Infrastructure Engineering - Research Publications

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    Simulating Runoff Under Changing Climatic Conditions: A Framework for Model Improvement
    Fowler, K ; Coxon, G ; Freer, J ; Peel, M ; Wagener, T ; Western, A ; Woods, R ; Zhang, L (American Geophysical Union, 2018-10-01)
    Rainfall-runoff models are often deficient under changing climatic conditions, yet almost no recent studies propose new or improved model structures, instead focusing on model intercomparison, input sensitivity, and/or quantification of uncertainty. This paucity of progress in model development is (in part) due to the difficulty of distinguishing which cases of model failure are truly caused by structural inadequacy. Here we propose a new framework to diagnose the salient cause of poor model performance in changing climate conditions, be it structural inadequacy, poor parameterization, or data errors. The framework can be applied to a single catchment, although larger samples of catchments are helpful to generalize and/or cross-check results. To generate a diagnosis, multiple historic periods with contrasting climate are defined, and the limits of model robustness and flexibility are explored over each period separately and for all periods together. Numerous data-based checks also supplement the results. Using a case study catchment from Australia, improved inference of structural failure and clearer evaluation of model structural improvements are demonstrated. This framework enables future studies to (i) identify cases where poor simulations are due to poor calibration methods or data errors, remediating these cases without recourse to structural changes; and (ii) use the remaining cases to gain greater clarity into what structural changes are needed to improve model performance in changing climate.
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    Improved Rainfall‐Runoff Calibration for Drying Climate: Choice of Objective Function
    Fowler, K ; Peel, M ; Western, A ; Zhang, L (American Geophysical Union, 2018-05)
    It has been widely shown that rainfall‐runoff models often provide poor and biased simulations after a change in climate, but evidence suggests existing models may be capable of better simulations if calibration strategies are improved. Common practice is to use “least squares”‐type objective functions, which focus on hydrological behavior during high flows. However, simulation of a drying climate may require a more balanced consideration of other parts of the flow regime, including mid‐low flows and drier years in the calibration period, as a closer analogue of future conditions. Here we systematically test eight objective functions over 86 catchments and five conceptual model structures in southern and eastern Australia. We focus on performance when evaluated over multiyear droughts. The results show significant improvements are possible compared to least squares calibration. In particular, the Refined Index of Agreement (based on sum of absolute error, not sum of squared error) and a new objective function called the Split KGE (which gives equal weight to each year in the calibration series) give significantly better split‐sample results than least squares approaches. This improvement held for all five model structures, regardless of basin characteristics such as slope, vegetation, and across a range of climatic conditions (e.g., mean precipitation between 500 and 1,500 mm/yr). We recommend future studies to avoid least squares approaches (e.g., optimizing NSE or KGE with no prior transformation on streamflow) and adopt these alternative methods, wherever simulations in a drying climate are required.
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    Simulating runoff under changing climatic conditions: Revisiting an apparent deficiency of conceptual rainfall-runoff models
    Fowler, KJA ; Peel, MC ; Western, AW ; Zhang, L ; Peterson, TJ (AMER GEOPHYSICAL UNION, 2016-03)
    Abstract Hydrologic models have potential to be useful tools in planning for future climate variability. However, recent literature suggests that the current generation of conceptual rainfall runoff models tend to underestimate the sensitivity of runoff to a given change in rainfall, leading to poor performance when evaluated over multiyear droughts. This research revisited this conclusion, investigating whether the observed poor performance could be due to insufficient model calibration and evaluation techniques. We applied an approach based on Pareto optimality to explore trade‐offs between model performance in different climatic conditions. Five conceptual rainfall runoff model structures were tested in 86 catchments in Australia, for a total of 430 Pareto analyses. The Pareto results were then compared with results from a commonly used model calibration and evaluation method, the Differential Split Sample Test. We found that the latter often missed potentially promising parameter sets within a given model structure, giving a false negative impression of the capabilities of the model. This suggests that models may be more capable under changing climatic conditions than previously thought. Of the 282[347] cases of apparent model failure under the split sample test using the lower [higher] of two model performance criteria trialed, 155[120] were false negatives. We discuss potential causes of remaining model failures, including the role of data errors. Although the Pareto approach proved useful, our aim was not to suggest an alternative calibration strategy, but to critically assess existing methods of model calibration and evaluation. We recommend caution when interpreting split sample results.