Using historical source data to understand urban flood risk: a socio-hydrological modelling application at Gregorio Creek, Brazil

This is an Accepted Manuscript of an article published by Taylor & Francis in Hydrological Sciences Journal on [date of publication], available online: https://doi.org/10.1080/02626667.2020.1740705.The city of São Carlos, state of São Paulo, Brazil, has a historical coexistence between society and floods. Unplanned urbanization in this area is a representative feature of how Brazilian cities have developed, undermining the impact of natural hazards. The Gregório Creek catchment is an enigma of complex dynamics concerning the relationship between humans and water in Brazilian cities. Our hypothesis is that social memory of floods can improve future resilience. In this paper we analyse flood risk dynamics in a small urban catchment, identify the impacts of social memory on building resilience and propose measures to reduce the risk of floods. We applied a socio-hydrological model using data collected from newspapers from 1940 to 2018. The model was able to elucidate human–water processes in the catchment and the historical source data proved to be a useful tool to fill gaps in the data in small urban basins

Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. Thus, there is still a gap in research with regard to the use of social media as a proxy for rainfall-runoff estimations and flood forecasting. To address this, we propose using a transformation function that creates a proxy variable for rainfall by analysing geo-social media messages and rainfall measurements from authoritative sources, which are later incorporated within a hydrological model for streamflow estimation. We found that the combined use of official rainfall values with the social media proxy variable as input for the Probability Distributed Model (PDM), improved streamflow simulations for flood monitoring. The combination of authoritative sources and transformed geo-social media data during flood events achieved a 71% degree of accuracy and a 29% underestimation rate in a comparison made with real streamflow measurements. This is a significant improvement on the respective values of 39% and 58%, achieved when only authoritative data were used for the modelling. This result is clear evidence of the potential use of derived geo-social media data as a proxy for environmental variables for improving flood early-warning systems

AGORA-GeoDash: a geosensor dashboard for real-time flood risk monitoring

Flood management is an important approach to reduce damage caused by floods. In this context, technological \ud architectures which work in real-time are needed. However, Brazil has faced many structural difficulties in \ud obtaining updated information on the current state of its rivers. To address this problem, this paper outlines a \ud geosensor dashboard called AGORA-GeoDash, which processes data streams from wireless sensor networks \ud and makes them available in the form of a set of performance indicators that are essential to support real-time \ud decision-making in flood risk monitoring. The dashboard was built on open-source frameworks, made use of \ud geoservices that comply with the standards of Open Geospatial Consortium, and established a Wireless Sensor \ud Network which monitors the rivers of São Carlos/SP in Brazil. The analysis of the indicators available in two \ud rainfall events revealed that the dashboard can provide the key information required for the decision-making \ud process involved in flood risk managementFAPESP processos n. 2008/58161-1, 2011/23274-3, 2012/18675-1, 2012/22550-0CNPq processo n. 307637/2012-3FINEP (MAPLU) 01.10.0701.0

The role of immune suppression in COVID-19 hospitalization: clinical and epidemiological trends over three years of SARS-CoV-2 epidemic

Specific immune suppression types have been associated with a greater risk of severe COVID-19 disease and death. We analyzed data from patients >17 years that were hospitalized for COVID-19 at the “Fondazione IRCCS Ca′ Granda Ospedale Maggiore Policlinico” in Milan (Lombardy, Northern Italy). The study included 1727 SARS-CoV-2-positive patients (1,131 males, median age of 65 years) hospitalized between February 2020 and November 2022. Of these, 321 (18.6%, CI: 16.8–20.4%) had at least one condition defining immune suppression. Immune suppressed subjects were more likely to have other co-morbidities (80.4% vs. 69.8%, p < 0.001) and be vaccinated (37% vs. 12.7%, p < 0.001). We evaluated the contribution of immune suppression to hospitalization during the various stages of the epidemic and investigated whether immune suppression contributed to severe outcomes and death, also considering the vaccination status of the patients. The proportion of immune suppressed patients among all hospitalizations (initially stable at <20%) started to increase around December 2021, and remained high (30–50%). This change coincided with an increase in the proportions of older patients and patients with co-morbidities and with a decrease in the proportion of patients with severe outcomes. Vaccinated patients showed a lower proportion of severe outcomes; among non-vaccinated patients, severe outcomes were more common in immune suppressed individuals. Immune suppression was a significant predictor of severe outcomes, after adjusting for age, sex, co-morbidities, period of hospitalization, and vaccination status (OR: 1.64; 95% CI: 1.23–2.19), while vaccination was a protective factor (OR: 0.31; 95% IC: 0.20–0.47). However, after November 2021, differences in disease outcomes between vaccinated and non-vaccinated groups (for both immune suppressed and immune competent subjects) disappeared. Since December 2021, the spread of the less virulent Omicron variant and an overall higher level of induced and/or natural immunity likely contributed to the observed shift in hospitalized patient characteristics. Nonetheless, vaccination against SARS-CoV-2, likely in combination with naturally acquired immunity, effectively reduced severe outcomes in both immune competent (73.9% vs. 48.2%, p < 0.001) and immune suppressed (66.4% vs. 35.2%, p < 0.001) patients, confirming previous observations about the value of the vaccine in preventing serious disease

