André St-Hilaire

A Review of Statistical Water Temperature Models

The use of statistical models to simulate or to predict stream water temperature is becoming an increasingly important tool in water resources and aquatic habitat management. This article provides an overview of the existing statistical water temperature models. Different models have been developed and used to analyze water temperature-environmental variables relationship. These are grouped into two major categories: deterministic and statistical/stochastic models. Generally, deterministic models require numerous input data (e.g., depth, amount of shading, wind velocity). Hence, they are more appropriate for analyzing different impact scenarios due to anthropogenic effects (e.g., presence of reservoirs, thermal pollution and deforestation). In contrast to the deterministic models, the main advantage of the statistical models is their relative simplicity and relative minimal data requirement. Parametric models such as linear and non-linear regression are popular methods often used for shorter time scales (e.g., daily, weekly). Ridge regression presents an advantage when the independent variables are highly correlated. The periodic models present advantages in dealing with seasonality that often exists in periodic time series. Non-parametric models (e.g., k-nearest neighbours, artificial neural networks) are better suited for analysis of nonlinear relationships between water temperature and environmental variables. Finally, advantages and disadvantages of existing models and studies are discussed.

A Nonstationary Extreme Value Analysis for the Assessment of Changes in Extreme Annual Wind Speed over the Gulf of St. Lawrence, Canada

Abstract Changes in the extreme annual wind speed in and around the Gulf of St. Lawrence (Canada) were investigated through a nonstationary extreme value analysis of the annual maximum 10-m wind speed obtained from the North American Regional Reanalysis (NARR) dataset as well as observed data from selected stations of Environment Canada. A generalized extreme value distribution with time-dependent location and scale parameters was used to estimate quantiles of interest as functions of time at locations where significant trend was detected. A Bayesian method, the generalized maximum likelihood approach, is implemented to estimate the parameters. The analysis yielded shape parameters very close to 0, suggesting that the distribution can be modeled using the Gumbel distribution. A similar analysis using a nonstationary Gumbel model yielded similar quantiles with narrower credibility intervals. Overall, little change was detected over the period 1979–2004. Only 7% of the investigated grids exhibited trends at...

Frequency analysis of maximum annual suspended sediment concentrations in North America / Analyse fréquentielle des maximums annuels de concentration en sédiments en suspension en Amérique du Nord

Abstract Suspended sediments are a natural component of aquatic ecosystems, but when present in high concentrations they can become a threat to aquatic life and can carry large amounts of pollutants. Suspended sediment concentration (SSC) is therefore an important abiotic variable used to quantify water quality and habitat availability for some species of fish and invertebrates. This study is an attempt to quantify and predict annual extreme events of SSC using frequency analysis methods. Time series of daily suspended sediment concentrations in 208 rivers in North America were analysed to provide a large-scale frequency analysis study of annual maximum concentrations. Seasonality and the correlation of discharges and annual peak of suspended sediment concentration were also analysed. Peak concentrations usually occur in spring and summer. A significant correlation between extreme SSC and associated discharge was detected only in half of the stations. Probability distributions were fitted to station data recorded at the stations to estimate the return period for a specific concentration, or the concentration for a given return period. Selection criteria such as the Akaike and Bayesian information criterion were used to select the best statistical distribution in each case. For each selected distribution, the most appropriate parameter estimation method was used. The most commonly used distributions were exponential, lognormal, Weibull and Gamma. These four distributions were used for 90% of stations.

Statistical Models and the Estimation of Low Flows

he present paper provides a brief review of statistical models that are commonly used in the estimation of low flows both at sites with a reliable streamflow record and sites remote from data sources. Opportunities are identified for the regional estimation of low-flow characteristics at ungauged sites. The adaptation of the neighbourhood regionalization approach, commonly used in regional flood frequency analysis, can be extended to low-flow variables. Estimation approaches extending the usefulness of recession information in regional low-flow frequency analysis to ungauged sites using a canonical correlation analysis approach for the identification of hydrological neighbourhoods is described. The validity of recession parameters when estimated from very short hydrological data records is also discussed. Promising new directions for future research in the field of statistical low-flow frequency estimation are identified.

Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada

This study compares three linear models and one non-linear model, specifically multiple linear regression (MLR) with ordinary least squares (OLS) estimates, robust regression, ridge regression, and artificial neural networks (ANNs), to identify an appropriate transfer function in statistical downscaling (SD) models for the daily maximum and minimum temperatures (Tmax and Tmin) and daily precipitation occurrence and amounts (Pocc and Pamount). This comparison was made over twenty-five observation sites located in five different Canadian provinces (British Columbia, Saskatchewan, Manitoba, Ontario, and Québec). Reanalysis data were employed as atmospheric predictor variables of SD models. Predictors of linear transfer functions and ANN were selected by linear correlations coefficient and mutual information, respectively. For each downscaled case, annual and monthly models were developed and analysed. The monthly MLR, annual ANN, annual ANN, and annual MLR yielded the best performance for Tmax, Tmin, Pocc and Pamont according to the modified Akaike information criterion (AICu). A monthly MLR is recommended for the transfer functions of the four predictands because it can provide a better performance for the Tmax and as good performance as the annual MLR for the Tmin, Pocc, and Pamount. Furthermore, a monthly MLR can provide a slightly better performance than an annual MLR for extreme events. An annual MLR approach is also equivalently recommended for the transfer functions of the four predictands because it showed as good a performance as monthly MLR in spite of its mathematical simplicity. Robust and ridge regressions are not recommended because the data used in this study are not greatly affected by outlier data and multicollinearity problems. An annual ANN is recommended only for the Tmin, based on the best performance among the models in terms of both the RMSE and AICu.

Water Renewal Estimates for Aquaculture Developments in the Richibucto Estuary, Canada

Water renewal in semi-enclosed coastal areas is crucial for the supply of oxygen and seston and for the removal of organic loadings from finfish or shellfish aquaculture sites. Water renewal depends on hydrodynamic processes and can have a complex spatial distribution due to irregular topographic features. This study describes some physical oceanography observations gathered in the Richibucto estuary, New Brunswick, Canada, and provides an estimate of the spatial distribution of water renewal in the North Arm, a location in the estuary where the largest American oyster (Crassostrea virginica) aquaculture operation in eastern Canada is located. The estuary changes from a well mixed estuary to a partially stratified estuary depending on runoff conditions. Tides are mixed but mainly diurnal due to the nearby presence of the second M2 amphidromic point in the Gulf of St. Lawrence. Tidal amplitudes vary from 0.3 to 0.6 m and show a slight increase some 35 km upstream. Currents in the main channel can reach over 0.60 m s−1 during ebb and 0.3 m s−1 during flood, with a slack water period of approximately 8 h. Low frequency sea level fluctuations have a range of 0.5 m at the mouth and are coherent within the estuary. Hydrodynamic and advection-dispersion models are used to calculate the spatial distribution of the local renewal time (LRT) in the North Arm for high and low freshwater discharge conditions, using the dissolved tracer method. Results show that the LRT varies from less than 5 d at the downstream end of the North Arm to over 100 d further upstream. When averaged over the entire North Arm, the integral renewal time (IRT) is estimated to vary only from 8 to 21 d depending on the season. The LRT and IRT estimates are major improvements over conventional renewal estimates using tidal prism methods.

Flood processes in Canada: Regional and special aspects

This paper provides an overview of the key processes that generate floods in Canada, and a context for the other papers in this special issue – papers that provide detailed examinations of specific floods and flood-generating processes. The historical context of flooding in Canada is outlined, followed by a summary of regional aspects of floods in Canada and descriptions of the processes that generate floods in these regions, including floods generated by snowmelt, rain-on-snow and rainfall. Some flood processes that are particularly relevant, or which have been less well studied in Canada, are described: groundwater, storm surges, ice-jams and urban flooding. The issue of climate change-related trends in floods in Canada is examined, and suggested research needs regarding flood-generating processes are identified.

