Tracing the Long-Term Evolution of Land Cover in an Alpine Valley 1820–2015 in the Light of Climate, Glacier and Land Use Changes

Alpine glacial environments and their fluvial systems are among those landscapes most comprehensively affected by climate change. Typically, studies on the consequences of climate change in such environments, e.g., glacier retreat, cover a maximum of 70 years, reflecting the availability of orthophotos or satellite images. This study addresses the long-term transformation processes in a glaciated catchment and highlights the role of human agency in a changing Alpine environment. In order to identify land cover changes between 1820 and 2015 in the Long-Term Ecosystem Research (LTER) site “Jamtal” (Tyrol, Austria) we apply a “regressive-iterative GIS reconstruction method” combining both historical maps and optical remote sensing data. Below 2,100 m a.s.l. the Jamtal experienced a massive 62% decline of unvegetated debris areas and bedrock outcrops (so-called “wasteland”) that was mainly transformed to grassland and sparsely wooded areas. Forests increased by an outstanding 323% and grassland was replaced by sparsely or densely wooded areas. This primarily reflects the abandonment of agricultural uses at unfavourable remote sites. In the higher (formerly) glaciated subbasin, ice-covered areas declined by 55%, which was associated with a major (82%) growth of exposed wasteland. Concurrently, Alpine grassland expanded by 196% and krumholz even by 304%. Approximately half of the new fluvial system that evolved in deglaciated areas between 1870 and 1921 still existed in 2015. Unconsolidated debris buried almost one fifth of the new channels, and almost one third was colonized by vegetation. Recent data show that the deglaciation process is much faster than the colonization process by Alpine vegetation. Accordingly, the extent of wasteland has expanded and potentially amplifies the sediment supply to the fluvial system. Alterations in high Alpine hydrological and sediment/debris regimes significantly affect human use in lower, more favourable areas of the Alpine region. The long-term investigation of the Alpine landscape reveals that the transformation processes have accelerated in recent decades.

Possible contribution of passive radiometers to the analysis of the evolution of fog episodes

<p>The Regional Agency for the Environment of Veneto (ARPAV - North East Italy) through its Meteorological Center, has been managing some passive radiometers since 2005, one located in the Po Valley in the historic center of Padua (210,000 inh.) and the other in a wide pre-Alpine valley (Val Belluna) in the municipality of Feltre (20,000 inh.). Both instruments are located on the roof of the host building and scan the atmosphere with a thermal profile every 5 min. In the Po Valley and in Val Belluna there are frequent episodes of fog, especially in the autumn / winter season, which can sometimes persist throughout the day.</p> <p>Both radiometers are MTP-5 HE produced by Attex and are able to obtain the thermal profile up to 1 km, with an interpolated value every 50 m, in almost all weather conditions, using a single channel centered on the absorption of the molecular oxygen microwave at 60 GHz. This simple type of radiometer, very useful for studying the characteristics of thermal inversion, or super-adiabatic heating of the first layers of the atmosphere, is widely used to characterize the PBL (Planetary Boundary Layer) in terms of atmospheric stability.</p> <p>The proposed study seeks to explore the possibility of using thermal profile data from passive radiometers to study fog evolution only at the Padua site, where other meteorological information is also available, such as professional weather stations, webcams and visibilimeters. A statistic of the phenomenon will be presented, with the help of satellite images, from data series of over 10 years, together with some case studies, which will try to highlight the limits and effectiveness of this new approach.</p>

Triggering Bimodal Radial Stem Growth in Pinus sylvestris at a Drought-Prone Site by Manipulating Stem Carbon Availability

A bimodal radial growth (RG) pattern, i.e., growth peaks in spring and autumn, was repeatedly found in trees in the Mediterranean regions, where summer drought causes reduction or cessation of cambial activity. In a dry inner Alpine valley of the Eastern Alps (Tyrol, Austria, 750 m asl), Pinus sylvestris shows unimodal RG with onset and cessation of cambial activity in early April and late June, respectively. A resumption of cambial activity after intense summer rainfall was not observed in this region. In a field experiment, we tested the hypothesis that early cessation of cambial activity at this drought-prone site is an adaptation to limited water availability leading to an early and irreversible switch of carbon (C) allocation to belowground. To accomplish this, the C status of young P. sylvestris trees was manipulated by physical blockage of phloem transport (girdling) 6 weeks after cessation of cambial cell division. Influence of manipulated C availability on RG was recorded by stem dendrometers, which were mounted above the girdling zone. In response to blockage of phloem flow, resumption of cambial activity was detected above girdling after about 2 weeks. Although the experimentally induced second growth surge lasted for the same period as in spring (c. 2 months), the increment was more than twice as large due to doubling of daily maximum RG rate. After girdling, wood anatomical traits above girdling no longer showed any significant differences between earlywood and latewood tracheids indicating pronounced effects of C availability on cell differentiation. Below girdling, no reactivation of cambial activity occurred, but cell wall thickness of last formed latewood cell was reduced due to lack of C supply after girdling. Intense RG resumption after girdling indicates that cessation of cambial activity can be reversed by manipulating C status of the stem. Hence, our girdling study yielded strong support for the hypothesis that belowground organs exert high C sink strengths on the drought-prone study site. Furthermore, this work highlights the need of in-depth experimental studies in order to understand the interactions between endogenous and exogenous factors on cambial activity and xylem cell differentiation more clearly.

