ORCID Profile
0000-0003-4121-151X
Current Organisations
University of Melbourne
,
University of Zurich
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Publisher: Bundesanstalt für Gewässerkunde, Koblenz
Date: 2019
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-19965
Abstract: & & Temporary streams are common in headwater catchments and serve as important ecological and hydrological links between these catchments and downstream perennial rivers. However, our understanding of temporary streams in headwater catchments is limited due to a lack of high spatiotemporal resolution data of the three main hydrological states of these streams: dry streambed, standing water and flowing water. In this study, we used a custom designed multi-sensor monitoring system to collect high spatiotemporal resolution state data of the temporary streams in the 0.12 km& sup& & /sup& upper Studibach catchment, a pre-alpine headwater catchment in Alptal, Switzerland. The monitoring system was installed at 30 locations in the stream network. The state data was used to determine: (1) the temporary stream regime for every monitoring location based on the permanence of each hydrological state, (2) the state change thresholds (antecedent soil moisture, precipitation amount and intensity, and discharge at the outlet) for every monitoring location, and (3) the state change patterns in the stream network during precipitation events. The temporary stream regimes, and the state change thresholds and patterns were compared to topographic, land cover and channel characteristics to determine if these factors can explain the variability in temporary stream dynamics. The results show that there are four different landscape areas with distinctive temporary stream dynamics in the catchment, and that a steep forested section with coarse streambed material often disconnects the flowing parts of the upper and lower stream network.& &
Publisher: Copernicus GmbH
Date: 15-05-2019
Abstract: Abstract. Flowing stream networks dynamically extend and retract, both seasonally and in response to precipitation events. These network dynamics can dramatically alter the drainage density, and thus the length of subsurface flow pathways to flowing streams. We mapped flowing stream networks in a small Swiss headwater catchment during different wetness conditions and estimated their effects on the distribution of travel times to the catchment outlet. For each point in the catchment, we determined the subsurface transport distance to the flowing stream based on the surface topography, and the surface transport distance along the flowing stream to the outlet. We combined the distributions of these travel distances with assumed surface and subsurface flow velocities to estimate the distribution of travel times to the outlet. These calculations show that the extension and retraction of the stream network can substantially change the mean travel time and the shape of the travel time distribution. During wet conditions with a fully extended flowing stream network, the travel time distribution was strongly skewed to short travel times, but as the network retracted during dry conditions, the distribution of the travel times became more uniform. Stream network dynamics are widely ignored in catchment models, but our results show that they need to be taken into account when modeling solute transport and interpreting travel time distributions.
Publisher: Elsevier BV
Date: 2013
Publisher: Copernicus GmbH
Date: 27-11-2019
DOI: 10.5194/HESS-23-4825-2019
Abstract: Abstract. Flowing stream networks dynamically extend and retract, both seasonally and in response to precipitation events. These network dynamics can dramatically alter the drainage density and thus the length of subsurface flow pathways to flowing streams. We mapped flowing stream networks in a small Swiss headwater catchment during different wetness conditions and estimated their effects on the distribution of travel times to the catchment outlet. For each point in the catchment, we determined the subsurface transport distance to the flowing stream based on the surface topography and determined the surface transport distance along the flowing stream to the outlet. We combined the distributions of these travel distances with assumed surface and subsurface flow velocities to estimate the distribution of travel times to the outlet. These calculations show that the extension and retraction of the stream network can substantially change the mean travel time and the shape of the travel time distribution. During wet conditions with a fully extended flowing stream network, the travel time distribution was strongly skewed to short travel times, but as the network retracted during dry conditions, the distribution of the travel times became more uniform. Stream network dynamics are widely ignored in catchment models, but our results show that they need to be taken into account when modeling solute transport and interpreting travel time distributions.
Publisher: MDPI AG
Date: 25-10-2019
DOI: 10.3390/S19214645
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 ( % 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.
Publisher: SAGE Publications
Date: 04-06-2013
Publisher: Wiley
Date: 10-2014
DOI: 10.1002/HYP.10347
No related grants have been discovered for Rick Assendelft.