scholarly journals A high-resolution snow distribution on alpine catchments over Southern Columbia mountains

2019 ◽  
Author(s):  
Hamidreza Shams
Keyword(s):  
High Resolution ◽  
Columbia Mountains ◽  
Snow Distribution ◽  
Alpine Catchments

Impact and uncertainty of climate projections on Alpine catchments using high-resolution models

2021 ◽  
Author(s):  
Jorge Sebastian Moraga ◽  
Nadav Peleg ◽  
Simone Fatichi ◽  
Peter Molnar ◽  
Paolo Burlando
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Variability ◽  
Soil Conditions ◽  
Internal Climate Variability ◽  
Mountainous Catchments ◽  
Precipitation Decrease ◽  
Alpine Catchments ◽  
Impacts Of Climate Change

<p>Hydrological processes in mountainous catchments will be subject to climate change on all scales, and their response is expected to vary considerably in space. Typical hydrological studies, which use coarse climate data inputs obtained from General Circulation Models (GCM) and Regional Climate Models (RCM), focus mostly on statistics at the outlet of the catchments, overlooking the effects within the catchments. Furthermore, the role of uncertainty, especially originated from natural climate variability, is rarely analyzed. In this work, we quantified the impacts of climate change on hydrological components and determined the sources of uncertainties in the projections for two mostly natural Swiss alpine catchments: Kleine Emme and Thur. Using a two-dimensional weather generator, AWE-GEN-2d, and based on nine different GCM-RCM model chains, we generated high-resolution (2 km, 1 hour) ensembles of gridded climate inputs until the end of the 21<sup>st</sup> century. The simulated variables were subsequently used as inputs into the fully distributed hydrological model Topkapi-ETH to estimate the changes in hydrological statistics at 100-m and hourly resolutions. Increased temperatures (by 4°C, on average) and changes in precipitation (decrease over high elevations by up to 10%, and increase at the lower elevation by up to 15%) results in increased evapotranspiration rates in the order of 10%, up to a 50% snowmelt, and drier soil conditions. These changes translate into important shifts in streamflow seasonality at the outlet of the catchments, with a significant increase during the winter months (up to 40%) and a reduction during the summer (up to 30%). Analysis at the sub-catchment scale reveals elevation-dependent hydrological responses: mean annual streamflow, as well as high and low flow extremes, are projected to decrease in the uppermost sub-catchments and increase in the lower ones. Furthermore, we computed the uncertainty of the estimations and compared them to the magnitude of the change signal. Although the signal-to-noise-ratio of extreme streamflow for most sub-catchments is low (below 0.5) there is a clear elevation dependency. In every case, internal climate variability (as opposed to climate model uncertainty) explains most of the uncertainty, averaging 85% for maximum and minimum flows, and 60% for mean flows. The results highlight the importance of modelling the distributed impacts of climate change on mountainous catchments, and of taking into account the role of internal climate variability in hydrological projections.</p>

scholarly journals High resolution snow distribution data from complex Arctic terrain: a tool for model validation

2002 ◽  
Vol 2 (3/4) ◽  
pp. 147-155 ◽  
Author(s):  
Ch. Jaedicke ◽  
A. D. Sandvik
Keyword(s):  
High Resolution ◽  
Snow Cover ◽  
Numerical Models ◽  
The Arctic ◽  
Distribution Data ◽  
Blowing Snow ◽  
Data Set ◽  
Meteorological Model ◽  
Drift Model ◽  
Snow Distribution

Abstract. Blowing snow and snow drifts are common features in the Arctic. Due to sparse vegetation, low temperatures and high wind speeds, the snow is constantly moving. This causes severe problems for transportation and infrastructure in the affected areas. To minimise the effect of drifting snow already in the designing phase of new structures, adequate models have to be developed and tested. In this study, snow distribution in Arctic topography is surveyed in two study areas during the spring of 1999 and 2000. Snow depth is measured by ground penetrating radar and manual methods. The study areas encompass four by four kilometres and are partly glaciated. The results of the surveys show a clear pattern of erosion, accumulation areas and the evolution of the snow cover over time. This high resolution data set is valuable for the validation of numerical models. A simple numerical snow drift model was used to simulate the measured snow distribution in one of the areas for the winter of 1998/1999. The model is a two-level drift model coupled to the wind field, generated by a mesoscale meteorological model. The simulations are based on five wind fields from the dominating wind directions. The model produces a satisfying snow distribution but fails to reproduce the details of the observed snow cover. The results clearly demonstrate the importance of quality field data to detect and analyse errors in numerical simulations.

scholarly journals Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain

2018 ◽  
Vol 12 (10) ◽  
pp. 3137-3160 ◽  
Author(s):  
Franziska Gerber ◽  
Nikola Besic ◽  
Varun Sharma ◽  
Rebecca Mott ◽  
Megan Daniels ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Variability ◽  
Complex Terrain ◽  
Small Scale ◽  
Near Surface ◽  
Precipitation Patterns ◽  
Simulated Precipitation ◽  
Precipitation Processes ◽  
Snow Distribution ◽  
Snow Precipitation

