Abstract
AbstractIdentifying the main controlling factors of the stream temperature (Tw) variability is important to target streams sensitive to climate and other drivers of change. The thermal sensitivity (TS), based on relationship between air temperature (Ta) and Tw, of a given stream can be used for quantifying the streams sensitivity to future climate change. This study aims to compare TS for a wide range of temperate streams located within a large French catchment (110,000 km2) using 4 years of hourly data (2008–2012) and to cluster stations sharing similar thermal variabilities and thereby identify environmental key drivers that modify TS at the regional scale. Two successive classifications were carried out: (a) first based on Ta–Tw relationship metrics including TS and (b) second to establish a link between a selection of environmental variables and clusters of stations. Based on weekly Ta–Tw relationships, the first classification identified four thermal regimes with differing annual Tw in terms of magnitude and amplitudes in comparison with Ta. The second classification, based on classification and regression tree method, succeeded to link each thermal regime to different environmental controlling factors. Streams influenced by both groundwater inflows and shading are the most moderated with the lowest TS and an annual amplitude of Tw around half of the annual amplitude of Ta. Inversely, stations located on large streams with a high distance from source and not (or slightly) influenced by groundwater inflows nor shading showed the highest TS, and so, they are very climate sensitive. These findings have implications for guiding river basin managers and other stakeholders in implementing thermal moderation measures in the context of a warming climate and global change.
Highlights
Grenouillet, 2013; Jonsson & Jonsson, 2009; Webb, Hannah, Moore, Brown, & Nobilis, 2008)
To account for the relative sensitivity of Tw during extreme months, we introduced two metrics, which are the differences between the monthly Ta (MTa) and Tw in January (ΔTJan) and in August (ΔTAug) averaged between 2012 and 2016: i =PNy ðMTaJanðiÞ − MTwJanðiÞÞ
We proposed a framework to compare thermal sensitivity (TS) for 127 stations located on temperate streams between 2008 and 2012 and to cluster stations sharing similar natural thermal regimes, not influence by anthropogenic effects
Summary
Grenouillet, 2013; Jonsson & Jonsson, 2009; Webb, Hannah, Moore, Brown, & Nobilis, 2008). Some regional-scale studies have used spatial thermal regime classification based on a large set of catchment properties (Chu et al, 2009; Laizé et al, 2017; Maheu, Poff, & St-Hilaire, 2016; Rivers-Moore, Dallas, & Morris, 2013; Tague, Farrell, Grant, Lewis, & Rey, 2007) These studies succeeded in identifying key drivers that influence the thermal regime of streams at the regional scale. TS may evolve into the future due to the changing drivers considered above, TS computed for a specific period of record gives insight of which streams have the greatest sensitivity to climate based on contemporary conditions, which can be used as a baseline for responsiveness (Kelleher et al, 2012) This integrated variable cannot distinguish the cause and effect of groundwater and riparian vegetation shading on Tw variability (Chang & Psaris, 2013; Chu et al, 2009; O'Driscoll & DeWalle, 2006). The relative importance of environmental variables on the TS of streams is investigated, and the implication for river management and river restoration is discussed
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