1 / 21

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

hope
Download Presentation

Adapting parameterization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related