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. Our 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 hydrologic 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.

In response to a growing number of natural and anthropogenic threats, the long-term sustainability of coastal river deltas and wetlands has come into question worldwide. Tools such as remote sensing and numerical modeling have been implemented in an effort to monitor and predict the hydro-geomorphological evolution of our coasts. Hydrological connectivity is known to play an important role in deltaic evolution by delivering flow, sediment, and nutrients to the interior of deltaic islands/wetlands. However, estimating connectivity typically requires detailed field work or numerical modeling, which is difficult to implement over broad spatial and temporal scales. In the present work, we investigate the potential of using remote sensing to estimate hydrological connectivity in the Wax Lake Delta (WLD) and Atchafalaya Delta region of the Louisiana coast. During a three-hour window, five difference maps of water level in the WLD and surrounding wetlands were collected using UAVSAR L-band radar in repeat-pass interferometric mode. We then modeled the WLD subsection of the domain using a 2D shallow-water hydrodynamic model configured to run on the same discharge, tide, and wind conditions as recorded at nearby monitoring stations during the observational window, with vegetation parameterized as a source of additional drag in the deltaic islands. Modeling allowed us to determine the relative influence of tides, vegetation, and wind on WLD water levels, which could then be extrapolated to infer the behavior throughout the rest of the domain. Over the observational window, UAVSAR measured a cumulative loss of over 22 megatons of water from non-channelized wetlands as tides fell. We find that the model tends to under-predict the observed water level draw-down, as well as the degree of hydrological activity in proximal islands that we observe in the UAVSAR data. Models that neglect the influence of wind underestimate the volume of water leaving the islands by up to two-thirds, suggesting the importance of wind on deltaic hydrodynamics during the observational window. With the information gained from the numerical modeling, as well as the computation of information theory statistics, we extend the WLD results to analyze and quantify the water level behavior in the surrounding wetlands and Atchafalaya delta.