82055942-386a-4a8b-b2a1-0c3eea12b168 English ISO/IEC 8859-1 (also known as Latin 1) dataset dataset NERC EDS Environmental Information Data Centre Lancaster Environment Centre, Library Avenue, Bailrigg Lancaster LA1 4AP UK info@eidc.ac.uk https://eidc.ac.uk/ EIDC website The Environmental Information Data Centre (EIDC) is the UK's national data centre for terrestrial and freshwater sciences. information pointOfContact 2025-11-13T16:18:29 UK GEMINI 2.3 WGS 84 Flood risk assessment of the Luanhe river basin under different development strategies and climate scenarios 2021-08-09 publication https://catalogue.ceh.ac.uk/id/82055942-386a-4a8b-b2a1-0c3eea12b168 10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168 doi: Zhao, J.H., Liang, Q.H., Chen, H.L. (2021). Flood risk assessment of the Luanhe river basin under different development strategies and climate scenarios. NERC EDS Environmental Information Data Centre 10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168 The dataset describes the data needed for and results produced by the flood risk assessment framework under different development strategies of Luanhe river basin under a changing climate. The Luanhe river basin is located in the northeast of the North China Plain (115°30' E-119°45' E, 39°10' N-42°40'N) of China, is an essential socio-economic zone on its own in North-Eastern China, and also directly contributes to and influences the socio-economic development of the Beijing-Tianjin-Hebei region. The dataset here used for investigating the flood risk includes: (1) uplifts of future climate scenarios to 2030 (2) the validation results of a historical event that happened in 2012 (3) the flood inundation prediction under different development strategies and climate scenarios to 2030 (4) and the spatial resident density map in Luanhe river basin to 2030. Wherein, the uplifts of the future climate change is generated based on the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset and will be applied to the future design rainfall to represent the future climate scenarios; a 2012 event is select to validate the flood model, and the remote sensing data is adopted as real-world observation data; considering the uplifts and future land use data as input, the validated flood model is applied to produce flood inundation prediction under different development strategies and climate scenarios to 2030; and the inundation results are used to overlay the Gridded Population of the World, Version 4 (GPWv4) and then calculate the flood risk map of the local resident. These data are mainly open data or produced by authors. With all these data, the flood risk of the Luanhe river basin in the near future (2030) can be assessed. Full details about this dataset can be found at https://doi.org/10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168 Zhao, J. Loughborough University j.zhao@lboro.ac.uk pointOfContact Zhao, J.H. Loughborough University j.zhao@lboro.ac.uk https://orcid.org/0000-0002-0494-1743 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Liang, Q.H. Loughborough University q.liang@lboro.ac.uk author Chen, H.L. Loughborough University h.chen2@lboro.ac.uk author NERC EDS Environmental Information Data Centre info@eidc.ac.uk custodian NERC EDS Environmental Information Data Centre info@eidc.ac.uk publisher Loughborough University j.zhao@lboro.ac.uk owner hydrology modelling climate change GEMET - Concepts, version 4.1.3 2019-01-28 creation Climate and climate change Hydrology Modelling otherRestrictions no limitations otherRestrictions This resource is available under the terms of the Open Government Licence otherRestrictions If you reuse this data, you should cite: Zhao, J.H., Liang, Q.H., Chen, H.L. (2021). Flood risk assessment of the Luanhe river basin under different development strategies and climate scenarios. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/82055942-386a-4a8b-b2a1-0c3eea12b168 textTable grid English utf8 environment 104.028 120.99 32.951 43.858 Comma-separated values (CSV) TIFF NERC EDS Environmental Information Data Centre info@eidc.ac.uk distributor https://catalogue.ceh.ac.uk/datastore/eidchub/82055942-386a-4a8b-b2a1-0c3eea12b168 Download the data Download a copy of this data fileAccess https://data-package.ceh.ac.uk/sd/82055942-386a-4a8b-b2a1-0c3eea12b168.zip Supporting information Supporting information available to assist in re-use of this dataset information dataset dataset Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 The base data (population, climate scenarios) are mainly based on the open data (GPWv4, NEX-GDDP), and the flood inundation results are produced by the in-house high-performance integrated hydrodynamic modelling system (HiPIMS). The uplifts of climate scenarios are calculated by fitting a Log-Pearson type III distribution to the NEX-GDDP dataset to calculate the magnitudes of 100-year design rainfall at each pixel within the domain for the 'retrospective run'/historical period (i.e. 1950~2005) without climate change to give (mm/day), and future periods (2006~2030) with climate change (RCP4.5 and RCP8.5) to provide (mm/day). And this pixel-based climate change uplift is then averaged to the city-wide mask to create a city-based uplift (rcp45.csv, rcp85.csv). The parameters of the flood model are calibrated to the 2012 event, wherein the remote sensing data is considered as real-world observation (RemoteSensing_validation.tif and Validation.tif). And this calibrated model is then applied to the future development strategies (reflected by land use data) and climate change scenarios (reflected by uplifts) to calculate the flood inundation map for the future scenarios in Luanhe river basin (Baseline.tif, Conservation*.tif, Expansion*.tif, Sustainability*.tif, Trend*.tif). Base on the inundation map, the flood risk map of the local resident can be calculated by overlaying it on the GPWv4 data (Popu_2000.tif, Popu_2005.tif, Popu_2010.tif, Popu_2015.tif, Popu_2020.tif, Popu_2025.tif, Popu_2030.tif).