Quantifying Uncertainty in Projections of Desertification in Central Asia Using Bayesian Networks

Desertification presents major environmental challenges in Central Asia, driven by climatic and anthropogenic factors. The present study quantifies desertification risk through an integrated approach using Bayesian networks and the ESAS model, offering a holistic perspective on desertification dynamics. Four key variables—vegetation cover, precipitation, land-use intensity, and soil quality—were incorporated into a Bayesian model to evaluate their influence on desertification. A probabilistic model was developed to gauge desertification intensity, with simulations conducted at 200 geospatial points. Hazard maps for 2030–2050 were produced under climate scenarios SSP245 and SSP585, incorporating projected land-use changes. All procedures for desertification risk assessment, land-use mapping, and climate downscaling were performed using the Google Earth Engine platform. The findings suggest a 4% increase in desertification risk under SSP245 and an 11% increase under SSP585 by 2050, with the greatest threats observed in western regions such as Kazakhstan, Uzbekistan, and Turkmenistan. Sensitivity analysis indicated that vegetation quality exerts the strongest influence on desertification, reflected by a Vegetation Quality Index (VQI) ranging from 1.582 (low in Turkmenistan) to 1.692 (very low in Kazakhstan). A comparison of the Bayesian and ESAS models revealed robust alignment, evidenced by an R2 value of 0.82, a Pearson correlation coefficient of 0.76, and an RMSE of 0.18. These results highlight the utility of Bayesian networks as an effective tool for desertification assessment and scenario analysis, underscoring the urgency of targeted land management and proactive climate adaptation. Although reclaimed land presents opportunities for afforestation and sustainable agriculture, carefully considering potential trade-offs with biodiversity and ecosystem services remains essential.