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Newcastle University

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  • This dataset consists of an ecology-focused survey of stillwaters along the rivers Yure and Swale and sediment flux measurements recorded at sites along the river Esk. The dataset results from a study which was part of the Rural Economy and Land Use (RELU) programme. The project analysed the complex network of natural and socio-economic relationships around angling in the river environment, including institutions of governance and land use practices at a range of interconnected scales. The sustainability, integrity and ecological value of river catchments are currently major issues for science. The management of freshwaters and their ecologies requires addressing processes that work across the boundaries between the natural environment, economy and society. This research focused upon these cross-cutting processes in an interdisciplinary, holistic assessment of river environments through the case of angling. Angling benefits from and influences river quality, design and management. It also links urban and rural environments and is an economic driver for the rural economy, involving about 4 million people in England and Wales and contributing 6 billion pounds to the economy through freshwater angling alone. This research aimed to provide insights into how environmental and socio-economic drivers for rural change work. This project therefore aimed to identify and analyse the complex network of influences and feedbacks around angling in the rural environment. These include natural and socio-economic influences, interdisciplinary research from both natural and social science disciplines (aquatic ecology, geomorphology, anthropology, sociology, human geography), as well as stakeholders from government, NGOs and the local community. This project focused upon three rivers in northern England - the Esk, Ure and Swale - in the course of an integrated and fine-grained study. The postal survey and business interviews from this study are available at the UK Data Archive under study number 6580 (see online resources). Further documentation for this study may be found through the RELU Knowledge Portal and the project's ESRC funding award web page (see online resources).

  • This data contains the time series flow discharge results of hydrological simulation of the River Trent at Colwick using UKCP09 Weather Generator inputs for a variety of time slices and emissions scenarios. The Weather Generator (WG) inputs were run on a hydrological model (Leathard et al., unpublished), calibrated using the observed record 1961-2002. Each simulation is derived from 100 30-year time series of weather at the WG location 4400355 for Control, Low, Medium and High emissions scenarios for the 2020s, 2030s, 2040s, 2050s and 2080s time slices. The datasets include the relevant accompanying input WG data. Full details about this dataset can be found at https://doi.org/10.5285/986d3df3-d9bf-42eb-8e18-850b8d54f37b

  • Hourly precipitation (mm) recorded at distributed points around Kampala between April 2019 and March 2020. Only timestamps where data were available from all sensors have been included. There are 8094 records in total and no missing values. Timestamps are recorded as “YYYY-MM-DD hh:mm:ss”. The geographic coordinates of the sensors are provided in GeoJSON format. The column names in the CSV file correspond to the “id” field in the GeoJSON file. Full details about this dataset can be found at https://doi.org/10.5285/3df031ad-34ec-4abc-8528-f8f20bad12b8

  • Data comprise modelled flood extents for the Kampala district produced by simulating rainfall events over a 5m Digital Elevation Model (DEM) using a 2D finite-volume hydrodynamic model. The DEM was obtained from Makerere University and rainfall events were sampled across a range of depths and durations (for 20, 40, 60, 80 and 100 mm of rainfall over 1, 3 and 6 hours using flood depth thresholds of 0.1, 0.2 and 0.3 mm). The effects of infiltration were included within green areas based on spatial data obtained from Makerere University. Maximum depths were converted into extents using various thresholds. Full details about this dataset can be found at https://doi.org/10.5285/e53dea2e-cb25-4f0f-b5f9-937eecf15aff

