Luca Carturan

Current Behaviour and Dynamics of the Lowermost Italian Glacier (Montasio Occidentale, Julian Alps)

Abstract Smaller glaciers (<0.5 km2) react quickly to environmental changes and typically show a large scatter in their individual response. Accounting for these ice bodies is essential for assessing regional glacier change, given their high number and contribution to the total loss of glacier area in mountain regions. However, studying small glaciers using traditional techniques may be difficult or not feasible, and assessing their current activity and dynamics may be problematic. In this paper, we present an integrated approach for characterizing the current behaviour of a small, avalanche‐fed glacier at low altitude in the talian lps, combining geomorphological, geophysical and high‐resolution geodetic surveying with a terrestrial laser scanner. The glacier is still active and shows a detectable mass transfer from the accumulation area to the lower ablation area, which is covered by a thick debris mantle. The glacier owes its existence to the local topo‐climatic conditions, ensured by high rock walls which enhance accumulation by delivering avalanche snow and reduce ablation by providing topographic shading and regulating the debris budget of the glacier catchment. In the last several years the glacier has displayed peculiar behaviour compared with most glaciers of the uropean lps, being close to equilibrium conditions in spite of warm ablation seasons. Proportionally small relative changes have also occurred since the Little Ice Age maximum. Compared with the majority of other Alpine glaciers, we infer for this glacier a lower sensitivity to air temperature and a higher sensitivity to precipitation, associated with important feedback from increasing debris cover during unfavourable periods.

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.

Enhanced estimation of glacier mass balance in unsampled areas by means of topographic data

Abstract A new method was developed to estimate the mass balance in unsampled areas from existing datasets. Three years of mass-balance data from two glaciers in the central Italian Alps were used to develop and test a multiple-regression method based exclusively on a 10m resolution digital terrain model. The introduction of a relative elevation attribute, which expresses the degree of wind exposure of the gridcells, notably increased the amount of explainable variance in winter balance with respect to altitude itself. The summer balance is highly correlated with elevation, but, in order to obtain reliable extrapolations, the clear-sky shortwave radiation and the diurnal cloud-cover cycle had to be taken into account. The net annual mass balance on a glacier system comprising the two monitored glaciers was calculated by applying both a single regression of winter and summer balance with altitude and the new regression method. The consistency of results was assessed against measured net balances and snow-cover maps drawn in the ablation season. The results of the new method were in close agreement with observations and proved to be less sensitive to the spatial representation of the sampled areas.

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.

Deploying a Communicating Automatic Weather Station on an Alpine Glacier

Abstract The cost and effort of installing and maintaining an automatic weather station (AWS) on a glacier may be mitigated by the possibility of gathering sensor data in near real-time, and of controlling and programming the station remotely. In this paper we report our experience with upgrading an existing AWS, operating over an Italian glacier, from a mere datalogger into a networked sensing station. Design choices, energy constraints and power-aware programming of the station determined by harsh environment are discussed. Deployment operations and results are described. The upgraded AWS provides low-power connectivity from a remote location and is able to serve as a base station for a wireless sensor network working in the glacier.

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.

TOPMELT 1.0: A topography-based distribution function approachto snowmelt simulation for hydrological modelling at basin scale

Enhanced temperature-index distributed models for snowpack simulation, incorporating air temperature and a term for clear sky potential solar radiation, are increasingly used to simulate the spatial variability of the snow water equivalent. This paper presents a new snowpack model (termed TOPMELT) which integrates an enhanced temperature-index model into the ICHYMOD semidistributed basin-scale hydrological model by exploiting a statistical representation of the distribution of clear sky potential solar radiation. This is obtained by discretizing the full spatial distribution of clear sky potential solar radiation into a number of radiation classes. The computation required to generate a spatially distributed water equivalent reduces to a single calculation for each radiation class. This turns into a potentially significant advantage when parameter sensitivity and uncertainty estimation procedures are carried out. The radiation index may be also averaged in time over given time periods. Thus, the model resembles a classical temperatureindex model when only one radiation class for each elevation band and a temporal aggregation of 1 year is used, whereas it approximates a fully distributed model by increasing the number of the radiation classes and decreasing the temporal aggregation. TOPMELT is integrated within the semidistributed ICHYMOD model and is applied at an hourly time step over the Aurino Basin (also known as the Ahr River) at San Giorgio (San Giorgio Aurino), a 614 km2 catchment in the Upper Adige River basin (eastern Alps, Italy) to examine the sensitivity of the snowpack and runoff model results to the spatial and temporal aggregation of the radiation fluxes. It is shown that the spatial simulation of the snow water equivalent is strongly affected by the aggregation scales. However, limited degradation of the snow simulations is achieved when using 10 radiation classes and 4 weeks as spatial and temporal aggregation scales respectively. Results highlight that the effects of space–time aggregation of the solar radiation patterns on the runoff response are scale dependent. They are minimal at the scale of the whole Aurino Basin, while considerable impact is seen at a basin scale of 5 km2.

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.