Improving Channel Hydrological Connectivity in Coastal Hydrodynamic Models With Remotely Sensed Channel Networks. Issue 3 (6th March 2022)

• Record Type: Journal Article
• Authors: Zhang, Xiaohe; Wright, Kyle; Passalacqua, Paola; Simard, Marc; Fagherazzi, Sergio
• Is Part Of: Journal of geophysical research. Volume 127:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-06
• Subjects: coastal numerical model -- flow propagation -- remote‐sensed channel network -- channel geometry -- model performance -- cost function; Geomorphology -- Periodicals; 551.3
• DOI: 10.1029/2021JF006294
• Languages: English
• ISSNs: 2169-9003

Abstract

Abstract: Coastal wetlands are nourished by rivers and periodical tidal flows through complex, interconnected channels. However, in hydrodynamic models, channel dimensions with respect to model grid size and uncertainties in topography preclude the correct propagation of tidal and riverine signals. It is therefore crucial to enhance channel geomorphic connectivity and simplify sub‐channel features based on remotely sensed networks for practical computational applications. Here, we utilize channel networks derived from diverse remote sensing imagery as a baseline to build a ∼10 m resolution hydrodynamic model that covers the Wax Lake Delta and adjacent wetlands (∼360 km 2 ) in coastal Louisiana, USA. In this richly gauged system, intensive calibrations are conducted with 18 synchronous field‐observations of water levels taken in 2016, and discharge data taken in 2021. We modify channel geometry, targeting realism in channel connectivity. The results show that a minimum channel depth of 2 m and a width of four grid elements (approximatively 40 m) are required to enable a realistic tidal propagation in wetland channels. The optimal depth for tidal propagation can be determined by a simplified cost function method that evaluates the competition between flow travel time and alteration of the volume of the channels. The integration of high spatial‐resolution models and remote sensing imagery provides a general framework to improve models performance in salt marshes, mangroves, deltaic wetlands, and tidal flats. Plain Language Summary: In hydrodynamic models, it is common to smooth topographic data to build the numerical grid. However, in coastal wetlands dissected by complex channel networks, this process would prevent an appropriate representation of channels for tidal propagation. This might lead to unreliable calculations of water fluxes between channels and wetlands. To address this issue, we modify channel geometry using remotely sensed data to enhance connectivity in simulations. Channel depth and width are determined by comparing model results to high‐resolution field measurements. We develop a simplified cost function that can determine the optimal channel depth for tidal propagation without running computationally expensive simulations. Our results provide a framework to improve model performance of tidal flows along wetland channels by integrating numerical simulations, a simplified cost function, and remotely sensed channel networks. Key Points: Remote‐sensed channel network can enhance hydrological connectivity in numerical models We provide a simplified cost function method to determine the minimum channel depth in numerical models A minimum 2‐m channel depth and a 4‐grid width are required to simulate tidal propagation in wetland channels