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X - 22LERAT ET AL.: VALUE OF UPSTREAM FLOW MEASUREMENTSFigure 8 shows the distribution of the five parameters of the UP-Meas model associated Figure 8395with different corruption configurations. The parameters controlling the water balancein the RR module (soil moisture accounting store capacity, S, and exchange parameter,IGF ) compensate the excess of water coming from the upstream catchment UP: Sincreases to retain more water in the soil moisture reservoir and IGF decreases to artificiallyexport more water outside of the catchment. Thus the corruption is a way to400increase the relative weight of measurements in downstream simulations if we accept thattel-00392240, version 1 - 5 Jun 2009405410errors associated with the propagation component are small compared to the errors of therainfall-runoff component. This is coherent with the superiority of the UP-Meas modelover the benchmark.In conclusion, the information provided by upstream flow measurements can be evenmore useful than what was observed in the previous section: a systematic and artificalincrease of its weight in the semidistributed modeling scheme via our corruption strategyproved to be beneficial. Of course, a limited number of cases exist where the upstream flowdata is not of appropriate quality and this procedure could prove counter-productive, as weindicated in the previsous section (outliers on the Figure 5). An efficient way to increase Figure 5the upstream flow measurement weight would be to parameterize it using a multiplyingfactor on upstream flows in the UP-Meas model. Calibration of this parameter could theneliminate the cases of uninformative upstream flows. This goes beyond the scope of thispaper but will be done in future work.D R A F T July 24, 2008, 3:43pm D R A F T
LERAT ET AL.: VALUE OF UPSTREAM FLOW MEASUREMENTS X - 235.3. What happens when the model is run with calculated upstream flowinstead of measured values?The two previous sections have shown the critical value of upstream flow measurements415in a semidistributed model. Unfortunately, these data are not always available, especiallyduring the validation period where we want our models to be able to extrapolate withoutany flow measurements. In this section, we compare the results of the benchmark and theUP-Sim models.Table 3 shows that simulations improve significantly on the UP stations from benchmark Table 3420to UP-Sim but not on the downstream point TOTAL. It is striking that CEff statistics ontel-00392240, version 1 - 5 Jun 2009425430435the TOTAL station are nearly identical for both models. Indeed, one would have expectedsimulations on both points to benefit from the use of upstream flow measurements duringthe calibration process of UP-Sim.This counter-intuitive result, however, confirms some results which had been consideredrather paradoxical in the literature: Boyle et al. [2001] and Ajami et al., [2004] foundthat the distribution of the parameters over several subcatchments did not improve thesimulations compared to a simple uniform parameterization scheme (as in our benchmarkmodel). The fact that we used upstream flow measurements during the calibration process,which was not the case in the studies of Boyle et al.[2001] and Ajami et al.[2004], waseven a further advantage for the parameter-distributed strategy (UP-Sim model), but itdid not change the conclusion.Figure 9 provides insights into this result by comparing simulations of the benchmark Figure 9and the UP-Sim model on the example of the Vienne river (tributary of the Loire river inthe center of France). Figure 9 shows the simulations of the two models on the downstreampoint TOTAL and their breakdown into two components as per equations 1 and 3: theD R A F T July 24, 2008, 3:43pm D R A F T
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Université Pierre et Marie CurieEc
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3Remerciementstel-00392240, version
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Table des matièresIntroduction gé
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TABLE DES MATIÈRES 7tel-00392240,
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TABLE DES MATIÈRES 9FGParamètres
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Introduction générale 13Contextet
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Introduction générale 15Probléma
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Introduction générale 17Crue de d
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Première partietel-00392240, versi
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Chapitre 1Apports latéraux dans le
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Chapitre 1. Apports latéraux dans
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Chapitre 2Présentation de l’éch
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Chapitre 2. Présentation de l’é
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Chapitre 3Choix des modèles hydrol
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Chapitre 3. Choix des modèles hydr
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Chapitre 4Stratégie de calage et d
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Chapitre 4. Stratégie de calage et
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Chapitre 5Jusqu’où est-il néces
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Chapitre 5. Jusqu’où est-il néc
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Chapitre 6Sensibilité d’un modè
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Chapitre 6. Sensibilité d’un mod
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Chapitre 7Simplifications de la str
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Chapitre 7. Simplifications de la s
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Conclusion générale 177Dans le ca
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Conclusion générale 179Quel est l
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BibliographieAbbott, M. et Refsgaar
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BIBLIOGRAPHIE 183Brutsaert, W. (200
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BIBLIOGRAPHIE 185Jakeman, A. et Hor
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BIBLIOGRAPHIE 187MEEDDAT (2008). Ba
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BIBLIOGRAPHIE 189Oudin, L., Andréa
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BIBLIOGRAPHIE 191Shamseldin, A., O
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Annexe ATable de correspondance ent
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Annexe A. Table de correspondance e
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Annexe BCaractéristiques et cartes
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Annexe B. Caractéristiques et cart
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Légende des cartes8 kmMaillage SAF
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Annexe CLes débits négatifs dans
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Annexe C. Les débits négatifs dan
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Annexe DApproximation de la courbe
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Annexe D. Approximation de la courb
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Annexe EAlgorithmique et codes C de
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Annexe E. Algorithmique et codes C
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Annexe Ftel-00392240, version 1 - 5
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Annexe F. Paramètres et critères
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Annexe F. Paramètres et critères
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Annexe GParamètres et critères de
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Annexe G. Paramètres et critères
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Annexe G. Paramètres et critères
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Annexe Htel-00392240, version 1 - 5
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Towards robust methods to couple lu
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5forecasting (Smith et al., 2009, t
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tion for a hydraulic model.These tw
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Apart from these few examples, the
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150nel (see Figure 1.d).All the dat
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4.2 Lumped Rainfall-Runoff Model245
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∫ tO(t) = Ψ(t)+0with: Ψ(t) = L[
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