Estimation of snow load and avalanche hazard assessment in Southeastern Afghanistan through remote sensing techniques

The estimation of snow load is vital for hazard assessment and disaster risk management in Southeastern Afghanistan, a region distinguished by its high-elevation mountainous terrain and a high incidence of snow avalanches. Due to the limited availability of ground-based observations, remote sensing techniques have been widely utilized to analyze snow cover, avalanche frequency, and climatic trends. The Snow Avalanche Frequency Estimation (SAFE) model, which employs Landsat satellite data, to emphasize remote sensing’s role in assessing climatic has successfully mapped zones susceptible to avalanches over a span of 32 years. Recent advancements in multispectral and passive microwave remote sensing technologies have significantly enhanced the evaluation of snow-covered areas and snow water equivalent (SWE), thereby facilitating flood prediction and effective water resource management. Climate change has intensified fluctuations in snowfall patterns, and consequently elevating the risks associated with snow-related hazards. This study explores various methodologies for estimating ground snow load, including empirical models, remote sensing applications, and machine learning approaches. Furthermore, it examines risk assessment frameworks and mapping techniques aimed at improving disaster preparedness. The findings underscore the necessity for region-specific modeling strategies and enhanced data collection methods to tackle the challenges presented by climatic variability. Future research should prioritize the integration of advanced geospatial technologies with in-situ measurements to improve the accuracy of snow hazard predictions in the complex mountainous landscapes of Afghanistan.