Massimiliano Pasqui

Legal immigrants: invasion of alien microbial communities during winter occurring desert dust storms

BackgroundA critical aspect regarding the global dispersion of pathogenic microorganisms is associated with atmospheric movement of soil particles. Especially, desert dust storms can transport alien microorganisms over continental scales and can deposit them in sensitive sink habitats. In winter 2014, the largest ever recorded Saharan dust event in Italy was efficiently deposited on the Dolomite Alps and was sealed between dust-free snow. This provided us the unique opportunity to overcome difficulties in separating dust associated from “domestic” microbes and thus, to determine with high precision microorganisms transported exclusively by desert dust.ResultsOur metagenomic analysis revealed that sandstorms can move not only fractions but rather large parts of entire microbial communities far away from their area of origin and that this microbiota contains several of the most stress-resistant organisms on Earth, including highly destructive fungal and bacterial pathogens. In particular, we provide first evidence that winter-occurring dust depositions can favor a rapid microbial contamination of sensitive sink habitats after snowmelt.ConclusionsAirborne microbial depositions accompanying extreme meteorological events represent a realistic threat for ecosystem and public health. Therefore, monitoring the spread and persistence of storm-travelling alien microbes is a priority while considering future trajectories of climatic anomalies as well as anthropogenically driven changes in land use in the source regions.

An Integrated Assessment of the Impacts of Changing Climate Variability on Agricultural Productivity and Profitability in an Irrigated Mediterranean Catchment

Climate change is likely to have a profound effect on many agricultural variables, although the extent of its influence will vary over the course of the annual farm management cycle. Consequently, the effect of different and interconnected physical, technical and economic factors must be modeled in order to estimate the effects of climate change on agricultural productivity. Such modeling commonly makes use of indicators that summarize the among environmental factors that are considered when farmers plan their activities. This study uses net evapotranspiration (ETN), estimated using EPIC, as a proxy index for the physical factors considered by farmers when managing irrigation. Recent trends suggest that the probability distribution function of ETN may continue to change in the near future due to changes in the irrigation needs of crops. Also, water availability may continue to vary due to changes in the rainfall regime. The impacts of the uncertainties related to these changes on costs are evaluated using a Discrete Stochastic Programming model representing an irrigable Mediterranean area where limited water is supplied from a reservoir. In this context, adaptation to climate change can be best supported by improvements to the collective irrigation systems, rather than by measures aimed at individual farms such as those contained within the rural development policy.

Functional clustering for Italian climate zones identification

This work presents a functional clustering procedure applied to meteorological time series. Our proposal combines time series interpolation with smoothing penalized B-spline and the partitioning around medoids clustering algorithm. Our final goal is to obtain homogeneous climate zones of Italy. We compare this approach to standard methods based on a combination of principal component analysis and Cluster Analysis (CA) and we discuss it in relation to other functional clustering approaches based on Fourier analysis and CA. We show that a functional approach is simpler than the standard methods from a methodological and interpretability point of view. Indeed, it becomes natural to find a clear connection between mathematical results and physical variability mechanisms. We discuss how the choice of the basis expansion (splines, Fourier) affects the analysis and propose some comments on their use. The basis for classification is formed by monthly values of temperature and precipitation recorded during the period 1971–2000 over 95 and 94 Italian monitoring stations, respectively. An assessment based on climatic patterns is presented to prove the consistency of the clustering and a comparison of results obtained with different methods is used to judge the functional data approach.

Modelling risk hurricane elements in potentially affected areas by a GIS system

In the last decade, modelling hurricanes in potentially affected areas using geographical information systems (GIS) and geospatial cyberinfrastracture (GCI) has become a major topic of research. Despite some basic approaches, some unsolved questions are still under discussion. The disastrous effects of hurricanes on communities are well known, however there is a need to better understand the hazard contributions of the different components related to a hurricane, such as storm surges, floods and high winds. In this paper, the selected approach is to determine an onset zoning from a set of attributes that are considered to govern the hurricane while examining the influence of each individual component that produces the final exposure. To this end, this study assesses the different components using parameters derived from topography, bathymetry and hurricane physical indexes. Key attributes are the river network, the topography, the wetness index and the offline bathymetry. Complementary data include the CMORPH rain dataset and the hurricane track together with its structure model, both based on National Oceanic and Atmospheric Administration (NOAA) datasets. Total hazard results were then overlaid with population data in the overall assessment of elements at risk. The approach, which made use of a number of available global and free datasets, was then validated on a regional basis using ground data collected by the World Food Programme (WFP) over the study area (Central America region) for a specific hurricane.

Modelling Hurricane Related Hazards and Risk through GIS for Early Warning Systems

Andrea Taramelli1, Massimiliano Pasqui2, Laura Melelli3, Monia Santini4 and Alessandro Sorichetta5 1ISPRA, Institute for Environmental Research, via di Casalotti, 300, Roma, 2Institute of Biometeorology – National Research Council, Roma, 3Department of Earth Science, University of Perugia, Perugia, 4euroMediterranean Center for Climate Changes, Lecce, 5Dipartimento di Scienze della Terra “Ardito Desio” , Universita degli Studi di Milano, Milano, Italy

Water management in a semi-arid region: an analogue algorithm approach for rainfall seasonal forecasting

Methods and results of this recent branch of atmospheric sciences must be the most simple and accessible as possible. For this reason, the Institute of Biometeorology, (part of the National Research Council, http://www.ibimet.cnr.it), has developed a physically — based statistical approach to obtain seasonal forecasts, regarding rainfall precipitation, over Sahel region.

Una valutazione integrata degli impatti produttivi ed economici del cambiamento della variabilità climatica in un’area mediterranea irrigua

Una valutazione integrata degli impatti produttivi ed economici del cambiamento della variabilita climatica in un’area mediterranea irrigua Quest’articolo analizza l’effetto dei cambiamenti climatici su temperature, piovosita, esigenze irrigue e disponibilita idrica di un’area mediterranea, integrando relazioni bio-fisiche ed economiche. L’analisi della distribuzione di probabilita (Pdf) delle variabili climatiche mostra che nel futuro prossimo aumentera la probabilita di avere temperature e fabbisogni irrigui alti e disponibilita idrica bassa. Un modello economico simula le scelte delle imprese agricole data l’incertezza rappresentata dalla variabilita delle Pdf. Le colture soffrono soprattutto per la minore disponibilita idrica, che si puo contrastare migliorando le infrastrutture irrigue collettive. La produzione del latte bovino patisce l’aumento delle temperature, che si puo mitigare con sistemi di raffrescamento.

Predicting Rainfall Fields from Lightning Records: A Hierarchical Bayesian Approach

Mixed models (linear and nonlinear) belong to a class of models in which some of the effects are fixed and some are random; formalization of these models is easily achieved in a hierarchical Bayesian framework. Here we propose a space-time mixed model to link rain measures and lightning counts in a given area of Central Italy.