Abstract
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.
Highlights
For Numerical Weather Prediction (NWP), the importance of snow on the ground is mostly related to its capacity to modify the surface albedo as well as heat and moisture exchanges between the surface and the atmosphere [1,2]
Pre-existing numerical modeling components were gathered to form the National Surface and River Prediction System (NRSPS; [8]). This system aims to provide the best possible representation of the current and future states of the land surface and of the movement of water over and through the soil column and through the lake and river network without any feedback to an atmospheric model. One of these modeling components is the Canadian Land Data Assimilation System (CaLDAS), and it produces snow analyses based on a simulated first guess and available observations
Efforts have been mainly dedicated at producing snow analyses that improve atmospheric predictions (i.e., NWP): in situ snow depth observations are assimilated using an optimal interpolation method
Summary
For Numerical Weather Prediction (NWP), the importance of snow on the ground is mostly related to its capacity to modify the surface albedo as well as heat and moisture exchanges between the surface and the atmosphere [1,2]. The amount of water stored in the snowpack and the timing of melting are critical variables for quality hydrological forecasts [3,4,5,6]. These factors help the community to predict and prepare for floods and droughts, and can have extensive societal implications (e.g., https://www.canada.ca/en/environment-climate-change/services/wateroverview/quantity/costs-of-flooding.html, last accessed on 6 December 2021). At Environment and Climate Change Canada (ECCC), efforts were mainly dedicated to producing snow analyses that improve atmospheric predictions [7].
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