Improving gridded snow water equivalent products in British Columbia , Canada : multi-source data fusion by neural network models
Improving gridded snow water equivalent products in British Columbia , Canada : multi-source data fusion by neural network models
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DOI：
10.5194/tc-2017-56
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发表时间：
2017
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影响因子：
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通讯作者：
A. Snauffer;W. Hsieh;Alex J. Cannon;M. Schnorbus
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文献类型：
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作者：
A. Snauffer;W. Hsieh;Alex J. Cannon;M. Schnorbus
估计地表雪水当量（SWE）在混合高山环境中的季节性融化是特别困难的高植被密度，地形起伏和积雪的地区。这三个混杂因素主宰了加拿大不列颠哥伦比亚省的大部分地区。一个人工神经网络（ANN）创建的预测使用6个网格SWE产品先前评估的BC。相关的时空协变量也被列为预测因子，并从位于整个BC站的人工积雪调查的观测结果被用作目标数据。平均绝对误差（MAEs）和年际相关性4月调查发现使用交叉验证。使用三个表现最好的SWE产品（ANN3）的人工神经网络具有全省最低的平均站MAE。ANN3优于每个产品以及产品的手段和多元线性回归（MLR）模型在所有的BC的五个自然地理区域，除了BC平原。随后与可变入渗能力（维克）水文模型生成的预测进行比较，发现ANN 3可以更好地估计维克域和大多数区域内的SWE。ANN3的上级性能优于单个产品、产品平均值、MLR和维克，在全省范围内具有统计学显著性。
Estimates of surface snow water equivalent (SWE) in mixed alpine environments with seasonal melts are particularly difficult in areas of high vegetation density, topographic relief, and snow accumulations. These three confounding factors dominate much of the province of British Columbia (BC), Canada. An artificial neural network (ANN) was created using as predictors six gridded SWE products previously evaluated for BC. Relevant spatiotemporal covariates were also included as predictors, and observations from manual snow surveys at stations located throughout BC were used as target data. Mean absolute errors (MAEs) and interannual correlations for April surveys were found using crossvalidation. The ANN using the three best-performing SWE products (ANN3) had the lowest mean station MAE across the province. ANN3 outperformed each product as well as product means and multiple linear regression (MLR) models in all of BC’s five physiographic regions except for the BC Plains. Subsequent comparisons with predictions generated by the Variable Infiltration Capacity (VIC) hydrologic model found ANN3 to better estimate SWE over the VIC domain and within most regions. The superior performance of ANN3 over the individual products, product means, MLR, and VIC was found to be statistically significant across the province.