What drives basin scale spatial variability of snowpack properties in northern Colorado
What drives basin scale spatial variability of snowpack properties in northern Colorado
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科罗拉多州北部积雪特性的盆地尺度空间变异性的驱动因素
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
S. Fassnacht
中科院分区：
文献类型：
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作者：
G. Sexstone;S. Fassnacht
本研究结合野外测量和自然资源保护局（NRCS）的业务雪数据，以了解流域尺度（100 ~ 1000 s km 2）下雪密度和雪水当量（SWE）变化的驱动因素。在一个多元线性回归雪密度模型中分析了历史雪道积雪密度观测值，以直接从雪深测量值估计SWE。在2011年和2012年4月1日或前后完成了积雪调查，并结合NRCS运行测量结果，调查了接近峰值积雪时SWE的空间变异性。开发了二元关系和多元线性回归模型，以了解雪密度和SWE与地形变量（使用地理信息系统（GIS）导出）的关系。雪密度的变化最好用一年中的天数、雪深、UTM东距和海拔来解释。利用雪密度模型直接由雪深测量值计算雪偏量具有较强的统计性能，模型验证表明，该模型可在原始数据集的范围内转换为独立数据。这种直接从雪深测量值估算SWE的途径在评估盆地尺度的积雪特性时非常有用，因为许多耗时的SWE测量值通常是不可行的。与先前开发的雪密度模型的比较表明，将雪密度模型校准到特定流域可以改善该尺度下的SWE估计，并且应该考虑用于未来的流域尺度分析。在2011年和2012年的水年（WY）期间，海拔和位置（UTM东向和/或UTM北向）是最重要的SWE模型变量，表明地形降水和风暴路径模式可能驱动流域尺度SWE变化。地形曲率也被证明是一个重要的变量，但在较小程度上的规模感兴趣。
This study uses a combination of field measure- ments and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow den- sity and snow water equivalent (SWE) variability at the basin scale (100s to 1000s km 2 ). Historic snow course snowpack density observations were analyzed within a multiple lin- ear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were com- pleted on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE near peak snow accumulation. Bivariate relations and multiple linear regression models were devel- oped to understand the relation of snow density and SWE with terrain variables (derived using a geographic informa- tion system (GIS)). Snow density variability was best ex- plained by day of year, snow depth, UTM Easting, and el- evation. Calculation of SWE directly from snow depth mea- surement using the snow density model has strong statisti- cal performance, and model validation suggests the model is transferable to independent data within the bounds of the original data set. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time- consuming measurements of SWE are often not feasible. A comparison with a previously developed snow density model shows that calibrating a snow density model to a specific basin can provide improvement of SWE estimation at this scale, and should be considered for future basin scale analy- ses. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and/or UTM Northing) were the most important SWE model variables, suggesting that oro- graphic precipitation and storm track patterns are likely driv- ing basin scale SWE variability. Terrain curvature was also shown to be an important variable, but to a lesser extent at the scale of interest.