1,721,013 research outputs found
Effects of wildfires on peak discharges in watersheds
Full text linkIt is acknowledged that wildfires can alter hydrologic processes in watersheds, increasing runoff peak discharge, which is one of the most important hydrological variables used in water resources applications. Then it is evident that usual rainfall-runoff methods should be modified in some of their components in order to model the different watershed response in burned condition. This paper contains some considerations on the estimation of the SCS runoff Curve Number, needed for the calculation of peak discharge in the small urban basin of San Giuliano in L’Aquila (Italy), which has been recently affected by a wildfire, causing a significant reduction in the forest cover
Precipitation and temperature trends over central Italy (Abruzzo Region): 1951–2012
No full textThis study analyses spatial and temporal trends of precipitation and temperatures over Abruzzo Region (central Italy), using historical climatic data from a dense observation network. The results show a general, although not significant, negative trend in the regionally averaged annual precipitation (− 1.8% of the yearly mean rainfall per decade). This reduction is particularly evident in winter, especially at mountain stations (average − 3% change/decade). Despite this general decreasing trend, a partial rainfall recovery is observed after the 1980s. Furthermore, the majority of meteorological stations register a significant warming over the last 60 years, (mean annual temperature increase of + 0.15 °C/decade), which reflects a rise in both minimum and maximum temperatures, with the latter generally increasing at a faster rate. Spring and summer are the seasons which contribute most to the general temperature increase, in particular at high elevation sites, which exhibit a more pronounced warming (+ 0.24 °C/decade). However, this tendency has not been uniform over 1951–2012, but it has been characterised by a cooling phenomenon in the first 30 years (1951–1981), followed by an even stronger warming during the last three decades (1982–2012). Finally, correlations between the climatic variables and the dominant teleconnection patterns in the Mediterranean basin are analysed to identify the potential influence of large-scale atmospheric dynamics on observed trends in Abruzzo. The results highlight the dominant role of the East-Atlantic pattern on seasonal temperatures, while more spatially heterogeneous associations, depending on the complex topography of the region, are identified between winter precipitation and the North Atlantic Oscillation, East-Atlantic and East-Atlantic/Western Russian patterns
Sito web del progetto MOVIDA (MOdello per la Valutazione Integrata del Danno Alluvionale)
No full textRiver basin planning: from qualitative to quantitative flood risk assessment: the case of Abruzzo Region (central Italy)
Full text linkFlood risk assessments are becoming an essential tool for a rational decision-making in river basin planning throughout Europe. In order to comply with the prescriptions of the Floods Directive, quantitative approaches, based on the estimation of the expected annual damage as a risk indicator, should increasingly be used for flood risk mapping in addition to the traditional qualitative methods. In this paper, a comparative application of the two methodologies to the river basins of the Abruzzo Region (central Italy) was performed for evaluating their strengths and weaknesses. The analysis was limited to direct damage estimation. The results showed that qualitative mapping could not be considered a fully effective tool for the identification of appropriate flood risk management strategies due to its limits in representing the real flood risk scenarios, mainly related to the use of a coarse hazard classification. Therefore, if it could be used at the basin scale for a preliminary identification of high-risk areas, it should be integrated with a more analytical assessment of the expected losses with quantitative models. However, a sensitivity analysis on the quantitative methodology showed that the large uncertainties inherent in damage modelling (with difference factors ranging from 2 to 10) may hinder the reliability of the estimates, with possible repercussions on the results of economic appraisals. Two case studies on the implementation of flood risk reduction measures confirmed that damage model uncertainty had huge influence on the results of cost–benefit analyses, with net present values switching from negative to positive according to the selection of the damage functions and the economic values of the exposed assets
On the Influence of Input Data Quality to Flood Damage Estimation: The Performance of the INSYDE Model
Full text linkIN-depth SYnthetic Model for Flood Damage Estimation (INSYDE) is a model for the
estimation of flood damage to residential buildings at the micro-scale. This study investigates the
sensitivity of INSYDE to the accuracy of input data. Starting from the knowledge of input parameters
at the scale of individual buildings for a case study, the level of detail of input data is progressively
downgraded until the condition in which a representative value is defined for all inputs at the census
block scale. The analysis reveals that two conditions are required to limit the errors in damage
estimation: the representativeness of representatives values with respect to micro-scale values and
the local knowledge of the footprint area of the buildings, being the latter the main extensive variable
adopted by INSYDE. Such a result allows for extending the usability of the model at the meso-scale,
also in different countries, depending on the availability of aggregated building data
Regional flood risk analysis for agricultural crops: Insights from the implementation of AGRIDE-c in central Italy
