Soil salinization is a critical environmental and socio‐economic concern with global implications, and its severity is expected to amplify under changing climate. The impact of climate change on salinization in Central Asia is still not fully understood. This study addresses this gap by employing a digital soil mapping (DSM) framework. Cubist, random forest (RF), and quantile regression forests (QRF) are utilized to project variations in soil surface salinity (0‐10 cm) in Central Asia from 2025 to 2100 under two shared socio‐economic pathways (SSPs): SSP2‐4.5 and SSP5‐8.5. These models are developed using data from 20 global climate models (GCMs) obtained from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results reveal that the RF model exhibits superior predictive capability in estimating soil salinity. RF performed on the calibration set with a coefficient of determination (R2) of 0.86, root mean square error (RMSE) of 9.84 and 9.90 dS m−1, ratio of performance to interquartile distance (RPIQ) of 3.09 and 3.07, and a Nash–Sutcliffe efficiency (NSE) of 0.86. The multi‐GCM ensemble means revealed the potential for varying degrees of salinization in Central Asia, with higher levels predominantly observed in the southeast and southwest of the study area, particularly downstream of the river and in the lakeside areas. Temporal analysis of soil salinity evolution reveals an overall increase in salinity across the region, with more notable changes projected under SSP5‐8.5. Specifically, the projected increase rate in soil salinity for Central Asia was 0.0005 dS m−1/year under SSP2‐4.5 and 0.01 dS m−1/year under SSP5‐8.5. Turkmenistan is notable for possessing the highest regional average of soil salinity, with the exception of a declining trend observed within this area. The remaining regions of Central Asia exhibit an upward trend in average soil salinity, particularly noteworthy under the SSP5‐8.5 scenario, where variations in soil salinity are more obvious. These findings hold significant potential in enhancing our understanding of how Central Asia responds to global change, advances toward sustainable development, and enhances comprehension of the dynamics within the region.

中文翻译：

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CMIP6强迫情景下中亚土壤盐碱化的未来变化


土壤盐碱化是一个具有全球影响的重要环境和社会经济问题，其严重程度预计会随着气候变化而加剧。气候变化对中亚盐碱化的影响尚不完全清楚。本研究通过采用数字土壤测绘（DSM）框架来解决这一差距。利用立体派、随机森林 (RF) 和分位数回归森林 (QRF) 来预测 2025 年至 2100 年中亚土壤表面盐度 (0-10 cm) 的变化，并采用两种共享的社会经济路径 (SSP)：SSP2- 4.5 和 SSP5-8.5。这些模型是使用从耦合模型比对项目第 6 阶段 (CMIP6) 获得的 20 个全球气候模型 (GCM) 的数据开发的。结果表明，RF 模型在估算土壤盐分方面表现出优异的预测能力。对校准集执行 RF，确定系数 (R2) 为 0.86，均方根误差 (RMSE) 为 9.84 和 9.90 dS m−1，性能与四分位距离之比 (RPIQ) 为 3.09 和 3.07，纳什– 萨特克利夫效率 (NSE) 为 0.86。多 GCM 集合意味着中亚地区存在不同程度的盐碱化潜力，较高水平主要出现在研究区域的东南部和西南部，特别是河流下游和湖滨地区。土壤盐分演变的时间分析揭示了整个地区盐分的总体增加，预计 SSP5-8.5 下的变化更为显着。具体而言，SSP2-4.5 下中亚土壤盐分预计增加率为 0.0005 dS m−1/年，SSP5-8.5 下为 0.01 dS m−1/年。 土库曼斯坦以拥有最高的区域平均土壤盐分而闻名，但该地区观察到的下降趋势除外。中亚其他地区的平均土壤盐分呈上升趋势，特别是在SSP5-8.5情景下，土壤盐分变化更为明显。这些发现对于增强我们对中亚如何应对全球变化、推进可持续发展以及增强对该地区动态的理解具有重大潜力。