Influence of low-level blocking and turbulence on the microphysics of a mixed-phase cloud in an inner-Alpine valley

Previous studies that investigated orographic precipitation have primarily focused on isolated mountain barriers. Here we investigate the influence of low-level blocking and shear-induced turbulence on the cloud microphysics and precipitation formation in a complex inner-Alpine valley. The analysis focuses on a mid-level cloud in a post-frontal environment and a low-level feeder cloud induced by an in-valley circulation. Observations were obtained from an extensive set of instruments including ground-based remote sensing instrumentation, in situ instrumentation on a tethered-balloon system and ground-based precipitation measurements. During this event, the boundary layer was characterized by a blocked low-level flow and enhanced turbulence in the region of strong vertical wind shear at the boundary between the blocked layer in the valley and the stronger cross-barrier flow aloft. Cloud radar observations indicated changes in the microphysical cloud properties within the turbulent shear layer including enhanced linear depolarization ratio (i.e., change in particle shape or density) and increased radar reflectivity (i.e., enhanced ice growth). Based on the ice particle habits observed at the surface, we suggest that riming, aggregation and needle growth occurred within the turbulent layer. Collisions of fragile ice crystals (e.g., dendrites, needles) and the Hallett–Mossop process might have contributed to secondary ice production. Additionally, in situ instrumentation on the tethered-balloon system observed the presence of a low-level feeder cloud above a small-scale topographic feature, which dissipated when the low-level flow turned from a blocked to an unblocked state. Our observations indicate that the low-level blocking (due to the downstream mountain barrier) created an in-valley circulation, which led to the production of local updrafts and the formation of a low-level feeder cloud. Although the feeder cloud did not enhance precipitation in this particular case (since the majority of the precipitation sublimated when falling through a subsaturated layer above), we propose that local flow effects such as low-level blocking can induce the formation of feeder clouds in mountain valleys and on the leeward slope of foothills upstream of the main mountain barrier, where they can act to enhance orographic precipitation through the seeder–feeder mechanism.

Measuring changes in snowpack SWE continuously on a landscape scale using lake water pressure

<p>The seasonal snowpack is a globally important water resource that is notoriously difficult to measure. Existing instruments make measurements of falling or accumulating snow water equivalent (SWE) that are susceptible to bias, and most can represent only a point in the landscape. Furthermore the global array of SWE sensors is too sparse and too poorly distributed to be an adequate constraint on snow in weather and climate models. We present a new approach to monitoring snowpack SWE from time series of lake water pressure. We tested our method in the lowland Finnish Arctic and in an alpine valley and high-mountain cirque in Switzerland, and found that we could measure changes in SWE and their uncertainty through snowfalls with little bias and with an uncertainty comparable to or better than that achievable by other instruments. More importantly, our method inherently senses change over the whole lake surface which can be several square kilometres, or hundreds of million of times larger than the aperture of a pluviometer. This large scale makes our measurements directly comparable to the grid cells of weather and climate models. We find, for example, snowfall biases of up to 100% in operational forecast models AROME-Arctic and COSMO-1. Seasonally-frozen lakes are widely distributed at high latitudes and are particularly common in mountain ranges, hence our new method is particularly well suited to the widespread, autonomous monitoring of snow-water resources in remote areas that are largely unmonitored today. This is potentially transformative in reducing uncertainty in regional precipitation and runoff in seasonally-cold climates.</p>

Multivariate postprocessing using Cholesky-based multivariate Gaussian regression

<p>To obtain reliable joint probability forecasts, multivariate postprocessing of numerical weather predictions (NWPs) must take into account dependencies among the univariate forecast errors—across different forecast horizons, locations or atmospheric quantities. We develop a framework for multivariate Gaussian regression (MGR), a flexible multivariate postprocessing technique with advantages over state-of-the-art methods.</p><p>In MGR both mean forecasts and parameters describing their error covariance matrix may be modeled simultaneously on NWP-derived predictor variables. The bivariate case is straightforward and has been used to postprocess horizontal wind vector forecasts, but higher dimensions present two major difficulties: ensuring the estimated error covariance matrix is positive definite and regularizing the high model complexity.</p><p>We tackle these problems by parameterizing the covariance through the entries of its basic and modified Cholesky decompositions. This ensures its positive definiteness and is the crucial fact making it possible to link parameters with predictors in a regression.  When there is a natural order to the variables, we can also sensibly reduce complexity through a priori restrictions of the parameter space.</p><p>MGR forecasts take the form of full joint parametric distributions—in contrast to ensemble copula coupling (ECC) that obtains samples from the joint distribution. This has the advantage that joint probabilities or quantiles can be easily derived.</p><p>Our novel method is applied to postprocess NWPs of surface temperature at an Alpine valley station for ten distinct lead times more than one week in the future.  All the mean forecasts and their full error covariance matrix are modelled on NWP-derived variables in one step. MGR outperforms ECC in combination with nonhomogeneous Gaussian regression.</p>