Stream Temperature Surges Under Urbanization and Climate Change: Data, Models, and Responses¹

Abstract

Multiple anthropogenic stressors, including increased watershed imperviousness, destruction of the riparian vegetation, increased siltation, and changes in climate, will impact streams over the coming century. These stressors will alter water temperature, thus influencing ecological processes and stream biota. Quantitative tools are needed to predict the magnitude and direction of altered thermal regimes. Here, empirical relationships were derived to complement a simple model of in‐stream temperature [developed by Caissie et al. Canadian Journal of Civil Engineering25 (1998) 250; Journal of Hydrology251 (2001) 14], including seasonal temperature shifts linked to land use, and temperature surges linked to localized rainstorms; surges in temperature averaged about 3.5°C and dissipated over about 3 h. These temperature surges occurred frequently at the most urbanized sites (up to 10% of summer days) and could briefly increase maximum temperature by >7°C. The combination of empirical relationships and model show that headwater streams may be more pervasively impacted by urbanization than by climate change, although the two stressors reinforce each other. A profound community shift, from common cold and coolwater species to some of the many warmwater species currently present in smaller numbers, may be expected, as shown by a count of days on which temperature exceeds the “good growth” range for coldwater species.

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).…”

“…Storm runoff drains directly into the stream from parking lots, buildings, and roads with the aid of more than 70 drainage pipes. Rapid runoff during summer storms transfers the heat stored in the urban infrastructure directly to the stream in minutes (Nelson and Palmer, 2007). Three summers of stream temperature monitoring of Boone Creek reveals up to 40 instances per summer in which temperatures rise more than 1 °C within 15 min (Anderson et al, 2007).…”

Changes in river water temperature between 1980 and 2012 in Yongan watershed, eastern China: Magnitude, drivers and models

“…2). The higher population densities further imply generation of a considerable quantity of domestic and industry sewage discharge to river segment HG, which is another heat source contributing to warming of river water in urban areas (Kinouchi, 2007;Nelson and Palmer, 2007;Kaushal et al, 2010;Xin and Kinouchi, 2013;Lepori et al, 2014). This premise is supported by the higher water warming rate in the HG segment than for air temperature during the winter (Fig.…”

Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model

“…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 …”

“…Thus, in the case of stream temperature modelling in ungauged catchments, some studies consider only physiographic characteristics as predictor variables (e.g. Scott et al, 2002;Jones et al, 2006;Nelson and Palmer, 2007;Hrachowitz et al, 2010), while others also include climatic variables (e.g. Isaak et al, 2010;Ruesch et al, 2012;Moore et al, 2013), stream morphological factors such as channel width or bed gravel size (e.g.…”