Integrating Connectivity Into Hydrodynamic Models: An Automated Open-Source Method to Refine an Unstructured Mesh Using Remote Sensing

Abstract

Hydrodynamic models are an essential tool for studying the movement of water and other materials across the Earth surface. However, models remain limited by practical constraints on size and resolution, particularly in coastal environments containing topologically complex and multi-scale channel/wetland networks. Unstructured meshes have helped address this problem by allowing resolution to vary spatially, and many models support local mesh refinement using breaklines or internal regions-of-interest. However, there remains no standardized, objective, or easily reproducible method to implement internal features between different users. The present study aims to address whether remote sensing can fill in that gap, by embedding information about landscape structure and connectivity directly into model meshes. We present an automated image processing methodology for preserving dynamically active connected features in the unstructured shallow-water model ANUGA, while reducing computational demand in less active areas of the domain. The Unstructured Mesh Refinement Method (UMRM) converts a binary input raster into a collection of closed, simple polygons which can be used to refine the mesh, meanwhile preserving connectivity and enforcing model-related constraints. We apply this workflow to a large-scale model of two coastal river deltas, and refine our mesh using time-series of optical Planet imagery, InSAR measurements of water level change, and topographic data. We compare the results of the connectivity-preserving mesh to results from a mesh using a uniform mesh resolution, and find that the UMRM decreased the computational demand by a third, without any discernible loss in accuracy when compared to in-situ and remotely sensed water level measurements.