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To evaluate models as hypotheses, we developed the method of Flux Mapping toconstruct a hypothesis space based on dominant runoff generating mechanisms.Acceptable model runs, defined as total simulated flow with similar (andminimal) model error, are mapped to the hypothesis space given their simulatedrunoff components. In each modeling case, the hypothesis space is the result ofan interplay of factors: model structure and parameterization, chosen errormetric, and data information content. The aim of this study is to disentanglethe role of each factor in model evaluation. We used two model structures(SACRAMENTO and SIMHYD), two parameter sampling approaches (Latin HypercubeSampling of the parameter space and guided-search of the solution space), threewidely used error metrics (Nash-Sutcliffe Efficiency - NSE, Kling-GuptaEfficiency skill score - KGEss, and Willmott refined Index of Agreement - WIA),and hydrological data from a large sample of Australian catchments. First, wecharacterized how the three error metrics behave under different error typesand magnitudes independent of any modeling. We then conducted a series ofcontrolled experiments to unpack the role of each factor in runoff generationhypotheses. We show that KGEss is a more reliable metric compared to NSE andWIA for model evaluation. We further demonstrate that only changing the errormetric -- while other factors remain constant -- can change the model solutionspace and hence vary model performance, parameter sampling sufficiency, and orthe flux map. We show how unreliable error metrics and insufficient parametersampling impair model-based inferences, particularly runoff generationhypotheses.

Evaluating Catchment Models as Multiple Working Hypotheses: on the Role  of Error Metrics, Parameter Sampling, Model Structure, and Data Information  Content