Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data

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Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data. / Loicq, Pierre; Moatar, Florentina; Jullian, Yann; Dugdale, Stephen J.; Hannah, David M.

In: Science of the Total Environment, Vol. 624, 15.05.2018, p. 480-490.

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@article{434a815e9a2c4ed998095b5d3b2e4272,
title = "Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data",
abstract = "Modelling river temperature at the catchment scale is needed to understand how aquatic communities may adapt to current and projected climate change. In small and medium rivers, riparian vegetation can greatly reduce maximum water temperature by providing shade. It is thus important that river temperature models are able to correctly characterise the impact of this riparian shading. In this study, we describe the use of a spatially-explicit method using LiDAR-derived data for computing the riparian shading on direct and diffuse solar radiation. The resulting data are used in the T-NET one-dimensional stream temperature model to simulate water temperature from August 2007 to July 2014 for 270 km of the Loir River, an indirect tributary of the Loire River (France). Validation is achieved with 4 temperature monitoring stations spread along the Loir River. The vegetation characterised with the LiDAR approach provides a cooling effect on maximum daily temperature (Tmax) ranging from 3.0 °C (upstream) to 1.3 °C (downstream) in late August 2009. Compared to two other riparian shading routines that are less computationally-intensive, the use of our LiDAR-based methodology improves the bias of Tmax simulated by the T-NET model by 0.62 °C on average between April and September. However, difference between the shading routines reaches up to 2 °C (monthly average) at the upstream-most station. Standard deviation of errors on Tmax is not improved. Computing the impact of riparian vegetation at the hourly timescale using reach-averaged parameters provides results close to the LiDAR-based approach, as long as it is supplied with accurate vegetation cover data. Improving the quality of riparian vegetation data should therefore be a priority to increase the accuracy of stream temperature modelling at the regional scale.",
keywords = "LiDAR, Loir River, Regional scale, Riparian shading, River temperature modelling",
author = "Pierre Loicq and Florentina Moatar and Yann Jullian and Dugdale, {Stephen J.} and Hannah, {David M.}",
year = "2018",
month = may,
day = "15",
doi = "10.1016/j.scitotenv.2017.12.129",
language = "English",
volume = "624",
pages = "480--490",
journal = "Science of the Total Environment",
issn = "0048-9697",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data

AU - Loicq, Pierre

AU - Moatar, Florentina

AU - Jullian, Yann

AU - Dugdale, Stephen J.

AU - Hannah, David M.

PY - 2018/5/15

Y1 - 2018/5/15

N2 - Modelling river temperature at the catchment scale is needed to understand how aquatic communities may adapt to current and projected climate change. In small and medium rivers, riparian vegetation can greatly reduce maximum water temperature by providing shade. It is thus important that river temperature models are able to correctly characterise the impact of this riparian shading. In this study, we describe the use of a spatially-explicit method using LiDAR-derived data for computing the riparian shading on direct and diffuse solar radiation. The resulting data are used in the T-NET one-dimensional stream temperature model to simulate water temperature from August 2007 to July 2014 for 270 km of the Loir River, an indirect tributary of the Loire River (France). Validation is achieved with 4 temperature monitoring stations spread along the Loir River. The vegetation characterised with the LiDAR approach provides a cooling effect on maximum daily temperature (Tmax) ranging from 3.0 °C (upstream) to 1.3 °C (downstream) in late August 2009. Compared to two other riparian shading routines that are less computationally-intensive, the use of our LiDAR-based methodology improves the bias of Tmax simulated by the T-NET model by 0.62 °C on average between April and September. However, difference between the shading routines reaches up to 2 °C (monthly average) at the upstream-most station. Standard deviation of errors on Tmax is not improved. Computing the impact of riparian vegetation at the hourly timescale using reach-averaged parameters provides results close to the LiDAR-based approach, as long as it is supplied with accurate vegetation cover data. Improving the quality of riparian vegetation data should therefore be a priority to increase the accuracy of stream temperature modelling at the regional scale.

AB - Modelling river temperature at the catchment scale is needed to understand how aquatic communities may adapt to current and projected climate change. In small and medium rivers, riparian vegetation can greatly reduce maximum water temperature by providing shade. It is thus important that river temperature models are able to correctly characterise the impact of this riparian shading. In this study, we describe the use of a spatially-explicit method using LiDAR-derived data for computing the riparian shading on direct and diffuse solar radiation. The resulting data are used in the T-NET one-dimensional stream temperature model to simulate water temperature from August 2007 to July 2014 for 270 km of the Loir River, an indirect tributary of the Loire River (France). Validation is achieved with 4 temperature monitoring stations spread along the Loir River. The vegetation characterised with the LiDAR approach provides a cooling effect on maximum daily temperature (Tmax) ranging from 3.0 °C (upstream) to 1.3 °C (downstream) in late August 2009. Compared to two other riparian shading routines that are less computationally-intensive, the use of our LiDAR-based methodology improves the bias of Tmax simulated by the T-NET model by 0.62 °C on average between April and September. However, difference between the shading routines reaches up to 2 °C (monthly average) at the upstream-most station. Standard deviation of errors on Tmax is not improved. Computing the impact of riparian vegetation at the hourly timescale using reach-averaged parameters provides results close to the LiDAR-based approach, as long as it is supplied with accurate vegetation cover data. Improving the quality of riparian vegetation data should therefore be a priority to increase the accuracy of stream temperature modelling at the regional scale.

KW - LiDAR

KW - Loir River

KW - Regional scale

KW - Riparian shading

KW - River temperature modelling

UR - http://www.scopus.com/inward/record.url?scp=85038208926&partnerID=8YFLogxK

U2 - 10.1016/j.scitotenv.2017.12.129

DO - 10.1016/j.scitotenv.2017.12.129

M3 - Article

AN - SCOPUS:85038208926

VL - 624

SP - 480

EP - 490

JO - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

ER -