Water depths during the Sheffield Flood of 1864. Information adapted from: Machan, Peter. 'The Dramatic Story of The Sheffield Flood?' 1999 ISBN 1-901587-05-3. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-07-01 and migrated to Edinburgh DataShare on 2017-02-21.

Locations and descriptions of damage which occurred during the Sheffield Flood of 1864. Information adapted from: Machan, Peter. 'The Dramatic Story of The Sheffield Flood?' 1999 ISBN 1-901587-05-3. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-07-06 and migrated to Edinburgh DataShare on 2017-02-21.

Locations and details of victims whose deaths occurred during the Sheffield Flood of 1864. Information adapted from: Machan, Peter. 'The Dramatic Story of The Sheffield Flood?' 1999 ISBN 1-901587-05-3. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-07-06 and migrated to Edinburgh DataShare on 2017-02-21.

This dataset shows Sheffield City Council Strategic Flood Risk Assessment Zone 3ai (Developed floodplain). It was produced for Sheffield City Council by JE Jacobs in 2008 and shows areas subject to flooding up to (and including) once in every 20 years on average.

A map showing locations of structures affected by Alternatives A1, D, and E of the Pearl River Federal Flood Risk Management Project

This dataset concerns recovery after major floods in the UK 2007 and Germany 2013. It contains two sheets. The first sheet contains data from a survey of residents and local businesses in Catcliffe, between Sheffield and Rotherham in South Yorkshire and in Dreiflüsse-Eck, the "Three Rivers Corner" in Passau in Bavaria conducted in late 2018 and early 2019. The second sheet conatisn data from a survey with flood experts in the UK and Germany.

The two events were comparable in terms of impacts, levels of preparedness and government response and show similar patterns of speed and quality of recovery. The two case study areas are similar in size and were amongst places most badly hit in each country. Both floods were considered to be "game-changers" and resulted in a heightened awareness of flood risk, increased investment in flood defences and an increasing emphasis on citizens taking more responsibility for flood preparedness.

This paper offers a protocol for conducting a quantified assessment of the relative merits of both existing and proposed methods of Natural Flood Management (NFM). Assessment is based on the rarely used concept of flood-excess volume (FEV), which approximately quantifies the volume of water one wishes to eliminate via flood-mitigation schemes, and is exemplified using publicly available river-gauge data for recent well-known extreme-flood events in Yorkshire, UK. The following question motivates the study: what fraction of the FEV is reduced, and at what cost, by a particular (suite of) flood-mitigation measure(s)? The approach presented admits juxtaposed cost assessments, of disparate and popular NFM measures, that are neither available nor considered in existing flood-mitigation policy. With promulgation of a societally useful protocol in mind, quantification and interpretation of alternative cost scenarios are facilitated using the authors' novel visualisation of flood-alleviation basins as partially filled, realistically constructible, two-metre-deep square lakes of side-length approximately one-to-two kilometres.

In the first case study, a hypothetical flood-alleviation scheme for the River Calder at Mytholmroyd, comprising flow-attenuation features, tree planting and peat restoration, and reservoir storage, is critically assessed alongside reasonable cost estimates. The clear quantification and visual representation of the analysis indicate that the fractions of FEV reduced by the NFM measures, while not insignificant, are dwarfed by the careful draw-down of reservoirs. The second case study, of the River Don at Sheffield, extends the analysis via a range of scenarios that attempt to sample realistic seasonal rainfall distributions across a catchment, and in doing so elucidates both the potential and uncertainty of numerous mitigation schemes under different conditions. We corroborate the growing consensus that, while NFM measures can reduce low-level flooding locally, the spatial scale at which they are effective is limited and may not upscale to the catchment level. Our FEV analysis and protocol thus not only offers a concise quantification of the effectiveness of disparate and in-tandem flood mitigation measures but also further highlights the issue of NFM scalability.

