A Model for Evaluating Stream Temperature Response to Climate Change Scenarios in Wisconsin

Westenbroek, Stephen M.; Stewart, Jana S.; Buchwald, Cheryl A.; Mitro, Matthew G.; Lyons, John D.; Greb, Steven R.

doi:10.1061/41143(394)1

Cited by 7 publications

(11 citation statements)

References 9 publications

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“…This water temperature metric is an ecologically meaningful representation of the summer thermal maximum in rivers in the north-central United States (Lyons et al, 2009). The water temperature model was developed from an artificialneural-net statistical analysis of continuously recorded summer water temperatures from 304 sites on Wisconsin streams and rivers during the period 1990-2008 Stewart et al, 2006;Westenbroek et al, 2011). Inputs to the model included segment-specific information on climate (air temperature, precipitation) and catchment topography, geology, soils, and land cover.…”

Section: Methodsmentioning

confidence: 99%

Predicted effects of future climate warming on thermal habitat suitability for Lake Sturgeon (Acipenser fulvescens, Rafinesque, 1817) in rivers in Wisconsin, USA

Lyons

Stewart

2014

J. Appl. Ichthyol.

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SummaryThe Lake Sturgeon (Acipenser fulvescens, Rafinesque, 1817) may be threatened by future climate warming. The purpose of this study was to identify river reaches in Wisconsin, USA, where they might be vulnerable to warming water temperatures. In Wisconsin, A. fulvescens is known from 2291 km of large-river habitat that has been fragmented into 48 discrete river-lake networks isolated by impassable dams. Although the exact temperature tolerances are uncertain, water temperatures above 28-30°C are potentially less suitable for this coolwater species. Predictions from 13 downscaled global climate models were input to a lotic water temperature model to estimate amounts of potential thermally less-suitable habitat at present and for 2046-2065. Currently, 341 km (14.9%) of the known habitat are estimated to regularly exceed 28°C for an entire day, but only 6 km (0.3%) to exceed 30°C. In 2046-2065, 685-2164 km (29.9-94.5%) are projected to exceed 28°C and 33-1056 km (1.4-46.1%) to exceed 30°C. Most river-lake networks have cooler segments, large tributaries, or lakes that might provide temporary escape from potentially less suitable temperatures, but 12 short networks in the Lower Fox and Middle Wisconsin rivers totaling 93.6 km are projected to have no potential thermal refugia. One possible adaptation to climate change could be to provide fish passage or translocation so that riverine Lake Sturgeon might have access to more thermally suitable habitats.

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Section: Methodsmentioning

confidence: 99%

Predicted effects of future climate warming on thermal habitat suitability for Lake Sturgeon (Acipenser fulvescens, Rafinesque, 1817) in rivers in Wisconsin, USA

Lyons

Stewart

2014

J. Appl. Ichthyol.

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“…Arscott et al, 2001;Mayer, 2012;Imholt et al, 2013) or linear mixed modelling (Macedo et al, 2013), but also more advanced techniques such as geographically weighted regression (Pratt and Chang, 2012;Chang and Psaris, 2013), networked kriging models (Gardner and Sullivan, 2004;Isaak et al, 2010;Ruesch et al, 2012) or machine learning techniques (e.g. Westenbroek et al, 2010;Hill et al, 2013;DeWeber and Wagner, 2014).…”

Section: Investigation Of a New Modelling Approachmentioning

confidence: 99%

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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“…Our approach is strongly inspired by the physically based models which have been used for decades to predict water temperature along stream reaches (e.g. Brown, 1969;Sinokrot and Stefan, 1993;Westhoff et al, 2007). However, it differs from these models in the sense that we seek a much simpler expression for stream temperature, expressed as a function of variables which are readily available at the regional scale.…”

Section: Investigation Of a New Modelling Approachmentioning

confidence: 99%

“…Assuming a well-mixed water column and a negligible longitudinal heat dispersion, the mass and energy-balance equations along a stream reach read (adapted from Westhoff et al, 2007)…”

Section: Derivation Of the Analytical Solution To The Energy-balance Equationmentioning

confidence: 99%

“…This fostered the development of many stochastic and deterministic models (e.g. Mohseni et al, 1998;Segura et al, 2014;Chang and Psaris, 2013;DeWeber and Wagner, 2014;Meier et al, 2003;Westhoff et al, 2007). The former type relies on a statistical analysis to empirically relate stream temperature to climatic and physiographic variables, such as air temperature, discharge, altitude or channel width (see Benyahya et al, 2007, for a complete review of this subject).…”

Section: Introductionmentioning

confidence: 99%

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Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physically-based analytical model

Gallice¹,

Schaefli²,

Lehning³

et al. 2015

Preprint

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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 interannual variation of stream temperature. This study presents a new model to estimate the monthly mean stream temperature of ungauged rivers over multiple years in an Alpine country (Switzerland). Contrary to the models developed to date, which mostly rely upon statistical regression to express stream temperature as a function of physiographic and climatic variables, this one rests upon the analytical solution to a simplified version of the energy-balance equation over an entire stream network. This physically-based approach presents some advantages: (1) the functional form linking stream temperature to the predictor variables is directly obtained from first principles, (2) the spatial extent over which the predictor variables are averaged naturally arises during model development, and (3) 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 root mean square error 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 basis of the model can be used to gain more insight into the stream temperature dynamics at regional scales.

show abstract

A Model for Evaluating Stream Temperature Response to Climate Change Scenarios in Wisconsin

Cited by 7 publications

References 9 publications

Predicted effects of future climate warming on thermal habitat suitability for Lake Sturgeon (Acipenser fulvescens, Rafinesque, 1817) in rivers in Wisconsin, USA

Predicted effects of future climate warming on thermal habitat suitability for Lake Sturgeon (Acipenser fulvescens, Rafinesque, 1817) in rivers in Wisconsin, USA

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 physically-based analytical model

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