A global streamflow reanalysis for 1980–2018

Global and continental scale hydrological reanalysis datasets receive growing attention due to their increasing number of applications, ranging from water resources management, climate change studies, water related hazards and policy support. Until recently, their use was mostly limited to qualitative assessments, due to their coarse spatial and temporal resolution, large uncertainty and bias in the model output, and limited extent of the dataset in space and time. This research reports on the setup of a gridded hydrological model with quasi-global coverage, able to reproduce a seamless 39-year streamflow simulation in all world’s medium to large river basins. The model was calibrated at 1226 river sections with a total drainage area of 51 million km2 within 66 countries, using ECMWF’s latest atmospheric reanalysis ERA5. A performance assessment revealed large improvements in reproducing past discharge observations, in comparison to the calibration used in the current operational setup of the hydrological model as part of the Copernicus – Global Flood Awareness System (GloFAS, www.globalfloods.eu), with median scores of Kling-Gupta Efficiency KGE = 0.67 and correlation r = 0.8. The simulation bias was also dramatically reduced and narrowed around zero, with more than 60% of stations showing percent bias within ±20%. Pronounced regional differences in the simulation results remain, pointing out the need for detailed investigation of the hydrological processes in specific regions, including parts of Africa and South Asia. In addition, observed discharges with high data quality is key to achieving skillful model output. The new calibrated model will become part of the operational runs of GloFAS in the next system release foreseen for Spring 2020, together with a near real time extension of the streamflow reanalysis

EFAS-Meteo: A European daily high-resolution gridded meteorological data set for 1990 - 2011

Data sets of spatially irregular meteorological observations interpolated to a regular grid are not only important for climate analyses but are also essential in order to derive climatologies for rainfall-runoff models which require meteorological data sets as input forcing. For example, in the European Flood Awareness System long term observed meteorological data are used to drive the hydrological model LISFLOOD to obtain long term time series of simulated discharges at a pan-European scale. Those long term time series of simulated “proxy” discharges can then be used for statistical analysis, e.g., to derive return periods or other time series derivatives. In this report, we present a comprehensive pan European high-resolution gridded daily data set (EFAS-Meteo) of precipitation, surface temperature (mean, minimum and maximum), wind speed, vapour pressure, calculated radiation and evapotranspiration (potential evapotranspiration, bare soil and open water evapotranspiration). The data set was created as part of the development of EFAS and has been continuously updated throughout the last years.JRC.H.7-Climate Risk Managemen

Do demographic and socio-economic factors predict Sense of Coherence among university students?

Introduction. The COVID-19 pandemic and related containment measures have been threatful for psychological well-being, particularly for young people such as university students. Sense of Coherence (SoC) can help in coping with stressful and anxiety-provoking situations. Aim. The aim of this study is to describe the levels of SoC and to investigate the socioeconomic, and demographic predictors in a sample of students attending Florence University, in the timespan between August, 17th and October, 3rd 2020. Method and results. The cross-sectional online survey was completed by 2,996 students. Higher levels of SoC have been found among males and for respondents reporting a better socioeconomic condition. Regarding the dimensions of SoC, lower levels were reported for comprehensibility and manageability, higher for meaningfulness. Conclusions. These results reinforce the need to plan and implement health promotion interventions aimed to support and sustain university students in general and specifically those at higher risk of low level of SoC

Younger age at onset and sex predict celiac disease in children and adolescents with type 1 diabetes: an Italian multicenter study

OBJECTIVE— To estimate the prevalence of biopsy-confirmed celiac disease in Italian children and adolescents with type 1 diabetes and to assess whether age at onset of type 1 diabetes is independently associated with diagnosis of celiac disease. RESEARCH DESIGNANDMETHODS— The study group was a clinic-based cohort of children and adolescents with type 1 diabetes cared for in 25 Italian centers for childhood diabetes. Yearly screening for celiac disease was performed using IgA/IgG anti-gliadin and IgA anti-endomysium antibodies. RESULTS— Of the 4,322 children and adolescents (age 11.8 4.2 years) identified with type 1 diabetes, biopsy-confirmed celiac disease was diagnosed in 292 (prevalence 6.8%, 95% confidence interval [CI] 6.0 –7.6), with a higher risk seen in girls than in boys (odds ratio [OR] 1.93, 1.51–2.47). In 89% of these, diabetes was diagnosed before celiac disease. In logistic regression analyses, being younger at onset of diabetes, being female, and having a diagnosis of a thyroid disorder were independently associated with the risk of having diabetes and celiac disease. In comparison with subjects who were older than 9 years at onset of diabetes, subjects who were younger than 4 years at onset had an OR of 3.27 (2.20–4.85). CONCLUSIONS— We have provided evidence that 1) the prevalence of biopsy-confirmed celiac disease in children and adolescents with type 1 diabetes is high (6.8%); 2) the risk of having both diseases is threefold higher in children diagnosed with type 1 diabetes at age 4 years than in those age 9 years; and 3) girls have a higher risk of having both diseases than boys

