Dynamic averaging of rainfall-runoff model simulations within non stationary climate
conditions
Nicolas Le Moine & Ludovic Oudin
Univ. Paris 6
1IAHS 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
2IAHS 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?
3IAHS 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
4IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Rainfall-Runoff Model
P PE
Methodology: Identifying long-term shifts of the hydric state of a catchment through modelling
5IAHS 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
6IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Mean of the period
Low frequency signal
Methodology: Designing a weighting function
7IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
8IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Methodology: Designing a weighting function
Methodology: Designing a weighting function
9IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Methodology: Designing a weighting function
10IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Prob. of non exceedance of Low Freq. anomaly
Methodology: Designing a weighting function
11IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Prob. of non exceedance of Low Freq. anomaly
Methodology: Designing a weighting function
12IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Prob. of non exceedance of Low Freq. anomaly
Methodology: Calibrating bi-polar models
13IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Methodology: Using Bi-polar models in validation
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Detailed Results on Axe Creek: calibration period 1
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Detailed Results on Axe Creek: validation period 4
Comparative results for Bias
17IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Gilbert River Axe Creek
Comparative results for Bias
18IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Bani River
Comparative results for KGE
19IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Gilbert River Axe Creek
Comparative results for KGE
20IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
Bani River
Conclusion
21IAHS Joint Assembly Gothenburg. Hw15 Testing simulation and forecasting models in non-stationary conditions
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