Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML
Development of a Deep Learning Emulator for a Distributed Groundwater–Surface Water Model: ParFlow-ML
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DOI：
10.3390/w13233393
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发表时间：
2021-12
期刊：
影响因子：
3.4
通讯作者：
Hoang Tran;E. Leonarduzzi;Luis De la Fuente;R. B. Hull;Vineet Bansal;Calla Chennault;P. Gentine;Peter Melchior;L. Condon;R. Maxwell
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文献类型：
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作者：
Hoang Tran;E. Leonarduzzi;Luis De la Fuente;R. B. Hull;Vineet Bansal;Calla Chennault;P. Gentine;Peter Melchior;L. Condon;R. Maxwell
综合水文模型求解代表自然过程的耦合数学方程，包括地下水、非饱和水流和地表径流。然而，这些模型在计算上是昂贵的。最近的研究表明，机器学习(ML)和深度学习(DL)可以用来模拟地球系统中复杂的物理过程。在这项研究中，我们演示了DL模型如何以很少的计算费用模拟瞬时的、三维的综合水文模型模拟。该仿真器基于先前用于建模视频动态的DL模型PredRNN。仿真器根据原始模型中使用的物理参数、水力传导性和地形等输入进行训练，并产生空间分布的输出(例如压头)，根据这些输出可以计算诸如径流和地下水位深度等量。模拟器和ParFlow的模拟结果对径流、地下水位深度和总蓄水量的平均相对偏差分别为0.070、0.092和0.032。此外，该仿真器的速度比ParFlow快42倍。考虑到这一有希望的概念证明，我们的结果为未来完全水文模型仿真的应用打开了大门，特别是在更大的尺度上。
Integrated hydrologic models solve coupled mathematical equations that represent natural processes, including groundwater, unsaturated, and overland flow. However, these models are computationally expensive. It has been recently shown that machine leaning (ML) and deep learning (DL) in particular could be used to emulate complex physical processes in the earth system. In this study, we demonstrate how a DL model can emulate transient, three-dimensional integrated hydrologic model simulations at a fraction of the computational expense. This emulator is based on a DL model previously used for modeling video dynamics, PredRNN. The emulator is trained based on physical parameters used in the original model, inputs such as hydraulic conductivity and topography, and produces spatially distributed outputs (e.g., pressure head) from which quantities such as streamflow and water table depth can be calculated. Simulation results from the emulator and ParFlow agree well with average relative biases of 0.070, 0.092, and 0.032 for streamflow, water table depth, and total water storage, respectively. Moreover, the emulator is up to 42 times faster than ParFlow. Given this promising proof of concept, our results open the door to future applications of full hydrologic model emulation, particularly at larger scales.