On the importance of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains

A modelling study was undertaken to evaluate the contribution of sublimation to an alpine snow mass balance in the Canadian Rocky Mountains. Snow redistribution and sublimation by wind, snowpack sublimation and snowmelt were simulated for two winters over an alpine ridge transect located in the Canada Rocky Mountains. The resulting snowcover regimes were compared to those from manual snow surveys. Simulations were performed using physically based blowing snow (PBSM) and snowpack ablation (SNOBAL) models. A hydrological response unit (HRU)-based spatial discretization was used rather than a more computationally expensive fully-distributed one. The HRUs were set up to follow an aerodynamic sequence, whereby eroded snow was transported from windswept, upwind HRUs to drift accumulating, downwind HRUs. That snow redistribution by wind can be adequately simulated in computationally efficient HRUs over this ridge has important implications for representing snow transport in large-scale hydrology models and land surface schemes. Alpine snow sublimation losses, in particular blowing snow sublimation losses, were significant. Snow mass losses to sublimation as a percentage of cumulative snowfall were estimated to be 20–32% with the blowing snow sublimation loss amounting to 17–19% of cumulative snowfall. This estimate is considered to be a conservative estimate of the blowing snow sublimation loss in the Canadian Rocky Mountains because the study transect is located in the low alpine zone where the topography is more moderate than the high alpine zone and windflow separation was not observed. An examination of the suitability of PBSM's sublimation estimates in this environment and of the importance of estimating blowing snow sublimation on the simulated snow accumulation regime was conducted by omitting sublimation calculations. Snow accumulation in HRUs was overestimated by 30% when neglecting blowing snow sublimation calculations

Parameter-state ensemble thinning for short-term hydrological prediction

The main sources of uncertainty in hydrological modelling can be summarized as structural errors, parameter errors, and data errors. Operational modellers are generally more concerned with predictive ability than model errors, and this paper presents a new, simple method to improve predictive ability. The method is called parameter-state ensemble thinning (P-SET). P-SET takes a large ensemble of continuous model runs and applies screening criteria to reduce the size of the ensemble. The goal is to find the most promising parameter-state combinations for analysis during the prediction period. Each prediction period begins with the same large ensemble, but the screening criteria are free to select a different sub-set of simulations for each separate prediction period. The case study is from June to October 2014 for a small (1324&thinsp;km2) watershed just north of Lake Superior in Ontario, Canada, using a Canadian semi-distributed hydrologic land-surface scheme. The study examines how well the approach works given various levels of certainty in the data, beginning with certainty in the streamflow and precipitation, followed by uncertainty in the streamflow and certainty in the precipitation, and finally uncertainty in both the streamflow and precipitation. The approach is found to work in this case when streamflow and precipitation are fairly certain, while being more challenging to implement in a forecasting scenario where future streamflow and precipitation are much less certain. The main challenge is determined to be related to parametric uncertainty and ideas for overcoming this challenge are discussed. The approach also highlights model structural errors, which are also discussed.</p

Development of the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale

International audienceEnvironment Canada has been developing a community environmental modelling system (Modélisation Environmentale Communautaire ? MEC), which is designed to facilitate coupling between models focusing on different components of the earth system. The ultimate objective of MEC is to use the coupled models to produce operational forecasts. MESH (MEC ? Surface and Hydrology), a configuration of MEC currently under development, is specialized for coupled land-surface and hydrological models. To determine the specific requirements for MESH, its different components were implemented on the Laurentian Great Lakes watershed, situated on the Canada-US border. This experiment showed that MESH can help us better understand the behaviour of different land-surface models, test different schemes for producing ensemble streamflow forecasts, and provide a means of sharing the data, the models and the results with collaborators and end-users. This modelling framework is at the heart of a testbed proposal for the Hydrologic Ensemble Prediction Experiment (HEPEX) which should allow us to make use of the North American Ensemble Forecasting System (NAEFS) to improve streamflow forecasts of the Great Lakes tributaries, and demonstrate how MESH can contribute to a Community Hydrologic Prediction System (CHPS)

Using the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale

International audienceEnvironment Canada has been developing a community environmental modelling system (Modélisation Environmentale Communautaire ? MEC), which is designed to facilitate coupling between models focusing on different components of the earth system. The ultimate objective of MEC is to use the coupled models to produce operational forecasts. MESH (MEC ? Surface and Hydrology), a configuration of MEC currently under development, is specialized for coupled land-surface and hydrological models. To determine the specific requirements for MESH, its different components were implemented on the Laurentian Great Lakes watershed, situated on the Canada?U.S. border. This experiment showed that MESH can help us better understand the behaviour of different land-surface models, test different schemes for producing ensemble streamflow forecasts, and provide a means of sharing the data, the models and the results with collaborators and end-users. This modelling framework is at the heart of a testbed proposal for the Hydrologic Ensemble Prediction Experiment (HEPEX) which should allow us to make use of the North American Ensemble Forecasting System (NAEFS) to improve streamflow forecasts of the Great Lakes tributaries, and demonstrate how MESH can contribute to a Community Hydrologic Prediction System (CHPS)

Identifying streamgage networks for maximizing the effectiveness of regional water balance modeling

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98795/1/wrcr20233.pd

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation

A synthesis of three decades of hydrological research at Scotty Creek, NWT, Canada

Scotty Creek, Northwest Territories (NWT), Canada, has been the focus of hydrological research for nearly three decades. Over this period, field and modelling studies have generated new insights into the thermal and physical mechanisms governing the flux and storage of water in the wetland-dominated regions of discontinuous permafrost that characterises much of the Canadian and circumpolar subarctic. Research at Scotty Creek has coincided with a period of unprecedented climate warming, permafrost thaw, and resulting land cover transformations including the expansion of wetland areas and loss of forests. This paper (1) synthesises field and modelling studies at Scotty Creek, (2) highlights the key insights of these studies on the major water flux and storage processes operating within and between the major land cover types, and (3) provides insights into the rate and pattern of the permafrost-thaw-induced land cover change and how such changes will affect the hydrology and water resources of the study region.</p

Canada’s Contributions to the SWOT Mission–Terrestrial Hydrology(SWOT-C TH)

The origins of the Surface Water and Ocean Topography (SWOT) mission date back to the mid-1970s with the launch of GOES-3 and SEASAT. These missions were then followed in 1992 by the Topex-Poseidon satellite, then by Jason-1 (2001), OSTM/Jason-2 (2008), and Jason 3 (2016), a series of joint satellite missions between NASA and CNES with a goal to monitor global ocean circulation. The proposed new SWOT mission will provide 120-km-wide swath interferometric coverage with a 20-km-wide gap at the nadir. The SWOT measurements will consist of water surface elevations and water surface slopes covering nearly all of the earth’s land surface at least once every 21 days. In 2010, NASA invited the Canadian Space Agency to contribute, and Canadian scientists welcomed the invitation to join the SWOT Science Definition Team and contribute to the experiments. The Canadian segment of the mission is known as the “SWOT-C” project. The SWOT satellite mission will provide unique opportunities in the Canadian context for water managers in both the public domain and in the private sector. This paper provides an overview of recent scientific progress by the SWOT-C Terrestrial Hydrology team, outlining current plans and progress towards applications and calibration post-launch