Aurélien Beaufort
email: aurelien.beaufort@irstea.fr

Florentina Moatar

Eric Sauquet

Pierre Loicq

David M Hannah

Géhco Géo-Hydrosystèmes

Aurélien

Influence of landscape and hydrological factors on stream-air temperature relationships at regional scale

Keywords: CART method, classification, regional scale, stream temperature, thermal sensitivity

Identifying 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 km 2 ) using 4 years of hourly data (2008)(2009)(2010)(2011)(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.

 et al., 2015). The rise of Ta is expected resulting in warmer stream Tw [START_REF] Garner | River temperature regimes of England and Wales: Spatial patterns, inter-annual variability and climatic sensitivity[END_REF][START_REF] Hannah | River water temperature in the United Kingdom: Changes over the 20th century and possible changes over the 21st century[END_REF][START_REF] Mohseni | Stream temperature/air temperature relationship: A physical interpretation[END_REF][START_REF] Van Vliet | Global river discharge and water temperature under climate change[END_REF], which could be exacerbated by the reducing of summer stream flows especially in temperate climate [START_REF] Isaak | Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a mountain river network[END_REF][START_REF] Van Vliet | Global river discharge and water temperature under climate change[END_REF]. In France, several studies have already highlighted an increase in Tw across various rivers [START_REF] Bustillo | A multimodel comparison for assessing water temperatures under changing climate conditions via the equilibrium temperature concept: Case study of the Middle Loire River, France[END_REF]Jackson, Fryer, Hannah, & Malcolm, 2017;[START_REF] Jackson | A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change[END_REF][START_REF] Jackson | Development of spatial regression models for predicting summer river temperatures from landscape characteristics: Implications for land and fisheries management[END_REF][START_REF] Moatar | Water temperature behaviour in the River Loire since 1976 and 1881[END_REF]. There is therefore a growing interest in understanding the spatio-temporal variability of river thermal regime given the likely effects of climate change (increase of both the Ta and the evapotranspiration, shift in river flow regimes and of groundwater inflows, etc.; [START_REF] Webb | Recent advances in stream and river temperature research[END_REF][START_REF] Moatar | La Loire à l'épreuve du changement climatique[END_REF]. The goal is to develop opportunities for mitigation and adaptive management of river systems [START_REF] Boisneau | Does global warming impact on migration patterns and recruitment of Allis shad (Alosa alosa L.) young of the year in the Loire River, France?[END_REF]Hrachowitz, Soulsby, Imholt, Malcolm, & Tetzlaff, 2010;[START_REF] Jackson | A novel approach for designing large-scale river temperature monitoring networks[END_REF][START_REF] Kurylyk | Preserving, augmenting, and creating cold-water thermal refugia in rivers: Concepts derived from research on the Miramichi River, New Brunswick (Canada)[END_REF].

The Tw variability, described by metrics of flow magnitude, frequency, duration, timing, and rate of change, on various timescales [START_REF] Jones | Thermal regime metrics and quantifying their uncertainty for North American streams[END_REF], is influenced by complex processes related to atmospheric, hydrogeological, geomorphic, and landscape characteristics and anthropogenic pressures, which could interact at multiple spatial scales [START_REF] Caissie | The thermal regime of rivers: A review[END_REF][START_REF] Hannah | River water temperature in the United Kingdom: Changes over the 20th century and possible changes over the 21st century[END_REF]. Numerous studies have highlighted the importance of riparian forest and groundwater inflows in moderating Tw variability [START_REF] Dugdale | Stream temperature under contrasting riparian forest cover: Understanding thermal dynamics and heat exchange processes[END_REF][START_REF] Garner | The role of riparian vegetation density, channel orientation and water velocity in determining river temperature dynamics[END_REF][START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF][START_REF] Lalot | Quantification of the contribution of the Beauce groundwater aquifer to the discharge of the Loire River using thermal infrared satellite imaging[END_REF][START_REF] Loicq | Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data[END_REF]. Identifying the main controlling factors of Tw variability remains an important task to target streams sensitive to climate change and to develop mitigation action to preserve aquatic ecosystems [START_REF] Jackson | A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change[END_REF].

Classification and regression tree (CART) of hydrological and landscape-dependent variables are informative and revealing methods to answer explore patterns without introducing an a priori structure of the link between explanatory variables and metrics describing Tw variability [START_REF] Arismendi | Descriptors of natural thermal regimes in streams and their responsiveness to change in the Pacific Northwest of North America[END_REF][START_REF] Casado | Influence of dam-induced hydrological regulation on summer water temperature: Sauce Grande River, Argentina[END_REF][START_REF] Chu | Linking the thermal regimes of streams in the Great Lakes Basin, Ontario, to landscape and climate variables[END_REF]. Some studies, in various parts of the world, considered explicitly and empirically the role of a limited number of basin properties on Tw (e.g., [START_REF] Garner | River temperature regimes of England and Wales: Spatial patterns, inter-annual variability and climatic sensitivity[END_REF]Hrachowitz et al., 2010;[START_REF] Jackson | A novel approach for designing large-scale river temperature monitoring networks[END_REF]Faye L. Jackson, Fryer, et al., 2017;Faye L. Jackson et al., 2018;Jackson, Hannah, et al., 2017 in the UK; [START_REF] Isaak | A hypothesis about factors that affect maximum summer stream temperatures across montane landscapes[END_REF][START_REF] Isaak | Effects of climate change and wildfire on stream temperatures and salmonid thermal habitat in a mountain river network[END_REF]Nelitz, MacIsaac, & Peterman, 2007, in North America).

Analyses in most of these studies are carried out on a site-by-site basis, which limits the extent to which broad patterns can be inferred [START_REF] Laizé | Climate and basin drivers of seasonal river water temperature dynamics[END_REF]). Some regional-scale studies have used spatial thermal regime classification based on a large set of catchment properties [START_REF] Chu | Linking the thermal regimes of streams in the Great Lakes Basin, Ontario, to landscape and climate variables[END_REF][START_REF] Laizé | Climate and basin drivers of seasonal river water temperature dynamics[END_REF][START_REF] Maheu | A classification of stream water temperature regimes in the conterminous USA: Classification of stream temperature regimes[END_REF][START_REF] Rivers-Moore | Towards setting environmental water temperature guidelines: A South African example[END_REF][START_REF] Tague | Hydrogeologic controls on summer stream temperatures in the McKenzie River basin, Oregon[END_REF]. These studies succeeded in identifying key drivers that influence the thermal regime of streams at the regional scale. Most of these studies use on metrics summarizing the warmest aspects of the Tw regime to examine the threats to cold-water species under climate change.

