Sea level rise is increasing the frequency of high tide flooding in coastal communities across the United States. Although the occurrence and severity of high-tide flooding will continue to increase, ...skillful prediction of high tide flooding on monthly-to-annual time horizons is lacking in most regions. Here, we present an approach to predict the daily likelihood of high tide flooding at coastal locations throughout the U.S. using a novel probabilistic modeling approach that relies on relative sea-level rise, tide predictions, and climatological non-tidal residuals as measured by NOAA tide gauges. A retrospective skill assessment using the climatological sea level information indicates that this approach is skillful at 61 out of 92 NOAA tide gauges where at least 10 high tide flood days occurred from 1997–2019. In this case, a flood day occurs when the observed water level exceeds the gauge-specific high tide flood threshold. For these 61 gauges, on average 35% of all floods are accurately predicted using this model, with over half of the floods accurately predicted at 18 gauges. The corresponding False-Alarm-Rate is less than 10% for all 61 gauges. Including mean sea level anomaly persistence at leads of 1 and 3 months further improves model skill in many locations, especially the U.S. Pacific Islands and West Coast. Model skill is shown to increase substantially with increasing sea level at nearly all locations as high tides more frequently exceed the high tide flooding threshold. Assuming an intermediate amount of relative sea level rise, the model will likely be skillful at 93 out of the 94 gauges projected to have regular flooding by 2040. These results demonstrate that this approach is viable to be incorporated into NOAA decision-support products to provide guidance on likely high tide flooding days. Further, the structure of the model will enable future incorporation of mean sea level anomaly predictions from numerical, statistical, andmachine learning forecast systems.
Rip currents are like rivers of fast-moving water that can quickly carry the unwary out to sea. They are not easy to recognize, especially to the untrained observer. Other than in-situ current ...measurements, there exists a number of methods that analyzes images and videos to detect rip currents. Most of these techniques base their detection on the appearance of rip currents such as foamy pattern, discoloration of water, and locations of breaking waves. Leading methods use either image processing or machine learning of images and/or video input. In this paper, we analyze the behavior of water movement rather than simply its appearance to detect rip currents. Specifically, we investigated several flow visualization methods and tune them to detect rip currents. Based on our study, we recommend two methods that allowed us to detect rip currents where other methods have failed. And because the methods originated as visualization techniques, any presence of rip currents are automatically highlighted. We also evaluated these two methods against previously annotated results by rip current experts, and found that our detections were sufficiently sensitive that some expert annotations were relabeled.
It is undeniable that citizen science contributes to the advancement of various fields of study. There are now software tools that facilitate the development of citizen science apps. However, apps ...developed with these tools rely on individual human skills to correctly collect useful data. Machine learning (ML)–aided apps provide on-field guidance to citizen scientists on data collection tasks. However, these apps rely on server-side ML support, and therefore need a reliable internet connection. Furthermore, the development of citizen science apps with ML support requires a significant investment of time and money. For some projects, this barrier may preclude the use of citizen science effectively. We present a platform that democratizes citizen science by making it accessible to a much broader audience of both researchers and participants. The SmartCS platform allows one to create citizen science apps with ML support quickly and without coding skills. Apps developed using SmartCS have client-side ML support, making them usable in the field, even when there is no internet connection. The client-side ML helps educate users to better recognize the subjects, thereby enabling high-quality data collection. We present several citizen science apps created using SmartCS, some of which were conceived and created by high school students.
