THE BLIND SIDE OF A RAIN GAUGES NETWORK: INTRODUCTORY THEORETICAL APPROACH, WITH A FIRST EXAMPLE OF APPLICATION.

One of the shortcomings implicit in the use of a network of rain gauges is to detect weather phenomena in pre-established geographical points that are stable over time. A discrete and finite number of measurement points are arranged to capture values of precipitation variables of atmospheric events.It happens that several of these precipitation events can impact areas that do not include any measurement point. This phenomenon reveals a blind side of the network: the hydrological values associated with such events are irreversibly and completely lost from the network.In this paper, a theoretical model suitable for estimating such events not captured by the network is described and proposed at an introductory level; introducing useful equations for estimating values such as: number of events, rainfall depths, rain volumes inferable on the ground.Starting from the hypothesis of isotropy and local homogeneity of some key variables, number of events and rainfall depth, we arrive at the synthesis of some significant relationships between the precipitation values of extremely isolated events completely not captured by the network, those measured by the network and the clusters of atmospheric events that generated both.The method allows these results to be obtained by making use only of the rainfall data provided by a network of rain gauges. The denser the network, the smaller the extent of such non-captured events; the more frequent the network measurement time is, the shorter the potentially deductible duration of such events.A first example of application shows that for a fairly dense network the estimated average annual rainfall not measured by a rain gauge can reach a value corresponding to 80% of the average annual total rainfall measured by the rain gauge itself. The results are confirmed by the literature. It must be taken into account that when calculating the volumes associated with this percentage lost, mainly small impact areas must be considered. In fact, the distribution of impact areas estimated for this application seems to favour smaller ones.