Bruce Davison

Characterizing snowmelt variability in a land‐surface‐hydrologic model

Abstract The simulation of the snowmelt period in Arctic landscapes has been a long‐standing challenge for hydro‐logic and atmospheric models, largely due to the high variability in end‐of‐winter snowpacks. This study examines an approach to improving the simulation of the spatial variability of snowmelt in a coupled land‐surface‐hydrology surface scheme (WATCLASS) during the spring melt period in a small Arctic basin representative of large parts of the Canadian and Siberian Arctic. Typical land‐surface model applications (such as in global and regional climate models, as well as single column experiments) are based on a priori estimates of land‐surf ace parameters and rarely use methods employed by hydrologists for parameter identification at the basin scale. In this study, the landscape‐based model parameters were estimated using two observational datasets, basin runoff and snow‐covered area determined from satellite images, rather than the a priori approach to parameter selection. Considerable improvement in the simulation of the sub‐grid variability of the snow cover and the subsequent melt was achieved by including windswept tundra and drift land classes based on topography, rather than the traditionally used vegetation. The added land classes allowed a much more realistic initialization of the spatially variable end‐ofwinter snow cover in the model and greatly improved the simulated variability in snow‐covered area throughout the melt period.

Flood processes in Canada: Regional and special aspects

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.

Modelling spatially distributed snowmelt and meltwater runoff in a small Arctic catchment with a hydrology land‐surface scheme (WATCLASS)

Abstract The fully distributed hydrology land‐surface scheme WATCLASS is used to simulate spring snowmelt runoff in a small Arctic basin, Trail Valley Creek, dominated by open tundra and shrub tundra vegetation. The model calculates snowmelt rates from a full surface energy balance, and a three‐layer soil model is used to simulate the infiltration into and the exchange of heat and moisture within the ground. The generated meltwater is delivered to the stream channel network by overland flow, interflow, and baseflow and subsequently routed out of the catchment. Subgrid spatial variability is handled by the model through the use of grouped response units (GRUs). The GRUs in WATCLASS are chosen according to vegetation land cover. Five spring snowmelt periods with a variety of initial end‐of‐winter snow cover and melt conditions were simulated and compared with observed runoff data. In a second step, the models ability to simulate spatially variable snow covered area (SCA) within the basin was tested by comparing model predictions to remotely sensed SCA. WATCLASS was able to predict runoff volumes (on average within 15% over five years of modelling) as well as timing of snowmelt and meltwater runoff for open tundra fairly accurately. However, the model underestimated melt in the energetically more complex shrub tundra areas of the basin. Furthermore, the observed high spatial variability of the SCA at a 1‐km resolution was not captured well by the model. Several recommendations are made to improve model performance in Arctic basins, including a more realistic implementation of the gradual deepening of the thawed layer during the spring, and the use of topographic information in the definition of land cover classes for the GRU approach.

Calibrating Environment Canada's MESH Modelling System over the Great Lakes Basin

Abstract This paper reports on recent progress towards improved predictions of a land surface-hydrological modelling system, Modélisation Environmentale–Surface et Hydrologie (MESH), via its calibration over the Laurentian Great Lakes Basin. Accordingly, a “global” calibration strategy is utilized in which parameters for all land class types are calibrated simultaneously to a number of sub-basins and then validated in time and space. Model performance was evaluated based on four performance metrics, including the Nash-Sutcliffe (NS) coefficient and simulated compared with observed hydrographs. Results from two calibration approaches indicate that in the model validation mode, the global strategy generates better results than an alternative calibration strategy, referred to as the “individual” strategy, in which parameters are calibrated individually to a single sub-basin with a dominant land type and then validated in a different sub-basin with the same dominant land type. The global calibration strategy was relatively successful despite the large number of calibration parameters (51) and relatively small number of model evaluations (1000) used in the automatic calibration procedure. The NS values for spatial validation range from 0.10 to 0.72 with a median of 0.41 for the 15 sub-basins considered. Results also confirm that a careful model calibration and validation is needed before any application of the model.

