1,721,012 research outputs found
Sistemi di monitoraggio meteorologico per l'analisi del campo di precipitazione in aree urbane finalizzati al preannuncio precoce di dissesti idrogeologici
Full text linkLo sviluppo dei sistemi informativi territoriali, insieme con quello di avanzate tecniche di monitoraggio ambientale, potrebbe risultare molto utile nell'ambito del preavviso e/o della mitigazione del rischio idrogeologico. Questo lavoro presenta un sistema integrato per il preannuncio del rischio idrogeologico il cui punto di forza risiede in un sistema di monitoraggio climatico e idrologico/geotecnico costituito da diversi sensori wireless che registrano una serie di informazioni relative a grandezze di tipo meteorologico, idrologico e geotecnico. I dati registrati e inviati ad una piattaforma web sono utilizzati per alimentare una rete neurale artificiale,
opportunamente creata e addestrata, che esegue il controllo in tempo reale della stabilità di una data area di studio. I risultati restituiti dalla rete neurale artificiale permettono di prevedere se e quando una frana si potrebbe attivare a seguito di precipitazioni intense e di lanciare automaticamente un messaggio di potenziale pericolo. Il sistema è organizzato secondo tre livelli che interagiscono tra di loro e, in particolare: un primo livello costituito dall'acquisizione dei dati dai sensori, un secondo livello di processamento dei dati e, infine, un livello di lancio del warning. Il sistema sviluppato è stato testato in un sito pilota nei pressi dell’area urbana di
Palermo. I primi risultati ottenuti hanno permesso di verificare il perfetto funzionamento del sistema nelle sue singole componenti ma anche nel suo insieme
Projecting Depth-Duration-Frequency Curves for Future Climate: a Case Study in the Mediterranean Area
Full text linkDepth-Duration-Frequency (DDF) curves are an essential tool in hydrological planning and risk management. However, the assumption of stationarity that is traditionally embedded in their derivation, is increasingly questioned by the impacts of climate change. This study focuses on adapting and projecting DDF curves for Sicily (Italy), which is experiencing an intensification of rainfall extremes, particularly for shorter durations. The proposed framework adapts the most up-to-date regional frequency analysis for the island by using an adaptation factor that incorporates the thermodynamic relationship between extreme precipitation and temperature, as well as future climate projections for temperature from an ensemble of regional climate models under the worst-case scenario. By the end of the century, the design rainfall estimates may require to be increased up to 50%, especially for hourly durations, to account for climate change effects. The results also highlight a strong spatial variability in the precipitation quantiles, with higher values observed in specific areas such as the north-eastern part of the island, which is characterized by small catchments and particularly prone to flash floods. Finally, this study provides a simple but still physical-based approach to updating DDF curves, that can be useful for engineers and practitioners, enhancing international efforts to mitigate climate change impacts through improved hydrological planning
Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a small sicilian catchment
Full text linkA PCA-based clustering algorithm for the identification of stratiform and convective precipitation at the event scale: an application to the sub-hourly precipitation of Sicily, Italy
Full text linkUnderstanding the structure of precipitation and its separation into stratiform and convective components is still today one of the important and interesting challenges for the scientific community. Despite this interest and the advances made in this field, the classification of rainfall into convective and stratiform components is still today not trivial. This study applies a novel criterion based on a clustering approach to analyze a high temporal resolution precipitation dataset collected for the period 2002–2018 over the Sicily (Italy). Starting from the rainfall events obtained from this dataset, the developed methodology makes it possible to classify the rainfall events into four different classes, which can be related to the convective and stratiform components of the events on the basis of their hyetograph shapes and average intensities. The results show that the occurrence of stratiform events is always much higher than the convective ones, especially in the winter and spring seasons, while from the summer to the mid-autumn the rainfall depth due to convective events results to be higher than that due to the stratiform events. Moreover, the comparison with a more widely accepted separation methodology demonstrates the physical consistency of the proposed methodology
Reconstruction of Maximum and Minimum Temperature Time Series for the Mediterranean’s Largest Island
No full textAnalyzing potential indicators of climate change necessitates access to extensive historical datasets.
However, measurement gauges are subject to frequent replacement or upgrades, resulting in spatial and/or
temporal inconsistencies that undermine the reliability of these datasets. This is the case of Sicily, the largest
island in the Mediterranean Sea, which is characterized by the presence of two different monitoring networks,
spanning partially different periods. By using a spatial interpolation method, we merge the information from
these networks and obtain continuous daily maximum and minimum temperature series for the 1980-2023
period in a 2x2 km grid
An Artificial Intelligence–Based Blending of Satellite products across Mediterranean Island of Sicily, Italy using GPM-IMERG V06 Final Run
Full text linkPrecipitation is the key input variable to hydrological models and its monitoring plays a significant role in water resources planning and improving flood and drought forecasting, also under climate change impacts. In recent years, many precipitation satellite products have been developed and released to the public; among these, the Integrated Multi-satellitE Retrievals from Global Precipitation Measurement (IMERG) is designed to address limitations and uncertainties related to traditional methods.
