Global analysis of the uncertainties prevailing in global-scale assessment of coastal flood damage and adaptation costs under 21st century sea-level rise

2020 ◽  
Author(s):  
Jeremy Rohmer ◽  
Daniel Lincke ◽  
Jochen Hinckel ◽  
Goneri Le Cozannet ◽  
Erwin Lambert
Keyword(s):  
Machine Learning ◽  
Sea Level Rise ◽  
Sea Level ◽  
21St Century ◽  
Global Scale ◽  
Flood Damage ◽  
Machine Learning Technique ◽  
Coastal Flood ◽  
Learning Technique ◽  
Adaptation Costs

<p>Global scale assessment 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 (SSP scenarios), of greenhouse gas emissions (RCP scenarios), and of sea-level rise (SLR). These uncertainties also include structural uncertainties related to the modeling of extreme sea levels, vulnerability functions, and the translation of flooding-induced damage to costs. This raises the following questions: what is the relative importance of each source of uncertainty in the final global-scale results? Which sources of uncertainty need to be considered? What uncertainties are of negligible influence? Hence, getting better insights in the role played by these uncertainties allows to ease their communication and to structure the message on future coastal impacts and induced losses. Using the integrated DIVA Model (see e.g. Hinkel et al., 2014, PNAS), we extensively explore the impact of these uncertainties in a global manner, i.e. by considering a large number (~3,000) of scenario combinations and by analyzing the associated results using a regression-based machine learning technique (i.e. regression decision trees). On this basis, we show the decreasing roles, over time, of the uncertainties in the extremes’ modeling together with the increasing roles of SSP and of RCP after 2030 and 2080 for the damage and adaptation costs respectively. This means that mitigation of climate change helps to reduce uncertainty of adaptation costs, and choosing a particular SSP reduces the uncertainty on the expected damages. In addition, the tree structure of the machine learning technique allows an in-depth analysis of the interactions of the different uncertain factors. These results are discussed depending on the SLR data selected for the analysis, i.e. before and after the recently released IPCC SROCC report on September 2019.</p>

scholarly journals Coastal flood damage and adaptation costs under 21st century sea-level rise

2014 ◽  
Vol 111 (9) ◽  
pp. 3292-3297 ◽  
Author(s):  
Jochen Hinkel ◽  
Daniel Lincke ◽  
Athanasios T. Vafeidis ◽  
Mahé Perrette ◽  
Robert James Nicholls ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
21St Century ◽  
Flood Damage ◽  
Coastal Flood ◽  
Adaptation Costs

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.

scholarly journals Quantifying the effect of sea level rise and flood defence – a point process perspective on coastal flood damage

2016 ◽  
Vol 16 (2) ◽  
pp. 559-576 ◽  
Author(s):  
M. Boettle ◽  
D. Rybski ◽  
J. P. Kropp
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Point Process ◽  
Extreme Events ◽  
Global Scale ◽  
Flood Damage ◽  
Greenhouse Gas Mitigation ◽  
Sea Levels ◽  
Stochastic Framework ◽  
Process Perspective

Abstract. In contrast to recent advances in projecting sea levels, estimations about the economic impact of sea level rise are vague. Nonetheless, they are of great importance for policy making with regard to adaptation and greenhouse-gas mitigation. Since the damage is mainly caused by extreme events, we propose a stochastic framework to estimate the monetary losses from coastal floods in a confined region. For this purpose, we follow a Peak-over-Threshold approach employing a Poisson point process and the Generalised Pareto Distribution. By considering the effect of sea level rise as well as potential adaptation scenarios on the involved parameters, we are able to study the development of the annual damage. An application to the city of Copenhagen shows that a doubling of losses can be expected from a mean sea level increase of only 11 cm. In general, we find that for varying parameters the expected losses can be well approximated by one of three analytical expressions depending on the extreme value parameters. These findings reveal the complex interplay of the involved parameters and allow conclusions of fundamental relevance. For instance, we show that the damage typically increases faster than the sea level rise itself. This in turn can be of great importance for the assessment of sea level rise impacts on the global scale. Our results are accompanied by an assessment of uncertainty, which reflects the stochastic nature of extreme events. While the absolute value of uncertainty about the flood damage increases with rising mean sea levels, we find that it decreases in relation to the expected damage.

scholarly journals Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures

2020 ◽  
Vol 20 (4) ◽  
pp. 1025-1044 ◽  
Author(s):  
Timothy Tiggeloven ◽  
Hans de Moel ◽  
Hessel C. Winsemius ◽  
Dirk Eilander ◽  
Gilles Erkens ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Flood Risk ◽  
Flood Hazard ◽  
Global Scale ◽  
Protection Measures ◽  
Benefit Cost Analysis ◽  
Coastal Flood ◽  
Expected Annual Damage ◽  
Coastal Flood Risk

