Future changes in soil salinization across Central Asia under CMIP6 forcing scenarios
Bibliographic Data
| ID | 21648960 |
|---|---|
| Authors | Xin Dong (0000-0001-9908-4838, College of Geography and Remote Sensing Sciences Xinjiang University Urumqi China), Xiaoqiang Dong (0000-0002-5443-4491, Xinjiang University), Jianli Ding (0000-0002-5024-931X, College of Geography and Remote Sensing Sciences Xinjiang University Urumqi China, corresponding author), Xiangyu Ge (0009-0008-2341-8051, College of Geography and Remote Sensing Sciences Xinjiang University Urumqi China) |
| Year | 2024 |
| Volume | 35 |
| Issue | 13 |
| Pages | 3981-3998 |
| Publication date | 2024-08-15 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Land Degradation and Development (JOURNAL) |
| Journal identifiers | ISSN: 1085-3278 • E-ISSN: 1099-145X |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/ldr.5194 |
| OpenAlex | W4400473960 |
| Language | EN |
| Citations received | 2 |
| References cited | 61 |
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 ( R 2 ) 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
Climate change · Climate model · Climatology · Coupled model intercomparison project · Salinity · Soil salinity · Soil water · Environmental Science · Plant Water Relations and Carbon Dynamics · Soil and Unsaturated Flow · Soil Geostatistics and Mapping · Geology · Soil Science
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |