Sources of uncertainty in modeling the glaciohydrological response of a Karakoram watershed to climate change

被引:95
作者
Ragettli, S. [1 ]
Pellicciotti, F. [1 ]
Bordoy, R. [1 ]
Immerzeel, W. W. [1 ,2 ]
机构
[1] Swiss Fed Inst Technol, Inst Environm Engn, ETH Hoenggerberg HIL 211, Zurich, Switzerland
[2] Univ Utrecht, Utrecht, Netherlands
关键词
glaciohydrological modeling; TOPKAPI-ETH; sources of uncertainty; projections of future streamflow; Himalaya-Karakoram; parameter information content; HAUT-GLACIER-DAROLLA; HUNZA RIVER-BASIN; SENSITIVITY-ANALYSIS; BALTORO GLACIER; SOLAR-RADIATION; MELT; RUNOFF; SNOW; ICE; ABLATION;
D O I
10.1002/wrcr.20450
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In the headwater catchments of the main Asian rivers, glaciohydrological models are a useful tool to anticipate impacts of climatic changes. However, the reliability of their projections strongly depends on the quality and quantity of data that are available for parameter estimation, model calibration and validation, as well as on the accuracy of climate change projections. In this study the physically oriented, glaciohydrological model TOPKAPI-ETH is used to simulate future changes in snow, glacier, and runoff from the Hunza River Basin in northern Pakistan. Three key sources of model uncertainty in future runoff projections are compared: model parameters, climate projections, and natural climate variability. A novel approach, applicable also to ungauged catchments, is used to determine which model parameters and model components significantly affect the overall model uncertainty. We show that the model is capable of reproducing streamflow and glacier mass balances, but that all analyzed sources of uncertainty significantly affect the reliability of future projections, and that their effect is variable in time and in space. The effect of parametric uncertainty often exceeds the impact of climate uncertainty and natural climate variability, especially in heavily glacierized subcatchments. The results of the uncertainty analysis allow detailed recommendations on network design and the timing and location of field measurements, which could efficiently help to reduce model uncertainty in the future.
引用
收藏
页码:6048 / 6066
页数:19
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