The primary purpose of this study is to provide a comprehensive assessment of precipitation estimates retrieved from the IMERG v6 Final Run over the Mediterranean island of Sicily (Italy) at daily and half-hourly temporal and at 0.1° spatial resolution for the first time using a quality-controlled sub-hourly gauge dataset. Sicily, which is characterized by a Mediterranean climate and a complex orography, experiences rather frequent short duration and high intensity precipitation originating from the interaction of steep orography on the coasts with winds carrying humid air masses from the Mediterranean Sea.
Previous studies have highlighted that most of the available satellite-based precipitation products show poor performance for capturing rainfall events at high temporal resolution particularly in coastal areas. Based on these findings, there is a critical need to put much effort to improve retrieval algorithms to account for coastal and morphological effects, thus enhancing satellite-based precipitation estimations for those areas. With this regard, this work also aims to show that a combination of multiple products may result in more accurate estimations especially for short duration events. This merging technique, which has been carried out exploiting artificial intelligence (AI) techniques, is shown to successfully reduce the error based on the comparison with data from a local rain gauge network. The results of the study will demonstrate the proficiency of the AI based approaches for improving remote-sensed daily and sub-hourly rainfall products even in coastal areas with complex orography
Effetto della risoluzione spaziale della mesh sulla modellazione delle frane attivate da precipitazione
Full text linkLe frane attivate da precipitazione sono, tra tutti gli eventi di dissesto idrogeologico, quelle che probabilmente si verificano con più frequenza. Il verificarsi di tali frane, soprattutto per la velocità con cui inizia e si sviluppa il fenomeno, può comportare conseguenze, a volte anche gravi, per le persone e le cose. In un tale scenario è chiaro che risulta di fondamentale importanza munirsi di strumenti per la previsione del fenomeno nel tentativo di prevenirne e/o mitigarne il rischio che questo comporta. L’utilizzo di modelli idrologici spazialmente distribuiti e a base fisica accoppiati con modelli di stabilità è, oggigiorno, uno tra i metodi più ricorrenti per la valutazione del rischio, a scala di bacino ed in termini distribuiti, legato al verificarsi di frane attivate da precipitazione. In questo contesto, una delle caratteristiche topografiche che maggiormente controlla la modellazione di tali fenomeni è la distribuzione spaziale della pendenza. In particolare, la pendenza agisce sull’attivazione di tali frane sia in maniera diretta, aumentando la sollecitazione del pendio, che in maniera indiretta, controllando la redistribuzione laterale dell’acqua sub-superficiale che, a sua volta, può determinare significative variazioni delle pressioni neutre tali da provocare l’innesco della frana.
Il presente studio analizza l’influenza della risoluzione spaziale del modello digitale delle elevazioni (DEM) utilizzato per la modellazione delle frane innescate da precipitazione con il modello eco-idrologico-di-stabilità tRIBS-VEGGIE-Landslide (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator - VEGetation Generator for Interactive Evolution). Il modello, a partire da un DEM, genera una griglia triangolare irregolare per descrivere la topografia dell’area di studio. La scelta del DEM da utilizzare risulta di fondamentale importanza poiché è da esso che vengono derivate la mesh di calcolo del modello e le pendenze che, come detto, giocano un ruolo fondamentale sulla analisi di stabilità di un pendio.
L’analisi è stata effettuata sul bacino Mameyes, del Luquillo Experimental Forest, Puerto Rico, dove in passato sono stati condotti altri studi di modellazione di frane attivate da precipitazione, molto frequenti nella località, con lo stesso modello. In particolare, per la modellazione, sono state considerate 5 mappe TINs derivate da 5 grid-DEM aventi una diversa risoluzione spaziale (10, 20, 30, 50 e 70 m).
