Accuracy of the Copernicus snow water equivalent (SWE) product and the impact of SWE calibration and assimilation on modelled SWE and streamflow was evaluated. Daily snowpack measurements were made at 12 locations from 2016 to 2019 across a 4104 km2 mixed-forest basin in the Great Lakes region of central Ontario, Canada. Sub-basin daily SWE calculated from these sites, observed discharge, and lake levels were used to calibrate a hydrologic model developed using the Raven modelling framework. Copernicus SWE was bias corrected during the melt period using mean bias subtraction and was compared to daily basin average SWE calculated from the measured data. Bias corrected Copernicus SWE was assimilated into the models using a range of parameters and the parameterizations from the model calibration. The bias corrected Copernicus product agreed well with measured data and provided a good estimate of mean basin SWE demonstrating that the product shows promise for hydrology applications within the study region. Calibration to spatially distributed SWE substantially improved the basin scale SWE estimate while only slightly degrading the flow simulation demonstrating the value of including SWE in a multi-objective calibration formulation. The particle filter experiments yielded the best SWE estimation but moderately degraded the flow simulation. The particle filter experiments constrained by the calibrated snow parameters produced similar results to the experiments using the upper and lower bounds indicating that, in this study, model calibration prior to assimilation was not valuable. The calibrated models exhibited varying levels of skill in estimating SWE but demonstrated similar streamflow performance. This indicates that basin outlet streamflow can be accurately estimated using a model with a poor representation of distributed SWE. This may be sufficient for applications where estimating flow is the primary water management objective. However, in applications where understanding the physical processes of snow accumulation, melt and streamflow generation are important, such as assessing the impact of climate change on water resources, accurate representations of SWE are required and can be improved via multi-objective calibration or data assimilation, as demonstrated in this study. Bias corrected Copernicus snow water equivalent (SWE) agreed well with measured data demonstrating the products utility for hydrology applications. Calibration to lake levels, streamflow, and SWE substantially improved model performance compared to a model calibrated to lake levels and streamflow only, thus demonstrating the value of including SWE in multi-objective calibration. image

The efficacy of field-based, photogrammetric point cloud, orthophoto and light detection and ranging datasets to describe forest structure and resolve forest-snowpack relationships in a mixed forest region was evaluated over two years at the point and transect scales. Hemispheric photo-derived canopy metrics correlated well with remotely sensed metrics, but tree bole metrics were not effectively derived from remotely sensed data. Significant differences in melt rate and snow-free date were found across forest type at the transect scale. Field and remotely sensed estimates of canopy cover were highly correlated with melt rate and snow-free date at the point scale, which aligns with previous literature and understanding of snowmelt processes. However, significant correlations were only present during the 2016 study year, which was attributed to canopy-controlled solar radiation-driven melt in 2016 versus more spatially uniform turbulent flux-driven melt in 2017. Peak snow water equivalent metrics were not correlated well with canopy or tree height metrics, contrary to previous research. This was likely due to mid-winter melt events throughout both study years, where a mix of accumulation and melt processes confounded forest-snowpack relationships. This study demonstrates that widely available remotely sensed data with a broad coverage can be used to: (i) describe forest-snowpack relationships in mixed hardwood, coniferous forests and (ii) elucidate the variability of forest-snowpack relationships under different climate conditions in this environment.

Stable isotope tracers of delta O-18 and delta H-2 are increasingly being applied in the study of water cycling in regional-scale watersheds in which human activities, like river regulation, are important influences. In 2015, delta O-18 and delta H-2 were integrated into a water quality survey in the Muskoka River Watershed with the aim to provide new regional-scale characterization of isotope hydrology in the 5,100-km(2) watershed located on the Canadian Shield in central Ontario, Canada. The forest dominated region includes similar to 78,000 ha of lakes, 42 water control structures, and 11 generating stations, categorized as "run of river." Within the watershed, stable isotope tracers have long been integrated into hydrologic process studies of both headwater catchments and lakes. Here, monthly surveys of delta O-18 and delta H-2 in river flow were conducted in the watershed between April 2015 and November 2016 (173 surface water samples from 10 river stations). Temporal patterns of stable isotopes in river water reflect seasonal influences of snowmelt and summer-time evaporative fractionation. Spatial patterns, including differences observed during extreme flood levels experienced in the spring of 2016, reflect variation in source contributions to river flow (e.g., snowmelt or groundwater versus evaporatively enriched lake storage), suggesting more local influences (e.g., glacial outwash deposits). Evidence of combined influences of source mixing and evaporative fractionation could, in future, support application of tracer-enabled hydrological modelling, estimation of mean transit times and, as such, contribute to studies of water quality and water resources in the region.

