MATHEMATICAL GENERALIZATION OF STREAM TEMPERATURE IN CENTRAL NEW ENGLAND<sup>1</sup>

Tasker, Gary D.; Burns, Alan W.

doi:10.1111/j.1752-1688.1974.tb00633.x

Cited by 15 publications

(12 citation statements)

References 2 publications

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“…It should be noted that Equation (6) needs to be modified when the sinusoidal function is fitted to a period shorter than one year (e.g., Tasker and Burns, 1974).…”

Section: Autoregressive Modelsmentioning

confidence: 99%

A Review of Statistical Water Temperature Models

Benyahya¹,

Caissie²,

St‐Hilaire³

et al. 2007

Canadian Water Resources Journal

198

180

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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.

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“…It should be noted that Equation (6) needs to be modified when the sinusoidal function is fitted to a period shorter than one year (e.g., Tasker and Burns, 1974).…”

Section: Autoregressive Modelsmentioning

confidence: 99%

A Review of Statistical Water Temperature Models

Benyahya¹,

Caissie²,

St‐Hilaire³

et al. 2007

Canadian Water Resources Journal

198

180

View full text Add to dashboard Cite

show abstract

“…. On peut utiliser différentes techniques de lissage ; les séries de Fourier utilisées depuis le XIX e siècle (Plantamour, 1863) et reprises par de nombreux auteurs (Ward, 1963 ;Tasker, 1974 ;Salas, 1980 ;Steele, 1982) dans cet article, contrairement à ce qui a été fait pour l'Étude Globale Rhône.…”

Section: Analyse Des Régimes Thermiques Du Rhône Et De Ses Principauxunclassified

1977-2006 : Trente années de mesures des températures de l'eau dans le Bassin du Rhône

Poirel¹,

Lauters²,

Desaint³

2008

Hydroécol. Appl.

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Résumé -La température de l'eau du Rhône et de ses affluents est mesurée au pas de temps horaire depuis 1977 sur une quinzaine de stations. La constitution de ces chroniques a nécessité un effort constant de métrologie, de validation et de critique sur plus de trente années pour obtenir des données fiables. L'analyse de ces données permet de dé-finir le régime thermique du Rhône et de mettre en évidence d'importantes augmentations thermiques (de +1 à +2• C. sur les moyennes annuelles). Ces augmentations sont plus élevées sur le Rhône aval et sur ses affluents chauds. L'effet est plus marqué au printemps et en été, excepté sur les stations soumises à un régime hydrologique nivo-glaciaire. Mots clés -température de l'eau, série chronologique, RhôneAbstract -Water temperature of the Rhone and its tributaries is measured hourly since 1977 on about fifteen stations. The installation and maintenance of these stations to get reliable data required a constant effort in metrology and data validation over thirty years. The analysis of these time series data characterizes the thermal regime of the Rhone and highlight significant increases in water temperature (+1 to +2• C on annual means). These increases are higher downstream on the Rhone and some warmer tributaries. The effect is more pronounced in spring and summer, except on sites subject to a glacial or nival hydrological regime.

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“…Daily streamfiow data from U. S. Geological Survey gages were obtained for 1948 through 1993 for several major rivers that are tributary to the Upper Illinois Waterway. Table 1 been proposed (Cluis, 1972;Kothandaraman, 1974;Tasker and Burns, 1974;Smith, 1981;Krajewski et al, 1982;among others). Stefan and Preud'homme (1993) have shown that simple linear regression models can be constructed relating water temperature to air temperature:…”

Section: Riversmentioning

confidence: 99%

“…Many authors have used a sinusoidal model to represent the annual cycle of water temperatures (Cluis, 1972;Kothandaraman, 1974;Tasker and Burns, 1974; among others). The monthly linear regression river temperature model for the DuPage River was replaced with a sinusoidal model.…”

Section: Sensitivity Analysismentioning

confidence: 99%

ESTIMATION OF WATER TEMPERATURE EXCEEDANCE PROBABILITIES USING THERMO‐HYDRODYNAMIC MODELING¹

Bradley¹,

Holly²,

Walker³

et al. 1998

J American Water Resour Assoc

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A continuous simulation approach is proposed for estimating water temperature exceedance probabilities using thermohydrodynamic modeling. The approach uses (1) a deterministic unsteady flow and heat transport model, (2) continuous hydrological and meteorological data for a long historical period, and (3) synthetic records of tributary water temperatures and other model inputs. Representative historical records of streamfiow, air temperatures, and other hydrometeorological variables are obtained from nearby gages. Stochastic modeling methods are used to construct synthetic records for other model inputs, including inflow watertemperatures. An application of this deterministic-stochastic approach is presented for a complex waterway in northeastern fllinois with heat discharges from several power plants and wastewater treatment plants. Statistical results from the continuous simulations are compared to results obtained from traditional event simulations. The application illustrates the information that engineers and biologists can obtain for (1) evaluating compliance with water temperature standards, and (2) assessing the effect of water temperatures on aquatic habitat.

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MATHEMATICAL GENERALIZATION OF STREAM TEMPERATURE IN CENTRAL NEW ENGLAND¹

Cited by 15 publications

References 2 publications

A Review of Statistical Water Temperature Models

A Review of Statistical Water Temperature Models

1977-2006 : Trente années de mesures des températures de l'eau dans le Bassin du Rhône

ESTIMATION OF WATER TEMPERATURE EXCEEDANCE PROBABILITIES USING THERMO‐HYDRODYNAMIC MODELING¹

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MATHEMATICAL GENERALIZATION OF STREAM TEMPERATURE IN CENTRAL NEW ENGLAND1

Cited by 15 publications

References 2 publications

A Review of Statistical Water Temperature Models

A Review of Statistical Water Temperature Models

1977-2006 : Trente années de mesures des températures de l'eau dans le Bassin du Rhône

ESTIMATION OF WATER TEMPERATURE EXCEEDANCE PROBABILITIES USING THERMO‐HYDRODYNAMIC MODELING1

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MATHEMATICAL GENERALIZATION OF STREAM TEMPERATURE IN CENTRAL NEW ENGLAND¹

ESTIMATION OF WATER TEMPERATURE EXCEEDANCE PROBABILITIES USING THERMO‐HYDRODYNAMIC MODELING¹