Predicting river water temperatures using the equilibrium temperature concept with application on Miramichi River catchments (New Brunswick, Canada)

Caissie, Daniel; Satish, Mysore G.; El‐Jabi, Nassir

doi:10.1002/hyp.5684

Cited by 91 publications

(118 citation statements)

References 48 publications

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“…This effect seems to be largely underestimated in the literature, since the lateral inflow of water has often been neglected in previous stream temperature models (e.g. Edinger et al, 1968;Bogan et al, 2003;Caissie et al, 2005;Bustillo et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…Bogan et al, 2004;Herb and Stefan, 2011), but nonetheless fails to be accounted for in many stream temperature models (e.g. Caissie et al, 2005;Bustillo et al, 2014). We therefore wish to emphasize the role of lateral water inflow in stream temperature, even in catchments -such as those used in this study -which are not impacted by groundwater infiltration originating from a deep aquifer.…”

Section: Discussionmentioning

confidence: 99%

“…Edinger et al, 1968;Bogan et al, 2003;Caissie et al, 2005;Bustillo et al, 2014). While a minority of authors considered the temperature distribution to be spatially homogeneous (Edinger et al, 1968;Caissie et al, 2005;Bustillo et al, 2014), most of them assumed the stream to be in a steady state or, equivalently, the stream celerity to be constant. In addition, they all assumed the river width to remain constant along the stream so as to analytically solve the energy-balance equation.…”

Section: Physics-inspired Statistical Modelmentioning

confidence: 99%

See 2 more Smart Citations

Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model

Gallice

Schaefli

Lehning

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. The development of stream temperature regression models at regional scales has regained some popularity over the past years. These models are used to predict stream temperature in ungauged catchments to assess the impact of human activities or climate change on riverine fauna over large spatial areas. A comprehensive literature review presented in this study shows that the temperature metrics predicted by the majority of models correspond to yearly aggregates, such as the popular annual maximum weekly mean temperature (MWMT). As a consequence, current models are often unable to predict the annual cycle of stream temperature, nor can the majority of them forecast the inter-annual variation of stream temperature. This study presents a new statistical model to estimate the monthly mean stream temperature of ungauged rivers over multiple years in an Alpine country (Switzerland). Contrary to similar models developed to date, which are mostly based on standard regression approaches, this one attempts to incorporate physical aspects into its structure. It is based on the analytical solution to a simplified version of the energy-balance equation over an entire stream network. Some terms of this solution cannot be readily evaluated at the regional scale due to the lack of appropriate data, and are therefore approximated using classical statistical techniques. This physics-inspired approach presents some advantages: (1) the main model structure is directly obtained from first principles, (2) the spatial extent over which the predictor variables are averaged naturally arises during model development, and (3) most of the regression coefficients can be interpreted from a physical point of view -their values can therefore be constrained to remain within plausible bounds. The evaluation of the model over a new freely available data set shows that the monthly mean stream temperature curve can be reproduced with a rootmean-square error (RMSE) of ±1.3 • C, which is similar in precision to the predictions obtained with a multi-linear regression model. We illustrate through a simple example how the physical aspects contained in the model structure can be used to gain more insight into the stream temperature dynamics at regional scales.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Physics-inspired Statistical Modelmentioning

confidence: 99%

See 1 more Smart Citation

Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model

Gallice

Schaefli

Lehning

et al. 2015

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…Studies of atmospheric forcing effects on stream temperature primarily focus on equilibrium temperature (Mohseni and Stefan, 1999;Bogan et al, 2003;Caissie et al, 2005), with some studies solving directly the heat budget equation using a range of meteorological parameters to predict stream temperatures (Sinokrot and Stefan, 1993;Caissie et al, 2007). Stochastically-based empir-ical models in the literature analyze the cumulative effects of atmospheric forcing (Bogan et al, 2006) with most focusing on direct air-temperature/stream-temperature relationships (Caissie et al, 1998(Caissie et al, , 2001O'Driscoll and DeWalle, 2006).…”

Section: Introductionmentioning

confidence: 99%

Changes in stream temperatures in response to restoration of groundwater discharge and solar heating in a culverted, urban stream

