Abstract. The carbon cycle in Arctic-boreal regions (ABR) is an important component of the planetary carbon balance, with growing concerns about the consequences of ABR warming on the global climate system. The greatest uncertainty in annual carbon dioxide (CO2) budgets exists during the non-growing season, primarily due to challenges with data availability and limited spatial coverage in measurements. The goal of this study was to determine the main environmental controls of non-growing season CO2 fluxes in ABR over a latitudinal gradient (45° N to 69° N) featuring four different ecosystem types: closed-crown coniferous boreal forest, open-crown coniferous boreal forest, erect-shrub tundra, and prostrate-shrub tundra. CO2 fluxes calculated using a snowpack diffusion gradient method (n = 560) ranged from 0 to 1.05 gC m2 day-1. To assess the dominant environmental controls governing CO2 fluxes, a Random Forest machine learning approach was used. We identified that soil temperature as the main control of non-growing season CO2 fluxes with 68 % of relative model importance, except when soil liquid water occurred during zero degree Celsius curtain conditions (Tsoil ≈ 0 °C and liquid water coexists with ice in soil pores). Under zero-curtain conditions, liquid water content became the main control of CO2 fluxes with 87 % of relative model importance. We observed exponential regressions between CO2 fluxes and soil temperature (RMSE = 0.024 gC m-2 day-1) in frozen soils, as well as liquid water content (RMSE = 0.137 gC m-2 day-1) in zero-curtain conditions. This study is showing the role of several variables on the spatio-temporal variability of CO2 fluxes in ABR during the non-growing season and highlight that the complex vegetation-snow-soil interactions in northern environments must be considered when studying what drives the spatial variability of soil carbon emission during the non-growing season.

Abstract. Systematic tile drainage is used extensively in agricultural lands to remove excess water and improve crop growth; however, tiles can also transfer nutrients from farmlands to downstream surface water bodies, leading to water quality problems. There is a need to simulate the hydrological behaviour of tile drains to understand the impacts of climate or land management change on agricultural runoff. The Cold Regions Hydrological Model (CRHM) is a physically based, modular modelling system that enables the creation of comprehensive models appropriate for cold regions by including a full suite of winter, spring, and summer season processes and coupling these together via mass and energy balances. A new tile drainage module was developed for CRHM to account for this process in tile-drained landscapes that are increasingly common in cultivated basins of the Great Lakes and northern Prairies regions of North America. A robust multi-variable, multi-criteria model performance evaluation strategy was deployed to examine the ability of the module with CRHM to capture tile discharge under both winter and summer conditions. Results showed that soil moisture is largely regulated by tile flow and lateral flow from adjacent fields. The explicit representation of capillary rise for moisture interactions between the rooting zone and groundwater greatly improved model simulations, demonstrating its significance in the hydrology of tile drains in loam soils. Water level patterns revealed a bimodal behaviour that depended on the positioning of the capillary fringe relative to the tile. A novel aspect of this module is the use of field capacity and its corresponding pressure head to provide an estimate of drainable water and thickness of the capillary fringe, rather than a detailed soil retention curve that may not always be available. Understanding the bimodal nature of soil water levels provides better insight into the significance of dynamic water exchange between soil layers below drains to improve tile drainage representation in models.