Enhanced tide model: Improving tidal predictions with integration of wind data

Shore & Beach ◽  
2020 ◽  
pp. 40-45
Author(s):  
Thomas Huff ◽  
Rusty Feagin ◽  
Jens Figlus
Keyword(s):  
Water Level ◽  
Empirical Model ◽  
Prediction Error ◽  
Tide Gauge ◽  
Water Levels ◽  
Wind Data ◽  
Flood Prediction ◽  
Tidal Model ◽  
The Public ◽  
Coastal Flood

Publicly available tidal predictions for coastlines are predominantly based on astronomical predictions. In shallow water basins, however, water levels can deviate from these predictions by a factor of two or more due to wind-induced fluctuations from non-regional storms. To model and correct these wind-induced tidal deviations, a two-stage empirical model was created: the Enhanced Tidal Model (ETM). For any given NOAA tide gauge location, this model first measured the wind-induced deviation based on a compiled dataset, and then adjusted the astronomical predictions into the future to create a 144-hour forecast. The ETM, when incorporating wind data, had only 76% of the error of NOAA astronomical tidal predictions (e.g. if NOAA had 1.0 ft. of error, ETM had only 0.76 ft. error from the observed water level). Certain ETM locations had approximately half (49%) as much prediction error as NOAA. With the improvement in tidal accuracy prediction, the ETM has the ability to significantly aid in navigation along with coastal flood prediction. We envision the ETM as a resource for industry and the public to make informed decisions that impact their livelihood.

scholarly journals Determining Extreme Still Water Levels for Design and Planning Purposes Incorporating Sea Level Rise: Sydney, Australia

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 95
Author(s):  
Phil J. Watson
Keyword(s):  
Sea Level Rise ◽  
Water Level ◽  
Sea Level ◽  
Tide Gauge ◽  
Water Levels ◽  
Extreme Value Analysis ◽  
Tide Gauge Record ◽  
Value Analysis ◽  
Mean Sea Level ◽  
The Impact

This paper provides an Extreme Value Analysis (EVA) of the hourly water level record at Fort Denison dating back to 1915 to understand the statistical likelihood of the combination of high predicted tides and the more dynamic influences that can drive ocean water levels higher at the coast. The analysis is based on the Peaks-Over-Threshold (POT) method using a fitted Generalised Pareto Distribution (GPD) function to estimate extreme hourly heights above mean sea level. The analysis highlights the impact of the 1974 East Coast Low event and rarity of the associated measured water level above mean sea level at Sydney, with an estimated return period exceeding 1000 years. Extreme hourly predictions are integrated with future projections of sea level rise to provide estimates of relevant still water levels at 2050, 2070 and 2100 for a range of return periods (1 to 1000 years) for use in coastal zone management, design, and sea level rise adaptation planning along the NSW coastline. The analytical procedures described provide a step-by-step guide for practitioners on how to develop similar baseline information from any long tide gauge record and the associated limitations and key sensitivities that must be understood and appreciated in applying EVA.

scholarly journals Coastal Flood Modeling Challenges in Defended Urban Backshores

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 450 ◽  
Author(s):  
Timu Gallien ◽  
Nikos Kalligeris ◽  
Marie-Pierre Delisle ◽  
Bo-Xiang Tang ◽  
Joseph Lucey ◽  
...  
Keyword(s):  
Water Levels ◽  
Flood Modeling ◽  
Flood Prediction ◽  
Integrated Framework ◽  
Hydrologic Processes ◽  
Coastal Flood ◽  
Event Prediction ◽  
Overland Flows ◽  
Hydraulic Infrastructure ◽  
Hydrologic Inputs

Coastal flooding is a significant and increasing hazard. There are multiple drivers including rising coastal water levels, more intense hydrologic inputs, shoaling groundwater and urbanization. Accurate coastal flood event prediction poses numerous challenges: representing boundary conditions, depicting terrain and hydraulic infrastructure, integrating spatially and temporally variable overtopping flows, routing overland flows and incorporating hydrologic signals. Tremendous advances in geospatial data quality, numerical modeling and overtopping estimation have significantly improved flood prediction; however, risk assessments do not typically consider the co-occurrence of multiple flooding pathways. Compound flooding refers to the combined effects of marine and hydrologic processes. Alternatively, multiple flooding source–receptor pathways (e.g., groundwater–surface water, overtopping–overflow, surface–sewer flow) may simultaneously amplify coastal hazard and vulnerability. Currently, there is no integrated framework considering compound and multi-pathway flooding processes in a unified approach. State-of-the-art urban coastal flood modeling methods and research directions critical to developing an integrated framework for explicitly resolving multiple flooding pathways are presented.

scholarly journals Evaluation of the Uncertainty of Flash Flood Prediction Using the RRI Model in Mountainous Rivers

