Impacts of uncertain river flow data on rainfall-runoff model calibration and discharge predictions

被引:199
作者
McMillan, Hilary [1 ]
Freer, Jim [2 ]
Pappenberger, Florian [3 ]
Krueger, Tobias [4 ]
Clark, Martyn [1 ]
机构
[1] Natl Inst Water & Atmospher Res Ltd, Christchurch, New Zealand
[2] Univ Bristol, Sch Geog Sci, Bristol, Avon, England
[3] European Ctr Medium Range Weather Forecasts, Reading RG2 9AX, Berks, England
[4] Univ E Anglia, Norwich NR4 7TJ, Norfolk, England
基金
英国自然环境研究理事会;
关键词
River flow; model calibration; rating curve; uncertainty; stage-discharge; gravel-bed river; GLOBAL OPTIMIZATION; BAYESIAN METHODS; EVOLUTION; PARAMETER; DYNAMICS; ASSAM;
D O I
10.1002/hyp.7587
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
In order to quantify total error affecting hydrological models and predictions, we must explicitly recognize errors in input data, model structure, model parameters and validation data. This paper tackles the last of these: errors in discharge measurements used to calibrate a rainfall-runoff model, caused by stage-discharge rating-curve uncertainty. This uncertainty may be due to several combined sources, including errors in stage and velocity measurements during individual gaugings, assumptions regarding a particular form of stage-discharge relationship, extrapolation of the stage-discharge relationship beyond the maximum gauging, and cross-section change due to vegetation growth and/or bed movement. A methodology is presented to systematically assess and quantify the uncertainty in discharge measurements due to all of these sources. For a given stage measurement, a complete PDF of true discharge is estimated. Consequently, new model calibration techniques can be introduced to explicitly account for the discharge error distribution. The method is demonstrated for a gravel-bed river in New Zealand, where all the above uncertainty sources can be identified, including significant uncertainty in cross-section form due to scour and re-deposition of sediment. Results show that rigorous consideration of uncertainty in flow data results in significant improvement of the model's ability to predict the observed flow. Copyright (C) 2010 John Wiley & Sons, Ltd.
引用
收藏
页码:1270 / 1284
页数:15
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