Several researchers analyse the relationship between Tw and Ta, with Ta taken as a surrogate of the main climatic drivers [START_REF] Ducharne | Importance of stream temperature to climate change impact on water quality[END_REF][START_REF] Garner | River temperature regimes of England and Wales: Spatial patterns, inter-annual variability and climatic sensitivity[END_REF]. Ta is a common variable, easily measured on the field, and it is strongly correlated to solar radiation [START_REF] Bustillo | A multimodel comparison for assessing water temperatures under changing climate conditions via the equilibrium temperature concept: Case study of the Middle Loire River, France[END_REF]. [START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF] studied the thermal sensitivity (TS) of streams to represent the relative sensitivity of Tw of a given stream to environmental change. TS is defined as the slope of the regression line between Ta and Tw, which can be linear (or logistic) and can be fitted on data averaged at different timescales [START_REF] Mohseni | Stream temperature/air temperature relationship: A physical interpretation[END_REF][START_REF] O'driscoll | Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA[END_REF][START_REF] Stefan | Stream temperature estimation from air temperature[END_REF]. TS summarizes the cumulative buffering effects of local landscape characteristics on stream temperatures. Although 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 [START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF]. However, this integrated variable cannot distinguish the cause and effect of groundwater and riparian vegetation shading on Tw variability [START_REF] Chang | Local landscape predictors of maximum stream temperature and thermal sensitivity in the Columbia River Basin, USA[END_REF][START_REF] Chu | Linking the thermal regimes of streams in the Great Lakes Basin, Ontario, to landscape and climate variables[END_REF][START_REF] O'driscoll | Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA[END_REF]. Understanding the importance of these driving factors is essential to develop appropriate strategies to mitigate and adapt to stream heating under anticipated climate warming.

The aim of our study is (a) to provide a comparison of thermal sensitivity (TS) across a wide range of French temperate streams, based on 4 years of hourly data (2008)(2009)(2010)(2011)(2012), and (b) to identify groups of streams with similar sensitivity and so infer the environmental key factors that control TS at the regional scale. For that purpose, two successive classifications of 127 stations located in the Loire catchment [START_REF] Beaufort | River temperature modelling by Strahler order at the regional scale in the Loire River basin, France[END_REF] 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 thermal regimes of stations. Finally, the relative importance of environmental variables on the TS of streams is investigated, and the implication for river management and river restoration is discussed.

| MATERIAL AND METHODS

| Sites and temperature data

| Basin description

The Loire basin (Figure 1) comprises a hydrographical network of 88,000 km and drains a catchment area of 117,000 km 2 . It is characterized by varying climates between the upstream and the downstream (annual rainfall between 550 and 2,100 mm/year and annual air temperature between 6 C and 12.5 C), landform (10% of the basin area >800 m; mean altitude = 300 m), and lithology (metamorphic, magmatic, and sedimentary rocks). The percentage of riparian vegetation, defined on a buffer zone of 10 m on both sides of the streams, is globally greater in the southern basin where the altitude is the highest (mean ratio of the riparian vegetation = 75%; dark green; Figure 1c). Streams located in the central part of the basin, mainly composed of sedimentary rocks, benefit more from groundwater contributions (Figure 1d). The main aquifers are found in the sedimentary rocks in the centre of the basin. The Beauce formations (12,700 km 2 ) are composed of many semipermeable aquifers [START_REF] Mohseni | Stream temperature/air temperature relationship: A physical interpretation[END_REF] with numerous groundwater inflows located at the north of the Loire basin. Some streams are very directly connected to this aquifer, and their flow depends on the level of the Beauce water table [START_REF] Baratelli | Estimation of stream-aquifer exchanges at regional scale using a distributed model: Sensitivity to instream water level fluctuations, riverbed elevation and roughness[END_REF].

| Field monitoring

Tw was monitored, hourly, at 127 stations managed by the French Agency for Biodiversity (http://www.naiades.eaufrance.fr), between July 2008 and December 2012, distributed across the Loire basin (Figure 1b). The monitoring stations are mainly located on streams with a Strahler order between 3 and 5 (78% of stations). All monitoring stations are located on streams with low direct human influence on the flow regime, and all time series of Tw have been scrutinized to discard streams influenced by dam operations. The mean annual water temperatures of these stations range from 7.5 C to 15.7 C. The highest mean annual temperatures were observed on large rivers such as the Loire (Strahler Order 8) and its main tributaries, where mean annual Tw ranged between 14 C and15.7 C between 2008 and2012 (Figure 1a;[START_REF] Beaufort | River temperature modelling by Strahler order at the regional scale in the Loire River basin, France[END_REF]. Colder temperatures (<9 C) were observed in the upstream reaches of the Loire River where the altitude is above 1,000 m. The annual Tw at stations located on small streams (51 stations <30 km from upstream sources) did not exceed 13 C (Figure 1b). values in time series). To limit biases in the calculation of indicators for each station, hydrological years with more than 15% of missing daily values (threshold based on previous studies [START_REF] Beaufort | River temperature modelling by Strahler order at the regional scale in the Loire River basin, France[END_REF]Aurélien Beaufort, Lamouroux, Pella, Datry, & Sauquet, 2018)) or with missing values during August or January are excluded from the analysis. The length of available records for the 127 stations time series varies between 1 (56 stations), 2 (29 stations), 3 (27 stations), and 4 years (15 stations).

The hourly Ta was taken from the SAFRAN (Système d'analyse fournissant des renseignements atmosphériques à la neige) reanalysis data (grid 8 km) at hourly time step between 2008 to 2012 [START_REF] Quintana-Seguí | Analysis of near-surface atmospheric variables: Validation of the SAFRAN analysis over France[END_REF][START_REF] Vidal | A 50-year high-resolution atmospheric reanalysis over France with the Safran system[END_REF]. Ta is extracted from the SAFRAN mesh (64 km 2 ) overlapping the station. The mean annual Ta of these stations ranges from 6.4 C to 12.5 C. The coldest temperatures are observed in the mountainous part of the basin (mean annual Ta < 10 C), whereas the warmest temperatures are observed in the west and in the sedimentary plain (mean annual Ta > 11 C).

Both hourly Ta and Tw have been averaged over the day and over the week in the next section.

| Metrics of air-water temperature relationship

We used four metrics to characterize the relation between air and water temperature. (a) Two of these metrics, the thermal sensitivity (TS) and intercept (b), provide information on the link between weekly Tw and Ta over the year. Weekly linear regressions were selected on the basis of the best mean R 2 fitted for the 127 stations in comparison with daily or logistic regressions.

