Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality
Benchmarking observational uncertainties for hydrology: rainfall, river discharge and water quality
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DOI：
10.1002/hyp.9384
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发表时间：
2012-12
影响因子：
3.2
通讯作者：
H. McMillan;T. Krueger;J. Freer
中科院分区：
文献类型：
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作者：
H. McMillan;T. Krueger;J. Freer
本审查和评论指出，需要关于观测数据中预期误差分布和大小的权威和简明信息。我们讨论了主导数据不确定性基准的必要组成部分，以及水文学的最新发展，这增加了这种指导的必要性。我们开始创建一个关于降雨、河流流量和水质(悬浮固体、磷和氮)等主要水文变量的数据不确定性特征的可访问信息目录。这包括演示如何量化不确定性，总结现有知识和现有的标准量化结果。特别是，综合多项研究的结果，可以得出控制数据不确定性大小的因素的结论，从而改进对这些不确定性的事先指导。降雨不确定度受空间尺度的影响，而河流流量不确定度受水流条件和量测方法的影响。水质变量呈现出一幅更复杂的图景，有许多分量误差。对于所有变量，很容易找到相对误差幅度超过40%的例子。我们考虑了数据不确定性对流域动态的解释、模型区域化和模型评估的影响。在总结这篇综述时，我们对未来量化数据不确定性的研究重点提出了建议，并强调了改善与观测不确定性有关的“参与文化”的必要性。版权所有©2012 John Wiley&Sons，Ltd.
This review and commentary sets out the need for authoritative and concise information on the expected error distributions and magnitudes in observational data. We discuss the necessary components of a benchmark of dominant data uncertainties and the recent developments in hydrology which increase the need for such guidance. We initiate the creation of a catalogue of accessible information on characteristics of data uncertainty for the key hydrological variables of rainfall, river discharge and water quality (suspended solids, phosphorus and nitrogen). This includes demonstration of how uncertainties can be quantified, summarizing current knowledge and the standard quantitative results available. In particular, synthesis of results from multiple studies allows conclusions to be drawn on factors which control the magnitude of data uncertainty and hence improves provision of prior guidance on those uncertainties. Rainfall uncertainties were found to be driven by spatial scale, whereas river discharge uncertainty was dominated by flow condition and gauging method. Water quality variables presented a more complex picture with many component errors. For all variables, it was easy to find examples where relative error magnitudes exceeded 40%. We consider how data uncertainties impact on the interpretation of catchment dynamics, model regionalization and model evaluation. In closing the review, we make recommendations for future research priorities in quantifying data uncertainty and highlight the need for an improved ‘culture of engagement’ with observational uncertainties. Copyright © 2012 John Wiley & Sons, Ltd.