Prediction of water temperature metrics using spatial modelling in the Eastern and Western Cape, South Africa

Abstract

Key aspects of a river's temperature regime are described by magnitudes, timing and durations of thermal events, and frequencies of extreme exceedance events. To understand alterations to thermal regimes, it is necessary to describe thermal time series based on these statistics. Classification of sites based on their thermal metrics, and understanding of spatial patterns of these thermal statistics, provides a powerful approach for comparing study sites against reference sites. Water temperature regime dynamics should be viewed regionally, where regional divisions have an inherent underpinning by an understanding of natural thermal variability. The aim of this research was to link key water temperature metrics to readilymapped environmental surrogates, and to produce spatial images of temperature metrics: 37 temperature metrics were derived for 12 months of sub-daily water temperatures at 90 sites in the Eastern Cape and Western Cape provinces, South Africa. These metrics were correlated with 16 environmental variables. Correlations enabled development of multiple regression models which facilitated mapping of temperature metrics over the study area. This approach has the potential to be applied at a national scale as more thermal time series are collected nationally. It is argued that the appropriateness of management decisions in rivers can be improved by including guidelines for thermal metrics at a regional scale. Such maps could facilitate incorporation of a temperature component into management guidelines for water resources.

Variation in thermal tolerances of native freshwater fishes in South Africa's Cape Fold Ecoregion: examining the east–west gradient in species' sensitivity to climate warming

“…Using 1 year of hourly water temperature data for each site, annual descriptive statistics (temperature metrics) were calculated using the indicators of thermal alteration (ITA) approach (Rivers‐Moore et al ., , ). The data for computing these metrics were recorded using Hobo UTB1‐001 TidBit V2 loggers (Onset Computer Corporation; http://www.onset.com) installed at the collection sites for at least 1 year.…”

“…Annual temperature metrics also included mean annual temperature, SD of mean annual temperature, mean of daily range, mean of annual minima, mean of annual maxima and degree days [ ° d, calculated by subtracting the threshold temperature (10°C in this case) from the daily mean temperature with the total degree days calculated by summing the differences for the year; Dallas et al ., ]. The number of degree days indicates the probability of stress to aquatic organisms; a greater number of degree days reflects greater levels of stress (Rivers‐Moore et al ., , ).…”

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 …”

“…Hrachowitz et al (2010), Imholt et al (2013) and Rivers-Moore et al (2012) expressed water temperature as a linear combination of climatic and physiographic variables for each month of the year separately. Their models were derived for a particular year, but can be transferred to other years by estimating stream temperature at a few measurement points using Mohseni's logistic equation and fitting the multi-linear regression model to the resulting values (Hrachowitz et al, 2010).…”

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

“…Both model types have usually been applied to a single stream reach or a limited number of catchments (e.g. Sinokrot and Stefan, 1993;Roth et al, 2010;Caissie et al, 2001;Caldwell et al, 2013;Grbić et al, 2013). As a response to the lack of stream temperature data, some studies have recently attempted to develop regionalized models.…”