Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year-to-year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in-situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics-based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in-situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics-based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in-situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time-series estimations of snow depth were observed. These results can be applied to any physics-based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments.

中文翻译：

---

重新审视雪分布模式：基于物理和机器学习的混合方法绘制亚北极雪分布图


由于当地地形、天气和植被特征，北极和高山景观中的积雪分布经常以每年重复的模式出现。先前的研究表明，通过多年的观测数据，这些降雪分布模式可以统计地集成到降雪过程建模工作流程中。雪水文学和机器学习 (ML) 的最新进展提高了我们利用原位观测、遥感数据集和可轻松获得大多数环境的简单景观特征来预测积雪分布的能力。在这里，我们提出了一种混合方法，将 ML 雪分布模式 (MLSDP) 地图与基于物理的雪过程模型结合起来。我们利用阿拉斯加苏厄德半岛亚北极研究区在雪水当量 (SWE) 峰值期间收集的数万个雪调查观测数据来训练随机森林 ML 算法。我们使用原位雪深和 SWE 观测数据以及光探测和测距数据集以及分布式温度分析传感器数据集来验证混合模型输出。将混合结果与基于物理的方法进行比较时，混合方法更准确地描述了积雪的空间模式、流雪区域以及随机森林 ML 训练数据集中未使用现场观测的年份。混合方法还显示，在观察到雪深时间序列估计的 61% 位置，均方根误差有所改善。这些结果可以应用于任何基于物理的模型，以改善雪的分布模式，以反映高纬度和高海拔寒冷地区环境中的观测条件。