Hydrological connectivity in coastal deltas is important for delivering flow, sediment, and nutrients to the island‐interiors. The roughness of island vegetation limits connectivity, but how important is the spatial distribution of vegetation? Using hydrodynamic modeling, we test the influence of vegetated percent cover, patch size, and stem density on the discharge‐fraction allocated to the islands of an idealized delta complex, modeled after Wax Lake Delta. We find that heterogeneity has negligible effects when vegetation is relatively sparse, but is important when vegetation is relatively dense and covers less than a “disconnectivity" threshold of 40% − 50% of the islands, near the theoretical percolation limit. Below this threshold, preferential flow‐paths develop in the islands, which alter the hydraulics and residence time distribution of the delta complex, and enhance potential sediment transport with respect to model runs with uniform vegetation. Patchiness has hydrogeomorphic and biogeochemical implications which should be considered when modeling deltaic systems.

Hydrologic connectivity controls the lateral exchange of water, solids, and solutes between rivers and floodplains, and is critical to ecosystem function, water treatment, flood attenuation, and geomorphic processes. This connectivity has been well‐studied, typically through the lens of fluvial flooding. In regions prone to heavy rainfall, the timing and magnitude of lateral exchange may be altered by pluvial flooding on the floodplain. We collected measurements of flow depth and velocity in the Trinity River floodplain in coastal Texas (USA) during Tropical Storm Imelda (2019), which produced up to 75 cm of rainfall locally. We developed a two‐dimensional hydrodynamic model at high resolution for a section of the Trinity River floodplain inspired by the compound flooding of Imelda. We then employed Lagrangian particle routing to quantify how residence times and particle velocities changed as flooding shifted from rainfall‐driven to river‐driven. Results show that heavy rainfall initiated lateral exchange before river discharge reached flood levels. The presence of rainwater also reduced floodplain storage, causing river water to be confined to a narrow corridor on the floodplain, while rainwater residence times were increased from the effect of high river flow. Finally, we analyzed the role of floodplain channels in facilitating surface‐water connectivity by varying model resolution in the floodplain. While the resolution of floodplain channels was important locally, it did not affect as much the overall floodplain behavior. This study demonstrates the complexity of floodplain hydrodynamics under conditions of heavy rainfall, with implications for sediment deposition and nutrient removal during floods.

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.

In geophysical systems, such as rivers, estuaries, and deltas, hydrodynamic models typically solve the depth-integrated "shallow water" equations in an Eulerian reference frame, which is concerned with fluxes through a given region of space-examples of these solvers include ANUGA ("ANUGA," 2019), Delft3D ("Delft3D," 2020), Frehd (Hodges, 2014) and others. However, the spatial and temporal characteristics of the movement of material through a landscape are often better understood using a Lagrangian reference frame (Doyle & Ensign, 2009), which follows the movement of individual objects or parcels. In this paper, we present an open-source Python package, dorado, which provides a transparent and accessible method for researchers to simulate passive Lagrangian particle transport on top of Eulerian hydrodynamic solutions. This mixed Eulerian-Lagrangian methodology adapts the routing functionality from the popular numerical model DeltaRCM (Liang et al., 2015a, 2015b) for use with the outputs of any shallow-water hydrodynamic solver.

Understanding the way fluvially transported materials are partitioned in river deltas is essential for predicting their morphological change and the fate of environmental constituents and contaminants. Translating water‐based partitioning estimates into fluxes of nonwater materials is often difficult to constrain because most materials are not uniformly distributed in the water column and may have characteristic transport pathways that differ from the mean flow. Here, we present a novel reduced‐complexity modeling approach for simulating the patterns of transport of a diverse range of suspended fluvial inputs influenced by vertical stratification and topographic steering. We utilize a mixed Eulerian‐Lagrangian modeling approach to estimate the patterns of nourishment and connectivity in the Wax Lake and Atchafalaya Deltas in coastal Louisiana. Using the reduced‐complexity particle routing model dorado, in conjunction with a calibrated ANUGA hydrodynamic model, we quantify how transport patterns in each system change as a function of a material's Rouse number and environmental conditions. We find that even small changes to local topographic steering lead to emergent system‐scale changes in patterns of fluvial nourishment, with greater channel‐island connectivity for positively buoyant materials than negatively buoyant materials, hydraulically sorting different materials in space. We also find that the nourishment patterns of some materials are more sensitive than others to changes in discharge, tidal conditions, and anthropogenic dredging. Our results have important implications for understanding the eco‐geomorphic evolution of deltas, and our modeling framework could have interdisciplinary implications for studying the transport of materials in other systems, including sediments, nutrients, wood, plastics, and biotic materials.