For each station, a linear regression is fitted between the weekly Tw (Tw7D) and the weekly Ta (Ta7D) and the distribution of slopes, hereafter called thermal sensitivity (TS), and intercept (b) were analysed (Equation 1; [START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF][START_REF] O'driscoll | Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA[END_REF].

Tw7D = Ta7D × TS + b: ð1Þ
(b) Two others metrics, ΔT Jan and ΔT Aug , are based on the seasonal difference between monthly Tw and Ta. For all stations, the monthly Tw (MTw) is the coldest in January and the warmest in August. 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 (ΔT Jan ) and in August (ΔT Aug ) averaged between 2012 and 2016:

ΔT Jan = P i = Ny i = 1 MTa Jan i ð Þ-MTw Jan i ð Þ ð Þ Ny , ð2Þ
ΔT Aug = P i = Ny i = 1 MTa Aug i ð Þ-MTw Aug i ð Þ ð Þ Ny , ð3Þ
where ΔT is the mean difference between monthly Ta (MTa) and Tw (MTw) calculated in January or August, MTa Jan (i) and MTa Aug (i) are respectively the monthly Ta in January and August of the year i, MTw Jan (i) and MTw Aug (i) are respectively the monthly Tw in January and August of the year i, and Ny is the number of year where monthly Ta and Tw are both available (1 ≤ Ny ≤ 4, see Section 2.1).

| Explanatory variables

A set of eight explanatory variables was selected to explain the should cool streams [START_REF] Isaak | The NorWeST summer stream temperature model and scenarios for the western U.S.: A crowdsourced database and new geospatial tools foster a user community and predict broad climate warming of rivers and streams[END_REF].

One variable was determined to characterize the riparian vegetation. A shading factor (SF), corresponding to a coefficient of reduction of the overall incident radiation, was estimated by [START_REF] Valette | SYRAH-CE: description des données et modélisation du risque d'altération de l'hydromorphologie des cours d'eau pour l'Etat des lieux DCE[END_REF]. SF gives the averaged vegetation cover (%) derived from a buffer of 10 m of vegetation polygons on both sides of reaches from the BD TOPO ® database, provided by Institut national de l'information géographique et forestière (IGN). SF has been calculated between 7 a.m. and 9 p.m. over the summer period between June 1 and September 30 over the period 2008-2012, when the effect of shading is at its annual seasonal maximum for the North Hemisphere. The model of [START_REF] Li | Modeled riparian stream shading: Agreement with field measurements and sensitivity to riparian conditions[END_REF] was implemented in its simplest version, that is, considering rectangular trees, located at the edge of the bank, without overhang.

SF = H × cotΨ × sinδ W × vc, ð4Þ
where H is the tree height (assumed to be 20 m everywhere), W is the stream width, estimated using the ESTIMKART empirical model (Lamouroux et al., 2010), Ψ is the solar elevation angle, δ is the angle between solar azimuth and the mean azimuth (0-180 ) of the river reach, and vc is the vegetation cover (%).

| Statistical classification and explanatory variables

| Thermal regimes clustering

To identify natural thermal regimes of stations sharing similar Ta-Tw relationship, an agglomerative hierarchical clustering (AHC) has been used. The AHC is based on the four metrics described above (TS, b, ΔT Jan , and ΔT Aug ). The Euclidean distance is used to measure the dissimilarity, and clusters are found with the Ward's minimum variance method. The stability of clusters is assessed through a bootstrap approach with the R package "fpc" [START_REF] Hennig | Thermal regimes in a large upland salmon river: A simple model to identify the influence of landscape controls and climate change on maximum temperatures[END_REF], and the similarity between each new cluster set and initial cluster was assessed with the Jaccard index [START_REF] Hennig | Cluster-wise assessment of cluster stability[END_REF][START_REF] Maheu | A classification of stream water temperature regimes in the conterminous USA: Classification of stream temperature regimes[END_REF]. The Jaccard coefficient ranges from 0 to 1, and a cluster with a coefficient larger than 0.75 can be considered as stable [START_REF] Maheu | A classification of stream water temperature regimes in the conterminous USA: Classification of stream temperature regimes[END_REF]. Each thermal regime identified is described in terms of magnitude (mean Tw over a month) and amplitude (differences between the maximum and minimum values of MTw) and compared with MTa.

| Identification of environmental drivers in thermal sensitivity

A CART is used to examine the relationship between TS and the set of explanatory variables described above. CART analysis [START_REF] Breiman | Classification and regression trees[END_REF]) is nonparametric and non-linear and does not introduce an a priori structure of the link between explanatory variables and the variable to be explained contrary to generalized linear models implicit assumption [START_REF] Breiman | Classification and regression trees[END_REF][START_REF] Ripley | Pattern recognition and neural networks[END_REF].

CART recursively partitions observations in a matched data set, consisting of TS (response) and the eight explanatory variables, into progressively smaller groups [START_REF] De'ath | Classification and regression trees: A powerful yet simple technique for ecological data analysis[END_REF]. Each partition is a binary split. During each recursion, splits for each explanatory variable are examined, and the split that leads to the most homogeneous subgroups with respect to the dependent variable is chosen. The interpretation of results summarized in a tree with series of logical ifthen conditions (tree nodes) is very simple. We used the R package which is constituted by selecting randomly 80% of the observations (80% of 127 stations × eight explanatory variables × TS), and the test set consists of the remaining 20%. We used the implementation in the R package "randomForest" [START_REF] Liaw | Classification and Regression by randomForest[END_REF]. The explanatory variable importance is given directly by the "randomForest" algorithm, which determines how much the mean square errors in prediction increases when that covariate is randomly permuted within the tree.

The random selection is performed 100 times, and the explanatory variables importance for each test set was then averaged.

| RESULTS

| Distribution of thermal sensitivity and link with catchment size

The R 2 values for weekly Ta and Tw ranged from.83 to.98, with values greater than.9 at 123 of the 127 sites. TS ranges from 0.42 (Figure 2b) to 1.2 (Figure 2a), and b ranges from 0.5 C to 7.5 C. Regression lines plotted for the 127 stations showed a higher range of values of Tw when Ta is high at the regional scale (Tw ranges between 15 C and 30 C when Ta is 25 C) than when Ta is low (Tw ranges between 0 C and 7.5 C when Ta is 0 C; Figure 2c). tance from source (Figure 3b). Streams with a distance from source higher than 100 km 2 obtain a TS higher than 0.7. For small and medium rivers (D < 100 km), the range of TS is large and between 0.42 and 1.

