Abstract

The threshold wind speed for snow transport is related to properties of the surface snowpack: snow particle bonding, cohesion, and kinetic friction. These properties are controlled by meteorological factors. A method is proposed that relates the threshold wind speed for the initiation of snow transport to standard surface meteorological observations. A complete dataset on the hourly threshold condition for snow transport as determined from visual observation was developed for 16 stations on the prairies of western Canada over six winters. The threshold wind speeds for wet snow transport are significantly different from those for dry snow transport. The majority of recorded threshold 10-m wind speeds ranged from 7 to 14 m s⁻¹ with an average of 9.9 m s⁻¹ for wet snow transport, and from 4 to 11 m s⁻¹ with an average of 7.7 m s⁻¹ for dry snow transport. The observations display a nonlinear but generally positive correlation between threshold wind speed and air temperature. An empirical model between threshold wind speed and air temperature was developed for dry snow conditions. The model, on average, provides a good estimate of the threshold wind speed.

Abstract

Within mountainous regions, estimating the exchange of sensible heat and water vapor between the surface and the atmosphere is an important but inexact endeavor. Measurements of the turbulence characteristics of the near-surface boundary layer in complex mountain terrain are relatively scarce, leading to considerable uncertainty in the application of flux-gradient techniques for estimating the surface turbulent heat and mass fluxes. An investigation of the near-surface boundary layer within a 7-ha snow-covered forest clearing was conducted in the Kananaskis River valley, located within the Canadian Rocky Mountains. The homogeneous measurement site was characterized as being relatively calm and sheltered; the wind exhibited considerable unsteadiness, however. Frequent wind gusts were observed to transport turbulent energy into the clearing, affecting the rate of energy transfer at the snow surface. The resulting boundary layer within the clearing exhibited perturbations introduced by the surrounding topography and land surface discontinuities. The measured momentum flux did not scale with the local aerodynamic roughness and mean wind speed profile, but rather was reflective of the larger-scale topographical disturbances. The intermittent nature of the flux-generating processes was evident in the turbulence spectra and cospectra where the peak energy was shifted to lower frequencies as compared with those observed in more homogeneous flat terrain. The contribution of intermittent events was studied using quadrant analysis, which revealed that 50% of the sensible and latent heat fluxes was contributed from motions that occupied less than 6% of the time. These results highlight the need for caution while estimating the turbulent heat and mass fluxes in mountain regions.

Abstract

The rapidly warming Arctic is experiencing permafrost degradation and shrub expansion. Future climate projections show a clear increase in mean annual temperature and increasing precipitation in the Arctic; however, the impact of these changes on hydrological cycling in Arctic headwater basins is poorly understood. This study investigates the impact of climate change, as represented by simulations using a high-resolution atmospheric model under a pseudo-global-warming configuration, and projected changes in vegetation, using a spatially distributed and physically based Arctic hydrological model, on a small headwater basin at the tundra–taiga transition in northwestern Canada. Climate projections under the RCP8.5 emission scenario show a 6.1°C warming, a 38% increase in annual precipitation, and a 19 W m⁻² increase in all-wave annual irradiance over the twenty-first century. Hydrological modeling results suggest a shift in hydrological processes with maximum peak snow accumulation increasing by 70%, snow-cover duration shortening by 26 days, active layer deepening by 0.25 m, evapotranspiration increasing by 18%, and sublimation decreasing by 9%. This results in an intensification of the hydrological regime by doubling discharge volume, a 130% increase in spring runoff, and earlier and larger peak streamflow. Most hydrological changes were found to be driven by climate change; however, increasing vegetation cover and density reduced blowing snow redistribution and sublimation, and increased evaporation from intercepted rainfall. This study provides the first detailed investigation of projected changes in climate and vegetation on the hydrology of an Arctic headwater basin, and so it is expected to help inform larger-scale climate impact studies in the Arctic.