  • The dataset collates the relative concentration of nearly 300 antimicrobial resistance (AMR) genes, and concentrations of polycyclic aromatic hydrocarbons (PAH) and potentially toxic elements (PTE; e.g., “metals”) found in soils across northeastern England during a sampling expedition in June 2016 by researchers at Newcastle University. Top soils (15cm depths; “A” horizon) were obtained from 24 rural and urban locations around Newcastle upon Tyne, representing a spectrum of landscape conditions relative to anticipated PTE contamination. There are three files related to different types of data collected: antimicrobial resistance genes, metal concentrations and PAH concentrations. The high-throughput analysis of nearly 300 AMR genes include many resistance traits representing major antibiotic classes: aminoglycosides, beta lactams, FCA (fluoroquinolone, quinolone, chloramphenicol, florfenicol and amphenicol resistance genes), MLSB (macrolide, lincosamide, streptogramin B), tetracycline, vancomycin, sulphonamide, and efflux pumps. PAH data represent the US Environmental Protection Agency priority polycyclic aromatic hydrocarbons as one of the measures of pollution impact. The other measure of impact is based on levels of twelve PTE represented by “total” and “two bio-available” concentrations, based on three extraction methods. Elements included aluminium, arsenic, beryllium, cadmium, chromium, copper, iron, lead, mercury, nickel, phosphorus, and zinc. Full details about this dataset can be found at https://doi.org/10.5285/35b49db6-8522-4c6b-a779-820268292603

  • 3D digital elevation models of Tsho Rolpa glacier lake, Nepal, generated from unmanned aerial vehicle (UAV) imagery, with a spatial resolution of 10 centimetres. It is combined with bathymetry data so that both the lakebed elevation (DTM) and the lake surface elevation (DSM) are obtained. Full details about this dataset can be found at https://doi.org/10.5285/8e483692-3b65-41d2-a7fd-5a3cd589a71c

  • [THIS DATASET HAS BEEN WITHDRAWN]. The dataset contains 1km gridded estimates of hourly rainfall for Great-Britain for the period 1990-2014. The estimates are derived by applying the nearest neighbour interpolation method to a national database of hourly raingauge observations collated by Newcastle University and the Centre for Ecology & Hydrology (CEH). These interpolated hourly estimates were then used to temporally disaggregate the CEH-GEAR daily rainfall dataset. The estimated rainfall on a given hour refers to the rainfall amount accumulated in the previous hour. The dataset also contains data indicating the distance between the grid point and the closest recording raingauge used in its interpolation. When this distance is greater than 50km, or there is zero rainfall recorded in the closest gauge, the daily value is disaggregated using a design storm. The dataset therefore also contains a flag indicating if the design storm was used. These data are provided as an indicator of the quality of the estimates. Full details about this dataset can be found at https://doi.org/10.5285/d4ddc781-25f3-423a-bba0-747cc82dc6fa

  • The data consists of identified exposed objects subject to flooding risk from the Tsho Rolpa Lake. The Tsho Rolpa Lake is the largest moraine-dammed proglacial lake in Nepal and was identified as one of the country’s most dangerous glacier lakes with a high possibility of outburst. Full details about this dataset can be found at https://doi.org/10.5285/3834d477-7a1d-4ad3-8a41-d38fc727dbd8

  • This dataset contains maximum water depth and maximum water velocity for 12 different Glacial Lake outburst floods (GLOFs) scenarios of the Tsho Rolpa Lake, Nepal. Also included is the water depth of dam breach flow and discharge of dam breach flow under each scenario. The GLOFs scenarios were created using a simple dam breach model. A high-performance hydrodynamic model was then used to simulate the resulting flood hydrodynamics. Full details about this dataset can be found at https://doi.org/10.5285/f4292d99-de93-4a28-a821-b2a6a826df4c

  • The dataset contains 1km gridded estimates of hourly rainfall for Great Britain for the period 1990-2016. The estimates are derived by applying the nearest neighbour interpolation method to a national database of hourly raingauge observations collated by Newcastle University and the UK Centre for Ecology & Hydrology (UKCEH). These interpolated hourly estimates were then used to temporally disaggregate the CEH-GEAR daily rainfall dataset. The estimated rainfall on a given hour refers to the rainfall amount accumulated in the previous hour. The dataset also contains data indicating the distance between the grid point and the closest recording raingauge used in its interpolation. When this distance is greater than 50km, or there is zero rainfall recorded in the closest gauge, the daily value is disaggregated using a design storm. The dataset therefore also contains a flag indicating if the design storm was used. These data are provided as an indicator of the quality of the estimates. Full details about this dataset can be found at https://doi.org/10.5285/fc9423d6-3d54-467f-bb2b-fc7357a3941f