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X - 18LERAT ET AL.: VALUE OF UPSTREAM FLOW MEASUREMENTSprising considering that the benchmark model does not exploit upstream flow informationduring the calibration process.In the following sections, we analyze the results of the different models in light of thethree questions concerning (1) the upstream flow’s influence on downstream simulations,315(2) the influence of errors on upstream flow measurements and (3) the changes in perfor-mance when measured upstream flow is replaced with a calculated value from an upstreamsubmodel.tel-00392240, version 1 - 5 Jun 20093203253305.1. What are the conditions necessary for upstream flow measurements toimpact downstream simulations?With this first question, our objective is to specify the conditions for measured upstreamflows to significantly improve downstream simulations. Here, we compare the resultsobtained with the benchmark model with the results obtained with the UP-Meas model.Table 3 clearly confirms the value of measured upstream flows: simulations on the Table 3downstream point TOTAL are clearly improved when comparing the performance of thebenchmark with the UP-Meas model, which explicitely uses these data. Mean classicalefficiency (CEff) in validation mode rises from 0.82 for the benchmark model to 0.87 forthe UP-Meas model. Similar behavior can be observed for all percentiles of the CEffdistribution over the 384 periods. This result was expected: it confirms the well-knownadvantage in using measured upstream boundary conditions in a semidistributed model.To push our analysis a step further, we tried to explain the values of the relative efficiencyREff(UP-Meas/Benchmark) by physical descriptors of the nested catchments UPand TOTAL. Among a large number of hydrological, hydrogeological and morphologicalindicators, the only descriptors that were found useful are related to the relative runoffD R A F T July 24, 2008, 3:43pm D R A F T
LERAT ET AL.: VALUE OF UPSTREAM FLOW MEASUREMENTS X - 19amplitude between the UP and TOTAL gaging stations. Figure 5 shows the REff effi- Figure 5ciencies plotted against three of these descriptors: (a) the surface ratio between UP andTOTAL catchments, (b) the ratio between the mean annual discharge on UP and TOTAL335stations, and (c) the ratio between the 99 th percentile of the discharge time-series on UPand TOTAL stations. This last indicator gives the ratio between high flows on the twostations. The choice of the 99 th percentile appeared as the best compromise between lowerpercentiles that do not capture the appropriate flood regimes and higher percentiles thatare influenced by sampling problems on extreme values.tel-00392240, version 1 - 5 Jun 2009340345350Figure 5 clearly shows that REff(UP-Meas/Benchmark) increases when upstream flow Figure 5measurements constitute a singificant part of the downstream flows. Large measuredupstream contributions reduce the importance of errors introduced by the UP-Meas modelon the intermediate area INT. As a result, the downstream simulation improves. Itis remarkable that the REff criterion does not converge toward 1 (perfect simulation)when the different descriptors tend toward 1 (corresponding to a nested station locatedimmediately upstream of the downstream station): when upstream contribution increasessubstantially, any inconsistency in the upstream flow data will have a critical impact onthe downstream simulation and the performance gain becomes more uncertain. This wasnoted in several cases (outliers showing null or negative REff values with a 99 th percentiledischarge ratio close to 1, see Figure 5 (c)). Upstream flow data quality appears to be animportant factor in our analysis; it will be studied in greater detail in section 5.2.Linear regressions between the descriptors and REff values were performed as indicatedin Figure 5: the coefficient of correlation increases from 0.62 with the surface ratio up to Figure 50.67 with the 99 th percentile discharge ratio. Note that a combination of several descriptorsD 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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tel-00392240, version 1 - 5 Jun 200
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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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Apart from these few examples, the
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tributed. Accordingly, the differen
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