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Overview Research Site Access and Downloads
Dataset Title
An Evaluation of New Snow Interception and Ablation Parameterisations in Continental, Subarctic and Maritime Needleleaf Forests
Dataset DOI
https://doi.org/10.1002/hyp.70573
Abstract
Snow interception by forest canopies occurs over 23% of the global land surface, influencing both subcanopy snow accumulation and land-surface energy exchanges. The processes governing snow interception are strongly influenced by both meteorological conditions and canopy density, resulting in differing process emergence in distinctive climates, seasons and forest types. Recent studies have revealed new relationships to represent snow interception and canopy snow ablation processes with potential applicability across a broader range of canopy structures and climatic conditions. To assess the effectiveness of these new parameterisations, they were implemented in the Cold Regions Hydrological Modelling platform as a new module called CanSnow, and evaluated against observations of snow water equivalent within and beneath the canopy at four sites in western and northern Canada: two continental climate sites (Marmot Creek and Fortress Mountain, Alberta), one subarctic site (Wolf Creek, Yukon Territory), and one temperate-maritime site (Russell Creek, British Columbia). The observed fraction of seasonal snowfall stored in the subcanopy snowpack at peak snow water equivalent (SWE) varied from 0.3 at Russell Creek, 0.4 at Marmot and Wolf Creek and 0.6 at Fortress Mountain. Uncalibrated simulation of canopy intercepted snow duration at Marmot Creek improved with the new CanSnow module, where the percent differences from observations decreased from −40.1% to 14%. CanSnow improved simulation of subcanopy snow accumulation at the temperate-maritime site, whereas improvements were smaller at the cold-continental and subarctic sites. Despite relatively smaller improvements in accuracy at cold-climate sites, the new model is based on an improved physical representation of initial interception and subsequent ablation of intercepted snow, increasing confidence in process diagnosis. An existing approach lacked a comprehensive representation of snow interception processes, resulting in generally larger errors in simulated SWE within and beneath the canopy. Based on diagnosis by CanSnow, at cold, low-wind sites, about half of annual snowfall was lost via sublimation of intercepted snow, whereas greater unloading at a cold, wind-exposed site reduced sublimation losses. At the high-snowfall temperate-maritime site, canopy snowmelt, meltwater drip and melt-induced unloading dominated, delivering the largest fraction of snowfall to the forest floor despite high initial interception efficiency.
Purpose
To answer the following research questions:

1 What is the fraction of seasonal snowfall stored in the subcanopy snowpack in mature needleleaf forests across continental, subarctic and temperate-maritime climates?

2 How accurately does a novel hydrological model simulate canopy snow load and subcanopy SWE across varying forest types and climates compared to an existing model?

3 How is snowfall in needleleaf forests partitioned into throughfall, interception, sublimation, unloading and melt/drip in differing climates and canopy structures?

Is Boundary Rectangular
○ Yes
◉ No
Research Site Location
Map Not Available
Display
View on Global Map
Does the data have access restrictions?
▣ No restriction (data is currently open to public)
◻ Limited (data is currently under embargo until publication)
◻ Limited (data involves intellectual property issues related to local or traditional knowledge)
◻ Limited (release of data may cause harm to the environment or to the public)
◻ Limited (pre-existing data has been used and is subject to access restrictions)
◻ Limited (data involves human subjects)
◻ Limited (data is supported by industry partnerships)
◻ Limited (data is supported by government partnerships)
Datasets and Real-time Data Feeds
Data Centre Postings
Download Links and Instructions
https://onlinelibrary.wiley.com/doi/10.1002/hyp.70573
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T-2026-09-02-Y1fvOEfY2KA0mnJY2pDMEM4Hw Dataset 1.3