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 270km 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 (T) 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 T 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 T 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.

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

“…The influence of the riparian vegetation shading on TS was highlighted by several studies (Chang & Psaris, 2013;Dugdale et al, 2018;Garner, Malcolm, et al, 2017;Hrachowitz et al, 2010; F.L. Loicq et al, 2018). 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 (Kelleher et al, 2012;O'Driscoll & DeWalle, 2006).…”

“…The Tw variability, described by metrics of flow magnitude, frequency, duration, timing, and rate of change, on various timescales (Jones & Schmidt, 2018), is influenced by complex processes related to atmospheric, hydrogeological, geomorphic, and landscape characteristics and anthropogenic pressures, which could interact at multiple spatial scales (Caissie, 2006;Hannah & Garner, 2015). Numerous studies have highlighted the importance of riparian forest and groundwater inflows in moderating Tw variability (Dugdale, Malcolm, Kantola, & Hannah, 2018;Garner, Malcolm, Sadler, & Hannah, 2017;Kelleher et al, 2012;Lalot et al, 2015;Loicq, Moatar, Jullian, Dugdale, & Hannah, 2018). 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 (Jackson et al, 2018).…”

Remote sensing of river corridors: A review of current trends and future directions

“…At reach scales, ALS has been used for riparian zone classification (Antonarakis, Richards, & Brasington, ; Gilvear, Tyler, & Davids, ; Michez et al, ), assessment of wood and debris retention (Abalharth, Hassan, Klinkenberg, Leung, & McCleary, ; Bertoldi, Gurnell, & Welber, ), upscaling from TLS models (Manners et al, ), creating rainfall interception models (Berezowski, Chormanski, Kleniewska, & Szporak‐Wasilewska, ), and for linking vegetation to morphological and anthropogenic contexts and needs (Bertoldi, Gurnell, & Drake, ; Cartisano et al, ; Picco, Comiti, Mao, Tonon, & Lenzi, ). At landform scales, ALS has been used to identify sources and volumes of woody debris (Kasprak, Magilligan, Nislow, & Snyder, ), the health of riparian ecosystems (Michez et al, ), the influence of vegetation on groundwater connectivity (Emanuel, Hazen, McGlynn, & Jencso, ), bank stability (McMahon et al, ), and water temperature through shading (Greenberg, Hestir, Riano, Scheer, & Ustin, ; Loicq, Moatar, Jullian, Dugdale, & Hannah, ; Wawrzyniak, Allemand, Bailly, Lejot, & Piegay, ). ALS therefore contributes heavily to our understanding of riparian vegetation and, despite potential drawbacks such as cost and mobilisation, is a key method to consider for monitoring activities.…”

Hydrologic Resilience from Summertime Fog and Recharge: A Case Study for Coho Salmon Recovery Planning

“…Stream temperature is an aggregate of conductive, convective and advective fluxes between water column, stream bed, hyporheic zone, groundwater, seeps of varying origin, shade and substrate type (Webb and Zhang 1997;Johnson 2004;Guenther et al 2014;Fullerton et al 2015). The dominant process influencing stream temperature is solar loading (Brown 1969;Torgersen et al 2001;Mayer 2012;Dugdale et al 2017;Dugdale et al 2018;Loicq et al 2018). A barrier to solar loading such as riparian vegetation or topographic shading reduces stream temperature (Bond et al 2015;Kalny et al 2017;Wawrzyniak et al 2017).…”