LONG-TERM PROJECTION OF STORM SURGE CHARACTERISTICS IN SUO-NADA SEA USING LARGE ENSEMBLE CLIMATE PROJECTION DATA

AbstractIt is five years since Hurricane Sandy heavily damaged the New York- New Jersey Metropolitan region, and the fuller character of the long-term response can be better understood. The long-term response to Hurricane Sandy and the flooding risks it illustrated are set in myriad of individual and collective decisions taken during the time following the event. While the physical vulnerability of this region to storm surge flooding and climate change risks including sea level rise has been well-documented within the scholarly literature, Sandy’s impact placed decision-makingpost extreme events into the forefront of public and private discussions about the appropriate response. Some of the most fundamental choices were made by individual homeowners who houses were damaged and in some cases made uninhabitable following the storm. These individuals were forced to make decisions regarding where they would live and whether Sandy’s impact would result in their moving. In the disaster recovery and rebuilding context, these early household struggles about whether to leave or stay are often lost in the wider and longer narrative of recovery. To examine this early phase, this paper presents results of a research study that documented the ephemeral evidence of the initial phase of recovery in coastal communities that were heavily impacted by Hurricane Sandy’s storm surge and flooding. Hurricane Sandy and the immediate response to the storm created conditions for a potential large-scale transformation with respect to settlement of the coastal zone. In the paper, we examine and analyze survey and interview results of sixty-one residents and two dozen local stakeholders and practitioners to understand the stresses and transitions experienced by flooded households and the implications for the longer term resiliency of the communities in which they are located.

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Alternate history: A synthetic ensemble of ocean chlorophyll concentrations

10.5194/egusphere-egu21-1375 ◽

2021 ◽

Author(s):

Geneviève Elsworth ◽

Nicole Lovenduski ◽

Karen McKinnon

Keyword(s):

Climate Variability ◽

Internal Variability ◽

Ensemble Member ◽

Global Ocean ◽

Large Ensemble ◽

Internal Climate Variability ◽

Surface Ocean ◽

Large Ensembles ◽

Long Term Trends

<p>Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long-term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long-term trends in the presence of internal variability. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.</p>

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Calibration of a reinforcement learning method with the ClimEx large ensemble and a weather generator for water management

10.5194/egusphere-egu21-12931 ◽

2021 ◽

Author(s):

Gabrielle Dallaire ◽

Richard Arsenault ◽

Pascal Côté ◽

Kenjy Demeester

Keyword(s):

Water Management ◽

Reinforcement Learning ◽

Hydrological Model ◽

Weather Generator ◽

Management Policy ◽

Learning Method ◽

Large Ensemble ◽

Model Driven ◽

Hydropower System

<p>Hydropower is a renewable source of energy that relies on efficient water planning and management. As the behavior of this natural resource is difficult to predict, water managers therefore use methods to help the decision-making process. Reinforcement Learning (RL) has been shown to be a potentially effective approach to overcome the limitations of the Stochastic Dynamic Programming (SDP) method that is commonly used for water management. However, convergence to a robust and efficient operating policy from RL methods requires large amounts of data, while long-term historical data is not always available. The objective of this study consists in using tools to generate long-term hydrological series to obtain an efficient parameterization of the management policy. This presentation introduces a comparison of calibration datasets used in a RL method for the optimal control of a hydropower system. This method aims to find a feedback policy that maximizes the production of a hydropower system over a mid-term horizon. Three streamflow datasets are compared on a real hydropower system for RL calibration: 1) the historical streamflow (35 years), 2) streamflow simulated by a hydrological model driven by a high-resolution large-ensemble climate model data (3500 years) from the ClimEx project, and 3) streamflow simulated by a hydrological model driven by climate data generated with a stochastic weather generator (5000 years). The GR4J hydrological model is employed for the hydrologic modelling aspect of the work. The reinforcement learning method is applied on the Lac-Saint-Jean water resources system in Quebec (Canada), where the hydrological regime is snowmelt-dominated. A bootstrapping method where multiple calibration and validation sets were resampled is used to conduct a robust statistical analysis for comparing the methods&#8217; performance. The performance of the calibrated management policy is evaluated with respect to the operational constraints of the system as well as the overall energy production. Preliminary results show that is possible to achieve effective management policies by using tools to generate long-term hydrological series to feed a RL method.</p>

