Winter severity index using widely available weather information

2012 ◽  
Vol 39 (4) ◽  
pp. 321 ◽  
Author(s):  
K. L. Dawe ◽  
S. Boutin
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Severity Index ◽  
Climate Index ◽  
Climate Indices ◽  
Climate Variables ◽  
Simple Method ◽  
Climate Region ◽  
Winter Severity

Context Changes in global climate and evidence of species’ responses to these changes have increased interest in relationships between climate variables and species demography and distributions. Although an important tool for many ecological questions, large-scale climate indices fail to provide the spatial resolution necessary to investigate drivers of change across small spatial scales. Climate variables that describe yearly climate variation at large spatial extents and small spatial grain are needed. Aim Here we develop a model for snow depth using snow water equivalent (SWE) data, which are readily available in a number of formats, to be included in a more general climate index. We use an existing winter severity index (WSI) for white-tailed deer to test the performance of the model. Methods We obtained data for 13 weather stations from north-western Canada, reporting both SWE and snow depth. We accumulated a snowpack from daily SWE of snowfall and then tested two methods for converting the SWE of the snowpack into the snow portion of the WSI. We then generalised the model for application to the northwest forest climate region. Key results Coefficients of determination (R2) relating the actual and predicted snow depth portion of the WSI ranged from 0.41 to 0.78, with only three stations being below 0.50. Coefficients of determination (R2) relating the actual and predicted WSI for the northwest climate region ranged from 0.58 to 0.88. Conclusions The SWE model predicts the snow portion of the WSI well for most stations and, when incorporated into the full WSI, provides a good measure of relative winter severity across space and time for most stations. Implications The method developed here could be applied elsewhere, where snow depth is an important factor in species ecology. The benefit of this approach is a comparatively simple method that maximises the use of widely available SWE data in place of snow-depth data.

Effect of snow depth on wolf activity and prey selection in north central Minnesota

1991 ◽  
Vol 69 (2) ◽  
pp. 283-287 ◽  
Author(s):  
Todd K. Fuller
Keyword(s):  
Feeding Behavior ◽  
Canis Lupus ◽  
Snow Depth ◽  
Prey Selection ◽  
Severity Index ◽  
North Central ◽  
Winter Activity ◽  
Induced Changes ◽  
Winter Population ◽  
Winter Severity

Wolf (Canis lupus) activity and interactions with white-tailed deer (Odocoileus virginianus) were monitored in north central Minnesota during six winters in which mean January–February snow depth alternated between shallow (19–26 cm) and relatively deep (40–47 cm) and winters (winter severity index; L. J. Verme. 1968. J. Wildl. Manage. 32: 566–574) alternated between mild (71–98) and moderately severe (126–137). Wolves traveled farther and more often and spent less time with other pack members in mild than in severe winters. Radio-marked wolves and deer used conifer cover less, and fewer deer were killed there, when snow was shallow. Similarly, fewer wolf-killed deer were found in and near deer concentration areas during mild winters. Of the 74 deer killed by wolves, the proportion that were fawns (54%) differed from the proportion of fawns in the winter population (27%), but neither varied with winter severity. Few deer killed by wolves appeared debilitated. Carcass consumption was high in all winters, regardless of their severity, but wolves scavenged less in mild than in severe winters (10 vs. 29% of deer carcasses observed). Thus, wolves changed winter activity, movement patterns, sociality, and feeding behavior in response to snow-induced changes in deer distribution and mobility.

scholarly journals Converting snow depth to snow water equivalent using climatological variables

2019 ◽  
Vol 13 (7) ◽  
pp. 1767-1784 ◽  
Author(s):  
David F. Hill ◽  
Elizabeth A. Burakowski ◽  
Ryan L. Crumley ◽  
Julia Keon ◽  
J. Michelle Hu ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
Weather Data ◽  
Validation Data ◽  
Simple Method ◽  
Avalanche Forecasting ◽  
Mean Temperature ◽  
Snow Water ◽  
The Difference

