A Statistical Framework for Spatial Boundary Estimation and Change Detection: Application to the Sahel–Sahara Climate Transition

Abstract

Spatial boundaries, such as ecological transitions or climatic regime interfaces, capture steep environmental gradients, and shifts in their structure can signal emerging environmental changes. Quantifying uncertainty in spatial boundary locations and formally testing for temporal shifts remain challenging, especially when boundaries are derived from noisy, gridded environmental data. We present a unified framework that combines heteroskedastic Gaussian process (GP) regression with a scaled maximum absolute difference (MAD) global envelope test (GET) to estimate spatial boundary curves and assess whether they evolve over time. The heteroskedastic GP provides a flexible probabilistic reconstruction of boundary lines, capturing spatially varying mean structure and location-specific variability, while the scaled Maximum Absolute GET test offers a rigorous hypothesis-testing tool for detecting departures from expected boundary behaviors. Simulation studies show that the proposed method achieves the correct size under the null and high power for detecting local boundary shifts. Applying our framework to the Sahel-Sahara transition zone, using annual Köppen-Trewartha climate classifications from 1960 to 1989, we find no statistically significant decade-scale changes in the arid/semi-arid or semi-arid/non-arid interfaces. However, the method successfully identifies localized boundary shifts during the extreme drought years of 1983–1984, consistent with climate studies documenting regional anomalies in these interfaces during that period.