The analysis of the spatial distribution of TS in the Loire basin

shows that the stations obtaining the smallest TS (TS < 0.5) are located in the sedimentary plain where the main aquifer formations are located (Figure 4). The stations with the highest TS (TS > 0.9) are located along large rivers in the sedimentary plain and in the western side of the basin where the altitudes are the lowest and Ta the highest. Finally, stations located in the regions with the highest altitude obtain a moderate TS lower than 0.7.

| Cluster classification analysis

The AHC yielded four clusters of station corresponding to four thermal regimes:

• WarmHighVar-warm and high variability (47 sites-37%): stations characterized by low b (<3 C) and high TS (>0.8). At these stations, MTw is higher than MTa in January and August with a median difference of 1.5 C (Table 1). These stations are those with the (Figure 5c).

The thermal regimes named "WarmHighVar" and "ColdLowVar"

were stable clusters and had a Jaccard coefficient larger than 0.7.

The thermal regimes called "WarmLowVar" and "ColdHighVar"

were less stable clusters and had Jaccard coefficients of 0.

| Drivers of thermal sensitivity (TS)

The CART model output leads to develop dichotomic tree plots to better visualize the effects of main drivers (Figure 7). The three most important explanatory variables used by the model to cluster stations as a function of their TS are SF, D (distance from source), and BFI (Figure 6). This is consistent with the RF model output where D, SF, and BFI are identified as the main environmental variables to explain the TS of streams (variable importance >15%; Figure 6). The variables • C1-low TS with high SF and high BFI: The combined effect of a high SF (SF > 30%) and a high BFI (BFI > 0.8) led to strongly reduce TS of streams (mean TS of 0.5; Figure 7). The 11 stations having these characteristics belong to the thermal regime ColdLowVar (Table 2).

Q
• C2 and C3-low and moderate TS with high SF: Streams with an SF higher than 30% and a BFI less than 0.8 belong mostly to the thermal regime ColdHighVar from AHC results (Figure 7). Q Aug has also an important influence, and we can see contrasts in terms of TS within this class. The TS was lower for the 17 stations located on streams with a Q Aug value higher than 5 L s -1 km -2 (mean TS = 0.67; C2) than for the 29 remaining stations in C3 with a Q Aug value less than 5 L s -1 km -2 (mean TS = 0.76).

• C4-moderate TS with low SF, low D, and high BFI: The six stations with SF less than 30%, a D less than 120 km, and a BFI greater than 0.8 have a moderate TS (mean TS of 0.71, C4) and belong to the two thermal regimes WarmLowVar and ColdLowVar (Table 2).

• C5 and C6-moderate and high TS with low SF, low D, and low BFI:

Stations located on small and medium streams (S < 120 km) with a BFI lower than 0.8 obtained moderate and high TS. The TS of the 13 stations located on streams with a higher slope (S > 2.5 m km -1 ) 2).

F I G U R E 5
• C7-high TS with low SF, low D, and high BFI: Stations located on streams with low SF (SF < 30%) and a high D (D > 120 km) have the highest TS (mean TS of 1; C7; Figure 7). The 23 stations having these characteristics belong to the thermal regime WarmHighVar (Table 2).

| DISCUSSION

| Regression robustness and comparison with other studies

In our case study, best correlations between Ta and Tw were obtained [START_REF] Mohseni | A nonlinear regression model for weekly stream temperatures[END_REF][START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF], which makes Ta-Tw relationship more non-linear for low values. In comparison with the studies using weekly Ta-Tw linear regressions, the R 2 values calculated on the 127 stations are on average higher (mean R 2 = 0.96) and comparable with the results of Webb (1992) and O' Driscoll and DeWalle (2006). The negative correlation between TS and b is also consistent with previous studies. Streams controlled by groundwater inflows are characterized by intercepts closer to the regional groundwater temperature and low slopes. Inversely, streams more sensitive to climate conditions have steeper slopes and lower intercepts closest to Ta.

Our TS and b range were consistent with other studies results for linear regression models using a weekly time scale (Table 3). These TS and b values were close to those found by Webb (1992), Stefan and[START_REF] Stefan | Stream temperature estimation from air temperature[END_REF][START_REF] Morrill | Estimating stream temperature from air temperature: Implications for future water quality[END_REF] except that we observe no negative b and the range of our TS and b is slightly higher (Figure 8). This can be explained by a higher number of streams used in our study and by the larger size of the watershed compared with other studies (Table 3)

| Groundwater influence on TS

In theory, groundwater influence is more visible on smaller streams because the volume of water is small and the travel time of the water from the source is short and not sufficient to equilibrate Tw with the atmosphere [START_REF] Beaufort | River temperature modelling by Strahler order at the regional scale in the Loire River basin, France[END_REF][START_REF] Mohseni | Stream temperature/air temperature relationship: A physical interpretation[END_REF]. Groundwater inflow is a heat source during winter and a heat sink during summer resulting in little seasonal variation in Tw and a low TS [START_REF] Hannah | Heat exchanges and temperatures within a salmon spawning stream in the Cairngorms, Scotland: Seasonal and sub-seasonal dynamics[END_REF][START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF].

Stations with a thermal regime ColdLowVar have low TS (median TS = 0.5) and a high intercept (median b = 5.7 C). In other studies, low TS could be due to the upstream influence of reservoirs or impoundments [START_REF] Erickson | Linear air/water temperature correlations for streams during open water periods[END_REF][START_REF] Morrill | Estimating stream temperature from air temperature: Implications for future water quality[END_REF] or to high groundwater contribution [START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF]. The 13 stations from the thermal regime ColdLowVar have a reduced annual variation of Tw (blue area; Figure 5a), and their low TS could be reasonably related to the groundwater inflows that decrease Tw response to changes in Ta and increase the thermal inertia of streams [START_REF] O'driscoll | Stream-air temperature relationships as indicators of groundwater inputs[END_REF]. This statement is reinforced by their location on small streams above the main aquifer formations (Figure 4). groundwater influences (C1 and C4; Figure 7). The decrease of TS is accentuated when a high BFI is combined with an SF higher than 30%

as on the 11 stations in C1 (mean TS = 0.5). The shading of riparian vegetation leads to increase the thermal moderation of surface water in summer by shading from solar radiation. The BFI appears as a very influential variable in TS (Figure 6). However, in the Loire basin, TS remains greater than 0.4 even when the BFI is higher than 0.8 and when b is higher than 6 C. In other studies, TS values are close to 0 when the BFI is close to 1 [START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF]. It could be suspected that the temperature of groundwater inflows feeding streams follows a seasonal trend correlated with Ta and more marked than those observed in the literature [START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF][START_REF] Krider | Air-water temperature relationships in the trout streams of southeastern Minnesota's carbonate-sandstone landscape[END_REF][START_REF] O'driscoll | Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA[END_REF]. This could also explain the high residuals of slope intercept regression for stations having a TS lower than 0.6 (Figure 3a).

