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@@ -6,13 +6,17 @@ Note : the icons do not appear on the toolbar for Linux users and the "menu" but
 
 Poulard Christine et Leblois Etienne, Inrae, Unité de recherche Riverly
 
-![Screenshot_AvecDeuxCruesCentennalesPourDebuter](uploads/4e438432a8c6f15be2d0a9ea7cb5ac5f/Screenshot_AvecDeuxCruesCentennalesPourDebuter.png)
+<img src="uploads/4e438432a8c6f15be2d0a9ea7cb5ac5f/Screenshot_AvecDeuxCruesCentennalesPourDebuter.png"width = "360">
+
+<i>Screenshot : a 10-year series of annual floods including 2 floods superior the the centennial flood</i>
 
 ## about Hydrology :
 
 This demo tool was initially designed for a first contact with frequential analysis in hydrology, Master level (french Engineering School). Let us consider the maximal flood discharge of each year, QY (max in a calendar year or, better, hydrological year), and let us assume this variable follows a known Gumbel distribution (the assumption is correct for return period under a given thresholk, largely ex...).
 
-Initial figure : sample of 10 QY The sample corresponding to 10 years of observation is represented as a time-series and with plotting positions in another graph with respect to the return period, denoted T, where a Gumbel distribution fitted on the sample is compared to the "theoretical" Gumbel one. to estimate the "right" Gumbel distribution parameters. Of course, withe only a few years of obervation the QY(T) relationship estimated by fitting Gumbel parameters has no reason to be very good ; the Confidence Interval is very thick at first.
+**Initial figure** : sample of 10 QY The sample corresponding to 10 years of observation is represented as a time-series and with plotting positions in another graph with respect to the return period, denoted T, where a Gumbel distribution fitted on the sample is compared to the "theoretical" Gumbel one. to estimate the "right" Gumbel distribution parameters. Of course, withe only a few years of obervation the QY(T) relationship estimated by fitting Gumbel parameters has no reason to be very good ; the Confidence Interval is very thick at first.
+
+**Let's draw attention to... ** : the specificity of this demo tool is that we KNOW A PRIORI the values of th flood quantiles _because we start from a pre-defined distribution we draw values from !_  Of course, in the real world we can know "the real quantiles", we can only estimate them from the observations we have...
 
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