Annual maximum water levels from tide gauges: Contributing factors and geographic patterns
Hourly time series from a quasi‐global set of 145 tide gauges are used to investigate annual maximum water levels at each station. High water levels are deconstructed into (1) a predicted tidal component, (2) a seasonal component, (3) a low‐frequency nontidal residual that accounts for sea level var...
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Veröffentlicht in: | Journal of geophysical research. Oceans 2013-05, Vol.118 (5), p.2535-2546 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Hourly time series from a quasi‐global set of 145 tide gauges are used to investigate annual maximum water levels at each station. High water levels are deconstructed into (1) a predicted tidal component, (2) a seasonal component, (3) a low‐frequency nontidal residual that accounts for sea level variability at time scales greater than a month but less than a year, and (4) a high‐frequency nontidal residual that captures variability particularly associated with storms at time scales greater than a month. The time‐averaged annual maximum water level correlates significantly with, and scales as 2.5 times, the water level standard deviation at the tide gauge stations. This relationship is used to estimate time‐averaged annual maximum water level on a nearly continuous global scale (excluding ice‐covered polar regions) by specifying variance maps of the tides from a tide model, the seasonal and low‐frequency residual components from satellite altimetry sea surface height, and the high‐frequency residual component from an atmospheric reanalysis product. The variance fields are combined to estimate time‐averaged annual maximum water levels that compare well with observed values at the tide gauge stations. Spatial patterns of annual maximum water levels and relative contributions from the tides and nontidal residual components are considered.
Key PointsMean annual maximum water levels can be specified on a global scaleTidal and non‐tidal contributions to annual maximum water levels are quantified |
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ISSN: | 2169-9275 2169-9291 |
DOI: | 10.1002/jgrc.20173 |