Elisa Destro

Subsurface thermal conductivity assessment in Calabria (southern Italy): a regional case study

Determining the suitability of a local area at a regional or local scale for the geothermal application of low enthalpy systems requires the knowledge of rock thermal conductivity values to evaluate the possibility of low-enthalpy heat exchange. A digital cartographic tool is also needed to synthesize the thermal properties of the underground. This tool should be easily accessible and upgradeable and thus suitable for territorial planning and environmental control. In order to address these key issues, a methodological approach was developed within the framework of the national VIGOR Project, dedicated to evaluating the geothermal potential in southern Italy. In this paper the region of Calabria was selected as a case study. Around 70 samples that were representative of the main geological formations were collected from all over the area. Thermal property tests were carried out both in dry and wet conditions, using a thermal device in accordance with the modified transient plane source method. The thermal conductivity values were then compared with data from the international literature. In order to consider the influence of the entire stratigraphic sequence on the thermal conductivity parameters, a geostatistical analysis of the available lithostratigraphic data was performed using the MATLAB toolbox Modalstrata, specially developed for this purpose. A comprehensive geothermal subsurface characterization of Calabria was thus obtained.

Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds

The systematic underestimation observed in debris flows early warning thresholds has been associated to the use of sparse rain gauge networks to represent highly non-stationary rainfall fields. Remote sensing products permit concurrent estimates of debris flow-triggering rainfall for areas poorly covered by rain gauges, but the impact of using coarse spatial resolutions to represent such rainfall fields is still to be assessed. This study uses fine resolution radar data for ∼100 debris flows in the eastern Italian Alps to (i) quantify the effect of spatial aggregation (1–20-km grid size) on the estimation of 5 debris flow triggering rainfall and on the identification of early warning thresholds and (ii) compare thresholds derived from aggregated estimates and rain gauge networks of different densities. The impact of spatial aggregation is influenced by the spatial organization of rainfall and by its dependence on the severity of the triggering rainfall. Thresholds from aggregated estimates show up to 8%–21% variations in the parameters whereas more than 10%–25% systematic variations result from the use of rain gauge networks, even for densities as high as 1/10 km−2. 10

Shallow Geothermal Exploration by Means of SkyTEM Electrical Resistivity Data: An Application in Sicily (Italy)

A novel procedure for estimating the geothermal energy exchanged by a unit volume was tested in northern Sicily (Italy), where public well data for depicting the complex geological setting were insufficient. An airborne electromagnetic survey was carried out in 2011, providing a 3D cell distribution of resistivity values. The integrated analysis of geological and resistivity data was used to identify six Litho-Electrical Units and to build a 3D geological model. This model was integrated with laboratory thermal conductivity measurements on rock samples, and was used to characterize the heat exchange at depths of up to 200 m, which in turn can be exploited for planning and designing geothermal heating and cooling plants using GSHP (Ground Source Heat Pump).

Evaluation of predictive models for post-fire debris flowsoccurrence in the western United States

Rainfall-induced debris flows in recently burned mountainous areas cause significant economic losses and human casualties. Currently, prediction of post-fire debris flows is widely based on the use of power-law thresholds and logistic regression models. While these procedures have served with certain success in existing operational warning systems, in this study we investigate the potential to improve the efficiency of current predictive models with machinelearning approaches. Specifically, the performance of a predictive model based on the random forest algorithm is compared with current techniques for the prediction of post-fire debris flow occurrence in the western United States. The analysis is based on a database of post-fire debris flows recently published by the United States Geological Survey. Results show that predictive models based on random forest exhibit systematic and considerably improved performance with respect to the other models examined. In addition, the random-forest-based models demonstrated improvement in performance with increasing training sample size, indicating a clear advantage regarding their ability to successfully assimilate new information. Complexity, in terms of variables required for developing the predictive models, is deemed important but the choice of model used is shown to have a greater impact on the overall performance.