• Snow, glaciers, wetlands, frozen ground and permafrost needed in hydrological models. • Water quality export by coupling biochemical transformations to cold regions processes. • Hydrological sensitivity to land use depends on cold regions processes. • Strong cold regions hydrological sensitivity to climate warming. Cold regions involve hydrological processes that are not often addressed appropriately in hydrological models. The Cold Regions Hydrological Modelling platform (CRHM) was initially developed in 1998 to assemble and explore the hydrological understanding developed from a series of research basins spanning Canada and international cold regions. Hydrological processes and basin response in cold regions are simulated in a flexible, modular, object-oriented, multiphysics platform. The CRHM platform allows for multiple representations of forcing data interpolation and extrapolation, hydrological model spatial and physical process structures, and parameter values. It is well suited for model falsification, algorithm intercomparison and benchmarking, and has been deployed for basin hydrology diagnosis, prediction, land use change and water quality analysis, climate impact analysis and flood forecasting around the world. This paper describes CRHM’s capabilities, and the insights derived by applying the model in concert with process hydrology research and using the combined information and understanding from research basins to predict hydrological variables, diagnose hydrological change and determine the appropriateness of model structure and parameterisations.

Abstract. Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.

Abstract. Climate warming may cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Few observations allow separating rain and snowmelt contributions to streamflow, so physically based models are needed for hydrological predictions and analyses. We develop an observational technique for detecting streamflow responses to snowmelt using incoming solar radiation and diel (daily) cycles of streamflow. We measure the 20th percentile of snowmelt days (DOS20), across 31 watersheds in the western US, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May, with warmer sites having earlier and more intermittent snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2 = 0.85), suggesting that a one-day earlier DOS20 corresponds with a one-day earlier DOQ25 and 0.7-day earlier DOQ50. Empirical projections of future DOS20 (RCP8.5, late 21st century), using space-for-time substitution, show that DOS20 will occur 11 ± 4 days earlier per 1 °C of warming, and that colder places (mean November–February air temperature, TNDJF <−8 °C) are 70 % more sensitive to climate change on average than warmer places (TNDJF > 0 °C). Moreover, empirical space-for-time based projections of DOQ25 and DOQ50 are about four and two times more sensitive to earlier streamflow than those from NoahMP-WRF. Given the importance of changing streamflow timing for headwater resources, snowmelt detection methods such as DOS20 based on diel streamflow cycles may constrain hydrological models and improve hydrological predictions.

Drylines are atmospheric boundaries separating dry from moist air that can initiate convection. Potential changes in the location, frequency, and characteristics of drylines in future climates are unknown. This study applies a multi-parametric algorithm to objectively identify and characterize the dryline in North America using convection-permitting regional climate model simulations with 4-km horizontal grid spacing for 13-years under a historical and a pseudo-global warming climate projection by the end of the century. The dryline identification is successfully achieved with a set of standardized algorithm parameters across the lee side of the Rocky Mountains from the Canadian Rockies to the Sierra Madres in Mexico. The dryline is present 27% of the days at 00 UTC between April and September in the current climate, with a mean humidity gradient magnitude of 0.16 g−1 kg−1 km−1. The seasonal cycle of drylines peak around April and May in the southern Plains, and in June and July in the northern Plains. In the future climate, the magnitude and frequency of drylines increase 5% and 13%, correspondingly, with a stronger intensification southward. Future drylines strengthen during their peak intensity in the afternoon in the Southern U.S. and Northeast Mexico. Drylines also show increasing intensities in the morning with future magnitudes that are comparable to peak intensities found in the afternoon in the historical climate. Furthermore, an extension of the seasonality of intense drylines could produce end-of-summer drylines that are as strong as mid-summer drylines in the current climate. This might affect the seasonality and the diurnal cycle of convective activity in future climates, challenging weather forecasting and agricultural planning.

