Exceedance probability statistics: The likelihood that coastal water levels will reach extreme elevations

2012 Oceans ◽  
2012 ◽  
Author(s):  
C. Lindley ◽  
C. Zervas
Keyword(s):  
Coastal Water ◽  
Water Levels ◽  
Exceedance Probability

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.

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.

Estimating the Probability of Compound Discharge/surge Events in a Complex Estuarine System Under Data Constraints 

2021 ◽  
Author(s):  
Victor M. Santos ◽  
Thomas Wahl ◽  
Robert Jane ◽  
Shubhra K. Misra ◽  
Kathleen D. White
Keyword(s):  
Storm Surge ◽  
Statistical Modelling ◽  
Water Levels ◽  
Exceedance Probability ◽  
Storm Event ◽  
Estuarine System ◽  
Outlier Removal ◽  
Sampling Schemes ◽  
Sabine Lake ◽  
Data Constraints

<p>Compound flooding may result from the interaction of two or more contributing processes, which may not be extreme themselves, but in combination lead to extreme impacts. Estuarine environments are particularly prone to compound flooding due to the interplay between coastal storm surge and river discharge processes, both often being driven by the same storm event. A detailed understanding of compounding mechanisms, including the dependence between flooding drivers, is necessary to avoid flood risk miscalculations when building/upgrading flood defences to mitigate risks associated with high impact events. Here, we use statistical methods to assess compound flooding potential in Sabine Lake, TX. Sabine Lake receives discharge from two rivers and is connected to the Gulf of Mexico coast through Sabine Pass. These geographic characteristics make it susceptible to compound flooding. We employ several trivariate statistical models (and simplified bivariate models for comparison) to examine the sensitivity of results to the choice of data pre-processing steps, statistical model setup, and outlier removal. We define a response function that represents water levels resulting from the interaction between discharge and storm surge inside Sabine Lake, and explore how the water level response is affected by including or ignoring dependencies between the contributing flooding drivers. Our results show that accounting for dependencies leads to water levels that are up to 30 cm higher for a 2% annual exceedance probability (AEP) event and up to 35 cm higher for a 1% AEP event, compared to assuming independence. We also find notable variations in the results across different sampling schemes, multivariate model configurations, and sensitivity to outlier removal. This highlights the need for testing various statistical modelling approaches in order to reliably capture potential compounding effects, especially under data constraints.</p><p> </p>

scholarly journals Evaluating the effectiveness of catchment-scale approaches in mitigating urban surface water flooding

2020 ◽  
Vol 378 (2168) ◽  
pp. 20190203 ◽  
Author(s):  
Charlie Ferguson ◽  
Richard Fenner
Keyword(s):  
Positive Impact ◽  
Drainage System ◽  
Water Levels ◽  
Tree Planting ◽  
Water Flooding ◽  
Exceedance Probability ◽  
Urban Drainage ◽  
Housing Estate ◽  
Catchment Scale ◽  
Urban Flood

The argument for natural flood management in the UK has strengthened in recent years with increasing awareness of the potential benefits gained from upstream interventions (especially improvements in water quality, public amenities and biodiversity). This study aims to develop an understanding of another potential benefit—interventions promoting free discharge at downstream urban drainage outfalls by moderating water levels in receiving watercourses. A novel, coupled model (linking dynamic TOPMODEL, HEC-RAS and Infoworks ICM) is calibrated for the Asker catchment in Dorset, England. This predominantly rural watershed drains to the town of Bridport, frequently submerging a surface drainage outfall in a nearby housing estate. Two forms of upstream, catchment-scale intervention (hillslope tree planting and in-channel large woody debris) are modelled to understand their impacts on the functioning of the drainage network during both the calibration period and a range of design storms. The results indicate that interventions have the greatest positive impact during frequent events. For example, during a storm with a 10% annual exceedance probability (AEP), upstream NFM could reduce outfall inundation by up to 3.75 h and remove any surcharging of flow within the drainage system in Bridport. In more severe storms, the results suggest interventions could slightly prolong the time the outfall was submerged. However, by slowing the wider catchment's response during the 3.3% AEP storm, upstream interventions allow more water to escape the urban drainage system and reduce the maximum surface flooding extent within the housing estate by 35%. This article is part of the theme issue ‘Urban flood resilience’.

