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

Benyahya, Loubna; St‐Hilaire, André; Ouarda, Taha B. M. J.; Bobée, Bernard; Dumas, Jacques

doi:10.1623/hysj.53.3.640

Cited by 39 publications

(27 citation statements)

References 51 publications

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“…For water resource and fisheries managers, it may be important to include other variables that may have an impact on the thermal regime of rivers, such as air temperature and flow. For this reason, the PAR method was extended by Benyahya et al (2007b) to incorporate other input variables (e.g., air temperature, streamflow) called exogenous variables and therefore the PAR model become a PARX model. A PARX model representing the water temperature series may be written in the following form…”

Section: Periodic Autoregressive Modelsmentioning

confidence: 99%

A Review of Statistical Water Temperature Models

Benyahya¹,

Caissie²,

St‐Hilaire³

et al. 2007

Canadian Water Resources Journal

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197

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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Section: Periodic Autoregressive Modelsmentioning

confidence: 99%

A Review of Statistical Water Temperature Models

Benyahya¹,

Caissie²,

St‐Hilaire³

et al. 2007

Canadian Water Resources Journal

Self Cite

197

180

View full text Add to dashboard Cite

show abstract

“…air temperature, solar radiation, conduction from soil, etc.) and anthropogenic disturbances of the natural thermal regime such as deforestation and hydroelectric development (Gras, 1969;Beschta et al, 1987;Benyahya et al, 2008). In recent years a number of studies have identified global warming as another major influence of water temperature (Sinokrot et al, 1995;Webb, 1996;Mohseni et al, 1998Pedersen and Sand-Jensen, 2007).…”

Section: Introductionmentioning

confidence: 99%

The Influence of Air Temperature on Water Temperature and the Concentration of Dissolved Oxygen in Newfoundland Rivers

Harvey

Lye

Khan

et al. 2011

Canadian Water Resources Journal

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Abstract:In this paper regression models are developed for predicting water temperature and the concentration of dissolved oxygen in rivers monitored by the Newfoundland and Labrador Real-Time Water Quality Monitoring (RTWQM) network. The developed models can be used to predict mean, maximum and minimum water temperatures and dissolved oxygen at the monthly, weekly and daily time scales. A nonlinear logistic model is found to best represent the S-shaped relationship between water temperature at the real-time stations and air temperature collected from meteorological stations 5-50 kilometers away. There is a clear tendency for monthly and weekly models to be more accurate for prediction than the daily models. Both linear and nonlinear exponential decay models were found to best represent the relationship between water temperature and dissolved oxygen at the real-time stations. A novel graphical method of linking air temperature to water temperature and dissolved oxygen has been developed and may prove to be a valuable simple tool in the assessment of the health of the rivers in the real-time network.Résumé : Ce papier décrit des modèles de régression qui prédisent la température de l'eau et la concentration d'oxygène dissous dans les rivières surveillées par le réseau Terre-Neuve-et-Labrador Real-Time Water Quality. On peut utiliser les modèles développés pour prédire les températures d'eau moyennes, maximales et minimales et les niveaux d'oxygène dissous dans l'eau aux échelles mensuelles, hebdomadaires et quotidiennes. Un modèle non-linéaire logistique représente mieux la relation entre la température de l'eau aux stations en temps réel et la température de l'air provenant des stations météorologiques de 5 à 50 kilomètres de distance. Il y a une tendance claire pour les modèles mensuels et hebdomadaires d'être plus précis pour la prédiction que les modèles quotidiens. Les modèles linéaires et non-linéaires de décroissance exponentielle représentent mieux la relation entre la température de l'eau et l'oxygène dissous aux stations en temps réel. Une nouvelle méthode graphique de relier la température de l'air à la température de l'eau et l'oxygène dissous a été développée; cette méthode devrait se révéler un outil précieux et simple pour évaluer la santé des rivières dans le réseau en temps réel.

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“…Daily air temperature and prior 7-day air temperature were important predictors in other stream temperature models [DeWeber and Wagner, 2014;Jackson et al, 2018]. This 7-day lag may result from a combination of factors including water flow from upstream, conductive heating from the ground which is slow to transfer heat and averages temperatures over extended periods, and through delayed surface and shallow ground-water inputs [Benyahya et al, 2008;Webb et al, 2010].…”

Section: Meteorological and Landscape Datamentioning

confidence: 98%

A hierarchical model of daily stream temperature for regional predictions

Hocking

O'Neil

Letcher

2018

Preprint

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Key Points:• Flexible approach to modeling daily stream temperature across broad space • Hierarchical modeling approach allows for inclusion of short observed stream temperature time series to fine-tune estimates to local conditions • Air temperature effects on stream temperature were moderated by precipitation and drainage area AbstractStream temperature is an important exogenous factor influencing populations of stream organisms such as fish, amphibians, and invertebrates. Many states regulate stream protections based on temperature. Therefore, stream temperature models are important, particularly for estimating thermal regimes in unsampled space and time. To help meet this need, we developed a hierarchical model of daily stream temperature and applied it across the eastern United States. Our model accommodates many of the key challenges associated with daily stream temperature models including the lagged response of water temperature to changes in air temperature, incomplete and widely-varying observed time series, spatial and temporal autocorrelation, and the inclusion of predictors other than air temperature. We used 248,517 daily stream temperature records from 1,352 streams to fit our model and 100,909 records were withheld for model validation. Our model had a root mean squared error of 0.61 C for the fitted data and 2.03 C for the validation data, indicating excellent fit and good predictive power for understanding regional temperature patterns. We then used our model to predict daily stream temperatures from 1980 -2015 for all streams <200 km 2 from Maine to Virginia. From these, we calculated derived stream metrics including mean July temperature, mean summer temperature, and the thermal sensitivity of each stream reach to changes in air temperature. Although generally water temperature follows similar latitudinal and altitudinal patterns as air temperature, there are considerable differences at the reach scale based on landscape and land-use factors.

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

Cited by 39 publications

References 51 publications

A Review of Statistical Water Temperature Models

A Review of Statistical Water Temperature Models

The Influence of Air Temperature on Water Temperature and the Concentration of Dissolved Oxygen in Newfoundland Rivers

A hierarchical model of daily stream temperature for regional predictions

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