Drylines are atmospheric boundaries separating dry from moist air that can initiate convection. Potential changes in the location, frequency, and characteristics of drylines in future climates are unknown. This study applies a multi-parametric algorithm to objectively identify and characterize the dryline in North America using convection-permitting regional climate model simulations with 4-km horizontal grid spacing for 13-years under a historical and a pseudo-global warming climate projection by the end of the century. The dryline identification is successfully achieved with a set of standardized algorithm parameters across the lee side of the Rocky Mountains from the Canadian Rockies to the Sierra Madres in Mexico. The dryline is present 27% of the days at 00 UTC between April and September in the current climate, with a mean humidity gradient magnitude of 0.16 g−1 kg−1 km−1. The seasonal cycle of drylines peak around April and May in the southern Plains, and in June and July in the northern Plains. In the future climate, the magnitude and frequency of drylines increase 5% and 13%, correspondingly, with a stronger intensification southward. Future drylines strengthen during their peak intensity in the afternoon in the Southern U.S. and Northeast Mexico. Drylines also show increasing intensities in the morning with future magnitudes that are comparable to peak intensities found in the afternoon in the historical climate. Furthermore, an extension of the seasonality of intense drylines could produce end-of-summer drylines that are as strong as mid-summer drylines in the current climate. This might affect the seasonality and the diurnal cycle of convective activity in future climates, challenging weather forecasting and agricultural planning.

• Organic layer dry thermal conductivity dominates ground thaw uncertainty. • Significant snowpack and active layer changes are expected under climate warming. • Data poor regions would benefit from pursuing physically based approaches to reduce uncertainty. To predict future hydrological cycling in permafrost-dominated regions requires consideration of complex hydrological interactions that involve cryospheric states and fluxes, and hence thermodynamics. This challenges many hydrological models, particularly those applied in the Arctic. This study presents the implementation and validation of set of algorithms representing permafrost and frozen ground dynamics, coupled into a physically based, modular, cold regions hydrological model at two tundra sites in northern Yukon Territory, Canada. Hydrological processes represented in the model include evapotranspiration, soil moisture dynamics, flow through organic and mineral terrain, ground freeze–thaw, infiltration to frozen and unfrozen soils, snowpack energy balance, and the accumulation, wind redistribution, sublimation, and canopy interception of snow. The model was able to successfully represent observed ground surface temperature, ground thaw and snow accumulation at the two sites without calibration. A sensitivity analysis of simulated ground thaw revealed that the soil properties of the upper organic layer dominated the model response; however, its performance was robust for a range of realistic physical parameters. Different modelling decisions were assessed by removing the physically based algorithms for snowpack dynamics and ground surface temperature and replacing them with empirical approaches. Results demonstrate that more physically based approaches should be pursued to reduce uncertainties in poorly monitored environments. Finally, the model was driven by three climate warming scenarios to assess the sensitivity of snow redistribution and ablation processes and ground thaw to warming temperatures. This showed great sensitivity of snow regime and soil thaw to warming, even in the cold continental climate of the northwestern Canadian Arctic. The results are pertinent to transportation infrastructure and water management in this remote, cold, sparsely gauged region where traditional approaches to hydrological prediction are not possible.

