Spatial and temporal variability of the solar radiation heat flux in streams of a forested catchment

Abstract Solar radiation is generally the largest contributing flux to the heat budget of streams and its estimation is crucial to predict stream water temperature with process-based models. The objective of this research is to quantify the spatial (between-site comparison of different stream sizes, within-site comparison at the reach scale) and temporal (seasonal, daily and hourly scales) variability in the transmission coefficient, which represents the proportion of incoming solar radiation reaching streams. We measured solar radiation at an open site with a meteorological station and at microclimate sites located in three streams of various sizes in the Miramichi River basin (Canada). During the summer, the percentage of incoming daily solar radiation reaching a stream varied from 8% in a small headwater stream (Trib) to 43% in a medium-sized stream (CatBk) and was close to 100% in a wide river (LSWM). We observed the largest variability between transmission coefficients for different stream sizes (range of variation = 92%) due to very different canopy closures, followed by variability at the reach scale between lateral positions (range = 21% between left and right banks) and between longitudinal positions (range = 11% between upstream and downstream sites), as measured at the medium-sized stream. Temporal variability was greatest at the seasonal scale where the transmission coefficient varied by 23% between May and September at the small headwater stream. The hourly variability of the transmission coefficient (i.e. associated with different solar angles) surpassed daily variability (i.e. associated with different cloud cover conditions), with coefficients of variation computed at the hourly time scale three to five times greater than at the daily time scale. Overall, this research offers insight regarding the handling of spatial and temporal variability of solar radiation which should provide further insight to improve process-based stream temperature models.