Abstract
Spatially differentiated snow-load assessment is essential for updating building provisions in Kazakhstan, where climatic contrasts and complex terrain produce substantial regional variations in snow accumulation. This study develops a national-scale methodology for ground snow load zoning consistent with the characteristic-value approach of Eurocode 1 (EN 1991-1-3:2003). The assessment uses observations from 210 Kazhydromet meteorological stations for 1971–2020 and snow survey data from 17 mountain river basins. Cluster analysis, minimum spanning tree regionalization, Gumbel extreme-value estimation, regional elevation-dependent regression relationships, and GIS mapping were combined to estimate characteristic ground snow loads with an annual exceedance probability of 0.02, corresponding to a 50-year mean return period. The resulting national map delineates nine snow-load zones, with assigned values of 0.8, 1.2, 1.5, 1.8, 2.4, 3.2, 4.0, 4.8 kPa and an open-ended highest class exceeding 5.6 kPa. Regional regression equations quantify elevation-related variations in snow loads within the investigated mountain and upland areas, complementing the geographical differentiation represented by the map. The results provide an observational and statistical basis for updating national snow-load provisions and selecting regionally appropriate ground snow loads for structural design. The underlying snow water equivalent data also support subsequent hydrological assessments of snow storage.