On air temperature fluctuations immediately above a glacier surface

Developing remote sensing techniques for measuring meteorological parameters in surface layers of snow field

A semantic web approach for built heritage representation

In a built heritage process, meant as a structured system of activities aimed at the investigation, preservation, and management of architectural heritage, any task accomplished by the several actors involved in it is deeply influenced by the way the knowledge is represented and shared. In the current heritage practice, knowledge representation and management have shown several limitations due to the difficulty of dealing with large amount of extremely heterogeneous data. On this basis, this research aims at extending semantic web approaches and technologies to architectural heritage knowledge management in order to provide an integrated and multidisciplinary representation of the artifact and of the knowledge necessary to support any decision or any intervention and management activity. To this purpose, an ontology-based system, representing the knowledge related to the artifact and its contexts, has been developed through the formalization of domain-specific entities and relationships between them

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

Spatially variable rate herbicide application on durum wheat in Sicily

Using the conventional farming system, durum wheat requires high rates of herbicide spraying. Herbicide residues can cause pollution of soil and ground water and, therefore, of the entire environment. In order to minimise the environmental impact of herbicides, a home made system for spatially variable rate crop input application was designed, developed and set up by the Department of Engineering and Technologies in Agriculture and Forestry (I.T.A.F.). This system consists of a DGPS, a portable computer, a specifically developed software and a device for applying rates proportionally related to the machine forward speed (DPA). Tests of spatially variable rate herbicide application were carried out in inland Sicily, on a field of 8.4 ha (where a three-year crop rotation, broad bean/vetch - durum wheat - durum wheat, was practised), using a sprayer modified for applying variable rates and equipped with the above mentioned system. The results are promising. The spatially variable rate herbicide application allowed an almost even grain yield over the entire field and a saving of 29% of herbicides with respect to the amounts normally used with the conventional farming system

Gradings of non-graded Hamiltonian Lie algebras

A thin Lie algebra is a Lie algebra graded over the positive integers satisfying a certain narrowness condition. We describe several cyclic grading of the modular Hamiltonian Lie algebras H(2\colon\n;\omega_2) (of dimension one less than a power of $p$) from which we construct infinite-dimensional thin Lie algebras. In the process we provide an explicit identification of H(2\colon\n;\omega_2) with a Block algebra. We also compute its second cohomology group and its derivation algebra (in arbitrary prime characteristic).Comment: 36 pages, to be published in J. Austral. Math. Soc. Ser.

Impact of mapping errors on the reliability of landslide hazard maps

International audienceIdentification and mapping of landslide deposits are an intrinsically difficult and subjective operation that requires a great effort to minimise the inherent uncertainty. For the Staffora Basin, which extends for almost 300 km2 in the northern Apennines, three landslide inventory maps were independently produced by three groups of geomorphologists. In comparing each map with the others, large positional discrepancies arise (in the range of 55?65%). When all three maps are overlain, the locational mismatch of landslide deposit polygons increases to over 80%. To assess the impact of these errors on predictive models of landslide hazard, for the study area discriminant models were built up from the same set of geological-geomorphological factors as predictors, and the occurrence of landslide deposits within each terrain-unit, derived from each inventory map, as dependent variable. The comparison of these models demonstrates that statistical modelling greatly minimises the impact of input data errors which remain, however, a major limitation on the reliability of landslide hazard maps

Mapping of penetrometer resistance in relation to tractor traffic using multivariate geostatistics

The traffic of agricultural machines can cause soil compaction and high variability of soil structure, both along normal lines and along those parallel to the field plane. The aim of this research is to investigate the potential of geostatistical techniques for understanding and evaluating the within-field spatial variability of soil compaction, caused by the traffic of agricultural machines and/or the action of tillage implements. In July 2003 soil cone penetrometer resistance was measured in a sandy-silt Cambisol of inland Sicily, where a three-year rotation wheat (Triticum durum Desf.) - wheat - tomato (Solanum lycopersicum L.) was adopted, along three parallel 3-m long transects, from the soil surface to a depth of 0.70 m. A multivariate geostatistical approach, including exploratory analysis, variography, stochastic simulation and post-processing of simulations was applied to produce thematic maps of penetrometer resistance and probability maps exceeding a critical value, corresponding to different examples of tractor movement. Penetrometer resistance variation was erratic at the surface but showed high spatial correlation between data measured at different depths. The maps of probabilistic compaction risk showed that the soil volume, exceeding the penetrometer resistance of 2.5 MPa, critical for root growth, increased from 20% to 40% after the tractor had passed through five times

Multivariate geostatistics for assessing and predicting soil compaction

The aim of this research is to investigate the potential of geostatistical techniques for understanding and evaluating the spatial variability of soil compaction, caused by the traffic of agricultural machines and/or the action of tillage implements. Soil cone penetrometer resistance was measured in a field of inland Sicily, along a transect of 3 m length, from the soil surface until 70 cm depth. The 3D mean maps showed a random variation on the surface and a high spatial correlation among penetrometer resistance data measured at different depths. The map corresponding to five tractor passes showed the largest extension of the areas characterised by the highest values of penetrometer resistance. The probability maps showed that at least 20% of the monitored soil volume can exceed the critical penetrometer resistance for root growth