Mise en place d’un laboratoire naturel sur le mont Covey Hill (Québec, Canada)

Le mont Covey Hill héberge des populations de salamandres rares et menacées dont les habitats sont maintenus en partie par\ud l’eau souterraine. Des travaux de recherche multidisciplinaires (UQAM, U. McGill, U. Montréal, IRBV, Centre Brace, SCABRIC et\ud ministères) ont permis de comprendre l’hydrologie ainsi que la répartition et les caractéristiques des habitats de salamandres. Ces projets ont contribué à la mise en place de stations de suivi écologique à long terme et de stations hydrométriques permanentes. En tant que propriétaire de terrains dédiés à la conservation, Conservation de la Nature assure la coordination de ces initiatives. Le mont Covey Hill est aujourd’hui un Laboratoire naturel unique au Québec, dédié à la compréhension intégrée et à long terme d’un écosystème fragile. Cet article a pour objectif de présenter le Laboratoire naturel par une description des recherches en cours et à venir

Microbial dynamics in maize-growing soil under different tillage and residue management

Non-Peer ReviewedMicroorganisms are involved in the fertility-related processes of agricultural fields. The long-term impact of tillage and residue management on soil microorganisms was studied over the growing season, in a sandy loam to loamy sand soil of southwestern Quebec. Tillage and residue treatments had been first imposed in fall 1991, on a maize (Zea mays L.) monoculture. Treatments consisted of no till, reduced tillage, and conventional tillage with crop residues either removed from (-R) or retained on (+R) experimental plots, laid out in a randomized complete block design. Soil microbial biomass carbon (SMB-C), soil microbial nitrogen (SMB-N) and phospholipid fatty acid (PLFA) concentrations were measured four times over the 2001 growing season i.e., in May 7 (preplanting), June 25, July 16, and September 29 (prior to corn harvest). The effect of time was larger than those of tillage or residue treatments. While SMB-C showed little seasonal change (160 μg C g-1 soil), SMB-N was responsive to post emergence mineral nitrogen fertilization, and PLFA analysis showed an increase in fungi and total PLFA throughout the season. The effect of residue was more pronounced than that of tillage, with increased SMB-C and SMB-N (61% and 96%) in +R plots compared to –R plots. This study illustrated that measuring soil quality based on soil microbial components must take into account seasonal changes in soil physical and chemical conditions

Use of AGNPS for watershed modeling in Quebec

A study was undertaken to determine the predictive capability of the AGNPS model with respect to surface runoff, peak flow, and sediment yield produced by rainfall-runoff events on a 26 km2 watershed in Quebec. Precipitation, stream discharge, surface runoff, and suspended sediment concentrations were monitored for rainfall-runoff events occurring from 1994-1996, inclusive. Data describing stream patterns, topography, soil type, and land use were collected and input to the model. Seven rainfall-runoff events were used for model calibration. Five storms were used to validate the model. Calibration curves were developed to correlate the antecedent precipitation index (AP1) to the SCS curve number. For model calibration, coefficients of performance of 0.12, 0.05, and 0.43 were obtained for peak flow, surface runoff, and sediment yield, respectively. For model validation, coefficients of performance of 0.02, and 0.01 were obtained for surface runoff, and sediment yield, respectively. Peak flow was generally overpredicted and yielded a CP'(A) of 2.07

Geostatistical methods for prediction of spatial variability of rainfall in a mountainous region

Reliable estimation of rainfall distribution in mountainous regions poses a great challenge not only due to highly undulating surface terrain and complex relationships between land elevation and precipitation, but also due to non-availability of abundant rainfall measurement points. Prediction of rainfall variability over mountainous islands is a logical step towards meaningful land use planning and water resources zoning. In this context, geostatistical techniques were developed for mapping the rainfall variability over the island of St. Lucia in the Caribbean, using the elevation information extracted from a Digital Elevation Model (DEM) and long-term mean monthly rainfall (MMR) data of 40 raingauge stations spread over 616 km2. The ordinary co-kriging (OCK) and collocated co-kriging (CCK) methods of interpolation were applied for the standardized rainfall depths associated with elevation, as the primary variate, and the surface elevation values as the secondary variate. The best semivariogram model algorithm generated, using either of the above co-kriging (CK) methods, was used to predict standardized values for the elevation points extracted from the DEM for which the rainfall depths were not known. The predicted values were further destandardized to generate the rainfall depth at the unmeasured locations. Ordinary kriging (OK) was then performed for the destandardized and observed rainfall depths to generate the prediction map of MMR over the entire island. These sequential steps were repeated for the MMR data of all twelve months to generate rainfall prediction maps over the island. The spherical semivariogram model fit well (0.84 < R2 < 0.98) for both the OCK and OK methods. The cross-validation error statistics of OCK presented in terms of coefficient of determination (R2), kriged root mean square error (KRMSE), and kriged average error (KAE) were within the ++acceptable limits (KAE close to zero, R2 close to one, and KRMSE from 0.55 to 1.45 for 40 raingauge locations) for most of the months. The exploratory data analysis, variogram model fitting, and generation of MMR prediction map through kriging were accomplished through use of ArcGIS and GS+ software

Improved curve number selection for runoff prediction

The three antecedent moisture conditions used in the SCS (Soil Conservation Service) curve number method of surface runoff volume prediction have been shown to be inapplicable in humid regions such as the Ottawa - St. Lawrence Lowlands. The antecedent precipitation index is an alternative indicator of soil moisture. Using a hydrologic database, calibration curves were developed to correlate antecedent precipitation index to the SCS curve number. Curve numbers were then input to the AGNPS hydrologic model. When compared to the three antecedent moisture conditions in the SCS curve number method, use of antecedent precipitation index as a soil moisture indicator considerably improved surface runoff volume simulations. However, peak flow was generally overpredicted by the AGNPS model