A Novel Method to Quantify Near-Surface Boundary-Layer Dynamics at Ultra-High Spatio-Temporal Resolution.

The lateral transport of heat above abrupt (sub-)metre-scale steps in land surface temperature influences the local surface energy balance. We present a novel experimental method to investigate the stratification and dynamics of the near-surface atmospheric layer over a heterogeneous land surface. Using a high-resolution thermal infrared camera pointing at synthetic screens, a 30 Hz sequence of frames is recorded. The screens are deployed upright and horizontally aligned with the prevailing wind direction. The screen's surface temperature serves as a proxy for the local air temperature. We developed a method to estimate near-surface two-dimensional wind fields at centimetre resolution from tracking the air temperature pattern on the screens. Wind field estimations are validated with near-surface three-dimensional short-path ultrasonic data. To demonstrate the capabilities of the screen method, we present results from a comprehensive field campaign at an alpine research site during patchy snow cover conditions. The measurements reveal an extremely heterogeneous near-surface atmospheric layer. Vertical profiles of horizontal and vertical wind reflect multiple layers of different static stability within 2 m above the surface. A dynamic, thin stable internal boundary layer (SIBL) develops above the leading edge of snow patches protecting the snow surface from warmer air above. During pronounced gusts, the warm air from aloft entrains into the SIBL and reaches down to the snow surface adding energy to the snow pack. Measured vertical turbulent sensible heat fluxes are shown to be consistent with air temperature and wind profiles obtained using the screen method and confirm its capabilities to investigate complex in situ near-surface heat exchange processes. Supplementary Information: The online version contains supplementary material available at 10.1007/s10546-022-00752-3.

Hatching phenology is lagging behind an advancing snowmelt pattern in a high-alpine bird.

To track peaks in resource abundance, temperate-zone animals use predictive environmental cues to rear their offspring when conditions are most favourable. However, climate change threatens the reliability of such cues when an animal and its resource respond differently to a changing environment. This is especially problematic in alpine environments, where climate warming exceeds the Holarctic trend and may thus lead to rapid asynchrony between peaks in resource abundance and periods of increased resource requirements such as reproductive period of high-alpine specialists. We therefore investigated interannual variation and long-term trends in the breeding phenology of a high-alpine specialist, the white-winged snowfinch, Montifringilla nivalis, using a 20-year dataset from Switzerland. We found that two thirds of broods hatched during snowmelt. Hatching dates positively correlated with April and May precipitation, but changes in mean hatching dates did not coincide with earlier snowmelt in recent years. Our results offer a potential explanation for recently observed population declines already recognisable at lower elevations. We discuss non-adaptive phenotypic plasticity as a potential cause for the asynchrony between changes in snowmelt and hatching dates of snowfinches, but the underlying causes are subject to further research.

Land surface phenology and greenness in Alpine grasslands driven by seasonal snow and meteorological factors.

Snow accumulation and melt have multiple impacts on Land Surface Phenology (LSP) and greenness in Alpine grasslands. Our understanding of these impacts and their interactions with meteorological factors are still limited. In this study, we investigate this topic by analyzing LSP dynamics together with potential drivers, using satellite imagery and other data sources. LSP (start and end of season) and greenness metrics were extracted from time series of vegetation and leaf area index. As explanatory variables we used snow accumulation, snow cover melt date and meteorological factors. We tested for inter-annual co-variation of LSP and greenness metrics with seasonal snow and meteorological metrics across elevations and for four sub-regions of natural grasslands in the Swiss Alps over the period 2003-2014. We found strong positive correlations of snow cover melt date and snow accumulation with the start of season, especially at higher elevation. Autumn temperature was found to be important at the end of season below 2000 m above sea level (m asl), while autumn precipitation was relevant above 2000 m asl, indicating climatic growth limiting factors to be elevation dependent. The effects of snow and meteorological factors on greenness revealed that this metric tends to be influenced by temperatures at high elevations, and by snow melt date at low elevations. Given the high sensitivity of alpine grassland ecosystems, these results suggest that alpine grasslands may be particularly affected by future changes in seasonal snow, to varying degree depending on elevation.

Snow depth variability in the Northern Hemisphere mountains observed from space.

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

Estimating below-canopy light regimes using airborne laser scanning: An application to plant community analysis.

