Identifying Precipitation Regimes in China Using Model-Based Clustering of Spatial Functional Data

TL;DR

This paper introduces a model-based clustering method for spatial functional data to identify precipitation regimes in China, accounting for spatial dependency and multi-scale characteristics.

Contribution

It develops a flexible functional linear model with Markov random field-based cluster memberships for spatially dependent meteorological data.

Findings

Successfully identified distinct precipitation regimes in China.

Demonstrated the effectiveness of the model in capturing spatial dependencies.

Provided insights for agricultural and water resource planning.

Abstract

The identification of precipitation regimes is important for many purposes such as agricultural planning, water resource management, and return period estimation. Since precipitation and other related meteorological data typically exhibit spatial dependency and different characteristics at different time scales, clustering such data presents unique challenges. In this paper, we develop a flexible model-based approach to cluster multi-scale spatial functional data to address such problems. The underlying clustering model is a functional linear model , and the cluster memberships are assumed to be a realization from a Markov random field with geographic covariates. The methodology is applied to a precipitation data from China to identify precipitation regimes.