10.29007/n72w ◽  
2018 ◽  
Author(s):  
Yosuke Nakamura ◽  
Koji Ikeuchi ◽  
Shiori Abe ◽  
Toshio Koike ◽  
Shinji Egashira
Keyword(s):  
Water Level ◽  
Lead Time ◽  
Hydrological Model ◽  
Flash Flood ◽  
Flood Damage ◽  
Water Levels ◽  
Uncertainty Distribution ◽  
Flood Prediction ◽  
The Mean ◽  
Level 2

In recent years, flood damage caused by flash floods in mountainous rivers has been frequently reported in Japan. In order to ensure a sufficient lead time for safe evacuation, it is necessary to predict river water levels in real time utilizing a hydrological model. In this study, we conducted flood prediction using the RRI model and rainfall forecasted for the next 6 hours in the Kagetsu River basin (136.1 km2) in July 2017, evaluated the uncertainty regarding the prediction, and illustrated the results using a box-plot. The evaluation found that the mean error of the forecasted water level was approximately - 0.3 m in the prediction for the initial 3 hours and -0.97 m at the 6th hour. Also, the study investigated the possibility of correcting water levels forecasted by clarifying an uncertainty distribution. As a result, the water level forecasted was found to be underestimated because it was predicted to rise as high as Warning Level 2, while the water level forecasted with bias correction was predicted to reach Warning Level 4. Moreover, the lead time was estimated to prolong by 2 hours. Overall, the study suggested that flood forecasting can be improved by considering the uncertainty involved in prediction.

scholarly journals Evolution of extreme Total Water Levels along the northern coast of the Iberian Peninsula

2011 ◽  
Vol 11 (2) ◽  
pp. 613-625 ◽  
Author(s):  
D. F. Rasilla Álvarez ◽  
J. C. García Codron
Keyword(s):  
Iberian Peninsula ◽  
Water Level ◽  
Sea Level ◽  
Tide Gauge ◽  
Water Levels ◽  
Northern Coast ◽  
Atmospheric Conditions ◽  
Total Water ◽  
Total Water Level

Abstract. This paper assesses the evolution of storminess along the northern coast of the Iberian Peninsula through the calculation of extreme (1%) Total Water Levels (eTWL) on both observed (tide gauge and buoy data) and hindcasted (SIMAR-44) data. Those events were first identified and then characterized in terms of oceanographic parameters and atmospheric circulation features. Additionally, an analysis of the long-term trends in both types of data was performed. Most of the events correspond to a rough wave climate and moderate storm surges, linked to extratropical disturbances following a northern track. While local atmospheric conditions seem to be evolving towards lesser storminess, their impact has been balanced by the favorable exposure of the northern coast of the Iberian Peninsula to the increasing frequency and strength of distant disturbances crossing the North Atlantic. This evolution is also correctly reproduced by the simulated long-term evolution of the forcing component (meteorological sea level residuals and wave run up) of the Total Water Level values calculated from the SIMAR 44 database, since sea level residuals have been experiencing a reduction while waves are arriving with longer periods. Finally, the addition of the rate of relative sea level trend to the temporal evolution of the atmospheric forcing component of the Total Water Level values is enough to simulate more frequent and persistent eTWL.

scholarly journals How historical information can improve extreme coastal water levels probability prediction: application to the Xynthia event at La Rochelle (France)

2014 ◽  
Vol 2 (11) ◽  
pp. 7061-7088 ◽  
Author(s):  
T. Bulteau ◽  
D. Idier ◽  
J. Lambert ◽  
M. Garcin
Keyword(s):  
Water Level ◽  
Coastal Water ◽  
Extreme Values ◽  
Tide Gauge ◽  
Heterogeneous Data ◽  
Water Levels ◽  
Exceedance Probability ◽  
Historical Information ◽  
Order Of Magnitude ◽  
Tide Gauge Measurements

Abstract. The knowledge of extreme coastal water levels is useful for coastal flooding studies or the design of coastal defences. While deriving such extremes with standard analyses using tide gauge measurements, one often needs to deal with limited effective duration of observation which can result in large statistical uncertainties. This is even truer when one faces the issue of outliers, those particularly extreme values distant from the others which increase the uncertainty on the results. In this study, we investigate how historical information, even partial, of past events reported in archives can reduce statistical uncertainties and relativize such outlying observations. A Bayesian Markov Chain Monte Carlo method is developed to tackle this issue. We apply this method to the site of La Rochelle (France), where the storm Xynthia in 2010 generated a water level considered so far as an outlier. Based on 30 years of tide gauge measurements and 8 historical events, the analysis shows that: (1) integrating historical information in the analysis greatly reduces statistical uncertainties on return levels (2) Xynthia's water level no longer appears as an outlier, (3) we could have reasonably predicted the annual exceedance probability of that level beforehand (predictive probability for 2010 based on data till end of 2009 of the same order of magnitude as the standard estimative probability using data till end of 2010). Such results illustrate the usefulness of historical information in extreme value analyses of coastal water levels, as well as the relevance of the proposed method to integrate heterogeneous data in such analyses.