Assimilação de dados na previsão de enchentes em tempo real em áreas urbanas com dados escassos

Flood forecasting techniques have been widely studied as a tool to mitigate damage from extreme events. However, their nature in urban areas developed without properly drainage planning, coupled with the scarcity of hydrological monitoring data, becomes a significant challenge for real-time flood forecasting. This doctoral thesis proposes deterministic methods of data assimilation for real-time hydrological forecasting. The methodology is developed using the semi-distributed hydrodynamic Storm Water Management Model (SWMM). It also aims to evaluate the impact of using traditional monitoring data together with citizen science data for model updating. The first and the second chapter present the general introduction and methodology of the thesis. The third chapter presents an automatic calibration tool - SWMM calibrator - developed to allow the adjustment of SWMM model parameters with data from multiple sources and to use observed level data as a priori knowledge. The fourth chapter deals with the use of citizen science data for urban model updating through a real-time estimator. The fifth chapter presents a data assimilation method by updating hydrological model inputs based on water level observations and evaluates the effectiveness of the technique in a distributed manner in the catchment. The proposed methodologies are validated in a case study at the Monjolinho urban catchment. The sixth chapter discusses general conclusions and recommendations. In conclusion, SWMM calibrator tool provides flexibility in calibration, allowing shaping the process according to the real-world limitations, and achieved satisfactory calibration results at Monjolinho catchment. The deterministic data assimilation methods proposed in the fourth and fifth chapters have shown effective results in a significant improvement in simulations accuracy.A previsão de enchentes para a mitigação dos danos causados por eventos extremos vem sendo amplamente estudada. No entanto, sua natureza em áreas urbanas desenvolvidas sem planejamento adequado de drenagem, associada a escassez de dados de monitoramento hidrológico apresentam um grande desafio para previsão de enchentes em tempo real. Esta tese de doutorado propõe novos métodos determinísticos de assimilação de dados em tempo real na previsão hidrológica, através do modelo hidrodinâmico semi-distribuído Storm Water Management Model (SWMM). Estes métodos visam contornar as limitações na previsão de enchentes em tempo real, em curto prazo e em bacias urbanas com dados escassos. Foi também avaliado o impacto do uso de dados de monitoramento tradicionais aliados a dados de ciência cidadã na atualização das simulações do modelo. O primeiro capítulo e o segundo capítulo trazem a introdução e metodologia gerais da tese. O terceiro capítulo apresenta uma ferramenta de calibração automática – SWMM calibrator – desenvolvida para permitir o ajuste de parâmetros do modelo SWMM com dados provenientes de múltiplos locais de monitoramento e utilizando dados observados de nível como conhecimento a priori. O quarto capítulo aborda a utilização de dados de ciência cidadã na atualização do modelo através de um estimador em tempo real. O quinto capítulo apresenta um método de assimilação de dados através da correção das entradas do modelo hidrológico baseado em observações de nível, e avalia a eficácia do método de forma distribuída na bacia. As metodologias propostas foram aplicadas para um estudo de caso na bacia urbana do Monjolinho. O sexto capítulo apresenta as conclusões e recomendações gerais. Em conclusão, a ferramenta SWMM calibrator disponibiliza flexibilidade na calibração, permitindo moldar o processo de acordo com as limitações de problemas reais, e alcançou resultados satisfatórios na calibração da bacia do Monjolinho. Os métodos determinísticos de assimilação de dados propostos no quarto e quinto capítulo mostraram resultados eficazes na redução do erro das simulações de nível do modelo, bem como mostraram resultados satisfatórios ao assimilar dados com uma distribuição temporal maior que o passo de tempo do modelo

