The transformed-stationary approach: A generic and simplified methodology for non-stationary extreme value analysis

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016)

Green balance in urban areas as an indicator for policy support: a multi-level application

Green spaces are increasingly recognised as key elements in enhancing urban resilience as they provide several ecosystem services. Therefore, their implementation and monitoring in cities are crucial to meet sustainability targets. In this paper, we provide a methodology to compute an indicator that assesses changes in vegetation cover within Urban Green Infrastructure (UGI). Such an indicator is adopted as one of the indicators for reporting on the key area “nature and biodiversity” in the Green City Accord (GCA). In the first section, the key steps to derive the indicator are described and a script, which computes the trends in vegetation cover using Google Earth Engine (GEE), is provided. The second section describes the application of the indicator in a multi-scale, policy-orientated perspective. The analysis has been carried out in 696 European Functional Urban Areas (FUAs), considering changes in vegetation cover inside UGI between 1996 and 2018. Results were analysed for the EU and the United Kingdom. The Municipality of Padua (Italy) is used as a case study to illustrate the results at the local level. Over the last 22 years, a slight upward trend characterised the vegetation growth within UGI in European FUAs. Within core cities and densily built-upcommuting zones, the trend was stable; in non-densely built-up areas, an upward trend was recorded. Vegetation cover in UGI has been relatively stable in European cities. However, a negative balance between abrupt changes in greening and browning has been recorded, affecting most parts of European cities (75% of core cities and 77% of commuting zones in densely built-up areas). This still indicates ongoing land take with no compensation of green spaces that are lost to artificial areas. Focusing on the FUA of Padua, a downward trend was observed in 33.3% and 12.9% of UGI in densely built-up and not-densely built-up areas, respectively. Within the FUA of Padua, most municipalities are characterised by a negative balance between abrupt greening and browning, both in non-densely built-up and densely built-up areas. This approach complements traditional metrics, such as the extent of UGI or tree canopy cover, by providing a valuable measure of condition of urban ecosystems and an instrument to monitor the impact of land take

Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change

In low-lying coastal areas, the co-occurrence of high sea level and precipitation resulting in large runoff may cause compound flooding (CF). When the two hazards interact, the resulting impact can be worse than when they occur individually. Both storm surges and heavy precipitation, as well as their interplay, are likely to change in response to global warming. Despite the CF relevance, a comprehensive hazard assessment beyond individual locations is missing, and no studies have examined CF in the future. Analyzing co-occurring high sea level and heavy precipitation in Europe, we show that the Mediterranean coasts are experiencing the highest CF probability in the present. However, future climate projections show emerging high CF probability along parts of the northern European coast. In several European regions, CF should be considered as a potential hazard aggravating the risk caused by mean sea level rise in the future

Urban heat island mitigation by green infrastructure in European Functional Urban Areas

The Urban Heat Island (UHI) effect is one of the most harmful environmental hazards for urban dwellers. Climate change is expected to increase the intensity of the UHI effect. In this context, the implementation of Urban Green Infrastructure (UGI) can partially reduce UHI intensity, promoting a resilient urban environment and contributing to climate change adaptation and mitigation. In order to achieve this result, there is a need to systematically integrate UGI into urban planning and legislation, but this process is subject to the availability of widely applicable, easily accessible and quantitative evidence. To offer a big picture of urban heat intensity and opportunities to mitigate high temperatures, we developed a model that reports the Ecosystem Service (ES) of microclimate regulation of UGI in 601 European cities. The model simulates the temperature difference between a baseline and a no-vegetation scenario, extrapolating the role of UGI in mitigating UHI in different urban contexts. Finally, a practical, quantitative indicator that can be applied by policymakers and city administrations has been elaborated, allowing to estimate the amount of urban vegetation that is needed to cool summer temperatures by a certain degree. UGI is found to cool European cities by 1.07 °C on average, and up to 2.9 °C, but in order to achieve a 1 °C drop in urban temperatures, a tree cover of at least 16% is required. The microclimate regulation ES is mostly dependent on the amount of vegetation inside a city and by transpiration and canopy evaporation. Furthermore, in almost 40% of the countries, more than half of the residing population does not benefit from the microclimate regulation service provided by urban vegetation. Widespread implementation of UGI, in particular in arid regions and cities with insufficient tree cover, is key to ensure healthy urban living conditions for citizens

Extreme sea levels at different global warming levels

The Paris agreement focused global climate mitigation policy on limiting global warming to 1.5 or 2 °C above pre-industrial levels. Consequently, projections of hazards and risk are increasingly framed in terms of global warming levels rather than emission scenarios. Here, we use a multimethod approach to describe changes in extreme sea levels driven by changes in mean sea level associated with a wide range of global warming levels, from 1.5 to 5 °C, and for a large number of locations, providing uniform coverage over most of the world’s coastlines. We estimate that by 2100 ~50% of the 7,000+ locations considered will experience the present-day 100-yr extreme-sea-level event at least once a year, even under 1.5 °C of warming, and often well before the end of the century. The tropics appear more sensitive than the Northern high latitudes, where some locations do not see this frequency change even for the highest global warming levels