I risultati hanno mostrato che l’utilizzo di una griglia irregolare permette di ridurre la perdita di accuratezza quando la distribuzione spaziale della pendenza è derivata a partire da una risoluzione spaziale più grossolana. La scelta della risoluzione spaziale influenza particolarmente i pattern di umidità del suolo (e quindi della stabilità) quando la redistribuzione laterale è significativa, la quale dipende, oltre che dalla pendenza, anche da altre caratteristiche come l’anisotropia del suolo e la forzante climatica. In condizioni stazionarie, invece, si è notato che l’utilizzo di diverse risoluzioni spaziali non ha impattato particolarmente i risultati, sia in termini di distribuzione spaziale delle zone instabili che in termini di valori di umidità in condizioni di instabilità. Infine, la scelta di una risoluzione più grossolana comporta una diminuzione dell’area totale instabile
Advancements and Challenges in Gridded Precipitation Datasets: Bias Correction and Downscaling Techniques for Enhanced Modeling in Sicily
No full textSpatial-temporal resolution advantages of Satellite Precipitation Products are limited by biases compared to ground observations. Various bias adjustment methods exist, from scaling methods to sophisticated techniques like Quantile Mapping (QM). However, many assume stationarity, leading to inaccuracies in variable climates. Alternative methods, instead, including Equidistant CDF
Matching (ECDFM), CDF transferring (CDFt), Quantile Delta Mapping (QDM), aim to address non-stationarity. Also, recent advancements in deep learning offer new methods, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, promising good performances in capturing spatial-temporal dependencies of climate phenomena.
Our study proposes a two-stage bias adjustment framework integrating multiple methodologies and advanced deep learning techniques to mitigate systematic bias in Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite estimatesfor the Sicily, Italy.
Also, to tackle one of the most important issues that affects semiarid regions as Sicily, i.e., precipitation datasets with zero-inflation, it exists different methods varying from Singularity Stochastic Removal (SSR) to complex zero-truncated models
Object-based image analysis technique for gully mapping using topographic data at very high resolution (VHR)
Full text linkAn accurate mapping of gullies is important since they are still major contributors of sediment to streams. Mapping gullies in many areas is difficult because of the presence of dense canopy, which precludes the identification through aerial photogrammetry and other traditional remote sensing methods. Moreover, the wide spatial extent of some gullies makes their identification and characterization through field surveys a very large and expensive proposition.
This work aims to develop an object-based image analysis (OBIA) to detect and map gullies based on a set of rules and morphological characteristics retrieved by very high resolution (VHR) imagery. A one-meter resolution LiDAR Digital Elevation Model (DEM) is used to derive different morphometric indexes, which are combined, by using different segmentation and classification rules, to identify gullies. The tool has been calibrated using, as reference, the perimeters of two relatively large gullies that have been measured during a field survey in the Calhoun Critical Zone Observatory (CCZO) area in the Southeastern United States
Green roof effects on the rainwater response in the Mediterranean area: first results of a Sicilian case study
Full text linkOver the last decades, we have been witnessing an increasing frequency of urban floods often attributed to the interaction between intensification of rainfall extremes due to climate change and increasing urbanization. Consequently, many studies have been trying to propose different new alternatives to mitigate ground effects of ever more frequent and severe extreme rainfall events in a context of growing urbanization, such as rain gardens, green roofs, permeable parking lots, etc., which are commonly referred to as green infrastructures.
With this regard, one of the most promising mitigation solutions is represented by multilayer green roofs. These systems, coupling classical green roofs with a rainwater harvesting system, results in a high capacity in retaining rainwater, thus improving the potential effects acted by classical green roofs on pluvial floods mitigation. These systems are particularly suited for applications in semi-arid climate, where a fraction of the rainwater can be detained during the more severe rainfall events, significantly reducing the pressure on drainage systems, and released in a later moment or reused, for instance, to sustain the vegetation during driest periods.
This study describes a multilayer green roof installed at the Department of Engineering of the University of Palermo (Sicily, Italy) and its preliminary results on its capacity to reduce the pressure of rainfall events on drainage systems in a Mediterranean context. The green roof has an extension of almost 35 m2 and is made of three different areas with different soil thickness (a mixture of volcanic material) and different Mediterranean vegetation. The green roof is equipped with multiple sensors to monitor the water level in the storage layer, soil water content, air and water temperature, and rainfall. Besides, a weighted rain gauge, a disdrometer, and a meteorological station for the collection of meteorological data are available as well.
An equal size classical roof area bordering the green roof installation is also monitored. Four different thermometers are used to measure the temperatures in different points of the roofs and a system of two rain barrels and two pressure sensors allows to collect and compare the rainwater coming from the green and the original roofs. Such an installation, differently from many others, has the advantage to allow a complete characterization of the potential benefits of a multilayer green roof through a comparison of the rainwater released by the two roof configurations at a rainfall event scale.
The study provides the preliminary results arising from the analysis of the two roof configurations' response to a series of rainfall events characterized by different duration and intensity
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