scholarly journals Snowpack Distribution Using Topographical, Climatological and Winter Season Index Inputs

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 3
Author(s):  
Douglas M. Hultstrand ◽  
Steven R. Fassnacht ◽  
John D. Stednick ◽  
Christopher A. Hiemstra
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Generalized Additive Models ◽  
Winter Season ◽  
Regression Tree ◽  
Distribution Patterns ◽  
Time Frame ◽  
Additive Models ◽  
Depth Measurement ◽  
Accumulation Index

A majority of the annual precipitation in many mountains falls as snow, and obtaining accurate estimates of the amount of water stored within the snowpack is important for water supply forecasting. Mountain topography can produce complex patterns of snow distribution, accumulation, and ablation, yet the interaction of topography and meteorological patterns tends to generate similar inter-annual snow depth distribution patterns. Here, we question whether snow depth patterns at or near peak accumulation are repeatable for a 10-year time frame and whether years with limited snow depth measurement can still be used to accurately represent snow depth and mean snow depth. We used snow depth measurements from the West Glacier Lake watershed, Wyoming, U.S.A., to investigate the distribution of snow depth. West Glacier Lake is a small (0.61 km2) windswept (mean of 8 m/s) watershed that ranges between 3277 m and 3493 m. Three interpolation methods were compared: (1) a binary regression tree, (2) multiple linear regression, and (3) generalized additive models. Generalized additive models using topographic parameters with measured snow depth presented the best estimates of the snow depth distribution and the basin mean amounts. The snow depth patterns near peak accumulation were found to be consistent inter-annually with an average annual correlation coefficient (r2) of 0.83, and scalable based on a winter season accumulation index (r2 = 0.75) based on the correlation between mean snow depth measurements to Brooklyn Lake snow telemetry (SNOTEL) snow depth data.

Size-related habitat use, aggregation patterns and abundance of anglerfish (Lophius budegassa) in the Mediterranean Sea determined by generalized additive modelling

2003 ◽  
Vol 83 (5) ◽  
pp. 1171-1178 ◽  
Author(s):  
Christos Maravelias ◽  
Costas Papaconstantinou
Keyword(s):  
Generalized Additive Models ◽  
Aegean Sea ◽  
Distribution Patterns ◽  
Habitat Associations ◽  
Additive Models ◽  
Seasonal Effects ◽  
Nursery Ground ◽  
Size Category ◽  
Aegean Area ◽  
Eastern Aegean

Spatial distribution patterns of black anglerfish, Lophius budegassa were examined in relation to size category, bathymetry, locational covariates, and season. Data were collected during a 2-y period (1998–1999) of quasi-synoptic seasonal sampling using demersal trawl surveys in the Aegean Sea. Generalized additive models (GAMs) were used to test the hypotheses that there was size-related variation in species' habitat associations and that the study area might serve as a nursery ground for black anglerfish. The current results supported these hypotheses. Data are presented that reveal size-dependent aggregation patterns of black anglerfish and an important habitat utilization of the north-eastern Aegean area. The modelled anglerfish abundances showed a strongly non-linear dependence on the explanatory covariates. The different size-classes exhibited significant seasonal effects and preferences for specific regions and distinct water depths. The present results also suggested that 1-y-old fish and potential spawners appeared to concentrate in the vicinity of the same areas. Two main areas of juvenile aggregations were detected in the deeper water regions of the study area on a seabed of around 300 m depth; both emerged in the proximity of the locations of larger fish. The bathymetric distribution of intermediate size anglerfish followed an inverse trend, with fish captured mainly in shallower waters. Results indicated a preferential aggregation of 1-y-old L. budegassa in the study area that is hypothesized to influence the supply of recruits to distant regions of the Aegean Sea.

Extreme snow loads in Austria

2021 ◽  
Author(s):  
Michael Winkler ◽  
Harald Schellander
Keyword(s):  
Structural Design ◽  
Snow Depth ◽  
Generalized Additive Models ◽  
Value Theory ◽  
Additive Models ◽  
Snow Load ◽  
Snow Loads ◽  
Snow Model ◽  
European Standards ◽  
Smooth Map

<p>In the framework of European standards for structural design, acceptable snow loads on constructions and buildings are based on maps for s<sub>k,</sub> the “characteristic snow load on the ground” with an average reoccurrence time of 50 years. The Austrian snow load standard is built on a very detailed zoning map from 2006, but underlying snow data is from the 1980s.</p><p>An updated snow load map for Austria is presented. It is based on 870 snow depth records with at least 30 years of regular daily observations between 1960 and 2019. ΔSNOW, a novel snow model, was used to simulate respective snow loads. Extreme value theory and generalized additive models led to a smooth map of extreme snow loads at 50x50m resolution. The methods are transparently published, reproducible and, thus, applicable in other regions as well.</p><p>The map can reasonably assign s<sub>k</sub> values up to 2000m altitude, a significant advantage compared to actual standards which are only valid up to 1500m. New insights in the spatial picture of extreme snow loads are provided and the quadratic altitude-s<sub>k</sub>-relation, which is widely used in snow load standards, is evaluated. Validation with station data reveals a higher accuracy for the presented map than for the currently used snow load map. The number of outliers, i.d. stations with significantly higher or lower s<sub>k</sub> values than the snow load maps would suggest, could be decreased in comparison with the actual standard. However, some problematic places remain, mostly in topographically and climatologically highly complex areas. In case the presented map will become a new base for future Austrian standards, those places will have to be treated in a special way.</p>

scholarly journals Microscale variability of snow depth using U.A.S. technology

2015 ◽  
Vol 9 (1) ◽  
pp. 1047-1075 ◽  
Author(s):  
C. De Michele ◽  
F. Avanzi ◽  
D. Passoni ◽  
R. Barzaghi ◽  
L. Pinto ◽  
...  
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Winter Season ◽  
Aerial Survey ◽  
Snow Accumulation ◽  
Volume Estimation ◽  
Accurate Estimation ◽  
Unmanned Aerial Systems ◽  
Spatial Integration ◽  
Interpolation Techniques

