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This study explores dynamic averaging of rainfall-runoff model simulations under non-stationary climate conditions. The research focuses on reconciling robustness and flexibility in model calibrations, using multi-model approaches and weighting functions to adapt parameterization and structure. Key methods include identifying long-term shifts in catchment hydric state, designing weighting functions, and calibrating bi-polar models. The findings highlight the importance of long-term variability, individual model behavioral parameters, and smooth weight variations over time. Further research is needed to assess the methodology on stationary catchments and explore the effects of time series length and objective functions.
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Dynamic averaging of rainfall-runoff model simulations within non stationary climate conditionsNicolas Le Moine & Ludovic OudinUniv. Paris 6 IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Coping with non stationary behaviors: models withmore constraints (and robustness) or more freedom (and flexibility)? • Adapting parameterization • Flexibility: Dynamic recalibration with climate analogs (de Vos et al., 2010). • Robustness: Constraining model parameter with multi-objective approach (with e.g. more weights on bias criterion) • Adapting model structure • Flexibility: Multi-model approach • Robustness: Choice of a fixed model structure that is relevant for more arid catchments and/or that is efficient when performing DSST IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Reconciling robustness and flexibility • Multi-model / Dynamic averaging / fuzzy comittee : A good idea involving arbitrary choices • Complementary objective functions for calibrating individually the models • A weighting function to average the simulated flows from the models • Is there a way to reduce the number of arbitrary choices? IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Data and models • 3 catchments with non-stationnary climate: • Axe Creek • Gilbert • Bani • One daily conceptual model: GR4J IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Methodology: Identifying long-term shifts of the hydric state of a catchment through modelling P PE Rainfall-Runoff Model IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 5
Methodology: Identifying long-term shifts of the hydric state of a catchment through modelling Low frequency signal Mean of the period IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 6
Methodology: Designing a weighting function IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 7
Methodology: Designing a weighting function IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 8
Methodology: Designing a weighting function IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 9
Methodology: Designing a weighting function Prob. of non exceedance of LowFreq. anomaly IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 10
Methodology: Designing a weighting function Prob. of non exceedance of LowFreq. anomaly IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 11
Methodology: Designing a weighting function Prob. of non exceedance of LowFreq. anomaly IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 12
Methodology: Calibrating bi-polar models IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Comparative results for Bias Gilbert River Axe Creek IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 17
Comparative results for Bias Bani River IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 18
Comparative results for KGE Axe Creek Gilbert River IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 19
Comparative results for KGE Bani River IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 20
Conclusion • A methodology focused on long-term variability • Robustness: each pole has a behavioural parameter set that works by itself • Flexibility: The weights may vary largely on a subperiod but smoothly in time • Need to test other settings • Assessing the methodology on stationary catchments • Effect of time series length • Objective functions IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
