Abstract:Successful applications of stochastic models for simulating and predicting daily stream temperature have been reported in the literature. These stochastic models have been generally tested on small rivers and have used only air temperature as an exogenous variable. This study investigates the stochastic modelling of daily mean stream water temperatures on the Moisie River, a relatively large unregulated river located in Québec, Canada. The objective of the study is to compare different stochastic approaches previously used on small streams to relate mean daily water temperatures to air temperatures and streamflow indices. Various stochastic approaches are used to model the water temperature residuals, representing short-term variations, which were obtained by subtracting the seasonal components from water temperature time-series. The first three models, a multiple regression, a second-order autoregressive model, and a Box and Jenkins model, used only lagged air temperature residuals as exogenous variables. The root-mean-square error (RMSE) for these models varied between 0Ð53 and 1Ð70°C and the second-order autoregressive model provided the best results.A statistical methodology using best subsets regression is proposed to model the combined effect of discharge and air temperature on stream temperatures. Various streamflow indices were considered as additional independent variables, and models with different number of variables were tested. The results indicated that the best model included relative change in flow as the most important streamflow index. The RMSE for this model was of the order of 0Ð51°C, which shows a small improvement over the first three models that did not include streamflow indices. The ridge regression was applied to this model to alleviate the potential statistical inadequacies associated with multicollinearity. The amplitude and sign of the ridge regression coefficients seem to be more in agreement with prior expectations (e.g. positive correlation between water temperature residuals of different lags) and make more physical sense.

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Abstract:A mass balance budget of the suspended sediment in the St. Lawrence River was established for the sector stretching from Cornwall, Ontario, to Quebec City, Quebec, for the period 1989±1993. The approach consisted of analysing the amount of sediment contributed by the dierent tributaries, on a watershed-by-watershed basis, through`sediment concentration±discharge' models incorporating more than 4000 data points collected since 1983. Lake Ontario contributes less than 3% of the particulate load at Quebec City, while St. Lawrence tributaries on the south and north shores contribute 19% and 13%, respectively, of the sediment load. Our ®ndings indicate that nearly 65% of the suspended sediments come from erosion of the bed and banks of the St. Lawrence River. This ®nding is broadly supported by numerous geomorphological and sedimentological observations and is consistent with the geological history of the river and the structures built on its banks in recent decades. Upstream±downstream mass balance studies conducted on individual river sectors indicate that the sources of erosion are located mainly in the Beauharnois Canal region, between Montreal and Les GreÁ ves, and further downstream, between the outlet of Lake Saint-Pierre and Portneuf.

Abstract:Water temperature is a key abiotic variable that modulates both water chemistry and aquatic life in rivers and streams. For this reason, numerous water temperature models have been developed in recent years. In this paper, a k-nearest neighbour model (KNN) is proposed and validated to simulate and eventually produce a one-day forecast of mean water temperature on the Moisie River, a watercourse with an important salmon population in eastern Canada. Numerous KNN model configurations were compared by selecting different attributes and testing different weight combinations for neighbours. It was found that the best model uses attributes that include water temperature from the two previous days and an indicator of seasonality (day of the year) to select nearest neighbours. Three neighbours were used to calculate the estimated temperature, and the weighting combination that yielded the best results was an equal weight on all three nearest neighbours. This nonparametric model provided lower Root Mean Square Errors (RMSE D 1Ð57°C), Higher Nash coefficient (NTD D 0Ð93) and lower Relative Bias (RB D 1Ð5%) than a nonlinear regression model (RMSE D 2Ð45°C, NTD D 0Ð83, RB D 3%). The k-nearest neighbour model appears to be a promising tool to simulate of forecast water temperature where long time series are available.