Roberto Seppi

Reconstructing Fluctuations of La Mare Glacier (Eastern Italian Alps) in the Late Holocene: New Evidence for a Little Ice Age Maximum Around 1600 AD

Abstract Field observations, old terrestrial photographs and maps, aerial orthophotos and detailed geomorphological mapping were used for compiling and validating a 119‐year cumulative record of terminus changes for a are lacier, astern talian lps. ate olocene glacier maxima preceding direct observations were reconstructed by applying age dating techniques (radiocarbon and lichenometry) to glacial deposits in the proglacial area of the glacier. Results show that the glacier reached its maximal position around 1600 ad, followed by smaller advances in the eighteenth century, while in the nineteenth century it did not reach or overrun these positions. A similar behaviour for neighbouring glaciers was reported by previous works, documenting absolute ate olocene maxima in the seventeenth or eighteenth centuries. By contrast, multi‐century reconstructions available for the north‐western lps show that in the nineteenth century, glaciers were at their maximum or very close to previous maxima achieved in the first half of the seventeenth century. Climatic causes for these discrepancies have been examined, analyzing multi‐proxy climatic reconstructions starting in 1500 ad, but also morphodynamic processes linked to the bedrock characteristics of a are lacier could have played a role in modulating its response to climatic changes.

DISCOVERY OF COLD ICE IN A NEW DRILLING SITE IN THE EASTERN EUROPEAN ALPS

During autumn 2011 we extracted the first ice cores drilled to bedrock in the eastern European Alps from a new drilling site on the glacier Alto dell’Ortles (3859 m, South Tyrol, Italy). Direct ice core observations and englacial temperature measurements provide evidence of the concomitant presence of shallow temperate firn and deep cold ice layers (ice below the pressure melting point). To the best of our knowledge, this is the first cold ice observed within a glacier of the eastern European Alps. These ice layers probably represent a unique remnant from the colder climate occurring before ~1980 AD. We conclude that the glacier Alto dell’Ortles is now changing from a cold to a temperate state. The occurrence of cold ice layers in this glacier enhances the probability that a climatic and environmental record is fully preserved in the recovered ice cores.

Physical and biological features of an active rock glacier in the Italian Alps

We report on the key physical features of an active rock glacier that influence the distribution of plants and arthropods. We also perform a comparison with neighboring scree slope and alpine grassland to test whether the environmental features of the rock glacier drive the presence of specific species assemblages. Compared with scree slope and grassland, the studied rock glacier provides particular physical features that determine the presence of unique species. Plant distribution is mainly driven by grain size. Arthropod distribution is linked to grain size, with cold-adapted species found on areas with coarse-grained deep debris, which also shows a distinctive temperature regime with very low values throughout the year. On the basis of these findings, we advance the hypothesis that rock glaciers provide specific ecological conditions creating potential refugia for cold-demanding species during warm climatic periods.

Life in harsh environments: carabid and spider trait types and functional diversity on a debris-covered glacier and along its foreland

1. Patterns of species richness and species assemblage composition of ground‐dwelling arthropods in primary successions along glacier forelands are traditionally described using a taxonomic approach. On the other hand, the functional trait approach could ensure a better characterisation of their colonisation strategies in these types of habitat.

A Pol-SAR Analysis for Alpine Glacier Classification and Snowline Altitude Retrieval

In this study, we investigated the use of synthetic aperture radar (SAR) polarimetry (Pol-SAR) and a supervised classification technique, support vector machine (SVM), for the classification of bare soil, ice, and snow, over the Ortles-Cevedale massif, (Eastern Italian Alps). We analyzed the importance of topographic correction on the backscattering and polarimetric SAR signature and the advantage of quad-pol with respect to dual-pol data. When backscattering values only are employed, the incidence angle used as input feature of the SVM classifier assures the best classification accuracy, 9.9% higher than the accuracy obtained with cosine corrected γ0 backscattering. The introduction of polarimetric features and decomposition parameters (such as Cloude-Pottier or Touzi decomposition parameters) increases the classification accuracy by 5.2% with respect to the backscattering case. The simulation of RADARSAT-2 data as Sentinel-1 like for dual-pol data shows a decrease of accuracy equal to 7.8% with respect to the fully polarimetric case (93.5%). The first Sentinel-1 image acquired on our test area was also employed for classification. We then tested the capability of C-band SAR to detect accumulation and ablation zones of the glaciers under the winter dry snow by setting up a multi-incidence angle and fully polarimetric SVM classifier, exploiting ascending and descending RADARSAT2 data. In this case, the accuracy increased by 14.7% combining different geometric acquisitions (88.9%) with respect to the single geometry case. Finally, from the resulting classification maps, we extracted the snowline altitude for a sample of three glaciers, using both optical and SAR data, comparing the different products.

