Dynamic risk of coastal flood and driving factors: Integrating local sea level rise and spatially explicit urban growth

2021 ◽  
pp. 129039
Author(s):  
Lilai Xu ◽  
Shenghui Cui ◽  
Xiaoming Wang ◽  
Jianxiong Tang ◽  
Vilas Nitivattananon ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Urban Growth ◽  
Driving Factors ◽  
Spatially Explicit ◽  
Coastal Flood ◽  
Dynamic Risk

scholarly journals Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
A. Hooijer ◽  
R. Vernimmen
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Flood Risk ◽  
Land Area ◽  
Area At Risk ◽  
Population Number ◽  
Coastal Flood ◽  
Elevation Data ◽  
Coastal Flood Risk ◽  
The Tropics

AbstractCoastal flood risk assessments require accurate land elevation data. Those to date existed only for limited parts of the world, which has resulted in high uncertainty in projections of land area at risk of sea-level rise (SLR). Here we have applied the first global elevation model derived from satellite LiDAR data. We find that of the worldwide land area less than 2 m above mean sea level, that is most vulnerable to SLR, 649,000 km2 or 62% is in the tropics. Even assuming a low-end relative SLR of 1 m by 2100 and a stable lowland population number and distribution, the 2020 population of 267 million on such land would increase to at least 410 million of which 72% in the tropics and 59% in tropical Asia alone. We conclude that the burden of current coastal flood risk and future SLR falls disproportionally on tropical regions, especially in Asia.

Multiple drivers of extreme sea levels in the northern Adriatic Sea

2021 ◽  
Author(s):  
Christian Ferrarin ◽  
Piero Lionello ◽  
Mirko Orlic ◽  
Fabio Raicich ◽  
Gianfausto Salvadori
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Adriatic Sea ◽  
Driving Factors ◽  
Northern Adriatic Sea ◽  
Relative Sea Level ◽  
Northern Adriatic ◽  
Sea Levels ◽  
Extreme Sea Levels ◽  
Relative Sea Level Rise

<p><span><span>Extreme sea levels at the coast result from the combination of astronomical tides with atmospherically forced fluctuations at multiple time scales. Seiches, river floods, waves, inter-annual and inter-decad</span></span><span><span>al dynamics and relative sea-level rise can also contribute to the total sea level. While tides are usually well described and predicted, the effect of the different atmospheric contributions to the sea level and their trends are still not well understood. Meso-scale atmospheric disturbances, synoptic-scale phenomena and planetary atmospheric waves (PAW) act at different temporal and spatial scales and thus generate sea-level disturbances at different frequencies. In this study, we analyze the 1872-2019 sea-level time series in Venice (northern Adriatic Sea, Italy) to investigate the relative role of the different driving factors in the extreme sea levels distribution. The adopted approach consists in 1) isolating the different contributions to the sea level by applying least-squares fitting and Fourier decomposition; 2) performing a multivariate statistical analysis which enables the dependencies among driving factors and their joint probability of occurrence to be described; 3) analyzing temporal changes in extreme sea levels and extrapolating possible future tendencies. The results highlight the fact that the most extreme sea levels are mainly dominated by the non-tidal residual, while the tide plays a secondary role. The non-tidal residual of the extreme sea levels is attributed mostly to PAW surge and storm surge, with the latter component becoming dominant for the most extreme events. The results of temporal evolution analysis confirm previous studies according to which the relative sea-level rise is the major driver of the increase in the frequency of floods in Venice over the last century. However, also long term variability in the storm activity impacted the frequency and intensity of extreme sea levels and have contributed to an increase of floods in Venice during the fall and winter months of the last three decades.</span></span></p>

EARLIEST DETECTION OF SEA-LEVEL RISE ACCELERATIONS TO INFORM UPGRADE/REPLACEMENT OF COASTAL FLOOD DEFENSE INFRASTRUCTURE

2019 ◽  
Author(s):  
ROBERT J. NICHOLLS ◽  
IVAN D. HAIGH ◽  
HAGEN RADTKE ◽  
FRANCISCO CALAFAT ◽  
PAOLO CIPOLLINI ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Coastal Flood ◽  
Flood Defense

A probabilistic methodology to estimate future coastal flood risk due to sea level rise

2008 ◽  
Vol 55 (12) ◽  
pp. 1062-1073 ◽  
Author(s):  
Matthew J. Purvis ◽  
Paul D. Bates ◽  
Christopher M. Hayes
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Flood Risk ◽  
Coastal Flood ◽  
Coastal Flood Risk

scholarly journals Modelling Coastal Flood Propagation under Sea Level Rise: A Case Study in Maria, Eastern Canada

