Classification of soft cliff dynamics using remote sensing and data mining techniques.

Coastal soft cliffs are subject to changes related to both marine and subaerial processes. It is imperative to comprehend the processes governing cliff erosion and develop predictive models for effective coastal protection. The primary objective of this study was to bridge the existing knowledge gap by elucidating the intricate relationship between changes in cliff system morphology and the driving forces behind these changes, all within the context of ongoing climate change. Therefore in this study, we employed various quantitative numerical methods to investigate the factors influencing coastal cliffs and the adjacent beaches. Our analysis involved the extraction of several morphological indicators, derived from terrestrial laser scanning data, which were then used to assess how cliffs respond to extreme weather events. The data span two winter storm seasons (2016-2018) and encompass three soft-cliff systems situated along the southern Baltic Sea, each characterized by distinct beach and cliff morphology. We conducted a detailed analysis of short-term cliff responses using various data mining techniques, revealing intricate mechanisms that govern beach and cliff changes. This comprehensive analysis has enabled the development of a classification system for soft cliff dynamics. Our statistical analysis highlights that each study area exhibits a unique conditional dependency between erosion processes and hydrometeorological conditions, both during and between storm events. Furthermore, our findings underscore the vulnerability of cliff coastlines to extreme water levels and episodes of intense precipitation.

Population, land use and economic exposure estimates for Europe at 100 m resolution from 1870 to 2020.

Understanding the influence of climate change on past extreme weather impacts is a vital research task. However, the effects of climate change are obscured in the observed impact data series due to the rapid evolution of the social and economic circumstances in which the events occurred. The HANZE v2.0 (Historical Analysis of Natural HaZards in Europe) dataset presented in this study quantifies the evolution of key socioeconomic drivers in Europe since 1870, namely land use, population, economic activity and assets. It consists of algorithms to reallocate baseline (2011) land use and population for any given year based on a large collection of historical subnational- and national-level statistics, and then disaggregate data on production and tangible assets by economic sector into a high-resolution grid. Raster datasets generated by the model enable reconstructing exposure within the footprint of any extreme event both at the time of occurrence and anytime between 1870 and 2020. This allows the separation of the effects of climate change from the effects of exposure change.

Future losses of ecosystem services due to coastal erosion in Europe.

Coastal erosion is a major issue facing Europe that will only worsen under future climate change and the resulting sea level rise. One effect of erosion is the loss of ecosystem services, which are provided by coastal areas, such as provisioning, regulating, habitat, and cultural services. These services can be quantified in monetary terms. Here, we present comprehensive estimates of future decline in coastal ecosystem services due to the erosion of sandy coastlines. We used datasets derived from remote sensing products: a pan-European land cover/use dataset (Corine Land Cover) and new global probabilistic coastal erosion projections constrained by artificial and topographical barriers to erosion. The results include historical changes (2000-2018) and projections under two emission scenarios (RCP4.5 and RCP8.5) for 2050 and 2100 together with uncertainty bounds. We estimate that in 2018, the coastal zone (excluding open sea) included 579,700 km2 of habitats generating 494 billion euros of services annually. The future sea-level rise could erode 1.0% [90% confidence interval 0.7-1.5%] of the 2018 area under RCP4.5, and 1.2% [0.7-2.2%] under RCP8.5. The decline in services would be even greater: 4.2% [3.0-6.1%] under RCP4.5, and 5.1% [3.3-8.5%] under RCP8.5. The highest absolute losses would be sustained by salt marshes, while relative losses would be highest in beaches, sands, and dunes. The most affected countries in relative economic terms would be Denmark, Albania, Greece, Estonia, and Finland, but countries such as Germany, the Netherlands, and France would be among those losing the largest share of their coastal ecosystem services. Regional analysis using NUTS 3 regions shows high diversity of the impacts, with many regions along the North Sea and eastern Mediterranean Sea that are heavily affected by coastal erosion-induced loss of ecosystem services. The study highlights the urgency of undertaking mitigation actions.

Convergence Between Developed and Developing Countries: A Centennial Perspective.

Are countries at a low level of socio-economic development catching up with developed countries over time or rather falling further behind? Existing work on the subject is not conclusive, partially due to methodological differences. The aim of the paper is to carry out a broader analysis with longer time series and a more diverse set of indicators. The study divides countries of the world into 21 developed "benchmark" countries and 156 developing countries. The distance between the benchmark and developing countries is measured using the "time lags" method, applied here to nine indicators covering topics such as the economy, health, education and the environment. The study further utilizes a probabilistic approach to extrapolate missing historical data for developing countries, so that the analysis can cover a full century starting in 1920 and ending with short-term projections to year 2020. The study finds that a majority of developing countries, and the population-weighted developing world as a whole, has reduced its lag in most indicators between 1920 and 2020. Progress was unevenly distributed, with East Asian and European countries converging the most with the benchmark, while most African countries have diverged along with some American ones. Catch-up in education attainment and life expectancy has been more successful than in infant survival rate, GDP per capita or technology adoption. The findings are put in context of United Nations' Sustainable Development Goals, showing how the time lag method could improve setting targets for some of the goals. Further, time lags are used to analyze the current demographic, economic and political situation of developing countries, identifying opportunities and risks for future catch-up with developed countries.