Abstract. Coastal flood hazard and exposure are expected to increase over the course of the 21st century, leading to increased coastal flood risk. In order to limit the increase in future risk, or even reduce coastal flood risk, adaptation is necessary. Here, we present a framework to evaluate the future benefits and costs of structural protection measures at the global scale, which accounts for the influence of different flood risk drivers (namely sea-level rise, subsidence, and socioeconomic change). Globally, we find that the estimated expected annual damage (EAD) increases by a factor of 150 between 2010 and 2080 if we assume that no adaptation takes place. We find that 15 countries account for approximately 90 % of this increase. We then explore four different adaptation objectives and find that they all show high potential in cost-effectively reducing (future) coastal flood risk at the global scale. Attributing the total costs for optimal protection standards, we find that sea-level rise contributes the most to the total costs of adaptation. However, the other drivers also play an important role. The results of this study can be used to highlight potential savings through adaptation at the global scale.

scholarly journals Global scale benefit-cost analysis of coastal flood adaptation to different flood risk drivers

2019 ◽  
Author(s):  
Timothy Tiggeloven ◽  
Hans de Moel ◽  
Hessel C. Winsemius ◽  
Dirk Eilander ◽  
Gilles Erkens ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Flood Risk ◽  
Flood Hazard ◽  
Global Scale ◽  
Protection Measures ◽  
Benefit Cost Analysis ◽  
Coastal Flood ◽  
Expected Annual Damage ◽  
Coastal Flood Risk

Abstract. Coastal flood hazard and exposure are expected to increase over the course of the 21st century, leading to increased coastal flood risk. In order to limit the increase in future risk, or even reduce coastal flood risk, adaptation is necessary. Here, we present a framework to evaluate the future benefits and costs of structural protection measures at the global scale, which accounts for the influence of different flood risk drivers (namely: sea-level rise, subsidence, and socioeconomic change). Globally, we find that the estimated expected annual damage (EAD) increases by a factor of 150 between 2010 and 2080, if we assume that no adaptation takes place. We find that 15 countries account for approximately 90 % of this increase. We then explore four different adaptation objectives and find that they all show high potential to cost-effectively reduce (future) coastal flood risk at the global scale. Attributing the total costs for optimal protection standards, we find that sea-level rise contributes the most to the total costs of adaptation. However, the other drivers also play an important role. The results of this study can be used to highlight potential savings through adaptation at the global scale.

scholarly journals Quantifying the effect of sea level rise and flood defence – a point process perspective on coastal flood damage

2015 ◽  
Vol 3 (10) ◽  
pp. 6229-6269
Author(s):  
M. Boettle ◽  
D. Rybski ◽  
J. P. Kropp
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Point Process ◽  
Extreme Events ◽  
Global Scale ◽  
Flood Damage ◽  
Greenhouse Gas Mitigation ◽  
Sea Levels ◽  
Stochastic Framework ◽  
Rising Sea Levels

Abstract. In contrast to recent advances in projecting sea levels, estimations about the economic impact of sea level rise are vague. Nonetheless, they are of great importance for policy making with regard to adaptation and greenhouse-gas mitigation. Since the damage is mainly caused by extreme events, we propose a stochastic framework to estimate the monetary losses from coastal floods in a confined region. For this purpose, we follow a Peak-over-Threshold approach employing a Poisson point process and the Generalised Pareto Distribution. By considering the effect of sea level rise as well as potential adaptation scenarios on the involved parameters, we are able to study the development of the annual damage. An application to the city of Copenhagen shows that a doubling of losses can be expected from a mean sea level increase of only 11 cm. In general, we find that for varying parameters the expected losses can be well approximated by one of three analytical expressions depending on the extreme value parameters. These findings reveal the complex interplay of the involved parameters and allow conclusions of fundamental relevance. For instance, we show that the damage always increases faster than the sea level rise itself. This in turn can be of great importance for the assessment of sea level rise impacts on the global scale. Our results are accompanied by an assessment of uncertainty, which reflects the stochastic nature of extreme events. While the uncertainty of flood damage increases with rising sea levels, we find that the error of our estimations in relation to the expected damage decreases.

Glacial Contributions to 21st Century Sea Level Rise

Eos ◽  
2020 ◽  
Vol 101 ◽  
Author(s):  
Kate Wheeling
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
21St Century ◽  
Mass Change ◽  
Sources Of Uncertainty

Researchers identify the main sources of uncertainty in projections of global glacier mass change, which is expected to add about 8–16 centimeters to sea level, through this century.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection
Sign in / Sign up

Export Citation Format

Share Document