Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs Atmosphere

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, USA, to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.

全球多数山地的年度降水以降雪为主，精准估算积雪层储水量对供水预报具有重要意义。山地地形可形成复杂的积雪分布、积累与消融格局，但地形与气象格局的相互作用往往会催生相似的年际积雪深度分布模式。本研究旨在探讨两个问题：其一，在10年时间尺度内，积雪峰值积累期前后的积雪深度格局是否具有可重复性；其二，即便积雪深度实测数据有限的年份，是否仍可用于精准表征积雪深度及流域平均积雪深度。
本研究采用美国怀俄明州西冰川湖流域的积雪深度实测数据，探究积雪深度的分布格局。西冰川湖流域面积为0.61 km²，受强风影响（平均风速8 m/s），海拔范围介于3277 m至3493 m之间。研究对比了三种插值方法：(1) 二元回归树（binary regression tree）；(2) 多元线性回归（multiple linear regression）；(3) 广义可加模型（generalized additive models）。
结果显示，结合地形参数与实测积雪深度的广义可加模型，对积雪深度分布及流域平均积雪量的估算效果最优。研究发现，峰值积累期前后的积雪深度格局具有稳定的年际一致性，年均决定系数（r²）达0.83；且基于流域平均积雪深度实测值与布鲁克林湖雪情遥测（SNOTEL）积雪深度数据的相关性，可通过冬季积雪积累指数（r²=0.75）实现格局的可推广性。