Geosciences ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 76 ◽  
Author(s):  
David Didier ◽  
Marion Bandet ◽  
Pascal Bernatchez ◽  
Dany Dumont
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Overland Flow ◽  
Water Levels ◽  
Model Complexity ◽  
Flood Mapping ◽  
Eastern Canada ◽  
Coastal Flood ◽  
The Impact ◽  
Flow Velocities

Coastal management often relies on large-scale flood mapping to produce sea level rise assessments where the storm-related surge is considered as the most important hazard. Nearshore dynamics and overland flow are also key parameters in coastal flood mapping, but increase the model complexity. Avoiding flood propagation processes using a static flood mapping is less computer-intensive, but generally leads to overestimation of the flood zone, especially in defended urban backshore. For low-lying communities, sea level rise poses a certain threat, but its consequences are not only due to a static water level. In this paper, the numerical process-based model XBeach is used in 2D hydrodynamic mode (surfbeat) to reproduce an observed historical flood in Maria (eastern Canada). The main goal is to assess the impacts of a future storm of the same magnitude in the horizon 2100 according to an increase in sea level rise. The model is first validated from in situ observations of waves and water levels observed on the lower foreshore. Based on field observations of a flood extent in 2010, the simulated flooded area was also validated given a good fit (59%) with the actual observed flood. Results indicate that the 2010 storm-induced surge generated overwash processes on multiple areas and net landward sediment transport and accumulation (washover lobes). The flood was caused by relatively small nearshore waves (Hs < 1 m), but despite small water depth (>1.2 m), high flow velocities occurred in the main street (U > 2 m/s) prior to draining in the salt marsh. The impact of sea level rise on the low-lying coastal community of Maria could induce a larger flood area in 2100, deeper floodwater, and higher flow velocities, resulting in higher hazard for the population.

A composite method for past events characterisation providing insights in past, present and future coastal flood hazards: Joining historical, statistical and modeling approaches

2020 ◽  
Author(s):  
Deborah Idier ◽  
Jeremy Rohmer ◽  
Rodrigo Pedreros ◽  
Sylvestre Le Roy ◽  
Jerome Lambert ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Meteorological Data ◽  
Critical Conditions ◽  
Flood Hazards ◽  
Flood Events ◽  
Coastal Flood ◽  
Set Up ◽  
Offshore Wave

&lt;p&gt;The characterisation of past coastal flood events is crucial for risk prevention. However, it is limited by the partial character of historical information on flood events and the lack or limited quality of past hydro-meteorological data. In addition coastal flood processes are complex, driven by many hydro-meteorological processes, making mechanisms and probability analysis challenging. These issues are tackled by joining historical, statistical and modelling approaches. We focus on a macrotidal site (G&amp;#226;vres, France) subject to overtopping and investigate the 1900-2010 period. A continuous hydro-meteorological database is built and a damage event database is set up based on archives, newspapers, maps and aerial photographies. Using together historic information, hindcasts and hydrodynamic models, we identified 9 flood events, among which 5 significant flood events (4 with high confidence: 1924, 1978, 2001, 2008; 1 with a lower confidence: 1904). These flood events are driven by the combination of sea-level rise, tide, atmospheric surge, offshore wave conditions and local wind. The critical conditions leading to flood are further analysed, including the effect of coastal defences, showing that the present coastal defences would not have allowed to face the hydro-meteorological conditions of 09/02/1924 for instance, whose bi-variate return periods of exceedance Tr (still water level relative to the mean sea level and significant wave height) is larger than 1000 y. In addition, Tr is expected to significantly decrease with the sea-level rise, reaching values smaller than 1 y, for 8 of the 9 historical events, for a sea-level rise of 0.63 m, which is equal to the median amount of sea-level rise projected by the 5&lt;sup&gt;th&lt;/sup&gt; Assessment Report of the IPCC in this region for RCP8.5 in 2100.&lt;/p&gt;

scholarly journals Allowances for evolving coastal flood risk under uncertain local sea-level rise

2016 ◽  
Vol 137 (3-4) ◽  
pp. 347-362 ◽  
Author(s):  
Maya K. Buchanan ◽  
Robert E. Kopp ◽  
Michael Oppenheimer ◽  
Claudia Tebaldi
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Flood Risk ◽  
Coastal Flood ◽  
Coastal Flood Risk

scholarly journals Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam

2013 ◽  
Vol 17 (1) ◽  
pp. 379-394 ◽  
Author(s):  
H. T. L. Huong ◽  
A. Pathirana
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Urban Growth ◽  
Flood Hazard ◽  
Extreme Rainfall ◽  
Urban Runoff ◽  
Growth Patterns ◽  
World City ◽  
Urban Heat