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Long-term variability of storm surge frequency in the Venice Lagoon: an update thanks to 18th century sea level observations

Natural Hazards and Earth System Science ◽

10.5194/nhess-15-527-2015 ◽

2015 ◽

Vol 15 (3) ◽

pp. 527-535 ◽

Cited By ~ 5

Author(s):

F. Raicich

Keyword(s):

Time Series ◽

Sea Level ◽

Storm Surge ◽

18Th Century ◽

Venice Lagoon ◽

Mean Sea Level ◽

Sea Level Anomalies ◽

Daily Data ◽

Remarkable Event

Abstract. Sea level observations made in the Venice Lagoon between 1751 and 1792 have been recovered, consisting of two time series of daily data on high and low waters in Venice and Chioggia. From comparisons with modern observations, the quality of the 18th century data appears to be good enough to allow a useful analysis. A composite time series of daily mean sea level is obtained by merging the 18th century data and 1872–2004 observations in Venice Punta della Salute. The absence of reliable information on vertical references prevents the connection of the two 18th century time series with each other and with modern observations. However, daily sea level anomalies relative to the mean sea level enable us to recognize storm surge events that appear to occur more frequently in the second half of the 18th century than in the late 19th and 20th centuries, particularly during the 1751–1769 period. The record-breaking storm surge of 4 November 1966 turns out to be a remarkable event even in comparison to the events extracted from the 18th century time series. Further work is required to fill the gap between the old and modern observations.

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Long-term variation of storm surge-associated waves in the Bohai Sea

Journal of Oceanology and Limnology ◽

10.1007/s00343-019-8257-z ◽

2019 ◽

Vol 37 (6) ◽

pp. 1868-1878 ◽

Cited By ~ 1

Author(s):

Yanping Wang ◽

Yongling Liu ◽

Xinyan Mao ◽

Yutao Chi ◽

Wensheng Jiang

Keyword(s):

Storm Surge ◽

Bohai Sea ◽

The Bohai Sea ◽

Term Variation

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A Numerical Modeling Approach to Predict the Effect of a Storm Surge Barrier on Hydrodynamics and Long-Term Transport Processes in a Partially Mixed Estuary

Estuaries and Coasts ◽

10.1007/s12237-016-0175-0 ◽

2016 ◽

Vol 40 (2) ◽

pp. 387-403 ◽

Cited By ~ 9

Author(s):

Jiabi Du ◽

Jian Shen ◽

Donna M. Bilkovic ◽

Carlton H. Hershner ◽

Mac Sisson

Keyword(s):

Numerical Modeling ◽

Storm Surge ◽

Transport Processes ◽

Modeling Approach

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Still normal? Contextualizing real-time data with long-term statistics to monitor anomalies and systematic changes in storm surge activity – Introduction of a prototype web tool storm surge monitor for the German coasts

10.5194/nhess-2021-75 ◽

2021 ◽

Author(s):

Xin Liu ◽

Insa Meinke ◽

Ralf Weisse

Keyword(s):

Decision Making ◽

Real Time ◽

Storm Surge ◽

Storm Surges ◽

Added Value ◽

Time Data ◽

Storm Activity ◽

The Public ◽

Real Time Data

Abstract. Storm surges represent a major threat to many low-lying coastal areas in the world. While most places can cope with or are more or less adapted to present-day risks, future risks may increase from factors such as sea level rise, subsidence, or changes in storm activity. This may require further or alternative adaptation and strategies. For most places, both forecasts and real-time observations are available. However, analyses of long-term changes or recent severe extremes that are important for decision-making are usually only available sporadically or with substantial delay. In this paper, we propose to contextualize real-time data with long-term statistics to make such information publicly available in near real-time. We implement and demonstrate the concept of a ”storm surge monitor” for tide gauges along the German North Sea and Baltic Sea coasts. It provides automated near real-time assessments of the course and severity of the ongoing storm surge season and its single events. The assessment is provided in terms of storm surge height, frequency, duration, and intensity. It is proposed that such near real-time assessments provide added value to the public and decision-making. It is further suggested that the concept is transferable to other coastal regions threatened by storm surges.

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