Deriving physioclimatic regions using advanced clustering techniques
The project team is happy to announce that the first EMERGENCE paper has been published in Environmental Modelling & Software. Our recent study titled “Derivation of characteristic physioclimatic regions through density-based spatial clustering of high-dimensional data” (Lehner, Enigl, and Schlögl 2025) introduces a novel methodology for physioclimatic regionalization, i.e., identifying geospatial clusters with similar physioclimatic attributes. In support of open science, the corresponding dataset is also freely available via zenodo (Lehner, Enigl, and Schlögl 2024).
Understanding Physioclimatic Regions
Physioclimatic regions are homogeneous geospatial entities that exhibit similar characteristics in both climatic conditions and the physiographic environment. These regions are crucial for various analyses in earth system sciences, including climate impact research, natural hazard modeling, and environmental monitoring.
Our Approach
To tackle the complexity of high-dimensional input data and nonlinear processes in nature, we developed a nonparametric clustering workflow. Our methodology includes:
- Principal Component Analysis (PCA) for linear dimension reduction.
- Uniform Manifold Approximation and Projection (UMAP) for nonlinear dimension reduction.
- Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for clustering.
- Random Forest for feature importance assessment.
Key Findings
Using a comprehensive dataset of climatological and geomorphometric indices from Austria, we identified seven distinct physioclimatic subregions. These include regions such as the Eastern Alps, Northern Austria, and Inner-alpine Valleys. Our approach revealed both agreement and differences compared to traditional classification methods, highlighting the need for quantitative performance evaluation and synoptic plausibility assessment.
Why It Matters
Our workflow offers a blueprint for delineating consistent geospatial regions for various applications. By leveraging advanced machine learning techniques, we can unearth new perspectives on regionalization and provide deeper insights into the underlying characteristics of these regions. This can aid in understanding complex environmental patterns and improve the accuracy of climate impact assessments.
Future Directions
We believe that our methodology can be adapted to other domains by incorporating domain-specific input data. This flexibility allows for tailored applications in areas such as agrometeorology, hydrology, and biogeography. We are excited to continue exploring the potential of this approach and contribute to the broader field of climate science.
Stay tuned for more updates on our research and findings!
Image Credit: Giammarco Boscaro via Unsplash