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Symptoms of performance degradation during multi-annual drought: a large-sample, multi-model study
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  • Luca Trotter,
  • Margarita Saft,
  • Murray Cameron Peel,
  • Keirnan James Andrew Fowler
Luca Trotter
University of Melbourne, University of Melbourne

Corresponding Author:[email protected]

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Margarita Saft
University of Melbourne, University of Melbourne
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Murray Cameron Peel
University of Melbourne, University of Melbourne
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Keirnan James Andrew Fowler
University of Melbourne, University of Melbourne
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Abstract

Hydrologic models are essential tools to understand and plan for the effect of changing climates; however, they are known to underperform in transitory climate conditions. Research to date identifies the inadequacy of models to perform during prolonged drought, but falls short on pinpointing how and which specific aspects of model performance are affected. Here, we study five conceptual rainfall-runoff models and their performance in 155 Australian catchments which recently experienced a 13-year long dry period, with a focus on a wide range of performance metrics. We show that model performance degrades extensively during the drought across most metrics, with overestimation of flow volumes driving the decline and representation of shape and variability of the hydrograph and the flow-duration curve being more resilient to the prolonged dry climate. This indicates that the overestimation is not linked to specific flow regimes, but is the result of proportional flow decline throughout the hydrograph, suggesting engagement of multiple catchment processes in determining the changes in flow during the drought across high and low flow periods as well as through faster and slower flow routes. Additionally, we show that in most cases model performance does not recover after the end of the drought and that the multi-annual nature of the drought is the likely reason for exacerbated performance decline due to accumulation and aggravation of errors over subsequent dry years. By promoting detailed investigation of models’ shortcomings, we hope to foster the development of more resilient model structures to improve applicability within climate change scenarios.