scholarly journals UAV-Based Estimate of Snow Cover Dynamics: Optimizing Semi-Arid Forest Structure for Snow Persistence

2021 ◽  
Vol 13 (5) ◽  
pp. 1036
Author(s):  
Adam Belmonte ◽  
Temuulen Sankey ◽  
Joel Biederman ◽  
John Bradford ◽  
Scott Goetz ◽  
...  
Keyword(s):  
Snow Cover ◽  
Forest Structure ◽  
Ponderosa Pine ◽  
Forest Health ◽  
Dry Forest ◽  
Multispectral Imagery ◽  
Forest Environment ◽  
Validation Data ◽  
Seasonal Snow ◽  
Seasonal Snow Cover

Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.

Effect of seasonal snow cover on litter decomposition in alpine forest

2013 ◽  
Vol 37 (4) ◽  
pp. 296-305 ◽  
Author(s):  
Qi-Qian WU ◽  
Fu-Zhong WU ◽  
Wan-Qin YANG ◽  
Zhen-Feng XU ◽  
Wei HE ◽  
...  
Keyword(s):  
Snow Cover ◽  
Litter Decomposition ◽  
Seasonal Snow ◽  
Alpine Forest ◽  
Seasonal Snow Cover

Modelling the hypsometric seasonal snow cover using meteorological parameters

2014 ◽  
Vol 60 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Snehmani ◽  
Anshuman Bhardwaj ◽  
Mritunjay Kumar Singh ◽  
R.D. Gupta ◽  
Pawan Kumar Joshi ◽  
...  
Keyword(s):  
Snow Cover ◽  
Meteorological Parameters ◽  
Seasonal Snow ◽  
Seasonal Snow Cover

scholarly journals Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

2018 ◽  
Vol 12 (4) ◽  
pp. 1137-1156 ◽  
Author(s):  
Paul J. Kushner ◽  
Lawrence R. Mudryk ◽  
William Merryfield ◽  
Jaison T. Ambadan ◽  
Aaron Berg ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Cover ◽  
Climate Prediction ◽  
Earth System Model ◽  
System Model ◽  
Prediction System ◽  
Earth System ◽  
Seasonal Snow ◽  
Observational Uncertainty ◽  
Seasonal Snow Cover

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

scholarly journals Sub-surface melting in a seasonal snow cover

1995 ◽  
Vol 41 (139) ◽  
pp. 474-482 ◽  
Author(s):  
Gary Koh ◽  
Rachel Jordan
Keyword(s):  
Surface Temperature ◽  
Snow Cover ◽  
Continuous Wave ◽  
Surface Melting ◽  
Balance Model ◽  
Fmcw Radar ◽  
Seasonal Snow ◽  
Wet Snow ◽  
Seasonal Snow Cover ◽  
Good Agreement

AbstractThe ability of solar radiation to penetrate into a snow cover combined with the low thermal conductivity of snow can lead to a sub-surface temperature maximum. This elevated sub-surface temperature allows a layer of wet snow to form below the surface even on days when the air temperature remains sub-freezing. A high-resolution frequency-modulated continuous wave (FMCW) radar has been used to detect the onset of sub-surface melting in a seasonal snow cover. The experimental observation of sub-surface melting is shown to be in good agreement with the predictions of a one-dimensional mass- and energy-balance model. The effects of varying snow characteristics and solar extinction parameters on the sub-surface melt characteristics are investigated using model simulations.

scholarly journals Long-term variations of the seasonal snow cover in Nordland, Norway: the influence of the North Atlantic Oscillation

2013 ◽  
Vol 54 (62) ◽  
pp. 25-34 ◽  
Author(s):  
Wilfred H. Theakstone
Keyword(s):  
North Atlantic Oscillation ◽  
Snow Cover ◽  
20Th Century ◽  
North Atlantic ◽  
Temporal Trends ◽  
The Arctic ◽  
Simple Task ◽  
Seasonal Snow ◽  
Synoptic Conditions ◽  
Seasonal Snow Cover

AbstractTemporal and spatial variations of the seasonal snow cover at 40 sites in Nordland county, Norway, since the last decade of the 19th century are examined. Nordland lies across the Arctic Circle. Annual maximum snow depths there have varied, reflecting the interaction of synoptic conditions, temperature and terrain. North/south and coastal/inland differences are evident, but common temporal trends are identified. Maximum snow depths are strongly related to the winter North Atlantic Oscillation index. Early in the 20th century, the index was positive and the associated stormy conditions resulted in a deep, prolonged snow cover. As the index declined in the 1920s, snow depths decreased sharply. Through much of the second half of the 20th century they increased as the index tended to become more positive. The start and duration of the period of continuous snow cover is influenced by the autumn NAO index. A decrease of duration around 1990 was particularly evident at low-lying stations and those in northern Nordland. The NAO has varied considerably over the past 120 years. Because of its influence, forecasting future trends of snow depth and snow-cover duration is not a simple task.

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