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Urban spread impact on GR4J parameters

Urban spread impact on GR4J parameters. Carina Furusho, Guillaume Thirel, Vazken Andréassian. July 23, 2013. Outline. IAHS Joint assembly. Gothenburg , 22-26 July 2013. Study sites The GR4J Model Level 2: Multi-parameterization on sub-periods Parameters evolution Scores evolution

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Urban spread impact on GR4J parameters

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  1. Urban spread impact on GR4J parameters Carina Furusho, Guillaume Thirel, Vazken Andréassian July 23, 2013

  2. Outline IAHS Joint assembly Gothenburg, 22-26 July 2013 Study sites The GR4J Model Level 2: Multi-parameterization on sub-periods • Parameters evolution • Scores evolution Level 3:Improvement of model behavior in non-stationary conditions • Adapting 1 non-stationary parameter • Splitting the watershed into 2

  3. Study sites ferson CREEK (134 km²) and blackberry creek (182 km²) Chicago Metropolitan Agency for Planning (CMAP), December , 2012

  4. Urbanization Urban fraction (%) Data interpolation Data available every 10years Threshold of housing density: 1 house per 4 hectares IAHS Joint assembly Gothenburg, 22-26 July 2013

  5. GR4J Perrin et al. (2003) X1: Soil moisture accounting store maximum capacity (mm) X2: Groundwater exchange X3: Routing store maximum capacity (mm) X4: Time base of the unit hydrograph (days) IAHS Joint assembly Gothenburg, 22-26 July 2013

  6. Outline IAHS Joint assembly Gothenburg, 22-26 July 2013 Study sites The GR4J Model Level 2: Multi-parameterization on sub-periods • Parameters evolution • Scores evolution Level 3:Improvement of model behaviour in non-stationary conditions

  7. Is there a correlation between the urban area increase and the variation of these parameters ? IAHS Joint assembly Gothenburg, 22-26 July 2013

  8. Scores variation P1 P2 P3 P4 P5 Is there a relation between the urban area increase and the variation of these scores ? What can be enhanced by including urban fraction information in GR4J simulations? Ferson creek 5 periods IAHS Joint assembly Gothenburg, 22-26 July 2013 Multi-parameterization on sub-periods

  9. Multi-parameterization on sub-periods Each Curve =Calibration Period Ferson Blackberry P0-complete P0-complete EVALUATION PERIOD EVALUATION PERIOD NSE : optimization on sqrt(Q ) IAHS Joint assembly Gothenburg, 22-26 July 2013

  10. Each Curve =Calibration Period Blackberry P0-complete EVALUATION PERIOD Ferson Bias IAHS Joint assembly Gothenburg, 22-26 July 2013 P0-complete EVALUATION PERIOD

  11. Each Curve =Calibration Period P0-complete Blackberry EVALUATION PERIOD Ferson Q95 High Flows IAHS Joint assembly Gothenburg, 22-26 July 2013 P0-complete EVALUATION PERIOD

  12. Outline IAHS Joint assembly Gothenburg, 22-26 July 2013 Study sites The GR4J Model Level 2: Multi-parameterization on sub-periods (main conclusions) • X3: encouraging trend following urban evolution • Bias: overall underestimation and significant deviation among ≠ calibration period curves • Q95: High flows underestimation Level 3: Improvement of model behavior in non-stationary conditions

  13. Non-stationary parameters NAT URB Urban rate increase Strategies: Replacing parameters by functions depending on the urban fraction. Example: X3 = f(urb%) 2) Splitting the basin into 2 sub-catchments. IAHS Joint assembly Gothenburg, 22-26 July 2013

  14. LEVEl 3 strategy 1: X3 = f (URB) Blackberry Ferson CALIBRATION PERIOD Original GR4J Nash: 0.74 Original GR4J Nash: 0.76 P0-complete P0-complete EVALUATION PERIOD EVALUATION PERIOD Modified (URB) Nash: 0.67 Modified (URB) Nash: 0.72 NSE Gothenburg, 22-26 July 2013 IAHS Joint assembly P0-complete P0-complete EVALUATION PERIOD EVALUATION PERIOD

  15. Bias CALIBRATION PERIOD GR4J P0 bias: 5.6% GR4J P0 bias: 6.3% P0-complete P0-complete EVALUATION PERIOD EVALUATION PERIOD Modified (URB) P0 bias: 4.4% Modified (URB) P0 bias: 2.7% Ferson Blackberry LEVEl 3 strategy 1: X3 = f (URB) Gothenburg, 22-26 July 2013 IAHS Joint assembly P0-complete P0-complete EVALUATION PERIOD EVALUATION PERIOD

  16. High flows: Percentile 0.95 CALIBRATION PERIOD GR4J MAPE:1.2% Modified (URB) MAPE: 0.7% GR4J MAPE: 1.2% P0-complete P0-complete EVALUATION PERIOD EVALUATION PERIOD Modified (URB) MAPE: 0.8% Ferson Gothenburg, 22-26 July 2013 IAHS Joint assembly Blackberry LEVEl 3 strategy 1: X3 = f (URB) P0-complete P0-complete EVALUATION PERIOD EVALUATION PERIOD MAPE*=Mean Absolute Percentage Error

  17. 2nd strategy: Splitting into 2 sub-catchments GR4J X1 , Xurb1 X2 , Xurb2 X3 , Xurb3 X4 , Xurb4 GR4J X1 , Xurb1 X2 , Xurb2 X3 , Xurb3 X4 , Xurb4 GR4J X1 , Xurb1 X2 , Xurb2 X3 , Xurb3 X4 , Xurb4 GR4J X1 , Xurb1 X2 , Xurb2 X3 , Xurb3 X4 , Xurb4 NAT URB GR4J X1 , Xurb1 X2 , Xurb2 X3 , Xurb3 X4 , Xurb4 Q1 tot = Qnat1*(1-URB1) + Qurb1*URB1 Q2tot = Qnat1*(1-URB1) + Qurb1*URB1 8 parameters Calibration over the complete period Q3tot = Qnat1*(1-URB1) + Qurb1*URB1 Q4 tot = Qnat1*(1-URB1) + Qurb1*URB1 Q5 tot = Qnat5*(1-URB5) + Qurb5*URB5

  18. Discharge flow distribution Level 3 : 2 strategies o: original GR4J x: 1st strategy: X3=f (urb%) +: 2ndstrategy: 8 parameters Q 0.95 Blackberry creek simulations IAHS Joint assembly Gothenburg, 22-26 July 2013 Ferson creek simulations

  19. Conclusions and future work IAHS Joint assembly Gothenburg, 22-26 July 2013 Few historical data may be enough to estimate the urban fraction of a catchment to improve simulations A larger sample of study cases should be tested to reduce the influence of other non-stationary factors influencing the system Both techniques improved the representation of higher flows, reduced the bias and have the potential to develop into solutions to this issue Changing the model structure (including reservoirs or other processes in parallel to the existing ones) works well for physically based models and might be another approach to look further.

  20. Thank youspecial thanks to Tom Over (USGS) webgr.irstea.frcarina.furusho@irstea.fr

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