Browsing by Author "Grabowski, Robert"
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Item Open Access Analysis of scaling relationships for flood parameters and peak discharge estimation in tropical regions data(Cranfield University, 2024-02-20 14:36) Grabowski, RobertHydrological data from the La Sierra catchment (Mexico), including input data used in the statistical modelling and output data from the scaling, correlation and regression analyses. Please see the readME file for the list of data and the published paper for more informationItem Open Access Data supporting the publication 'Clay swelling: role of cations in stabilizing/destabilizing mechanisms'(Cranfield University, 2022-01-18 16:34) Chen, Wenlong; Grabowski, Robert; Goel, SauravIn the compressed dataset, there are two subdirectories, one in the name of 'Example' and another 'PostprocessData'. The Example directory contains input files, output data and postprocessed data for case Na12 starting at a d-space of onelayer value, where files starts with in.* are input files for lammps software, files ending with .dat or .lmptrj are output files from lammps, and files ending with .mat are matlab processed data. The 'postporcessedata' contains matlab processed results for all simulations in this study, contains simulation for NaMMT, KMMT, CaMMT and NaBD starting at onelayer, twolayer and threelayer d-space values.Item Open Access Data: Catchment and Climatic Influences on Spatio-Temporal Variations in Suspended Sediment Transport Dynamics in Rivers(Cranfield University, 2023-08-08 18:01) hun Shin, Jae; Grabowski, Robert; Holman, IanThe excel file contains calculation results of Suspended sediment (SS) dynamics indicators using 3day Kalman interpolation incl. seasonal indicators at 120 selected sites. The file also contains site attribution data used in the model obtained from USGS. The coordinates are gauging stations.Item Open Access Data: Indicators of Suspended Sediment Transport Dynamics in Rivers(Cranfield University, 2023-07-26 11:26) hun Shin, Jae; Grabowski, Robert; Holman, IanThe dataset contains SS dynamics indicator calculation results for contiental USA, Honolulu and Puerto Rico. The indicators represent magnitude, frequency and timing. This secondary data has been created without gap filling.Item Open Access Effects of large wood on invertebrate assemblages in the bed of a lowland river using a functional trait approach(Cranfield University, 2019-01-02 11:35) Grabowski, Robert; Magliozzi, ChiaraThis data is related to a research project using invertebrate ecological data to investigate functional traits at large wood sites. The research took place in the Hammer Stream, United Kingdom. The data was collected between 2016 and 2017 and available as separate .csv files.Two types of data are available: 1. Abundances of invertebrates and Traits2 Environmental dataPlease see the "description.txt" and 'readme.txt' files for further explanationItem Open Access Land-River Interface Management Semi-Structured Interview Data(Cranfield University, 2022-12-07 13:58) Grabowski, Robert; Azhoni, AzhoniAbout the data: The data contained in this archive are collected through semi-structured interviews from officials in key organizations involved in land-river interface management in India. The interviews were conducted through online video conferencing and recorded which were later transcribed verbatim for analyzing the key challenges of land-river interface management in India. The Garret ranking data was collected at the end of the interviews and marked in the form by the interviewer. The research was conducted as part of a collaborative NERC Towards a Sustainable Earth project (2019-2022), with direct funding from the Indian Department of Biotechnology. File names: Each interview transcript is in different files with the file name indicating the type organizations the interview respondents represent, viz; Academic Institutions (AI), Central Government Agencies (CGA), Central Government Ministries (CGI), Non-governmental organizations (NG), and State Government Departments (SG). This files are being archived for future reference and for providing transparency to the research data.Item Open Access Sediment fingerprinting: source classification(Cranfield University, 2018-03-08 16:02) Vercruysse, Kim; Grabowski, RobertThis data is related to a research project using sediment fingerprinting based on Diffuse Reflectance Infrared Fourier Transform Spectrometry (DRIFTS) to estimate sediment source contributions to suspended sediment in rivers. The research took place in the River Aire catchment, United Kingdom. The data was collected between 2014 and 2017.Two types of data are available:1. Suspended sediment concentrations during high-flow events (SuspendedSedimentConcentration_RiverAire.csc). Samples were collected with a depth-integrating suspended sediment sampler from the side of the river at Brewery Wharf in the city center of Leeds (dates are indicated)2. Diffuse Reflectance Infrared Fourier Transform Spectrometry (DRIFTS) spectra of sediment samples Type of samples: - suspended sediment, - bed sediment, - sediment sources (*),- experimental mixtures of sediment source samples. Each type of sample is included in a separate .CSV file (**)(*) Sample IDs: CR, LR, MR (samples from eroding riverbanks in coals, limestone and millstone area respectively). CU, LU, MU (samples from uncultivated grassland soils in coals, limestone and millstone area respectively). U (urban street dust samples). Numbers 1-2-3 represent sub samples taken within one square meter.(**) First row of the columns in all DRIFTS files represent the wavelength (micrometer-1) ranging between 3799 and 651Item Open Access Using source-specific models to test the impact of sediment source classification on sediment fingerprinting(Wiley, 2018-08-31) Vercruysse, Kim; Grabowski, RobertSediment fingerprinting estimates sediment source contributions directly from river sediment. Despite being fundamental to the interpretation of sediment fingerprinting results, the classification of sediment sources and its impact on the accuracy of source apportionment remain underinvestigated. This study assessed the impact of source classification on sediment fingerprinting based on diffuse reflectance infrared Fourier transform spectrometry (DRIFTS), using individual, source‐specific partial least‐squares regression (PLSR) models. The objectives were to (a) perform a model sensitivity analysis through systematically omitting sediment sources and (b) investigate how sediment source‐group discrimination and the importance of the groups as actual sources relate to variations in results. Within the Aire catchment (United Kingdom), five sediment sources were classified and sampled (n = 117): grassland topsoil in three lithological areas (limestone, millstone grit, and coal measures); riverbanks; and street dust. Experimental mixtures (n = 54) of the sources were used to develop PLSR models between known quantities of a single source and DRIFTS spectra of the mixtures, which were applied to estimate source contributions from DRIFTS spectra of suspended (n = 200) and bed (n = 5) sediment samples. Dominant sediment sources were limestone topsoil (45 ± 12%) and street dust (43 ± 10%). Millstone and coals topsoil contributed on average 19 ± 13% and 14 ± 10%, and riverbanks 16 ± 18%. Due to the use of individual PLSR models, the sum of all contributions can deviate from 100%; thus, a model sensitivity analysis assessed the impact and accuracy of source classification. Omitting less important sources (e.g., coals topsoil) did not change the contributions of other sources, whereas omitting important, poorly‐discriminated sources (e.g., riverbank) increased the contributions of all sources. In other words, variation in source classification substantially alters source apportionment depending on source discrimination and source importance. These results will guide development of procedures for evaluating the appropriate type and number of sediment sources in DRIFTS‐PLSR sediment fingerprinting.