Modelling waves and surges during the 1953 storm

2005 ◽  
Vol 363 (1831) ◽  
pp. 1359-1375 ◽  
Author(s):  
J Wolf ◽  
R.A Flather
Keyword(s):  
Water Level ◽  
Sea Level ◽  
North Sea ◽  
Tide Gauge ◽  
High Water ◽  
Water Levels ◽  
Wind Fields ◽  
Sea Levels ◽  
Southern North Sea ◽  
Wave Models

Waves and sea levels have been modelled for the storm of 31 January–1 February 1953. Problems in modelling this event are associated with the difficulty of reconstructing wind fields and validating the model results with the limited data available from 50 years ago. The reconstruction of appropriate wind fields for surge and wave models is examined. The surges and waves are reproduced reasonably well on the basis of tide-gauge observations and the sparse observational information on wave heights. The maximum surge coincided closely in time with tidal high water, producing very high water levels along the coasts of the southern North Sea. The statistics of the 1953 event and the likelihood of recurrence are also discussed. Both surge and wave components were estimated to be approximately 1 in 50 year events. The maximum water level also occurred when the offshore waves were close to their maximum. The estimation of return period for the total water level is more problematic and is dependent on location. A scenario with the 1953 storm occurring in 2075, accounting for the effects of sea level rise and land movements, is also constructed, suggesting that sea level relative to the land could be 0.4–0.5 m higher than in 1953 in the southern North Sea, assuming a rise in mean sea level of 0.4 m.

scholarly journals How historical information can improve estimation and prediction of extreme coastal water levels: application to the Xynthia event at La Rochelle (France)

2015 ◽  
Vol 15 (6) ◽  
pp. 1135-1147 ◽  
Author(s):  
T. Bulteau ◽  
D. Idier ◽  
J. Lambert ◽  
M. Garcin
Keyword(s):  
Water Level ◽  
Coastal Water ◽  
Extreme Values ◽  
Tide Gauge ◽  
Heterogeneous Data ◽  
Water Levels ◽  
Exceedance Probability ◽  
Historical Information ◽  
Order Of Magnitude ◽  
Tide Gauge Measurements

Abstract. The knowledge of extreme coastal water levels is useful for coastal flooding studies or the design of coastal defences. While deriving such extremes with standard analyses using tide-gauge measurements, one often needs to deal with limited effective duration of observation which can result in large statistical uncertainties. This is even truer when one faces the issue of outliers, those particularly extreme values distant from the others which increase the uncertainty on the results. In this study, we investigate how historical information, even partial, of past events reported in archives can reduce statistical uncertainties and relativise such outlying observations. A Bayesian Markov chain Monte Carlo method is developed to tackle this issue. We apply this method to the site of La Rochelle (France), where the storm Xynthia in 2010 generated a water level considered so far as an outlier. Based on 30 years of tide-gauge measurements and 8 historical events, the analysis shows that (1) integrating historical information in the analysis greatly reduces statistical uncertainties on return levels (2) Xynthia's water level no longer appears as an outlier, (3) we could have reasonably predicted the annual exceedance probability of that level beforehand (predictive probability for 2010 based on data until the end of 2009 of the same order of magnitude as the standard estimative probability using data until the end of 2010). Such results illustrate the usefulness of historical information in extreme value analyses of coastal water levels, as well as the relevance of the proposed method to integrate heterogeneous data in such analyses.

scholarly journals What's streamflow got to do with it? A probabilistic simulation of the competing oceanographic and fluvial processes driving extreme along-river water levels

2019 ◽  
Author(s):  
Katherine A. Serafin ◽  
Peter Ruggiero ◽  
Kai A. Parker ◽  
David F. Hill
Keyword(s):  
Water Level ◽  
River Water ◽  
Extreme Event ◽  
Tide Gauge ◽  
High Water ◽  
Water Levels ◽  
Tribal Community ◽  
Indian Reservation ◽  
Environmental Forcing ◽  
Extreme Water Levels

Abstract. Extreme water levels driving flooding in estuarine and coastal environments are often compound events, generated by many individual processes like waves, storm surge, streamflow, and tides. Despite this, extreme water levels are typically modeled in isolated open coast or estuarine environments, potentially mischaracterizing the true risk to flooding facing coastal communities. We explore the variability of extreme water levels near the tribal community of La Push, within the Quileute Indian Reservation on the Washington state coast where a river signal is apparent in tide gauge measurements during high discharge events. To estimate the influence of multivariate forcing on high water levels, we first develop a methodology for statistically simulating discharge and river-influenced water levels in the tide gauge. Next, we merge probabilistic simulations of joint still water level and discharge occurrences with a hydraulic model that simulates along-river water levels. This methodology produces water levels from thousands of combinations of events not necessarily captured in the observational record. We show that the 100-yr ocean or 100-yr streamflow event does not always produce the 100-yr along-river water level. Along specific sections of river, both still water level and streamflow are necessary for producing the 100-yr water level. Understanding the relative forcing of extreme water levels along an ocean-to-river gradient will better prepare communities within inlets and estuaries for the compounding impacts of various environmental forcing, especially when a combination of extreme or non-extreme forcing can result in an extreme event with significant impacts.

