Estimating Regional Snow Line Elevation Using Public Webcam Images

Snow cover is of high relevance for the Earth’s climate system, and its variability plays a key role in alpine hydrology, ecology, and socioeconomic systems. Measurements obtained by optical satellite remote sensing are an essential source for quantifying snow cover variability from a local to global scale. However, the temporal resolution of such measurements is often affected by persistent cloud coverage, limiting the application of high resolution snow cover mapping. In this study, we derive the regional snow line elevation in an alpine catchment area using public webcams. We compare our results to the snow line information derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 snow cover products and find our results to be in good agreement therewith. Between October 2017 and the end of June 2018, snow lines derived from webcams lie on average 55.8 m below and 33.7 m above MODIS snow lines using a normalized-difference snow index (NDSI) of 0.4 and 0.1, respectively, and are on average 53.1 m below snow lines derived from Sentinel-2. We further analyze the superior temporal resolution of webcam-based snow cover information and demonstrate its effectiveness in filling temporal gaps in satellite-based measurements caused by cloud cover. Our findings show the ability of webcam-based snow line elevation retrieval to complement and improve satellite-based measurements

Improving surface heat flux estimation for a large lake through model optimization and two-point calibration: The case of Lake Geneva

Net Surface Heat Flux (SurHF) was estimated from 2008 to 2014 for Lake Geneva (Switzerland/France), using long‐term temperature depth profiles at two locations, hourly maps of reanalysis meteorological data from a numerical weather model and lake surface water temperatures from calibrated satellite imagery. Existing formulas for different heat flux components were combined into 54 different total SurHF models. The coefficients in these models were calibrated based on SurHF optimization. Four calibration factors characterizing the incoming long‐wave radiation, sensible, and latent heat fluxes were further investigated for the six best performing models. The combination of the modified parameterization of the Brutsaert equation for incoming atmospheric radiation and of similarity theory‐based bulk parameterization algorithms for latent and sensible surface heat fluxes provided the most accurate SurHF estimates. When optimized for one lake temperature profile location, SurHF models failed to predict the temperature profile at the other location due to the spatial variability of meteorological parameters between the two locations. Consequently, the optimal SurHF models were calibrated using two profile locations. The results emphasize that even relatively small changes in calibration factors, particularly in the atmospheric emissivity, significantly modify the estimated long‐term heat content. The lack of calibration can produce changes in the calculated heat content that are much higher than the observed annual climate change‐induced trend. The calibration improved parameterization of bulk transfer coefficients, mainly under low wind regimes

Density and community structure of soil- and bark-dwelling microarthropods along an altitudinal gradient in a tropical montane rainforest

Microarthropod communities in the soil and on the bark of trees were investigated along an elevation gradient (1,850, 2,000, 2,150, 2,300 m) in a tropical montane rain forest in southern Ecuador. We hypothesised that the density of microarthropods declines with depth in soil and increases with increasing altitude mainly due to the availability of resources, i.e. organic matter. In addition, we expected bark and soil communities to differ strongly, since the bark of trees is more exposed to harsher factors. In contrast to our hypothesis, the density of major microarthropod groups (Collembola, Oribatida, Gamasina, Uropodina) was generally low and decreased with altitude. However, as we predicted the density of each of the groups decreased with soil depth. Density of microarthropods on tree bark was lower than in soil. Overall, 43 species of oribatid mites were found, with the most abundant higher taxa being Poronota, pycnonotic Apheredermata, Mixonomata and Eupheredermata. The oribatid mite community on bark did not differ significantly from that in soil. The number of oribatid mite species declined with altitude (24, 23, 17 and 13 species at 1,850, 2,000, 2,150 and 2,300 m, respectively). Rarefaction curves indicate that overall about 50 oribatid mite species are to be expected along the studied altitudinal gradient. Results of this study indicate (1) that microarthropods may be limited by the quality of resources at high altitudes and by the amount of resources at deeper soil layers, and (2) that the bark of trees and the soil are habitats of similar quality for oribatid mites

Copy Number Variation in Patients with Disorders of Sex Development Due to 46,XY Gonadal Dysgenesis

