Coastal Water Level Fluctuations

<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 &#177;7 days of the occurrence of the ECWL event.&#160;Our analysis reveals a spatially coherent dependence pattern with strong positive dependence for gauges located between 52&#176; and 60&#176;N latitude, whereas a weak positive dependence across gauges in > 60&#176;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&#8217;s catastrophic winter storm Xaver that affected most of northern Europe. Furthermore, a multi-decadal (1889 &#8211; 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&#176; to 60&#176;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&#176;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. &#160;</p>

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Development of an Atlantic Canadian Coastal Water Level Neural Network Model

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2008jtecho569.1 ◽

2008 ◽

Vol 25 (11) ◽

pp. 2117-2132 ◽

Cited By ~ 3

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.

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Development of a regional neural network for coastal water level predictions

Ocean Engineering ◽

10.1016/s0029-8018(03)00083-0 ◽

2003 ◽

Vol 30 (17) ◽

pp. 2275-2295 ◽

Cited By ~ 57

Author(s):

Wenrui Huang ◽

Catherine Murray ◽

Nicholas Kraus ◽

Julie Rosati

Keyword(s):

Neural Network ◽

Water Level ◽

Coastal Water ◽

Coastal Water Level

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Temperature Trend Analysis in Urmia Lake Basin Compared with Water Level Fluctuations of the Lake

International Institute of Chemical, Biological & Environmental Engineering June 5-6, 2015 Istanbul (Turkey) ◽

10.15242/iicbe.c0615010 ◽

2015 ◽

Keyword(s):

Water Level ◽

Trend Analysis ◽

Temperature Trend ◽

Urmia Lake ◽

Water Level Fluctuations ◽

Lake Basin ◽

Urmia Lake Basin

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AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

International Conference on Technics Technologies and Education ◽

10.15547/ictte.2019.02.041 ◽

2019 ◽

pp. 95-103

Author(s):

Krum Videnov ◽

Vanya Stoykova

Keyword(s):

Water Level ◽

Real Time ◽

Early Warning ◽

Water Sources ◽

Water Levels ◽

Water Level Fluctuations ◽

Level Monitoring ◽

Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

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