Snowmelt Processes and Runoff at the Arctic Treeline: Ten Years of MAGS Research

Under the Mackenzie GEWEX Study, extensive snowmelt and runoff research was carried out at the Trail Valley and Havikpak Creek research basins at the tundra-forest transition zone near Inuvik, Northwest Territories. Process based research concentrated on snow accumulation, the spatial variability of energy fluxes controlling melt, local scale advection of sensible heat from snow-free patches to snow patches, percolation of meltwater through the snowpack, storage of meltwater in stream channels, and hillslope runoff. Building on these studies, process based models were improved, as shown by a better ability to model changes in snow-covered area during the melt period. In addition, various landsurface and hydrologic models were tested, demonstrating an enhanced capability to model melt related runoff. Future research is required to accurately model both snow-covered area and runoff at a variety of scales and to incorporate topographic and vegetation effects correctly in the models.

Low-Flows in Deterministic Modelling: A Brief Review

Deterministic hydrological models are limited in their ability to model low-flows, but as they are increasingly being used for low-flow studies, a review is timely if they are to be used for this purpose. The representations of the physical processes that govern low-flows are described for a selection of models developed and/or used in Canada. The models vary in the extent to which they incorporate low-flow processes, such as drawdown of storage in lakes, stream channels and wetlands, riparian evapotranspiration, freeze-up and bank storage. The representation of groundwater in the models is not physically-based and the hydrologic models must be coupled with distributed groundwater models to answer questions about groundwater withdrawals. Most models have not been rigorously tested for low-flow simulations. Considerable work remains to be done, especially in the adequate representation of low-flow processes, and in evaluation of models for low-flow studies by using low-flow specific evaluation criteria.

What is Missing from the Prescription of Hydrology for Land Surface Schemes

AbstractLand 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 i...

Application of SMOS Soil Moisture and Brightness Temperature at High Resolution With a Bias Correction Operator

The assimilation of soil moisture and brightness temperature (TB) are expected to improve the modeling of land surface processes, but are only available at a resolution that is far coarser than the scale of many hydrological processes. Due to systematic differences between model states and satellite observations, a bias correction operator is a necessary step in land data assimilation schemes and was evaluated as a method to disaggregate coarse-scale satellite observations to fine-scale model grid cells (~800 m). This was done by coupling the Modélisation Environmentale Communautaire-Surface Hydrology (MESH) Hydrological Land-Surface Scheme to the Community Microwave Emissions Model (CMEM) to simulate soil moisture and TB. By comparison, MESH-CMEM was found to be in good agreement with observations from the Soil Moisture and Ocean Salinity (SMOS) satellite at the scale of SMOS data products (R ≈ 0.55), with simulated TB being better correlated than soil moisture retrievals. Following bias correction, TB and soil moisture retrievals at 800-m resolution had comparable performance to coarse-resolution SMOS data. Bias correction of TB was more reliable than soil moisture. These findings indicate that both TB and soil moisture retrievals can be assimilated in a land surface model at moderate-to-high resolution with a simple observation operator.

Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach

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 Experiments (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.

Assimilation of SMOS soil moisture in the MESH model with the ensemble Kalman filter

Over the past decade, satellite soil moisture retrievals have showed great potential to improve land surface and hydrologic modeling, especially through an advanced data assimilation system. Data assimilation can be viewed as a process to optimally merge the model estimate and the observed information based upon some estimate of their error characteristics. This paper presents a case study of assimilating the Soil Moisture and Ocean Salinity (SMOS) satellite soil moisture retrievals (2010-2013) into a coupled land-surface and hydrological model MESH with an ensemble Kalman filter (EnKF). The assimilation experiment is conducted over the Great Lakes basin. The assimilation is validated against in situ soil moisture measurements (53 sites) from the Michigan Automated Weather Network, the Soil Climate Analysis Network, and the Fluxnet-Canada, in terms of the daily-spaced anomaly time series correlation coefficient (soil moisture skill). Results indicate that the assimilation of SMOS retrievals enhances the MESH models soil moisture skill.