  With more satellite and model precipitation data becoming available, new
analytical methods are needed that can take advantage of emerging data patterns
to make well informed predictions in many hydrological applications. We propose
a new strategy where we extract precipitation variability patterns and use
correlation map to build the resulting density map that serves as an input to
centroidal Voronoi tessellation construction that optimizes placement of
precipitation gauges. We provide results of numerical experiments based on the
data from the Alto-Adige region in Northern Italy and Oklahoma and compare them
against actual gauge locations. This method provides an automated way for
choosing new gauge locations and can be generalized to include physical
constraints and to tackle other types of resource allocation problems.