Realistic representation of monthly sea level anomalies in coastal regions has been a challenge for global ocean reanalyses. This is especially the case in coastal regions where sea levels are ...influenced by western boundary currents such as near the U.S. Atlantic Coast and the Gulf of Mexico. For these regions, most ocean reanalyses compare poorly to observations. Problems in reanalyses include errors in data assimilation and horizontal resolutions that are too coarse to simulate energetic currents like the Gulf Stream and Loop Current System. However, model capabilities are advancing with improved data assimilation and higher resolution. Here, we show that some current-generation ocean reanalyses produce monthly sea level anomalies with improved skill when compared to satellite altimetry observations of sea surface heights. Using tide gauge observations for coastal verification, we find the highest skill associated with the GLORYS12 and HYCOM ocean reanalyses. Both systems assimilate altimetry observations and have eddy-resolving horizontal resolutions (1/12°). We found less skill in three other ocean reanalyses (ACCESS-S2, ORAS5, and ORAP6) with coarser, though still eddy-permitting, resolutions (1/4°). The operational reanalysis from ECMWF (ORAS5) and their pilot reanalysis (ORAP6) provide an interesting comparison because the latter assimilates altimetry globally and with more weight, as well as assimilating ocean observations over continental shelves. We find these attributes associated with improved skill near many tide gauges. We also assessed an older reanalysis (CFSR), which has the lowest skill likely due to its lower resolution (1/2°) and lack of altimetry assimilation. ACCESS-S2 likewise does not assimilate altimetry, although its skill is much better than CFSR and only somewhat lower than ORAS5. Since coastal flooding is influenced by sea level anomalies, the recent development of skilful ocean reanalyses on monthly timescales may be useful for better understanding the physical processes associated with flood risks.
Coastal water level information is crucial for understanding flood occurrences and changing risks. Here, we validate the preliminary version (0.9) of NOAA’s Coastal Ocean Reanalysis (CORA), which is ...a 43-year reanalysis (1979–2021) of hourly coastal water levels for the Gulf of Mexico and Atlantic Ocean (i.e., the Gulf and East Coast region, or GEC). CORA-GEC v0.9 was conducted by the Renaissance Computing Institute using the coupled ADCIRC+SWAN coastal circulation and wave model. The model uses an unstructured mesh of nodes with varying spatial resolution that averages 400 m near the coast and is much coarser in the open ocean. Water level variations associated with tides and meteorological forcing are explicitly modeled, while lower-frequency water level variations are included by dynamically assimilating observations from NOAA’s National Water Level Observation Network. We compare CORA to water level observations that were either assimilated or not, and find that the reanalysis generally performs better than a state-of-the-art global ocean reanalysis (GLORYS12) in capturing the variability on monthly, seasonal, and interannual timescales as well as the long-term trend. The variability of hourly non-tidal residuals is also shown to be well resolved in CORA when compared to water level observations. Lastly, we present a case study of extreme water levels and coastal inundations around Miami, Florida to demonstrate an application of CORA for studying flood risks. Our assessment suggests that NOAA’s CORA-GEC v0.9 provides valuable information on water levels and flooding occurrence from 1979–2021 in areas that are experiencing changes across multiple time scales. CORA potentially can enhance flood risk assessment along parts of the U.S. Coast that do not have historical water level observations.
The Surf-camera Remote Calibration Tool (SurfRCaT) is a Python-based software application to calibrate and rectify images from pre-existing video cameras that are operating at coastal sites in the ...United States. The software enables remote camera calibration and subsequent image rectification by facilitating the remote-extraction of ground control points using airborne lidar observations, and guides the user through the entire process. No programming or code interaction are necessary to use the software. Calibration parameters and subsequent rectified image products derived from the software are saved locally. Users can apply SurfRCaT to any camera imagery that has stationary structures within the camera’s field of view. Given current recreational camera infrastructure, SurfRCaT could increase the number of potential quantitative coastal video monitoring stations in the United States by an order of magnitude.
