Kinematic Loggers-Development of Rugged Sensors and Recovery Systems for Field Measurements of Stone Rolling Dynamics and Impact Accelerations during Floods.

Discrete particle dynamics is one of the least understood aspects of river bedload transport, but in situ measurement of stone movement during floods poses a significant technical challenge. A promising approach to address this knowledge gap is to use sensors embedded within stones. Sensors must be waterproof and recoverable after being transported downstream and potentially buried by other sediment. To address this challenge rugged sensors (Kinematic Loggers) were developed for deployment inside stones (ranging in size from cobbles to boulders) during floods. The sensors feature a 9-axis inertial measurement unit, 3-axis high-g accelerometer, 128 MB flash memory, and a 433 MHz LoRa radio transmission module for sensor recovery. The sensors are enclosed in rugged waterproof housings for deployment in extreme conditions (i.e., bedload transport during floods). Novel relay units and drone-based recovery systems were also developed for finding the sensors after field deployments. Firmware to control the sensors and relay units was developed, as well as software for configuring the sensors and an android application for communicating with the sensors via the LoRa radio transmission module. This paper covers the technical development of the sensors, mounting them inside stones, and field recovery tests. Although designed for measurement of coarse bedload transport and particle dynamics during floods, the sensors are equally applicable for deployment in other harsh environments, such as to study landslide and rockfall dynamics.

Vegetation and longitudinal coarse sediment connectivity affect the ability of ecosystem restoration to reduce riverbank erosion and turbidity in drinking water.

It is a substantial challenge to quantify the benefits which ecosystems provide to water supply at scales large enough to support policy making. This study tested the hypothesis that vegetation could reduce riverbank erosion, and therefore contribute to reducing turbidity and the cost of water supply, during a large magnitude flood along a 62 km riparian corridor where land cover differed substantially from natural conditions. Several lines of evidence were used to establish the benefits that vegetation provided to reducing eleven riverbank erosion processes over 1688 observations. The data and analyses confirmed that vegetation significantly reduced the magnitude of the riverbank erosion process which was the largest contributor to total erosion volume. For this process, a 1% increase in canopy cover of trees higher than five metres reduced erosion magnitude by between 2 and 3%. Results also indicate that riverbank erosion was likely to be affected by direct changes to the riparian corridor which influenced longitudinal coarse sediment connectivity. When comparing the impact of these direct changes on a relative basis, sand and gravel extraction was likely to be the dominant contributor to changed erosion rates. The locations where erosion rates had substantially increased were of limited spatial extent and in general substantial change in river form had not occurred. This suggests that the trajectory of river condition and increasing turbidity are potentially reversible if the drivers of river degradation are addressed through an ecosystem restoration policy.

Tracing sediment sources in a mountainous forest catchment under road construction in northern Iran: comparison of Bayesian and frequentist approaches.

Development and land use change lead to accelerated soil erosion as a serious environmental problem in river catchments in Iran. Reliable information about the sources of sediment in catchments is therefore necessary to design effective control strategies. This study used a composite sediment source tracing procedure to determine the importance of forest road cuttings as a sediment source in a mountainous catchment located in northern Iran. A fallout radionuclide (137Cs) and 12 geochemical tracers (Ca, Cu, Fe, K, Mg, Mn, Na, Ni, OC, Pb, Sr and TN) were used to determine the relative contributions of three sediment source types (hillslopes, road cuttings and channel banks) to both suspended and bed sediment samples. Two mixing models based on different mathematical concepts were used to apportion the sediment sources: the mixture sampling importance resampling Bayesian model which incorporates the mass-balance matrix and a distribution model using normal and summed probability of normal distributions. The results of both mixing models indicated that sub-soil erosion from road cuttings and channel banks dominated the sources of river bed and suspended sediment samples, respectively. These results therefore highlight that conservation that works in the study area to remedy the sediment problem should initially focus on stabilisation and rehabilitation of road cuttings and channel banks. This successful application of a composite (radionuclide and geochemical) tracing technique for discriminating source end members characterised by different erosion processes underscores the importance of sub-soil erosion in this case study.

Accuracy of mixing models in predicting sediment source contributions.

Determining the source of sediment using geochemical properties is now a widely used approach in catchment management. However the outcome of these studies often depends on the type of model used to determine the relative contribution from difference sources. Here we test the accuracy and robustness of four widely used sediment mixing models using artificial mixtures of three well-distinguished geologic sources. Sub-samples from these three sources were mixed to create four groups of samples, each consisting of five samples, with known source contributions, 20 samples in total. The source contributions to the individual and groups of artificial sediment mixtures were calculated using each of the four mixing models: Modified Hughes, Modified Collins, Landwehr and Distribution models. Unlike Modified Collins and Landwehr models which use calculated values from each tracer property of individual sources (e.g. mean and standard deviation), Hughes model uses the measured fingerprint property of replicated samples from each source and Distribution model incorporate distribution of tracers and correlation between tracer properties for sediment samples and sources. For the 20 individual sample mixtures the Distribution model provided the closest estimates to the known sediment source contribution values (Mean Absolute Error (MAE)=10.8%, and standard error (SE)=0.9%). The Modified Hughes (MAE=13.5%, SE=1.1%), Landwehr (MAE=19%, SE=1.7) and Collins models (MAE=29%, SE=2.1%) were the next accurate models, respectively. For the groups of the samples the Modified Hughes was the most robust source contribution predictor with 5.4% error. The Distribution model (MAE=6.1%) and Landwehr model (MAE=7.8%) were the second and third accurate models. Collins model with MAE of 28.3% was a significantly weaker source contribution predictor than the three other models. This study demonstrates the dependence of source attribution on model selection. The study highlight the need to test mixing model using known source and mixture samples prior to applying them to field samples. The results indicate that the Distribution and Modified Hughes models provided the most accurate source attributions using geochemical fingerprint properties.