Stream temperatures in an Alpine area

Johnson, F.A.

doi:10.1016/0022-1694(71)90042-4

Cited by 53 publications

(42 citation statements)

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“…Similarly, both Pratt and Chang (2012) and Hill et al (2013) aimed at estimating mean stream temperature in summer and winter. Very few studies have actually attempted Bogan et al (2003) Eastern USA AE 596 30 Week R 2 = 0.80, σ e = 3.1 • C Chang and Psaris (2013) Western USA MLR, GWR 74 n/a Week, year R 2 = 0.52-0.62, σ e = 2.0-2.3 • C Daigle et al (2010) Western Canada Various 16 0.5 Month σ e = 0.9-2.8 • C DeWeber and Wagner (2014) Eastern USA ANN 1080 31 Day σ e = 1.8-1.9 • C Ducharne (2008) France MLR 88 7 Month R 2 = 0.88-0.96, σ e = 1.4-1.9 Gardner and Sullivan (2004) Eastern USA NKM 72 1 Day σ e = 1.4 • C Garner et al (2014) UK CA 88 18 Month n/a Hawkins et al (1997) Western USA MLR 45 ≥ 1 Year R 2 = 0.45-0.64 Hill et al (2013) Conterminous USA RF ∼ 1000 1/site Season, year σ e = 1.1-2.0 • C Hrachowitz et al (2010) UK MLR 25 1 Month, year R 2 = 0.50-0.84 Imholt et al (2013) UK MLR 23 2 Month R 2 = 0.63-0.87 Isaak et al (2010) Western USA MLR, NKM 518 14 Month, year R 2 = 0.50-0.61, σ e = 2.5-2.8 • C Isaak and Hubert (2001) Western USA PA 26 1/site Season R 2 = 0.82 Johnson (1971) New Zealand ULR 6 1 Month n/a Johnson et al (2014) UK NLR 36 1.5 Day R 2 = 0.67-0.90, σ e = 1.0-2.4 • C Jones et al (2006) Eastern USA MLR 28 3 Year R 2 = 0.57-0.73 Kelleher et al (2012) Eastern USA MLR 47 2 Day, week n/a Macedo et al (2013) Brazil LMM 12 1.5 Day R 2 = 0.86 Mayer (2012) Western USA MLR 104 ≥ 2 Week, month R 2 = 0.72, σ e = 1.8 • C Miyake and Takeuchi (1951) Japan ULR 20 n/a Month n/a Moore et al (2013) Western Canada MLR 418 1/site Year σ e = 2.1 • C Nelitz et al (2007) Western Canada CRT 104 1/site Year n/a Nelson and Palmer (2007) Western USA MLR 16 3 Season R 2 = 0.36-0.88 Ozaki et al (2003) Japan ULR 5 8 Day n/a Pratt and Chang (2012) Western USA MLR, GWR 51 1/site Season R 2 = 0.48-078 Risley et al (2003) Western USA ANN 148 0.25 Hour, season σ e = 1.6-1.8 • C Rivers- Moore et al (2012) South Africa MLR 90 1/site Month, year R 2 = 0.14-0.50 …”

Section: Few Models Can Predict the Stream Temperature Annual Cyclementioning

confidence: 99%

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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: Few Models Can Predict the Stream Temperature Annual Cyclementioning

confidence: 99%

“…The performance of their model was not tested using data from subsequent years, though. Johnson (1971) relied on another different technique to estimate the thermal regime of six rivers in New Zealand. He first fitted the stream temperature annual cycles with sine curves.…”

Section: A Gallice Et Al: Stream Temperature Prediction In Ungaugedmentioning

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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“…Climatological factors include air temperature, solar radiation, and precipitation (as both snow and rain); hydrological factors include discharge and channel form; and geological factors include base-flow contributions, such as ground-water seepage and channel form (Smith and Lavis, 1975;Webb and Nobilis, 1997). Physical land-surface characteristics of the basin, including forest cover, glacial influence, aspect, and elevation additionally affect stream temperatures (Johnson, 1971). The most significant environmental factors that influence stream temperature depend on time scale.…”

Section: Water-temperature Modelmentioning

confidence: 99%

“…Many different models of stream temperature have been developed, such as heat advection/dispersion transport models, models based on the concept of equilibrium temperature and processes of surface-heat transfer, seasonal and sinusoidal functions with respect to time, and linear regressions of air and stream temperature (Ward, 1963;Edinger and others, 1966;Brown, 1969;Johnson, 1971;Song and others, 1973;Crisp and Howson, 1982;Sinokrot and Stefan, 1993;Stefan and Preud'homme, 1993;Mohseni and others, 1998;Pilgrim and others, 1998).…”