Web cameras are transforming coastal environmental monitoring. Improvements in camera technology and image processing capabilities, paired with decreases in cost, enable widespread use of camera ...systems by researchers, managers and first responders for a growing range of environmental monitoring applications. Applications are related to transportation and commerce, preparedness, risk reduction and response, and stewardship of coastal resources. While web cameras are seemingly ubiquitous, operators often follow unique installation procedures and collect, store, and process imagery data in various ways. These inconsistencies significantly limit the ability for imagery data to be shared and utilized across research and operational disciplines. Similar to the early days of other remote sensing networks like High Frequency Radar, the benefits and downstream application of coastal imagery data can be greatly enhanced through centralized data access and standardization of data collection, analysis and dissemination. The NOAA National Ocean Service Web Camera Applications Testbed (WebCAT) was launched in 2017 in partnership with SECOORA, as a public-private partnership to address this coastal ocean observing standardization need. WebCAT is a pilot project relying on the private sector expertise of Surfline, Inc. to install and operate several web cameras capable of meeting the short-term needs of diverse users including NOAA, USGS, state health agencies, academia and others. The project aims to determine operational imagery collection, storage, processing, access, and archival requirements that will foster collaboration across research and operational user communities. Seven web cameras have been installed at six locations along the southeast U.S. coast (from Florida to North Carolina) for purposes including: counting animals on the beach and migrating right whales, identifying rip currents, validating wave runup models, and understanding human use of natural resources. Here we present a review of the state of coastal imagery data and an overview of the WebCAT project. Goals of an upcoming community workshop will also be presented along with our vision for how WebCAT can motivate a future sustained operational web camera network.
Abstract
The demand for nearshore wave observations is increasing due to spatial gaps and the importance of observations for accurate models and better understanding of inundation processes. Here, we ...show how water level (WL) standard deviation (sigma,
σ
) measurements at three acoustic NOAA tide gauges that utilize an Aquatrak sensor Duck, North Carolina, Bob Hall Pier (BHP) in Corpus Christi, Texas, and Lake Worth, Florida can be used as a proxy for significant wave height (
H
m0
). Sigma-derived
H
m0
is calibrated to best fit nearby wave observations and error is quantified through RMSE, normalized RMSE (NRMSE), bias, and a scatter index. At Duck and Lake Worth, a quadratic fit of sigma to nearby wave observations results in a
R
2
of 0.97 and 0.83, RMSE of 0.11 and 0.11 m, and NRMSE of 0.09 and 0.22, respectively. A linear fit between BHP sigma and
H
m0
is best, resulting in
R
2
0.62, RMSE of 0.22, and NRMSE of 0.26. Regression fits deviate across NOAA stations and from the classic relationship of
H
m0
= 4
σ
, indicating
H
m0
cannot be accurately estimated with this approach at these Aquatrak sites. The dynamic water level (DWL = still WL ± 2
σ
) is calculated over the historic time series showing climatological and seasonal trends in the stations’ daily maximums. The historical DWL and sigma wave proxy could be calculated for many NOAA tide gauges dating back to 1996. These historical wave observations can be used to fill observational spatial gaps, validate models, and improve understanding of wave climates.
Significance Statement
There is a large spatial gap in nearshore real-time observational wave data that can provide critical information to researchers and resource managers regarding inundation and erosion, help validate coastal hydrodynamic models, and provide the maritime community with products that help ensure navigational safety. This study utilizes existing infrastructure to help fill the demand for nearshore wave observations by deriving a proxy for wave height at three sites. This work shows spatial variability in the regression fits across the sites, which should be explored at more stations in future work. Multidecadal length time series were also used at the sites to investigate climatological and seasonal trends that provide insight into wave climates and wave driven processes important for coastal flooding.
This paper presents a machine learning approach for the automatic identification of rip currents with breaking waves. Rip currents are dangerous fast moving currents of water that result in many ...deaths by sweeping people out to sea. Most people do not know how to recognize rip currents in order to avoid them. Furthermore, efforts to forecast rip currents are hindered by lack of observations to help train and validate hazard models. The presence of web cams and smart phones have made video and still imagery of the coast ubiquitous and provide a potential source of rip current observations. These same devices could aid public awareness of the presence of rip currents. What is lacking is a method to detect the presence or absence of rip currents from coastal imagery. This paper provides expert labeled training and test data for rip currents. We use Faster R–CNN and a custom temporal aggregation stage to make detections from still images or videos with higher measured accuracy than both humans and other methods of rip current detection previously reported in the literature.
•Evidence that region based object detectors are applicable to amorphous and ephemeral objects such as rip currents.•Analysis showing rip current detection accuracy above existing published methods.•Data sets of rip current images and video for training and testing.
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