Effects of Meteorological Forcing Uncertainty on High-Resolution Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed
Effects of Meteorological Forcing Uncertainty on High-Resolution Snow Modeling and Streamflow Prediction in a Mountainous Karst Watershed
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DOI：
10.1016/j.jhydrol.2023.129304
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发表时间：
2023-02
影响因子：
6.4
通讯作者：
C. Tyson;Q. Longyang;B. Neilson;R. Zeng;Tianfang Xu
中科院分区：
文献类型：
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作者：
C. Tyson;Q. Longyang;B. Neilson;R. Zeng;Tianfang Xu
在美国西部山区，相当一部分的水供应来自通过喀斯特流域的融雪。准确模拟积雪为主的喀斯特盆地中的径流对于水资源管理非常重要。然而，由于这些流域的气象和水文地质过程的时空变异性很大，而且缺乏气候站，因此这一工作具有挑战性。为了克服这些挑战，使用基于物理的雪模型来模拟100 m分辨率的雪过程，并将计算出的融雪和潜在蒸散率输入深度学习模型来模拟径流。雪模型由区域尺度天气研究和预报（WRF）模型或北美陆地数据同化系统（NLDAS-2）的气象变量驱动。这两个数据集都使用在原始分辨率和缩小到100米分辨率的基础上地形调整，导致四套强迫。雪模型模拟结果从四组强迫模拟雪水当量（SWE）和融雪速率和时间显示出很大的差异。然而，在模拟的径流中，这种差异得到了抑制，因为深度学习模型部分不受输入偏差的影响，并且在使用雪模型结果进行训练时，对融雪和降雨的径流响应不同。虽然考虑的气象数据集产生了接近的径流模拟精度，平均模拟径流从四组强迫一致取得了更好的性能，这表明包括多个气象数据集在山区流域径流建模的价值。
In the mountainous Western U.S., a considerable portion of water supply originates as snowmelt passing through karst watersheds. Accurately simulating streamflow in snow-dominated, karst basins is important for water resources management. However, this has been challenging due to high spatiotemporal variability of meteorological and hydrogeological processes in these watersheds and scarcity of climate stations. To overcome these challenges, a physically based snow model is used to simulate snow processes at 100 m resolution, and the calculated snowmelt and potential evapotranspiration rates are fed into a deep learning model to simulate streamflow. The snow model was driven by meteorological variables from a regional scale Weather Research and Forecasting (WRF) model or from the North American Land Data Assimilation System (NLDAS-2). The two datasets were used both at the original resolution and downscaled to 100 m resolution based on orographic adjustments, leading to four sets of forcings. Snow model simulation results from the four sets of forcings showed large differences in simulated snow water equivalent (SWE) and snowmelt rate and timing. However, the differences were damped in simulated streamflow, as the deep learning model is partially immune to input bias and picked up different streamflow responses to snowmelt and rainfall when trained using snow model results. While the meteorological datasets considered yielded close streamflow simulation accuracy, averaging simulated streamflow from the four sets of forcings consistently achieved better performance, suggesting the value of including multiple meteorological datasets for modeling streamflow in mountainous watersheds.