Data assimilation of hyper-local water level sensors for real-time monitoring of coastal inundation

Details

• Journal Title:
  Coastal Engineering
• Personal Author:
  Son, Youngjun ; Di Lorenzo, Emanuele ; Park, Kyungmin ; Wipperfurth, Spenser ; Luo, Jian
• NOAA Program & Office:
  NOS (National Ocean Service)
• Description:
  As flood events become increasingly prevalent in coastal regions with sea level rise, multiple communities have deployed water level monitoring networks across estuaries in addition to existing tide gauges located primarily at immediate coasts. Due to the spatially-distributed nature of sensor deployments, however, water level data are only available at specific sensor locations during the time of monitoring. As a result, an information gap on water levels exists along estuarine channels outside of active monitoring locations. To fill such a gap, this study presents a physics-based empirical modeling approach to assimilate coastal water levels using observations from hyper-local water level sensors. We implement an Objective Analysis (OA) procedure for sensor observation datasets obtained from the Smart Sea Level Sensors project along the U.S. Georgia coasts, based on spatial covariance structures of water levels that are extracted from high-resolution coastal-ocean hydrodynamic simulations. The approach is validated using simulated water levels, which provide basis functions for spatial covariance information. Additionally, the implementation is validated using actual water levels from active monitoring stations. The assimilated results of water levels are compared to those obtained by the same OA procedure but with a commonly-used Gaussian covariance function, which lacks prior knowledge of spatial covariance structures. To demonstrate the capability of the assimilation approach, we extend its application to a hurricane event, during which other dynamic processes may be relevant to variability in coastal water levels. Overall, the presented approach provides an accurate and efficient estimation of estuarine water levels along channels, which can support community officials to promptly identify localized flood threats to critical infrastructure systems in coastal regions.
• Source:
  Coastal Engineering, 186, 104398
• DOI:
  https://doi.org/10.1016/j.coastaleng.2023.104398
• ISSN:
  0378-3839
• Format:
  pdf
• Publisher:
  Elsevier BV
• Document Type:
  Journal Article-Accepted Manuscript
• Funding:
  Grant no. NA22NOS4690219
• License:
  https://creativecommons.org/licenses/by-nc-nd/4.0/
• Rights Information:
  CC BY-NC-ND
• Rights Statement:
  The NOAA IR provides access to this content under the authority of the government's retained license to distribute publications and data resulting from federal funding. While users may legally access this content, the copyright owners retain rights that govern the reproduction, redistribution, and re-use of this work. The user is solely responsible for complying with applicable copyright law.
• Compliance:
  Library
• Main Document Checksum:
  urn:sha-512:2dd69f7f794ba8981146e9eae632ec0ef0c8a18ba088261eefb1e46c0be6ec41d0e7b15ff2d31511238caa824e20344920e3fc37ec2d767d1dfeaa570aa79c76
• Download URL:
  https://repository.library.noaa.gov/view/noaa/67942/noaa_67942_DS1.pdf
• File Type:
  [PDF - 3.18 MB ]