Abstract. We investigate the capabilities of photogrammetry-based surveys with Unmanned Aerial Systems (U.A.S.) to retrieve the snow depth distribution at cm resolution over a small alpine area (~300 000 m2). For this purpose, we have designed two field campaigns during the 2013/2014 winter season. In the first survey, realized at the beginning of the accumulation season, the digital elevation model of bare soil has been obtained. The second survey, made at the end of the accumulation season, allowed to determine the snow depth distribution as difference with respect to the previous aerial survey. 12 manual measurements of snow depth were collected at random positions in order to run a point comparison with U.A.S. measurements. The spatial integration of U.A.S. snow depth measurements allowed to estimate the snow volume accumulated over the area. We compare this volume estimation with the ones provided by classical interpolation techniques of the 12 point measurements. Results show that the U.A.S. technique provides an accurate estimation of point snow depth values (the average difference with reference to manual measurements is of −7.3 cm), and a distributed evaluation of the snow accumulation patterns. Moreover, the interpolation techniques considered return average differences in snow volume estimation, with respect to the one obtained through the U.A.S. technology, equal to ~21%.

Habitat partitioning and the influence of benthic topography and oceanography on the distribution of fin and minke whales in the Bay of Fundy, Canada

2007 ◽  
Vol 87 (1) ◽  
pp. 149-156 ◽  
Author(s):  
Simon N. Ingram ◽  
Laura Walshe ◽  
Dave Johnston ◽  
Emer Rogan
Keyword(s):  
Linear Relationship ◽  
Linear Models ◽  
Generalized Additive Models ◽  
Bottom Topography ◽  
Distribution Patterns ◽  
Additive Models ◽  
Bay Of Fundy ◽  
Fin Whale ◽  
Minke Whales ◽  
Fin Whales

We collected data on the distribution of fin whales (Balaenoptera physalus) and minke whales (Balaenoptera acutorostrata) in the Bay of Fundy, Canada from a whale-watching vessel during commercial tours between July and September 2002. A single observer recorded the positions, species, numbers and surface activity of whales encountered during boat tours. We controlled for biased search effort by calculating sightings rates for both species in cells measuring 2′ latitude by 2′ longitude throughout the study area. Sightings rates were calculated by dividing the number of sightings of fin and minke whales in each cell by the number of visits by the tour boat to that cell. We used generalized additive models and generalized linear models to examine the influence of benthic topography on whale distribution patterns. Models showed a non-linear relationship for minke whale sighting rates with increasing benthic slopes and a linear relationship for minke and fin whale sightings rates with increasing water depth. Sightings of minkes were concentrated in areas subject to tidal wakes near the northern tips of Grand Manan and Campobello Island. Fin whales were also found off the northern tip of Grand Manan but sighting rates for this species were highest in areas with less benthic sloping topography adjacent to the relatively deep Owen Basin. Foraging was recorded during 87% of all whale encounters and our results indicate that whale distribution in this area is likely to be influenced by depth, bottom topography and fine scale oceanographic features that facilitate foraging.

scholarly journals Generation of daily high-spatial resolution snow depth maps from in-situ measurement and time-lapse photographs

2020 ◽  
Vol 46 (1) ◽  
pp. 59-79
Author(s):  
J. Revuelto ◽  
E. Alonso-González ◽  
J.I. López-Moreno
Keyword(s):  
Snow Depth ◽  
Depth Distribution ◽  
Laser Scanner ◽  
Distribution Patterns ◽  
Time Lapse ◽  
Daily Basis ◽  
Small Scale ◽  
Mountain Areas ◽  
Depth Maps

Acquiring information on snow depth distribution at high spatial and temporal resolution in mountain areas is time consuming and generally these acquisitions are subjected to meteorological constrains. This work presents a simple approach to assess snow depth distribution from automatically observed snow variables and a pre-existing database of snow depth maps. By combining daily observations of in-situ snow depth, georectified time-lapse photography (snow presence or absence) and information on snowpack distribution during annual snow peaks determined with a Terrestrial Laser Scanner (TLS), a method was developed to simulate snow depth distribution on day-by-day basis. This method was tested is Izas Experimental Catchment, in the Central Spanish Pyrenees, a site with a large database of TLS observations, time-lapse images and nivo-meteorological variables for six snow seasons (from 2011 to 2017). The contrasted snow climatic characteristics among the snow seasons allowed analysis of the transferability of snowpack distribution patterns observed during particular seasons to periods without spatialized snow depth observations, by TLS or other procedures. The method i) determines snow depth ratio among the observed maximum snow depths and all other snow map pixels at the TLS yearly snow peak accumulation, ii ) rescales these ratios on a daily basis with time-lapse images information and iii) calculates the snow depth distribution with; the rescaled ratios and the snow depth observed at the automatic weather station. The average of the six TLS observed peaks was the combination showing optimal overall applicability. Despite its simplicity, these simulated values showed encouraging results when compared with snow depth distribution observed on particular dates. This was due primarily to the strong topographic control of small scale snow depth distribution on heterogeneous mountain areas, which has high inter- and intra-annual consistencies.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

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