The variable Q Aug is the mean specific discharge during the warmest month and represents the sustainability of low flows. It is a moderately influential variable in TS (Figure 6). It can be assumed that a stream with a high Q Aug , in the case of natural flowing, benefits from groundwater inflows and/or of important contribution of its tributaries allowing it to maintain a sufficient depth to moderate Tw in summer. CART analysis results showed that streams with a Q Aug value higher than 5 L s -1 km -2 , associated to an SF less than 30% and a BFI less than 0.8, have a lower TS than others stations (C2 vs. C3;

Figure 7), which seems to confirm our assumption. However, the importance of Q Aug remains applies to a subset of stations and the BFI remains the main variable representing the influence of groundwater inflows in our dataset. Sinokrot & Stefan, 1993). The riparian vegetation captures solar radiation and leads to reduced Tw resulting in a decrease of TS. This effect is particularly visible in summer when the solar radiation is the strongest and represents the main source of energy inputs (e.g., [START_REF] Hannah | A comparison of forest and moorland stream microclimate, heat exchanges and thermal dynamics[END_REF]. In addition, the riparian vegetation of the Loire basin is mainly composed of deciduous trees, which considerably limit the effect of shading in winter.

| Riparian shading influence on TS

The influence of the riparian vegetation shading on TS was highlighted by several studies [START_REF] Chang | Local landscape predictors of maximum stream temperature and thermal sensitivity in the Columbia River Basin, USA[END_REF][START_REF] Dugdale | Stream temperature under contrasting riparian forest cover: Understanding thermal dynamics and heat exchange processes[END_REF][START_REF] Garner | The role of riparian vegetation density, channel orientation and water velocity in determining river temperature dynamics[END_REF]Hrachowitz et al., 2010;F.L. Jackson et al., 2017;[START_REF] Loicq | Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data[END_REF]. However, it is still complex to characterize the own effect of riparian shading, and shading effect is regularly lumped to other drivers of TS moderation [START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF][START_REF] O'driscoll | Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA[END_REF].

In our study, we tried to differentiate the effects of shading and of 0.8) and slightly higher intercept (median b = 2.9 C) than in thermal regime WarmHighVar (Table 1). Between thermal regimes Cold-HighVar and WarmHighVar, their ΔT Jan is similar (ΔT Jan = -1.6 C), but

Tw is clearly lower than Ta during August for stations in thermal regime ColdHighVar (median ΔT Aug = 1.9). The influence of the riparian vegetation shading is suspected. CART model results seems to confirm this assumption because all stations in ColdHighVar were identified with an SF higher than 30% (C2 and C4; Figure 7). The effects of shading could be accentuated when the specific discharge in August is higher than 5 L s -1 km -2 (C2; TS ≈ 0.67) because the thermal inertia of the streams is increased.

| Landscape factors influence

The distance from the source (D) is a key driver of TS (Figure 6).

CART model results showed that stations with a D higher than 120 km obtained the highest TS (TS = 1; C7; Figure 7). D is highly positively correlated with the drainage area, and several studies identified this driver as playing an important role in the TS of rivers [START_REF] Chang | Local landscape predictors of maximum stream temperature and thermal sensitivity in the Columbia River Basin, USA[END_REF][START_REF] Garner | River temperature regimes of England and Wales: Spatial patterns, inter-annual variability and climatic sensitivity[END_REF]Hrachowitz et al., 2010;[START_REF] Imholt | Influence of scale on thermal characteristics in a large montane river basin: Thermal dynamics at different scales[END_REF]. Some others studies have also identified the Strahler order, which is correlated to D (R 2 = 0.6), as a strong influence factor of TS [START_REF] Chang | Local landscape predictors of maximum stream temperature and thermal sensitivity in the Columbia River Basin, USA[END_REF][START_REF] Ducharne | Importance of stream temperature to climate change impact on water quality[END_REF][START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF][START_REF] Wehrly | Landscape-based models that predict July thermal characteristics of lower Michigan rivers[END_REF]. Streams with a high D and a large drainage area are weakly dependent on upstream conditions, and the travel time of the water body between upstream and downstream allows Tw to equilibrate with Ta [START_REF] Mohseni | Stream temperature/air temperature relationship: A physical interpretation[END_REF], leading to increase TS. Also, a longer D and a larger F I G R E 8 Representation of the range of TS and b found in reviewed publications for linear regression models of weekly Ta-Tw relationship catchment area corresponds to lower topographical slopes, slower flow velocities, and greater regional residence time, which allow more time for Tw to adjust to local Ta [START_REF] Mayer | Controls of summer stream temperature in the Pacific Northwest[END_REF].

Stations located on small and medium streams, not influenced by shading and groundwater inflows (SF < 30%; BFI < 0.8; and D < 120 km) belonging to cluster C5 and C6 (Figure 7), obtained a TS less than those of large rivers in C7. There is an influence of S because stations located on streams with a high slope (S < 2.5 m km -1 ) had a mean TS of 0.8 (C5; Figure 7), whereas others had a mean TS of 0.88 (Cluster C6; Figure 7). The stream slope is mostly linked to elevation (R 2 = 0.65). A higher slope increases the flow velocity, and the elevation influences Tw over the adiabatic lapse rates of Ta (Hrachowitz et al., 2010) and also through snow and glacier meltwater inflow [START_REF] Arora | Thermal discontinuities along a lowland river: The importance of urban areas and lakes[END_REF][START_REF] Morrill | Estimating stream temperature from air temperature: Implications for future water quality[END_REF], which may contribute to decrease TS. P and PET are not relevant in CART model, which may be explained by the relative climatic homogeneity of the study area (Cfb = temperate oceanic climate, Table 3).