Hydrodynamic modelling of compound flood drivers in estuaries

2020 ◽  
Author(s):  
Anais Couasnon ◽  
Dirk Eilander ◽  
Paul Bates ◽  
Hessel C. Winsemius ◽  
Philip J. Ward
Keyword(s):  
Time Series ◽  
Water Level ◽  
Transition Zone ◽  
River System ◽  
Water Levels ◽  
Flood Events ◽  
Sea Levels ◽  
Coastal Flood ◽  
Highly Nonlinear ◽  
Maximum Water

<p>Compound flooding in deltas and estuaries can be defined as the combination of various flood drivers leading to a significant flood impact (Zscheischler et al., 2018). For example, elevated sea-levels can impede flood drainage and create backwater effects that worsen flood damages. This was observed recently in March 2019 during cyclone Idai, where devastating floods from a high storm surge and discharge destroyed the port city of Beira. Even though the importance of accounting for compound flooding in flood risk assessments has been heavily underlined in recent literature, little research has been done on the impacts of compound flood events globally.</p><p>In this study, we investigate how compound flood hazard in estuaries is influenced by their various geophysical characteristics and the nature of their upstream river basins. The influence of riverine and coastal flood drivers on the water level varies along the estuary.  The water level at the river mouth is dependent on sea-levels, whereas one can expect this influence to reduce moving upstream in the river system and to become negligible completely upstream in large river systems. The location within a river system where both riverine and coastal flood drivers significantly contribute to the water level is referred to as the transition zone (Bilskie and Hagen, 2018).</p><p>We set up a model experiment to compare maximum water levels across realistic estuary types and boundary conditions. We use the 1-D unsteady hydrodynamic model LISFLOOD-FP to simulate water level time series for average and anomalous compound flood events of sea-levels and discharge. For each estuary type, resulting water level time series are analyzed to quantify the contribution of each flood driver in the maximum water level obtained along the complete coastal river profile and on the extent of the transition zone. We find that the interaction between the extreme sea level and extreme discharge is highly nonlinear and that this effect strongly varies depending on the estuary shape and length. We foresee this extensive overview of estuarine compound flood behavior to globally identify areas particularly vulnerable for interactions between extreme discharge and sea levels.</p>

scholarly journals PUMMA (Perangkat Ukur Murah untuk Muka Air) performance for water level monitoring of mangrove ecosystem in Pangandaran

2021 ◽  
Vol 925 (1) ◽  
pp. 012060
Author(s):  
N R Prasetiawan ◽  
D Novianto ◽  
A Setiawan ◽  
S Husrin ◽  
R Bramawanto ◽  
...  
Keyword(s):  
Water Level ◽  
Real Time ◽  
Tide Gauge ◽  
Mangrove Ecosystem ◽  
Water Levels ◽  
Tidal Range ◽  
Technical Problems ◽  
Mangrove Area ◽  
Data Gap ◽  
Water Level Measurements

Abstract PUMMA is a real-time tide gauge that has been operating in several locations in Indonesia. One of them was installed in a mangrove area of Pangandaran that supports both the fisheries and tourism sectors. Tidal dynamics is one of the factors that can affect fish abundance in the mangrove ecosystem. PUMMA Pangandaran monitors the water levels of the mangrove ecosystem in real-time 24/7 and produces CCTV images. This paper aims to analyze the performance of the PUMMA in Pangandaran based on data from water level measurements and image quality from CCTV. The results show that the tidal range in the waters of the mangrove ecosystem in Pangandaran is 1.3 m, with the maximum and minimum high tides being 0.79 m and -0.53 m. The tidal type in the mangrove ecosystem in Pangandaran is semidiurnal and affected by geometry of the estuary. The water level in the mangrove area was influenced by sediments that form a sandbar at the mouth of the Ciputrapinggan River, which controls the fluxes of seawater. There is a data gap of 368 hours during the operation period of PUMMA, and mostly due to technical problems that often occurred at the beginning of the installation. However, after March and April, its performance was improved with only three hours data gap. For the quality of CCTV images, good quality contributed to about 76.67% and only 5.06% on bad quality. Overall, PUMMA’s performance showed excellent reliability in monitoring the water levels and the conditions of the mangrove ecosystem.

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