Sensitivity of fluvial sediment source apportionment to mixing model assumptions: A Bayesian model comparison

Mixing models have become increasingly common tools for apportioning fluvial sediment load to various sediment sources across catchments using a wide variety of Bayesian and frequentist modeling approaches. In this study, we demonstrate how different model setups can impact upon resulting source apportionment estimates in a Bayesian framework via a one-factor-at-a-time (OFAT) sensitivity analysis. We formulate 13 versions of a mixing model, each with different error assumptions and model structural choices, and apply them to sediment geochemistry data from the River Blackwater, Norfolk, UK, to apportion suspended particulate matter (SPM) contributions from three sources (arable topsoils, road verges, and subsurface material) under base flow conditions between August 2012 and August 2013. Whilst all 13 models estimate subsurface sources to be the largest contributor of SPM (median ∼76%), comparison of apportionment estimates reveal varying degrees of sensitivity to changing priors, inclusion of covariance terms, incorporation of time-variant distributions, and methods of proportion characterization. We also demonstrate differences in apportionment results between a full and an empirical Bayesian setup, and between a Bayesian and a frequentist optimization approach. This OFAT sensitivity analysis reveals that mixing model structural choices and error assumptions can significantly impact upon sediment source apportionment results, with estimated median contributions in this study varying by up to 21% between model versions. Users of mixing models are therefore strongly advised to carefully consider and justify their choice of model structure prior to conducting sediment source apportionment investigations

High-temporal resolution fluvial sediment source fingerprinting with uncertainty: a Bayesian approach

This contribution addresses two developing areas of sediment fingerprinting research. Specifically, how to improve the temporal resolution of source apportionment estimates whilst minimizing analytical costs and, secondly, how to consistently quantify all perceived uncertainties associated with the sediment mixing model procedure. This first matter is tackled by using direct X-ray fluorescence spectroscopy (XRFS) and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analyses of suspended particulate matter (SPM) covered filter papers in conjunction with automatic water samplers. This method enables SPM geochemistry to be quickly, accurately, inexpensively and non-destructively monitored at high-temporal resolution throughout the progression of numerous precipitation events. We then employed a Bayesian mixing model procedure to provide full characterization of spatial geochemical variability, instrument precision and residual error to yield a realistic and coherent assessment of the uncertainties associated with source apportionment estimates. Applying these methods to SPM data from the River Wensum catchment, UK, we have been able to apportion, with uncertainty, sediment contributions from eroding arable topsoils, damaged road verges and combined subsurface channel bank and agricultural field drain sources at 60- and 120-minute resolution for the duration of five precipitation events. The results presented here demonstrate how combining Bayesian mixing models with the direct spectroscopic analysis of SPM-covered filter papers can produce high-temporal resolution source apportionment estimates that can assist with the appropriate targeting of sediment pollution mitigation measures at a catchment level

Scalability and reliability of web applications in a cloud environment illustrated by the example of Amazon Web Services platform

Celem pracy jest przedstawienie czytelnikowi różnych podejść do zagadnienia skalowalności i niezawodności aplikacji internetowych na przykładzie lidera dostawców rozwiązań chmurowych, jakim jest Amazon Web Services (AWS). Zaprezentowane zostaną serwisy AWS, które pomagają w pisaniu wysoce skalowalnych i niezawodnych aplikacji, sposób ich użycia oraz przykładowa usługa internetowa napisana w języku Scala. Usługa została przygotowana w dwóch wersjach – pierwszej, dla środowisk opartych na wykorzystaniu maszyn wirtualnych i kontenerów, oraz drugiej, utworzonej specjalnie na potrzeby przetwarzania bezserwerowego. Zadaniem aplikacji będzie zaprezentowanie sposobów tworzenia usług i witryn internetowych opartych na infrastrukturze udostępnianej przez AWS oraz pomoc w porównaniu opisywanych architektur.The purpose of the thesis is to familiarize a reader with various possible approaches to the subject of scalability and reliability illustrated by the example of Amazon Web Services (AWS), which is a leader in terms of providing cloud computing solutions. Multiple AWS services that help to write scalable and reliable web applications will be presented. The way how to use them along with exemplary web service written in the Scala language. The service has been implemented in two versions - first, for environments based on virtual machines or containers and the second one, created especially for serverless computing. The main goal of the application is to present the ways of creating web applications based on an infrastructure provided by AWS and help compare described architectures

Fingerprinting upland sediment sources : particle size-specific magnetic linkages between soils, lake and suspended sediments.

Accelerated erosion of fine-grained sediment is an environmental problem of international dimensions. Erosion control strategies and targeting of mitigation measures require robust and quantitative identification of sediment sources. Here, we use magnetic ‘fingerprinting’ to characterize soils, and examine their affinity with and contribution to suspended sediments transported within two subcatchments feeding Bassenthwaite Lake, northwest England. A high-resolution soil magnetic susceptibility survey was made using a field susceptometer (ZH Instruments, SM400 probe). Combining the spatial and vertical (down-profile) soil magnetic data, a subset of soil profiles was selected for detailed, laboratory-based magnetic remanence analyses. The magnetic properties of the catchment soils are highly particle size-dependent. Magnetic analyses were performed on the 31–63 μm fraction, for particle size-specific comparison both with the suspended sediments and lake sediments. Fuzzy cluster analysis groups the soil magnetic data into six clusters, apparently reflecting variations in parent material and horizon type, with three magnetically hard soils as unclassified outliers. Examination of the cluster affinity of the soils, suspended sediments and lake sediments indicates that topsoils of the upper Newlands Valley and subsoils around Keskadale Beck are a major source of the Newlands Beck suspended load, and the recent (post-nineteenth century) sediments in the deep lake basin. Older lake sediments show strong affinity with a small number of the Derwent suspended sediments and one of the Glenderamackin soils. A large number of Derwent suspended sediments show no affinity with any of the soils or lake sediments, instead forming a coherent, discrete and statistically unclassified group, possibly resulting from mixing between the magnetically hard subsoils of the medium to high-altitude Glenderamackin and Troutbeck areas and softer, lower altitude Glenderamackin soils. The lack of any affinity of these suspended sediments with the lake sediments may indicate deposition along the Derwent flood plain and/or in the shallow delta of Lake Bassenthwaite. Particle size-specific magnetic fingerprinting is thus shown to be both highly discriminatory and quantitatively robust even within the homogeneous geological units of this catchment area. Such a methodological approach has important implications for small–large scale catchment management where sources of sediment arising from areas with uniform geology have been difficult to determine using other approaches, such as geochemical or radionuclide analyses