Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice

Pluvial flood is a natural hazard occurring from extreme rainfall events that affect millions of people around the world, causing damages to their properties and lives. The magnitude of projected climate risks indicates the urgency of putting in place actions to increase climate resilience. Through this study, we develop a Machine Learning (ML) model to predict pluvial flood risk under Representative Concentration Pathways (RCP) 4.5 and 8.5 for future scenarios of precipitation for the period 2021-2050, considering different triggering factors and precipitation patterns. The analysis is focused on the case study area of the Metropolitan City of Venice (MCV) and considers 212 historical pluvial flood events occurred in the timeframe 1995-2020. The methodology developed implements spatiotemporal constraints in the ML model to improve pluvial flood risk prediction under future scenarios of climate change. Accordingly, a cross-validation approach was applied to frame a model able to predict pluvial flood at any time and space. This was complemented with historical pluvial flood data and the selection of nine triggering factors representative of territorial features that contribute to pluvial flood events. Logistic Regression was the most reliable model, with the highest AUC score, providing robust result both in the validation and test set. Maximum cumulative rainfall of 14 days was the most important feature contributing to pluvial flood occurrence. The final output is represented by a suite of risk maps of the flood-prone areas in the MCV for each quarter of the year for the period 1995-2020 based on historical data, and risk maps for each quarter of the period 2021-2050 under RCP4.5 and 8.5 of future precipitation scenarios. Overall, the results underline a consistent increase in extreme events (i.e., very high and extremely high risk of pluvial flooding) under the more catastrophic scenario RCP8.5 for future decades compared to the baseline

Involve to improve: A participatory approach for a Decision Support System for coastal climate change impacts assessment. The North Adriatic case

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Stochastic system dynamics modelling for climate change water scarcity assessment of a reservoir in the Italian Alps

Water management in mountain regions is facing multiple pressures due to climate change and anthropogenic activities. This is particularly relevant for mountain areas where water abundance in the past allowed for many anthropogenic activities, exposing them to future water scarcity. Here stochastic system dynamics modelling (SDM) was implemented to explore water scarcity conditions affecting the stored water and turbined outflows in the Santa Giustina (S. Giustina) reservoir (Autonomous Province of Trento, Italy). The analysis relies on a model chain integrating outputs from climate change simulations into a hydrological model, the output of which was used to test and select statistical models in an SDM for replicating turbined water and stored volume within the S. Giustina dam reservoir. The study aims at simulating future conditions of the S. Giustina reservoir in terms of outflow and volume as well as implementing a set of metrics to analyse volume extreme conditions.Average results on 30-year slices of simulations show that even under the short-term RCP4.5 scenario (2021-2050) future reductions for stored volume and turbined outflow are expected to be severe compared to the 14-year baseline (1999-2004 and 2009-2016; -24.9 % of turbined outflow and -19.9 % of stored volume). Similar reductions are expected also for the long-term RCP8.5 scenario (2041-2070; -26.2 % of turbined outflow and -20.8 % of stored volume), mainly driven by the projected precipitations having a similar but lower trend especially in the last part of the 2041-2070 period. At a monthly level, stored volume and turbined outflow are expected to increase for December to March (outflow only), January to April (volume only) depending on scenarios and up to +32.5 % of stored volume in March for RCP8.5 for 2021-2050. Reductions are persistently occurring for the rest of the year from April to November for turbined outflows (down to -56.3 % in August) and from May to December for stored volume (down to -44.1 % in June). Metrics of frequency, duration and severity of future stored volume values suggest a general increase in terms of low volume below the 10th and 20th percentiles and a decrease of high-volume conditions above the 80th and 90th percentiles. These results point at higher percentage increases in frequency and severity for values below the 10th percentile, while volume values below the 20th percentile are expected to last longer. Above the 90th percentile, values are expected to be less frequent than baseline conditions, while showing smaller severity reductions compared to values above the 80th percentile. These results call for the adoption of adaptation strategies focusing on water demand reductions. Months of expected increases in water availability should be considered periods for water accumulation while preparing for potential persistent reductions of stored water and turbined outflows. This study provides results and methodological insights that can be used for future SDM upscaling to integrate different strategic mountain socio-economic sectors (e.g. hydropower, agriculture and tourism) and prepare for potential multi-risk conditions