This study investigates scaling issues by evaluating snow processes and quantifying bias in snowpack properties across scale in a northern Great Lakes-St. Lawrence forest. Snow depth and density were measured along transects stratified by land cover over the 2015/2016 and 2016/2017 winters. Daily snow depth was measured using a time-lapse (TL) camera at each transect. Semivariogram analysis of the transect data was conducted, and no autocorrelation was found, indicating little spatial structure along the transects. Pairwise differences in snow depth and snow water equivalent (SWE) between land covers were calculated and compared across scales. Differences in snowpack between forested sites at the TL points corresponded to differences in canopy cover, but this relationship was not evident at the transect scale, indicating a difference in observed process across scale. TL and transect estimates had substantial bias, but consistency in error was observed, which indicates that scaling coefficients may be derived to improve point scale estimates. TL and transect measurements were upscaled to estimate grid scale means. Upscaled estimates were compared and found to be consistent, indicating that appropriately stratified point scale measurements can be used to approximate a grid scale mean when transect data are not available. These findings are important in remote regions such as the study area, where frequent transect data may be difficult to obtain. TL, transect, and upscaled means were compared with modelled depth and SWE. Model comparisons with TL and transect data indicated that bias was dependent on land cover, measurement scale, and seasonality. Modelled means compared well with upscaled estimates, but model SWE was underestimated during spring melt. These findings highlight the importance of understanding the spatial representativeness of in situ measurements and the processes those measurements represent when validating gridded snow products or assimilating data into models.

River ice breakup and resulting flood risk is a nearly annual concern for communities along the five major rivers draining to the James and Hudson Bay Coasts in Ontario (Moose, Albany, Attawapiskat, Winisk and Severn Rivers). Ice breakup within this region has historically been monitored using flight reconnaissance supplemented by assessment of hydrometric data. More recently, remote sensed imagery have been used to monitor near real-time ice breakup and flood risk. However, the near real-time remotely sensed breakup information was found to have limited utility in the absence of a broader spatial and temporal understanding of breakup progression. The primary purpose of this study was to develop a method for generating a dataset of breakup dates. A secondary objective was to calculate statistics from this dataset that can be used to provide context to operational near real-time imagery analysis and improve understanding of ice processes in the study area. An automated method for detecting river ice breakup dates from 2000 to 2017 using MODIS imagery was developed. This method uses a threshold-based technique that aims to maximize river coverage and minimize effects of cloud obstruction. Image processing was completed in the high-performance Google Earth Engine application which enabled iterative classification and model calibration. The breakup date dataset was used to calculate statistics on breakup timing, duration, annual variability and breakup order. An assessment of patterns within these data is discussed, relationships between breakup timing, duration and highwater years is explored and the operational utility of these statistics is described. The classification compared well with Water Survey of Canada derived breakup dates with mean bias ranging from -2.0 days to 6.7 days and mean absolute error of 3.4 days to 6.9 days across the rivers. Latitude and distance upstream were found to be primary controls on breakup timing with drainage network configuration and reach morphology also having an influence. No relationships between highwater years and calculated breakup statistics were found. It is recommended that future studies use the dataset developed in this study in combination with hydrometric and remotely sensed data to improve prediction of highwater and understanding of breakup processes within these rivers.

Snow water equivalent (SWE) is an important indicator used in hydrology, water resources, and climate change impact. There are various methods of estimating SWE (falling in 3 categories: indirect sensors, empirical models, and process‐based models), but few studies that provide comparison across these different categories to help users make decisions on monitoring site design or method selection. Five SWE estimation methods were compared against manual snow course data collected over 2 years (2015–2016) from the Dorset Environmental Science Centre, including the gamma‐radiation‐based CS725 sensor, 3 empirical estimation models (Sexstone snow density model, McCreight & Small snow density model, and a meteorology‐based model), and the University of British Columbia Watershed Model snow energy‐balance model. Snow depth, density, and SWE were measured at the Dorset Environmental Science Centre weather station in south‐central Ontario, on a daily basis over 6 winters from 2011 to 2016. The 2 snow density‐based models, requiring daily snow depth as input, gave the best performance (R2 of .92 and .92 for McCreight & Small and Sexstone models, respectively). The CS725 sensor that receives radiation coming from soil penetrating the snowpack provided the same performance (R2 = .92), proving that the sensor is an applicable method, although it is expensive. The meteorology‐based empirical model, requiring daily climate data including temperature, precipitation and solar radiation, gave the poorest performance (R2 = .77). The energy‐balance‐based University of British Columbia Watershed Model snow module, only requiring climate data, worked better than the empirical meteorology‐based model (R2 = .9) but performed worse than the density models or CS725 sensor. Given differences in application objectives, site conditions, and budget, this comparison across SWE estimation methods may help users choose a suitable method. For ongoing and new monitoring sites, installation of a CS725 sensor coupled with intermittent manual snow course measurements (e.g., weekly) is recommended for further SWE method estimation testing and development of a snow density model.

This paper demonstrates a methodology developed through applying and refining Lake Simcoe Region Conservation Authority (LSRCA) and Bradford's (2011) framework for setting ecological flow and water level targets. Each step of the methodology is described along with an example application within Lovers Creek subwatershed. The methodology requires subwatershed objectives and habitat specialists to be defined. Study nodes are selected and the reference streamflow regime is characterized along with the hydrological alteration that has occurred, or is expected to occur under a given scenario. Potential ecological responses to the various hydrologic alterations are then identified. Methods for setting overall ecosystem health and specific ecological objective flow targets are discussed and demonstrated. The targets are then integrated into a flow regime for each study node and a process for using this information for decision-making is suggested. The targets developed using the methodology presented in this paper are mainly limited by the accuracy of the hydrologic model and the quantified flow magnitudes. Recommendations for improving these components of the assessment are made. The unique approach presented in this paper provides explicit steps for developing flow targets for subwatersheds within Southern Ontario and beyond. This research contributes toward the advancement of ecological flow assessment within Southern Ontario, which provides opportunities for enhanced protection and restoration of ecosystem health across the Province.