Comparison of non-parametric and parametric water temperature models on the Nivelle River, France

Abstract Water temperature is an important abiotic variable in aquatic habitat studies and may be one of the factors limiting the potential fish habitat (e.g. salmonids) in a stream. Stream water temperatures are modelled using statistical approaches with air temperature and streamflow as exogenous variables in the Nivelle River, southern France. Two different models are used to model mean weekly maximum temperature data: a non-parametric approach, the k-nearest neighbours method (k-NN) and a parametric approach, the periodic autoregressive model with exogenous variables (PARX). The k-NN is a data-driven method, which consists of finding, at each point of interest, a small number of neighbours nearest to this value, and the prediction is estimated based on these neighbours. The PARX model is an extension of commonly-used autoregressive models in which parameters are estimated for each period within the years. Different variants of air temperature and flow are used in the model development. In order to test the performance of these models, a jack-knife technique is used, whereby model goodness of fit is assessed separately for each year. The results indicate that both models give good performances, but the PARX model should be preferred, because of its good estimation of the individual weekly temperatures and its ability to explicitly predict water temperature using exogenous variables.

A nested multivariate copula approach to hydrometeorological simulations of spring floods: the case of the Richelieu River (Québec, Canada) record flood

Floods have potentially devastating consequences on populations, industries and environmental systems. They often result from a combination of effects from meteorological, physiographic and anthropogenic natures. The analysis of flood hazards under a multivariate perspective is primordial to evaluate several of the combined factors. This study analyzes spring flood-causing mechanisms in terms of the occurrence, frequency, duration and intensity of precipitation as well as temperature events and their combinations previous to and during floods using frequency analysis as well as a proposed multivariate copula approach along with hydrometeorological indices. This research was initiated over the Richelieu River watershed (Quebec, Canada), with a particular emphasis on the 2011 spring flood, constituting one of the most damaging events over the last century for this region. Although some work has already been conducted to determine certain causes of this record flood, the use of multivariate statistical analysis of hydrologic and meteorological events has not yet been explored. This study proposes a multivariate flood risk model based on fully nested Archimedean Frank and Clayton copulas in a hydrometeorological context. Several combinations of the 2011 Richelieu River flood-causing meteorological factors are determined by estimating joint and conditional return periods with the application of the proposed model in a trivariate case. The effects of the frequency of daily frost/thaw episodes in winter, the cumulative total precipitation fallen between the months of November and March and the 90th percentile of rainfall in spring on peak flow and flood duration are quantified, as these combined factors represent relevant drivers of this 2011 Richelieu River record flood. Multiple plausible and physically founded flood-causing scenarios are also analyzed to quantify various risks of inundation.

Suspended Sediment Concentrations Downstream of a Harvested Peat Bog: Analysis and Preliminary Modelling of Exceedances Using Logistic Regression

Acting as natural filters, peatlands are important wetland ecosystems in many northern countries, including Canada. To harvest peat, the vegetation must be removed and the harvested area ditched to drain and dry the peat. Drainage ditches are often designed to route water to settling ponds prior to releasing runoff into nearby water bodies. The present study investigated one key water quality variable, suspended sediment concentration (SSC), downstream of settling ponds in an actively harvested peatland. Time series of SSC for two spring seasons (2001-2002) were recorded at two sites using optical back scatterometers (OBS) calibrated in situ. SSC values exceeded the New Brunswick provincial guideline of 25 mg/L between 53.6 and 86.0% of the time. Even when the threshold was raised to relatively high values such as 500 mg/L, the percentage of exceedance remained relatively high (between 11 and 60%). A statistical model of SSC exceedance, based on logistic regression, was tested to investigate which hydrological forcings may explain high SSC values. Various independent variables were used in conjunction with an autoregressive component and were compared using different goodness of fit criteria. For a threshold of 500 mg/L, the best fit among all the logistic regression models tested included lag 1 and 2 autoregressive terms, as well as five-day cumulative precipitation, air temperature and three-day lagged discharge. The model was able to correctly predict 82% of exceedances.