Feedback effects between plant and flower-visiting insect communities along a primary succession gradient

Primary successions of glacier forelands are unique model systems to investigate community dynamics and assembly processes. However, successional changes of plant and insect communities have been mainly analysed separately. Therefore, changes in plant–insect interactions along successional gradients on glacier forelands remain unknown, despite their relevance to ecosystem functioning. This study assessed how successional changes of the vegetation influenced the composition of the flower-visiting insect assemblages of two plant species, Leucanthemopsis alpina (L.) Heyw. and Saxifraga bryoides L., selected as the only two insect-pollinated species occurring along the whole succession. In addition, we investigated the links between reproductive output of these plants and pollinator abundance through experimental exclusion of pollinators. Plant community structure changed along the succession, affecting the distribution and the abundance of insects via idiosyncratic responses of different insect functional groups. L. alpina interacted with ubiquitously distributed pollinators, while S. bryoides pollinators were positively associated with insect-pollinated plant species density and S. bryoides abundance. With succession proceeding, insect assemblages became more functionally diverse, with the abundance of parasitoids, predators and opportunists positively related to an increase in plant cover and diversity. The reproductive output of both plant species varied among successional stages. Contrary to our expectation, the obligate insect-pollinated L. alpina showed a reproductive output rather independent from pollinator abundance, while the reproductive output of the self-fertile S. bryoides seemed linked to pollinator abundance. Observing ecological interactions and using functional traits, we provided a mechanistic understanding of community assembly processes along a successional gradient. Plant community diversity and cover likely influenced insect community assembly through bottom-up effects. In turn, pollinators regulate plant reproductive output through top-down control. We emphasise that dynamics of alpine plant and insect communities may be structured by biotic interactions and feedback processes, rather than only be influenced by harsh abiotic conditions and stochastic events.

WESNEP: A Wireless Environmental Sensor Network for Permafrost Studies

The aim of this paper is to give an overview of WESNEP, an environmental wireless sensor network which is currently being developed at the University of Pavia in order to study alpine permafrost. After a brief introduction on environmental sensor networks and the definition of permafrost, the motivation of WESNEP project and the architecture of the network are described, finally the main benefits expected from the project are presented.

A multitemporal probabilistic error correction approach to SVM classification of alpine glacier exploiting sentinel-1 images (Conference Presentation)

In mountain regions and their forelands, glaciers are key source of melt water during the middle and late ablation season, when most of the winter snow has already melted. Furthermore, alpine glaciers are recognized as sensitive indicators of climatic fluctuations. Monitoring glacier extent changes and glacier surface characteristics (i.e. snow, firn and bare ice coverage) is therefore important for both hydrological applications and climate change studies. Satellite remote sensing data have been widely employed for glacier surface classification. Many approaches exploit optical data, such as from Landsat. Despite the intuitive visual interpretation of optical images and the demonstrated capability to discriminate glacial surface thanks to the combination of different bands, one of the main disadvantages of available high-resolution optical sensors is their dependence on cloud conditions and low revisit time frequency. Therefore, operational monitoring strategies relying only on optical data have serious limitations. Since SAR data are insensitive to clouds, they are potentially a valid alternative to optical data for glacier monitoring. Compared to past SAR missions, the new Sentinel-1 mission provides much higher revisit time frequency (two acquisitions each 12 days) over the entire European Alps, and this number will be doubled once the Sentinel1-b will be in orbit (April 2016). In this work we present a method for glacier surface classification by exploiting dual polarimetric Sentinel-1 data. The method consists of a supervised approach based on Support Vector Machine (SVM). In addition to the VV and VH signals, we tested the contribution of local incidence angle, extracted from a digital elevation model and orbital information, as auxiliary input feature in order to account for the topographic effects. By exploiting impossible temporal transition between different classes (e.g. if at a given date one pixel is classified as rock it cannot be classified as glacier ice in a following date) we here propose an innovative post classification correction based on SVM classification probabilities. Optical data, i.e. Landsat-8 and Sentinel-2, have been employed, when available, for training sample collection. Detailed field observations from two glaciers located in the Ortles-Cevedale massif (Eastern Italian Alps) have been employed for validation.

Combined GPR and TDR measurements for snow thickness and density estimation

The Ground Penetrating Radar (GPR) is a technique capable to perform a fast monitoring of snowpack and glaciers, providing an estimation of some snow parameters like thickness, density and Snow Water Equivalent (SWE). The most important quantity to know to understand GPR data is the wave velocity in the snow, in order to transform the traveltime in depth. Independent measurements of wave velocity could be performed using the Time Domain Reflectometry (TDR) or estimating the electrical permittivity from density measurements. Here an evaluation of the accuracy of TDR measurements to estimate the wave velocity is proposed and the results are corroborated by independent measurements of snow height and density.

Seasonal river discharge forecast in alpine catchments using snow map time series and support vector regression approach

The prediction of monthly mean discharge is critical for water resources management. Statistical methods applied on discharge time series are traditionally used for predicting this kind of slow response hydrological events. With this paper we present a Support Vector Regression (SVR) system able to predict monthly mean discharge considering discharge and snow cover extent (250 meters resolution obtained by MODIS images) time series as inputs. Additional meteorological and climatic variables are also tested as inputs for the SVR approach. The prediction system has been evaluated on 14 catchments in South Tyrol (Northern Italy). Considering as a reference the estimates based on the average discharge computed on the past 10 years, which is a common practice for water resources management in the study region, the percentage root mean square error (RMSE%) is reduced of 11% and 6% for a prediction lag of 1 and 3 months respectively.