Changing Values Among Argentine and American Nurses

This article is based upon the data of questioning carried out in 1964 in an American and an Argentine hospital. The authors assumed that Argentine and American nurses could be compared in terms of changes which have already been heralded by the American case. In order to measure the hypothesized difference in values, they used three pattern — variables each operating in one hypothesis, i. e. affectivity — affective neutrality, diffuseness — specificity, and particularism — universalism. However, the results showed that the hypothesis No. 1 and No. 3 had to be rejected, i. e. Argentine nurses tended to share more neutral affectivity and universal criteria than their American counterparts. Neither has hypothesis No. 2 been fully proved. After a full interpretation of the material the authors conclude that modernization conceptualized in terms of Parsons pattern — variables does not occur directly on behavioral level, but through an intermediate stage, normatively and ideally conceived

Inventory of GIS-Based Decision Support Systems Addressing Climate Change Impacts on Coastal Waters and Related Inland Watersheds

A Decision Support System (DSS) is a computer-based software that can assist decision makers in their decision process, supporting rather than replacing their judgment and, at length, improving effectiveness over efficiency. Environmental DSS are models based tools that cope with environmental issues and support decision makers in the sustainable management of natural resources and in the definition of possible adaptation and mitigation measures [2]. DSS have been developed and used to address complex decision-based problems in varying fields of research. For instance, in environmental resource management, DSS are generally classified into two main categories: Spatial Decision Support Systems (SDSS) and Environmental Decision Supports Systems (EDSS) [3-5]. SDSS provide the necessary platform for decision makers to analyse geographical information in a flexible manner, while EDSS integrate the relevant environmental models, database and assessment tools – coupled within a Graphic User Interface (GUI) – for functionality within a Geographical Information System (GIS) [1,4-6]. In some detail, GIS is a set of computer tools that can capture, manipulate, process and display spatial or geo-referenced data in which the enhancement of spatial data integration, analysis and visualization can be conducted [8-9]. These functionalities make GIS-tools useful for efficient development and effective implementation of DSS within the management process. For this purpose they are used either as data managers (i.e. as a spatial geo-database tool) or as an end in itself (i.e. media to communicate information to decision makers)

A multi-risk methodology for the assessment of climate change impacts in coastal zones

Climate change threatens coastal areas, posing significant risks to natural and human systems, including coastal erosion and inundation. This paper presents a multi-risk approach integrating multiple climate-related hazards and exposure and vulnerability factors across different spatial units and temporal scales. The multi-hazard assessment employs an influence matrix to analyze the relationships among hazards (sea-level rise, coastal erosion, and storm surge) and their disjoint probability. The multi-vulnerability considers the susceptibility of the exposed receptors (wetlands, beaches, and urban areas) to different hazards based on multiple indicators (dunes, shoreline evolution, and urbanization rate). The methodology was applied in the North Adriatic coast, producing a ranking of multi-hazard risks by means of GIS maps and statistics. The results highlight that the higher multi-hazard score (meaning presence of all investigated hazards) is near the coastline while multi-vulnerability is relatively high in the whole case study, especially for beaches, wetlands, protected areas, and river mouths. The overall multi-risk score presents a trend similar to multi-hazard and shows that beaches is the receptor most affected by multiple risks (60% of surface in the higher multi-risk classes). Risk statistics were developed for coastal municipalities and local stakeholders to support the setting of adaptation priorities and coastal zone management plans

Chapter Inventory of GIS-Based Decision Support Systems Addressing Climate Change Impacts on Coastal Waters and Related Inland Watersheds

Cosmology & the univers

On the Application of GIS-based Decision Support Systems to study climate change impacts on coastal systems and associated ecosystems

One of the most remarkable achievements by scientists in the field of global change in recent years is the improvedunderstanding of climate change issues. Its effects on human environments, particularly coastal zones and associated watersystems, are now a huge challenge to environmental resource managers and decision makers. International and regionalregulatory frameworks have been established to guide the implementation of interdisciplinary methodologies, useful toanalyse water-related systems issues and support the definition of management strategies against the effects of climatechange. As a response to these concerns, several decision support systems (DSS) have been developed and applied toaddress climate change through geographical information systems (GIS) and multi-criteria decision analysis (MCDA)techniques; linking the DSS objectives with specific functionalities leading to key outcomes, and aspects of the decisionmaking process involving coastal and waters resources. An analysis of existing DSS focusing on climate change impacts oncoastal and related ecosystems was conducted by surveying the open literature. Consequently, twenty DSS were identifiedand are comparatively discussed according to their specific objectives and functionalities, including a set of criteria (generaltechnical, specific technical and applicability) in order to better inform potential users and concerned stakeholders throughthe evaluation of a DSS’ actual application.Key words: Climate change, Decision support, GIS, regulations, Environmen

