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

Beaufort, Aurélien; Moatar, Florentina; Sauquet, Éric; Loicq, Pierre; Hannah, David M.

doi:10.1002/hyp.13608

Cited by 35 publications

(31 citation statements)

References 72 publications

(149 reference statements)

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“…Water level was obtained from the WT measurement sites measuring water level every 10 min along with WT and aggregated daily by the median. The baseflow index (BFI) was introduced as a hydrological indicator corresponding to the ratio of the low flow to the total river flow ( Beaufort et al, 2020 ). As used by Beaufort et al (2020) , BFI was calculated in our study to reflect the influence of groundwater.…”

Section: Methodsmentioning

confidence: 99%

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Georges

Michez

Piégay

et al. 2021

PeerJ

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Managers need to know how to mitigate rising stream water temperature (WT) due to climate change. This requires identifying the environmental drivers that influence thermal regime and determining the spatial area where interventions are most effective. We hypothesized that (i) extreme thermal events can be influenced by a set of environmental factors that reduce thermal sensitivity and (ii) the role played by those factors varies spatially. To test these hypotheses, we (i) determined which of the environmental variables reported to be the most influential affected WT and (ii)identified the spatial scales over which those environmental variables influenced WT. To this end, the influence of multi-scale environmental variables, namely land cover, topography (channel slope, elevation), hydromorphology (channel sinuosity, water level, watershed area, baseflow index) and shade conditions, was analyzed on the three model variables (day thermal sensitivity, night thermal sensitivity, and non-convective thermal flux) in the model developed by Georges et al. (2021) of the temporal thermal dynamics of daily maximum WT during extreme events. Values were calculated on six spatial scales (the entire upstream catchment and the associated 1 km and 2 km circular buffer, and 50 m wide corridors on each side of the stream with the associated 1 km and 2 km circular buffer). The period considered was 17 extreme days during the summer identified by Georges et al. (2021) based on WT data measured every 10 min for 7 years (2012–2018) at 92 measurement sites. Sites were located evenly throughout the Wallonia (southern Belgium) hydrological network. Results showed that shade, baseflow index (a proxy of the influence of groundwater), water level and watershed area were the most significant variables influencing thermal sensitivity. Since managers with finite financial and human resources can act on only a few environmental variables, we advocate restoring and preserving the vegetation cover that limits solar radiation on the watercourse as a cost-effective solution to reduce thermal sensitivity. Moreover, management at small spatial scale (50 m riparian buffer) should be strategically promoted (for finance and staffing) as our results show that a larger management scale is not more effective in reducing thermal sensitivity to extreme events.

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Section: Methodsmentioning

confidence: 99%

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Georges

Michez

Piégay

et al. 2021

PeerJ

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“…9), probably due to decrease in Q (up to -2 %/decade, see Fig. S12), greater thermal sensitivity, and the absence of mitigating factors like riparian vegetation shading or groundwater inputs (Kelleher et al, 2012;Beaufort et al, 2020).…”

Section: Implications For River Management and Aquatic Biotamentioning

confidence: 99%

Regional, multi-decadal analysis reveals that stream temperature increases faster than air temperature

Seyedhashemi

Vidal

Diamond

et al. 2021

Preprint

Self Cite

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Abstract. Stream temperature appears to be increasing globally, but its rate remains poorly constrained due to a paucity of long-term data and difficulty in parsing effects of hydroclimate and landscape variability. Here, we address these issues using the physically-based thermal model T-NET (Temperature-NETwork) coupled with the EROS semi-distributed hydrological model to reconstruct past daily stream temperature and streamflow at the scale of the entire Loire River basin in France (105 km2 with 52278 reaches). Stream temperature increased for almost all reaches in all seasons (mean = +0.38 °C/decade) over the 1963–2019 period. Increases were greatest in spring and summer with a median increase of +0.38 °C (range = +0.11– +0.76 °C) and +0.44 °C (+0.08– +1.02 °C) per decade, respectively. Rates of stream temperature increases were greater than for air temperature across seasons for 50–86 % of reaches. Spring and summer increases were typically the greatest in the southern headwaters (up to +1 °C/decade) and in the largest rivers (Strahler order > 5). Importantly, air temperature and streamflow exerted joint influence on stream temperature trends, where the greatest stream temperature increases were accompanied by similar trends in air temperature (up to +0.71 °C/decade) and the greatest decreases in streamflow (up to −16 %/decade). Indeed, for the majority of reaches, positive stream temperature anomalies exhibited synchrony with positive anomalies in air temperature and negative anomalies in streamflow, highlighting the dual control exerted by these hydroclimatic drivers. Moreover, spring and summer stream temperature, air temperature, and streamflow time series exhibited common change-points occurring in the late 1980s, suggesting a temporal coherence between changes in the hydroclimatic drivers and a rapid stream temperature response. Critically, riparian vegetation shading mitigated stream temperature increases by up to 16 % in smaller streams (i.e., < 30 km from the source). Our results provide strong support for basin-wide increases in stream temperature due to joint effects of rising air temperature and reduced streamflow. We suggest that some of these climate change-induced effects can be mitigated through the restoration and maintenance of riparian forests, and call for continued high-resolution monitoring of stream temperature at large scales.

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“…However, this assumption is unreasonable in some cases because data labeling is usually expensive and time-consuming. erefore, we use the accuracy of the model as a comparison index to compare the particle swarm and the neural network optimized by ADAM [43,44]. e application mathematics mining effect of these three models on the three sample sets is shown in Figure 5.…”

Section: Mining Of Applied Mathematics Educational Resourcesmentioning

confidence: 99%

Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification

Zhou²

2021

Mobile Information Systems

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Facing the massive data of higher education institutions, data mining technology is an intelligent information processing technology that can effectively discover knowledge from the massive data and can discover important information that people have previously ignored from the huge data information. This article is dedicated to the development of applied mathematics education resource mining technology based on edge computing and data stream classification. First of all, this article establishes a resource system architecture suitable for existing applied mathematics education through edge computing technology, which can effectively improve the efficiency of data mining. Secondly, the data stream classification algorithm is used for information extraction and classification integration of massive applied mathematical education data. This method provides potential and valuable information for decision-makers and education practitioners. Finally, the simulation and performance test of the system verify that it has the functions of mathematical information mining and data processing. This system will provide strong support for applied mathematics education reform.

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

Cited by 35 publications

References 72 publications

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Which environmental factors control extreme thermal events in rivers? A multi-scale approach (Wallonia, Belgium)

Regional, multi-decadal analysis reveals that stream temperature increases faster than air temperature

Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification

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