About

Dr. Katherine Anarde joined the faculty at NC State in August 2021 as an Assistant Professor within the Civil, Construction and Environmental Engineering Department, specializing in coastal engineering and geomorphology. Her research combines observational and numerical approaches to investigate coastal hazards, focusing on how storm (acute) and climate (chronic) hazards influence the habitability of coastlines. Her work on acute hazards includes studying tropical cyclone impacts on sandy coastlines, ocean wave measurement, beach erosion during storms, meteotsunami generation, and infrastructure vulnerability. On the climate hazard side, her research monitors the effects of sea level rise on coastal communities, including the occurrence and impacts of 'sunny day' floods and human alterations to natural barrier island processes. Her interdisciplinary approach involves collaboration with social scientists and coastal stakeholders to address issues of coastal resilience and climate justice.

Research topics

• Computer Science
• Oceanography
• Machine Learning
• Geology
• Artificial Intelligence
• Climatology
• Physical geography
• Meteorology
• Mathematics
• Statistics
• Environmental science
• Cartography
• Geomorphology
• Geography
• Ecology

Selected publications

• Data From the Drain: A Sensor Framework That Captures Multiple Drivers of Chronic Coastal Floods

  Water Resources Research · 2023 · 34 citations

  • Environmental science
  • Geography
  • Oceanography

  Abstract Tide gauge water levels are commonly used as a proxy for flood incidence on land. These proxies are useful for projecting how sea‐level rise (SLR) will increase the frequency of coastal flooding. However, tide gauges do not account for land‐based sources of coastal flooding and therefore flood thresholds and the proxies derived from them likely underestimate the current and future frequency of coastal flooding. Here we present a new sensor framework for measuring the incidence of coastal floods that captures both subterranean and land‐based contributions to flooding. The low‐cost, open‐source sensor framework consists of a storm drain water level sensor, roadway camera, and wireless gateway that transmit data in real‐time. During 5 months of deployment in the Town of Beaufort, North Carolina, 24 flood events were recorded. Twenty‐five percent of those events were driven by land‐based sources—rainfall, combined with moderate high tides and reduced capacity in storm drains. Consequently, we find that flood frequency is higher than that suggested by proxies that rely exclusively on tide gauge water levels for determining flood incidence. This finding likely extends to other locations where stormwater networks are at a reduced drainage capacity due to SLR. Our results highlight the benefits of instrumenting stormwater networks directly to capture multiple drivers of coastal flooding. More accurate estimates of the frequency and drivers of floods in low‐lying coastal communities can enable the development of more effective long‐term adaptation strategies.

  DOI

• Labeling Poststorm Coastal Imagery for Machine Learning: Measurement of Interrater Agreement

  Earth and Space Science · 2021 · 25 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Abstract Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data‐driven models are only as good as the data used for training, and this points to the importance of high‐quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time‐consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.

  DOI

• Dune Dynamics Drive Discontinuous Barrier Retreat

  Geophysical Research Letters · 2021 · 29 citations

  • Geology
  • Climatology
  • Geomorphology

  Abstract Barrier islands and spits tend to migrate landward in response to sea‐level rise through the storm‐driven process of overwash, but overwash flux depends on the height of the frontal dunes. Here, we explore this fundamental linkage between dune dynamics and barrier migration using the new model Barrier3D. Our experiments demonstrate that discontinuous barrier retreat is a prevalent behavior that can arise directly from the bistability of foredune height, occurring most likely when the storm return period and characteristic time scale of dune growth are of similar magnitudes. Under conditions of greater storm intensity, discontinuous retreat becomes the dominant behavior of barriers that were previously stable. Alternatively, higher rates of sea‐level rise decrease the overall likelihood of discontinuous retreat in favor of continuous transgression. We find that internal dune dynamics, while previously neglected in exploratory barrier modeling, are an essential component of barrier evolution on time scales relevant to coastal management.

  DOI

Frequent coauthors

• Ian Reeves

  29 shared

• Phillipe A. Wernette

  Great Lakes Science Center

  21 shared

• Laura J. Moore

  18 shared

• Marion Tissier

  Université de Pau et des Pays de l'Adour

  16 shared

• Ryan S. Mieras

  University of North Carolina Wilmington

  13 shared

• A. Brad Murray

  13 shared

• Evan B. Goldstein

  Florida Atlantic University

  13 shared

• Miyuki Hino

  University of North Carolina at Chapel Hill

  12 shared

Education

• Ph.D., Civil Engineering

  University of California, Berkeley

  1995

• M.S., Civil Engineering

  University of California, Berkeley

  1991

• B.S., Civil Engineering

  University of California, Berkeley

  1988

Similar researchers at North Carolina State University

• Jonathan Lindseyh-101
• Edward Breitschwerdth-100
• José Alonsoh-94
• Rob Dunnh-94
• Frances S. Liglerh-92