Continuous snowpack monitoring using upward-looking ground-penetrating radar technology
Continuous snowpack monitoring using upward-looking ground-penetrating radar technology
复制标题
DOI：
10.3189/2014jog13j084
复制
发表时间：
2014-01-01
影响因子：
3.4
通讯作者：
Eisen, Olaf
中科院分区：
文献类型：
--
作者：
Schmid, Lino;Heilig, Achim;Eisen, Olaf
雪层和水渗透是雪崩形成的关键因素。到目前为止，只有破坏性的方法可以提供这种信息。雷达技术允许对积雪进行连续的、非破坏性的扫描，从而可以跟踪内部特性的时间演变。我们在Weissfluhjoch研究基地（瑞士达沃斯）安装了一个上视探地雷达系统（upGPR）。在两个冬季（2010/11年和2011/12年），我们记录的数据，目的是定量确定积雪的属性及其时间演变。我们自动推导出雪高，精度约为5 cm，跟踪内部层的沉降（+/- 7 cm），并测量新雪量（+/- 10 cm）。使用外部积雪高度测量，我们确定了体积密度，与手动测量相比，平均误差为4.3%。雷达测得的雪水当量与人工测量值相差+/-5%。此外，我们还跟踪了积雪中干湿转变的位置，直到水蒸发到地面。基于过渡和独立的雪高测量，可以估计体积液态水含量及其时间演变。即使我们需要额外的信息来获得一些雪的属性，我们的研究结果表明，它是可能的定量推导出雪的属性与upGPR。
Snow stratigraphy and water percolation are key contributing factors to avalanche formation. So far, only destructive methods can provide this kind of information. Radar technology allows continuous, non-destructive scanning of the snowpack so that the temporal evolution of internal properties can be followed. We installed an upward-looking ground-penetrating radar system (upGPR) at the Weissfluhjoch study site (Davos, Switzerland). During two winter seasons (2010/11 and 2011/12) we recorded data with the aim of quantitatively determining snowpack properties and their temporal evolution. We automatically derived the snow height with an accuracy of about 5 cm, tracked the settlement of internal layers (+/- 7 cm) and measured the amount of new snow (+/- 10 cm). Using external snow height measurements, we determined the bulk density with a mean error of 4.3% compared to manual measurements. Radar-derived snow water equivalent deviated from manual measurements by +/- 5%. Furthermore, we tracked the location of the dry-to-wet transition in the snowpack until water percolated to the ground. Based on the transition and an independent snow height measurement it was possible to estimate the volumetric liquid water content and its temporal evolution. Even though we need additional information to derive some of the snow properties, our results show that it is possible to quantitatively derive snow properties with upGPR.