Abstract. The amount and phase of cold season precipitation accumulating in the upper Saint John River basin are critical factors in determining spring runoff, ice-jams, and flooding in downstream communities. To study the impact of winter and spring storms on the snowpack in the upper Saint John River (SJR) basin, the Saint John River Experiment on Cold Season Storms (SAJESS) utilized meteorological instrumentation, upper air soundings, human observations, and hydrometeor macrophotography during winter/spring 2020–21. Here, we provide an overview of the SAJESS study area, field campaign, and existing data networks surrounding the upper SJR basin. Initially, meteorological instrumentation was co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick, in early December 2020. This was followed by an intensive observation period that involved manual observations, upper-air soundings, a multi-angle snowflake camera, macrophotography of solid hydrometeors, and advanced automated instrumentation throughout March and April 2021. The resulting datasets include optical disdrometer size and velocity distributions of hydrometeors, micro rain radar output, near-surface meteorological observations, and wind speed, temperature, pressure and precipitation amounts from a K63 Hotplate precipitation gauge, the first one operating in Canada. These data are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2022). We also include a synopsis of the data management plan and data processing, and a brief assessment of the rewards and challenges of utilizing community volunteers for hydro-meteorological citizen science.

The southern Canadian Rockies are prone to extreme precipitation that often leads to high streamflow, deep snowpacks, and avalanche risks. Many of these precipitation events are associated with rain–snow transitions, which are highly variable in time and space due to the complex topography. A warming climate will certainly affect these extremes and the associated rain–snow transitions. The goal of this study is to investigate the characteristics and variability of rain–snow transitions aloft and how they will change in the future. Weather Research and Forecasting (WRF) simulations were conducted from 2000 to 2013 and these were repeated in a warmer pseudo-global warming (PGW) future. Rain–snow transitions occurred aloft throughout the year over the southern Canadian Rockies, but their elevations and depths were highly variable, especially across the continental divide. In PGW conditions, with future air temperatures up to 4–5°C higher on average over the Canadian Rockies, rain–snow transitions are projected to occur more often throughout the year, except during summer. The near-0°C conditions associated with rain–snow transitions are expected to increase in elevation by more than 500 m, resulting in more rain reaching the surface. Overall, this study illustrates the variability of rain–snow transitions, which often impact the location of the snowline. This study also demonstrates the non-uniform changes under PGW conditions, due in part to differences in the types of weather patterns that generate rain–snow transitions across the region.

Abstract The Canadian Rockies are a triple-continental divide, whose high mountains are drained by major snow-fed and rain-fed rivers flowing to the Pacific, Atlantic, and Arctic Oceans. The objective of the April–June 2019 Storms and Precipitation Across the continental Divide Experiment (SPADE) was to determine the atmospheric processes producing precipitation on the eastern and western sides of the Canadian Rockies during springtime, a period when upslope events of variable phase dominate precipitation on the eastern slopes. To do so, three observing sites across the divide were instrumented with advanced meteorological sensors. During the 13 observed events, the western side recorded only 25% of the eastern side’s precipitation accumulation, rainfall occurred rather than snowfall, and skies were mainly clear. Moisture sources and amounts varied markedly between events. An atmospheric river landfall in California led to moisture flowing persistently northward and producing the longest duration of precipitation on both sides of the divide. Moisture from the continental interior always produced precipitation on the eastern side but only in specific conditions on the western side. Mainly slow-falling ice crystals, sometimes rimed, formed at higher elevations on the eastern side (>3 km MSL), were lifted, and subsequently drifted westward over the divide during nonconvective storms to produce rain at the surface on the western side. Overall, precipitation generally crossed the divide in the Canadian Rockies during specific spring-storm atmospheric conditions although amounts at the surface varied with elevation, condensate type, and local and large-scale flow fields.

Abstract The collection efficiency of a typical precipitation gauge-shield configuration decreases with increasing wind speed, with a high scatter for a given wind speed. The high scatter in the collection efficiency for a given wind speed arises in part from the variability in the characteristics of falling snow and atmospheric turbulence. This study uses weighing gauge data collected at the Marshall Field Site near Boulder, Colorado, during the WMO Solid Precipitation Intercomparison Experiment (SPICE). Particle diameter and fall speed data from a laser disdrometer were used to show that the scatter in the collection efficiency can be reduced by considering the fall speed of solid precipitation particles. The collection efficiency was divided into two classes depending on the measured mean-event particle fall speed during precipitation events. Slower-falling particles were associated with a lower collection efficiency. A new transfer function (i.e., the relationship between collection efficiency and other meteorological variables, such as wind speed or air temperature) that includes the fall speed of the hydrometeors was developed. The root-mean-square error of the adjusted precipitation with the new transfer function with respect to a weighing gauge placed in a double fence intercomparison reference was lower than using previously developed transfer functions that only consider wind speed and air temperature. This shows that the measured fall speed of solid precipitation with a laser disdrometer accounts for a large amount of the observed scatter in weighing gauge collection efficiency.

