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Deepti Joshi Andre St-Hilaire Taha B. M. J. Ouarda Anik Daigle Nathalie Thiemonge 《水文科学杂志》2013,58(11):1996-2010
ABSTRACTThis work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.
Editor D. Koutsoyiannis; Associate editor K. Hamed 相似文献
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AbstractRiver water temperature regimes are expected to change along with climate over the next decades. This work focuses on three important salmon rivers of eastern Canada, two of which warm up most summers to temperatures higher than the Atlantic salmon lethal limit (>28°C). Water temperature was monitored at 53 sites on the three basins during 2–18 summers, with about half of these sites either known or potential thermal refugia for salmon. Site-specific statistical models predicting water temperature, based on 10 different climate scenarios, were developed in order to assess how many of these sites will remain cool enough to serve as refugia in the future (2046–2065). The results indicate that, while 19 of the 23 identified refugia will persist, important increases in the occurrence and duration of temperature events in excess of 24°C and 28°C, respectively, in the mainstems of the rivers, will lead to higher demands for thermal refugia in the salmonid populations.
Editor Z.W. Kundzewicz; Associate editor T. Okruszko 相似文献
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André St‐Hilaire Taha B. M. J. Ouarda Zoubeida Bargaoui Anik Daigle Laurent Bilodeau 《水文研究》2012,26(9):1302-1310
Water temperature is a key abiotic variable that modulates both water chemistry and aquatic life in rivers and streams. For this reason, numerous water temperature models have been developed in recent years. In this paper, a k‐nearest neighbour model (KNN) is proposed and validated to simulate and eventually produce a one‐day forecast of mean water temperature on the Moisie River, a watercourse with an important salmon population in eastern Canada. Numerous KNN model configurations were compared by selecting different attributes and testing different weight combinations for neighbours. It was found that the best model uses attributes that include water temperature from the two previous days and an indicator of seasonality (day of the year) to select nearest neighbours. Three neighbours were used to calculate the estimated temperature, and the weighting combination that yielded the best results was an equal weight on all three nearest neighbours. This nonparametric model provided lower Root Mean Square Errors (RMSE = 1·57 °C), Higher Nash coefficient (NTD = 0·93) and lower Relative Bias (RB = ? 1·5%) than a nonlinear regression model (RMSE = 2·45 °C, NTD = 0·83, RB = ? 3%). The k‐nearest neighbour model appears to be a promising tool to simulate of forecast water temperature where long time series are available. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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The development of ecosystem-based indicators requires the broadening of a view of the community, from fish species to all the species (macrobenthic and fish) correctly captured by a given sampling gear. Many scientific surveys already have such integrated databases. The present note aims to demonstrate that existing databases, herein from dedicated coastal nursery surveys, are actually underexploited. Such databases contain information on non-commercial taxa, which could greatly improve our knowledge on the organisation and functioning of coastal ecosystems. Using two datasets, a “complete” dataset composed of commercial and not-commercial epibenthic trawled species (fish and invertebrate) and a “subset” dataset characterized by commercial and routinely surveyed species (mainly fish and cephalopods), different measures of functional diversity are compared to identify the functional gains of including epibenthic species. The results show that, when included in the analyses, epibenthic taxa provide gains of functional information, associated mainly with the community feeding traits, i.e. organisms composing the primary and secondary consumer levels of the coastal nursery food web. Failure to include some of the primary (zooplanktivores and suspension feeders) and secondary consumers (detritivores–scavengers) in coastal survey analyses may, for instance, hamper our understanding of energy flux between the benthic and water column compartments of these ecosystems. The results also suggest that the exclusion of some taxa associated with these two food web compartments, may lead to the underestimation of the functional redundancy in coastal ecosystems. 相似文献
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Anik Daigle Andr St-Hilaire Valrie Ouellet Julie Corriveau Taha B.M.J. Ouarda Laurent Bilodeau 《Journal of Hydrology》2009,370(1-4):29-38
A data-driven model is designed using artificial neural networks (ANN) to predict the average onset for the annual water temperature cycle of North-American streams. The data base is composed of daily water temperature time series recorded at 48 hydrometric stations in Québec (Canada) and northern US, as well as the geographic and physiographic variables extracted from the 48 associated drainage basins. The impact of individual and combined drainage area characteristics on the stream annual temperature cycle starting date is investigated by testing different combinations of input variables. The best model allows to predict the average temperature onset for a site, given its geographical coordinates and vegetation and lake coverage characteristics, with a root mean square error (RMSE) of 5.6 days. The best ANN model was compared favourably with parametric approaches. 相似文献
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Claire L. Raftery D. Shaun Bloomfield Peter T. Gallagher Daniel B. Seaton David Berghmans Anik De Groof 《Solar physics》2013,286(1):111-124
We calculated the temperature response of the 171 Å passbands of the Sun Watcher using APS detectors and image Processing (SWAP) instrument onboard the PRoject for OnBoard Autonomy 2 (PROBA2) satellite. These results were compared to the temperature responses of the Extreme Ultraviolet Imaging Telescope (EIT) onboard the Solar and Heliospheric Observatory (SOHO), the Transition Region and Coronal Explorer (TRACE), the twin Extreme Ultraviolet Imagers (EUVI) onboard the Solar TErrestrial RElations Observatory (STEREO) A and B spacecraft, and the Atmospheric Imaging Assembly (AIA) onboard the Solar Dynamics Observatory (SDO). Multiplying the wavelength-response functions for each instrument by a series of isothermal synthetic spectra and integrating over the range 165?–?195 Å produced temperature-response functions for the six instruments. Each temperature response was then multiplied by sample differential emission-measure functions for four different solar conditions. For any given plasma condition (e.g. quiet Sun, active region), it was found that the overall variation with temperature agreed remarkably well across the six instruments, although the wavelength responses for each instrument have some distinctly different features. Deviations were observed, however, when we compared the response of any one instrument to different solar conditions, particularly for the case of solar flares. 相似文献
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A methodology for planning an optimized river water temperature monitoring network is presented. The methodology is based on sampling of the physio-climatic variability of the region to be monitored. Physio-climatic metrics are selected to describe the study region, based on principal component analysis. The sites to be monitored are then identified based on a k-means clustering in the multidimensional space defined by the selected metrics. The methodology is validated on an existing dense water temperature network in Haute-Savoie, France. Different configurations of more or less dense network scenarios are evaluated by assessing their ability to estimate water temperature indices at ungauged locations. An optimized network containing 83 sites is found to provide satisfactory estimations for seven ecologically and biologically meaningful thermal indices defined to characterize brown trout thermal habitat. 相似文献
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