Technical note: Analyzing river network dynamics and the active length–discharge relationship using water presence sensors

Abstract

Abstract. Despite the importance of temporary streams for the provision of key ecosystem services, their experimental monitoring remains challenging because of the practical difficulties in performing accurate high-frequency surveys of the flowing portion of river networks. In this study, about 30 electrical resistance (ER) sensors were deployed in a high relief 2.6 km2 catchment of the Italian Alps to monitor the spatio-temporal dynamics of the active river network during 2 months in the late fall of 2019. The setup of the ER sensors was customized to make them more flexible for the deployment in the field and more accurate under low flow conditions. Available ER data were compared to field-based estimates of the nodes' persistency (i.e., a proxy for the probability to observe water flowing over a given node) and then used to generate a sequence of maps representing the active reaches of the stream network with a sub-daily temporal resolution. This allowed a proper estimate of the joint variations of active river network length (L) and catchment discharge (Q) during the entire study period. Our analysis revealed a high cross-correlation between the statistics of individual ER signals and the flow persistencies of the cross-sections where the sensors were placed. The observed spatial and temporal dynamics of the actively flowing channels also highlighted the diversity of the hydrological behavior of distinct zones of the study catchment, which was attributed to the heterogeneity in catchment geology and stream-bed composition. Our work emphasizes the potential of ER sensors for analyzing spatio-temporal dynamics of active channels in temporary streams, discussing the major limitations of this type of technology emerging from the specific application presented herein.

Eco-hydrological modelling of channel network dynamics—part 1: stochastic simulation of active stream expansion and retraction

“…The synchronicity between increases (decreases) of Q and network expansion (contraction) implies the existence of a bijective correspondence between Q at the outlet and the spatial configuration of the active nodes in the network. This complies with the one-to-one relation between Q and active length L frequently observed in the literature [19,30,34,40,45,46]. Under the aforementioned assumptions, whenever a streamflow Q with duration D is observed at the outlet, the corresponding active network configuration is made by nodes that are active for a fraction of time which is at least equal to D , because they are active also in all the more expanded configurations observed during higher flow levels.…”

“…The coupling is allowed by the one-to-one relation between streamflow and active length, which was commonly observed in many practical settings (e.g. [30,40,45,46]). One of the main advantages of the model is the explicit description of the link between climate, discharge and stream network dynamics.…”

Probabilistic Description of Streamflow and Active Length Regimes in Rivers

“…While the estimation of L from field surveys is highly time‐consuming, the ensuing data are considered to be quite robust, as most of the uncertainty relies on the practical definition of an “active stream” through the identification of threshold widths and/or velocities. In the literature, the parameter b has been often assessed using relatively few river network surveys (Godsey & Kirchner, 2014; Jensen et al., 2017) and its estimate seems to be relatively stable when the sampling frequency changes (Zanetti et al., 2021). Thus, we suggest that relatively few surveys (e.g., from 5 to 10) performed under different hydrologic conditions should be enough to estimate the value of the scaling exponent b and enable the extension of the analytical streamflow regime classification to the active length regime.…”

“…In fact, the soil composition and the vegetation features change along the altimetric gradient of the catchment (Figure 3a). The Valfredda catchment has been extensively studied in previous work (Botter & Durighetto, 2020; Durighetto & Botter, 2021; Durighetto et al., 2020; Zanetti et al., 2021). The Valfredda river has an alpine climate, characterized by cold snowy winters and wet summers.…”

Harnessing hyperspectral imagery to map surface water presence and hyporheic flow properties of headwater stream networks

“…Recent methodological developments, such as the deployment of flow presence-absence sensors and drone surveys (e.g. Dugdale et al, 2022;Carbonneau et al, 2020;Zanetti et al, 2022), provide important constraints, but tend to be limited in space or time. Water presence can be detected in large open water (Wang et al, 2022) bodies or main stem river reaches with width greater than existing satellite imagery pixel resolutions (∼10-30 m pixel, Wang et al, 2022;Qin et al, 2021;Verma et al, 2021).…”