Soil Organic Carbon Projections and Climate Adaptation Strategies across Pacific Rim Agro-ecosystems

Abstract

Abstract. In Pacific Rim regions highly exposed to climate variability, accurate projections of soil organic carbon (SOC) are critical for furture effective land management and climate adaptation strategies. This study integrated digital soil mapping with CMIP6-based climate projections to estimate the spatiotemporal distribution of SOC stocks in subtropical (Zhuoshui River) and tropical (Laonong River) watersheds in Taiwan. We collected 1377 soil samples and data on 18 environmental covariates and modeled SOC stocks at a 20-m resolution through the Cubist and random forest algorithms, which were also combined with regression kriging. The Cubist-based kriging model was discovered to achieve the highest performance in SOC stock prediction. Forested areas were found to contain >80 % of SOC stocks, and tropical zones were discovered to store substantially less carbon than subtropical zones. Future emission scenarios revealed spatial heterogeneity in SOC stock dynamics. In scenario SSP1-2.6, a maximum SOC stock decline of approximately 20.9 % was predicted, particularly for uplands, because of erosion induced by extreme rainfall events (R95p and R99p), whereas in scenarios SSP2-4.5 and SSP5-8.5, increases of 7.9 % to 58 % were predicted, respectively; particularly corresponded to forested areas because of enhanced productivity caused by increased TNx and TXx (extremes of minimum and maximum temperature). Partial least squares path modeling revealed a climate–topography interaction in SOC stocks, dominated by topography and followed by prolonged dry spells. Examining the interactions between climatic extremes, landscape types, and SOC stocks is essential for enhancing soil resilience and ensuring stable SOC stocks in the future.