Development of an Air–Water Temperature Relationship Model to Predict Climate-Induced Future Water Temperature in Estuaries

Cho, Hong-Yeon; Lee, Khil-Ha

doi:10.1061/(asce)ee.1943-7870.0000499

Cited by 27 publications

(22 citation statements)

References 2 publications

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Section: Introductionmentioning

confidence: 90%

“…In particular, we assume that air temperature is a proxy for the integrated effect of the relevant processes and fluxes (Livingstone and Padisá k 2007) and that can be used as the unique input variable of the model. A similar assumption has been introduced also in studies concerning other environments, like rivers, estuaries, and bays (Morrill et al 2005;Cho and Lee 2012).We apply a simple lumped model (Piccolroaz et al 2013) that has been developed to estimate water temperature in the well-mixed surface layer (approximately corresponding to the epilimnion) as a function of air temperature only to several different lakes. The model is based on physical considerations, but it is elaborated in the simple form of an ordinary differential equation that contains a small number of parameters (from four to eight in different versions).…”

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confidence: 99%

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Prediction of surface temperature in lakes with different morphology using air temperature

Toffolon

Piccolroaz

Majone

et al. 2014

Limnology & Oceanography

119

137

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Temperature of the surface layer of temperate lakes is reconstructed by means of a simplified model on the basis of air temperature alone. The comparison between calculated and observed data shows a remarkable agreement (Nash-Sutcliffe efficiency indices always larger than 0.87, mean absolute errors of approximately 1uC) for all 14 lakes investigated (Mara, Sparkling, Superior, Michigan, Huron, Erie, Ontario, Biel, Zurich, Constance, Garda, Neusiedl, Balaton, and Baikal, in west-to-east order), which present a wide range of morphological and hydrological characteristics. Differently from a pure heat flux balance approach, where the different fluxes are determined on the basis of independent relationships, the input data directly inform parameters of a simple model that, in turn, provides meaningful information about the properties of the real system. The dependence of the model parameters on the main morphological indicators is presented, which allows for a quantitative description of the strong influence of the mean depth of the lake on the thermal inertia and the hysteresis pattern between air and lake surface temperatures.The temperature of the surface layer is a crucial factor for the hydrodynamics and ecology of lakes. Water temperature changes may have important direct and indirect ecological effects via their influence on the lifehistory processes of organisms (metabolism, growth, reproduction) and the properties of the habitats (Winder and Sommer 2012). Temperature variations affect foodweb structures and the availability of nutrients for the biological systems in a lake. The effects of temperature on physical and chemical characteristics of lakes may also modify the distribution of individual taxa from the microbiological to the top predator scale (Eggermont and Heiri 2012;Wojtal-Frankiewicz 2012;De Senerpont Domis et al. 2013).Surface temperature is the result of heat fluxes at the lake surface (short-wave solar radiation, long-wave radiation from and to the lake, sensible and latent heat exchange) and at the boundaries (inflows and outflows, groundwater exchange, precipitation, etc.), and of heat transport due to mixing within the lake. All these fluxes depend on several variables (solar radiation, air temperature, wind speed and direction, cloudiness, relative humidity, etc.), some of which may be difficult or expensive to measure reliably and with sufficient precision. Moreover, some of the variables can change significantly over the lake surface and in time (e.g., wind), and reconstructing their spatial variation can be a particularly hard task. Predicting water temperature is nonetheless a desired goal and models of different types and of different complexity have been proposed, ranging from simple regression models (Livingstone and Lotter 1998;Sharma et al. 2008) to more complex process-based numerical one-dimensional Perroud et al. 2009;Thiery et al. 2014) and three-dimensional models (Wahl and Peeters 2014).In this work, we aim at obtaining results that are accurate enough to reliably p...

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Section: Introductionmentioning

confidence: 90%

mentioning

confidence: 99%

Prediction of surface temperature in lakes with different morphology using air temperature

Toffolon

Piccolroaz

Majone

et al. 2014

Limnology & Oceanography

119

137

View full text Add to dashboard Cite

show abstract

“…In this instance, most species are primarily sensitive to changes in water temperature. Given a relation developed between air and water temperature (as in the harmonic analysis by Cho and Lee, 2012), it is possible to estimate water temperature from the available datasets. However, hurricane circulation can only be approximated by the GCMs (Tapiador, 2008), and a hurricane climatology can only be represented by dynamic downscaling techniques (Emanuel and others, 2008).…”

Section: The Climate Sensitivities Of Southeast Us Species and Ecosmentioning

confidence: 99%

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

Wootten¹,

Smith²,

Boyles³

et al. 2014

Open-File Report

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“…The data-driven approach has been introduced and applied to solve coastal marine environment problems (Cho and Lee, 2011;Bazi et al, 2012;Kim et al, 2014), such as regional coastal water quality (Mahajan et al, 1999;Jin et al, 2003;Chau, 2006;Palani et al, 2008;Singh et al, 2009) and marine ecosystem modeling (Arhonditsis et al, 2002;Cerco et al, 2010;Tian et al, 2011;Kolovoyiannis and Tsirtsis, 2013) using statistics, data mining, or machine learning. The most important issue in data-driven modeling is the construction of a probabilistic model composed of logical arguments or mathematical equations that represent or sufficiently approximate the true generating mechanism of a given phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Bayesian structural equation modeling for coastal management: The case of the Saemangeum coast of Korea for water quality improvements

Kim

Park

2017

Ocean & Coastal Management

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Development of an Air–Water Temperature Relationship Model to Predict Climate-Induced Future Water Temperature in Estuaries

Cited by 27 publications

References 2 publications

Prediction of surface temperature in lakes with different morphology using air temperature

Prediction of surface temperature in lakes with different morphology using air temperature

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

Bayesian structural equation modeling for coastal management: The case of the Saemangeum coast of Korea for water quality improvements

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