Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

Vionnet, Vincent; Mortimer, Colleen; Brady, Michael S.; Arnal, Louise; Brown, Ross

doi:10.5194/essd-13-4603-2021

Cited by 23 publications

(19 citation statements)

References 43 publications

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“…The ERA5-Land dataset was chosen over the available ground truth SWE observations like the CanSWE dataset (Canadian historical Snow Water Equivalent dataset; Vionnet et al, 2021) because (a) the ERA5-Land dataset is available over the entire modeling domain, while CanSWE is only available for the Canadian portion, (b) the ERA5-Land dataset is gridded, allowing for similar comparison approaches as used for AET and SSM, and (c) the ERA5-Land dataset is available on a daily scale, while the frequency of data in CanSWE varies from biweekly to monthly, which would have limited the number of data available for model evaluation. We, however, provide a comparison of the ERA5-Land and CanSWE observations by comparing the grid cells containing at least one snow observation station available in CanSWE and derive the KGE of those two time series over the days on which both datasets provide estimates.…”

Section: Model Evaluation Setup and Datasetsmentioning

confidence: 99%

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

Mai

Shen

Tolson

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

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Section: Model Evaluation Setup and Datasetsmentioning

confidence: 99%

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

Mai

Shen

Tolson

et al. 2022

Hydrol. Earth Syst. Sci.

Self Cite

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“…We also evaluated the model's robustness in simulating two auxiliary variables, snow water equivalent (SWE) and actual evapotranspiration (AET) (Mai et al, 2022). The Canadian historical SWE station data (CanSWE) (Vionnet et al, 2021) at four selected locations (one station at each sub-domain) were used to compare simulated SWE at the grid exactly over the corresponding CanSWE station. Supplementary Figure S2 shows the resultant plots and it is evident that the model is able to represent the dynamics of SWE at selected stations.…”

Section: Calibration and Validationmentioning

confidence: 99%

The Impacts of Climate Change on Land Hydroclimatology of the Laurentian Great Lakes Basin

et al. 2022

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The freshwater resources of the Laurentian Great Lakes basin contribute significantly to the environment and economy of the region. With the impacts of climate change becoming more evident, sustainable management of the freshwater resources of the Laurentian Great Lakes basin is important. This study uses 36 simulations from 6 regional climate models to quantify trends and changes in land-area precipitation and temperature in two future periods (mid-century, 2035–2064 and end-century, 2065–2094) with reference to a baseline period (1951–2005) for two emission scenarios (RCP4.5 and RCP 8.5). Climatic forcings from these 36 simulations are used as input to a calibrated and validated hydrological model to assess changes in land snowpack and actual evapotranspiration, and runoff to lake. Ensemble results show wetter (7 to 15% increase in annual precipitation) and warmer (2.4–5.0°C increase in annual mean temperature) future conditions on GL land areas. Seasonal and monthly changes in precipitation and mean temperature are more sporadic, for instance although precipitation is projected to increase overall, in some scenarios, summer precipitation is expected to decrease. Projected increases in highest one-day precipitation and decreases in number of wet days indicate possible increases in extreme precipitation in future. Minimum temperature is expected to increase in a higher rate than maximum temperature. Ensemble results from the hydrological model show projected decrease in snowpack (29–58%). Similarly, actual evapotranspiration is projected to increase, especially during summer months (up to 0.4 mm/day). Annually, runoff is expected to increase (up to 48% in Superior, 40% in Michigan-Huron, 25% Erie and 28% in Ontario). Seasonal and monthly changes in runoff are more sporadic (e.g., projected decrease up to 17% in Erie subdomain in October). Such contrasting patterns of changes in land hydroclimatology of the GL basin will pose challenges to sustainable management of the water resources of the basin in future.

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“…Snow datasets covering Canada and the United States (US), including Alaska, were combined to propose a detailed evaluation of the different CaLDAS experiments using independent observations that are not assimilated in CaLDAS_REF. In Canada, observations of daily snow water equivalent (SWE) and snow depth (SD) were taken from the Canadian historical Snow Water Equivalent dataset (CanSWE; [38]). CanSWE combines manual (snow surveys) and automatic SWE observations (snow pillows and passive gamma sensors) collected by national, provincial and territorial agencies as well as hydro-power companies and their partners.…”

Section: Evaluation Datamentioning

confidence: 99%

“…Outlier detection was also applied to automatic measurements. More detailed are provided in [38]. Daily SWE and SD observations in the continental US and Alaska were obtained from the SNOTEL network of automatic snow pillows [39].…”

Section: Evaluation Datamentioning

confidence: 99%

Improving Snow Analyses for Hydrological Forecasting at ECCC Using Satellite-Derived Data

et al. 2021

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As part of the National Hydrological Services Transformation Initiative, Environment and Climate Change Canada (ECCC) designed and implemented the National Surface and River Prediction System (NSRPS) in order to provide surface and river flow analysis and forecast products across Canada. Within NSRPS, the Canadian Land Data Assimilation System (CaLDAS) produces snow analyses that are used to initialise the land surface model, which in turn is used to force the river routing component. Originally, CaLDAS was designed to improve atmospheric forecasts with less focus on hydrological processes. When snow data assimilation occurs, the related increments remove/add water from/to the system, which can sometimes be problematic for streamflow forecasting, in particular during the snowmelt period. In this study, a new snow analysis method introduces multiple innovations that respond to the need for higher quality snow analyses for hydrological purposes, including the use of IMS snow cover extent data instead of in situ snow depth observations. The results show that the new snow assimilation methodology brings an overall improvement to snow analyses and substantially enhances water conservation, which is reflected in the generally improved streamflow simulations. This work represents a first step towards a new snow data assimilation process in CaLDAS, with the final objective of producing a reliable snow analysis to initialise and improve NWP as well as environmental predictions, including flood and drought forecasts.

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Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

Cited by 23 publications

References 43 publications

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)

The Impacts of Climate Change on Land Hydroclimatology of the Laurentian Great Lakes Basin

Improving Snow Analyses for Hydrological Forecasting at ECCC Using Satellite-Derived Data

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