Coastal zones are exposed to a range of coastal hazards including sea-level rise with its related effects. At the same time, they are more densely populated than the hinterland and exhibit higher rates of population growth and urbanisation. As this trend is expected to continue into the future, we investigate how coastal populations will be affected by such impacts at global and regional scales by the years 2030 and 2060. Starting from baseline population estimates for the year 2000, we assess future population change in the low-elevation coastal zone and trends in exposure to 100-year coastal floods based on four different sea-level and socio-economic scenarios. Our method accounts for differential growth of coastal areas against the land-locked hinterland and for trends of urbanisation and expansive urban growth, as currently observed, but does not explicitly consider possible displacement or out-migration due to factors such as sea-level rise. We combine spatially explicit estimates of the baseline population with demographic data in order to derive scenario-driven projections of coastal population development. Our scenarios show that the number of people living in the low-elevation coastal zone, as well as the number of people exposed to flooding from 1-in-100 year storm surge events, is highest in Asia. China, India, Bangladesh, Indonesia and Viet Nam are estimated to have the highest total coastal population exposure in the baseline year and this ranking is expected to remain largely unchanged in the future. However, Africa is expected to experience the highest rates of population growth and urbanisation in the coastal zone, particularly in Egypt and sub-Saharan countries in Western and Eastern Africa. The results highlight countries and regions with a high degree of exposure to coastal flooding and help identifying regions where policies and adaptive planning for building resilient coastal communities are not only desirable but essential. Furthermore, we identify needs for further research and scope for improvement in this kind of scenario-based exposure analysis.

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Future coastal population growth and exposure to sea-level rise and coastal flooding--a global assessment.

Flood losses in coastal cities will rise due to increasing populations and assets. Research now quantifies average losses in the 136 largest coastal cities. Estimated at approximately US$6 billion in 2005, average annual losses could increase to US$52 billion by 2050 on the basis of projected socio-economic change alone. If climate change and subsidence are also considered, current protection will need to be upgraded to avoid unacceptable losses. Flood exposure is increasing in coastal cities1,2 owing to growing populations and assets, the changing climate3, and subsidence4,5,6. Here we provide a quantification of present and future flood losses in the 136 largest coastal cities. Using a new database of urban protection and different assumptions on adaptation, we account for existing and future flood defences. Average global flood losses in 2005 are estimated to be approximately US$6 billion per year, increasing to US$52 billion by 2050 with projected socio-economic change alone. With climate change and subsidence, present protection will need to be upgraded to avoid unacceptable losses of US$1 trillion or more per year. Even if adaptation investments maintain constant flood probability, subsidence and sea-level rise will increase global flood losses to US$60–63 billion per year in 2050. To maintain present flood risk, adaptation will need to reduce flood probabilities below present values. In this case, the magnitude of losses when floods do occur would increase, often by more than 50%, making it critical to also prepare for larger disasters than we experience today. The analysis identifies the cities that seem most vulnerable to these trends, that is, where the largest increase in losses can be expected.

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Future flood losses in major coastal cities

[1] Data analysis has been one of the core activities in scientific research, but limited by the availability of analysis methods in the past, data analysis was often relegated to data processing. To accommodate the variety of data generated by nonlinear and nonstationary processes in nature, the analysis method would have to be adaptive. Hilbert-Huang transform, consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in geophysical research. In this review, we will briefly introduce the method, list some recent developments, demonstrate the usefulness of the method, summarize some applications in various geophysical research areas, and finally, discuss the outstanding open problems. We hope this review will serve as an introduction of the method for those new to the concepts, as well as a summary of the present frontiers of its applications for experienced research scientists.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2007RG000228

A review on Hilbert‐Huang transform: Method and its applications to geophysical studies

Machine learning methods for solar radiation forecasting: A review

Review of photovoltaic power forecasting

This study illustrates a methodology to assess the economic impacts of climate change at a city scale and benefits of adaptation, taking the case of sea level rise and storm surge risk in the city of Copenhagen, capital of Denmark. The approach is a simplified catastrophe risk assessment, to calculate the direct costs of storm surges under scenarios of sea level rise, coupled to an economic input-output (IO) model. The output is a risk assessment of the direct and indirect economic impacts of storm surge under climate change, including, for example, production and job losses and reconstruction duration, and the benefits of investment in upgraded sea defences. The simplified catastrophe risk assessment entails a statistical analysis of storm surge characteristics, geographical-information analysis of population and asset exposure combined with aggregated vulnerability information. For the city of Copenhagen, it is found that in absence of adaptation, sea level rise would significantly increase flood risks. Results call for the introduction of adaptation in long-term urban planning, as one part of a comprehensive strategy to manage the implications of climate change in the city. Mitigation policies can also aid adaptation by limiting the pace of future sea level rise.

/pdf/assessing-climate-change-impacts-sea-level-rise-and-storm-6l7df5reg8.pdf

Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen

Summary. Many long instrumental climate records are available and might provide useful information in climate research.These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather-stations by means of a suitable two-factor model allows us to check the reliability of the series. An adapted penalized log-likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.

Detection and correction of artificial shifts in climate series

A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production

One of the main objectives of statistical climatology is to extract relevant information hidden in complex spatial–temporal climatological datasets. To identify spatial patterns, most well-known statistical techniques are based on the concept of intra- and intercluster variances (like the k-means algorithm or EOFs). As analyzing quantitative extremes like heavy rainfall has become more and more prevalent for climatologists and hydrologists during these last decades, finding spatial patterns with methods based on deviations from the mean (i.e., variances) may not be the most appropriate strategy in this context of studying such extremes. For practitioners, simple and fast clustering tools tailored for extremes have been lacking. A possible avenue to bridging this methodological gap resides in taking advantage of multivariate extreme value theory, a well-developed research field in probability, and to adapt it to the context of spatial clustering. In this paper, a novel algorithm based on this plan ...

Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France

One major concern of climate change is the possible rise of temperature extreme events, in terms of occurrence and intensity. To study this phenomenon, reliable daily series are required, for instance to compute daily-based indices: high-order quantiles, annual extrema, number of days exceeding thresholds, and so on. Because observed series are likely to be affected by changes in the measurement conditions, adapted homogenization procedures are required. Although a very large number of procedures have been proposed for adjustment of observed series at a monthly time scale, few have been proposed for adjustment of daily temperature series. This article proposes a new adjustment method for temperature series at a daily time scale. This method, called spline daily homogenization (SPLIDHOM), relies on an indirect nonlinear regression method. Estimation of the regression functions is performed by cubic smoothing splines. This method is able to adjust the mean of the series as well as high-order quantiles and moments of the series. When using well-correlated series, SPLIDHOM improves the results of two widely used methods, as a result of an optimal selection of the smoothing parameter. Applications to the Toulouse, France, temperature series are shown as a real example.

Olivier Mestre

Papers

Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen

Detection and correction of artificial shifts in climate series

A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production

Clustering of Maxima: Spatial Dependencies among Heavy Rainfall in France

SPLIDHOM: A Method for Homogenization of Daily Temperature Observations