Water is a vital resource, and can also sometimes be a destructive force. As such, it is important to manage this resource. The prediction of stream flows is an important component of this management. Hydrological models are very useful in accomplishing this task. The objective of this study is to develop and apply an optimization method useful for calibrating a deterministic model of the daily flows of the Miramichi River watershed (New Brunswick). The model used is the CEQUEAU model. The model is calibrated by applying a genetic algorithm. The Nash-Sutcliffe efficiency criterion, modified to penalize physically unrealistic results, was used as the objective function. The model was calibrated using flow data (1975-2000) from a gauging station on the Southwest Miramichi River (catchment area of 5050 km 2 ), obtaining a Nash-Sutcliffe criterion of 0.83. Model validation was performed using flow data (2001-2009) from the same station (Nash-Sutcliffe criterion value of 0.80). This suggests that the model calibration is sufficiently robust to be used for future predictions. A second model validation was performed using data from three other measuring stations on the same watershed. The model performed well in all three additional locations (Nash -Sutcliffe criterion values of 0.77, 0.76 and 0.74), but was performing less well when applied to smaller sub-basins. Nonetheless, the relatively strong performance of the model suggests that it could be used to predict flows anywhere in the watershed, but caution is suggested for applications in small sub-basins. The performance of the CEQUEAU model was also compared to a simple benchmark model (average of each calendar day). A sensitivity analysis was also performed. KeywordsHydrological Modeling, Genetic Algorithm, CEQUEAU Model, Beta Function, Miramichi River J. Boisvert et al. 152
This study deals with incomplete bivariate data in hydrology, where information contained in a hydrological series of relatively long length (X, the auxiliary variable) is utilized to enhance the quality of the quantile estimates for a series of shorter length (Y, the variable of main interest), when there is an association between X and Y. It is suggested that bivariate models for representing (X, Y) be constructed by means of copulas, which allows for flexibility in choosing both the marginals and the bivariate distributions. Parameter estimation is done by maximum likelihood (ML), where all the unknown parameters of the bivariate model are estimated simultaneously. A case study using flow records at three gauging stations on the St. John River (New Brunswick, Canada) is used to demonstrate the interest of using bivariate distributions for modeling incomplete data. By using (X, Y) bivariate data observed on the St. John River, the probability density function (pdf) obtained from a univariate frequency analysis of Y (Model A), is compared to the pdf constructed using a bivariate model relating X to Y (Model B). It is shown that Model B reduces the variability in the Y pdf as compared to the pdf obtained from Model A, and also corrects the quantile estimates for Y through a location shift.
The availability of hydrometric data, as well as its spatial distribution, is important for water resources management. An overly dense network or an under developed network can cause inaccurate hydrological regional estimates. The objective of this study is to propose a methodology for rationalizing a network, specifically the New Brunswick Hydrometric Network. A hierarchical clustering analysis allowed dividing the province into two regions (North and South), based on latitude and high flow timing. These groups were subsequently split separately into three homogeneous subgroups, based on the generalized extreme value (GEV) distribution shape parameter of each station for annual maximum flow series. An entropy method was then applied to compute the amount of information shared between stations, ranking each station’s importance. A station with a lot of shared information is redundant (less important), whereas one with little shared information is unique (very important). The entropy method appears to be a useful decisional tool in a network rationalization.
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