Congenital Central Hypoventilation Syndrome (CCHS) is a rare condition characterized by an alveolar hypoventilation due to a deficient autonomic central control of ventilation and a global autonomic ...dysfunction. Paired-like homeobox 2B (PHOX2B) mutations are found in most of the patients with CCHS. In recent years, the condition has evolved from a life-threatening neonatal onset disorder to include broader and milder clinical presentations, affecting children, adults and families. Genes other than PHOX2B have been found responsible for CCHS in rare cases and there are as yet other unknown genes that may account for the disease. At present, management relies on lifelong ventilatory support and close follow up of dysautonomic progression. BODY: This paper provides a state-of-the-art comprehensive description of CCHS and of the components of diagnostic evaluation and multi-disciplinary management, as well as considerations for future research.
Awareness and knowledge of the diagnosis and management of this rare disease should be brought to a large health community including adult physicians and health carers.
A combined snow modelling approach integrating remote sensing data, in-situ data, and an improved hydrological model is presented. Complementary information sources are evaluated in terms of its ...value for constraining the model parameters and to overcome limitations of individual data such as inadequate scale representation. The study site consists of the Upper Fagge river basin in the Austrian Alps featuring the Weisssee Snow Research Site. The available remote sensing datasets include Terra MODIS based medium resolution and Landsat-7/8 and Sentinel-2A based high resolution fractional snow covered area maps. Recently, Sentinel-1 based wet snow covered area maps have become increasingly available. To the knowledge of the authors the first evaluation of their value for snow-hydrological modelling is presented. Besides conventional small footprint station data, in-situ time-series of snow water equivalent (SWE) of a Cosmic-Ray Neutron Sensor (CRNS) having a footprint of several hectares is additionally used. For including these data the model now provides respective outputs such as fractional snow cover, wet/dry snow surface and SWE areal means equivalent to the CRNS sensor footprint. By means of 40,000 model runs the high complementary value of representative SWE data and remote sensing information was assessed with most promising results achieved by combining high resolution fractional snow covered area maps with CRNS-SWE data. Regarding mean SWE or mean snow covered area in the catchment the ensemble spreads are reduced by two thirds compared to the results of a benchmark simulation based only on runoff for model calibration. Wet snow covered area maps have a high potential for simulating SWE at Weisssee Snow Research Site but introduce additional uncertainties for runoff simulations likely caused by the uncertain detection of the snow covered area from Sentinel-1 backscatter. The approach has high potential for water resources management in gauged and ungauged mountain basin and gives guidance for efficient data assimilation schemes.
•A framework to analyse the explanatory value of complementary snow data is presented.•The applicability of the different data is addressed in terms of scale and accuracy.•Cosmic-Ray Neutron Sensing increases model realism in gauged and ungauged basins.•High resolution optical and SAR based remote sensing data outperform MODIS data.
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 the snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4,000 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.
The characteristics of an aboveground cosmic‐ray neutron sensor (CRNS) are evaluated for monitoring a mountain snowpack in the Austrian Alps from March 2014 to June 2016. Neutron counts were compared ...to continuous point‐scale snow depth (SD) and snow‐water‐equivalent (SWE) measurements from an automatic weather station with a maximum SWE of 600 mm (April 2014). Several spatially distributed Terrestrial Laser Scanning (TLS)‐based SD and SWE maps were additionally used. A strong nonlinear correlation is found for both SD and SWE. The representative footprint of the CRNS is in the range of 230–270 m. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, no complete signal saturation was observed. These results imply that CRNS could be transferred into an unprecedented method for continuous detection of spatially averaged SD and SWE for alpine snowpacks, though with sensitivity decreasing with increasing SWE. While initially different functions were found for accumulation and melting season conditions, this could be resolved by accounting for a limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m−3 and above. In contrast to prior studies with shallow snowpacks, interannual transferability of the results is very high regardless of presnowfall soil moisture conditions. This underlines the unexpectedly high potential of CRNS to close the gap between point‐scale measurements, hydrological models, and remote sensing of the cryosphere in alpine terrain.
