论文标题
评估集水模型作为多个工作假设：关于误差指标，参数采样，模型结构和数据信息内容的作用
Evaluating Catchment Models as Multiple Working Hypotheses: on the Role of Error Metrics, Parameter Sampling, Model Structure, and Data Information Content
论文作者
论文摘要
为了评估模型作为假设，我们开发了通量映射的方法，以基于主要的径流生成机制来构建假设空间。可接受的模型运行，定义为具有相似（和最小）模型误差的总模拟流，鉴于其模拟径流组件，映射到了假设空间。在每种建模情况下，假设空间是因素相互作用的结果：模型结构和参数化，选择的误差指标和数据信息内容。这项研究的目的是解散每个因素在模型评估中的作用。 We used two model structures (SACRAMENTO and SIMHYD), two parameter sampling approaches (Latin Hypercube Sampling of the parameter space and guided-search of the solution space), three widely used error metrics (Nash-Sutcliffe Efficiency - NSE, Kling-Gupta Efficiency skill score - KGEss, and Willmott refined Index of Agreement - WIA), and hydrological data from a large sample of Australian catchments.首先，我们表征了三个误差指标在不同的误差类型下的行为如何独立于任何建模。然后，我们进行了一系列受控的实验，以解开每个因素在径流产生假设中的作用。我们表明，与NSE和WIA进行模型评估相比，KGESS是一个更可靠的度量标准。我们进一步证明，只有更改误差度量（而其他因素保持恒定）才能改变模型解决方案空间，因此可以改变模型性能，参数采样充足和 /或通量图。我们展示了不可靠的误差指标和参数采样不足的基于模型的推论，尤其是径流产生假设。
To evaluate models as hypotheses, we developed the method of Flux Mapping to construct a hypothesis space based on dominant runoff generating mechanisms. Acceptable model runs, defined as total simulated flow with similar (and minimal) model error, are mapped to the hypothesis space given their simulated runoff components. In each modeling case, the hypothesis space is the result of an interplay of factors: model structure and parameterization, chosen error metric, and data information content. The aim of this study is to disentangle the role of each factor in model evaluation. We used two model structures (SACRAMENTO and SIMHYD), two parameter sampling approaches (Latin Hypercube Sampling of the parameter space and guided-search of the solution space), three widely used error metrics (Nash-Sutcliffe Efficiency - NSE, Kling-Gupta Efficiency skill score - KGEss, and Willmott refined Index of Agreement - WIA), and hydrological data from a large sample of Australian catchments. First, we characterized how the three error metrics behave under different error types and magnitudes independent of any modeling. We then conducted a series of controlled experiments to unpack the role of each factor in runoff generation hypotheses. We show that KGEss is a more reliable metric compared to NSE and WIA for model evaluation. We further demonstrate that only changing the error metric -- while other factors remain constant -- can change the model solution space and hence vary model performance, parameter sampling sufficiency, and or the flux map. We show how unreliable error metrics and insufficient parameter sampling impair model-based inferences, particularly runoff generation hypotheses.