Light is a key driver of forest biodiversity and functioning. Light regimes beneath tree canopies are mainly driven by the solar angle, topography, and vegetation structure, whose three-dimensional complexity creates heterogeneous light conditions that are challenging to quantify, especially across large areas. Remotely sensed canopy structure data from airborne laser scanning (ALS) provide outstanding opportunities for advancement in this respect. We used ALS point clouds and a digital terrain model to produce hemispherical photographs from which we derived indices of nondirectional diffuse skylight and direct sunlight reaching the understory. We validated our approach by comparing the performance of these indices, as well as canopy closure (CCl) and canopy cover (CCo), for explaining the light conditions experienced by forest plant communities, as indicated by the Landolt indicator values for light (L light) from 43 vegetation surveys along an elevational gradient. We applied variation partitioning to analyze how the independent and joint statistical effects of light, macroclimate, and soil on the spatial variation in plant species composition (i.e., turnover, Simpson dissimilarity, ß SIM) depend on light approximation methodology. Diffuse light explained L light best, followed by direct light, CCl and CCo (R2  = .31, .23, .22, and .22, respectively). The combination of diffuse and direct light improved the model performance for ß SIM compared with CCl and CCo (R2  = .30, .27 and .24, respectively). The independent effect of macroclimate on ß SIM dropped from an R 2 of .15 to .10 when diffuse light and direct light were included. The ALS methods presented here outperform conventional approximations of below-canopy light conditions, which can now efficiently be quantified along entire horizontal and vertical forest gradients, even in topographically complex environments such as mountains. The effect of macroclimate on forest plant communities is prone to be overestimated if local light regimes and associated microclimates are not accurately accounted for.

Alpine Grassland Phenology as Seen in AVHRR, VEGETATION, and MODIS NDVI Time Series - a Comparison with In Situ Measurements.

This study evaluates the ability to track grassland growth phenology in the Swiss Alps with NOAA-16 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time series. Three growth parameters from 15 alpine and subalpine grassland sites were investigated between 2001 and 2005: Melt-Out (MO), Start Of Growth (SOG), and End Of Growth (EOG).We tried to estimate these phenological dates from yearly NDVI time series by identifying dates, where certain fractions (thresholds) of the maximum annual NDVI amplitude were crossed for the first time. For this purpose, the NDVI time series were smoothed using two commonly used approaches (Fourier adjustment or alternatively Savitzky-Golay filtering). Moreover, AVHRR NDVI time series were compared against data from the newer generation sensors SPOT VEGETATION and TERRA MODIS. All remote sensing NDVI time series were highly correlated with single point ground measurements and therefore accurately represented growth dynamics of alpine grassland. The newer generation sensors VGT and MODIS performed better than AVHRR, however, differences were minor. Thresholds for the determination of MO, SOG, and EOG were similar across sensors and smoothing methods, which demonstrated the robustness of the results. For our purpose, the Fourier adjustment algorithm created better NDVI time series than the Savitzky-Golay filter, since latter appeared to be more sensitive to noisy NDVI time series. Findings show that the application of various thresholds to NDVI time series allows the observation of the temporal progression of vegetation growth at the selected sites with high consistency. Hence, we believe that our study helps to better understand largescale vegetation growth dynamics above the tree line in the European Alps.

Anchorage of mature conifers: resistive turning moment, root-soil plate geometry and root growth orientation.

Eighty-four mature Norway spruce (Picea abies L. Karst), silver fir (Abies alba Mill) and Scots pine (Pinus sylvestris L.) trees were winched over to determine the maximum resistive turning moment (M(a)) of the root-soil system, the root-soil plate geometry, the azimuthal orientation of root growth, and the occurrence of root rot. The calculation of M(a), based on digital image tracking of stem deflection, accounted not only for the force application and its changing geometry, but also for the weight of the overhanging tree, representing up to 42% of M(a). Root rot reduced M(a) significantly and was detected in 25% of the Norway spruce and 5% of the silver fir trees. Excluding trees with root rot, differences in M(a) between species were small and insignificant. About 75% of the variance in M(a) could be explained by one of the four variables--tree mass, stem mass, stem diameter at breast height squared times tree height, and stem diameter at breast height squared. Among the seven allometric variables assessed above ground, stem diameter at breast height best described the root-soil plate dimensions, but the correlations were weak and the differences between species were insignificant. The shape of the root-soil plate was well described by a depth-dependent taper model with an elliptical cross section. Roots displayed a preferred azimuthal orientation of growth in the axis of prevailing winds, and the direction of frequent weak winds matched the orientation of growth better than that of rare strong winds. The lack of difference in anchorage parameters among species probably reflects the similar belowground growth conditions of the mature trees.