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• 1. [Article] Archival Water-Level Measurements: Recovering Historical Data to Help Design for the Future

  Improving methods of assessing risk and designing structures to withstand extreme events and changing sea levels is a vital component of strategies for reducing risk to coastal resources and assets. An ...

  Citation × Citation Citation Abstract Copy Title: Archival Water-Level Measurements: Recovering Historical Data to Help Design for the Future Author: Talke, Stefan A., Jay, David A. Year: 2017 Improving methods of assessing risk and designing structures to withstand extreme events and changing sea levels is a vital component of strategies for reducing risk to coastal resources and assets. An obvious approach to improving the statistical robustness of risk assessments is to increase the number, time span, and quality of available water-level data sets, and to assess trends and non-stationarity. In this report we discuss efforts to recover, digitize, and analyze hundreds of station-years of lost-and-forgotten tide data and other water-level measurements that extend back to the early 19th century. To date, more than 6,500 station-years of previously lost or forgotten tide data have been identified, of which more than 1,600 station-years have been recovered and more than 550 station-years digitized. An additional 500+ years of once-a-day river-stage measurements in tidal rivers have been recovered and digitized. Approximately 300,000 documents have been recovered. In this report we also demonstrate how data recovery can help engineers better characterize their water-level environment and understand long-term trends, resulting in better risk assessment and ultimately more robust design of new infrastructure and adaptation of existing infrastructure. Long records help improve hazard estimates and can help characterize whether statistics are nonstationarity over climate-relevant time scales. Further, long and more complete records exhibit more natural variability and can be used to validate numerical simulations of historical storms (e.g., the 1893 New York hurricane). Historical tide data can help inform how tidal datums such as High Water and Low Water have changed, can be used to hindcast historical river flow, and provide a much richer view of long-term trends in local sea level. Used with numerical models, historical data help explain how anthropogenic activities have changed tide range and river slope, with the largest effects often observed far inland from the coast. Moreover, numerical models combined with tide data can be used to assess whether local engineering or other long-term changes have influenced storm surge risk. In conclusion, improved assessment of the past can help us better plan for the future. Title: Archival Water-Level Measurements: Recovering Historical Data to Help Design for the Future Author: Talke, Stefan A., Jay, David A. Year: 2017 Improving methods of assessing risk and designing structures to withstand extreme events and changing sea levels is a vital component of strategies for reducing risk to coastal resources and assets. An obvious approach to improving the statistical robustness of risk assessments is to increase the number, time span, and quality of available water-level data sets, and to assess trends and non-stationarity. In this report we discuss efforts to recover, digitize, and analyze hundreds of station-years of lost-and-forgotten tide data and other water-level measurements that extend back to the early 19th century. To date, more than 6,500 station-years of previously lost or forgotten tide data have been identified, of which more than 1,600 station-years have been recovered and more than 550 station-years digitized. An additional 500+ years of once-a-day river-stage measurements in tidal rivers have been recovered and digitized. Approximately 300,000 documents have been recovered. In this report we also demonstrate how data recovery can help engineers better characterize their water-level environment and understand long-term trends, resulting in better risk assessment and ultimately more robust design of new infrastructure and adaptation of existing infrastructure. Long records help improve hazard estimates and can help characterize whether statistics are nonstationarity over climate-relevant time scales. Further, long and more complete records exhibit more natural variability and can be used to validate numerical simulations of historical storms (e.g., the 1893 New York hurricane). Historical tide data can help inform how tidal datums such as High Water and Low Water have changed, can be used to hindcast historical river flow, and provide a much richer view of long-term trends in local sea level. Used with numerical models, historical data help explain how anthropogenic activities have changed tide range and river slope, with the largest effects often observed far inland from the coast. Moreover, numerical models combined with tide data can be used to assess whether local engineering or other long-term changes have influenced storm surge risk. In conclusion, improved assessment of the past can help us better plan for the future. Close

• 2. [Article] Adaptation of Classical Tidal Harmonic Analysis to Nonstationary Tides, with Application to River Tides

  One of the most challenging areas in tidal analysis is the study of nonstationary signals with a tidal component, as they confront both current analysis methods and dynamical understanding. A new analysis ...

  Citation × Citation Citation Abstract Copy Title: Adaptation of Classical Tidal Harmonic Analysis to Nonstationary Tides, with Application to River Tides Author: Matte, Pascal, Jay, David A., Zaron, Edward D. Year: 2013 One of the most challenging areas in tidal analysis is the study of nonstationary signals with a tidal component, as they confront both current analysis methods and dynamical understanding. A new analysis tool has been developed, NS_TIDE, adapted to the study of nonstationary signals, in this case, river tides. It builds the nonstationary forcing directly into the tidal basis functions. It is implemented by modification of T_TIDE; however, certain concepts, particularly the meaning of a constituent and the Rayleigh criterion, are redefined to account for the smearing effects on the tidal spectral lines by nontidal energy. An error estimation procedure is included that constructs a covariance matrix of the regression coefficients, based on either an uncorrelated or a correlated noise model. The output of NS_TIDE consists of time series of subtidal water levels [mean water level (MWL)] and tidal properties (amplitudes and phases), expressed in terms of external forcing functions. The method was tested using records from a station on the Columbia River, 172 km from the ocean entrance, where the tides are strongly altered by river flow. NS_TIDE hindcast explains 96.4% of the signal variance with a root-mean-square error of 0.165 m obtained from 288 parameters, far better than traditional harmonic analysis (38.5%, 0.604 m, and 127 parameters). While keeping the benefits of harmonic analysis, its advantages compared to existing tidal analysis methods include its capacity to distinguish frequencies within tidal bands without losing resolution in the time domain or data at the endpoints of the time series. Title: Adaptation of Classical Tidal Harmonic Analysis to Nonstationary Tides, with Application to River Tides Author: Matte, Pascal, Jay, David A., Zaron, Edward D. Year: 2013 One of the most challenging areas in tidal analysis is the study of nonstationary signals with a tidal component, as they confront both current analysis methods and dynamical understanding. A new analysis tool has been developed, NS_TIDE, adapted to the study of nonstationary signals, in this case, river tides. It builds the nonstationary forcing directly into the tidal basis functions. It is implemented by modification of T_TIDE; however, certain concepts, particularly the meaning of a constituent and the Rayleigh criterion, are redefined to account for the smearing effects on the tidal spectral lines by nontidal energy. An error estimation procedure is included that constructs a covariance matrix of the regression coefficients, based on either an uncorrelated or a correlated noise model. The output of NS_TIDE consists of time series of subtidal water levels [mean water level (MWL)] and tidal properties (amplitudes and phases), expressed in terms of external forcing functions. The method was tested using records from a station on the Columbia River, 172 km from the ocean entrance, where the tides are strongly altered by river flow. NS_TIDE hindcast explains 96.4% of the signal variance with a root-mean-square error of 0.165 m obtained from 288 parameters, far better than traditional harmonic analysis (38.5%, 0.604 m, and 127 parameters). While keeping the benefits of harmonic analysis, its advantages compared to existing tidal analysis methods include its capacity to distinguish frequencies within tidal bands without losing resolution in the time domain or data at the endpoints of the time series. Close