On hypothesis testing in hydrology: Why falsification of models is still a really good idea
On hypothesis testing in hydrology: Why falsification of models is still a really good idea
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DOI：
10.1002/wat2.1278
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发表时间：
2018-05-01
影响因子：
8.2
通讯作者：
Beven, Keith J.
中科院分区：
文献类型：
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作者：
Beven, Keith J.
这篇观点文章认为，就测试模型作为集水区功能的假设而言，没有现有的方法可以充分处理建模过程中数据和水文过程的潜在认知不确定性。建议采用拒绝主义框架作为前进的道路，其中，在进行任何模型模拟之前，对输入和评估数据中的不确定性进行评估，以确定可接受性的限制。可接受性的限制也可能取决于建模的目的，因此我们可以更严格地判断模型是否真正适合目的。在这个框架中可以评估不同的模型结构和参数集，尽管考虑到建模过程中不确定性的认知性质，主观因素必然存在。减少认知不确定性的影响，并允许更严格的假设检验，最有效的方法之一是委托更好的观察方法。模型拒绝是一件好事，因为它要求我们变得更好，从而推动科学的进步。本文的分类是：水科学bbb水文过程科学bbb方法
This opinion piece argues that in respect of testing models as hypotheses about how catchments function, there is no existing methodology that adequately deals with the potential for epistemic uncertainties about data and hydrological processes in the modeling processes. A rejectionist framework is suggested as a way ahead, wherein assessments of uncertainties in the input and evaluation data are used to define limits of acceptability prior to any model simulations being made. The limits of acceptability might also depend on the purpose of the modeling so that we can be more rigorous about whether a model is actually fit-for-purpose. Different model structures and parameter sets can be evaluated in this framework, albeit that subjective elements necessarily remain, given the epistemic nature of the uncertainties in the modeling process. One of the most effective ways of reducing the impacts of epistemic uncertainties, and allow more rigorous hypothesis testing, would be to commission better observational methods. Model rejection is a good thing in that it requires us to be better, resulting in advancement of the science. This article is categorized under:Science of Water > Hydrological Processes Science of Water > Methods