Istituto Universitario Di Studi Superiori Di Pavia

About: Istituto Universitario Di Studi Superiori Di Pavia is a education organization based out in Pavia, Italy. It is known for research contribution in the topics: Pulsar & Neutron star. The organization has 162 authors who have published 566 publications receiving 22605 citations.

Modelling increasing natural-hazard risk due to urban growth in Kathmandu Valley, Nepal

Abstract: <p>In our rapidly urbanizing world, many hazard-prone regions face significant challenges when it comes to risk-informed urban development. This study specifically addresses this issue by investigating evolving spatial interactions between natural hazards, ever-increasing urban areas, and social vulnerability in Kathmandu Valley, Nepal. The methodology used in this work considers: (1) the characterization of flood hazard and liquefaction susceptibility using pre-existing global models; (2) the simulation of future urban built-up areas using the cellular-automata SLEUTH (Slope, Land use, Excluded areas, Urban extent, Transportation, Hillshade) model, which requires satellite imagery for statistical calibration and validation; and (3) the assessment of social vulnerability using a social vulnerability index tailored for the case-study area. Results show that the total built-up area in Kathmandu will increase to 352 km<sup>2</sup> by 2050, which is effectively double the equivalent 2018 figure of 177 km<sup>2</sup>. The most socially vulnerable villages will account for 29% of the total built-up area in 2050, which is 11% more than their current proportion. Built-up areas in the 100-year and 1000-year return period floodplains will respectively increase from 38 km<sup>2</sup> and 49 km<sup>2</sup> today to 83 km<sup>2</sup> and 108 km<sup>2</sup><sub></sub>in 2050. In the same time frame, built-up areas in liquefaction-susceptible zones will expand by  13 km<sup>2</sup> to 47 km<sup>2</sup>. The results of this study illustrate how, where, and to which extent risks from natural hazards can evolve in socially vulnerable regions. Ultimately, this study emphasizes an urgent need to implement effective policy measures (e.g., land-use regulations) for reducing tomorrow's natural-hazard risks.</p>

Error-Uncertainty Trade-off Judgment for Ensemble Member Selection of Regional Climate Models and Climate Change Impacts Modelling

Abstract: Abstract: Climate change and its impacts on the environment have become more than ever a worldwide challenging issue. Hence, decision makers are seeking reliable climate-impact models to take long term appropriate actions against this phenomena. In this study, ten regional climate models (RCMs) obtained from the European Coordinated Downscaling Experiment (EURO CORDEX) platform are evaluated on the Chiese river catchment located in the northeast of Italy. The models’ ensembles are assessed in terms of the uncertainty and error calculated through different statistical and error indices. The uncertainties are investigated in terms of signal (increase, decrease or neutral changes in the variables) and value uncertainties. Together with the spatial analysis of the data over the catchment, the weighted averaged values are used for the models’ evaluations and data projections. Using weighted catchment variables, climate change impacts are assessed on 10 different hydro-climatological variables showing the changes in the temperature, precipitation, rainfall events’ features and the hydrological variables of the Chiese catchment between historical (1991–2000) and future (2071–2080) decades under RCP (Representative Concentration Path for increasing greenhouse gas emissions) scenario 4.5.  The results show that, even though the multi-model ensemble mean (MMEM) could cover the outputs’ uncertainty of the models, it increases the error of the outputs. On the other hand, the RCM with the least error could cause high signal and value uncertainties for the results. Hence, different multi-model subsets of ensembles (MMEM-s) of ten RCMs are obtained through a proposed algorithm for different impact models’ calculation and projection, making tradeoffs between two important shortcomings of model outputs, which are error and uncertainty. The single model (SM) and multi-model (MM) outputs imply that catchment warming is obvious in all cases and, therefore, evapotranspiration will be intensified in the future where there are about 1.28 C° and 6% value uncertainties for monthly temperature increase and the decadal relative balance of evapotranspiration. While rainfall events feature higher intensity and shorter duration in the SM, there are no significant differences for the mentioned features in the MM, showing high signal uncertainties in this regard. The unchanged catchment rainfall events’ depth can be observed in two SM and MM approaches implying good signal certainty for the depth feature trend; there is still high uncertainty about the depth values. As a result of climate change, the percolation component change is negligible, with low signal and value uncertainties, while decadal evapotranspiration and discharge uncertainties show the same signal and value. While extreme events and their anomalous outcomes direct the uncertainties in rainfall events' features values towards zero, they remain critical for yearly maximum catchment discharge in 2071–2080 as the highest value uncertainty is observed for this variable.Keywords: climate change; regional climate model; specific region; ensemble evaluation; spatial analysis; impact model; error; uncertainty; hydrological variables