| Implication for river management and river restoration

The study of streams TS makes it possible to identify the most sensi- highly sensitive to the effects of global warming. For these streams having a large wet width (>50 m), the effects of shading from riparian vegetation are very small, and actions to reduce TS are limited. Thermal anomalies could be detected by aerial infrared survey [START_REF] Wawrzyniak | Longitudinal and temporal thermal patterns of the French Rhône River using Landsat ETM+ thermal infrared images[END_REF] and be preserved by limiting advective thermal mixing [START_REF] Kurylyk | Preserving, augmenting, and creating cold-water thermal refugia in rivers: Concepts derived from research on the Miramichi River, New Brunswick (Canada)[END_REF] or activated by geomorphological restoration of streams [START_REF] Eschbach | Thermal-infrared remote sensing of surface water-groundwater exchanges in a restored anastomosing channel (Upper Rhine River, France)[END_REF][START_REF] Loheide | Quantifying stream-aquifer interactions through the analysis of remotely sensed thermographic profiles and in situ temperature histories[END_REF]. On small and medium streams, it is necessary to preserve and/or favour the presence of riparian vegetation to moderate TS [START_REF] Fabris | Integrating process-based flow and temperature models to assess riparian forests and temperature amelioration in salmon streams[END_REF]. The effects will be most pronounced, in comparison with large streams, because of their smaller width, but investments have to be made strategically [START_REF] Isaak | The NorWeST summer stream temperature model and scenarios for the western U.S.: A crowdsourced database and new geospatial tools foster a user community and predict broad climate warming of rivers and streams[END_REF][START_REF] Johnson | Seeing the landscape for the trees: Metrics to guide riparian shade management in river catchments[END_REF]. From a watershed management perspective, stream shading would be less effective in streams where

Tw is already strongly moderated by groundwater inflows but more effective along losing reaches or stream reaches distant from groundwater inflows [START_REF] O'driscoll | Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA[END_REF].

Streams with a low TS have a limited surface water heating during summer and may provide thermal refuges for thermo-sensitive aquatic species (macroinvertebrates, stream-dwelling amphibians, and fish species; [START_REF] Isaak | The NorWeST summer stream temperature model and scenarios for the western U.S.: A crowdsourced database and new geospatial tools foster a user community and predict broad climate warming of rivers and streams[END_REF]. In order to limit these warmings and preserve ecosystems, it seems important to identify streams constituting cold-water thermal refuges (with low TS) and to restore and preserve thermal diversity in the hydrographical network [START_REF] Torgersen | Primer for identifying cold-water refuges to protect and restore thermal diversity in riverine landscapes[END_REF]. However, the main factors limiting TS (BFI and SF) could change in the future, and several streams could become much more sensitive to environmental change [START_REF] Leach | Empirical stream thermal sensitivities may underestimate stream temperature response to climate warming[END_REF]. For example, the loss in groundwater inflows would result in greater meteorological controls increasing the annual amplitude of Tw [START_REF] O'driscoll | Stream-air temperature relations to classify stream-ground water interactions in a karst setting, central Pennsylvania, USA[END_REF]. Limiting water abstraction during lowflow periods may avoid a disconnection of groundwater/surface water exchanges and ensure environmental flows during the summer [START_REF] Elmore | Effects of environmental water transfers on stream temperatures: Effects of environmental water transfers[END_REF]. Some cooling strategies proposed to reconnect streams to floodplains and to facilitate greater lateral and hyporheic flow exchanges [START_REF] Beechie | Restoring salmon habitat for a changing climate: Restoring salmon habitat for a changing climate[END_REF]Daniel Caissie & Luce, 2017;[START_REF] Kurylyk | Preserving, augmenting, and creating cold-water thermal refugia in rivers: Concepts derived from research on the Miramichi River, New Brunswick (Canada)[END_REF] but need to be tested at a regional scale.

To apply efficient and effective actions, river managers have to focus on small and medium streams and can use the environmental variables identified in our classification results as indicators to assess the climate sensitivity of unmonitored streams.

| CONCLUSION

In this study, we proposed a framework to compare thermal sensitiv- The Tw metrics and the environmental variables remain simple to determine and can easily be applied in others catchments at a regional scale. One of the perspectives to this work would be to explore if main controlling factors of the Tw variability identified here are the same in different climate and physiographical regions elsewhere. We observe that almost invariability streams studied in reviewed publications for linear regression models of weekly Ta-Tw relationship (Table 3) correspond to temperate and continental climatic regions. It would be interesting to study streams from different climatic contexts to understand how controls of TS may vary. Furthermore, it would be insightful to explore how TS may be modified by anthropogenic effects (dams, weirs, and other flow augmentation/abstractions, etc.). Management agencies can use our findings on thermal sensitivity for prioritizing restoration areas to moderate stream temperature and undertake mitigation and adaptation actions to protect sensitive aquatic species in the context of a changing environment.

  Moreover, gaps in Tw time series between 2008 and 2012 exist, and the proportion of missing values is about 35% on average for the 127 stations (80 stations with more than 20% of missing F I G U R E 1 Presentation of the Loire catchment: (a) altitude and location of monitoring stations, (b) location of the 127 water temperature monitoring stations presented with the spatial distribution of mean annual stream temperatures (TwA), (c) vegetation cover besides streams (Valette, Piffady, Chandesris, & Souchon, 2012), and (d) main aquifer formations and basin lithology

  observed spatial pattern in TS and identify main drivers of thermal streams moderation. The variable selection was based on the most pertinent variables identified in literature and on the results of a principal component analysis (not presented here) to minimize dependency between variables. The distance from the source (D in km) and the elevation (E in m) are determined at the location of each monitoring station. The slope of the river reach (S in m m -1 ) where the station is located is determined with BD ALTI ® 25-m resolution DTM dataset (IGN Paris, France). A higher S increases the flow velocity, and E influences Tw through the association with the adiabatic lapse rates of Ta and also through snow and glacier meltwater inflow, which should cool Tw at higher elevations. Streams with a high D have more time to equilibrate their Tw with Ta. Two hydrological indicators were also introduced. (a) The baseflow index (BFI) was estimated with the method of the Institute of Hydrology (1980) between 2008 and 2012. The BFI is a measure of the proportion of the low-flow component to the total river flow with values between 0 and 1. Details on calculation can be found in Gustard, Bullock, and Dixon (1992). Low values are related to catchments with no storage capacity and also to catchments exposed to very high climate variability resulting in severe low-flow and quick run-off in response to rainfall events. High values are observed where artificial reservoirs, large aquifers, and storage in snow packs moderate the variability of daily flow. In our study, BFI is considered as a proxy of groundwater influence. The discharge Q was not monitored at Tw station, and each Tw station was coupled to the nearest gauging station (distance between both stations ranges from 10 m to 15 km). The matching is based on two criteria: (a) The gauging stations has to be located in the same or nearby streams and (b) the difference of catchment area between the location where Tw was measured and the location where Q was measured was kept to a maximum of ±20%. The daily discharge was extracted from the French river flow monitoring network (HYDRO database, http://www.hydro. eaufrance.fr/). (b) The average specific discharge in August (Q Aug ) is calculated at each station between 2008 and 2012. The goal is to measure the capacity of the catchment to produce a flow in summer, when precipitation is low. The specific discharge is the ratio between the discharge and the corresponding catchment area (in L s -1 km -2 ) and is used to standardize discharge for basin area. Two climatic variables were determined from the Safran reanalysis data: (a) the mean summer cumulated precipitation (P in mm) and (b) the mean summer potential evapotranspiration (PET in mm) both calculated between June 1 and September 30 of each year between 2008 and 2012 for the entire upstream area of each monitoring station. Streams with wetter basin (high P and low PET) are expected to have higher water yields and more groundwater contributions that

