Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its trans-boundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the United States and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1 million square kilometer study domain. The study comprises 13 models covering a wide range of model types from Machine Learning based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulated streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways: either by aggregating model outputs and the reference to basin-level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are: (1) The comparison of models regarding streamflow reveals the superior quality of the Machine Learning based model in all experiments performance; even for the most challenging spatio-temporal validation the ML model outperforms any other physically based model. (2) While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. (3) The regionally calibrated models – while losing less performance in spatial and spatio-temporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas as well as agricultural regions in the US. (4) Comparisons of additional model outputs (AET, SSM, SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to basin scale can lead to different conclusions than a comparison at the native grid scale. This is especially true for variables with large spatial variability such as SWE. (5) A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, SWE) reveals overall excellent performing locally calibrated models (i.e., HYMOD2-lumped) as well as regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The Machine Learning based model was not included here as is not setup to simulate AET, SSM, and SWE. (6) All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download data and model outputs.

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper discusses the scientific and technical challenges of the large-scale modelling of cold region systems and reports recent modelling developments, focussing on MESH, the Canadian community hydrological land surface scheme. New cold region process representations include improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole pond storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multistage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km2). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. These illustrate the current capabilities and limitations of cold region modelling, and the extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.

Abstract The Yukon River Basin (YRB) is one of the most important river networks shared between Canada and The United States, and is one of the largest river basins in the subarctic region of North America. The Canadian part of the YRB is characterized by steeply sloped, partly glaciated mountain headwaters that generate considerable runoff during melt of glaciers and seasonal snowcover. Snow redistribution, snowmelt, glacier melt and freezing–thawing soil processes in winter and spring along with summertime rainfall‐runoff and evapotranspiration processes are thus key components of streamflow generation in the basin, making conceptual rainfall‐runoff models unsuitable for this cold region. Due to the remote high latitudes and high altitudes of the basin, there is a paucity of observational data, making heavily calibrated conceptual modeling approaches infeasible. At the request of the Yukon Government, this project developed and operationalized a streamflow forecasting system for the Yukon River and several of its tributary rivers using a distributed land surface modeling approach developed for large‐scale implementation in cold regions. This represents a substantial advance in bringing operational hydrological forecasting to the Canadian subarctic for the first time. This experience will inform future research to operation improvements as Canada develops a nationally coordinated flood forecast system.

Abstract The Global Environmental Multiscale Model (GEM) is currently in operational use for data assimilation and forecasting at 25–15 km scales; regional 10 km scales over North America; and 2.5 km scales over Canada. To evaluate the GEM model for forecasting applications in Iran, global daily temperature and precipitation outputs of GEM at a 25 km scale were compared to data sets from hydrometeorological stations and the De Martonne climate classification method was used to demarcate climate zones for comparisons. GEM model outputs were compared to observations in each of these zones. The results show good agreement between GEM outputs and measured daily temperatures with Kling‐Gupta efficiencies of 0.76 for the arid, 0.71 for the semiarid, and 0.78 for the humid regions. There is also an agreement between GEM outputs and measured annual precipitation with differences of 50% for the arid, 36% for the semiarid, and 15% for the humid region. There is a ~13% systematic difference between the elevation of stations and the average elevation of corresponding GEM grid cells; differences in elevation associated with forcing data sets can be potentially corrected using environmental lapse rates. Compared with hydrometeorological data sets, the GEM model precipitation outputs are less accurate than temperature outputs, and this may influence the accuracy of potential Iranian forecasting operations utilizing GEM. The results of this study provide an understanding of the operation and limitations of the GEM model for climate change and hydro‐climatological studies.

Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

AbstractHydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequen...

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources, but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper reports scientific developments over the past decade of MESH, the Canadian community hydrological land surface scheme. New cold region process representation includes improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multi-stage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. This imposes extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.

• Development of the ensemble-based data assimilation framework is examined. • GRACE assimilation improves the simulation of snow estimates at the basin and grid scales. • Data assimilation can effectively constrain the amplitude of modeled water storage dynamics. • GRACE data assimilation improves the simulation of high flows during snowmelt season. Accurate estimation of snow mass or snow water equivalent (SWE) over space and time is required for global and regional predictions of the effects of climate change. This work investigates whether integration of remotely sensed terrestrial water storage (TWS) information, which is derived from the Gravity Recovery and Climate Experiment (GRACE), can improve SWE and streamflow simulations within a semi-distributed hydrology land surface model. A data assimilation (DA) framework was developed to combine TWS observations with the MESH (Modélisation Environnementale Communautaire – Surface Hydrology) model using an ensemble Kalman smoother (EnKS). The snow-dominated Liard Basin was selected as a case study. The proposed assimilation methodology reduced bias of monthly SWE simulations at the basin scale by 17.5% and improved unbiased root-mean-square difference (ubRMSD) by 23%. At the grid scale, the DA method improved ubRMSD values and correlation coefficients for 85% and 97% of the grid cells, respectively. Effects of GRACE DA on streamflow simulations were evaluated against observations from three river gauges, where it effectively improved the simulation of high flows during snowmelt season from April to June. The influence of GRACE DA on the total flow volume and low flows was found to be variable. In general, the use of GRACE observations in the assimilation framework not only improved the simulation of SWE, but also effectively influenced streamflow simulations.

Abstract. Cold region hydrology is very sensitive to the impacts of climate warming. Impacts of warming over recent decades in western Canada include glacier retreat, permafrost thaw, and changing patterns of precipitation, with an increased proportion of winter precipitation falling as rainfall and shorter durations of snow cover, as well as consequent changes in flow regimes. Future warming is expected to continue along these lines. Physically realistic and sophisticated hydrological models driven by reliable climate forcing can provide the capability to assess hydrological responses to climate change. However, the provision of reliable forcing data remains problematic, particularly in data-sparse regions. Hydrological processes in cold regions involve complex phase changes and so are very sensitive to small biases in the driving meteorology, particularly in temperature and precipitation, including precipitation phase. Cold regions often have sparse surface observations, particularly at high elevations that generate a large amount of runoff. This paper aims to provide an improved set of forcing data for large-scale hydrological models for climate change impact assessment. The best available gridded data in Canada are from the high-resolution forecasts of the Global Environmental Multiscale (GEM) atmospheric model and outputs of the Canadian Precipitation Analysis (CaPA), but these datasets have a short historical record. The EU WATCH ERA-Interim reanalysis (WFDEI) has a longer historical record but has often been found to be biased relative to observations over Canada. The aim of this study, therefore, is to blend the strengths of both datasets (GEM-CaPA and WFDEI) to produce a less-biased long-record product (WFDEI-GEM-CaPA) for hydrological modelling and climate change impact assessment over the Mackenzie River Basin. First, a multivariate generalization of the quantile mapping technique was implemented to bias-correct WFDEI against GEM-CaPA at 3 h ×0.125∘ resolution during the 2005–2016 overlap period, followed by a hindcast of WFDEI-GEM-CaPA from 1979. The derived WFDEI-GEM-CaPA data are validated against station observations as a preliminary step to assess their added value. This product is then used to bias-correct climate projections from the Canadian Centre for Climate Modelling and Analysis Canadian Regional Climate Model (CanRCM4) between 1950 and 2100 under RCP8.5, and an analysis of the datasets shows that the biases in the original WFDEI product have been removed and the climate change signals in CanRCM4 are preserved. The resulting bias-corrected datasets are a consistent set of historical and climate projection data suitable for large-scale modelling and future climate scenario analysis. The final historical product (WFDEI-GEM-CaPA, 1979–2016) is freely available at the Federated Research Data Repository at https://doi.org/10.20383/101.0111 (Asong et al., 2018), while the original and corrected CanRCM4 data are available at https://doi.org/10.20383/101.0162 (Asong et al., 2019).

Abstract. Permafrost is an important feature of cold-region hydrology, particularly in river basins such as the Mackenzie River basin (MRB), and it needs to be properly represented in hydrological and land surface models (H-LSMs) built into existing Earth system models (ESMs), especially under the unprecedented climate warming trends that have been observed. Higher rates of warming have been reported in high latitudes compared to the global average, resulting in permafrost thaw with wide-ranging implications for hydrology and feedbacks to climate. The current generation of H-LSMs is being improved to simulate permafrost dynamics by allowing deep soil profiles and incorporating organic soils explicitly. Deeper soil profiles have larger hydraulic and thermal memories that require more effort to initialize. This study aims to devise a robust, yet computationally efficient, initialization and parameterization approach applicable to regions where data are scarce and simulations typically require large computational resources. The study further demonstrates an upscaling approach to inform large-scale ESM simulations based on the insights gained by modelling at small scales. We used permafrost observations from three sites along the Mackenzie River valley spanning different permafrost classes to test the validity of the approach. Results show generally good performance in reproducing present-climate permafrost properties at the three sites. The results also emphasize the sensitivity of the simulations to the soil layering scheme used, the depth to bedrock, and the organic soil properties.