Effects of uncertainties in hydrological modelling. A case study of a mountainous catchment in Southern Norway

被引:31
|
作者
Engeland, Kolbjorn [1 ]
Steinsland, Ingelin [2 ,3 ]
Johansen, Stian Solvang [4 ]
Petersen-Overleir, Asgeir [4 ]
Kolberg, Sjur [3 ]
机构
[1] Norwegian Water Resources & Energy Directorate NV, POB 5091 Majorstuen, N-0301 Oslo, Norway
[2] Norwegian Univ Sci & Technol, Dept Math Sci, N-7491 Trondheim, Norway
[3] SINTEF Energy Res, N-7465 Trondheim, Norway
[4] Statkraft, N-0284 Oslo, Norway
关键词
Hydrological modelling; Streamflow uncertainty; Precipitation uncertainty; Temperature uncertainty; PROBABILISTIC FORECASTS; AIR-TEMPERATURE; FLOOD; CALIBRATION; DECOMPOSITION; RAINFALL; IMPACTS; ERROR; MODIS;
D O I
10.1016/j.jhydrol.2016.02.036
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In this study, we explore the effect of uncertainty and poor observation quality on hydrological model calibration and predictions. The Osali catchment in Western Norway was selected as case study and an elevation distributed HBV-model was used. We systematically evaluated the effect of accounting for uncertainty in parameters, precipitation input, temperature input and streamflow observations. For precipitation and temperature we accounted for the interpolation uncertainty, and for streamflow we accounted for rating curve uncertainty. Further, the effects of poorer quality of precipitation input and streamflow observations were explored. Less information about precipitation was obtained by excluding the nearest precipitation station from the analysis, while reduced information about the streamflow was obtained by omitting the highest and lowest streamflow observations when estimating the rating curve. The results showed that including uncertainty in the precipitation and temperature inputs has a negligible effect on the posterior distribution of parameters and for the Nash-Sutcliffe (NS) efficiency for the predicted flows, while the reliability and the continuous rank probability score (CRPS) improves. Less information in precipitation input resulted in a shift in the water balance parameter P-corr, a model producing smoother streamflow predictions, giving poorer NS and CRPS, but higher reliability. The effect of calibrating the hydrological model using streamflow observations based on different rating curves is mainly seen as variability in the water balance parameter P-corr. When evaluating predictions, the best evaluation scores were not achieved for the rating curve used for calibration, but for rating curves giving smoother streamflow observations. Less information in streamflow influenced the water balance parameter P-corr, and increased the spread in evaluation scores by giving both better and worse scores. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码:147 / 160
页数:14
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