Section: Water-temperature Modelmentioning

confidence: 99%

“…Three indirect factors affecting the change in stream temperature caused by global climate warming not considered by the water-temperature-regression model are (1) forest or vegetation cover change, (2) alternative sources of cooler or warmer water (i.e., natural and artificial reservoirs), and (3) precipitation. Forest cover affects stream temperatures by reducing the magnitude of extremes and reducing the seasonal variations in temperature (Johnson, 1971). If a basin is logged or the vegetation removed, studies have shown that significant increases in water temperature will occur (Brown and Krygier, 1970).…”

Section: Stream-temperature Projections and Implicationsmentioning

confidence: 99%

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Water temperature of streams in the Cook Inlet basin, Alaska, and implications of climate change

Kyle¹,

Brabets²

2001

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FOREWORDThe U.S. Geological Survey (USGS) is committed to serve the Nation with accurate and timely scientific information that helps enhance and protect the overall quality of life, and facilitates effective management of water, biological, energy, and mineral resources. Information on the quality of the Nation's water resources is of critical interest to the USGS because it is so integrally linked to the long-term availability of water that is clean and safe for drinking and recreation and that is suitable for industry, irrigation, and habitat for fish and wildlife. Escalating population growth and increasing demands for the multiple water uses make water availability, now measured in terms of quantity and quality, even more critical to the long-term sustainability of our communities and ecosystems.The USGS implemented the National WaterQuality Assessment (NAWQA) Program to support national, regional, and local information needs and decisions related to water-quality management and policy. Shaped by and coordinated with ongoing efforts of other Federal, State, and local agencies, the NAWQA Program is designed to answer: What is the condition of our Nation's streams and ground water? How are the conditions changing over time? How do natural features and human activities affect the quality of streams and ground water, and where are those effects most pronounced? By combining information on water chemistry, physical characteristics, stream habitat, and aquatic life, the NAWQA Program aims to provide science-based insights for current and emerging water issues. NAWQA results can contribute to informed decisions that result in practical and effective water-resource management and strategies that protect and restore water quality.Since 1991, the NAWQA Program has implemented interdisciplinary assessments in more than 50 of the Nation's most important river basins and aquifers, referred to as Study Units. Collectively, these Study Units account for more than 60 percent of the overall water use and population served by public water supply, and are representative of the Nation's major hydrologic landscapes, priority ecological resources, and agricultural, urban, and natural sources of contamination.Each assessment is guided by a nationally consistent study design and methods of sampling and analysis. The assessments thereby build local knowledge about water-quality issues and trends in a particular stream or aquifer while providing an understanding of how and why water quality varies regionally and nationally. The consistent, multi-scale approach helps to determine if certain types of water-quality issues are isolated or pervasive, and allows direct comparisons of how human activities and natural processes affect water quality and ecological health in the Nation's diverse geographic and environmental settings. Comprehensive assessments on pesticides, nutrients, volatile organic compounds, trace metals, and aquatic ecology are developed at the national scale through comparative analysis of the Study-Unit findings.The US...

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Water–air temperature relationships in a Devon river system and the role of flow

Webb¹,

Clack²,

Walling³

2003

Hydrological Processes

313

325

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Abstract:The nature of the water-air temperature relationship, and its moderation by discharge, were investigated for catchments ranging in size from 2Ð1 to 601 km 2 in the Exe basin, Devon, UK and for data relating to hourly, daily and weekly time bases. The sensitivity and explanatory power of simple water-air temperature regression models based on hourly data were improved by incorporation of a lag, which increased with catchment size, although relationships became more sensitive and less scattered as the time base of data increased from hourly to weekly mean values. Significant departures from linearity in water-air temperature relationships were evident for hourly, but not for daily mean or weekly mean, data. A clear tendency for relationships between water and air temperatures to be stronger and more sensitive for flows below median levels was apparent, and multiple regression analysis also revealed water temperature to be inversely related to discharge for all catchments and time-scales. However, discharge had a greater impact in accounting for water temperature variation at shorter time-scales and in larger catchments.

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Stream temperatures in an Alpine area

Cited by 53 publications

References 1 publication

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

Water temperature of streams in the Cook Inlet basin, Alaska, and implications of climate change

Water–air temperature relationships in a Devon river system and the role of flow

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