Fine-scale assessment of desertification risk in Tarim Populus euphratica National Nature Reserve based on RWEQ and MEDALUS models

Desertification risk assessment in arid regions is frequently hindered by the coarse spatial resolution of available soil datasets. Conventional linear approaches cannot fully meet the complex dynamic and static driving ecosystem in highly fragmented habitats. Consequently, it is often required to accurately identify the risk sources and micro-geomorphic variations in sustainable ecology. In this study, a high-precision desertification risk assessment was constructed in the extreme arid zones, thereby revealing the spatiotemporal evolution patterns of the ecosystem. A typical ecologically fragile region, the Tarim Populus Euphratica National Nature Reserve was selected as the study area. A framework was then developed as follows. Initially, a machine learning model with the random forest (RF) was employed to spatially downscale soil texture data from a 1 km resolution to 30 m. High-resolution remote sensing predictors were integrated with empirical field sampling data to enhance baseline accuracy. Subsequently, the comprehensive desertification risk level (CDRL) was formulated using a hierarchical zoning matrix. Dynamic wind erosion driving forces were also coupled to quantify by the revised wind erosion equation (RWEQ). The static ecosystem sensitivity was characterized by the desertification sensitivity index (DSI). Furthermore, the Theil-Sen Median trend analysis, the Mann-Kendall test, alongside the optimal multivariable stratified geodetector (OMGD) were applied to quantitatively decode the spatiotemporal evolution and the multi-factor nonlinear driving mechanisms of regional desertification risks over the period from 2000 to 2024. The results showed that: 1) The robust performance of spatial downscaling was achieved in the R² values from 0.79 to 0.82, a Kling-Gupta efficiency (KGE) between 0.64 and 0.70, after in-situ measurements. The high-resolution mapping also captured the spatial heterogeneity of micro-geomorphic features that were previously obscured. 2) The desertification risk in the reserve presents a gradient distribution pattern, being low along the river and high in peripheral areas. Extremely high-risk zones are mainly concentrated on the south bank of the Tarim River. 3) Temporal trend analyses revealed that the overall ecological status of the reserve maintained a resilient baseline. Furthermore, 73.64% of the area exhibited no significant changes over the study period. A spatial divergence emerged in the evolving areas: 12.82% of the area on the north bank shared the significant reduction of the risks, while 13.54% of the area on the southern edge was ongoing degradation, indicating a delicate dynamic balance. 4) The driving force detection indicated that the soil properties functioned as the dominant baseline factors, with their explanatory power (q values) consistently exceeding 0.91. Notably, the explanatory power of the vegetation quality index increased significantly from 0.69 in 2000 to 0.82 in 2024, indicating the growing positive impact of anthropogenic ecological water conveyance projects. In conclusion, the evaluation framework successfully facilitated a fine-scale, mechanism diagnosis of desertification risk, compared with the conventional coarse-scale ones. There was a dynamic balance between ecological restoration and natural degradation. According to these mechanistic insights, the differentiated spatial strategy was strongly recommended to implement targeted engineering for the 13.54% of degrading areas on the south bank. The findings can provide a strong reference for the ecological connectivity of the river corridor.