Comparison and Analysis of Different Discrete Methods and Entropy-Based Methods in Rain Gauge Network Design

Abstract

A reasonable rain gauge network layout can provide accurate regional rainfall data and effectively support the monitoring, development and utilization of water resources. Currently, an increasing number of network design methods based on entropy targets are being applied to network design. The discretization of data is a common method of obtaining the probability in calculations of information entropy. To study the application of different discretization methods and different entropy-based methods in the design of rain gauge networks, this paper compares and analyzes 9 design results for rainy season rain gauge networks using three commonly used discretization methods (A1, SC and ST) and three entropy-based network design algorithms (MIMR, HT and HC) from three perspectives: the joint entropy, spatiality, and accuracy of the network, as evaluation indices. The results show that the variation in network information calculated by the A1 and ST methods for rainy season rain gauge data is too large or too small compared to that calculated by the SC method, and also that the MIMR method performs better in terms of spatiality and accuracy than the HC and HT methods. The comparative analysis results provide a reference for the selection of discrete methods and entropy-based objectives in rain gauge network design, and provides a way to explore a more suitable rain gauge network layout scheme.