In this study we construct and compare three different deep learning (DL) models for estimating daily snow water equivalent (SWE) from high-resolution gridded meteorological fields over the Rocky Mountain region. To train the DL models, Snow Telemetry (SNOTEL) station-based SWE observations are used as the prediction target. All DL models produce higher median Nash-Sutcliffe Efficiency (NSE) values than a conceptual SWE model and interpolated gridded data sets, although mean squared errors also tend to be higher. Sensitivity of the SWE prediction to the model's input variables is analyzed using an explainable artificial intelligence (XAI) method, yielding insight into the physical relationships learned by the models. This method reveals the dominant role precipitation and temperature play in snowpack dynamics. In applying our models to estimate SWE throughout the Rocky Mountains, an extrapolation problem arises since the statistical properties of SWE (e.g., annual maximum) and geographical properties of individual grid points (e.g., elevation) differ from the training data. This problem is solved by normalizing the SWE with its historical maximum value to alleviate extrapolation for all tested DL models. Our work shows that the DL models are promising tools for estimating SWE, and sufficiently capture relevant physical relationships to make them useful for spatial and temporal extrapolation of SWE values.

中文翻译：

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使用时态深度学习模型估计落基山脉每日雪水当量

在本研究中，我们构建并比较了三种不同的深度学习 (DL) 模型，用于根据落基山脉地区的高分辨率网格气象场估算每日雪水当量 (SWE)。为了训练 DL 模型，使用基于雪遥测 (SNOTEL) 站的 SWE 观测作为预测目标。与概念性 SWE 模型和插值网格数据集相比，所有 DL 模型都会产生更高的 Nash-Sutcliffe 效率 (NSE) 中值，但均方误差也往往更高。使用可解释的人工智能 (XAI) 方法分析 SWE 预测对模型输入变量的敏感性，从而深入了解模型学到的物理关系。该方法揭示了降水和温度在积雪动力学中的主导作用。在应用我们的模型来估计整个落基山脉的 SWE 时，由于 SWE 的统计特性（例如，年度最大值）和各个网格点的地理特性（例如，海拔）与训练数据不同，因此出现了外推问题。这个问题是通过将 SWE 与其历史最大值标准化来解决的，以减轻对所有测试的 DL 模型的外推。我们的工作表明，DL 模型是估计 SWE 的有前途的工具，并且充分捕获相关的物理关系，使其可用于 SWE 值的空间和时间外推。