EFAS upgrade for the extended model domain

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication.JRC.E.1-Disaster Risk Managemen

EFAS upgrade for the extended model domain

This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the European policymaking process. The scientific output expressed does not imply a policy position of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use that might be made of this publication.JRC.E.1-Disaster Risk Managemen

Social media analysis for situational awareness and impact assessment in flood risk management

The interactions among people on social media are a form of distributed intelligence, as they allow people to make sense of a developing event collectively. Social media users can contribute to creating a "sensor" for citizen-generated data that modeling or monitoring systems can assimilate during a crisis. However, social media platforms may not provide the functionality of summarizing useful information for crisis responders. We developed a platform to streamline the processing of text and images extracted from Twitter in near real-time during floods to solve this problem. Social media analysis can improve situational awareness in the form of a map or a report. When combined with risk analysis and socio-economic data, it could shorten the time needed to fill the time gap between the definition of the risk and the actual impact of a flood. Emergency managers could aggregate annotated data to confirm a forecast or monitor an event’s development. Crisis responders can filter social media messages to distill specific needs when they must act quickly. Finally, we explore a quantitative integration of social media information into geospatial information systems to compute the flood extent in urban areas.Las interacciones entre las personas a través de las redes sociales son una forma de inteligencia distribuida, ya que permiten dar sentido a un evento en desarrollo de manera colectiva. Los usuarios de redes sociales pueden contribuir a crear un "sensor"para que datos generados por los ciudadanos puedan ser asimilados durante una crisis por sistemas de modelado o monitoreo. Sin embargo, las plataformas de redes sociales no ofrecen una funcionalidad para resumir la información útil para la gestión de una crisis. Para resolver este problema, desarrollamos una plataforma para agilizar el procesamiento de texto e imágenes extraídos de Twitter en tiempo casi-real durante inundacione. El análisis de redes sociales puede mejorar la conciencia situacional a través de mapas o informes. Cuando se combina con el análisis de riesgos y datos socioeconómicos, podría acortar el tiempo entre la definición del riesgo y el impacto real de una inundación. Los administradores de emergencias podrían utilizar datos anotados automáticamente para confirmar un pronóstico o monitorear el desarrollo de un evento. Las personas encargadas de la gestión de una crisis pueden filtrar los mensajes de redes sociales para destilar aquellos que atienden necesidades específicas, en particularmente en los casos en que deben actuar rápidamente. Finalmente, exploramos una integración cuantitativa de la información de las redes sociales en sistemas de información geoespacial para calcular la extensión las inundaciones en áreas urbana

GloFAS hydrological reanalysis for 1980-2018

This collection includes output of global hydrological simulations produced in the context of the Copernicus Emergency Management Service - Global Flood Awareness System (CEMS-GloFAS). Output maps have quasi global extent and 0.1 degree (~10 km) grid resolution.JRC.E.1-Disaster Risk Managemen

Younger age at onset and sex predict celiac disease in children and adolescents with type 1 diabetes: an Italian multicenter study

OBJECTIVE— To estimate the prevalence of biopsy-confirmed celiac disease in Italian children and adolescents with type 1 diabetes and to assess whether age at onset of type 1 diabetes is independently associated with diagnosis of celiac disease. RESEARCH DESIGNANDMETHODS— The study group was a clinic-based cohort of children and adolescents with type 1 diabetes cared for in 25 Italian centers for childhood diabetes. Yearly screening for celiac disease was performed using IgA/IgG anti-gliadin and IgA anti-endomysium antibodies. RESULTS— Of the 4,322 children and adolescents (age 11.8 4.2 years) identified with type 1 diabetes, biopsy-confirmed celiac disease was diagnosed in 292 (prevalence 6.8%, 95% confidence interval [CI] 6.0 –7.6), with a higher risk seen in girls than in boys (odds ratio [OR] 1.93, 1.51–2.47). In 89% of these, diabetes was diagnosed before celiac disease. In logistic regression analyses, being younger at onset of diabetes, being female, and having a diagnosis of a thyroid disorder were independently associated with the risk of having diabetes and celiac disease. In comparison with subjects who were older than 9 years at onset of diabetes, subjects who were younger than 4 years at onset had an OR of 3.27 (2.20–4.85). CONCLUSIONS— We have provided evidence that 1) the prevalence of biopsy-confirmed celiac disease in children and adolescents with type 1 diabetes is high (6.8%); 2) the risk of having both diseases is threefold higher in children diagnosed with type 1 diabetes at age 4 years than in those age 9 years; and 3) girls have a higher risk of having both diseases than boys