Abstracts
Résumé
Nous proposons onze nouvelles variables pour classifier, caractériser et analyser les facteurs de variabilité spatiale des régimes hydrologiques des affluents du fleuve Saint-Laurent au Québec. Ces variables se rapportent exclusivement aux débits mensuels et utilisent quatre (volume d’écoulement, période d’occurrence, durée et amplitude de variabilité intra-annuelle des débits) des cinq critères proposés par Richter et al. (1996) pour caractériser écologiquement les régimes hydrologiques.
L’analyse en composantes principales de ces onze variables hydrologiques a permis d’extraire trois composantes principales significatives après rotation d’axes par la méthode varimax. La première composante principale est associée aux débits saisonniers hivernaux et aux mois d’occurrence des débits mensuels maximums et minimums. La seconde composante est associée aux débits saisonniers printaniers et au rapport entre ces débits et les débits estivaux. Enfin, la dernière composante est associée au coefficient d’immodération (rapport entre les débits mensuels maximums et minimums) et aux débits mensuels minimums. La variance totale expliquée par ces trois composantes, à part presqu’égale, est d’environ 83%. Sur la base des signes de notes factorielles sur les trois composantes principales, les 72 rivières analysées ont été groupées en huit régimes hydrologiques naturels non contigus dans l’espace. Les caractéristiques de chaque régime hydrologique ont été clairement définies.
Quant aux facteurs environnementaux qui influencent la variabilité spatiale des régimes hydrologiques, il est apparu que les six variables hydrologiques associées aux trois composantes principales sont principalement influencées par la température de l’air ainsi que la superficie couverte par les forêts, les lacs et les marais.
Mots-clés:
- Régimes hydrologiques naturels,
- caractéristiques des débits,
- débits saisonniers et mensuels,
- Facteurs environnementaux,
- analyse en composantes principales,
- analyse de corrélation,
- Saint-Laurent,
- Québec
Abstract
Several classifications of hydrologic regimes have already been proposed in Quebec. However, these classifications are exclusively based upon the magnitude of discharge (annual and monthly discharge, annual maximum and minimum discharge). This hydrologic parameter isn’t sufficient to describe the ecological hydrologic regime. Thus, Richter et al. (1996) suggested five fundamental characteristics to describe hydrologic regimes that regulate ecological processes in river ecosystems :
1. The magnitude of the water condition at any given time. It is a measure of the availability or suitability of a habitat. It defines such habitat attributes as wetted area or habitat volume, or the position of the water table relative to wetland or riparian plant rooting zones.
2. The timing of occurrence of particular water conditions can determine whether certain life-cycle requirements can influence the degree of stress or mortality associated with extreme water conditions such as flood or droughts.
3. The frequency of occurrence of specific water conditions such as droughts or floods may be tied to reproduction or mortality events for various species, thereby influencing population dynamics.
4. The duration of time over which a particular life-cycle phase can be completed or the degree to which stressful effects such as inundation or drought can accumulate.
5. The rate of change (range) in water conditions may be related to the stranding of certain organisms along the water’s edge, in ponded depressions, or the ability of plant roots to maintain contact with phreatic water supplies.
The application of these characteristics requires a daily discharge time series, but these data are not always available. To overcome this difficulty, we propose eleven new hydrological variables exclusively based upon monthly discharge data. These new variables describe four (magnitude, timing of occurrence, duration of time and the rate of change) of the five characteristics of hydrologic regimes suggested by Richter et al. (1996). The eleven new variables are as follows: seasonal discharge coefficients (%); monthly maximum and minimum discharge coefficients (%); median Julian day of occurrence of maximum monthly discharge; median Julian day of occurrence of monthly minimum discharge; spring and winter seasonal discharge ratios; spring and summer seasonal discharge ratios and monthly maximum and minimum discharge ratios.
We have isolated, using principal component analysis (PCA), three significant principal components after varimax rotation. The first principal component was linked with the magnitude of winter discharge and the timing of monthly maximum and minimum discharge. The second principal component was associated with the magnitude of spring seasonal discharge and the spring and summer seasonal discharge ratio. The third component was linked with the coefficient of immoderation (monthly maximum/minimum discharge ratio) and the magnitude of monthly minimum discharge. The three principal components explain, almost weight for weight, about 83% of the total variance. On the basis of signs of loadings for these three components, 72 rivers were analysed and grouped into eight natural hydrologic regimes that are not geographically contiguous. For example, the first hydrologic regime was characterized by high winter discharge (> 12%), timing of monthly maximum discharge in April, high summer discharge (> 54%), high spring and summer seasonal discharge ratios (> 3.5), high monthly maximum and minimum discharge (> 12) and low monthly minimum discharge (< 3%).
We have also analysed the relation between the six hydrological variables associated with the three principal components and the thirteen environmental factors that can influence the spatial variability of these hydrological variables. The environmental factors were as follows: the drainage area (km2); the length of rivers (km); the mean basin slope (%); the forest surface area (%); the swamp and lake surface area (%); the annual precipitation; the seasonal winter (October to March) precipitation (mm); the number of rainy days in the winter; the mean annual temperature (ºC); the mean winter seasonal temperature (ºC); the mean summer seasonal temperature (ºC) and the number of winter days with temperature > 0ºC. The correlation analysis revealed the following mean results:
- The winter seasonal discharge was influenced by the forest surface area (negative correlation) and both annual and seasonal temperature (positive correlation).
- The timing of the monthly maximum discharge was influenced by the length of rivers (positive correlation), the forest and lake surface area (positive correlation) and both annual and seasonal temperatures (negative correlation).
- The spring seasonal discharge was influenced by the length of rivers (negative correlation), the mean basin slope (positive correlation), the forest surface area (positive correlation), the lake surface area (negative correlation), the annual precipitation (negative correlation) and the winter and summer seasonal temperature (negative correlation).
- The spring and summer seasonal discharge ratio was negatively correlated with the drainage basin, the length of rivers, the mean basin drainage, the annual precipitation and the number of winter days with temperature > 0ºC, but was positively correlated with annual and seasonal temperature.
- The monthly maximum and minimum discharge was positively correlated with forest surface area but negatively correlated with lake surface area, annual and seasonal temperature.
- The monthly minimum discharge was negatively correlated with forest surface area but positively correlated with annual and seasonal discharge.
From this correlation analysis, it appeared that temperature was the only factor that influenced the spatial variability of all hydrological variables, followed by forest and lake surface area. The influence of precipitation on this spatial variability was very weak.
Keywords:
- Natural hydrologic regimes,
- characteristics of discharge,
- seasonal and monthly discharge,
- environmental factors,
- principal components analysis,
- correlation analysis,
- Quebec
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