Infrastructure Engineering - Research Publications
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ItemCharacterizing dominant hydrological processes under uncertainty: evaluating the interplay between model structure, parameter sampling, error metrics, and data information content(Copernicus Publications, 2020-03-23)<p>Hydrological models are conventionally evaluated in terms of their response surface or likelihood surface constructed with the model parameter space. To evaluate models as hypotheses, we developed the method of <em>Flux Mapping</em> to construct a hypothesis space based on model process representation. Here we defined the hypothesis space based on dominant runoff generating mechanisms, and acceptable model runs are defined as total simulated flow with similar (and minimal) model error simulated by distinct combinations of runoff components. We demonstrate that the hypothesis space in each modeling case is the result of interplay between the factors of model structure, parameter sampling, choice of error metric, and data information content. The aim of this study is to disentangle the role of each factor in this interplay. We used two model structures (SACRAMENTO and SIMHYD), two parameter sampling approaches (small samples based on guided-search and large samples based on Latin Hypercube Sampling), three widely used error metrics (NSE, KGE, and WIA &#8212; Willmott&#8217;s Index of Agreement), and hydrological data from a range of Australian catchments. First, we characterized how the three error metrics behave under different error regimes independent of any modeling. We then conducted a series of controlled experiments, i.e. a type of one-factor-at-a-time sensitivity analysis, to unpack the role of each factor in runoff simulation. We show that KGE is a more reliable error metric compared to NSE and WIA for model evaluation. We also argue that robust error metrics and sufficient parameter sampling are necessary conditions for evaluating models as hypotheses under uncertainty. We particularly argue that sampling sufficiency, regardless of the sampling strategy, should be further evaluated based on its interaction with other modeling factors determining the model response. We conclude that the interplay of these modeling factors is complex and unique to each modeling case, and hence generalizing model-based inferences should be done with caution particularly in characterizing hydrological processes in large-sample hydrology.</p>
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ItemOn the relationship between the variability of catchment hydroclimate and physiography, and the uncertainty of runoff generation hypotheses(Copernicus Publications, 2021-03-04)<p>Question #20 of the UPH aspires to disentangle and reduce model prediction uncertainty. One feasible approach is to first formulate the relationship between variability (of real-world hydrological processes and catchment characteristics) and uncertainty (of model components and variables), which links the UPH theme of &#8220;modelling methods&#8221; to &#8220;time variability and change&#8221; and &#8220;space variability and scaling&#8221;. Building on this premise, we explored the relationship between runoff generation hypotheses, derived from a large ensemble of catchment model simulations, and catchment characteristics (physiographic, climatic, and streamflow response characteristics) across a large sample of 221 Australian catchments. Using ensembles of 10<sup>6&#160;</sup>runs of SIMHYD model for each catchment, runoff generation hypotheses were formulated based on the interaction of 3 runoff generating fluxes of SIMHYD, namely intensity-based, wetness-based, and slow responses. The hypotheses were derived from model runs with acceptable performance and sufficient parameter sampling. For model performance acceptability, we benchmarked Kling-Gupta Efficiency (KGE) skill score against the calendar day average observed flow, a catchment-specific and more informative benchmark than the conventional observed flow mean. The relative parameter sampling sufficiency was also defined based on the comparative efficacy of two common model parameterisation routines of Latin Hypercube Sampling and Shuffled Complex Evolution for each catchment. Across 186 catchments with acceptable catchment models, we examined the association of uncertain runoff generation hypotheses (i.e. ensemble of modeled runoff fluxes) with 22 catchment attributes. We used the Flux Mapping method (https://doi.org/10.1029/2018WR023750) to characterise the uncertainty of runoff generation hypotheses, and a range of daily and annual summary statistics to characterise catchment attributes. Among the metrics used, Spearman rank correlation coefficient (R<sub>s</sub>) was the most informative metric to capture the functional connectivity of catchment attributes with the internal dynamics of model runoff fluxes, compared to linear Pearson correlation and distance correlation coefficients. We found that streamflow characteristics generally