Abstract

A physically based hydrological model for the upper Baker River basin (UBRB) in Patagonia was developed using the modular Cold Regions Hydrological Model (CRHM) in order to better understand the processes that drive the hydrological response of one of the largest rivers in this region. The model includes a full suite of blowing snow, intercepted snow, and energy balance snowmelt modules that can be used to describe the hydrology of this cold region. Within this watershed, snowfall, wind speed, and radiation are not measured; there are no high-elevation weather stations; and existing weather stations are sparsely distributed. The impact of atmospheric data from ECMWF interim reanalysis (ERA-Interim) and Climate Forecast System Reanalysis (CFSR) on improving model performance by enhancing the representation of forcing variables was evaluated. CRHM parameters were assigned for local physiographic and vegetation characteristics based on satellite land cover classification, a digital elevation model, and parameter transfer from cold region environments in western Canada. It was found that observed precipitation has almost no predictive power [Nash–Sutcliffe coefficient (NS) < 0.3] when used to force the hydrologic model, whereas model performance using any of the reanalysis products—after bias correction—was acceptable with very little calibration (NS > 0.7). The modeled water balance shows that snowfall amounts to about 28% of the total precipitation and that 26% of total river flow stems from snowmelt. Evapotranspiration losses account for 7.2% of total precipitation, whereas sublimation and canopy interception losses represent about 1%. The soil component is the dominant modulator of runoff, with infiltration contributing as much as 73.7% to total basin outflow.

Abstract

How mountain hydrology at different elevations will respond to climate change is a challenging question of great importance to assessing changing water resources. Here, three North American Cordilleran snow-dominated basins—Wolf Creek, Yukon; Marmot Creek, Alberta; and Reynolds Mountain East, Idaho—each with good meteorological and hydrological records, were modeled using the physically based, spatially distributed Cold Regions Hydrological Model. Model performance was verified using field observations and found adequate for diagnostic analysis. To diagnose the effects of future climate, the monthly temperature and precipitation changes projected for the future by 11 regional climate models for the mid-twenty-first century were added to the observed meteorological time series. The modeled future was warmer and wetter, increasing the rainfall fraction of precipitation and shifting all three basins toward rainfall–runoff hydrology. This shift was largest at lower elevations and in the relatively warmer Reynolds Mountain East. In the warmer future, there was decreased blowing snow transport, snow interception and sublimation, peak snow accumulation, and melt rates, and increased evapotranspiration and the duration of the snow-free season. Annual runoff in these basins did not change despite precipitation increases, warming, and an increased prominence of rainfall over snowfall. Reduced snow sublimation offset reduced snowfall amounts, and increased evapotranspiration offset increased rainfall amounts. The hydrological uncertainty due to variation among climate models was greater than the predicted hydrological changes. While the results of this study can be used to assess the vulnerability and resiliency of water resources that are dependent on mountain snow, stakeholders and water managers must make decisions under considerable uncertainty, which this paper illustrates.

Abstract

On the Canadian Prairies, agricultural practices result in millions of hectares of standing crop stubble that gradually emerges during snowmelt. The importance of stubble in trapping wind-blown snow and retaining winter snowfall has been well demonstrated. However, stubble is not explicitly accounted for in hydrological or energy balance snowmelt models. This paper relates measurable stubble parameters (height, width, areal density, and albedo) to the snowpack energy balance and snowmelt with the new, physically based Stubble–Snow–Atmosphere Model (SSAM). Novel process representations of SSAM quantify the attenuation of shortwave radiation by exposed stubble, the sky and vegetation view factors needed to solve longwave radiation terms, and a resistance scheme for stubble–snow–atmosphere fluxes to solve for surface temperatures and turbulent fluxes. SSAM results were compared to observations of radiometric snow-surface temperature, stubble temperature, snow-surface solar irradiance, areal-average turbulent fluxes, and snow water equivalent from two intensive field campaigns during snowmelt in 2015 and 2016 over wheat and canola stubble in Saskatchewan, Canada. Uncalibrated SSAM simulations compared well with these observations, providing confidence in the model structure and parameterization. A sensitivity analysis conducted using SSAM revealed compensatory relationships in energy balance terms that result in a small increase in net snowpack energy as stubble exposure increases.