Abstract. We present a simple method that allows snow depth measurements to be converted to snow water equivalent (SWE) estimates. These estimates are useful to individuals interested in water resources, ecological function, and avalanche forecasting. They can also be assimilated into models to help improve predictions of total water volumes over large regions. The conversion of depth to SWE is particularly valuable since snow depth measurements are far more numerous than costlier and more complex SWE measurements. Our model regresses SWE against snow depth (h), day of water year (DOY) and climatological (30-year normal) values for winter (December, January, February) precipitation (PPTWT), and the difference (TD) between mean temperature of the warmest month and mean temperature of the coldest month, producing a power-law relationship. Relying on climatological normals rather than weather data for a given year allows our model to be applied at measurement sites lacking a weather station. Separate equations are obtained for the accumulation and the ablation phases of the snowpack. The model is validated against a large database of snow pillow measurements and yields a bias in SWE of less than 2 mm and a root-mean-squared error (RMSE) in SWE of less than 60 mm. The model is additionally validated against two completely independent sets of data: one from western North America and one from the northeastern United States. Finally, the results are compared with three other models for bulk density that have varying degrees of complexity and that were built in multiple geographic regions. The results show that the model described in this paper has the best performance for the validation data sets.

scholarly journals Converting Snow Depth to Snow Water Equivalent Using Climatological Variables

2019 ◽  
Author(s):  
David F. Hill ◽  
Elizabeth A. Burakowski ◽  
Ryan L. Crumley ◽  
Julia Keon ◽  
J. Michelle Hu ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mean Squared Error ◽  
Independent Set ◽  
Weather Data ◽  
Mean Annual Precipitation ◽  
Validation Data ◽  
Data Set ◽  
Simple Method ◽  
Snow Water

Abstract. We present a simple method that allows snow depth measurements to be converted to snow water equivalent (SWE) estimates. These estimates are useful to individuals interested in water resources, ecological function, and avalanche forecasting. They can also be assimilated into models to help improve predictions of total water volumes over large regions. The conversion of depth to SWE is particularly valuable since snow depth measurements are far more numerous than costlier and more complex SWE measurements. Our model regresses SWE against snow depth and climatological (30-year normal) values for mean annual precipitation (MAP) and mean February temperature, producing a power-law relationship. Relying on climatological normals rather than weather data for a given year allows our model to be applied at measurement sites lacking a weather station. Separate equations are obtained for the accumulation and the ablation phases of the snowpack, which introduces day of water year (DOY) as an additional variable. The model is validated against a large database of snow pillow measurements and yields a bias in SWE of less than 0.5 mm and a root-mean-squared-error (RMSE) in SWE of approximately 65 mm. When the errors are investigated on a station-by-station basis, the average RMSE is about 5 % of the MAP at each station. The model is additionally validated against a completely independent set of data from the northeast United States. Finally, the results are compared with other models for bulk density that have varying degrees of complexity and that were built in multiple geographic regions. The results show that the model described in this paper has the best performance for the validation data set.

scholarly journals Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 363
Author(s):  
George Duffy ◽  
Fraser King ◽  
Ralf Bennartz ◽  
Christopher G. Fletcher
Keyword(s):  
Time Scales ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Snow Accumulation ◽  
High Latitudes ◽  
Percentage Points ◽  
Snow Water ◽  
Time Periods ◽  
Predicted Values ◽  
Good Agreement

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

scholarly journals Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data

2021 ◽  
Vol 13 (4) ◽  
pp. 554
Author(s):  
A. A. Masrur Ahmed ◽  
Ravinesh C Deo ◽  
Nawin Raj ◽  
Afshin Ghahramani ◽  
Qi Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Climate Index ◽  
Climate Indices ◽  
High Temporal Resolution ◽  
Synoptic Scale ◽  
Time Step ◽  
Gated Recurrent Unit