The 7.12 woodland creation scheme is located to the south of Underbank Reservoir, near Stocksbridge, South Yorkshire, within the Yorkshire Southern Pennine Fringe landscape character area, about 500m north of the Peak District National Park boundary. The landscape is agricultural with gently sloping fields and drystone walls against a wooded and pastoral backdrop of the wider Little Don Valley. The proposed new woodland is contiguous with existing woodland along the northern and eastern boundaries that is also owned and managed by Yorkshire Water. It will form part of a wider project to link existing mature woodland at Underbank reservoir with woodland at Midhope reservoir creating a wider wooded landscape delivering multiple objectives including carbon sequestration, recreation, biodiversity and water quality.

The woodland design has been carefully considered to maximise multiple benefits including nature recovery and biodiversity net gain as well as helping address local climate emergency targets and contributing towards the wider woodland creation ambition in the Northern Forest. The woodland mix will be 64% native broadleaves with 36% conifer to complement the adjacent woodland, provide quality timber and help improve carbon sequestration. Species choice has been informed by the ESC 2080 climate scenario and site-specific soil survey to ensure future climate resilience. Open space provision within the woodland accommodates and buffers both archaeological interest, informed by an archaeology survey, a powerline wayleave and provides new recreation opportunities. Biodiversity enhancement through creation of edge habitat and species selection will help with local target species such as Willow Tit and bats. A detailed ecology survey identified little existing ecological interest on the land holding so the project will result in a positive uplift in biodiversity value and enhanced connectivity with contiguous woodland.

Another important objective is water quality. Given the proximity of the reservoir, catchment management for water quality is a key output for Yorkshire Water. A change of land use to woodland will also enhance natural flood management which is a wider objective for the Environment Agency in the Little Don catchment area, a reactive catchment upstream of Sheffield which historically suffers from flooding issues.

The site is close to the town of Stocksbridge and the Underbank reservoir has established recreation users. New recreation routes within this project will enhance this and open up new areas for access. The area is heavily used by walkers and there is a high level of local horse ownership. The creation of new woodland infrastructure will provide an opportunity to create a new bridleway link promoting horse riding and cycling and tying in with a new recreation network in the Little Don linking multiple reservoirs. New permissive footpaths will also be created that connect with the existing path network around Underbank reservoir providing new circular route options.

This dataset contains results from testing carried out at a laboratory facility at the University of Sheffield (UK) as part of the CENTAUR project. CENTAUR is an EC funded Horizon 2020 Innovation Action. The project has developed a system to reduce flood risk in urban areas by utilising existing available storage capacity in urban drainage networks through the use of a gate installed in an existing manhole. The gate is controlled by Fuzzy Logic, using data from level sensors.

The laboratory facility is described in the paper 'Demonstrating a Fuzzy Logic algorithm for real-time flow control in a full-scale laboratory environment' which is currently under review with the Urban Water Journal (https://www.tandfonline.com/toc/nurw20/current). Further details of the sensors and logging system are provided in 'Data_File_Column_Descriptions.csv'.

The file 'Test_Record.csv' describes all tests carried out. This dataset contains 83 csv data files in for days when good data was collected, these are zipped into 'DataFiles.zip'. Each csv file within the .zip contains the test results for one day, the files are named with the date of testing in the format yymmdd. The csv data files do not include column headers, but a full description of the data in each column is provided in 'Data_File_Column_Descriptions.csv'. The csv files contain data from all sensors, but the time period of the data from each sensor (or sensor set) and timesteps are not the same, hence for each sensor / sensor set there is a separate time column. The sampling interval for the level sensors is given in column 26 of 'Test_Record.csv', this will be correct for the test period, but outside the tests the interval was often increased and this may be seen in the data files. The gate / FCD sampling interval is the same as the Fuzzy Logic interval in column 27 of 'Test_Record.csv', although the position is only reported when the gate / FCD is active - i.e. not fully open. At the end of a test the FCD will return to the fully open position (100%), but this final datapoint is not recorded. The flow rate and downstream valve position sampling interval are given in column 12 of 'Test_Record.csv'.

Test numbers and fuzzy logic version ids are simplified for the journal paper 'Demonstrating a Fuzzy Logic algorithm for real-time flow control in a full-scale laboratory environment' which is currently under review with the Urban Water Journal. A correlation between the information in the paper and in 'Test_Record.csv' can be found in 'Paper_Test_Numbers.csv'.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 641931.