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Www.irstea.fr Pour mieux affirmer ses missions, le Cemagref devient Irstea Carina Furusho, Guillaume Thirel, Vazken Andréassian July 23, 2013 Urban spread.

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Presentation on theme: "Www.irstea.fr Pour mieux affirmer ses missions, le Cemagref devient Irstea Carina Furusho, Guillaume Thirel, Vazken Andréassian July 23, 2013 Urban spread."— Presentation transcript:

1 Pour mieux affirmer ses missions, le Cemagref devient Irstea Carina Furusho, Guillaume Thirel, Vazken Andréassian July 23, 2013 Urban spread impact on GR4J parameters

2 2 Outline IAHS JOINT ASSEMBLY GOTHENBURG, 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 3 Study sites FERSON CREEK (134 KM²) AND BLACKBERRY CREEK (182 KM²) Chicago Metropolitan Agency for Planning (CMAP), December, 2012

4 4 Urbanization DATA INTERPOLATION Urban fraction (%) Data available every 10years Threshold of housing density: 1 house per 4 hectares IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013

5 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, JULY 2013

6 6 Outline IAHS JOINT ASSEMBLY GOTHENBURG, 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 7 Is there a correlation between the urban area increase and the variation of these parameters ? IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013

8 8 Scores variation 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, JULY 2013 MULTI-PARAMETERIZATION ON SUB-PERIODS P1P2P3P4P5

9 9 NSE : optimization on sqrt(Q ) IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013 MULTI-PARAMETERIZATION ON SUB-PERIODS Blackberry Ferson EVALUATION PERIOD P0-complete Each Curve =Calibration Period

10 10 Bias IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013 Blackberry Ferson EVALUATION PERIOD P0-complete EVALUATION PERIOD P0-complete Each Curve =Calibration Period

11 11 Q95 High Flows IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013 Blackberry Ferson Each Curve =Calibration Period EVALUATION PERIOD P0-complete EVALUATION PERIOD P0-complete

12 12 Outline IAHS JOINT ASSEMBLY GOTHENBURG, 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 13 Non-stationary parameters URBAN RATE INCREASE Strategies: 1)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, JULY 2013 URB NAT

14 14 NSE IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013 Modified (URB) Nash: 0.67 Original GR4J Nash: 0.76 Original GR4J Nash: 0.74 Modified (URB) Nash: 0.72 LEVEL 3 STRATEGY 1: X3 = F (URB) BlackberryFerson P0-complete EVALUATION PERIOD CALIBRATION PERIOD EVALUATION PERIOD

15 15 Bias IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013 GR4J P0 bias: 5.6% Modified (URB) P0 bias: 2.7% LEVEL 3 STRATEGY 1: X3 = F (URB) GR4J P0 bias: 6.3% Modified (URB) P0 bias: 4.4% BLACKBERRY FERSON P0-complete EVALUATION PERIOD CALIBRATION PERIOD EVALUATION PERIOD

16 16 High flows: Percentile 0.95 BLACKBERRY IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013 GR4J MAPE:1.2% Modified (URB) MAPE: 0.8% GR4J MAPE: 1.2% Modified (URB) MAPE: 0.7% MAPE*=Mean Absolute Percentage Error FERSON LEVEL 3 STRATEGY 1: X3 = F (URB) P0-complete EVALUATION PERIOD CALIBRATION PERIOD EVALUATION PERIOD

17 17 2 nd strategy: Splitting into 2 sub-catchments GR4J X1, Xurb1 X2, Xurb2 X3, Xurb3 X4, Xurb4 Q 1 tot = Qnat 1 *(1-URB 1 ) + Qurb 1 *URB 1 GR4J X1, Xurb1 X2, Xurb2 X3, Xurb3 X4, Xurb4 Q 2 tot = Qnat 1 *(1-URB 1 ) + Qurb 1 *URB 1 GR4J X1, Xurb1 X2, Xurb2 X3, Xurb3 X4, Xurb4 Q 3 tot = Qnat 1 *(1-URB 1 ) + Qurb 1 *URB 1 GR4J X1, Xurb1 X2, Xurb2 X3, Xurb3 X4, Xurb4 Q 4 tot = Qnat 1 *(1-URB 1 ) + Qurb 1 *URB 1 GR4J X1, Xurb1 X2, Xurb2 X3, Xurb3 X4, Xurb4 Q 5 tot = Qnat 5 *(1-URB 5 ) + Qurb 5 *URB 5 8 parameters Calibration over the complete period URB NAT

18 18 Discharge flow distribution BLACKBERRY CREEK SIMULATIONS IAHS JOINT ASSEMBLY GOTHENBURG, JULY 2013 FERSON CREEK SIMULATIONS +: 2 nd strategy: 8 parameters x: 1 st strategy: X3=f (urb%) o: original GR4J Level 3 : 2 strategies Q 0.95

19 19 Conclusions and future work IAHS JOINT ASSEMBLY GOTHENBURG, 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 Pour mieux affirmer ses missions, le Cemagref devient Irstea Thank you special thanks to Tom Over (USGS) webgr.irstea.fr


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