Wavelet-based analysis of aeolian sand dynamics and adaptive mitigation strategies for desert highways

Scientific analysis of aeolian sand environments is fundamental for sustainable disaster mitigation along desert highways. However, significant regional variability in wind energy conditions complicates accurate characterization of wind regimes and introduces uncertainty in determining optimal monitoring timescales. Moreover, prevailing sand control measures often rely on standardized designs rather than site-specific adaptive strategies. To address these issues, this study proposes an integrated framework for aeolian environment analysis and develops targeted disaster mitigation strategies tailored for desert highways. The proposed framework employs wavelet transform to unravel the periodic characteristics of wind speed time series and integrates multi-source data (including ERA5 wind datasets, sand samples, ASTER GDEM, and multi-temporal remote sensing imagery) to enable a comprehensive aeolian environmental assessment. Concurrently, a suite of adaptive strategies is formulated to mitigate disaster risks along desert highways. Validated through a case study of the Tumushuk-Kunyu Desert Highway in Xinjiang, China, the framework exhibits high accuracy: predictions of annual aeolian sand transport activity show relative errors mostly below 7% against long-term reference sequences, and the calculated resultant drift direction exhibits a strong correlation with observed dune migration, yielding an R-squared value of 0.96. These findings confirm the framework's reliability and provide a robust basis for designing adaptive, location-specific mitigation strategies, thereby enhancing the sustainability of desert highway infrastructure.