Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow

IF 3.5 2区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY Journal of Geophysical Research: Earth Surface Pub Date : 2024-02-21 DOI:10.1029/2023JF007336
Zhipeng Chen, Feng Luo, Ruijie Li, Chi Zhang
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Abstract

The two-way interactions between biological and physical processes, bio-geomorphic feedback, play a vital role in landscape formation and evolution in salt marshes. Patchy vegetation represents a typical form of scale-dependent feedback in salt marshes and is primarily responsible for the formation of efficient drainage networks. The intuitive manifestation of scale-dependent feedback is the heterogeneity of flow and landscape. Process-based modeling is an essential tool for exploring flow heterogeneity, but calculations for small spatial scales and over long time frames can be prohibitively costly. In this study, we proposed a deep learning model architecture, UNet-Flow, based on convolutional neural networks (CNNs), which is used to build a surrogate model to simulate a flow field induced by salt marsh patchy vegetation. After optimizing and evaluating the model, we discovered that UNet-Flow exhibits a speed improvement of over four orders of magnitude compared to single-process simulations using the free surface flow model TELEMAC-2D, with acceptable levels of error. Furthermore, we proposed an approach that combines the process-based model SISYPHE with the deep learning method to model geomorphic heterogeneity. After numerous simulations of flow heterogeneity modeling using UNet-Flow, we obtained a significant logarithmic relationship between scale-dependent feedback strength and vegetation stem density, and an ascending-descending trend in feedback strength was observed as the number or surface area of vegetation patches increased. Finally, we investigated the relationship between geomorphic heterogeneity and vegetation-related variables. This study represents a noteworthy effort to study bio-geomorphology using deep learning methods.

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基于卷积神经网络 UNet-Flow 的盐沼植被斑块诱导的水流和地貌异质性建模
生物与物理过程之间的双向互动,即生物地貌反馈,在盐沼地貌形成和演化过程中发挥着至关重要的作用。成片植被是盐沼中一种典型的规模依赖反馈形式,主要负责形成高效的排水网络。规模依赖性反馈的直观表现是水流和地貌的异质性。基于过程的建模是探索水流异质性的重要工具,但对小空间尺度和长时间段的计算成本过高。在本研究中,我们提出了一种基于卷积神经网络(CNNs)的深度学习模型架构--UNet-Flow,用于建立一个代用模型来模拟盐沼斑块植被诱导的流场。在对模型进行优化和评估后,我们发现 UNet-Flow 与使用自由表面流模型 TELEMAC-2D 的单进程模拟相比,速度提高了四个数量级以上,而且误差水平可以接受。此外,我们还提出了一种方法,将基于过程的模型 SISYPHE 与深度学习方法相结合,对地貌异质性进行建模。在使用 UNet-Flow 对水流异质性建模进行大量模拟后,我们发现规模反馈强度与植被茎干密度之间存在显著的对数关系,并且随着植被斑块数量或表面积的增加,反馈强度呈上升-下降趋势。最后,我们研究了地貌异质性与植被相关变量之间的关系。这项研究是利用深度学习方法研究生物地貌学的一项值得关注的努力。
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来源期刊
Journal of Geophysical Research: Earth Surface
Journal of Geophysical Research: Earth Surface Earth and Planetary Sciences-Earth-Surface Processes
CiteScore
6.30
自引率
10.30%
发文量
162
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