scholarly journals Effect of Fluvial Discharges and Remote Non-Tidal Residuals on Compound Flood Forecasting in San Francisco Bay

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2481
Author(s):  
Babak Tehranirad ◽  
Liv Herdman ◽  
Kees Nederhoff ◽  
Li Erikson ◽  
Robert Cifelli ◽  
...  
Keyword(s):  
San Francisco ◽  
Coastal Water ◽  
Large Scale ◽  
San Francisco Bay ◽  
Flood Forecasting ◽  
Water Levels ◽  
Global Ocean ◽  
Coastal Flood ◽  
Extreme Water Levels ◽  
Recent Emergence

Accurate and timely flood forecasts are critical for making emergency-response decisions regarding public safety, infrastructure operations, and resource allocation. One of the main challenges for coastal flood forecasting systems is a lack of reliable forecast data of large-scale oceanic and watershed processes and the combined effects of multiple hazards, such as compound flooding at river mouths. Offshore water level anomalies, known as remote Non-Tidal Residuals (NTRs), are caused by processes such as downwelling, offshore wind setup, and also driven by ocean-basin salinity and temperature changes, common along the west coast during El Niño events. Similarly, fluvial discharges can contribute to extreme water levels in the coastal area, while they are dominated by large-scale watershed hydraulics. However, with the recent emergence of reliable large-scale forecast systems, coastal models now import the essential input data to forecast extreme water levels in the nearshore. Accordingly, we have developed Hydro-CoSMoS, a new coastal forecast model based on the USGS Coastal Storm Modeling System (CoSMoS) powered by the Delft3D San Francisco Bay and Delta community model. In this work, we studied the role of fluvial discharges and remote NTRs on extreme water levels during a February 2019 storm by using Hydro-CoSMoS in hindcast mode. We simulated the storm with and without real-time fluvial discharge data to study their effect on coastal water levels and flooding extent, and highlight the importance of watershed forecast systems such as NOAA’s National Water Model (NWM). We also studied the effect of remote NTRs on coastal water levels in San Francisco Bay during the 2019 February storm by utilizing the data from a global ocean model (HYCOM). Our results showed that accurate forecasts of remote NTRs and fluvial discharges can play a significant role in predicting extreme water levels in San Francisco Bay. This pilot application in San Francisco Bay can serve as a basis for integrated coastal flood modeling systems in complex coastal settings worldwide.

scholarly journals Extreme Coastal Water Levels Exacerbate Fluvial Flood Hazards in Northwestern Europe

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Poulomi Ganguli ◽  
Bruno Merz
Keyword(s):  
Coastal Water ◽  
River Discharge ◽  
Storm Surges ◽  
Water Levels ◽  
Peak Discharge ◽  
Flood Hazards ◽  
Coastal Zones ◽  
Winter Storm ◽  
Return Levels ◽  
River Flood

Abstract Compound flooding, such as the co-occurrence of fluvial floods and extreme coastal water levels (CWL), may lead to significant impacts in densely-populated Low Elevation Coastal Zones. They may overstrain disaster management owing to the co-occurrence of inundation from rivers and the sea. Recent studies are limited by analyzing joint dependence between river discharge and either CWL or storm surges, and little is known about return levels of compound flooding, accounting for the covariance between drivers. Here, we assess the compound flood severity and identify hotspots for northwestern Europe during 1970–2014, using a newly developed Compound Hazard Ratio (CHR) that compares the severity of compound flooding associated with extreme CWL with the unconditional T-year fluvial peak discharge. We show that extreme CWL and stronger storms greatly amplify fluvial flood hazards. Our results, based on frequency analyses of observational records during 2013/2014’s winter storm Xaver, reveal that the river discharge of the 50-year compound flood is up to 70% larger, conditioned on the occurrence of extreme CWL, than that of the at-site peak discharge. For this event, nearly half of the stream gauges show increased flood hazards, demonstrating the importance of including the compounding effect of extreme CWL in river flood risk management.

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