@article{Lv-2019-Evaluation,
title = "Evaluation of SNODAS Snow Water Equivalent in Western Canada and Assimilation Into a Cold Region Hydrological Model",
author = "Lv, Zhibang and
Pomeroy, John W. and
Fang, Xing",
journal = "Water Resources Research, Volume 55, Issue 12",
volume = "55",
number = "12",
year = "2019",
publisher = "American Geophysical Union (AGU)",
url = "https://gwf-uwaterloo.github.io/gwf-publications/G19-34002",
doi = "10.1029/2019wr025333",
pages = "11166--11187",
abstract = "Snow water equivalent (SWE) is one of the most hydrologically important physical properties of a snowpack. The U.S. National Weather Service's Snow Data Assimilation System (SNODAS) provides snow products at high spatial ({\textasciitilde}1 km2) and temporal (daily) resolution for the contiguous United States and southern Canada. This study evaluated the SNODAS SWE product in the boreal forest, prairie, and Canadian Rockies of western Canada against extensive snow survey measurements. SNODAS was found to work well in sheltered environments, to overestimate SWE under needle‐leaf forests, and to be unable to capture the spatial variation of SWE in windswept prairie and alpine environments. Results indicate that SNODAS SWE accuracy is strongly influenced by the missing blowing snow redistribution and canopy energetics and snow interception and sublimation processes in the mass balance calculations of the SNODAS model and by erroneous precipitation data forcing the model. To demonstrate how errors caused by missing processes can be corrected in areas with low assimilation frequency, SNODAS data were assimilated into a physically based hydrological model created using the modular Cold Region Hydrological Modelling (CRHM) platform that includes blowing and intercepted snow redistribution and subcanopy melt energetic processes. This approach decreased the overestimation of SWE compared to SNODAS from 135 to 79{\%} in the study area and suggests that snow assimilation modeled SWE quality can be improved if snow redistribution, sublimation, and subcanopy melt processes are incorporated.",
}
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<abstract>Snow water equivalent (SWE) is one of the most hydrologically important physical properties of a snowpack. The U.S. National Weather Service’s Snow Data Assimilation System (SNODAS) provides snow products at high spatial (~1 km2) and temporal (daily) resolution for the contiguous United States and southern Canada. This study evaluated the SNODAS SWE product in the boreal forest, prairie, and Canadian Rockies of western Canada against extensive snow survey measurements. SNODAS was found to work well in sheltered environments, to overestimate SWE under needle‐leaf forests, and to be unable to capture the spatial variation of SWE in windswept prairie and alpine environments. Results indicate that SNODAS SWE accuracy is strongly influenced by the missing blowing snow redistribution and canopy energetics and snow interception and sublimation processes in the mass balance calculations of the SNODAS model and by erroneous precipitation data forcing the model. To demonstrate how errors caused by missing processes can be corrected in areas with low assimilation frequency, SNODAS data were assimilated into a physically based hydrological model created using the modular Cold Region Hydrological Modelling (CRHM) platform that includes blowing and intercepted snow redistribution and subcanopy melt energetic processes. This approach decreased the overestimation of SWE compared to SNODAS from 135 to 79% in the study area and suggests that snow assimilation modeled SWE quality can be improved if snow redistribution, sublimation, and subcanopy melt processes are incorporated.</abstract>
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%0 Journal Article
%T Evaluation of SNODAS Snow Water Equivalent in Western Canada and Assimilation Into a Cold Region Hydrological Model
%A Lv, Zhibang
%A Pomeroy, John W.
%A Fang, Xing
%J Water Resources Research, Volume 55, Issue 12
%D 2019
%V 55
%N 12
%I American Geophysical Union (AGU)
%F Lv-2019-Evaluation
%X Snow water equivalent (SWE) is one of the most hydrologically important physical properties of a snowpack. The U.S. National Weather Service’s Snow Data Assimilation System (SNODAS) provides snow products at high spatial (~1 km2) and temporal (daily) resolution for the contiguous United States and southern Canada. This study evaluated the SNODAS SWE product in the boreal forest, prairie, and Canadian Rockies of western Canada against extensive snow survey measurements. SNODAS was found to work well in sheltered environments, to overestimate SWE under needle‐leaf forests, and to be unable to capture the spatial variation of SWE in windswept prairie and alpine environments. Results indicate that SNODAS SWE accuracy is strongly influenced by the missing blowing snow redistribution and canopy energetics and snow interception and sublimation processes in the mass balance calculations of the SNODAS model and by erroneous precipitation data forcing the model. To demonstrate how errors caused by missing processes can be corrected in areas with low assimilation frequency, SNODAS data were assimilated into a physically based hydrological model created using the modular Cold Region Hydrological Modelling (CRHM) platform that includes blowing and intercepted snow redistribution and subcanopy melt energetic processes. This approach decreased the overestimation of SWE compared to SNODAS from 135 to 79% in the study area and suggests that snow assimilation modeled SWE quality can be improved if snow redistribution, sublimation, and subcanopy melt processes are incorporated.
%R 10.1029/2019wr025333
%U https://gwf-uwaterloo.github.io/gwf-publications/G19-34002
%U https://doi.org/10.1029/2019wr025333
%P 11166-11187
Markdown (Informal)
[Evaluation of SNODAS Snow Water Equivalent in Western Canada and Assimilation Into a Cold Region Hydrological Model](https://gwf-uwaterloo.github.io/gwf-publications/G19-34002) (Lv et al., GWF 2019)
ACL
- Zhibang Lv, John W. Pomeroy, and Xing Fang. 2019. Evaluation of SNODAS Snow Water Equivalent in Western Canada and Assimilation Into a Cold Region Hydrological Model. Water Resources Research, Volume 55, Issue 12, 55(12):11166–11187.