Global-scale changes to extreme ocean wave events due to anthropogenic warming

Extreme surface ocean waves are often primary drivers of coastal flooding and erosion over various time scales. Hence, understanding future changes in extreme wave events owing to global warming is of socio-economic and environmental significance. However, our current knowledge of potential changes in high-frequency (defined here as having return periods of less than 1 year) extreme wave events are largely unknown, despite being strongly linked to coastal hazards across time scales relevant to coastal management. Here, we present global climate-modeling evidence, based on the most comprehensive multi-method, multi-model wave ensemble, of projected changes in a core set of extreme wave indices describing high-frequency, extra-tropical storm-driven waves. We find changes in high-frequency extreme wave events of up to ∼50%-100% under RCP8.5 high-emission scenario; which is nearly double the expected changes for RCP4.5 scenario, when globally integrated. The projected changes exhibit strong inter-hemispheric asymmetry, with strong increases in extreme wave activity across the tropics and high latitudes of the Southern Hemisphere region, and a widespread decrease across most of the Northern Hemisphere. We find that the patterns of projected increase across these extreme wave events over the Southern Hemisphere region resemble their historical response to the positive anomaly of the Southern Annular Mode. Our findings highlight that many countries with low-adaptive capacity are likely to face increasing exposure to much more frequent extreme wave events in the future

African heritage sites threatened as sea-level rise accelerates

The African coast contains heritage sites of ‘Outstanding Universal Value’ that face increasing risk from anthropogenic climate change. Here, we generated a database of 213 natural and 71 cultural African heritage sites to assess exposure to coastal flooding and erosion under moderate (RCP 4.5) and high (RCP 8.5) greenhouse gas emission scenarios. Currently, 56 sites (20%) are at risk from a 1-in-100-year coastal extreme event, including the iconic ruins of Tipasa (Algeria) and the North Sinai Archaeological Sites Zone (Egypt). By 2050, the number of exposed sites is projected to more than triple, reaching almost 200 sites under high emissions. Emissions mitigation from RCP 8.5 to RCP 4.5 reduces the number of very highly exposed sites by 25%. These findings highlight the urgent need for increased climate change adaptation for heritage sites in Africa, including governance and management approaches, site-specific vulnerability assessments, exposure monitoring, and protection strategies

More meteorological events that drive compound coastal flooding are projected under climate change

Compound flooding arises from storms causing concurrent extreme precipitation and meteorological tides, that is the superposition of storm surge and waves. This flooding can severely affect densely populated low-lying coastal areas. Here, combining output from climate and ocean models, we analyse the concurrence probability of the meteorological conditions driving compound flooding. We show that, under a high emissions scenario, the concurrence probability would increase globally by more than 25% by 2100 compared to present. In latitudes above 40o north, compound flooding could become more than 2.5 times as frequent, in contrast to parts of the subtropics where it would weaken. Changes in extreme precipitation and meteorological tides account for most (77% and 20%, respectively) of the projected change in concurrence probability. The evolution of the dependence between precipitation and meteorological tide dominates the uncertainty in the projections. Our results indicate that not accounting for these effects in adaptation planning could leave coastal communities insufficiently protected against flooding

Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk

This study provides a literature-based comparative assessment of uncertainties and biases in global to world-regional scale assessments of current and future coastal flood risks, considering mean and extreme sea-level hazards, the propagation of these into the floodplain, people and coastal assets exposed, and their vulnerability. Globally, by far the largest bias is introduced by not considering human adaptation, which can lead to an overestimation of coastal flood risk in 2100 by up to factor 1300. But even when considering adaptation, uncertainties in how coastal societies will adapt to sea-level rise dominate with a factor of up to 27 all other uncertainties. Other large uncertainties that have been quantified globally are associated with socio-economic development (factors 2.3–5.8), digital elevation data (factors 1.2–3.8), ice sheet models (factor 1.6–3.8) and greenhouse gas emissions (factors 1.6–2.1). Local uncertainties that stand out but have not been quantified globally, relate to depth-damage functions, defense failure mechanisms, surge and wave heights in areas affected by tropical cyclones (in particular for large return periods), as well as nearshore interactions between mean sea-levels, storm surges, tides and waves. Advancing the state-of-the-art requires analyzing and reporting more comprehensively on underlying uncertainties, including those in data, methods and adaptation scenarios. Epistemic uncertainties in digital elevation, coastal protection levels and depth-damage functions would be best reduced through open community-based efforts, in which many scholars work together in collecting and validating these data

A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts

There are numerous global ocean wave reanalysis and hindcast products currently being distributed and used across different scientific fields. However, there is not a consistent dataset that can sample across all existing products based on a standardized framework. Here, we present and describe the first coordinated multi-product ensemble of present-day global wave fields available to date. This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, includes general and extreme statistics of significant wave height (Hs), mean wave period (Tm) and mean wave direction (θm) computed across 1980–2014, at different frequency resolutions (monthly, seasonally, and annually). This coordinated global ensemble has been derived from fourteen state-of-the-science global wave products obtained from different atmospheric reanalysis forcing and downscaling methods. This data set has been processed, under a specific framework for consistency and quality, following standard Data Reference Syntax, Directory Structures and Metadata specifications. This new comprehensive dataset provides support to future broad-scale analysis of historical wave climatology and variability as well as coastal risk and vulnerability assessments across offshore and coastal engineering applications