Nassir El-Jabi

Modelling of maximum daily water temperatures in a small stream using air temperatures

Maximum daily stream water temperature information is very important when assessing fish habitat in terms of distribution and fish growth rate. For instance, coldwater fishes such as the Atlantic salmon can be adversely affected by these maximum summer temperatures or by those exacerbated by land use practices such as deforestation. The present study deals with the modelling of maximum daily stream water temperatures using regression and stochastic models to relate air and water temperatures in Catamaran Brook, a small stream in New Brunswick where long-term multidisciplinary habitat research is being carried out. The regression model was a logistic type function while the stochastic model was based on the autocorrelation structure of the water temperature time series. The first step in the stochastic modelling was to establish the long-term annual component in stream water temperatures. This was possible by fitting a combination of a Fourier and Sine function to stream water temperatures. The short-term residual temperatures (departure from the long-term annual component) were modelled using a second order Markov process. Results showed that the regression type model was only possible on a weekly basis with a root-mean-square error (RMSE) of 1.93°C. Alternatively, the stochastic model showed that it was possible to predict maximum daily water temperatures for small streams using air temperatures only. The RMSE varied between 1.48 and 1.62°C on an annual basis from 1992 to 1997, which were lower than the regression model. Calibrations were carried out on a seasonal basis as well as during the summer of each year. However, the improvements in the modelling were less than 0.1°C. It was also noted that the empirical coefficient linking air to water temperatures residuals varied on a seasonal and summer basis, and this coefficient was related to summer discharge. Although variable, this empirical coefficient did not improve the modelling significantly on a seasonal or summer basis.

Instream Flow Assessment: From Holistic Approaches to Habitat Modelling

The conflict between the ever-increasing demand for river water (hydroelectric development, irrigation, drinking water, etc.) and the environmental need for sustaining flows during drought and low flow periods is a recurring problem in water resource management. Competition between water abstraction (offstream use) and instream flow needs (minimum flow for the protection of fish habitat) will undoubtedly increase in the future, as it is estimated that worldwide more than 50% of total accessible runoff is presently being used. Instream flow methods are the primary and essential tools used during environmental impact assessments to evaluate the level of aquatic habitat protection for rivers under reduced flow conditions. Since the 1970s, methodologies, applicable to various scopes of water utilization and of a range of sophistication, have been developed. These can be classified into three categories: historical streamflow, river hydraulics and habitat preference methods. New methods are widely used in North America, but important questions related to validation and range of applicability remain. Existing and new instream flow methods also need to take into consideration changing environmental conditions such as climate change, in establishing a level of protection. The research reported here focuses on the current knowledge, strengths and weaknesses of a variety of instream flow methods as well as their associated level of aquatic habitat protection. Recommendations are provided for future instream flow research depending on the range of complexity of applied methods being contemplated.

Hydrometeorological Trends in the Miramichi River, Canada: Implications for Atlantic Salmon Growth

Abstract Hydrometeorological conditions are important determinants of the distribution and productivity of Atlantic salmon Salmo salar in freshwater habitats. Environmental conditions, such as temperature, precipitation, and streamflow, affect an aquatic organisms growth rate, developmental rate, behavior, and ultimate survival. In the Miramichi River, New Brunswick, Canada, climatic and hydrological conditions have been monitored for over 30 years, providing a unique opportunity to examine long-term changes in temperature, precipitation, and streamflow and their effects on juvenile Atlantic salmon. Average air temperature in the Miramichi region increased over time, particularly in spring and summer months. Coincident changes in water temperature were not observed, with only summer water temperature increasing in the Southwest Miramichi River. Extreme summer water temperatures in the Southwest Miramichi River regularly exceeded the upper temperature threshold for growth of Atlantic salmon (22.5°C). Few ...