Weather circulation patterns associated with extreme precipitation events in Italy

Abstract: <p>In the last years, many countries in Europe have been experiencing an increased frequency of extreme precipitation leading to natural disasters like floods and landslides. In Italy, the majority of the country’s natural disasters have been related to extreme precipitation. Floods and landslides have led to the country experiencing great loss in its social and economic structure. Early warning systems are important to stakeholders such as Disaster Risk Managers to make informed decisions in relation to a forecasted disaster.</p><p>Extreme precipitation is often associated with specific circulation patterns. Precursor information about atmospheric circulation patterns can therefore act as an indicator of an oncoming extreme precipitation event. The objective of this work is to identify the weather circulation patterns associated with extreme precipitation events over Italy.</p><p>E-OBS precipitation datasets were used to identify the most intense extreme precipitation events for each season for the period 1990-2020 across Italy. Mean sea level pressure and 500 hPa geopotential height from the ERA5 dataset were used to identify circulation anomalies associated with the extreme events. The analysis is performed by clustering extreme precipitation events into three homogeneous climatic zones in Italy defined following the Köppen-Geiger classification.</p><p>Results show that extreme precipitation events are always associated with an intense low pressure system located within the Euro-Mediterranean region. Depending on the location of precipitation extremes across different climatic zones, low pressure location changes, also modifying the atmospheric circulation and the associated moisture transport. Namely, for precipitation extremes occurring in the Italian peninsula, the low pressure is located in central-western Europe, while for extremes in Sardinia and Sicily, low pressure is in the Mediterranean. </p>

Future changes in sub-daily extreme precipitation over a complex-orography area from a convection-permitting climate model

Abstract: Sub-daily extreme precipitation can generate fast hydro-geomorphic hazards such as flash floods and debris flows, which cause fatalities and damages especially in mountainous regions. Reliable projections of extreme future precipitation is fundamental for risk management and adaptation strategies. Convection-permitting climate models (CPMs) esplicitely resolve large convective systems and represent local processes, especially sub-daily extreme precipitation, more realistically than coarser resolution models, thus leading to higher confidence in their projections. Given their high computation cost, however, the available CPM simulations cover relatively short time periods (10–20 years), too short for deriving precipitation frequency analyses with conventional extreme value methods based on annual maxima or threshold exceedances.In this work, we evaluate the potential of a non-asymptotic approach based on “ordinary” events, the so-called Simplified Metastatistical Extreme Value (SMEV), to provide information on the future change of short-duration precipitation extremes. We focus on a complex-orography region in the Eastern Italian Alps, where significant changes in sub-daily annual maxima have been already observed. The study is based on COSMO-crCLIM model simulations at 2.2 km resolution under the RCP8.5 scenario and uses three 10-year time periods: historical 1996-2005 (the control period), near-future 2041-2050 and far future 2090-2099. We estimate extreme precipitation for durations ranging from 1 h to 24 h and assess the projected changes with respect to the control period. Specifically, we analyze annual maxima, return levels up to 50 years, and the parameters of the statistical model. A bootstrap procedure is used to evaluate the uncertainty of the estimates, and a permutation test is applied to assess the statistical significance of the projected changes. We compare our results with a modified Generalized Extreme Value (GEV) approach, recently applied for the study of extremes in CPM future time periods.We found that annual maxima and higher return levels exhibit a general increase in the future especially for the far future and the shorter event durations. On average, the magnitude of the far future change decreases with the precipitation temporal scale. The changes show an interesting spatial organization that can be associated with the orography of the region: significant future increases are mostly located at high elevations, while lowlands and coastal zones show no clear pattern.This work shows that SMEV reduces the uncertainty in the estimates of higher return levels compared to GEV and can thus provide improved estimates of their future changes from short CPM runs. These findings advance our knowledge about the projected changes in extreme precipitation and their spatial distribution at the different time scales. They can thus help improving risk management and adaptation strategies.

Complements on Elasticity Problems

Abstract: Elasticity problems are probably the most common use of the finite element method. Historically, they were indeed at the origin of the method. We have already considered in Chap. 8, in particular in Sect. 8.12, standard formulations of elasticity problems based on displacement variables. Considerations on the choice of elements have also be presented in Sect. 8.14.1. Our main concern will now be to present mixed methods using explicitly an approximation of the stress tensor, in which the equilibrium condition is strongly imposed on each element.