Effects of Topgraphic Attributes and Water-Table Depths on the Soil Salinity Accumulation in Arid Land

Bannari, A.; Al-Ali, Z.; Kadhem, G.

doi:10.1109/igarss47720.2021.9555038

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…The use of models allows the control of salinity by knowing its spatial distribution and its evolution over time [6,7]. However, monitoring the process and mapping the soil salinity are not carried out with sufficient reliability without the integration of several factors [8]. Given the complexity of the mechanism controlling soil salinity, the use of a multicriteria approach makes it possible to identify areas at high risk of salinity and ensure the spatiotemporal monitoring of chemical soil degradation [9].…”

Section: Introductionmentioning

confidence: 99%

Mapping Soil Salinity Risk by Using an Index Approach

Chaaou

Chikhaoui

Naïmi

et al. 2022

The 2nd International Laayoune Forum on Biosaline Agriculture

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Section: Introductionmentioning

confidence: 99%

Mapping Soil Salinity Risk by Using an Index Approach

Chaaou

Chikhaoui

Naïmi

et al. 2022

The 2nd International Laayoune Forum on Biosaline Agriculture

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Future changes in soil salinization across Central Asia under CMIP6 forcing scenarios

Dong,

Ding,

2024

Land Degrad Dev

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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.

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