Abstract. Urban development increases flood risk in cities due to local changes in hydrological and hydrometeorological conditions that increase flood hazard, as well as to urban concentrations that increase the vulnerability. The relationship between the increasing urban runoff and flooding due to increased imperviousness is better perceived than that between the cyclic impact of urban growth and the urban rainfall via microclimatic changes. The large-scale, global impacts due to climate variability and change could compound these risks. We present the case of a typical third world city – Can Tho (the biggest city in Mekong River Delta, Vietnam) – faced with multiple future challenges, namely: (i) the likely effect of climate change-driven sea level rise, (ii) an expected increase of river runoff due to climate change as estimated by the Vietnamese government, (iii) increased urban runoff driven by imperviousness, and (iv) enhancement of extreme rainfall due to urban growth-driven, microclimatic change (urban heat islands). A set of model simulations were used to construct future scenarios, combining these influences. Urban growth of the city was projected up to year 2100 based on historical growth patterns, using a land use simulation model (Dinamica EGO). A dynamic limited-area atmospheric model (WRF), coupled with a detailed land surface model with vegetation parameterization (Noah LSM), was employed in controlled numerical experiments to estimate the anticipated changes in extreme rainfall patterns due to urban heat island effect. Finally, a 1-D/2-D coupled urban-drainage/flooding model (SWMM-Brezo) was used to simulate storm-sewer surcharge and surface inundation to establish the increase in the flood hazard resulting from the changes. The results show that under the combined scenario of significant change in river level (due to climate-driven sea level rise and increase of flow in the Mekong) and "business as usual" urbanization, the flooding of Can Tho could increase significantly. The worst case may occur if a sea level rise of 100 cm and the flow from upstream happen together with high-development scenarios. The relative contribution of causes of flooding are significantly different at various locations; therefore, detailed research on adaptation are necessary for future investments to be effective.

scholarly journals Unravelling the Importance of Uncertainties in Global-Scale Coastal Flood Risk Assessments under Sea Level Rise

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 774
Author(s):  
Jeremy Rohmer ◽  
Daniel Lincke ◽  
Jochen Hinkel ◽  
Gonéri Le Cozannet ◽  
Erwin Lambert ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Flood Risk ◽  
Global Scale ◽  
Short Term ◽  
Sea Levels ◽  
Coastal Flood ◽  
Extreme Sea Levels ◽  
Coastal Flood Risk ◽  
Adaptation Costs

Global scale assessments of coastal flood damage and adaptation costs under 21st century sea-level rise are associated with a wide range of uncertainties, including those in future projections of socioeconomic development (shared socioeconomic pathways (SSP) scenarios), of greenhouse gas concentrations (RCP scenarios), and of sea-level rise at regional scale (RSLR), as well as structural uncertainties related to the modelling of extreme sea levels, data on exposed population and assets, and the costs of flood damages, etc. This raises the following questions: which sources of uncertainty need to be considered in such assessments and what is the relative importance of each source of uncertainty in the final results? Using the coastal flood module of the Dynamic Interactive Vulnerability Assessment modelling framework, we extensively explore the impact of scenario, data and model uncertainties in a global manner, i.e., by considering a large number (>2000) of simulation results. The influence of the uncertainties on the two risk metrics of expected annual damage (EAD), and adaptation costs (AC) related to coastal protection is assessed at global scale by combining variance-based sensitivity indices with a regression-based machine learning technique. On this basis, we show that the research priorities in terms of future data/knowledge acquisition to reduce uncertainty on EAD and AC differ depending on the considered time horizon. In the short term (before 2040), EAD uncertainty could be significantly decreased by 25 and 75% if the uncertainty of the translation of physical damage into costs and of the modelling of extreme sea levels could respectively be reduced. For AC, it is RSLR that primarily drives short-term uncertainty (with a contribution ~50%). In the longer term (>2050), uncertainty in EAD could be largely reduced by 75% if the SSP scenario could be unambiguously identified. For AC, it is the RCP selection that helps reducing uncertainty (up to 90% by the end of the century). Altogether, the uncertainty in future human activities (SSP and RCP) are the dominant source of the uncertainty in future coastal flood risk.

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