Abstract Accurate snowfall measurement is challenging because it depends on the precipitation gauge used, meteorological conditions, and the precipitation microphysics. Upstream of weighing gauges, the flow field is disturbed by the gauge and any shielding used usually creates an updraft, which deflects solid precipitation from falling in the gauge, resulting in significant undercatch. Wind shields are often used with weighing gauges to reduce this updraft, and transfer functions are required to adjust the snowfall measurements to consider gauge undercatch. Using these functions reduces the bias in precipitation measurement but not the root-mean-square error (RMSE). In this study, the accuracy of the Hotplate precipitation gauge was compared to standard unshielded and shielded weighing gauges collected during the WMO Solid Precipitation Intercomparison Experiment program. The analysis performed in this study shows that the Hotplate precipitation gauge bias after wind correction is near zero and similar to wind corrected weighing gauges. The RMSE of the Hotplate precipitation gauge measurements is lower than weighing gauges (with or without an Alter shield) for wind speeds up to 5 m s −1 , the wind speed limit at which sufficient data were available. This study shows that the Hotplate precipitation gauge measurement has a low bias and RMSE due to its aerodynamic shape, making its performance mostly independent of the type of solid precipitation.

A new flow for Canadian young hydrologists: Key scientific challenges addressed by research cultural shiftsCaroline Aubry-Wake1, Lauren D. Somers2,3, Hayley Alcock4, Aspen M. Anderson5, Amin Azarkhish6, Samuel Bansah7, Nicole M. Bell8, Kelly Biagi9, Mariana Castaneda-Gonzalez10, Olivier Champagne9, Anna Chesnokova10, Devin Coone6, Tasha-Leigh J. Gauthier11, Uttam Ghimire6, Nathan Glas6, Dylan M. Hrach11, Oi Yin Lai14, Pierrick Lamontagne-Halle3, Nicolas R. Leroux1, Laura Lyon3, Sohom Mandal12, Bouchra R. Nasri13, Natasa Popovic11, Tracy. E. Rankin14, Kabir Rasouli15, Alexis Robinson16, Palash Sanyal17, Nadine J. Shatilla9, 18, Brandon Van Huizen11, Sophie Wilkinson9, Jessica Williamson11, Majid Zaremehrjardy191 Centre for Hydrology, University of Saskatchewan, Saskatoon, SK, Canada2 Civil and Environmental Engineering, Massachusetts Institute of Technology, MA, USA3 Department of Earth and Planetary Sciences, McGill University, Montreal QC4 Department of Natural Resource Science, McGill University, Montreal, QC, Canada5 Department of Earth Sciences, Simon Fraser University, Burnaby, BC, Canada6 School of Engineering, University of Guelph, Ontario, ON, Canada7 Department of Geological Sciences, University of Manitoba, Winnipeg, Canada8 Centre for Water Resources Studies, Department of Civil & Resource Engineering, Dalhousie University, Halifax, NS, Canada9 School of Geography and Earth Sciences, McMaster University, Hamilton, ON, Canada.10 Department of Construction Engineering, Ecole de technologie superieure, Montreal, QC, Canada11 Department of Geography & Environmental Management, University of Waterloo, Waterloo, ON, Canada12 Department of Geography and Environmental Studies, Ryerson University, Toronto, ON, Canada13 Department of Mathematics and Statistics, McGill University, Montreal, Qc, Canada14 Geography Department, McGill University, Montreal, QC, Canada15 Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada16 Department of Geography and Planning, University of Toronto, Toronto, ON17 Global Institute for Water Security, University of Saskatchewan.18 Lorax Environmental Services Ltd, Vancouver, BC, Canada.19 Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB, Canada

Recent studies of water flow through dry porous media have shown progress in simulating preferential flow propagation. However, current methods applied to snowpacks have neglected the dynamic nature of the capillary pressure, such as conditions for capillary pressure overshoot, resulting in a rather limited representation of the water flow patterns through snowpacks observed in laboratory and field experiments. Indeed, previous snowmelt models using a water entry pressure to simulate preferential flow paths do not work for natural snowpack conditions where snow densities are less than 380 kg m−3. Because preferential flow in snowpacks greatly alters the flow velocity and the timing of delivery of meltwater to the base of a snowpack early in the melt season, a better understanding of this process would aid hydrological predictions. This study presents a 2‐D water flow through snow model that solves the non‐equilibrium Richards equation. This model, coupled with random perturbations of snow properties, can represent realistic preferential flow patterns. Using 1‐D laboratory data, two model parameters were linked to snow properties and model boundary conditions. Parameterizations of these model parameters were evaluated against 2‐D snowpack observations from a laboratory experiment, and the resulting model sensitivity to varying inputs and boundary conditions was calculated. The model advances both the physical understanding of and ability to simulate water flow through snowpacks and can be used in the future to parameterize 1‐D snowmelt models to incorporate flow variations due to preferential flow path formation.

The timing and magnitude of snowmelt discharge and subsequent runoff are controlled by both matrix and preferential flows of water through snowpacks. Matrix flow can be estimated using the Richards equation, and recently, preferential flow in snowpacks has been represented in 2D and 3D models. A challenge for representing preferential flow through porous media in 2D or 3D is capillary pressure overshoot in 1D. Soil studies have developed sophisticated and largely realistic approaches to represent capillary pressure overshoot, but it has not been addressed in snowpack water flow models. Here a 1D nonequilibrium Richards equation model is implemented with dynamic capillary pressure and is combined with a new concept of entrapment of liquid water within the pore space. This new model well represented capillary pressure overshoot, as estimated by published capillary pressure measurements in snow samples of various grain sizes under different rates of liquid water infiltration. Three model parameters were calibrated, and their impacts on model outputs were evaluated. This improvement is a substantial step toward better understanding and simulating physical processes occurring while liquid water percolates an initially dry snowpack.