"

  rpart" (Version 4.1, Therneau & Atkinson, 2018) for implementing the CART model. The random forest (RF) model was used to assess the importance of explanatory variables for the prediction of TS and to evaluate the robustness of the classification. RF combines decision trees obtained by resampling the calibration set[START_REF] Breiman | Random forests[END_REF],

  The relationship between TS and b shows a moderate negative correlation with R 2 ≈ 0.7. Stations with the lowest TS (<0.6) and the highest b have the highest residual and seemed to follow a different pattern than other stations (Figure3a; all the points are located above the regression line). Stations having a moderate TS between 0.6 and 0.9 are most often observed across the Loire River basin, and their associated b ranges from 1 C to 5 C. Stations with a high TS (>0.9) and a small b (<3) follow the same trend and have small residuals of the slope intercept regression. The analysis of weekly Ta-Tw relationship indicates that TS generally increases with stream size and the dis-

  highest annual amplitude of MTw reaching 18 C with similar annual MTa amplitude (red area; Figure 5a,b). Their MTws are the warmest during summer and exceed 21 C on average. • WarmLowVar-warm and low variability (23 sites-18%): These stations are characterized by a smaller TS and a higher b (median b = 3.5 C) than stations from WarmHighVar. MTa is less than MTw in winter with a median deviation of 3 C. In August, the MTw is very close to MTa, and ΔT Aug does not exceed 1 C. They have annual MTw amplitude of 14 C and MTw smaller than 4 C in F I G U R E 3 Distribution of TS: (a) relationship between b and TS of weekly Tw-Ta linear regressions and (b) TS as a function of the distance from the source (D) of monitoring stations F I G U R E 2 Tw-Ta weekly linear regression: (a) for the station with the highest TS, (b) for the station with the lowest TS, and (c) for the 127 stations fitted on data available between 2008 and 2012. Dashed lines represent the curve x = y, solid lines represent the weekly linear regression curves for each station, and the black solid lines in (c) correspond to weekly linear regression curves of stations represented in (a) and (b), and grey points in (c) represents all the observations summer in comparison with the MTw of stations from WarmHighVar (yellow area; Figure 5a,b). • ColdHighVar-cold and high variability (44 sites-35%): Stations have MTw higher than MTa in January by 2 C (Table 1). Inversely, in August, MTw is less than MTa by 2 C. The TS and b of stations have rather average values with medians of 0.7 and 2.9, respectively. They have annual MTw amplitude of 14 C and MTw less than 5 C in summer in comparison with the MTw of stations from WarmHighVar (green area; Figure 5a,b). • ColdLowVar-cold and low variability (13 sites-10%): Stations demonstrate the lowest TS of each class (TS < 0.7) and the highest b (greater than 4.9 C). The differences between MTw and MTa are high, in comparison with other thermal regimes, whether in August (MTa > MTw by 3.5 C) or in January (MTa < MTw by 4 C). These stations are those with the lowest annual MTw amplitude of 9 C, which is one half less than the amplitude of stations from WarmHighVar (blue area; Figure 5a). The MTw of stations from ColdLowVar is the lowest during summer (MTw = 15 C) and the warmest during winter in comparison with others thermal regimes

  55 and 0.61, respectively. The analysis of the deviation from the mean annual Tw (MTw) and of MTw averaged over the four clusters identified by AHC led to distinguish significantly different thermal regime in terms of magnitude and amplitudes (Figure 5a,c) in comparison with MTa (Figure 5b,d). The MTa patterns of each cluster demonstrate a very similar amplitude and magnitude (Figure 5b,d) with annual amplitude close to 18 C following the same amplitude of thermal regime WarmHighVar. The different response of each thermal regime to same climate conditions suggests other controlling factors than climate determine the annual amplitude and magnitude of Tw.

F

  I G U R E 4 Spatial distribution of TS calculated between 2012 and 2016 on weekly Ta-Tw regressions stations clustering. Others variables have a lower influence on TS and are not used in the dichotomic tree plot from the CART model.

  Representation of (a) the deviation from the mean annual Tw, (b) the deviation from the mean annual Ta, (c) the monthly Tw, and (d) the monthly Ta averaged over the four thermal regimes identified by agglomerative hierarchical clustering. The colour area bars represent ± standard deviation of each series F I G U R E 6 Variable importance to explain TS for all stations ranking from the highest to the lowest obtained from random forest have a lower sensitivity (mean TS of 0.81; C5) in comparison with the 28 remaining stations from C6 (mean TS of 0.88; C6). The 13 stations in C5 mostly belong to the thermal regime War-mLowVar, whereas the 28 stations in C6 mostly belong to the thermal regime WarmHighVar (Table

  with linear regression models and at the weekly time step, with a mean R 2 of 0.96 (standard deviation [SD] = 0.02) determined for the 127 stations. Weekly Ta-Tw linear regressions slightly outperform daily Ta-Tw linear regressions (mean R 2 = 0.88; SD = 0.03) as well as F I G U R E 7 Regression and classification on tree developed for TS for all explanatory variables. In each cluster, the mean TS, the mean b, their standard deviation on (in brackets), and the number of stations (n) are presented. Histograms under each branch indicate the thermal regimes of stations identified by the agglomerative hierarchical clustering analysis T A B L E 2 Explanatory variables presented for each thermal regime identify with the agglomerative hierarchical clustering

  . O'Driscoll and DeWalle (2006), Kelleher et al. (2012), and Krider et al. (2013) obtained lower values of TS and higher b values for their studied streams located in karst basins.

