approach considering differences in physical catchment descriptors (PCDs) is required. Such a regionalization approach can be regarded as a top-down method, in the sense that factors controlling parameter variability are identified first within the entire region under study, and then such information is exploited to predict runoff in a smaller sub-region. Regionalization results reveal that consideration of interrelation s between dependent variables, which here are the parameters of the rainfall-runoff model, can improve performance of regression as a regionalization method. Breaking the parameter correlation structure inherent in the model, and exploiting merely relationships between model parameters and PCDs (no matter how weakly related they are), can result in a significant decrease in regionalization performance. Also, high significance of regression between values of PCDs and DRCs does not guarantee a set of parameters with a good predictive power. When there is a reason to believe that, in the sense of hydrological behaviour, a gauged catchment resembles the ungauged catchment, then it may be worthwhile to adopt the entire set of calibrated parameters from the gauged catchment instead of deriving quantitative relationships between catchment descriptors and model parameters. Copyright © 2003 John Wiley & Sons, Ltd.

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Citations
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Journal ArticleDOI

Downward approach to hydrological prediction

TL;DR: The downward approach as mentioned in this paper is a necessary counterpoint to the mechanistic "reductionist" approach that dominates current hydrological model development and provides a systematic framework to learning from data, including the testing of hypotheses at every step of analysis.
Journal ArticleDOI

A comparison of regionalisation methods for catchment model parameters

TL;DR: In this article, the relative performance of a range of methods for transposing catchment model parameters to ungauged catchments was examined and the results indicated that two methods perform best.
Journal ArticleDOI

Calibration of hydrological model parameters for ungauged catchments

TL;DR: In this paper, a different approach for the transfer of entire parameter sets from one catchment to another is discussed, where parameter sets are considered as tranferable if the corresponding model performance (defined as the Nash-Sutclife efficiency) on the donor catchment is good and the regional statistics: means and variances of annual discharges estimated from catchment properties and annual climate statistics for the recipient catchment are well reproduced by the model.
Journal ArticleDOI

Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins

TL;DR: In this article, the authors evaluate a threshold-based flash flood warning method, by considering a wide range of climatic and physiographic conditions, and by focusing on ungauged basins.
Journal ArticleDOI

Ensemble predictions of runoff in ungauged catchments

TL;DR: In this paper, a new approach to regionalization of conceptual rainfall-runoff models is presented on the basis of ensemble modeling and model averaging, which represents an improvement on the established procedure of regressing parameter values against numeric catchment descriptors.
References
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Journal ArticleDOI

River flow forecasting through conceptual models part I — A discussion of principles☆

TL;DR: In this article, the principles governing the application of the conceptual model technique to river flow forecasting are discussed and the necessity for a systematic approach to the development and testing of the model is explained and some preliminary ideas suggested.

A physically based, variable contributing area model of basin hydrology

Mike Kirkby, +1 more
TL;DR: In this paper, a hydrological forecasting model is presented that attempts to combine the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple lumped parameter basin models.
Journal ArticleDOI

A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant

TL;DR: In this paper, a hydrological forecasting model is presented that combines the important distributed effects of channel network topology and dynamic contributing areas with the advantages of simple luminescence.
Journal ArticleDOI

Scale issues in hydrological modelling: A review

TL;DR: A framework is provided for scaling and scale issues in hydrology and a more holistic perspective dealing with dimensional analysis and similarity concepts is addressed, which deals with complex processes in a much simpler fashion.
Journal ArticleDOI

A new procedure for gridding elevation and stream line data with automatic removal of spurious pits

TL;DR: In this article, a morphological approach to the interpolation of regular grid digital elevation models (DEMs) from surface specific elevation data points and selected stream lines is described, which has given rise to a computationally efficient interpolation procedure which couples the minimization of a terrain specific roughness penalty with an automatic drainage enforcement algorithm.
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