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 supporting: 'Green Nourishment: An Innovative Nature-Based Solution for Coastal Erosion'(Cranfield University, 2022-10-13 16:22) Chen, Wenlong; Grabowski, Robert; Goel, SauravCoastal erosion poses an urgent threat to life and property in low-lying regions. Sand nourishment is increasing used as a nature-based solution but requires significant natural resources and replenishment over time. In this study, a novel form of nourishment is explored that combines shoreface nourishment and seagrass restoration to mitigate coastal erosion (i.e. green nourishment). Using the coastal morphodynamic model Xbeach, the impact of seagrass planting on wave energy dissipation, sediment erosion and transport, and morphological evolution of a cross-shore profile was studied for mild wave conditions and an intense storm. Model results indicate that a seagrass meadow enhanced the wave energy dissipation provided by a shoreface nourishment and suggest that it may be particularly effective in sediment transport mitigation when implemented in the sheltered nearshore area. The morphological feature of the shoreface nourishment reduced the wave strength on the seagrass meadow and reduced the rate of seagrass destroyed by deposition or erosion over the grass height after storm event. Green nourishment also reduced beach foreshore erosion caused by a simulated storm event. An alternative, more cost-effective planting technique using seagrass seeds was explored, which showed similar coastal erosion protection benefits to seagrass transplants. This modelling study found that green nourishment is potentially an effective nature-based solution for coastal erosion and flooding on sandy coasts, and future studies are recommended to evaluate its morphological, ecological and flood risk reduction benefits in the field.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 DATASET "De-icer Mobilisation by Rainfall"(Cranfield University, 2024-08-02) Exton, Benjamin; Grabowski, RobertHPLC data of propylene glycol in runoff (mobilised de-icer in rainfall)Item Open Access Dataset "Impact of Carbon Sources in Airport De-icing Compounds on the Growth of Sphaerotilus natans"(Cranfield University, 2024-09-12) Exton, Benjamin; Grabowski, Robert; Hassard, Francis; Medina Vaya, AngelItem 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 Impact of the Oxford canal on water quality and ecological status of the River Cherwell.(Cranfield University, 2021-09) Dowdeswell-Downey, Luke; Grabowski, Robert; Campo Moreno, PabloGood quality water provides numerous ecosystem services and it is a legal requirement of the Water Framework Directive for all waterbodies to be achieving good status. Achieving good water quality and ecological status remains a critical issue throughout most waterbodies within the UK. Waterbodies can have an impact on one another, however, determining the impact of tributaries and canal interactions on rivers remains a challenge. This study aims to investigate whether the Oxford canal has had a negative impact upon water quality and ecological status of the River Cherwell. New water quality and sediment samples were collected, upstream and downstream of where the river and canal interact at two sections at 16 sampling locations. Historical invertebrate data was examined at three locations; upstream, between and downstream of these two sections along the River Cherwell and compared to the nearby River Windrush, which does not interact with any canals. Results showed no statistical decreases in river water quality downstream of where the canal joins the river. Conversely, there was evidence that interaction with the canal water may have decreased river nutrient levels, such as nitrate, by a dilution effect. Furthermore, mixing with river water may have significantly increased orthophosphate and conductivity in the canal (p< 0.05). Similarly, invertebrate data showed no negative impact from the canal on the river in terms of taxa richness, BMWP, ASPT, and diversity indices. However, results suggested that the River Cherwell has had higher invertebrate metrics and indices when compared to the River Windrush. Overall, this study found no evidence to indicate that the Oxford canal negatively impacted water quality and ecological status of the River Cherwell.Item 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 Sewage fungus occurrence in English rivers(Cranfield University, 2024-02-05 11:13) Grabowski, Robert; Exton, Ben; Hassard, FrancisThe dataset contains the location and date (month/year) of sewage fungus observed in rivers by Environment Agency environment officers (2000-2020). The data were obtained via a standard data request to the Environment Agency. Spatial locations were limited to a 10 x 10 km resolution by the Environment Agency, and no information was provided on the river or pollution source, to comply with their data protection rules.Item 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.