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Adapting parameterization

Dynamic averaging of rainfall-runoff model simulations within non stationary climate conditions Nicolas Le Moine & Ludovic Oudin Univ. Paris 6. Coping with non stationary behaviors: models with more constraints (and robustness) or more freedom (and flexibility)?. Adapting parameterization

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Adapting parameterization

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  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. Methodology: Designing a weighting function IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 7

  8. Methodology: Designing a weighting function IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 8

  9. Methodology: Designing a weighting function IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 9

  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 10

  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 11

  12. 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

  13. Methodology: Calibrating bi-polar models IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions

  14. Methodology: Using Bi-polar models in validation

  15. Detailed Results on Axe Creek: calibration period 1

  16. Detailed Results on Axe Creek: validation period 4

  17. Comparative results for Bias Gilbert River Axe Creek IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 17

  18. Comparative results for Bias Bani River IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 18

  19. Comparative results for KGE Axe Creek Gilbert River IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 19

  20. Comparative results for KGE Bani River IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions 20

  21. 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

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