Coastal water level variations

2012 ◽  
pp. 145-199
Keyword(s):  
Water Level ◽  
Coastal Water ◽  
Water Level Variations ◽  
Coastal Water Level

Compounding Effects of Riverine and Coastal Floods and Its Implications for Coastal Urban Flood Resilience

2020 ◽  
Author(s):  
Poulomi Ganguli ◽  
Bruno Merz
Keyword(s):  
Water Level ◽  
Coastal Water ◽  
Flood Hazard ◽  
Storm Surges ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Positive Dependence ◽  
Urban Flood ◽  
River Floods ◽  
Coastal Water Level

<p>Globally more than 600 million people reside in the low elevation (< 10 meters elevation) coastal zone. The densely populated low-lying deltas are vulnerable to flooding primarily in two ways: (1) Due to extreme coastal water level (ECWL) because of either storm surges or heavy rain-induced river floods generated by a severe storm episode. (2) Co-occurrence or successive occurrence of ECWL and river floods as a result of storm-producing synoptic weather conditions leading to compound floods that causes a severe impact than when each of these extremes occurs in an isolation at different times. Most of the earlier assessments that analyzed compound floods, often do not consider the delay between rainfall and streamflow events. River runoff, which also includes subsurface groundwater recharge component, cannot be adequately described by extreme precipitation alone. While most of the literature is limited to analyzing joint dependence between variables considering only central dependence, challenges to flood hazard assessment include difficulty in delineating the severity of riverine floods, especially due to long upper tails of the variables that influence interdependencies between underlying drivers. Despite uncertainties, utilizing the rich database of northwestern Europe, here we assess compound flood severity and its trend by examining spatial interdependencies between annual maxima coastal water level (as an indicator of ECWL) and d-day lagged peak discharge within ±7 days of the occurrence of the ECWL event. Our analysis reveals a spatially coherent dependence pattern with strong positive dependence for gauges located between 52° and 60°N latitude, whereas a weak positive dependence across gauges in > 60°N latitude. Based on a newly proposed index, Compound Hazard Ratio (CHR) that compares the severity of compound floods with at-site design floods, our proof-of-principal analysis suggests nearly half of the stream gauges show amplifications in fluvial flood hazard during 2013/2014’s catastrophic winter storm Xaver that affected most of northern Europe. Furthermore, a multi-decadal (1889 – 2014) temporal evolution of compound flood reveals the existence of a flood-rich period between 1960s and 1980s, especially for the mid-latitude gauges (located within 47° to 60°N), which might be closely linked to the North Atlantic Oscillation (NAO) teleconnection pattern prevailing in the region. On the other hand, gauges at high-latitude (> 60°N) show decreasing to no trend in compound floods. The approach presented here can serve as a basis for developing coastal urban flood risk management portfolios aiding improved resilience and reduce vulnerability in the affected areas.  </p>

Development of an Atlantic Canadian Coastal Water Level Neural Network Model

2008 ◽  
Vol 25 (11) ◽  
pp. 2117-2132 ◽  
Author(s):  
Guoqi Han ◽  
Yu Shi
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Sea Level ◽  
Coastal Water ◽  
Water Levels ◽  
Atlantic Canada ◽  
Level Data ◽  
Coastal Water Level ◽  
Term Data

Abstract Coastal water-level information is essential for coastal zone management, navigation, and oceanographic research. However, long-term water-level observations are usually only available at a limited number of locations. This study discusses a complementary and simple neural network (NN) approach, to predict water levels at a specified coastal site from the data gathered at other nearby or remote permanent stations. A simple three-layer, feed-forward, back-propagation network and a neural network ensemble, named Atlantic Canadian Coastal Water Level Neural Network (ACCSLENNT) models, was developed to correlate the nonlinear relationship of sea level data among stations by learning from their historical characteristics. Instantaneous hourly observations of water level from five stations along the coast of Atlantic Canada—Argentia, Belledune, Halifax, North Sydney, and St. John’s—are used to formulate and validate the ACCSLENNT models. Qualitative and quantitative comparisons of the network output with target observations showed that despite significant changes in sea level amplitudes and phases in the study area, appropriately trained NN models could provide accurate and robust long-term predictions of both tidal and nontidal (tide subtracted) water levels when only short-term data are available. The robust results indicate that the NN models in conjunction with limited permanent stations are able to supplement long-term historical water-level data along the Atlantic Canadian coast. Because field data collection is usually expensive, the ACCSLENNT models provide a cost-effective alternative to obtain long-term data along Atlantic Canada.

Development of a regional neural network for coastal water level predictions

2003 ◽  
Vol 30 (17) ◽  
pp. 2275-2295 ◽  
Author(s):  
Wenrui Huang ◽  
Catherine Murray ◽  
Nicholas Kraus ◽  
Julie Rosati
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Coastal Water ◽  
Coastal Water Level

Presentation and assessment of tides and water level records for geophysical investigations

1970 ◽  
Vol 7 (2) ◽  
pp. 607-625 ◽  
Author(s):  
G. C. Dohler ◽  
L. F. Ku
Keyword(s):  
Water Level ◽  
Power Spectra ◽  
Rate Of Change ◽  
Regression Technique ◽  
Level Data ◽  
Water Level Data ◽  
Geophysical Investigations ◽  
Water Level Variations ◽  
Mean Water Level ◽  
Change In Mean

The methods and problems involved in collecting water level data are explained, and the processing and formats of the data are illustrated. The trend of the change in mean water level is plotted and the corresponding rate of change is estimated by the regression technique. The power spectra of the water level variations are plotted to illustrate these variations in terms of frequencies.

scholarly journals Dynamics of fortnightly water level variations along a tide-dominated estuary with negligible river discharge

2021 ◽  
Author(s):  
Erwan Garel ◽  
Ping Zhang ◽  
Huayang Cai
Keyword(s):  
Water Level ◽  
Phase Difference ◽  
River Discharge ◽  
Water Levels ◽  
Level Increase ◽  
Upstream Direction ◽  
Water Level Variations ◽  
Mean Water Level ◽  
The Mean ◽  
Guadiana Estuary

Abstract. Observations indicate that the fortnightly fluctuations in mean water level increase in amplitude along the lower half of a tide-dominated estuary (The Guadiana estuary) with negligible river discharge but remain constant upstream. Analytical solutions reproducing the semi-diurnal wave propagation shows that this pattern results from reflection effects at the estuary head. The phase difference between velocity and elevation increases from the mouth to the head (where the wave has a standing nature) as the high and low water levels get progressively closer to slack water. Thus, the tidal (flood-ebb) asymmetry in discharge is reduced in the upstream direction. It becomes negligible along the upper estuary half, as the mean sea level remains constant despite increased friction due to wave shoaling. Observations of a flat mean water level along a significant portion of an upper estuary, easier to obtain than the phase difference, can therefore indicate significant reflection of the propagating semi-diurnal wave at the head. Details of the analytical model shows that changes in the mean depth or length of semi-arid estuaries, in particular for macrotidal locations, affect the fortnightly tide amplitude, and thus the upstream mass transport and inundation regime. This has significant potential impacts on the estuarine environment.

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