Search

To maintain an optimum balance between security and efficiency of maritime transport in shallow waterways with a lot of deep-draft ship traffic such as in the St. Lawrence Waterway, it is particularly important to accurately estimate the ship squat, which is the reduction of the underkeel clearance between a vessel at rest and in motion. Recently, a squat model based on a regression tree was developed. The skill of this model to predict squat in the St. Lawrence Waterway exceeded the performance of 10 empirical models commonly used by the operational and regularity agencies. Although this approach is promising, two main problems were noticed: (1) the predictions obtained by the regression tree are not smooth and (2) the squat predicted with this model is not always monotonically increasing with ship speed (Froude number). In this paper, a stepwise regression tree algorithm is used to model squat. This approach has the same advantages as the regression tree (allowing the representation of complex and nonlinear relationships) and solves both of the aforementioned problems. Furthermore, the squat predictions of the new stepwise regression model outperform the predictions of the regression tree model and the Eryuzlu model, which is currently used by the Canadian Coast Guard. This new model could provide a handy tool for mariners to get real-time squat predictions in the St. Lawrence River. We also provide an algorithm that can be used to fit a squat model for any other economically important shallow waterway.

In shallow waterways such as the St. Lawrence River, an accurate prediction of the squat is important to ensure a balance between the security and the efficiency of traffic. The Canadian Coast Guard is now studying the squat phenomenon and considering to reassess the actual underkeel clearance standards of the St. Lawrence Waterway. Hence, a field campaign was conducted with 12 deep draft ship sailings, during which the maximal squat was measured with on-the-fly global positioning system. All the variables that may influence the squat (speed, draught, water level, etc.) were also measured. Twenty of the empirical models that are used in practice to predict the squat were tested and the Canadian Coast Guard recommended to either optimize these models or develop new models. Therefore, statistical approaches to model the squat of deep draft ships that navigate on the St. Lawrence Waterway are proposed in this paper. The Eryuzlu model, which is presently used by the Canadian Coast Guard, was optimized by modeling its errors with a stepwise regression. New models were also developed with the regression tree technique. The performance of the statistical models was better than 10 empirical models that are considered the most suitable to predict the maximal squat in the St. Lawrence Waterway. The models built by regression tree gave the best predictions.

Sewage sludge decontamination requires the removal of toxic metals and it can be performed by chemical leaching. The commercialization of such a decontamination process needs to consider the preservation of the sludge fertilizing properties and the ease of dewatering of the acidic treated sludge. Moreover, operating costs must be acceptable under optimal operational parameters. Chemical leaching assays were performed at the laboratory bench scale with biological sludge. Afterwards, a multicriteria analysis has been conducted to determine the optimal operational parameters allowing the sludge decontamination while meeting all performance criteria. The analysis pointed out adequate working ranges in terms of sulfuric acid, ferric chloride, and hydrogen peroxide concentrations to be used for chemical decontamination of the sludge. Moreover, it allowed establishing a mathematical equation to help identify the optimal working range for further studies having different contamination scenarios.

The shape of a flood hydrograph is a random event. This study develops a method for describing statistical properties of the shape of a flood hydrograph. Two shape variables, namely, shape mean ( Sm) and shape variance (Sv), are defined to express the randomness of a flood hydrograph. The two-parameter beta probability density function ~pdf! is adopted to represent the shape of a flood hydrograph. The parameters are estimated using the shape variables. On the basis of this approach, one can derive the design-flood hydrograph shape corresponding to various combinations of flood peak, volume, and duration. The applicability of the proposed method is demonstrated by using observed flood data from the Ashuapmushuan basin in the province of Quebec, Canada. It is concluded that the method is appropriate for representing the statistical properties of the shape of a flood hydrograph.

It is often necessary to estimate extreme events at sites where little or no hydrometric data are available. In such cases, one may use a regional estimation procedure, utilizing data available from other stations in the same hydrologic region. In general, a regional flood frequency procedure consists of two steps, delineation of hydrologically homogeneous regions and regional estimation. This paper focuses on the development of a regional flood frequency procedure based on canonical correlation analysis (CCA) and its application to data from a northern Canadian basin in which floods are dominated by spring snowmelt. This CCA-based procedure allows the joint regional estimation of spring flood peaks and volumes. The CCA method allows the determination of pairs of canonical variables such that the correlation between the canonical variables of one pair is maximized and between the variables of different pairs is equal to zero. Therefore, it is possible to infer hydrological canonical variables, knowing the physiographical-meteorological canonical variables. The methodology developed was applied to the St. Maurice River basin system, which is operated by Hydro-Quebec and characterized by the relatively low precision of flow data available. Results show that the proposed method allows for a significant reduction in the 100-year spring flood and volume quantile estimation bias and mean square error. The study also shows that, in 60% of cases, the method that was previously used overestimates quantile values, which leads to an overdesign of retention structures.

The presence of zeroes in a record of low flows usually has been interpreted as being indicative of a stream that goes dry during some periods. However, in some instances, zeroes may appear in a gauging record simply because the actual discharge was below a measurement threshold. For example, in some regions it is sometimes true that the lower limit of a stream gauge is above the true stream bottom, and any (censored) discharges not registered by the gauges are recorded as zeroes. This paper presents a comparison of alternative methods for treatment of zeroes in low-flow estimation procedures when tail models are employed for modeling purposes. Simulation experiments indicate that a Weibull tail model, as fitted using a likelihood-based method for inclusion of censored data, is the best estimator of the 10-year low-flow quantile when zeroes in a data set have arisen as a consequence of censoring. When zeroes in a data set exist because the stream is an ephemeral one, a lognormal mixed tail model fitted us...

In the fields of statistics and hydrology, a considerable amount of research has been directed toward robust methods of estimation. This paper broadly reviews these methods and presents results of the application of a specific robust estimation procedure, namely trimming, in flood frequency analysis. Using simulation, the effect of various proportions of symmetric trimming on the estimation of moments, distribution parameters, and quantiles is examined. The influence of the sample size and parent distribution parameters on the estimation performance is also investigated for several return periods and for three different fitting methods for the log-Pearson Type III distribution. While the results point to the potential usefulness of robust estimators in hydrological analysis, they also serve as a precaution that certain distribution fitting techniques may be overly robust to extreme observations in the sample (those in the tails).

In hydraulic design, one often needs to estimate flood quantiles for use as design values. It is important to assess the estimation error by constructing confidence intervals (CIs) for these quantiles. Fitting probability distributions to hydrologic data is used widely for estimating quantiles of hydrological variables. The two-parameter gamma (G2) is among the distributions commonly used, but the three-parameter generalized gamma (GG3) (also known as Kritsky-Menkel distribution) is an alternative when more shape flexibility is needed to fit the data. We use an approximate method to construct CIs for the quantiles of the G2 and GG3 distributions. This method is shown to be useful for hydrological applications where the data record is short.