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)

Resilience of large investments and critical infrastructures in Europe to climate change

This technical report describes the key findings, methodological aspects and underlying assumptions and limitations of the research activities undertaken by the JRC in the CCMFF project financed by DG CLIMA. The project provides the first comprehensive multi-hazard multi-sector risk assessment for Europe under climate change and identifies the most vulnerable and impacted regions in Europe throughout the 21st century. It significantly contributes to a better understanding and awareness of hazard impacts that is crucial for the management of future climate risks.JRC.H.7-Climate Risk Managemen

Small Island Developing States under threat by rising seas even in a 1.5 °C warming world

Small Island Developing States (SIDS) have long been recognized as some of the planet’s most vulnerable areas to climate change, notably to rising sea levels and coastal extremes. They have been crucial in raising ambitions to keep global warming below 1.5 °C and in advancing the difficult debate on loss and damage. Still, quantitative estimates of loss and damage for SIDS under different mitigation targets are lacking. Here we carry out an assessment of future flood risk from slow-onset sea-level rise and episodic sea-level extremes along the coastlines of SIDS worldwide. We show that by the end of this century, without adaptation, climate change would amplify present direct economic damages from coastal flooding by more than 14 times under high-emissions scenarios. Keeping global warming below 1.5 °C could avoid almost half of unmitigated damage, depending on the region. Achieving this climate target, however, would still not prevent several SIDS from suffering economic losses that correspond to considerable shares of their GDP, probably leading to forced migration from low-lying coastal zones. Our results underline that investments in adaptation and sustainable development in SIDS are urgently needed, as well as dedicated support to assisting developing countries in responding to loss and damage due to climate change

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

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk

A Fast pH-Switchable and Self-Healing Supramolecular Hydrogel Carrier for Guided, Local Catheter Injection in the Infarcted Myocardium

Minimally invasive intervention strategies after myocardial infarction use state-of-the-art catheter systems that are able to combine mapping of the infarcted area with precise, local injection of drugs. To this end, catheter delivery of drugs that are not immediately pumped out of the heart is still challenging, and requires a carrier matrix that in the solution state can be injected through a long catheter, and instantaneously gelates at the site of injection. To address this unmet need, a pH-switchable supramolecular hydrogel is developed. The supramolecular hydrogel is switched into a liquid at pH > 8.5, with a viscosity low enough to enable passage through a 1-m long catheter while rapidly forming a hydrogel in contact with tissue. The hydrogel has self-healing properties taking care of adjustment to the injection site. Growth factors are delivered from the hydrogel thereby clearly showing a reduction of infarct scar in a pig myocardial infarction model

U.S. IOOS coastal and ocean modeling testbed : inter-model evaluation of tides, waves, and hurricane surge in the Gulf of Mexico

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Oceans 118 (2013): 5129–5172, doi:10.1002/jgrc.20376.A Gulf of Mexico performance evaluation and comparison of coastal circulation and wave models was executed through harmonic analyses of tidal simulations, hindcasts of Hurricane Ike (2008) and Rita (2005), and a benchmarking study. Three unstructured coastal circulation models (ADCIRC, FVCOM, and SELFE) validated with similar skill on a new common Gulf scale mesh (ULLR) with identical frictional parameterization and forcing for the tidal validation and hurricane hindcasts. Coupled circulation and wave models, SWAN+ADCIRC and WWMII+SELFE, along with FVCOM loosely coupled with SWAN, also validated with similar skill. NOAA's official operational forecast storm surge model (SLOSH) was implemented on local and Gulf scale meshes with the same wind stress and pressure forcing used by the unstructured models for hindcasts of Ike and Rita. SLOSH's local meshes failed to capture regional processes such as Ike's forerunner and the results from the Gulf scale mesh further suggest shortcomings may be due to a combination of poor mesh resolution, missing internal physics such as tides and nonlinear advection, and SLOSH's internal frictional parameterization. In addition, these models were benchmarked to assess and compare execution speed and scalability for a prototypical operational simulation. It was apparent that a higher number of computational cores are needed for the unstructured models to meet similar operational implementation requirements to SLOSH, and that some of them could benefit from improved parallelization and faster execution speed.This project was supported by NOAA via the U.S. IOOS Office (award: NA10NOS0120063 and NA11NOS0120141

Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil

[EN] Stochastic upscaling of flow and reactive solute transport in a tropical soil is performed using real data collected in the laboratory. Upscaling of hydraulic conductivity, longitudinal hydrodynamic dispersion, and retardation factor were done using three different approaches of varying complexity. How uncertainty propagates after upscaling was also studied. The results show that upscaling must be taken into account if a good reproduction of the flow and transport behavior of a given soil is to be attained when modeled at larger than laboratory scales. The results also show that arrival time uncertainty was well reproduced after solute transport upscaling. This work represents a first demonstration of flow and reactive transport upscaling in a soil based on laboratory data. It also shows how simple upscaling methods can be incorporated into daily modeling practice using commercial flow and transport codes.The authors thank the financial support by the Brazilian National Council for Scientific and Technological Development (CNPq) (Project 401441/2014-8). The doctoral fellowship award to the first author by the Coordination of Improvement of Higher Level Personnel (CAPES) is acknowledged. The first author also thanks the international mobility grant awarded by CNPq, through the Sciences Without Borders program (Grant Number: 200597/2015-9). The international mobility grant awarded by Santander Mobility in cooperation with the University of Sao Paulo is also acknowledged. DHI-WASI is gratefully thanked for providing a FEFLOW license.Almeida De-Godoy, V.; Zuquette, L.; Gómez-Hernández, JJ. (2019). Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil. 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Implementation of a program for type 2 diabetes based on the Chronic Care Model in a hospital-centered health care system: "the Belgian experience"

Background: Most research publications on Chronic Care Model (CCM) implementation originate from organizations or countries with a well-structured primary health care system. Information about efforts made in countries with a less well-organized primary health care system is scarce. In 2003, the Belgian National Institute for Health and Disability Insurance commissioned a pilot study to explore how care for type 2 diabetes patients could be organized in a more efficient way in the Belgian healthcare setting, a setting where the organisational framework for chronic care is mainly hospital-centered. Methods: Process evaluation of an action research project (2003-2007) guided by the CCM in a well-defined geographical area with 76,826 inhabitants and an estimated number of 2,300 type 2 diabetes patients. In consultation with the region a program for type 2 diabetes patients was developed. The degree of implementation of the CCM in the region was assessed using the Assessment of Chronic Illness Care survey (ACIC). A multimethod approach was used to evaluate the implementation process. The resulting data were triangulated in order to identify the main facilitators and barriers encountered during the implementation process. Results: The overall ACIC score improved from 1.45 (limited support) at the start of the study to 5.5 (basic support) at the end of the study. The establishment of a local steering group and the appointment of a program manager were crucial steps in strengthening primary care. The willingness of a group of well-trained and motivated care providers to invest in quality improvement was an important facilitator. Important barriers were the complexity of the intervention, the lack of quality data, inadequate information technology support, the lack of commitment procedures and the uncertainty about sustainable funding. Conclusion: Guided by the CCM, this study highlights the opportunities and the bottlenecks for adapting chronic care delivery in a primary care system with limited structure. The study succeeded in achieving a considerable improvement of the overall support for diabetes patients but further improvement requires a shift towards system thinking among policy makers. Currently primary care providers lack the opportunities to take up full responsibility for chronic care