The importance of incorporating diurnally fluctuating stream discharge in stream temperature energy balance models

Abstract

Although stream temperature energy balance models are useful to predict temperature through time and space, a major unresolved question is whether fluctuations in stream discharge reduce model accuracy when not exactly represented. However, high‐frequency (e.g., subdaily) discharge observations are often unavailable for such simulations, and therefore, diurnal streamflow fluctuations are not typically represented in energy balance models. These fluctuations are common due to evapotranspiration, snow pack or glacial melt, tidal influences within estuaries, and regulated river flows. In this work, we show when to account for diurnally fluctuating streamflow. To investigate how diurnal streamflow fluctuations affect predicted stream temperatures, we used a deterministic stream temperature model to simulate stream temperature along a reach in the Quilcayhuanca Valley, Peru, where discharge varies diurnally due to glacial melt. Diurnally fluctuating streamflow was varied alongside groundwater contributions via a series of computational experiments to assess how uncertainty in reach hydrology may impact simulated stream temperature. Results indicated that stream temperatures were more sensitive to the rate of groundwater inflow to the reach compared with the timing and amplitude of diurnal fluctuations in streamflow. Although incorporating observed diurnal fluctuations in discharge resulted in a small improvement in model RMSE, we also assessed other diurnal discharge signals and found that high amplitude signals were more influential on modelled stream temperatures when the discharge peaked at specific times. Results also showed that regardless of the diurnal discharge signal, the estimated groundwater flux to the reach only varied from 1.7% to 11.7% of the upstream discharge. However, diurnal discharge fluctuations likely have a stronger influence over longer reaches and in streams where the daily range in discharge is larger, indicating that diurnal fluctuations in stream discharge should be considered in certain settings.

Effects of Topographic Resolution and Geologic Setting on Spatial Statistical River Temperature Models

“…TIR data presents a snapshot in time; while we report on how representative these TIR data are of interannual variability, hydroclimatic variability across years will undoubtedly influence hydrological processes. Our remote sites and thus models lack discharge data, which is a critical component of river temperature energy budgets (Baker et al, 2018). Land use in our study areas is dominated by forestry; changes to the forest landscape have occurred between 2008/2009 and 2017 (Linke et al, 2017), and these changes can modify river flow and thermal regimes (Moore et al, 2005; Smerdon et al, 2009, but see also Alexander, 2006; Kirchner et al, 2020).…”

“…Air temperature measurements are extracted by overlaying the TIR sample points on the raster. River discharge is an important parameter for river thermal regimes (Baker et al, 2018). In the remote setting of this study river gauges are absent.…”

“…The importance of thermal refugia for coldwater species in rivers will continue to increase in a warming climate (Corey et al, 2019;Dugdale et al, 2016). Modeling fine-scale inflows is complicated because surface water and groundwater thermal regimes are governed by different thermo-mechanical processes and flux at different amplitudes and magnitudes (Anderson, 2005;Glose et al, 2017). Some fine-scale inflows may also be ephemeral, further complicating modeling efforts (Ebersole et al, 2015;Leibowitz et al, 2008).…”

Salmonid thermal habitat contraction in a hydrogeologically complex setting

“…Precipitation was negatively correlated with temperature in all MLR and SSN models and was the least important parameter in the RF models (Tables 4 and 5). Increases in precipitation lead to increases in discharge, thereby increasing the thermal capacity of lentic and lotic bodies (Baker et al 2018).…”

Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models

“…Process-based stream temperature models simulate the real-world mass and energy fluxes driving stream temperature dynamics (e.g. Baker et al, 2018;Dugdale et al, 2017;Loinaz et al, 2013;Null et al, 2010;Yearsley, 2009) and consequently have the potential to elucidate the spatially and temporally variable effect of riparian tree cover on river temperature. These models often contain routines capable of simulating the impact of riparian shading on radiative (and to a lesser extent, turbulent) fluxes at the stream surface (e.g.…”