have the most important influence on runoff generation hypotheses, followed by climate and then physiographic attributes. Particularly, daily flow coefficient of variability (Qcv) and skewness (Q Skewness), followed by the same summary statistics of precipitation (Pcv and&#160;P Skewness), were most important. These four attributes are strongly correlated with one another, and represent the dynamics of the rainfall-runoff signal within a catchment system. A higher&#160;Pcv&#160;denotes a higher day-to-day variability in rainfall on the catchment, responded by a higher&#160;Qcv&#160;flow response. A higher variability in rainfall propagates through the catchment model and translates into a higher degree of equifinality in model runoff fluxes, which implies larger uncertainties of runoff generation hypotheses at catchment scale, and hence a greater challenge for reliable/realistic simulation and prediction of streamflow.</p>
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ItemEquifinality and Flux Mapping: A New Approach to Model Evaluation and Process Representation Under Uncertainty(AMER GEOPHYSICAL UNION, 2019-11)Abstract Uncertainty analysis is an integral part of any scientific modeling, particularly within the domain of hydrological sciences given the various types and sources of uncertainty. At the center of uncertainty rests the concept of equifinality, that is, reaching a given endpoint (finality) through different pathways. The operational definition of equifinality in hydrological modeling is that various model structures and/or parameter sets (i.e., equal pathways) are equally capable of reproducing a similar (not necessarily identical) hydrological outcome (i.e., finality). Here we argue that there is more to model equifinality than model structures/parameters, that is, other model components can give rise to model equifinality and/or could be used to explore equifinality within model space. We identified six facets of model equifinality, namely, model structure, parameters, performance metrics, initial and boundary conditions, inputs, and internal fluxes. Focusing on model internal fluxes, we developed a methodology called flux mapping that has fundamental implications in understanding and evaluating model process representation within the paradigm of multiple working hypotheses. To illustrate this, we examine the equifinality of runoff fluxes of a conceptual rainfall‐runoff model for a number of different Australian catchments. We demonstrate how flux maps can give new insights into the model behavior that cannot be captured by conventional model evaluation methods. We discuss the advantages of flux space, as a subspace of the model space not usually examined, over parameter space. We further discuss the utility of flux mapping in hypothesis generation and testing, extendable to any field of scientific modeling of open complex systems under uncertainty.
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ItemNo Preview AvailableEquifinality and process-based modelling(American Geophysical Union, 2018-11-26)Equifinality is understood as one of the fundamental difficulties in the study of open complex systems, including catchment hydrology. A review of the hydrologic literature reveals that the term equifinality has been widely used, but in many cases inconsistently and without coherent recognition of the various facets of equifinality, which can lead to ambiguity but also methodological fallacies. Therefore, in this study we first characterise the term equifinality within the context of hydrological modelling by reviewing the genesis of the concept of equifinality and then presenting a theoretical framework. During past decades, equifinality has mainly been studied as a subset of aleatory (arising due to randomness) uncertainty and for the assessment of model parameter uncertainty. Although the connection between parameter uncertainty and equifinality is undeniable, we argue there is more to equifinality than just aleatory parameter uncertainty. That is, the importance of equifinality and epistemic uncertainty (arising due to lack of knowledge) and their implications is overlooked in our current practice of model evaluation. Equifinality and epistemic uncertainty in studying, modelling, and evaluating hydrologic processes are treated as if they can be simply discussed in (or often reduced to) probabilistic terms (as for aleatory uncertainty). The deficiencies of this approach to conceptual rainfall-runoff modelling are demonstrated for selected Australian catchments by examination of parameter and internal flux distributions and interactions within SIMHYD. On this basis, we present a new approach that expands equifinality concept beyond model parameters to inform epistemic uncertainty. The new approach potentially facilitates the identification and development of more physically plausible models and model evaluation schemes particularly within the multiple working hypotheses framework, and is generalisable to other fields of environmental modelling as well.