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

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

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

Phosphorous losses in surface and subsurface runoff from a snowmelt event on an agricultural field in Quebec

Phosphorus concentrations exceed water quality guidelines in most of the major rivers in southeastern Quebec. The problem is particularly important in the Pike River, which drains into the Missiquoi Bay of Lake Champlain. Elevated phosphorus concentrations can lead to a reduction in the palatability of drinking water, a decrease in diversity of aquatic life, and loss of recreational opportunities. These problems have been observed in the Bay. An agricultural field in southeastern Quebec was instrumented to measure and evaluate the partitioning of phosphorus between surface runoff and subsurface drainage, year round. The snowmelt event was the dominant surface and subsurface event for the 2000/2001 water year. The 2000/2001 water year was unusually dry, which resulted in a limited number of surface and subsurface runoff events. The annual depth of surface runoff at one site was 87.5 mm. The estimated depth of subsurface runoff of the snowmelt event was 93.7 mm. Subsurface drainage flow was 51.7% of the total volume of runoff from the field during the snowmelt event. The total phosphorus load in surface runoff for the spring snowmelt was 166.4 g/ha. The estimated total phosphorus load in subsurface drainage for the spring snowmelt was 98.2 g/ha. The estimated subsurface total phosphorus load accounted for 37.1% of the total loads during the snowmelt. Therefore, the subsurface drains proved to be a significant pathway for phosphoru

Development of a hydrology multimedia courseware

A computer assisted learning approach was developed to enhance course material for an undergraduate hydrology and water management course, through the use of multimedia courseware. The courseware was installed under 32bit versions of the Windows operating system. CDROM's were developed as the courseware distribution media. Course material was placed in a Windows graphic user interface using Microsoft Visual Basic versions 4-5. Multimedia files, including images, sound, and movies were added to enhance visualization. Students were able to navigate through the courseware in a non-linear fashion akin to multimedia hyperlink technology. The courseware contained all of the conventional course material, in text format, with multimedia additions. Simulation and prediction tools were added to aid students in problem visualization and problem solving. To modify the course contents, the instructor made changes directly to the CD and re-issued an updated CD to the students

Simulating nitrogen dynamics under water table management systems with DRAINMOD-N

There is increasing interest in water table management to control nitrate leaching in eastern Canada. However, little is known about the impacts of this practice in the region. DRAINMOD-N, a recently developed drainage water quality model, offers the potential to evaluate nitrate leaching. The model was validated by comparing simulated results with measurements of water table depth, drain flow, cumulative nitrate leaching, and cumulative denitrification, from conventional drainage and subirrigation field plots planted to corn. Replicated plots of 15 m x 75 m were either under conventional free drainage or subirrigation at a weir setting of 0.5 m below the soil surface. DRAINMOD-N predicted water table depth to within a range of ± 160 to 210 mm, drain flow to within ± 2 mm/d and nitrate leaching to within ± 8 kg N/ha. DRAINMOD-N models denitrification using first order kinetics. This did not accurately describe field measurements of cumulative denitrification, as by day of year 270 cumulative denitrification was underestimated by 64 to 83%. Therefore, the model was modified by replacing the original denitrification function with the Michaelis-Menten relationship, which simulates denitrification as a first order process when nitrate is limiting and as a zero order process for non-limiting nitrate. This modification had little effect (< 2%) on the modified model's prediction of nitrate leaching. For cumulative denitrification, however, the prediction error with the modified model was of 23 to 60% less than with DRAINMOD-N