Abstract. Snow distribution in complex alpine terrain and its evolution in the future climate is important in a variety of applications including hydropower, avalanche forecasting and freshwater resources. However, it is still challenging to quantitatively forecast precipitation, especially over complex terrain where the interaction between local wind and precipitation fields strongly affects snow distribution at the mountain ridge scale. Therefore, it is essential to retrieve high-resolution information about precipitation processes over complex terrain. Here, we present very-high-resolution Weather Research and Forecasting model (WRF) simulations (COSMO–WRF), which are initialized by 2.2 km resolution Consortium for Small-scale Modeling (COSMO) analysis. To assess the ability of COSMO–WRF to represent spatial snow precipitation patterns, they are validated against operational weather radar measurements. Estimated COSMO–WRF precipitation is generally higher than estimated radar precipitation, most likely due to an overestimation of orographic precipitation enhancement in the model. The high precipitation amounts also lead to a higher spatial variability in the model compared to radar estimates. Overall, an autocorrelation and scale analysis of radar and COSMO–WRF precipitation patterns at a horizontal grid spacing of 450 m show that COSMO–WRF captures the spatial variability normalized by the domain-wide variability in precipitation patterns down to the scale of a few kilometers. However, simulated precipitation patterns systematically show a lower variability on the smallest scales of a few hundred meters compared to radar estimates. A comparison of spatial variability for different model resolutions gives evidence for an improved representation of local precipitation processes at a horizontal resolution of 50 m compared to 450 m. Additionally, differences of precipitation between 2830 m above sea level and the ground indicate that near-surface processes are active in the model.

scholarly journals Altitudinal dependency of snow amounts in two small alpine catchments: can catchment-wide snow amounts be estimated via single snow or precipitation stations?

2011 ◽  
Vol 52 (58) ◽  
pp. 153-158 ◽  
Author(s):  
Thomas Grünewald ◽  
Michael Lehning
Keyword(s):  
Laser Scanning ◽  
Snow Water Equivalent ◽  
Altitudinal Gradients ◽  
Mountainous Regions ◽  
Winter Snow ◽  
Catchment Areas ◽  
Scale Models ◽  
Snow Water ◽  
Snow Distribution ◽  
Alpine Catchments

AbstarctThe spatial distribution and the local amount of snow in mountainous regions strongly depend on the spatial characteristics of snowfall, snow deposition and snow redistribution. Uniform altitudinal gradients can only represent a part of these influences but are without alternative for use in larger-scale models. How well altitudinal gradients represent the true snow distribution has not been assessed. We analyse altitudinal characteristics of snow stored in two high-alpine catchments in Switzerland. Peak winter snow depths were monitored using high-resolution airborne laser scanning technology. These snow depths were transferred to snow water equivalent by applying simple density estimations. From these data, altitudinal gradients were calculated for the total catchment areas and for selected subareas characterized by different accumulation patterns. These gradients were then compared with gradients resulting from automated snow depth measurements obtained from several snow stations on different height levels located in the catchments, and with estimations from climatological precipitation gradients. The analysis showed that neither precipitation gradients nor flat-field stations estimate catchment-wide snow amounts accurately. While the climatological gradient showed different trends for different areas and years, the snow stations tended to overestimate mean snow amounts.

scholarly journals Extracting High Resolution Snow Distribution Information with Inexpensive Autonomous Cameras

10.29007/93gh ◽  
2018 ◽  
Author(s):  
Pauline Millet ◽  
Hendrik Huwald ◽  
Steven V. Weijs
Keyword(s):  
High Resolution ◽  
Low Cost ◽  
Time Lapse ◽  
Distortion Correction ◽  
Snow Covered Area ◽  
Covered Area ◽  
Digital Elevation ◽  
Snow Distribution ◽  
Elevation Model ◽  
Time Lapse Photography

This study details a procedure to derive high resolution snow cover information using low-cost autonomous cameras. Images from time lapse photography of target areas are used to obtain temporally resolved binary snow-covered area information. Various image processing steps, such as distortion correction, alignment, projection using the Digital Elevation Model (DEM), and classification using clustering are described. Several innovations, such as matching the mountain silhouette with the DEM, and application of specific filters are described to make this terrestrial remote sensing method generally applicable to derive valuable snow information.

Observations of the spatial structure of interstellar hydrogen

1967 ◽  
Vol 31 ◽  
pp. 45-46
Author(s):  
Carl Heiles
Keyword(s):  
High Resolution ◽  
Spatial Structure ◽  
Velocity Dispersion ◽  
Temperature Variations

High-resolution 21-cm line observations in a region aroundlII= 120°,b11= +15°, have revealed four types of structure in the interstellar hydrogen: a smooth background, large sheets of density 2 atoms cm-3, clouds occurring mostly in groups, and ‘Cloudlets’ of a few solar masses and a few parsecs in size; the velocity dispersion in the Cloudlets is only 1 km/sec. Strong temperature variations in the gas are in evidence.

Combining integrated systems-biology approaches with intervention-based experimental design provides a higher-resolution path forward for microbiome research

2019 ◽  
Vol 42 ◽  
Author(s):  
J. Alfredo Blakeley-Ruiz ◽  
Carlee S. McClintock ◽  
Ralph Lydic ◽  
Helen A. Baghdoyan ◽  
James J. Choo ◽  
...  
Keyword(s):  
Systems Biology ◽  
High Resolution ◽  
Experimental Design ◽  
Integrated Systems ◽  
Microbiome Research ◽  
Constructive Criticism

Abstract The Hooks et al. review of microbiota-gut-brain (MGB) literature provides a constructive criticism of the general approaches encompassing MGB research. This commentary extends their review by: (a) highlighting capabilities of advanced systems-biology “-omics” techniques for microbiome research and (b) recommending that combining these high-resolution techniques with intervention-based experimental design may be the path forward for future MGB research.

Sign in / Sign up

Export Citation Format

Share Document