Participative-based early warning model using volunteer geographic information systems for flood forecasting

Este trabalho apresenta uma nova proposta metodológica de previsão de enchentes: o Modelo de Alerta Hidrológico com Base Participativa (MAHP). O MAHP consiste em um modelo de previsão de enchentes em bacias urbanas que integra Informações Geográficas Voluntárias (VGI) e redes de sensores sem fio (WSN). A principal contribuição deste modelo é o uso de dados de fontes heterogêneas (sensores aliados a dados fornecidos por voluntários) com o objetivo de reduzir a incerteza na previsão de enchentes. O modelo MAHP foi dividido em módulos, cada um deles é responsável por uma atividade no processo de previsão de enchentes. Embora o modelo possua diversos módulos auxiliares, pode-se resumir o modelo MAHP em três módulos principais: aquisição de dados; previsão de precipitações e, por fim, o módulo responsável pela previsão das enchentes. Para o módulo de aquisição de dados foram desenvolvidas metodologias para uso de dados voluntários de nível da água e sensores medidores de nível foram instalados para a composição da rede de sensores sem fio em pontos estratégicos nos canais fluviais da cidade. No módulo de previsão da precipitação do modelo MAHP foram desenvolvidos dois softwares de previsão, sendo um modelo de previsão da precipitação conceitual e um empírico. Para o funcionamento do módulo responsável pela previsão das enchentes foi feita a modelagem da bacia urbana de São Carlos no modelo SWMM (Storm Water Management Model). As simulações chuva-vazão realizadas com a bacia modelada apresentaram ajustes satisfatórios quando comparadas com eventos de enchente reais. Como o uso de informações voluntárias na previsão de enchentes é um conceito bastante novo, outra importante contribuição do trabalho foi propor parâmetros espaço-temporais que influenciam na qualidade da previsão ocasionada pelo uso de dados VGI. Existem vários cenários e combinações de uso de informações voluntárias que podem influenciar na previsão de enchentes. Neste trabalho foi considerada apenas uma destas combinações. Devido à ausência de dados VGI reais em eventos de enchente recentes foram utilizados dados de nível medidos por sensores para simular dados voluntários. Foram levantadas diversas hipóteses para que a inserção de dados voluntários no modelo MAHP tenham maior influência na redução da incerteza na previsão de enchentes.This work presents a new approach forflood forecasting: Hydrological Alert Model with Participatory Base (HAMPB). The HAMPB consists of a flood forecasting model applied to urban basins integrating Volunteered Geographic Information (VGI) and Wireless Sensor Networks (WSN). The main contribution of this model is the use of heterogeneous data sources (convencional sensors and volunteered data) aiming to reduce the uncertainty in the flood forecasting. The HAMPB model was divided in modules, which are responsible for the forecasting process activities. Although the model has multiple auxiliary modules, we can summarize the HAMPB model in three modules: data acquisition; rainfall forecasting and, finally, the module responsible for flood forecasting. Telemetric water level sensors were installed at strategic points in river channels of the city to create the WSN. In order to use the volunteered information, a methodology was proposed tdevelop the acquisition module. The rainfall forecasting module consists of two forecasting models: an empirical model and a conceptual model. The conceptual prediction model presented closest predictions of observed rainfall compared to the forecast of the empirical model. In order to apply the flood forecasting methodology, we modelled the urban basin of São Carlos using SWMM model. The rainfall-runoff simulations performed with the basin model showed satisfactory adjustments compared with actual flood events. Since the use of voluntary information on flood forecasting is a fairly new concept, another important contribution of this work was the proposition of spatiotemporal parameters that influence on the forecast caused by the use of VGI data. There are many scenarios and combinations which using volunteered information can be helpful in the flood forecasting. In this work we consider only one combination. Due to absence of real volunteered data, we use sensor data to simulate VGI data. Several hypotheses have been raised to the inclusion of volunteers in HAMPB data model to produce more relevant results than using traditional methods of forecasting

