Richard Turcotte

Determination of the drainage structure of a watershed using a digital elevation model and a digital river and lake network

Abstract Distributed hydrological models require a detailed definition of a watersheds internal drainage structure. The conventional approach to obtain this drainage structure is to use an eight flow direction matrix (D8) which is derived from a raster digital elevation model (DEM). However, this approach leads to a rather coarse drainage structure when monitoring or gauging stations need to be accurately located within a watershed. This is largely due to limitations of the D8 approach and the lack of information over flat areas and pits. The D8 approach alone is also unable to differentiate lakes from plain areas. To avoid these problems a new approach, using a digital river and lake network (DRLN) as input in addition to the DEM, has been developed. This new approach allows for an accurate fit between the DRLN and the modelled drainage structure, which is represented by a flow direction matrix and a modelled watercourse network. More importantly, the identification of lakes within the modelled network is now possible. The proposed approach, which is largely rooted in the D8 approach, uses the DRLN to correct modelled flow directions and network calculations. For DEM cells overlapped by the DRLN, flow directions are determined using DRLN connections only. The flow directions of the other DEM cells are evaluated with the D8 approach which uses a DEM that has been modified as a function of distance to the DRLN. The proposed approach has been tested on the Chaudiere River watershed in southern Quebec, Canada. The modelled watershed drainage structure showed a high level of coherence with the DRLN. A comparison between the results obtained with the D8 approach and those obtained by the proposed approach clearly demonstrated an improvement over the conventionally modelled drainage structure. The proposed approach will benefit hydrological models which require data such as a flow direction matrix, a river and lake network and sub-watersheds for drainage structure information.

Calibration of watershed models.

Published by the American Geophysical Union as part of the Water Science and Application Series, Volume 6. During the past four decades, computer-based mathematical models of watershed hydrology have been widely used for a variety of applications including hydrologic forecasting, hydrologic design, and water resources management. These models are based on general mathematical descriptions of the watershed processes that transform natural forcing (e.g., rainfall over the landscape) into response (e.g., runoff in the rivers). The user of a watershed hydrology model must specify the model parameters before the model is able to properly simulate the watershed behavior.

A Comparison of the Canadian Global and Regional Meteorological Ensemble Prediction Systems for Short-Term Hydrological Forecasting

AbstractMeteorological ensemble prediction systems (M-EPS) are generally set up at lower resolution than for their deterministic counterparts. Operational hydrologists are thus more prone to selecting deterministic meteorological forecasts for driving their hydrological models. Limited-area implementation of meteorological models may become a convenient way of providing the sought after higher-resolution meteorological ensemble forecasts. This study aims to compare the Canadian operational global EPS (M-GEPS) and the experimental regional EPS (M-REPS) for short-term operational hydrological ensemble forecasting over eight watersheds, for which performance and reliability was assessed. Higher-resolution deterministic forecasts were also available for the study. Results showed that both M-EPS provided better performance than their deterministic counterparts when comparing their mean continuous ranked probability score (MCRPS) and mean absolute error (MAE), especially beyond a 24-h horizon. The global and re...

Present and future Laurentian Great Lakes hydroclimatic conditions as simulated by regional climate models with an emphasis on Lake Michigan-Huron

Regional climate modelling represents an appealing approach to projecting Great Lakes water supplies under a changing climate. In this study, we investigate the response of the Great Lakes Basin to increasing greenhouse gas and aerosols emissions using an ensemble of sixteen climate change simulations generated by three different Regional Climate Models (RCMs): CRCM4, HadRM3 and WRFG. Annual and monthly means of simulated hydro-meteorological variables that affect Great Lakes levels are first compared to observation-based estimates. The climate change signal is then assessed by computing differences between simulated future (2041–2070) and present (1971–1999) climates. Finally, an analysis of the annual minima and maxima of the Net Basin Supply (NBS), derived from the simulated NBS components, is conducted using Generalized Extreme Value distribution. Results reveal notable model differences in simulated water budget components throughout the year, especially for the lake evaporation component. These differences are reflected in the resulting NBS. Although uncertainties in observation-based estimates are quite large, our analysis indicates that all three RCMs tend to underestimate NBS in late summer and fall, which is related to biases in simulated runoff, lake evaporation, and over-lake precipitation. The climate change signal derived from the total ensemble mean indicates no change in future mean annual NBS. However, our analysis suggests an amplification of the NBS annual cycle and an intensification of the annual NBS minima in future climate. This emphasizes the need for an adaptive management of water to minimize potential negative implications associated with more severe and frequent NBS minima.

Analyse de l’identification de la fonte de neige printanière avec QuickSCAT dans le Sud du Québec, Canada

Snowmelt detection during springtime is a major issue for dam management in northern areas. The Seawinds scatterometer onboard QuikSCAT has proven to be a useful tool to identify liquid water within the snowpack. The study presents an analysis of snowmelt detection using QuikSCAT data in southern Québec, Canada, with the optimization of a backscattering coefficient threshold on every grid point (0.1° grid for years 2001–2007). The threshold considers interpolated air temperatures from local meteorological stations used as reference. Throughout the studied period (2001–2007), the mean accuracy for snowmelt detection is 94%. Nevertheless, the detection of snowmelt is less accurate over open crop land (no forest), even if the effect of forest cover fraction variations on the backscattering coefficient is negligible. Based on an observed relationship between the winter mean backscattering coefficient and the optimized threshold, a simple method for snowmelt detection is proposed. This method makes use of a dynamic empirical threshold linked to the mean backscattering coefficient by a linear function during winter prior to snowmelt. The proposed method provides a mean detection accuracy of 86.0% (2001–2007), better than a method based on a variable threshold with fixed bias. This dynamic threshold approach has the advantage of accounting for strong interannual snow condition variability. This can be shown by the effect of winter mean temperatures driving the surface conditions specific to every winter, which have a significant impact on the mean backscattering coefficient.