Snow processes in mountain forests: interception modeling for coarse-scale applications
Snow processes in mountain forests: interception modeling for coarse-scale applications
复制标题
山地森林的降雪过程：粗尺度应用的拦截建模
DOI：
10.5194/hess-24-2545-2020
复制
发表时间：
2019
影响因子：
6.3
通讯作者：
J. Monnet
中科院分区：
文献类型：
--
作者：
N. Helbig;D. Moeser;M. Teich;L. Vincent;Y. Lejeune;J. Sicart;J. Monnet
抽象。森林冠层的积雪拦截控制了冠层下积雪的空间异质性，导致森林和非森林地区在各种尺度上存在显着差异。被林冠截留的雪也能极大地改变地表覆盖度。因此，准确地模拟积雪拦截对于水文、天气和气候预测等各种模型应用非常重要。由于雪截留的直接测量的困难，以前的经验雪截留模型仅在点尺度上开发。空间上广泛的数据集的缺乏，阻碍了雪拦截模型在不同的雪气候，森林类型，并在各种空间尺度的验证，并减少了雪拦截在粗尺度模型的准确表示。我们提出了两个新的经验模型的空间平均值和一个标准差的雪拦截来自一个广泛的雪拦截数据集收集在瑞士阿尔卑斯山的万年青针叶林。除了开放站点降雪，子网格模型输入参数包括DSM（数字表面模型）和/或天空视图因子的标准偏差，这两者都可以很容易地预先计算。这两个模型的验证进行了不同的天气条件下，在地理上不同的位置获得的积雪拦截数据集。来自美国落基山脉和法国阿尔卑斯山的积雪拦截数据集与建模的积雪拦截进行了很好的比较，两个模型的空间平均值的归一化均方根误差（NRMSE）≤ 10%，标准差的NRMSE ≤ 13%。与以前的模型相比，空间平均截留的雪水当量，所提出的模型表现出改进的模型性能。我们的研究结果表明，所提出的积雪拦截模型可以应用在粗糙的陆面模型网格单元提供了一个足够精细的尺度DSM是可以得到次网格森林参数。
Abstract. Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.