Key Points
First application of monitoring a mountain snowpack via cosmic‐ray neutron sensing (CRNS)
High correlation with both snow depth and water equivalent obtained via Terrestrial Laser Scanning (TLS)
No complete saturation of CRNS signal even for snowpacks up to 600 mm snow water equivalent
The study presents a data set of the interannual and intraannual snow depth distribution recorded by terrestrial laser scanning (TLS) scans at Weisssee snow research site in Austria between November ...2014 and May 2018. The data set comprises 23 snow‐on digital elevation models, one snow‐off digital elevation model, and the difference raster calculated between a snow‐off and snow‐on scans. The relative accuracy of the TLS scans was determined by measuring the distance between snow‐free planes from the snow‐on and snow‐off scans and shows mean values smaller than 0.03 m and standard deviations ranging between 0.02 and 0.1 m. The reliability of the snow depths derived from TLS was further assessed by comparing snow depths from snow probing, Global Navigation Satellite System measurements, and continuous snow depth measurements from the weather station. Comparison of the different measurement methods shows average deviations of less than 0.1 m. The data can be used for analysis and modeling of snow distribution or for assessing the representativeness of snow sensors or other remote sensing products.
Key Points
The 23 TLS snow depth maps from 11.2014 to 05.2018 show the spatial distribution of snow depth around an alpine weather and snow station
The average deviations between snow depth measurements generated from TLS and observations used for validation are lower than 0.1 m
The data can be used for interannual and intraannual snow distribution analysis and snow hydrological modeling
Knowledge of the spatial snow distribution and its interannual persistence is of interest for a broad spectrum of issues in cryospheric sciences. In this study, snow depths derived from airborne ...laser scanning are analyzed for interannual persistence of the seasonal snow cover in a partly glacierized mountain area (~36 km2). At the end of five accumulation periods, the snow-covered area varied by 16% of its temporal mean. Mean snow depth of the total area ranged by a factor of two (1.31–2.58 m), with a standard deviation of 0.42 m. Interannual correlation coefficients of snow depth distribution were in the range 0.68–0.84. Of the investigated area, 75% was found to be interannually persistent. The remaining area showed variable snow cover from year to year, caused by occasional avalanches and changes in surface topography as a result of glacier retreat. Snow cover underwent a change from a homogeneous distribution on the former glacier surface to a more heterogeneous snow cover in the recently deglaciated terrain. A geostatistical analysis shows interannual persistence in scaling behavior of snow depth in ice-free terrain with scale break distances at 20 m. Scale-invariant behavior of snow depth is indicated over >100 m on smooth glacier surfaces.
•Basin-wide SWE is derived from Lidar data.•Mean SWE in the catchment has an uncertainty in the order of 15%.•SES accurately simulates the variability of SWE on the watershed scale.•SWE is superior ...to snow covered area for the reduction of parameter uncertainties.
In the present paper multi-temporal Lidar (Light detection and ranging) data and Landsat images are used to assess the spatial variability of snow at the end of the accumulation season (April–May) in a glacierized catchment (167km2) in Tyrol, Austria. Snow cover characteristics in the Tyrolean Alps have been analysed using regular snow measurements and snow course data. Results are used for the conversion of basin-wide Lidar snow depth into snow water equivalent (SWE). When considering different possible error sources, uncertainties of the mean basin-wide SWE obtained from Lidar are between 12% and 16%. Available distributions of SWE and snow covered area (SCA) in the catchment are used for the calibration and validation of the fully distributed hydrological model SES. The study focuses especially on the simulation of snow accumulation and the corresponding variability of snow. Observed accumulation patterns are related to the topography (elevation, slope and curvature), and according parameter settings of the hydrological model are derived by means of Monte Carlo simulations. The majority of the model runs simulates SCA for various datasets with an accuracy of 85–95%. The paper demonstrates that using SWE data is superior to SCA for constraining model parameter ranges. Results at the watershed scale are in agreement with respect to the total water volume of the snow cover with deviations lower than 5% between SWE from Lidar or from the hydrological model.
Glacio-hydrological models combine both glacier and catchment hydrology modeling and are used to assess the hydrological response of high-mountain glacierized catchments to climate change. To capture ...the uncertainties from these model combinations, it is essential to compare the outcomes of several model entities forced with the same climate projections. For the first time, we compare the results of two completely independent glacio-hydrological models: (i) HQsim-GEM and (ii) AMUNDSEN. In contrast to prevailing studies, we use distinct glacier models and glacier initialization times. At first glance, the results achieved for future glacier states and hydrological characteristics in the Rofenache catchment in Ötztal Alps (Austria) appear to be similar and consistent, but a closer look reveals clear differences. What can be learned from this study is that low-complexity models can achieve higher accuracy in the calibration period. This is advantageous especially when data availability is weak, and priority is given to efficient computation time. Furthermore, the time and method of glacier initialization play an important role due to different data requirements. In essence, it is not possible to make conclusions about the model performance outside of the calibration period or more specifically in the future. Hence, similar to climate modeling, we suggest considering different modeling approaches when assessing future catchment discharge or glacier evolution. Especially when transferring the results to stakeholders, it is vital to transparently communicate the bandwidth of future states that come with all model results.
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