A Low-Cost, Multi-Sensor System to Monitor Temporary Stream Dynamics in Mountainous Headwater Catchments

Abstract

While temporary streams account for more than half of the global discharge, high spatiotemporal resolution data on the three main hydrological states (dry streambed, standing water, and flowing water) of temporary stream remains sparse. This study presents a low-cost, multi-sensor system to monitor the hydrological state of temporary streams in mountainous headwaters. The monitoring system consists of an Arduino microcontroller board combined with an SD-card data logger shield, and four sensors: an electrical resistance (ER) sensor, temperature sensor, float switch sensor, and flow sensor. The monitoring system was tested in a small mountainous headwater catchment, where it was installed on multiple locations in the stream network, during two field seasons (2016 and 2017). Time-lapse cameras were installed at all monitoring system locations to evaluate the sensor performance. The field tests showed that the monitoring system was power efficient (running for nine months on four AA batteries at a five-minute logging interval) and able to reliably log data (<1% failed data logs). Of the sensors, the ER sensor (99.9% correct state data and 90.9% correctly timed state changes) and flow sensor (99.9% correct state data and 90.5% correctly timed state changes) performed best (2017 performance results). A setup of the monitoring system with these sensors can provide long-term, high spatiotemporal resolution data on the hydrological state of temporary streams, which will help to improve our understanding of the hydrological functioning of these important systems.

Classifying Streamflow Duration: The Scientific Basis and an Operational Framework for Method Development

“…Daily or more frequent data are typically collected using deployed devices with data storage capabilities and referred to as continuous data, whereas less frequently collected data are referred to as discrete data. Continuous hydrologic data include the measuring of stage, velocity, water temperature, electrical resistance, and time-lapse imagery [142][143][144]. Less frequent observations or discrete data are typically collected using field observations (e.g., in-person, field cameras, landowner interviews [87,145,146] or remotely sensed observations (e.g., aerial photos, satellite images; [7,147,148] of flow status.…”

Analysing river network dynamics and active length - discharge relationship using water presence sensors

“…Two main techniques are reported in the literature for the deployment of ER sensors. A first technique consists in manufacturing a sensor made up of two distinct parts: i) the head containing the electrodes, which is located on the channel bed; and ii) the logger used to measure and record the response of the sensor head, which is typically located nearby (Bhamjee et al, 2016;Peirce and Lindsay, 2015;Assendelft and vanMeerveld, 2019). The second technique, instead, consists in converting already existing temperature sensors (Blasch et al, 2004;Adams et al, 2006;Jaeger and Olden, 2012) or commercially available temperature/light data loggers into ER sensors (Chapin et al, 2014;Goulsbra et al, 2014;Jensen et al, 2019;Kaplan et al, 2019;Paillex et al, 2020).…”

“…CC BY 4.0 License. conditions along the whole perimeter of the cross section where the sensors were placed, unless the stream bed was properly reshaped to convey the entire water flow towards the sensors (Assendelft and vanMeerveld, 2019). Additionally, while ER timeseries were often used to represent the spatial and temporal evolution of the active network in dynamical rivers, the statistical properties of individual ER timeseries have never been compared with independent empirical estimates of the local persistency of the channel segments hosting these sensors.…”

Aqua temporaria incognita

“…Other studies have used low‐cost electrical resistance (Bhamjee & Lindsay, 2011; Blasch, Ferré, Christensen, & Hoffmann, 2002; Chapin, Todd, & Zeigler, 2014; Goulsbra, Lindsay, & Evans, 2009; Paillex, Siebers, Ebi, Mesman, & Robinson, 2020; Sherrod, Sauck, & Werkema, 2012) or temperature (Constantz, 2008; Ronan, Prudic, Thodal, & Constantz, 1998) sensors to determine the onset and cessation of flow. The sensor networks developed by Bhamjee, Lindsay, and Cockburn (2016) and Assendelft and van Meerveld (2019) even allow differentiation of standing water (pools) and flowing water. Even though the initial tests of these sensors are promising, their use has yet to become commonplace, likely due to the need to invest in sensor development and maintenance.…”