Arman Haddadchi

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 (Bleone 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.

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.

Evaluation of suspended load transport rate using transport formulas and artificial neural network models (Case study: Chelchay Catchment)

Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r=0.69) and lower Root Mean Square Error (RMSE=0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.

Assessment of bed-load predictors based on sampling in a gravel bed river

Bedload transport in alluvial channels has been extensively studied and different equations based on field and/or experimental data have been proposed. Prediction of bed-load transport rate using different equations results in wide ranges which are not always reliable. In this study, some of the universal bedload predictors were evaluated with measured load by a Helley-Smith sampler in the Node River, a gravel bed river in the northeast part of Iran. From 19 sets of data, 14 series of data were used to evaluate the bed-load transport equations. The results show that the equations presented by Van Rijn, Meyer-Peter and Mueller, and Ackers and White may adequately predict bedload transport in the range of field data.

An alternative method for interpreting jet erosion test (JET) data: Part 2. Application

This paper reports the results of jet tester experiments on soil samples of uniform properties which allow quantitative application of the new theory proposed in part 1 of these publications. This theory explores the possibly that a more adequate indicator of soil erodibility may be obtained by using the mass (and so volume) of soil eroded by the jet and the depth of scour penetration, rather than by using penetration depth alone, as assumed in the commonly-used data interpretation method. It is shown that scour geometry can be well described using a generalized form of the Gaussian function, defined by its standard deviation and maximum depth. Using a published expression for jet kinetic energy flux, the new theory divides this flux into that used to erode soil, and the remainder which is dissipated in a variety of ways. Jet experiments on a specially-prepared uniform soil sample are reported which provide the key to determining the spatial variability in the profile resistance to erosion offered by field soils. This resistance is expressed in the work required to erode unit mass of soil, denoted as J (in J/kg). The paper also gives results obtained on the profile variation in J for jet tests carried out at riverine sites on the upper Brisbane River, Queensland, Australia. As expected in most natural soil profiles, the results show an increase in J with depth in the profile. The soil resistance (J) is compared to the traditional interpretation of soil erodibility, (kd). The graphical comparison of these two indicators illustrates the inverse type of relationship between them which is expected from their respective definitions, but this relationship is associated with significant scatter. Possible reasons for this scatter are given, together with comments on jet tester experience in a wide variety of soil types. Copyright

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.