  CART model results showed that all stations of the thermal regime ColdLowVar have BFI greater than 0.8, which seems to confirm T A B L E 3 TS, b, and R 2 values found in reviewed publications for linear regression models of weekly Ta-Tw relationship The value in brackets corresponds to the average value.

  Shortwave (solar) radiation is one of the most influential factors that influence stream temperature and is related directly to the amount of shading provided by riparian vegetation (O'Driscoll & DeWalle, 2006;

  groundwater inflows. The only study of TS and b does not allow to clearly make this distinction because the effect of riparian vegetation shading could be mixed with the effect of groundwater inflows. The ΔT Jan and ΔT Aug were introduced in the AHC model to help make this distinction. Stations in thermal regime WarmHighVar have a high TS (TS > 0.8) combined to a small intercept (b < 3.5 C) and are supposed the most influenced by climate and Ta. Their amplitude and magnitude of MTa and MTw are very similar and follow the same trend (amplitude of 18 C; Figure 5a) and do not seem to be moderated by any drivers. Stations in thermal regime ColdHighVar have a lower TS (TS <

  tive streams to environmental change (high TS) and potentially the most sensitive to the effects of climate change. The stationarity of all processes influencing Tw is difficult to estimate because it implies the use of physically based models directly integrating energy fluxes because the only study of TS tends to underestimate the warming of climate change (Leach & Moore, 2019). However, streams studied here have a natural thermal regime and are not influenced by anthropogenic activities. These natural streams sensitive to environmental changes in the present time (high TS) will always be sensitive to environmental changes in the future without human actions. The goal of our approach is to identify the most climate-sensitive streams, linking them to environmental or hydrological features, to guide stakeholders to pay particular attention to them. Our analysis identified D, BFI, and SF as the main factors influencing TS in the Loire basin. The major streams of the Loire catchment (D > 120 km) show the highest TS value (mean TS = 1) and appear

  ity (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. On the basis of weekly Ta-Tw relationships, four thermal regimes were identified with differing annual Tw in terms of magnitude and amplitudes in comparison with Ta. We linked each cluster to different environmental controlling factors as inferred by TS. This highlighted that shading from riparian vegetation, groundwater inflows, and the distance from the source of streams were the main drivers of the moderation of streams located in the Loire catchment. Streams influenced by both groundwater inflows and shading are the most moderated with the lowest TS and an annual amplitude of Tw around half the annual amplitude of Ta. Inversely, stations located on large streams or on streams slightly or not influenced by groundwater inflows and/or shading showed the highest TS and are very climate sensitive. Their Tw amplitude and magnitude were very close to those of Ta; consequently, these rivers are deemed the most sensitive to the effects of future climate change.

  Metrics averaged for each thermal regime determined with the agglomerative hierarchical clustering

	Cluster		TS	b	ΔT Jan	ΔT Aug
	WarmHighVar-warm and high variability	Max	1.2	3.5	-0.4	1.1
	47 sites-37%	Med	0.9	2.2	-1.4	-1.6
		Min	0.8	0.6	-2.7	-4.2
	WarmLowVar-warm and low variability	Max	0.8	4.7	-1.7	1.0
	23 sites-18%	Med	0.8	3.5	-3.1	-0.3
		Min	0.7	2.5	-4.0	-1.1
	ColdHighVar-cold and high variability	Max	0.8	4.3	-0.8	5.8
	44 sites-35%	Med	0.7	2.9	-1.8	1.9
		Min	0.6	1.2	-4.1	0.7
	ColdLowVar-cold and low variability	Max	0.7	7.6	-2.9	4.7
	13 sites-10%	Med	0.5	5.7	-4.0	3.5
		Min	0.4	4.9	-5.7	1.1

Aug and S are also used to differentiate clusters in the CART model and obtained a moderate importance close to 8% with RF. Elevation (E) is identified as the fourth relevant variable with RF model (variable importance = 11%; Figure

6

) but is not used by the CART model for

T A B L E 1

-1 km -2 ) P (mm) PET (mm)

  SD = 0.05) and weekly (mean R 2 = 0.93; SD = 0.03) logistic regressions. The weekly time step is more accurate because this time step filters out the lag time between Ta and Tw peaks, which can be of several days. In contrast to other studies (e.g.,[START_REF] Kelleher | Investigating controls on the thermal sensitivity of Pennsylvania streams[END_REF], taking into account a non-linear relationship between Ta and Tw did not improve the performance of the regressions. This is probably explained by the fact that the Loire basin is not subject to Ta (min weekly Ta across the Loire basin between

	daily (mean R 2 = 0.86; 2008 and 2012 = -8 C) as low as in cold, continental regions studied
	in the contiguous United States (min Ta = -20 C; Omid Mohseni, Ste-
	fan, &
	WarmHighVar	Max	8	896	323	12.6	60	0.81	1.1	294.0	308.7
		Med	5	122	101	0.5	20	0.72	2	211.2	283.1
		Min	3	19	10	0.1	0	0.51	0.3	144.5	258.6
	WarmLowVar	Max	5	145	1120	29.7	29	0.95	3.7	352.2	313.1
		Med	4	36	282	3.3	15	0.74	3	214.0	284.9
		Min	2	7	88	0.1	0	0.64	0.2	140.9	261.0
	ColdHighVar	Max	6	96	755	26.1	77	0.78	9.9	302.9	313.5
		Med	4	26	232	3.3	50	0.71	4	231.3	281.8
		Min	2	4	41	0.1	30	0.49	0.1	151.4	256.4
	ColdLowVar	Max	5	73	231	3.8	71	0.92	1.7	237.1	312.6
		Med	3	24	122	1.5	50	0.86	5	188.0	283.2
		Min	2	6	65	0.1	19	0.81	0.5	148.0	248.6

ACKNOWLEDGMENTS

The authors wish to thank the two reviewers for their valuable comments, suggestions, and positive feedback on the manuscript. The research project was funded by the French Agency for Biodiversity (AFB). Thanks are due to the AFB, the DREAL Auvergne, the DREAL Pays de Loire and the DREAL Centre for the temperature data.

DATA AVAILABILITY STATEMENT

Temperature used in this study are available from the French Biodiversity Agency (AFB; www.afbiodiversite.fr). Restrictions apply to the availability of these data, which were used under license herein. Data are available at http://www.naiades.eaufrance.fr with the permission of the French Biodiversity Agency (www.naiades.eaufrance. fr/formulaire/contact). Discharge data are available from the French river flow monitoring network (HYDRO database, http://www.hydro. eaufrance.fr/). Restrictions apply to the availability of meteorological data, which were used under license herein with the permission of Météo France (www.meteofrance.fr).