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LERAT ET AL.: VALUE OF UPSTREAM FLOW MEASUREMENTS X - 21where median(Q UP ) is the median of the upstream streamflow time series and α and β arethe two coefficients of the transformation. They are chosen to preserve the initial medianfor the sake of continuity and to modify the 99 th percentile by known perturbation of-50%, -25%, +25% and +50%. An example of this corruption is presented in Figure 6. Figure 6375Note that the benchmark model is not affected by upstream flow corruption because ittel-00392240, version 1 - 5 Jun 2009380385does not use upstream flow data as an input.Figure 7 shows the statistics of the relative efficiencies REff(UP-Meas/Benchmark) in Figure 7validation mode for different levels of corruption. First, we see that substantial corruptionof upstream flow measurements does not destabilize the UP-Meas model’s performance.The model is still better than the benchmark, with positive values for all the statistics ofthe REff criteria. This result reinforces the value of upstream flow measurements becausethey still contribute to a significant performance gain on downstream simulations whenthe corruption level rises up to 50%.Second, the UP-Meas model slightly improves with positive corruption of upstream flowdata (+25% and +50%). This result is unexpected in that it was expected that upstreamflow corruption could only degrade downstream simulations. How can this result beinterpreted? First, upstream flows may be systematically underestimated. In this case,the positive corruption compensates this underestimation.This is difficult to believeconsidering the size and the variety of our nested catchment data set. Moreover, it would390imply that the flow data are only biaised toward underestimation, whereas overestimationis also probable.Second, the increase in upstream flows induced by the positive corruption scenario modifiesthe balance between the propagation component and the rainfal-runoff component.D R A F T July 24, 2008, 3:43pm D R A F T