Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

Abstract

A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.

Investigating snowpack across scale in the northern Great Lakes–St. Lawrence forest region of Central Ontario, Canada

“…Alternatively, the GlobSnow estimate may have been less than the true mean during melt. This is consistent with previous literature that has found that product estimates that assimilate point depths (such as GlobSnow) typically underestimate depth and SWE due to assimilation of point measurements in exposed areas (such as airports) that ablate more rapidly than the heterogeneous landscape (including forest cover) that the grid cell represents (Brown, Tapsoba, & Derksen, ; Grünewald & Lehning, ; Neumann et al, ). Passive microwave SWE estimates have known error when water content is present in the snow pack (Dietz et al, ).…”

Evaluation of long term Northern Hemisphere snow water equivalent products

“…Correlations between GlobSnow and the R4 products are strong across 305 most snow covered regions of the northern hemisphere (GS-R4), with the exception of parts of Arctic Canada and the ephemeral snow zones of both North America and Eurasia (note that alpine areas are masked in the GlobSnow product). As noted earlier, the performance of GlobSnow is closely tied to the density of snow depth data used as inputs to the retrievals (Larue et al, 2017;Brown et al, 2018) which likely contributes to the low correlations in parts of Arctic Canada where there are relatively few snow depth observations. The NASA AMSR-E historical dataset exhibits very weak anomaly correlations with the R4 datasets (Nh-R4), and even negative correlations over the boreal forest of North America and parts of central and eastern Siberia.…”

“…The ability of GlobSnow to retain sensitivity to deeper snow than the AMSR-E products is due to the assimilation of daily surface snow depth observations which work to 'nudge' the retrievals to higher values (Pulliainen, 2006). In observation sparse regions such as northern Quebec, the GlobSnow 185 retrieval must rely more on the passive microwave retrievals, which increases uncertainty in these areas (Larue et al, 2017;Brown et al, 2018) compared to forested, deep snow regions with a dense observation network such as Finland (Takala et al, 2011).…”

Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada

“…The potential formation of clusters of grains, which increases the effective snow grain size, is not taken into account, generating uncertainties (Picard et al, 2013). Several studies have shown that DMRT-ML needed an effective scaling factor to represent the stickiness between snow grains and to correct the snow microstructure representation (Brucker et al, 2011;Roy et al, 2013;Royer et al, 2017). Larue et al (2018) have shown that a mean snow stickiness parameter (τ snow ) of 0.17 was optimal to simulate T Bsnow over boreal snow in Quebec (RMSE of 27 K) when DMRT-ML is driven by Crocus snow profiles.…”

“…This contribution does not only depend on the fraction of forest cover, but also on the biomass (liquid water content, LWC), the vegetation volume, and the canopy structure (stem, leaf, trunk) (Franklin, 1987). To adjust snowpack model simulations, several studies suggest using radiative transfer models, coupled to a snowpack model, to take into account the different contributions to the PMW signal at the top of the atmosphere and to directly assimilate PMW satellite observations (Brucker et al, 2011;Durand et al, 2011;Langlois et al, 2012;Roy et al, 2016). However, the assimilation of PMW must be used with care, and a good understanding of the interactions between the properties and microwave emission of the snowpack is crucial to avoid degradation of the SWE estimates.…”