1,721,021 research outputs found
A General Formulation to Describe Empirical Rainfall Thresholds for Landslides
Full text linkAbstractIn this paper, a brief description of the Generalized FLaIR Model (GFM, De Luca and Versace, 2016) is provided, that is able to reproduce all the empirical thresholds proposed in literature, aimed to forecast landslides triggered by rainfall. In particular, this paper focuses on Antecedent Precipitation (AP) schemes. The paper demonstrates that these are particular solutions of the GFM and will exemplify this using AP schemes for NE Italy1, Seattle2 and Nicaragua - El Salvador3
Early warning of rainfall-induced landslides based on empirical mobility function predictor
No full textA stochastic real time predictor of rainfall-induced landslides has been developed. It couples
the empirical slope stability model FLaIR with a point rainfall stochastic model. FLaIR model
introduces a slope mobility function which links the occurrence of a slide movement to the
characteristics of antecedent rainfall. Point rainfall external intermittence, namely the alternation of
storms and dry periods, is modelled as an alternating renewal process (ARP). The properties of the
ARP allow to assume that, during a storm, the future evolution of the mobility function depends only, in
stochastic sense, on the hyetograph observed after the beginning of the ongoing storm (internal
intermittence). Thus, the expected value of the mobility function is empirically evaluated by selecting,
from the historical data set, only the storms with characteristics similar to the ongoing one. The
predictor has been calibrated and validated on the basis of a nearly 48 years long hourly rainfall data
record, collected by the rain gauge of Lanzo, in Northern Italy, close to the slope of Pessinetto, where
six earth flows occurred during the observation period. The obtained results show that the proposed
model provides reliable real time predictions of the slope mobility function up to a lead time of six
hours. The proposed predictor has been also tested as a part of an early warning system against earth
flows to be operated at the slope of Pessinetto, by defining two threshold values of the mobility
function, corresponding to alert and alarm level, respectively. The obtained results show that, by
properly setting the levels of probability of exceeding the two thresholds, at which the corresponding
messages are launched by the system, it is possible, with a low number of false and missing messages,
to gain some hours for effectively activating risk mitigation procedures
Rainfall height stochastic modeling as a support tool for floods and flowslides early warning
No full textEvaluation of the mechanisms underlying the kainate-induced impairment of [3H]dopamine release in the rat striatum.
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A space-time generator for rainfall nowcasting: The PRAISEST model
Full text linkThe paper introduces a stochastic technique for forecasting rainfall in space-time domain: the PRAISEST Model (Prediction of Rainfall Amount Inside Storm Events: Space and Time). The model is based on the assumption that the rainfall height H accumulated on an interval Δ between the instants iΔt and (i+1)Δt and on a spatial cell of size ΔxΔy is correlated either with a variable Z, representing antecedent precipitation at the same point, either with a variable W, representing simultaneous rainfall at neighbour cells. The mathematical background is given by a joined probability density fH,W,Z (h,w,z) in which the variables have a mixed nature, that is a finite probability for null value and infinitesimal probabilities for the positive values. As study area, the Calabria region, in Southern Italy, has been selected. The region has been discretised by 10 km10 km cell grid, according to the raingauge network density in this area. Storm events belonging to 1990-2004 period were analyzed to test performances of the PRAISEST model. © 2009 Author(s)
Functional damage of dopamine nerve terminals following intrastriatal kainic acid injection
No full textRainfall nowcasting by at site stochastic model P.R.A.I.S.E
Full text linkThe paper introduces a stochastic model to forecast rainfall heights at site: the P.R.A.I.S.E. model (Prediction of Rainfall Amount Inside Storm Events). PRAISE is based on the assumption that the rainfall height H i+1 accumulated on an interval Δt between the instants i Δt and (i+1) Δt is correlated with a variable Zi(v) representing antecedent precipitation. The mathematical background is given by a joined probability density fHi+1,Z i(v) (hi+1,zi(v)) in which the variables have a mixed nature, that is a finite probability in correspondence to the null value and infinitesimal probabilities in correspondence to the positive values. As study area, the Calabria region, in Southern Italy, was selected, to test performances of the PRAISE model
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