• Organic layer dry thermal conductivity dominates ground thaw uncertainty. • Significant snowpack and active layer changes are expected under climate warming. • Data poor regions would benefit from pursuing physically based approaches to reduce uncertainty. To predict future hydrological cycling in permafrost-dominated regions requires consideration of complex hydrological interactions that involve cryospheric states and fluxes, and hence thermodynamics. This challenges many hydrological models, particularly those applied in the Arctic. This study presents the implementation and validation of set of algorithms representing permafrost and frozen ground dynamics, coupled into a physically based, modular, cold regions hydrological model at two tundra sites in northern Yukon Territory, Canada. Hydrological processes represented in the model include evapotranspiration, soil moisture dynamics, flow through organic and mineral terrain, ground freeze–thaw, infiltration to frozen and unfrozen soils, snowpack energy balance, and the accumulation, wind redistribution, sublimation, and canopy interception of snow. The model was able to successfully represent observed ground surface temperature, ground thaw and snow accumulation at the two sites without calibration. A sensitivity analysis of simulated ground thaw revealed that the soil properties of the upper organic layer dominated the model response; however, its performance was robust for a range of realistic physical parameters. Different modelling decisions were assessed by removing the physically based algorithms for snowpack dynamics and ground surface temperature and replacing them with empirical approaches. Results demonstrate that more physically based approaches should be pursued to reduce uncertainties in poorly monitored environments. Finally, the model was driven by three climate warming scenarios to assess the sensitivity of snow redistribution and ablation processes and ground thaw to warming temperatures. This showed great sensitivity of snow regime and soil thaw to warming, even in the cold continental climate of the northwestern Canadian Arctic. The results are pertinent to transportation infrastructure and water management in this remote, cold, sparsely gauged region where traditional approaches to hydrological prediction are not possible.

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. The impact of transient changes in climate and vegetation on the hydrology of small Arctic headwater basins has not been investigated before, particularly in the tundra–taiga transition region. This study uses weather and land cover observations and a hydrological model suitable for cold regions to investigate historical changes in modelled hydrological processes driving the streamflow response of a small Arctic basin at the treeline. The physical processes found in this environment and explicit changes in vegetation extent and density were simulated and validated against observations of streamflow discharge, snow water equivalent and active layer thickness. Mean air temperature and all-wave irradiance have increased by 3.7 ∘C and 8.4 W m−2, respectively, while precipitation has decreased 48 mm (10 %) since 1960. Two modelling scenarios were created to separate the effects of changing climate and vegetation on hydrological processes. Results show that over 1960–2016 most hydrological changes were driven by climate changes, such as decreasing snowfall, evapotranspiration, deepening active layer thickness, earlier snow cover depletion and diminishing annual sublimation and soil moisture. However, changing vegetation has a significant impact on decreasing blowing snow redistribution and sublimation, counteracting the impact of decreasing precipitation on streamflow, demonstrating the importance of including transient changes in vegetation in long-term hydrological studies. Streamflow dropped by 38 mm as a response to the 48 mm decrease in precipitation, suggesting a small degree of hydrological resiliency. These results represent the first detailed estimate of hydrological changes occurring in small Arctic basins, and can be used as a reference to inform other studies of Arctic climate change impacts.

Abstract A better understanding of cold regions hydrological processes and regimes in transitional environments is critical for predicting future Arctic freshwater fluxes under climate and vegetation change. A physically based hydrological model using the Cold Regions Hydrological Model platform was created for a small Arctic basin in the tundra-taiga transition region. The model represents snow redistribution and sublimation by wind and vegetation, snowmelt energy budget, evapotranspiration, subsurface flow through organic terrain, infiltration to frozen soils, freezing and thawing of soils, permafrost and streamflow routing. The model was used to reconstruct the basin water cycle over 28 years to understand and quantify the mass fluxes controlling its hydrological regime. Model structure and parameters were set from the current understanding of Arctic hydrology, remote sensing, field research in the basin and region, and calibration against streamflow observations. Calibration was restricted to subsurface hydraulic and storage parameters. Multi-objective evaluation of the model using observed streamflow, snow accumulation and ground freeze/thaw state showed adequate simulation. Significant spatial variability in the winter mass fluxes was found between tundra, shrubs and forested sites, particularly due to the substantial blowing snow redistribution and sublimation from the wind-swept upper basin, as well as sublimation of canopy intercepted snow from the forest (about 17% of snowfall). At the basin scale, the model showed that evapotranspiration is the largest loss of water (47%), followed by streamflow (39%) and sublimation (14%). The models streamflow performance sensitivity to a set of parameter was analysed, as well as the mean annual mass balance uncertainty associated with these parameters.