Exposure and vulnerability estimation for modelling flood losses to commercial assets in Europe.

Commercial assets comprise buildings, machinery and equipment, which are susceptible to floods. Existing damage models and exposure estimation methods for this sector have limited transferability between flood events and therefore limited potential for pan-European applications. In this study we introduce two methodologies aiming at improving commercial flood damage modelling: (1) disaggregation of economic statistics to obtain detailed building-level estimates of replacement costs of commercial assets; (2) a Bayesian Network (BN) damage model based primarily on post-disaster company surveys carried out in Germany. The BN model is probabilistic and provides probability distributions of estimated losses, and as such quantitative uncertainty information. The BN shows good accuracy of predictions of building losses, though overestimates machinery/equipment loss. To test its suitability for pan-European flood modelling, the BN was applied to three case studies, comprising a coastal flood in France (2010) and fluvial floods in Saxony (2013) and Italy (2014). Overall difference between modelled and reported average loss per company was only 2-19% depending on the case study. Additionally, the BN model achieved better results than six alternative damage models in those case studies (except for one model in the Italian case study). Further, our exposure estimates mostly resulted in better predictions of the damage models compared to previously published pan-European exposure data, which tend to overestimate exposure. All in all, the methods allow easy modelling of commercial flood losses in the whole of Europe, since they are applicable even if only publicly-available datasets are obtainable. The methods achieve a higher accuracy than alternative approaches, and inherently provide confidence intervals, which is particularly valuable for decision making under high uncertainty.

Trends in flood losses in Europe over the past 150 years.

Adverse consequences of floods change in time and are influenced by both natural and socio-economic trends and interactions. In Europe, previous studies of historical flood losses corrected for demographic and economic growth ('normalized') have been limited in temporal and spatial extent, leading to an incomplete representation of trends in losses over time. Here we utilize a gridded reconstruction of flood exposure in 37 European countries and a new database of damaging floods since 1870. Our results indicate that, after correcting for changes in flood exposure, there has been an increase in annually inundated area and number of persons affected since 1870, contrasted by a substantial decrease in flood fatalities. For more recent decades we also found a considerable decline in financial losses per year. We estimate, however, that there is large underreporting of smaller floods beyond most recent years, and show that underreporting has a substantial impact on observed trends.

Measuring Central and Eastern Europe's Socio-Economic Development Using Time Lags.

This paper applies the 'time lag' method to a set of social and economic indicators, examining the development of Central and Eastern Europe since the first world war. Originally used to assess technology diffusion, this method allows comparison of levels of development between states and through a long period of time. It presents how many years have elapsed between achieving a certain level of development between countries. The results show that the countries of Central and Eastern Europe have only narrowly converged with a set of 23 highly-developed 'benchmark' states. Development in monetary terms (gross domestic product per capita) is the indicator where this region lags most. Employment structure, life expectancy or infant mortality show much smaller lags. Communist states were closest to the West in the 1960s-early 1970s and struggled thereafter. They are still mostly lagging more today than at their peak before transformation despite the progress achieved in absolute terms after the fall of centrally-planned economy.

Application of empirical wave run-up formulas to the Polish Baltic Sea coast.

Advanced, multidimensional models are typically applied when researching processes occurring in the nearshore. Relatively simple, empirical equations are commonly used in coastal engineering practice in order to estimate extreme wave run-up on beaches and coastal structures. However, they were mostly calibrated to the characteristics of oceanic coasts, which have different wave regime than a semi-enclosed basin like the Baltic Sea. In this paper we apply the formulas to the Polish Baltic Sea coast. The equations were adjusted to match local conditions in two test sites in Miedzyzdroje and Dziwnówek, where beaches are under continuous video surveillance. Data from WAM wave model and coastal gauge stations were used, as well as precise measurements of the beaches' cross-sections. More than 600 run-up events spanning from June to December 2013 were analysed, including surges causing dune erosion. Extreme wave run-up R2% was calculated and presented as a percentage value indicating what part of the beach was inundated. The method had a root-mean-square error of 6.1 and 6.5 percentage points depending on the test site. We consider it is a fast and computationally undemanding alternative to morphodynamic models. It will constitute a part of the SatBaltyk Operating System-Shores, delivering forecasts of wave run-up on the beaches for the entire Polish coastline.