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

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.