Centroidal Voronoi Tessellation Based Methods for Optimal Rain Gauge Location Prediction

TL;DR

This paper introduces a novel method using centroidal Voronoi tessellation and correlation maps to optimize the placement of rain gauges based on precipitation variability patterns, demonstrated with regional data.

Contribution

The paper presents a new automated approach for selecting rain gauge locations leveraging Voronoi tessellation and correlation analysis, adaptable to physical constraints and other resource allocation problems.

Findings

Method effectively predicts optimal gauge locations.

Numerical experiments show improved placement over existing sites.

Approach is adaptable to various hydrological resource allocation tasks.

Abstract

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.