Accuracy of mixing models in predicting sediment source contributions

International audienceDetermining 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

Sediment fingerprinting in fluvial systems: review of tracers, sediment sources and mixing models

International audienceSuspended sediments in fluvial systems originate from a myriad of diffuse and point sources, with the relative contribution from each source varying over time and space. The process of sediment fingerprinting focuses on developing methods that enable discrete sediment sources to be identified from a composite sample of suspended material. This review identifies existing methodological steps for sediment fingerprinting including fluvial and source sampling, and critically compares biogeochemical and physical tracers used in fingerprinting studies. Implications of applying different mixing models to the same source data are explored using data from 41 catchments across Europe, Africa, Australia, Asia, and North and South America. The application of seven commonly used mixing models to two case studies from the US (North Fork Broad River watershed) and France (Bléone watershed) with local and global (genetic algorithm) optimization methods identified all outputs remained in the acceptable range of error defined by the original authors. We propose future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the modified Collins (using correction factors) and Hughes (relying on iterations involving all data, and not only their mean values) models with optimization using genetic algorithms to best predict the relative contribution of sediment sources to fluvial systems

Sediment fingerprinting in fluvial systems: review of tracers, sediment sources and mixing models

Suspended sediments in fluvial systems originate from a myriad of diffuse and point sources, with the relative contribution from each source varying over time and space. The process of sediment fingerprinting focuses on developing methods that enable discrete sediment sources to be identified from a composite sample of suspended material. This review identifies existing methodological steps for sediment fingerprinting including fluvial and source sampling, and critically compares biogeochemical and physical tracers used in fingerprinting studies. Implications of applying different mixing models to the same source data are explored using data from 41 catchments across Europe, Africa, Australia, Asia, and North and South America. The application of seven commonly used mixing models to two case studies from the US (North Fork Broad River watershed) and France (Bléone watershed) with local and global (genetic algorithm) optimization methods identified all outputs remained in the acceptable range of error defined by the original authors. We propose future sediment fingerprinting studies use models that combine the best explanatory parameters provided by the modified Collins (using correction factors) and Hughes (relying on iterations involving all data, and not only their mean values) models with optimization using genetic algorithms to best predict the relative contribution of sediment sources to fluvial systems

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