Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution
Scaling Precipitation Input to Spatially Distributed Hydrological Models by Measured Snow Distribution
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通过测量的雪分布将降水输入缩放到空间分布水文模型
DOI：
10.3389/feart.2016.00108
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发表时间：
2016
影响因子：
2.9
通讯作者：
M. Bavay
中科院分区：
文献类型：
--
作者：
Christian Vögeli;M. Lehning;N. Wever;M. Bavay
准确了解高山地形的积雪分布对于洪水风险评估、雪崩预警或供水和水力发电管理等各种应用至关重要。为了模拟高山地形的季节性积雪发展，基于物理的空间分布模型 Alpine3D 是合适的。该模型通常由自动气象站（AWS）观测的空间插值驱动，导致大气强迫的空间分布出现错误。随着遥感技术的最新进展，可以获取高空间分辨率和精度的积雪深度图。在这项工作中，使用基于机载数字传感器（ADS）的夏季和冬季数字表面模型计算出的积雪深度分布图来缩放降水输入数据，旨在提高Alpine3D模拟积雪空间分布的准确性。提出了一种简单的降水量缩放和重新分布方法，并分析了其性能。仅当下雪时才应用缩放方法。对于降雨，降水量通过插值进行分布，并使用简单的气温阈值来确定降水阶段。结果发现，模拟域的空间积雪分布精度可以得到显着提高。绝对雪深误差的标准偏差减少了 3.4 倍，小于 20 厘米。当在模拟域中使用代表性输入源时，雪分布的平均绝对误差会减少。对于年际缩放，即使使用不同冬季的遥感数据集，模型性能也可以得到改善。总之，利用遥感数据处理降水输入，可以替代诸如风雪雪崩优先沉积和雪迁移等复杂过程，提高空间积雪分布的建模性能。
Accurate knowledge on snow distribution in alpine terrain is crucial for various applications such as flood risk assessment, avalanche warning or managing water supply and hydro-power. To simulate the seasonal snow cover development in alpine terrain, the spatially distributed, physics-based model Alpine3D is suitable. The model is typically driven by spatial interpolations of observations from automatic weather stations (AWS), leading to errors in the spatial distribution of atmospheric forcing. With recent advances in remote sensing techniques, maps of snow depth can be acquired with high spatial resolution and accuracy. In this work, maps of the snow depth distribution, calculated from summer and winter digital surface models based on Airborne Digital Sensors (ADS), are used to scale precipitation input data, with the aim to improve the accuracy of simulation of the spatial distribution of snow with Alpine3D. A simple method to scale and redistribute precipitation is presented and the performance is analysed. The scaling method is only applied if it is snowing. For rainfall the precipitation is distributed by interpolation, with a simple air temperature threshold used for the determination of the precipitation phase. It was found that the accuracy of spatial snow distribution could be improved significantly for the simulated domain. The standard deviation of absolute snow depth error is reduced up to a factor 3.4 to less than 20 cm. The mean absolute error in snow distribution was reduced when using representative input sources for the simulation domain. For inter-annual scaling, the model performance could also be improved, even when using a remote sensing dataset from a different winter. In conclusion, using remote sensing data to process precipitation input, complex processes such as preferential snow deposition and snow relocation due to wind or avalanches, can be substituted and modelling performance of spatial snow distribution is improved.