Remotely sensed soil moisture forecasting through satellite-based sensors to estimate the future state of the underlying soils plays a critical role in planning and managing water resources and sustainable agricultural practices. In this paper, Deep Learning (DL) hybrid models (i.e., CEEMDAN-CNN-GRU) are designed for daily time-step surface soil moisture (SSM) forecasts, employing the gated recurrent unit (GRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and convolutional neural network (CNN). To establish the objective model’s viability for SSM forecasting at multi-step daily horizons, the hybrid CEEMDAN-CNN-GRU model is tested at 1st, 5th, 7th, 14th, 21st, and 30th day ahead period by assimilating a comprehensive pool of 52 predictor dataset obtained from three distinct data sources. Data comprise satellite-derived Global Land Data Assimilation System (GLDAS) repository a global, high-temporal resolution, unique terrestrial modelling system, and ground-based variables from Scientific Information Landowners (SILO) and synoptic-scale climate indices. The results demonstrate the forecasting capability of the hybrid CEEMDAN-CNN-GRU model with respect to the counterpart comparative models. This is supported by a relatively lower value of the mean absolute percentage and root mean square error. In terms of the statistical score metrics and infographics employed to test the final model’s utility, the proposed CEEMDAN-CNN-GRU models are considerably superior compared to a standalone and other hybrid method tested on independent SSM data developed through feature selection approaches. Thus, the proposed approach can be successfully implemented in hydrology and agriculture management.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals Distributed snow and rock temperature modelling in steep rock walls using Alpine3D

2017 ◽  
Vol 11 (1) ◽  
pp. 585-607 ◽  
Author(s):  
Anna Haberkorn ◽  
Nander Wever ◽  
Martin Hoelzle ◽  
Marcia Phillips ◽  
Robert Kenner ◽  
...  
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Depth ◽  
Spatial Scales ◽  
Swiss Alps ◽  
Balance Model ◽  
Near Surface ◽  
Surface Rock ◽  
Depth Data ◽  
Rock Temperature

Abstract. In this study we modelled the influence of the spatially and temporally heterogeneous snow cover on the surface energy balance and thus on rock temperatures in two rugged, steep rock walls on the Gemsstock ridge in the central Swiss Alps. The heterogeneous snow depth distribution in the rock walls was introduced to the distributed, process-based energy balance model Alpine3D with a precipitation scaling method based on snow depth data measured by terrestrial laser scanning. The influence of the snow cover on rock temperatures was investigated by comparing a snow-covered model scenario (precipitation input provided by precipitation scaling) with a snow-free (zero precipitation input) one. Model uncertainties are discussed and evaluated at both the point and spatial scales against 22 near-surface rock temperature measurements and high-resolution snow depth data from winter terrestrial laser scans.In the rough rock walls, the heterogeneously distributed snow cover was moderately well reproduced by Alpine3D with mean absolute errors ranging between 0.31 and 0.81 m. However, snow cover duration was reproduced well and, consequently, near-surface rock temperatures were modelled convincingly. Uncertainties in rock temperature modelling were found to be around 1.6 °C. Errors in snow cover modelling and hence in rock temperature simulations are explained by inadequate snow settlement due to linear precipitation scaling, missing lateral heat fluxes in the rock, and by errors caused by interpolation of shortwave radiation, wind and air temperature into the rock walls.Mean annual near-surface rock temperature increases were both measured and modelled in the steep rock walls as a consequence of a thick, long-lasting snow cover. Rock temperatures were 1.3–2.5 °C higher in the shaded and sunny rock walls, while comparing snow-covered to snow-free simulations. This helps to assess the potential error made in ground temperature modelling when neglecting snow in steep bedrock.

scholarly journals Snowpack variability and trends at long-term stations in northern Colorado, USA

2015 ◽  
Vol 371 ◽  
pp. 131-136 ◽  
Author(s):  
S. R. Fassnacht ◽  
M. Hultstrand
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Entire Period ◽  
Northern Colorado ◽  
Snow Water ◽  
Highly Correlated ◽  
The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

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