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ARS Home » Southeast Area » Florence, South Carolina » Coastal Plain Soil, Water and Plant Conservation Research » Research » Publications at this Location » Publication #405221
Research Project: Innovative Technologies and Practices to Enhance Water Quantity and Quality Management for Sustainable Agricultural Systems in the Southeastern Coastal Plain

Location: Coastal Plain Soil, Water and Plant Conservation Research

Title: Predictive model for characterizing bioclimatic variability within Köppen-Geiger global climate classification scheme

Author
item Sohoulande, Clement

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 8/8/2023
Publication Date: 10/29/2023
Citation: Sohoulande Djebou, D.C. 2023. Predictive model for characterizing bioclimatic variability within Köppen-Geiger global climate classification scheme. 2023 ASA, CSSA, SSSA INTERNATIONAL ANNUAL MEETING in ST. LOUIS, MISSOURI, OCTOBER 29- NOVEMBER 1 2023.

Interpretive Summary: Abstract only

Technical Abstract: Worldwide, climate classification schemes are useful for characterizing bioclimatic potentials of terrestrial ecosystems. Among these schemes, the Köppen-Geiger climate classification system has been increasingly used in a wide range of environmental fields across the globe. However, the Köppen-Geiger climate information is not sufficient to fully comprehend the dynamics of ecosystem components such as vegetation and water resources. In fact, the spatial distribution of vegetation and water resources is known to be highly influenced by the variability of bioclimatic factors. Hence, connecting global bioclimatic variability to the Köppen-Geiger classification system could leverage the interpretability of climate classes at the regional level. This study developed a combined entropy and probabilistic approach for characterizing bioclimatic variability within the Köppen-Geiger climate classification system. The bioclimatic variables used are half-degree gridded precipitation, surface temperature, leaf area index, and liquid water equivalence anomalies. In the approach, entropy-based disorder index (DI) values were quantified for individual variables and thresholds of DI percentiles were used to discretize bioclimatic variability zones at the global scale. Multivariate logistic regression models were later applied to the DI zone attributes and long-term averages of bioclimatic variables to estimate Köppen-Geiger climate classes. Statistics sustain variable models’ likelihoods (0.04=McFadden’s pseudo R2=0.92) but consistent estimates (0.87=Count R2=0.99) within the global climate classes.