• 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.

Hydrological conditions in cold regions have been shown to be sensitive to climate change. However, a detailed understanding of how regional climate and basin landscape conditions independently influence the current hydrology and its climate sensitivity is currently lacking. This study, therefore, compares the climate sensitivity of the hydrology of two basins with contrasted landscape and meteorological characteristics typical of eastern Canada: a forested boreal climate basin (Montmorency) versus an agricultural hemiboreal climate basin (Acadie). The physically based Cold Regions Hydrological Modelling (CRHM) platform was used to simulate the current and future hydrological processes. Both basin landscape and regional climate drove differences in hydrological sensitivities to climate change. Projected peak SWE were highly sensitive to warming, particularly for milder baseline climate conditions and moderately influenced by differences in landscape conditions. Landscape conditions mediated a wide range of differing hydrological processes and streamflow responses to climate change. The effective precipitation was more sensitive to warming in the forested basin than in the agricultural one, due to reductions in forest canopy interception losses with warming. Under present climate, precipitation and discharge were found to be more synchronized in the greater relief and slopes of the forested basin, whereas under climate change, they are more synchronized in the agricultural basin due to reduced infiltration and storage capacities. Flow through and over agricultural soils translated the increase in water availability under a warmer and wetter climate into higher peak discharges, whereas the porous forest soils dampened the response of peak discharge to increased available water. These findings help diagnose the mechanisms controlling hydrological response to climate change in cold regions forested and agricultural basins.

• A real-time ice-jam risk assessment system was developed to better regulate reservoir discharges. • The modelling system improves ice-jam flood predictions considering the influence of reservoir regulation. • Machine learning with deterministic modelling provides more accurate ice-jam flood predictions of regulated rivers. • The modelling system was successfully verified for the Sanhuhekou bend reach regulated by the Sanshenggong reservoir. To effectively alleviate ice-jam flood disasters, it is necessary to carry out hazard assessments and predictions of ice-jam flooding influenced by the operational scheme of a reservoir. However, traditional hydrologic flood routing techniques cannot effectively address the huge uncertainties caused by the many factors that lead to ice-jam flooding. In this paper, a hazard assessment system for regulating flood discharge schemes is developed; it is composed of a machine learning (ML) model, Long Short-Term Memory (LSTM), and a river-ice dynamic model (RIVICE) within a probabilistic method. The modelling system is to aid in the challenge of predicting ice-jam flooding downstream of reservoirs. The LSTM model forecasts the downstream flow under the operational discharge scheme and, combined with the RIVICE model, the backwater level profile of ice jams can be forecasted. Furthermore, a set of backwater level profiles can be provided by probabilistic modelling, and the probability of ice-jam flood inundation can be calculated by comparing backwater levels with the elevation of the river bank; this information can be used to warn of the hazard induced by operational discharges to better aid in the preparedness and mitigation of ice-jam floods. This system was tested successfully for the ice-cover breakup period in the spring of 2008 and 2018 along the Sanhuhekou bend reach of the Yellow River in China.