Disorders of sex development (DSD), ranging in severity from mild genital abnormalities to complete sex reversal, represent a major concern for patients and their families. DSD are often due to disruption of the genetic programs that regulate gonad development. Although some genes have been identified in these developmental pathways, the causative mutations have not been identified in more than 50% 46,XY DSD cases. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to analyse copy number variation in 23 individuals with unexplained 46,XY DSD due to gonadal dysgenesis (GD). Here we describe three discrete changes in copy number that are the likely cause of the GD. Firstly, we identified a large duplication on the X chromosome that included DAX1 (NR0B1). Secondly, we identified a rearrangement that appears to affect a novel gonad-specific regulatory region in a known testis gene, SOX9. Surprisingly this patient lacked any signs of campomelic dysplasia, suggesting that the deletion affected expression of SOX9 only in the gonad. Functional analysis of potential SRY binding sites within this deleted region identified five putative enhancers, suggesting that sequences additional to the known SRY-binding TES enhancer influence human testis-specific SOX9 expression. Thirdly, we identified a small deletion immediately downstream of GATA4, supporting a role for GATA4 in gonad development in humans. These CNV analyses give new insights into the pathways involved in human gonad development and dysfunction, and suggest that rearrangements of non-coding sequences disturbing gene regulation may account for significant proportion of DSD cases

Towards a webcam-based snow cover monitoring network: methodology and evaluation

Snow cover variability has a significant impact on climate and the environment and is of great socioeconomic importance for the European Alps. Terrestrial photography offers a high potential to monitor snow cover variability, but its application is often limited to small catchment scales. Here, we present a semiautomatic procedure to derive snow cover maps from publicly available webcam images in the Swiss Alps and propose a procedure for the georectification and snow classification of such images. In order to avoid the effort of manually setting ground control points (GCPs) for each webcam, we implement a novel registration approach that automatically resolves camera parameters (camera orientation; principal point; field of view, FOV) by using an estimate of the webcams' positions and a high-resolution digital elevation model (DEM). Furthermore, we propose an automatic image-to-image alignment to correct small changes in camera orientation and compare and analyze two recent snow classification methods. The resulting snow cover maps indicate whether a DEM grid is snow-covered, snow-free, or not visible from webcams' positions. GCPs are used to evaluate our novel automatic image registration approach. The evaluation reveals a root mean square error (RMSE) of 14.1 m for standard lens webcams (FOV<48∘) and a RMSE of 36.3 m for wide-angle lens webcams (FOV≥48∘). In addition, we discuss projection uncertainties caused by the mapping of low-resolution webcam images onto the high-resolution DEM. Overall, our results highlight the potential of our method to build up a webcam-based snow cover monitoring network

Geometric accuracy assessment of coarse-resolution satellite datasets: a study based on AVHRR GAC data at the sub-pixel level

AVHRR Global Area Coverage (GAC) data provide daily global coverage of the Earth, which are widely used for global environmental and climate studies. However, their geolocation accuracy has not been comprehensively evaluated due to the difficulty caused by onboard resampling and the resulting coarse resolution, which hampers their usefulness in various applications. In this study, a correlation-based patch matching method (CPMM) was proposed to characterize and quantify the geo-location accuracy at the sub-pixel level for satellite data with coarse resolution, such as the AVHRR GAC dataset. This method is neither limited to landmarks nor suffers from errors caused by false detection due to the effect of mixed pixels caused by a coarse spatial resolution, and it thus enables a more robust and comprehensive geometric assessment than existing approaches. Data of NOAA-17, MetOp-A and MetOp-B satellites were selected to test the geocoding accuracy. The three satellites predominately present west shifts in the across-track direction, with average values of −1.69, −1.9, −2.56 km and standard deviations of 1.32, 1.1, 2.19 km for NOAA-17, MetOp-A, and MetOp-B, respectively. The large shifts and uncertainties are partly induced by the larger satellite zenith angles (SatZs) and partly due to the terrain effect, which is related to SatZ and becomes apparent in the case of large SatZs. It is thus suggested that GAC data with SatZs less than 40∘ should be preferred in applications. The along-track geolocation accuracy is clearly improved compared to the across-track direction, with average shifts of −0.7, −0.02 and 0.96 km and standard deviations of 1.01, 0.79 and 1.70 km for NOAA-17, MetOp-A and MetOp-B, respectively. The data can be accessed from https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002 (Stengel et al., 2017) and https://doi.org/10.5067/MODIS/MOD13A1.006 (Didan, 2015)