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Time Scale Dependent Sensitivities of XinAnJiang Model Parameters

Time Scale Dependent Sensitivities of XinAnJiang Model Parameters. Lu, Minjiao and Li, Xiao Department of Civil and Environmental Engineering Nagaoka Universi t y of Technology Niigata, Japan Nov. 21, 2010. Calibration. ?. Background:. One Catchment

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Time Scale Dependent Sensitivities of XinAnJiang Model Parameters

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  1. Time Scale Dependent Sensitivities of XinAnJiang Model Parameters Lu, Minjiao and Li, Xiao Department of Civil and Environmental Engineering Nagaoka University of Technology Niigata, Japan Nov. 21, 2010

  2. Calibration ? Background: One Catchment One model(mostly XinAnJiang model in China) Two data sets: monthly daily Parameter Set A B

  3. What to do Understand the sensitivities of the parameters and their time scale dependency(this talk) Develop a optimization scheme to automatically determine the parameter sets(Ms. Li Xiao’s talk) Study the transferability between parameter sets at different time scales(for simple models, many works have been done and published)

  4. Sensitivity analysis techniques Local approaches: only local derivatives are considered. The major drawback of these approaches is their inability to account for parameter interactions, and then their proneness to underestimating true model sensitivities. Most SA in journals are local (Saltelli,1999) Global approaches: consider the effect of a factor while others changes. Many techniques have been developed that can be applied even to non-linear non-monotonic models(Saltelli, 2004).

  5. Sensitivity analysis in hydrology There are numerous papers reporting results sensitivity analysis of different methods and different models. Vemuri et al. (1969) pointed out that sensitivity analysis should be an integral part of nearly every hydrologic study. McCuen (1973) showed the importance of sensitivity analysis and proposed a mathematical framework of sensitivity. Bathurst (1986) made a sensitivity analysis of the Système Hydrologique Européen. Lenhart et al. (2002) carried out a comparison of two simple approaches of sensitivity analysis. Tang et al. (2007) compared four sensitivity analysis methods, one local and three global. and showed the superiority of global methods.

  6. The XAJ model

  7. Coefficients for data adjustment: Cp : rainfall Cep : potential evaporation Parameters of the XAJ model There are 15 parameters in the model we used. Parameters for runoff components separation and routing: SM, EX, KI, KG, Cs, ci, cg Parameters for runoff generation: B, imp, WUM, WLM, WDM, C

  8. Daily input data Daily XAJ model Daily simulated discharge Monthly Annual Daily NS Monthly NS Annually NS Daily observed discharge Monthly Annual Measure of the model performance Nash’s model efficiency is used.

  9. Some results of local SA Change one parameter while keeping others unchanged.

  10. Morris method Represent the value space of k factors by a k-dimensional p-level grid Randomly sample r points from the value space for each factor Calculate elementary effects by using Calculate sensitivity measures

  11. Sensitivity measures Measure m: mean of r local derivatives in the value spaceshowing the sensitivity of each factor. Measure m*:mean of absolute values of r local derivatives showing the sensitivity of each factor. Useful for non-monotonic model output. Measure s:standard deviation of r local derivatives in the value spaceshowing the interaction of each factor with others.

  12. Sensitivities at annual scale Parameters for input data adjustment, Cp and Cep are most sensitive at annual scale They interact strongly.

  13. Sensitivities at monthly scale Parameters controlling runoff component separation and routing are relative sensitive. Also there are quite strong interactions among these parameters Cp and Cep are excluded.

  14. Sensitivities at daily scale Parameters controlling runoff component separation and routing are relative sensitive. Also there are quite strong interactions among these parameters. Cp and Cep are excluded.

  15. Sensitivities at annual scale again Some parameters controlling runoff generation and runoff routing show strong sensitivities at annual scale after excluding Cp and Cep. Those controlling runoff generation are quite independent. Cp and Cep are excluded.

  16. Conclusions • Each parameter has interaction with others at all time scales; • Parameters for data adjustment are sensitive and interact each other at annual scale; • Parameters controlling runoff separation and routing are sensitive at monthly and daily scale; • Some parameters from above two groups become sensitive at annual scale after excluding Cp and Cep.

  17. Better understanding of parameter sensitivity Thanks! Better strategy for parameter optimization More efficient parameter calibration

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