Nonstationary frequency analysis of extreme daily precipitation amounts in Southeastern Canada using a peaks-over-threshold approach

In this paper, a statistical inference of Southeastern Canada extreme daily precipitation amounts is proposed using a classical nonstationary peaks-over-threshold model. Indeed, the generalized Pareto distribution (GPD) is fitted to excess time series derived from annual averages of independent precipitation amount events above a fixed threshold, the 99th percentile. Only the scale parameter of the fitted distribution is allowed to vary as a function of a covariate. This variability is modeled using B-spline function. Nonlinear correlation and cross-wavelet analysis allowed identifying two dominant climate indices as covariates in the study area, Arctic Oscillation (AO) and Pacific North American (PNA). The nonstationary frequency analysis showed that there is an east-west behavior of the AO index effects on extreme daily precipitation amounts in the study area. Indeed, the higher quantiles of these events are conditional to the AO positive phase in Atlantic Canada, while those in the more southeastern part of Canada, especially in Southern Quebec and Ontario, are negatively related to AO. The negative phase of PNA also gives the best significant correlation in these regions. Moreover, a regression analysis between AO (PNA) index and conditional quantiles provided slope values for the positive phase of the index on the one hand and the negative phase and on the other hand. This statistic allows computing a slope ratio which permits to sustain the nonlinear relation assumption between climate indices and precipitation and the development of the nonstationary GPD model for Southeastern Canada extremes precipitation modeling.

Study of hydrological phenomena by extreme value theory

We describe and give hydrological applications of a probabilistic model based on extreme value theory which can be used to study the values of a hydrologic process that exceed a certain threshold level QB.This model is useful in estimating extreme events XTof return period T based on N years of available hydrologic record. We also present easy-to-use tables which give confidence intervals for XT.The hydrologic applications reported are a flood frequency analysis, a methodology for estimating flood damage, an estimation of precipitation probabilities, and a prediction of extreme tide levels.

Comparison of the annual minimum flow and the deficit below threshold approaches: case study for the province of New Brunswick, Canada

A low-flow frequency analysis using the annual minimum flow (AMF) and the deficit below threshold (DBT) approaches was carried out for 30 hydrometric stations across the province of New Brunswick. The AMF method considers only the annual minimum events, and the DBT method considers all low flows below a certain threshold (or truncation level). In the present study, the DBT method characterizes low flow in terms of deficit intensity, which is becoming increasingly important in both water and aquatic resources management. The annual minimum series were fitted using the three-parameter Weibull distribution (AMF–WEI3), and the intensity data series were fitted using the two-parameter Weibull distribution (DBT–WEI2) and the generalized Pareto distribution (DBT–GP). All parameter estimates were obtained using the maximum likelihood method. The AMF–WEI3 and DBT–GP approaches provided a good fit to at-site data in terms of annual minimum flow and deficit intensity, respectively. However, the present study showed ...

Flood analysis and flood projections under climate change in New Brunswick

Floods events are a key component in river engineering including the design and risk assessment of various projects. In this study, a flood frequency analysis was carried out to determine flood characteristics in New Brunswick under present and future climate. For current flood characteristics, an analysis was carried out using 56 hydrometric stations across the province using the Generalized Extreme Value (GEV) distribution and the three-parameter lognormal distribution function. A regional flood frequency analysis was also carried out using regression equations. Results showed that current regional flood equations were very consistent among distribution functions. Results were also consistent with previous studies. To study floods under climate change, seven catchments were selected within the province and these catchments were further analyzed using artificial neural network (ANN) models for two climate scenarios. As such, future climate data were extracted from the third-generation Coupled Climate Model (CGCM3.1) under the greenhouse gas emission scenarios B1 and A2. The climate variables (temperature and precipitation) were downscaled using the delta change approach, and future river discharges were predicted. A frequency analysis was then carried out on these seven stations using the GEV distribution function. Results showed that for the period 2010–2100, average temperatures are projected to increase between 2.9°C (B1) and 5.2°C (A2) in New Brunswick. As for precipitation, the mean annual precipitation showed an increase of 9 to 12% compared to current conditions. Results also showed an increase in flood flows. The increase in low-return floods (e.g. 2-year) was generally higher than the increase of higher return floods (e.g. 100-year). Depending on the scenario and the future time period, the increase in low-return floods was about 30%, and about 15% for higher return floods. A Regional Climate Index (RCI) was used to links floods to their frequency under future climate scenarios.

Représentation stochastique des dommages dans les plaines inondables

The development of river basins subject to inundation requires a proper method of estimating possible damage resulting from a flood with a given return period.In the present study, a distribution function is developed, with the capability of providing an acceptable estimation of possible damage which may be incurred during a known flood.Based on the theory of extreme values in stochastic processes, a distribution function for the damage suffered is derived, assuming that the values of the extremes of exceedances are independant and identically distributed in the time interval (0, t] for a given year and season.