Uncertainty in water resource model parameters used for climate change impact assessment
Uncertainty in water resource model parameters used for climate change impact assessment
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DOI：
10.1002/hyp.5819
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发表时间：
2005-10
影响因子：
3.2
通讯作者：
R. Wilby
中科院分区：
文献类型：
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作者：
R. Wilby
尽管其公认的局限性，集总概念模型继续广泛用于气候变化影响评估。因此，重要的是要了解水资源预测中的不确定性的相对大小，这些不确定性来自模型校准期的选择、模型结构和模型参数集的非唯一性。此外，还应该承认与排放情景的选择、气候模型集合成员、降尺度技术等相关的外部不确定性来源。为此，CATCHMOD概念水平衡模型被用来预测泰晤士河在金斯顿的日流量变化，使用参数集来自不同的子集的训练数据，包括完整的记录。蒙特卡洛抽样也被用来探讨参数的稳定性和可识别性的背景下，历史的气候变化。在简单的模型结构中，在土壤表面反映降雨接受度的参数被认为是高度敏感的训练期间，这意味着气候的变化导致变化的水文行为的泰晤士河流域。与排放情景的选择相比，更复杂的模型结构的参数的非唯一性导致不同训练期的预测年平均流量分位数的变化相对较小。然而，这是不是一年以下的流量统计，流量变化的不确定性，由于等效性是在冬季高于夏季，并在规模上的排放情景的不确定性。因此，建议使用概念性水平衡模型进行气候变化影响评估时，应定期进行敏感性分析，以量化由于参数不稳定性、可识别性和非唯一性造成的不确定性。版权所有© 2005年约翰威利父子有限公司。
Despite their acknowledged limitations, lumped conceptual models continue to be used widely for climate‐change impact assessments. Therefore, it is important to understand the relative magnitude of uncertainties in water resource projections arising from the choice of model calibration period, model structure, and non‐uniqueness of model parameter sets. In addition, external sources of uncertainty linked to choice of emission scenario, climate model ensemble member, downscaling technique(s), and so on, should be acknowledged. To this end, the CATCHMOD conceptual water balance model was used to project changes in daily flows for the River Thames at Kingston using parameter sets derived from different subsets of training data, including the full record. Monte Carlo sampling was also used to explore parameter stability and identifiability in the context of historic climate variability. Parameters reflecting rainfall acceptance at the soil surface in simpler model structures were found to be highly sensitive to the training period, implying that climatic variability does lead to variability in the hydrologic behaviour of the Thames basin. Non‐uniqueness of parameters for more complex model structures results in relatively small variations in projected annual mean flow quantiles for different training periods compared with the choice of emission scenario. However, this was not the case for subannual flow statistics, where uncertainty in flow changes due to equifinality was higher in winter than summer, and comparable in magnitude to the uncertainty of the emission scenario. Therefore, it is recommended that climate‐change impact assessments using conceptual water balance models should routinely undertake sensitivity analyses to quantify uncertainties due to parameter instability, identifiability and non‐uniqueness. Copyright © 2005 John Wiley & Sons, Ltd.