<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/CINECAstyle.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-20T07:53:38Z</responseDate><request verb="GetRecord" identifier="oai:iris.unica.it:11584/266363" metadataPrefix="oai_dc">https://iris.unica.it/oai/request</request><GetRecord><record><header><identifier>oai:iris.unica.it:11584/266363</identifier><datestamp>2022-10-20T09:39:49Z</datestamp><setSpec>com_11584_207615</setSpec><setSpec>com_11584_111066</setSpec><setSpec>col_11584_265854</setSpec></header><metadata><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:doc="http://www.lyncode.com/xoai" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
<dc:title>Modelli di preannuncio delle piene in piccoli bacini ed incertezze legate alla densita' della rete pluviometrica.</dc:title>
<dc:creator>SEONI, ALESSANDRO</dc:creator>
<dc:subject>campionamento precipitazione</dc:subject>
<dc:subject>distributed hydrologic model</dc:subject>
<dc:subject>flash flood</dc:subject>
<dc:subject>flood frequency</dc:subject>
<dc:subject>frequenza portate</dc:subject>
<dc:subject>modellazione distribuita</dc:subject>
<dc:subject>piena improvvisa</dc:subject>
<dc:subject>rain gauge</dc:subject>
<dc:subject>rainfall sampling</dc:subject>
<dc:subject>rainfall threshold</dc:subject>
<dc:subject>soglia pluviometrica</dc:subject>
<dc:subject>Settore ICAR/02 - Costruzioni Idrauliche e Marittime e Idrologia</dc:subject>
<dc:description>Small catchments with fast hydrological responding geomorphology are commonly prone to Flash&#xd;
Floods, which have to be predicted on the basis of meteorological forecast and radar nowcasting, rather&#xd;
than continuously monitoring river water levels. Moreover, real-time rainfall observations at high&#xd;
resolution in time and space are required to understand the watershed processes involved. However&#xd;
small catchments are generally scarcely instrumented and only a sparse rain gauge network is available.&#xd;
This work analyze different flood forecasting techniques and investigates their applicability on some&#xd;
small catchments located in the east side of southern central Sardinia, where only a small number of&#xd;
rain gauges is available. The first part of the work investigates the sensitivity and accuracy of&#xd;
hydrological processes simulation of flooding events, using rain gauge networks of different density&#xd;
with a statistical approach, by means of a long synthetic rainfall dataset simulated at high resolution in&#xd;
time and space. Analyses on a set of 12 basins of different sizes, ranging from 15 up to 1800 km2, are&#xd;
performed on the hydrological response of two simplified rainfall-runoff models: a lumped and a&#xd;
distributed model. Results highlight a strong dependence of model performance with the event severity,&#xd;
and show that even in very small basins and regardless of the model approach implemented, to&#xd;
guarantee satisfying hydrological simulations, more rain gauges than those generally available are&#xd;
required. A final comparison with a real case, although restricted to very few rain gauges, seems to&#xd;
confirm the outcomes of the synthetic approach.&#xd;
In the second part of the work, different Flash Flood forecasting techniques are tested on two small&#xd;
basins (sizing 121 and 53 km2) provided with long observations at the rain gauges and hydrometric&#xd;
stations. These techniques are classified in two main forecasting approaches: RTCM (Rainfall&#xd;
Thresholds based on Conceptual Models) e RFTDM (Runoff and Frequency Thresholds based on&#xd;
Distributed Modelling).&#xd;
RTCM are strictly deterministic and provide rainfall thresholds for the entire basin through simple&#xd;
operational curves, obtained by applying different event-based lumped models in inverse mode, and&#xd;
taking into account only initial soil moisture content and event duration. These techniques are&#xd;
operationally easy and could be rapidly transferred to other catchments. Meanwhile they provide fairly&#xd;
good forecasting performances when base flow is rather low, even if an high false alarm rate is usually&#xd;
exhibited.&#xd;
RFTDM rely on a physically based distributed model which simulates continuously all hydrological&#xd;
basin processes: in this work tRIBS (TIN based Real Time Integrated Basin Simulator) is applied for its&#xd;
efficiency and computational speed. In particular two different approaches are proposed: Direct&#xd;
method, in which the model is part of a forecasting chain running continuously in real-time and&#xd;
simulating directly maximum floods on the basis of meteorological forecasts; Statistic method, through&#xd;
flood frequency analyses (FFA) on observed and simulated discharges, provides probabilistic flood&#xd;
predictions comparing occurrence frequencies rather than discharges. Results highlight a significant&#xd;
reduction of false alarms with respect to the RTCM, preserving good prediction skills in different&#xd;
operational conditions. Meanwhile, compared with the direct method, the expected forecasting&#xd;
improvement using statistic method has not detected, regardless of the probability distribution chosen&#xd;
for FFA. It’s important to note that Statistic method, notwithstanding the laborious setting, allows to&#xd;
creating alert maps for flash flooding. Outcomes suggests to pay particular attention when using alert&#xd;
maps produced by few rain gauges due to distortions induced through rainfall field sampling.</dc:description>
<dc:date>2015-05-08</dc:date>
<dc:type>info:eu-repo/semantics/doctoralThesis</dc:type>
<dc:identifier>http://hdl.handle.net/11584/266363</dc:identifier>
<dc:language>ita</dc:language>
<dc:relation>numberofpages:238</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:publisher>Università degli Studi di Cagliari</dc:publisher>
<dc:rights>license:Non specificato</dc:rights>
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