Anderson

Thaxton

et al. 2010

Journal of Hydrology

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Changes in stream temperatures in response to restoration of groundwater discharge and solar heating in a culverted, urban stream A B S T R A C TBoone Creek is a mountainous headwater stream that lies within an urbanized environment in north-western North Carolina. The primary source of thermal pollution in Boone Creek is the urban infrastruc-ture, which affects stream temperatures through (1) heated runoff, which creates temperature surges and (2) the elimination of groundwater-surface water interactions. In this study, we use a Monte Carlo ther-mal mixing model to predict the thermal impact of removing a 700-m-long culvert. Our thermal mixing model balances stream discharge and temperatures with surface-heat exchange parameters and restored baseflow. We calculate the daily-average groundwater discharge velocity at 15 locations in the stream using signal decay in streambed temperatures, and utilize a Monte Carlo implementation of the hetero-geneous baseflow field in the thermal mixing model. We also calculate surface-heat exchange per unit area for conditions upstream and downstream of the existing culvert and utilize those values in the ther-mal mixing model. Our modeled temperatures suggest a decrease in summer stream temperatures down-stream of the culvert that average 1.35 ° C and 1.17 ° C for upstream and downstream surface-heat exchange conditions, respectively. The results of our study have implications for the balance between baseflow and the urban infrastructure in any high-gradient urban stream that experiences similar ther-mal effects.

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“…Examples of deterministic models include the US Fish and Wildlife SNTEMP model (Bartholow, 1989), the SSTEMP model (Bartholow, 1999) and the CEQUEAU hydrological and water temperature model (Morin & Couillard, 1990;St-Hilaire et al, 2003). Other references of simpler deterministic water temperature models include those of Gu et al (1998) and Caissie et al (2005), who used an equilibrium temperature concept.…”

Section: Introductionmentioning

confidence: 99%

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

Benyahya

St‐Hilaire

Ouarda

et al. 2008

Hydrological Sciences Journal

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Water temperature is an important abiotic variable in aquatic habitat studies and may be one of the factors limiting the potential fish habitat (e.g. salmonids) in a stream. Stream water temperatures are modelled using statistical approaches with air temperature and streamflow as exogenous variables in the Nivelle River, southern France. Two different models are used to model mean weekly maximum temperature data: a non-parametric approach, the k-nearest neighbours method (k-NN) and a parametric approach, the periodic autoregressive model with exogenous variables (PARX). The k-NN is a data-driven method, which consists of finding, at each point of interest, a small number of neighbours nearest to this value, and the prediction is estimated based on these neighbours. The PARX model is an extension of commonly-used autoregressive models in which parameters are estimated for each period within the years. Different variants of air temperature and flow are used in the model development. In order to test the performance of these models, a jack-knife technique is used, whereby model goodness of fit is assessed separately for each year. The results indicate that both models give good performances, but the PARX model should be preferred, because of its good estimation of the individual weekly temperatures and its ability to explicitly predict water temperature using exogenous variables.Key words stream water temperature; non-parametric vs parametric models; PARX, k-nearest neighbours Comparaison de modèles paramétriques et non-paramétriques de température de l'eau de la Rivière Nivelle, France Résumé La température de l'eau est une variable très importante pour les études d'habitat aquatique. Elle peut être un facteur limitant pour plusieurs espèces de poisson, telles que les salmonidés. Cet article présente une modélisation statistique de la température de l'eau en utilisant la température de l'air et le débit comme variables explicatives. Les données utilisées dans la modélisation numérique sont les températures hebdomadaires de la rivière Nivelle (France). Deux modèles de température de l'eau sont alors proposés et comparés, soit la méthode non-paramétrique des k plus proches voisins (VPP) et le modèle périodique autorégressif avec variables exogènes (PARX). La méthode des VPP consiste à chercher dans tout l'historique, les k plus proches voisins qui serviront à estimer la température actuelle. Le modèle PARX est un modèle autorégressif dont les paramètres de chaque variable explicative sont estimés indépendamment pour chaque période de l'année. Plusieurs attributs de température de l'eau et du débit sont considérés. La performance des modèles a été évaluée pour chaque année en utilisant une technique de validation croisée de type "jack-knife". Les résultats préliminaires ont montré que le modèle PARX et le modèle VPP présentent une performance similaire dans la simulation des températures hebdomadaires. Toutefois, le modèle PARX demeure le plus approprié, car il préserve la persistance des séries périodiques ...

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Predicting river water temperatures using the equilibrium temperature concept with application on Miramichi River catchments (New Brunswick, Canada)

Cited by 91 publications

References 48 publications

Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model

Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model

Changes in stream temperatures in response to restoration of groundwater discharge and solar heating in a culverted, urban stream

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

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