Quantifying spatial and temporal discharge dynamics of an event in a first order stream, using distributed temperature sensing

Abstract

Abstract. Understanding the spatial distribution of discharge can be important for water quality and quantity modeling. Non-steady flood waves can, particularly as a result of short high intensity summer rainstorms, influence small headwater streams significantly. The aim of this paper is to quantify the spatial and temporal dynamics of stream flow in a headwater stream during a summer rainstorm. These dynamics include gains and losses of stream water, the effect of bypasses that become active and hyporheic exchange fluxes that may vary over time as a function of discharge. We use an advectiondispersion model coupled with an energy balance model to simulate in-stream water temperature, which we compare with high resolution temperature observations obtained with Distributed Temperature Sensing. This model was used as a learning tool to stepwise unravel the complex puzzle of instream processes subject to varying discharge. Hypotheses were tested and rejected, which led to more insight in the spatial and temporal dynamics in discharge and hyporheic exchange processes. We showed that, for the studied stream infiltration losses increase during a small rain event, while gains of water remained constant over time. We conclude that, eventually, part of the stream water bypassed the main channel during peak discharge. It also seems that hyporheic exchange varies with varying discharge in the first 250 m of the stream; while further downstream it remains constant. Because we relied on solar radiation as the main energy input, we were only able to apply this method during a small summer storm and low flow conditions. However, when additional (artificial) energy is available, the presented method is also applicable in larger streams, during higher flow conditions or longer storms.

What causes cooling water temperature gradients in forested stream reaches?

“…Thus, the evaluation statistics compared favourably to those observed in previous studies using similar models (e.g. Westhoff et al, 2011). The model was biased towards slight overprediction (as indicated by percent bias), but in all cases this was < 2.0 %.…”

“…Several studies have documented daytime cooling gradients (instantaneous decreases in downstream temperature) under forest canopies located downstream of open (no trees) land use, although the magnitude of reported cooling effects varied between studies (e.g. Brown et al, 1971;Rutherford et al, 1997;McGurck, 1989;Keith et al, 1998;Zwieniecki and Newton, 1999;Torgerson et al, 1999;Story et al, 2003;Westhoff et al, 2011). For example, McGurck (1989), Keith et al (1998) and Story et al (2003) observed gradients of between 4.0 and 9.2 • C km −1 .…”

What causes cooling water temperature gradients in a forested stream reach?

“…Brown et al, 1971;Rutherford et al, 1997;McGurck, 1989;Keith et al, 1998;Zwieniecki and Newton, 1999;Torgerson et al, 1999;Story et al, 2003;Westhoff et al, 2011). For example, McGurck (1989), Keith et al (1998) and Story et al (2003) observed gradients of between 4.0 and 9.2 • C km −1 .…”

Exploring Streamwater Mixing Dynamics via Handheld Thermal Infrared Imagery

“…In rivers, temperature exerts an important control on physiochemical characteristics of water [15] and it is a driver of Water 2017, 9, 358 2 of 16 numerous biological processes, controlling the structure of ecological communities and habitat complexity [16,17]. Approaches for measuring stream water temperature cover a wide range of methods including temperature loggers and thermometers [18][19][20], fiber-optic distributed temperature sensing (FO-DTS) [21,22] and thermal infrared (TIR) cameras [23][24][25].…”