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

Abstract

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.

Nocturnal river water temperatures: Spatial and temporal variations

“…This is consistent with Webb et al (2003) who found that linear regression models for Tw had greater explanatory power when discharge was below the median. On average, the lag interval between dTa and dTw is of the order 3 hours but depends on thermal inertia and the velocity of flow through the drainage network.…”

“…An alternate to the energy balance approach is to infer Tw from air temperature (Ta) (Erikson and Stefan 2000;Stefan and Preud'homme, 1993;Webb et al 2003). Both Ta and Tw are forced by solar radiation and weather conditions so Ta is widely used as a surrogate, (recognising that Ta is not always a useful predictor -for instance when local Ta is strongly influenced by groundwater or artificial heat from urban surfaces and reservoirs).…”

“…When Ta is the primary predictor of Tw in statistical models, then the coefficients can be assumed to reflect local environmental and catchment controls. For example, previous studies have shown that regression parameters are related to catchment area (Ozaki et al 2003;Webb et al 2003), extent of upstream riparian shade and groundwater inputs (Johnson et al 2013;Toone et al, 2011), baseflow index, percent forest cover and stream order (Chang and Psaris, 2013).…”

“…Unfortunately, comprehensive measurement of the energy fluxes and hydromorphological processes governing heat dynamics is seldom undertaken, particularly at the spatial and temporal scales needed to understand (sub-)daily Tw over extended periods Webb and Zhang, 1997). An alternate to the energy balance approach is to infer Tw from air temperature (Ta) (Erikson and Stefan 2000;Stefan and Preud'homme, 1993;Webb et al 2003). Both Ta and Tw are forced by solar radiation and weather conditions so Ta is widely used as a surrogate, (recognising that Ta is not always a useful predictor -for instance when local Ta is strongly influenced by groundwater or artificial heat from urban surfaces and reservoirs).…”

“…When Ta is the primary predictor of Tw in statistical models, then the coefficients can be assumed to reflect local environmental and catchment controls. For example, previous studies have shown that regression parameters are related to catchment area (Ozaki et al 2003;Webb et al 2003), extent of upstream riparian shade and groundwater inputs (Johnson et al 2013;Toone et al, 2011), baseflow index, percent forest cover and stream order (Chang and Psaris, 2013).The purpose of this paper is to undertake the first comprehensive assessment of spatial and temporal variations in nTw and then show how a conventional model of dTw can be extended to nTw. We also seek to establish the extent to which riparian shade reduces both dTw and nTw, and the circumstances under which this management option might be less effective.…”