A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis

Extreme weather and climate related events, from river flooding to droughts and tropical cyclones, are likely to become both more severe and more frequent in the coming decades, and the damages caused by these events will be felt across all sectors of society. In the face of this threat, policy-and decision-makers are increasingly calling for new approaches and tools to support risk management and climate adaptation pathways that can capture the full extent of the impacts. In this frame, a GIS-based Bayesian Network (BN) approach is presented for the capturing and modelling of multi-sectoral flooding damages against future 'what-if' scenarios. Building on a risk-based conceptual framework, the BN model was trained and validated by exploiting data collected from the 2014 Secchia River flooding event, as well as other contextual variables. Moreover, a novel approach to defining the structure of the BN was performed, reconfiguring the model according to expert judgment and data-based validation. The model showed a good predictive capacity for damages in the agricultural, industrial and residential sectors, predicting the severity of damages with a classification accuracy of about 60% for each of these assessment endpoints. 'What-if' scenario analysis was performed to understand the potential impacts of future changes in i) land use patterns and ii) increasing flood depths resulting from more severe flood events. The output of the model showed a rising probability of experiencing high monetary damages under both scenarios. In spite of constraints within the case study dataset, the results of the appraisal show good promise, and together with the designed BN model itself represent a valuable support for disaster risk management and reduction actions against extreme river flooding events, enabling better informed decision making

DESARROLLO DE COLUMNAS CAPILARES POR CROMATOGRAFÍA DE GASES. APLICACIÓN A LA ENANTIOSEPARACIÓN DE PLAGUISCIDAS QUIRALES

Objetivos generales: Se propone el desarrollo de columnas capilares de cromatografía de gases (GC) conteniendo fases estacionarias quirales con diversos selectores químicos. Para esto se prepararán capilares de distintas dimensiones y de espesor perfectamente conocido en los que se depositará/ligará una película polimérica líquida conteniendo los selectores quirales, que se aplicarán a la enantioseparación de plaguicidas quirales de las familias de fenoxiácidos, ariloxifenoxiácidos e imidazolinonas. Se desarrollarán columnas conteniendo familias de ciclodextrinas disueltos o fijado al polímero; diferentes líquidos iónicos quirales con un contraión apropiado que proporcione líquidos de diferentes polaridades; se evaluará la capacidad de enantioreconocimiento en diferentes polímeros solventes con el objeto de incrementar la universalidad de las columnas obtenidas y se estudiarán plaguicidas en muestras medioambientales. El proyecto apunta a la fabricación de columnas capilares quirales de GC de diversas estructuras químicas. Se trata de un insumo imprescindible en los laboratorios de control de calidad de compuestos quirales (volátiles y semi-volátiles). El control de pureza enantiomérica requiere de métodos quirales de alta sensibilidad en la cuantificación, dado que usualmente el compuesto activo está presente en porcentajes mayores al 99% frente a menos de un 1% de su isómero óptico. Las columnas quirales, tanto en cromatografía de líquidos (LC) como para GC, presentan dos características que las distinguen de cualquier columna aquiral: i) son altamente específicas y, a priori, no es posible predecir el éxito de una enantioseparación con una columna en particular, por lo que es necesario contar con una multiplicidad de columnas para la resolución de enantiómeros diversos, muchas veces incluso, pertenecientes a una misma familia química y ii) su menor vida útil en comparación con columnas de uso general. En síntesis, el desarrollo de columnas capilares conteniendo fases estacionarias generales y quirales como las que aquí se propone resulta de sumo interés por varios motivos: i. desarrollo de tecnología para la fabricación de columnas quirales analíticas para GC en capilares que podrían sustituir a las columnas comerciales que actualmente se importan; ii. obención de columnas quirales a costos muy inferiores (estimativamente entre U$150 – U$250, dependiendo de la geometría y selectores); iii. desarrollo de métodos de control de calidad para el análisis de agroquímicos que se comercializan, o en un futuro cercano lo harán, como enantiómeros puros y que actualmente no se los controla como tales