Blowing snow is ubiquitous in cold, windswept environments. In some regions, blowing snow sublimation losses can ablate a notable fraction of the seasonal snowfall. It is advantageous to predict alpine snow regimes at the spatial scale of snowdrifts (≈1 to 100 m) because of the role of snow redistribution in governing the duration and volume of snowmelt. However, blowing snow processes are often neglected due to computational costs. Here, a three‐dimensional blowing snow model is presented that is spatially discretized using a variable resolution unstructured mesh. This represents the heterogeneity of the surface explicitly yet, for the case study reported, gained a 62% reduction in computational elements versus a fixed‐resolution mesh and resulted in a 44% reduction in total runtime. The model was evaluated for a subarctic mountain basin using transects of measured snow water equivalent (SWE) in a tundra valley. Including blowing snow processes improved the prediction of SWE by capturing inner‐annual snowdrift formation, more than halved the total mean bias error, and increased the coefficient of variation of SWE from 0.04 to 0.31 better matching the observed CV (0.41). The use of a variable resolution mesh did not dramatically degrade the model performance. Comparison with a constant resolution mesh showed a similar CV and RMSE as the variable resolution mesh. The constant resolution mesh had a smaller mean bias error. A sensitivity analysis showed that snowdrift locations and immediate up‐wind sources of blowing snow are the most sensitive areas of the landscape to wind speed variations.

Nature manifests itself in space and time. The spatiotemporal complexity of processes such as precipitation, temperature, and wind, does not allow purely deterministic modeling. Spatiotemporal random fields have a long history in modeling such processes, and yet a single unified framework offering the flexibility to simulate processes that may differ profoundly does not exist. Here we introduce a blueprint to efficiently simulate spatiotemporal random fields that preserve any marginal distribution, any valid spatiotemporal correlation structure, and intermittency. We suggest a set of parsimonious yet flexible marginal distributions and provide a rule of thumb for their selection. We propose a new and unified approach to construct flexible spatiotemporal correlation structures by combining copulas and survival functions. The versatility of our framework is demonstrated by simulating conceptual cases of intermittent precipitation, double‐bounded relative humidity, and temperature maxima fields. As a real‐word case we simulate daily precipitation fields. In all cases, we reproduce the desired properties. In an era characterized by advances in remote sensing and increasing availability of spatiotemporal data, we deem that this unified approach offers a valuable and easy‐to‐apply tool for modeling complex spatiotemporal processes.