Linking Urban Floods to Citizen Science and Low Impact Development in Poorly Gauged Basins under Climate Changes for Dynamic Resilience Evaluation

Cities must develop actions that reduce flood risk in the face of extreme rainfall events. In this study, the dynamic resilience of the Gregorio catchment (São Carlos, Brazil) was assessed. The catchment lacks environmental monitoring and suffers from recurrent floods. The resilience curves were made considering the water depth in the drainage system as the performance index, obtained by simulations with SWMM and HEC-RAS. The calibration of the flood extension was performed using citizen science data. The contribution to increasing the dynamic resilience by implementing decentralized low impact development (LID) practices was also evaluated. For this purpose, bioretention cells were added to the SWMM simulations. The resilience curves were then calculated for the current and future climate scenario, with and without LID, for return periods of 5, 10, 50, and 100 years and duration of 30, 60, and 120 min. Intensity–duration–frequency curves (IDFs) updated by the regional climate model MIROC5 for 2050 and 2100 were used. The results showed a significant improvement in the system’s resilience for light storms and the current period due to LID practice interventions. Efficiencies were reduced for moderate and heavy storms with no significant drops in floodwater depth and resilience regardless of the scenario

Inflammation and platelet activation in peripheral arterial occlusive disease

Objectives: Epidemiological evidence indicates that inflammation accompanies the progression of atherosclerosis. Aim of the cross-sectional study was to define relations between platelet activation and inflammation in patients with mild to severe (stages II\u2013IV) peripheral arterial obliterative disease (PAOD) and matched controls. The effect of chronic administration of low dose aspirin was investigated.Design and Methods: Subjects were studied in a single occasion. C-reactive protein (CRP) and two indices of in vivo platelet activation were measured: the urinary excretion of 11-dehydro-TXB2 by immunoassay and circulating platelet-monocyte aggregates (PMA) by flow cytometry. Results: Plasma PMA and urinary 11-dehydro-TXB2 were significantly increased in PAOD patients compared to controls (p<0.01 for all). A positive correlation between 11-dehydro-TXB2, and CRP was found in the study population (rs=0.63, p<0.001). Using logistic regression analysis, CRP was the only independent correlate of 11-dehydro-TXB2 (\u3b2CRP = 11.9; p<0.01), whereas only the presence of PAOD was an independent predictor of high PMA levels (\u3b2PAOD = 13.7; p=0.001). Chronic administration of aspirin reduced 11-dehydro-TXB2 but not PMA and CRP.Conclusions: There is evidence that platelet activation in patients with PAOD is related to the vascular disease and dependent on the severity of inflammation

Phenotypical characterization of circulating cell subsets in pyoderma gangrenosum patients: The experience of the Italian immuno-pathology group

Background No data are available as to the phenotype of circulating lymphocyte subsets in pyoderma gangrenosum (PG). Aim To analyse the expression of different chemokine receptors associated to T-helper (Th)1 (CCR5), Th2 (CCR4) and Th17 (CCR6), as well as the regulatory T-cell subset (Treg) and dendritic cell polarization in the blood of newly diagnosed untreated PG patients. Materials and methods Multi-parameter flow cytometry was performed on blood samples from 10 PG patients collected at first diagnosis among centres belonging to the Italian Immuno-pathology Group. Blood samples from 10 age- and sex-matched healthy controls (HC) were used as controls. Results PG patients are characterized by an over-expression in the blood of the CD4+CCR5+ and CD4+CCR6+ and a down-regulation of CD4+CCR4+ counts with respect to healthy subjects. Moreover, they show increased levels of myeloid derived dendritic cells type1 and reduced levels of the Treg CD4+CD25highFOXP3+ subset. Conclusions The pattern of chemokine expression argues in favour of a Th1 (CCR5+) and Th17 (CCR6+) polarization with a down-regulation of Th2 (CCR4+)