No full textThe development of reliable models for different exposed sectors is key for obtaining more comprehensive flood damage and risk assessments. This study discusses the implementation of the recent conceptual damage model for crops, AGRIDE-c, to a central region of Italy, in order to demonstrate its potential for application and related challenges from a flood risk assessment perspective at the river basin scale. The process for adaptation of model's components to the local characteristics of the investigated area is first described, including the extension of the analytical flood loss model to annual vegetables and perennial crops (here exemplified for grapevine) not covered in the original AGRIDE-c. Moreover, being a multi-variable damage model, with also some input parameters of uncertain estimation, this study examines the implications of modelling assumptions on damage and risk results to obtain general insights from a modeller's perspective. Finally, the issue of spatial transferability is discussed by comparing model outcomes for cereals in the analysed region and in the Po Plain, for which AGRIDE-c was originally developed. The results highlight the importance, in practical applications, (i) to determine a confidence band for loss estimates that can help for more informed decision making processes and (ii) when transferring models, to verify the similarity between the physical and economic contexts of implementation (and, if necessary, adjust model's input and/or assumptions), in order to prevent inaccurate or distorted damage and risk estimations
Machine learning and hydrodynamic proxies for enhanced rapid tsunami vulnerability assessment
Full text linkCoastal communities in various regions of the world are exposed to risk from tsunami inundation, requiring reliable modeling tools for implementing effective disaster preparedness and management strategies. This study advocates for comprehensive multi-variable models and emphasizes the limitations of traditional univariate fragility functions by leveraging a large, detailed dataset of ex-post damage surveys for the 2011 Great East Japan tsunami, hydrodynamic modeling of the event, and advanced machine learning techniques. It investigates the complex interplay of factors influencing building vulnerability to tsunami, with a specific focus on the hydrodynamic effects associated to tsunami propagation on land. Novel synthetic variables representing shielding and debris impact mechanisms prove to be suitable proxies for water velocity, offering a practical solution for rapid damage assessments, especially in post-event scenarios or large-scale analyses. Machine learning then emerges as a promising approach to tackle the complexities of vulnerability assessment, while providing valuable and interpretable insights.Hydrodynamic modelling and machine learning-based methods can effectively model tsunami damage mechanisms and represent an improvement over traditional univariate fragility functions for vulnerability assessments
INSYDE-BI: A Flood Damage Model for Residential Buildings in Burundi
No full textIn recent years, the frequency and intensity of floods have increased globally, posing a significant threat to communities and their infrastructure. This paper introduces the adaptation of an existing flood damage model, INSYDE, originally developed for the context of Italy, to the unique conditions of the Republic of Burundi, Africa. The proposed model, named INSYDE-BI, aims to enhance understanding of flood-induced damages to residential buildings in Burundi, considering the hazard and building factors specific to the region. The adaptation process involves the examination of local building practices and vulnerability factors as well as the modeling of specific damage mechanisms to certain building’s components. By integrating these region-specific elements, INSYDE-BI seeks to provide a more accurate and contextually relevant assessment of flood-related risks and damages for residential structures in Burundi
INSYDE-content: a synthetic, multi-variable flood damage model for household contents
Full text linkThis paper introduces INSYDE-content, a novel, probabilistic, multi-variable synthetic model designed to estimate flood damage for household contents on a component-by-component basis. The model addresses a critical gap in current modeling tools, which often overlook the significance of household contents in overall damage assessments. Developed through an expert-based approach and grounded in the scientific and technical literature, INSYDE-content leverages desk-based data to characterize model features, including uncertainty treatment arising from incomplete input data. A validation test on two historical flood events and a sensitivity analysis are performed to assess the model's performance and explore the contribution of input variables to damage estimation, confirming its robustness and interpretability. For illustrative purposes, in this study INSYDE-content has been tailored to the specific hazard, vulnerability and exposure characteristics of Northern Italy; nonetheless, its adaptable structure supports broader applicability across diverse regional settings, provided suitable customization is applied
An integrated regionalization framework for incorporating flood seasonality into agricultural flood risk assessments
Full text linkFlood risk to agriculture is strongly influenced by the timing of inundation relative to crop development stages, making flood seasonality a critical but often overlooked component in damage estimation. This study introduces a generalizable regionalization framework that combines hydrological clustering and machine learning to incorporate seasonal flood probability into agricultural risk assessment. The approach involves identifying clusters of gauged catchments with similar patterns of intra-annual flood occurrence and using supervised classification to extrapolate these seasonal regimes to ungauged catchments based on their physical attributes. The resulting spatially distributed maps of monthly flood probability can be then integrated with a flood damage model to calculate expected annual losses and support risk estimates across entire river districts. The proposed framework, applied in this study to the Po River District (Italy) for illustrative purposes, is scalable and adaptable to different regions, contributing to more robust and context-sensitive adaptation planning in agriculture. Results highlight the importance of accounting for flood seasonality in cost-benefit analyses within agricultural contexts, as neglecting intra-annual variability can lead to overestimated damage projections and suboptimal mitigation strategies
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