Stream temperature‐equilibrium temperature relationship

Abstract

[1] Equilibrium temperature is the water temperature at which the sum of all heat fluxes through the water surface is zero. It can be calculated from weather data. Mean weekly stream temperature was found to be linearly related to mean weekly equilibrium temperature above 0°C. The slopes and intercepts of the linear relationship were used to identify effects of shading, sheltering, cold water inputs (groundwater, meltwater, and deep reservoir releases) and warm water inputs (wastewater, cooling water, and lake surface water). The linearity hypothesis was confirmed for data from 596 U.S. Geological Survey stream gaging stations in the eastern and central United States. For approximately 15% (89 of 596, NSC ! 0.90) of stream gaging stations, weekly equilibrium temperature was a good estimator of weekly stream temperature with zero wind sheltering and sun shading. With sheltering and shading the number rose to 26% (156 of 596). For these streams the heat exchange through the water surface has the most controlling effect on stream temperatures. For the remaining 74% of streams the relationship between weekly stream temperature and weekly equilibrium temperature was also linear, but its slope was significantly less than 1.0 (even after calibration for shading and sheltering), indicating that processes other than surface heat exchange have an influence. Cold water inflows were dominant for 8% (46 of 596) of stream gaging sites. Less than 5% of all stream gaging stations showed evidence of unnatural heat inputs. Fitting an equilibrium temperature linearly to recorded stream temperatures, e.g., at a weekly timescale, can be of use to project stream temperatures under different weather-climate scenarios or to identify both natural and anthropogenic heat and hydrologic inputs to streams.

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

“…4) as the result of (1) equilibrium temperature effects (Bogan et al, 2003) and (2) the compounding effects of urbanization (Anderson et al, 2007;Nelson and Palmer, 2007;Herb et al, 2008). Groundwater temperatures that we measured in an observation well in the region average 10.6 ° C with minimal oscillation (<0.1 ° C).…”

“…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).…”

“…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). Groundwater forcing in the form of baseflow plays a moderating role in thermal oscillations within streams and streambeds, especially in small headwater streams where baseflow may be a large component of total stream discharge (Bogan et al, 2003).…”

The footprint of Asian monsoon dynamics in the mass and energy balance of a Tibetan glacier

“…Mölg and Scherer, 2012), instead of abruptly by one air temperature threshold; (iv) a second stability correction for stable conditions is available so the turbulence damping factor can be calculated from either equation 11 or 12 in Braithwaite (1995); (v) the energy flux from precipitation may be included in the SEB (standard equation; e.g. Bogan et al, 2003); and (vi) a second parameterization of L ↓ from air temperature, water vapor pressure and cloud cover (Klok and Oerlemans, 2002) provides an alternative to the original formulation (Mölg et al, 2009b).…”

Development of a GIS-based Model for River Water Temperature Interpolation

“…This is problematic as no universal model exists, which fits perfectly to any study area. Therefore, the model has to be customized according to the study area of this project (BOGAN et al 2003). The main and final result is the implementation of a GIS-based water temperature interpolation model, and a semi-automated workflow is processed in ArcGIS using Python Scripts.…”