Abstract

Forest clearings are common features of evergreen forests and produce snowpack accumulation and melt differing from that in adjacent forests and open terrain. This study has investigated the challenges in specifying the turbulent fluxes of sensible and latent heat to snowpacks in forest clearings. The snowpack in two forest clearings in the Canadian Rockies was simulated using a one-dimensional (1D) snowpack model. A trade-off was found between optimizing against measured snow surface temperature or snowmelt when choosing how to specify the turbulent fluxes. Schemes using the Monin–Obukhov similarity theory tended to produce negatively biased surface temperature, while schemes that enhanced turbulent fluxes, to reduce the surface temperature bias, resulted in too much melt. Uncertainty estimates from Monte Carlo experiments showed that no realistic parameter set could successfully remove biases in both surface temperature and melt. A simple scheme that excludes atmospheric stability correction was required to successfully simulate surface temperature under low wind speed conditions. Nonturbulent advective fluxes and/or nonlocal sources of turbulence are thought to account for the maintenance of heat exchange in low-wind conditions. The simulation of snowmelt was improved by allowing enhanced latent heat fluxes during low-wind conditions. Caution is warranted when snowpack models are optimized on surface temperature, as model tuning may compensate for deficiencies in conceptual and numerical models of radiative, conductive, and turbulent heat exchange at the snow surface and within the snowpack. Such model tuning could have large impacts on the melt rate and timing of the snow-free transition in simulations of forest clearings within hydrological and meteorological models.

Abstract

Gridded precipitation datasets are used in many applications such as the analysis of climate variability/change and hydrological modeling. Regridding precipitation datasets is common for model coupling (e.g., coupling atmospheric and hydrological models) or comparing different models and datasets. However, regridding can considerably alter precipitation statistics. In this global analysis, the effects of regridding a precipitation dataset are emphasized using three regridding methods (first-order conservative, bilinear, and distance-weighted averaging). The differences between the original and regridded dataset are substantial and greatest at high quantiles. Differences of 46 and 0.13 mm are noted in high (0.95) and low (0.05) quantiles, respectively. The impacts of regridding vary spatially for land and oceanic regions; there are substantial differences at high quantiles in tropical land regions, and at low quantiles in polar regions. These impacts are approximately the same for different regridding methods. The differences increase with the size of the grid at higher quantiles and vice versa for low quantiles. As the grid resolution increases, the difference between original and regridded data declines, yet the shift size dominates for high quantiles for which the differences are higher. While regridding is often necessary to use gridded precipitation datasets, it should be used with great caution for fine resolutions (e.g., daily and subdaily), because it can severely alter the statistical properties of precipitation, specifically at high and low quantiles.

Abstract

Small-scale topography and snow redistribution have important effects on snow-cover heterogeneity and the timing, rate, and duration of spring snowmelt in mountain tundra environments. However, land surface schemes (LSSs) are usually applied as a means to provide large-scale surface states and vertical fluxes to atmospheric models and do not normally incorporate topographic effects or horizontal fluxes in their calculations

A study was conducted in Granger Creek, an 8-km² catchment within Wolf Creek Research Basin in the Yukon Territory, Canada, to examine whether inclusion of the effects of wind redistribution of snow between landscape units, and slope and aspect in snowmelt calculations for tiles, could improve the simulation of snowmelt by an LSS.

Measured snow accumulation, reflecting overwinter wind redistribution of snow, was used to provide initial conditions for the melt simulation, and physically based algorithms from a small-scale hydrological model were used to calculate radiation on slopes during melt. Based on consideration of the spatial distribution of snow accumulation, topography, and shrub cover in the basin, it was divided into five landscapes units (tiles) for simulation of mass and energy balance using an LSS during melt. Effects of averaging initial conditions and forcing data on LSS model performance were contrasted against distributed simulations. Results showed that, in most of the cases, simulations using aggregated initial conditions and forcing data gave unsuccessful descriptions of snow ablation whereas the incorporation of both snow-cover redistribution and slope and aspect effects in an LSS improved the prediction of snowmelt rate, timing, and duration.

Abstract

Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates.