Accurate partitioning of precipitation into rain and snow remains a major source of uncertainty in hydrological projections for cold regions. This study quantifies how four precipitation phase-partitioning formulations influence simulated precipitation phase, snow storage and streamflow response in the Saskatchewan River Basin under historical (1981-2010) and late-century (2071-2100) climate conditions. Simulations were conducted under naturalised conditions, with all reservoir regulation, irrigation and water-management abstractions deactivated to isolate the effect of phase-partitioning choice, using the MESH land-surface hydroloical model forced with CanRCM4 climate projections spanning 1950 to 2100. Three empirical temperature-based methods (single-threshold, linear-transition and polynomial) and one physically based psychrometric formulation were evaluated using diagnostic variables including total precipitation, snow and rain fractions, annual maximum snow water equivalent (SWE), runoff, peak discharge and streamflow timing metrics. Late-century projections showed increases in total precipitation of 19%-30% and declines in snowfall fraction of 25%-34% across landforms. Reductions in annual maximum SWE ranged from approximately 5%-13% in the cordillera and foothills to 13%-19% in the plains. Inter-method spreads in projected SWE were small in colder landforms (< 4 percentage points) but reached 6-9 percentage points in the plains and lowlands, comparable in magnitude to the projected climate-change signal. Streamflow timing advanced consistently: spring pulse onset date advanced by a median of 27 days, centre-of-mass timing by 22 days and time to peak flow by 26 days. Peak discharge increased by a median of 12.6% and annual streamflow volume by 23.9%. Inter-method differences were smallest for spring onset and centre-of-mass timing (1-3 days) and largest for time to peak flow and peak discharge, with mean absolute deviations exceeding 10 days and 14 percentage points respectively under some empirical methods. Snowfall-fraction biases between empirical and physical methods were spatially structured, with time of emergence occurring as early as 1952 for the single-threshold and linear-transition methods and several decades later for the polynomial method. The physically based psychrometric formulation remained consistent across historical and future climates, whereas empirical temperature-threshold schemes exhibited systematic biases and increasing divergence under stronger warming. These findings highlight the importance of explicitly accounting for precipitation phase-partitioning uncertainty in climate-impact assessments for snow-dominated and mixed-regime river basins.

The MESH hydrological model, driven by a 10 km meteorological reanalysis, was deployed to simulate the Saskatchewan River Basin (SRB), a 406 000 km(2) cold-region basin in Western Canada with diverse climate zones and extensive human regulation. The model was validated using multi-source observation and enabled detailed assessment of the basin's water balance components, runoff generation processes and irrigation impacts on hydrology. The model achieved Kling-Gupta Efficiency values of 0.35-0.85 across 23 streamflow stations (2005-2016), indicating reliable capture of observed flow regimes and reservoir regulation effects. Simulated evapotranspiration correlated strongly with satellite estimates (GLEAM, r = 0.98), and the model realistically reproduced seasonal snowpack dynamics and GRACE-derived water storage variations, with minor underestimation of peak snow water equivalent. Glacier diagnostics revealed that total runoff from glacier-covered areas contributes similar to 2.9% of SRB's mean annual runoff, of which 0.75% is glacier ice melt. Glacier ice melt runoff contributions varied by sub-basin, with the highest proportions from high-elevation regions: 1.96% to the North Saskatchewan near Edmonton, 1.14% to the Bow near its mouth, 0.66% to the Oldman and 0.32% to the Red Deer. A negative glacier mass balance trend strongest in southern sub-basins, signals declining ice reserves and the long-term vulnerability of glacier-fed water supplies. Diagnosis of runoff processes revealed significant variability in runoff generation, particularly in mountain headwaters and identified snowmelt as the dominant contributor, involved in 84.2% of runoff generation, broken down as snowmelt 43.4%, rain-on-snowmelt 10.2% and mixed events 30.6% of the SRB's annual runoff. Rainfall events contributed 15.8% and events with rainfall involved totalled 56.6% of annual runoff. This highlights the complexity of runoff generation processes in the SRB and the substantial role of snowmelt in sustaining the basin's hydrology. The impact of irrigation on evapotranspiration and streamflow was significant, with irrigation increasing mean annual evapotranspiration by 26.4% and reducing streamflow in key locations by up to 11%. Overall, this study provides a comprehensive and validated understanding of the SRB's hydrology and water resources, emphasising the influence of interactions between natural processes and human interventions. The insights from this research can inform water management strategies, particularly those aimed at adapting to future environmental changes. The findings underscore the importance of MESH as a robust tool for coupled hydrological and water resources modelling in managed, diverse, cold-regions basins.

Continental high latitudes have been warming at higher rates than the global average, causing substantial permafrost thaw with widespread effects on soils, vegetation, streamflow seasonality and land subsidence. Complex feedbacks are controlled by precipitation changes and soil hydraulic and thermal properties, amongst many factors. The Mackenzie River Basin (MRB) is the largest drainage basin in Canada (1.8x106 km2), underlain by permafrost of various classes for most of its extent (70-80% by area). Changes to the MRB affect atmospheric feedback, inflows to the Arctic Ocean, and local environments and communities. This study aims to parameterize a land surface hydrology model (MESH) for the MRB to simulate the coevolution of hydrology and permafrost dynamics, and hence enable the investigation of future change impacts. MESH, designed to simulate cold region processes, couples energy and water exchanges and requires a deep soil profile and long spin-up periods to initialize the permafrost regime at depth. Calibration was restricted to a limited number of influential physical parameters using streamflow from representative sub-basins. Validation used streamflow, snowpack, and snow cover across the basin. Sensitivity and identifiability analyses at well-instrumented sub-surface temperature gauges identified key parameters for permafrost simulation, which were adjusted for consistency with the spatial distribution of permafrost occurrence provided by available datasets and maps. To confirm permafrost prediction accuracy, validations were performed at gauges where active layer depth or soil temperature were observed. The resulting model has high fidelity in simulating both the hydrology and permafrost dynamics in the basin. This suggests that the model can be used with confidence to predict the impacts of future climate, land use/cover, and management scenarios on the evolution of cold region hydrology and permafrost at large scales.

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Improving Mountain Hydrological Predictions by Better Representing Mountain Topography in Hydrological Land Surface Models 57 Pages Posted: 24 Feb 2024 See all articles by Zelalem TesemmaZelalem TesemmaUniversity of SaskatchewanJohn PomeroyUniversity of SaskatchewanAlain Pietroniroaffiliation not provided to SSRNBruce DavisonEnvironment and Climate Change CanadaFuad YassinUniversity of Saskatchewan Abstract Accounting for cold region hydrological processes improves model predictive reliability and fidelity for diagnosing the impact of climate warming on the hydrology of high mountains. Common issues in high mountain hydrological prediction include poor representation of cold regions physical processes, lack of high-resolution atmospheric forcing inputs, and sparse or inaccurate land surface data for model parametrization. Current hydrological land surface models lack detailed and adequate representation, including sub-grid segmentation, to account for local elevation, slope, aspect, topographic curvature, and sky view factors. This study aims to demonstrate the improvements possible in prediction in high mountain basins through improved hydrological representation of processes at a finer scale while maintaining computational efficiency. The Canadian land surface hydrology model, MESH (Modélisation Environmentale Communautaire-Surface and Hydrology) was reconfigured to address slope, aspect, and elevation in parametrizing its physically based representations of the blowing snow redistribution, snowpack energy balance, and glacier melt processes that are important in high mountains headwaters such as the Canadian Rockies. The modifications included sub-grid corrections to shortwave irradiance for terrain slope, aspect, and sky view. Adjustments for elevation were made to air temperature, specific humidity, air pressure and precipitation. These sub-grid values for elevation, temperature, and sky view were used to adjust longwave irradiance. The modified version of MESH is denoted as Mountain MESH, and its outcomes in the Bow River Basin and Peyto Glacier, Canadian Rockies, Alberta, exhibited a notable enhancement in simulating glacier annual mass balance and snow accumulation over a standard implementation of the MESH model. Errors in estimating annual glacier mass balance decreased by 69%, glacier wastage contribution by 83%, snow accumulation by 13-23%, and streamflow by 20% with Mountain MESH compared to the original MESH configuration. This suggests that explicitly addressing mountain terrain effects on cold regions processes can result in greatly improved snow, glacier and hydrological simulations by hydrological land surface schemes in high mountain environments and may be necessary for reliable prediction of mountain derived water resources. Keywords: Mountain hydrology, surface heterogeneity, sub-grid variability, Land surface model, snow hydrology, glacier mass balance Suggested Citation: Suggested Citation Tesemma, Zelalem and Pomeroy, John and Pietroniro, Alain and Davison, Bruce and Yassin, Fuad, Improving Mountain Hydrological Predictions by Better Representing Mountain Topography in Hydrological Land Surface Models. Available at SSRN: https://ssrn.com/abstract=4737884 Zelalem Tesemma (Contact Author) University of Saskatchewan ( email ) College of EducationSaskatoon, S7N 5A7Canada John Pomeroy University of Saskatchewan ( email ) College of EducationSaskatoon, S7N 5A7Canada Alain Pietroniro affiliation not provided to SSRN ( email ) No Address Available Bruce Davison Environment and Climate Change Canada ( email ) GatineauCanada Fuad Yassin University of Saskatchewan ( email ) College of EducationSaskatoon, S7N 5A7Canada Download This Paper Open PDF in Browser Do you have negative results from your research you’d like to share? Submit Negative Results Paper statistics Downloads 0 Abstract Views 9 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday.

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.

The interior of western Canada, like many similar cold mid- to high-latitude regions worldwide, is undergoing extensive and rapid climate and environmental change, which may accelerate in the coming decades. Understanding and predicting changes in coupled climate–land–hydrological systems are crucial to society, yet limited by lack of understanding of changes in cold region process responses and interactions, along with their representation in most current generation land surface and hydrological models. It is essential to consider the underlying processes and base predictive models on the proper physics, especially under conditions of non-stationarity where the past is no longer a reliable guide to the future and system trajectories can be unexpected. These challenges were forefront in the recently completed Changing Cold Regions Network (CCRN), which assembled and focused a wide range of multi-disciplinary expertise to improve the understanding, diagnosis, and prediction of change over the cold interior of western Canada. CCRN advanced knowledge of fundamental cold region ecological and hydrological processes through observation and experimentation across a network of highly instrumented research basins and other sites. Significant efforts were made to improve the functionality and process representation, based on this improved understanding, within the fine-scale Cold Regions Hydrological Modelling (CRHM) platform and the large-scale Modélisation Environmentale Communautaire (MEC) – Surface and Hydrology (MESH) model. These models were, and continue to be, applied under past and projected future climates, and under current and expected future land and vegetation cover configurations to diagnose historical change and predict possible future hydrological responses. This second of two articles synthesizes the nature and understanding of cold region processes and Earth system responses to future climate, as advanced by CCRN. These include changing precipitation and moisture feedbacks to the atmosphere; altered snow regimes, changing balance of snowfall and rainfall, and glacier loss; vegetation responses to climate and the loss of ecosystem resilience to wildfire and disturbance; thawing permafrost and its influence on landscapes and hydrology; groundwater storage and cycling, and its connections to surface water; and stream and river discharge as influenced by the various drivers of hydrological change. Collective insights, expert elicitation, and model application are used to provide a synthesis of this change over the CCRN region for the late-21st century.

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 (Modelisation 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.

Traditionally, hydrological models are only calibrated to reproduce streamflow regime without considering other hydrological state variables, such as soil moisture and evapotranspiration. Limited studies have been performed on constraining the model parameters, despite the fact that the presence of a large number of parameters may provide large degree of freedom, resulting in equifinality and poor model performance. In this study, a multi-objective optimization approach is adopted, and both streamflow and soil moisture data are calibrated simultaneously for an experimental study basin in the Saskatchewan Prairies in western Canada. The results of this study show that the multi-objective calibration improves model fidelity compared to the single objective calibration. Moreover, the study demonstrates that single objective calibration performed against only streamflow can fairly mimic the streamflow hydrograph but does not yield realistic estimation of other fluxes such as evapotranspiration and soil moisture (especially in deeper soil layers).

Reservoirs significantly affect flow regimes in watershed systems by changing the magnitude and timing of streamflows. Failure to represent these effects limits the performance of hydrological and land-surface models (H-LSMs) in the many highly regulated basins across the globe and limits the applicability of such models to investigate the futures of watershed systems through scenario analysis (e.g., scenarios of climate, land use, or reservoir regulation changes). An adequate representation of reservoirs and their operation in an H-LSM is therefore essential for a realistic representation of the downstream flow regime. In this paper, we present a general parametric reservoir operation model based on piecewise-linear relationships between reservoir storage, inflow, and release to approximate actual reservoir operations. For the identification of the model parameters, we propose two strategies: (a) a “generalized” parameterization that requires a relatively limited amount of data and (b) direct calibration via multi-objective optimization when more data on historical storage and release are available. We use data from 37 reservoir case studies located in several regions across the globe for developing and testing the model. We further build this reservoir operation model into the MESH (Modélisation Environmentale-Surface et Hydrologie) modeling system, which is a large-scale H-LSM. Our results across the case studies show that the proposed reservoir model with both parameter-identification strategies leads to improved simulation accuracy compared with the other widely used approaches for reservoir operation simulation. We further show the significance of enabling MESH with this reservoir model and discuss the interdependent effects of the simulation accuracy of natural processes and that of reservoir operations on the overall model performance. The reservoir operation model is generic and can be integrated into any H-LSM.

The main sources of uncertainty in hydrological modelling can be summarized as structural errors, parameter errors, and data errors. Operational modellers are generally more concerned with predictive ability than model errors, and this paper presents a new, simple method to improve predictive ability. The method is called parameter-state ensemble thinning (P-SET). P-SET takes a large ensemble of continuous model runs and applies screening criteria to reduce the size of the ensemble. The goal is to find the most promising parameter-state combinations for analysis during the prediction period. Each prediction period begins with the same large ensemble, but the screening criteria are free to select a different sub-set of simulations for each separate prediction period. The case study is from June to October 2014 for a small (1324 km2) watershed just north of Lake Superior in Ontario, Canada, using a Canadian semi-distributed hydrologic land-surface scheme. The study examines how well the approach works given various levels of certainty in the data, beginning with certainty in the streamflow and precipitation, followed by uncertainty in the streamflow and certainty in the precipitation, and finally uncertainty in both the streamflow and precipitation. The approach is found to work in this case when streamflow and precipitation are fairly certain, while being more challenging to implement in a forecasting scenario where future streamflow and precipitation are much less certain. The main challenge is determined to be related to parametric uncertainty and ideas for overcoming this challenge are discussed. The approach also highlights model structural errors, which are also discussed.

Abstract. Reservoirs significantly affect flow regimes in watershed systems by changing the magnitude and timing of streamflows. Failure to represent these effects limits the performance of hydrological and land surface models (H-LSMs) in the many highly regulated basins across the globe and limits the applicability of such models to investigate the futures of watershed systems through scenario analysis (e.g., scenarios of climate, land use, or reservoir regulation changes). An adequate representation of reservoirs and their operation in an H-LSM is therefore essential for a realistic representation of the downstream flow regime. In this paper, we present a general parametric reservoir operation model based on piecewise linear relationships between reservoir storage, inflow, and release, to approximate actual reservoir operations. For the identification of the model parameters, we propose two strategies: (a) a generalized parameterization that requires a relatively limited amount of data; and (b) direct calibration via multi-objective optimization when more data on historical storage and release are available. We use data from 37 reservoir case studies located in several regions across the globe for developing and testing the model. We further build this reservoir operation model into the MESH modelling system, which is a large-scale H-LSM. Our results across the case studies show that the proposed reservoir model with both of the parameter identification strategies leads to improved simulation accuracy compared with the other widely used approaches for reservoir operation simulation. We further show the significance of enabling MESH with this reservoir model and discuss the interdependent effects of the simulation accuracy of natural processes and that of reservoir operation on the overall model performance. The reservoir operation model is generic and can be integrated into any H-LSM.

Abstract. The main sources of uncertainty in hydrological modelling can be summarized as structural errors, parameter errors, and data errors. Operational modellers are generally more concerned with predictive ability than model errors, and Data Assimilation (DA) methods are commonly employed to merge models with observations to improve predictive ability. This paper presents an example of Approximate Bayesian Computing (ABC), or a simplified Particle Filter (PF), to simultaneously assimilate model states and parameters, calling the method Parameter-State Ensemble DA (P-SEDA). The case study is from June to October, 2014 for a small (1324 km 2 ) watershed just north of Lake Superior in Ontario, Canada using the Canadian semi-distributed hydrologic land-surface scheme MESH. The study examines how well the approach works given various levels of certainty in the data; beginning with certainty in the streamflow and precipitation, followed by uncertainty in the streamflow and certainty in the precipitation, and finally uncertainty in both the streamflow and precipitation. The approach is found to work in this case when streamflow and precipitation is fairly certain, while being more challenging to implement in a forecasting scenario where future streamflow and precipitation is much less certain. The main challenge is determined to be related to parametric uncertainty and ideas for overcoming this challenge are discussed.

Hydrologic model development and calibration have continued in most cases to focus only on accurately reproducing streamflows. However, complex models, for example, the so-called physically based models, possess large degrees of freedom that, if not constrained properly, may lead to poor model performance when used for prediction. We argue that constraining a model to represent streamflow, which is an integrated resultant of many factors across the watershed, is necessary but by no means sufficient to develop a high-fidelity model. To address this problem, we develop a framework to utilize the Gravity Recovery and Climate Experiment's (GRACE) total water storage anomaly data as a supplement to streamflows for model calibration, in a multiobjective setting. The VARS method (Variogram Analysis of Response Surfaces) for global sensitivity analysis is used to understand the model behaviour with respect to streamflow and GRACE data, and the BORG multiobjective optimization method is applied for model calibration. Two subbasins of the Saskatchewan River Basin in Western Canada are used as a case study. Results show that the developed framework is superior to the conventional approach of calibration only to streamflows, even when multiple streamflow-based error functions are simultaneously minimized. It is shown that a range of (possibly false) system trajectories in state variable space can lead to similar (acceptable) model responses. This observation has significant implications for land-surface and hydrologic model development and, if not addressed properly, may undermine the credibility of the model in prediction. The framework effectively constrains the model behaviour (by constraining posterior parameter space) and results in more credible representation of hydrology across the watershed.

This paper explores for the first time assimilation of the X-band soil moisture retrievals by the advanced microwave scanning radiometer-Earth observing system and the advanced microwave scanning radiometer 2 in Environment Canada's standalone Modelisation Environmentale Surface et Hydrologie model over the Great Lakes basin, in comparison with the assimilation of L-band soil moisture retrievals from the soil moisture and ocean salinity mission. A priori rescaling on satellite retrievals is performed by matching their cumulative distribution function (CDF) to the model surface soil moisture's CDF, in order to reduce the satellite-model bias in the assimilation system. The satellite retrievals, the open-loop model soil moisture (no assimilation), and the assimilation soil moisture estimates are validated against point-scale in situ measurements, in terms of the daily-spaced anomaly time series correlation coefficient R (soil moisture skill). Results show that assimilating X-band retrievals can improve the model soil moisture skill for both surface and root zone soil layers. The assimilation of L-band retrievals results in greater soil moisture skill improvement Delta RA-M (the assimilation skill minus the skill for the open loop model) than the assimilation of X-band products does, although the sensitivity of the assimilation to the satellite retrieval capability may become progressively weaker as the open-loop skill increases. The joint assimilation of X-band and L-band retrievals does not necessarily yield the greatest skill improvement. Overall, Delta RA-M exhibits a strong dependence upon the difference between the satellite retrieval skill and the open-loop surface soil moisture skill.

With recent advances in satellite microwave soil moisture estimation, particularly the launch of the Soil Moisture and Ocean Salinity satellite and the soil moisture active passive mission, there is an increased demand for exploiting the potential of satellite microwave soil moisture observations to improve the predictive capability of hydrologic and land surface models. This study presents the implementation of the 1-D version of the ensemble Kalman filter scheme to assimilate satellite soil moisture into Environment Canada's Standalone Modélisation Environmentale-Surface et Hydrologie (MESH) model that couples the Canadian land surface scheme with a distributed hydrological model. This paper examines the performance of the established assimilation scheme by conducting a series of synthetic assimilation experiments in which the satellite soil moisture and the reference ("true") solutions were derived from the MESH model simulations. The synthetic analyses have demonstrated the capability of the assimilation system, given the synthetic satellite soil moisture and the intentionally degraded model estimates, to accurately approximate the "true" surface layer and root-zone soil moisture solutions. The experiments have also revealed the impacts of a series of factors (ensemble size, vegetation cover, observing frequency, specification of observation, and model input error parameters) upon the quality of the assimilation estimates, which can provide an important guidance for the practical application of the assimilation scheme.

Land surface schemes (LSSs) are of potential interest both to hydrologists looking for innovative ways to simulate river flow and the land surface water balance and to atmospheric scientists looking to improve weather and climate predictions. This paper discusses three ideas, which are grounded in hydrological science, to improve LSS predictions of streamflow and latent heat fluxes. These three possibilities are 1) improved representation of lateral flow processes, 2) the appropriate representation of surface heterogeneity, and 3) calibration to streamflow as a way to account for parameter uncertainty. The current understanding of lateral hydrological processes is described along with their representation of a selected group of LSSs. Issues around spatial heterogeneity are discussed, and calibration in hydrologic models and LSSs is examined. A case study of an evapotranspiration-dominated basin with over 10 years of extensive observations in central Canada is presented. The results indicate that in this particular basin, calibration of streamflow presents atmospheric modelers with a unique opportunity to improve upon the current practice of using lookup tables to define parameter values. More studies are needed to determine if model calibration to streamflow is an appropriate method for generally improving LSS-modeled heat fluxes around the globe.

This paper provides an overview of the key processes that generate floods in Canada, and a context for the other papers in this special issue - papers that provide detailed examinations of specific floods and flood-generating processes. The historical context of flooding in Canada is outlined, followed by a summary of regional aspects of floods in Canada and descriptions of the processes that generate floods in these regions, including floods generated by snowmelt, rain-on-snow and rainfall. Some flood processes that are particularly relevant, or which have been less well studied in Canada, are described: groundwater, storm surges, ice-jams and urban flooding. The issue of climate change-related trends in floods in Canada is examined, and suggested research needs regarding flood-generating processes are identified.

To cite this article: James M. Buttle, Diana M. Allen, Daniel Caissie, Bruce Davison, Masaki Hayashi, Daniel L. Peters, John W. Pomeroy, Slobodan Simonovic, André St-Hilaire & Paul H. Whitfield (2016): Flood processes in Canada: Regional and special aspects, Canadian Water Resources Journal / Revue canadienne des ressources hydriques, DOI: 10.1080/07011784.2015.1131629 To link to this article: http://dx.doi.org/10.1080/07011784.2015.1131629