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

The QUEST-GSI WPd1 "Climate scenarios". The aim was to construct climate scenarios representing the effects of uncertainty and different rates of climate forcing. This dataset contains model data which construct climate scenarios. The project requires climate scenarios which (a) characterise the uncertainty in the climate change associated with a given forcing, including changes in climate variability and extreme events, and (b) allow the construction of generalised relationships between climate forcing and impact.

This dataset combines average daily temperature and soil moisture data from nine experimental plots at the climate change field site Climoor located in Clocaenog forest, NE Wales. Soil temperature is measured at 5 cm and 20 cm soil depth (degrees Celsius), and soil moisture is measured as soil volumetric water content (m3 per m3). The experimental field site consists of three untreated control plots (Plots 3, 6 and 9), three plots where the plant canopy air is artificially warmed during night time hours (Plots 1, 2 and 7) and three plots where rainfall is excluded from the plots at least during the plants growing season (Plots 4, 5 and 8). Data is an extension of the micromet datasets covering 1998-2015 and 2015-2016; adding the time period September 2016 to December 2021. Soil temperatures were measured with T107 sensors, and soil moisture was measured with CS616 sensors, both from Campbell scientific. Temperature and moisture data were recorded in minute intervals, and automatically averaged as half-hourly by the data logger. Half-hourly data were automatically transferred to CEH/UKCEH servers using remote telemetry. Data which were not recorded are marked with “NA”, faulty data were replaced with “-9999”. The Climoor field experiment intends to answer questions regarding the effects of warming and drought on ecosystem processes. The reported data are collected to monitor site specific environmental conditions and their development with time. These data are important to interpret results that are collected from the climate change manipulations imposed in the field. Full details about this dataset can be found at https://doi.org/10.5285/5d90d356-5b2c-4ce0-927a-e26efacff015

This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate. Full details about this dataset can be found at https://doi.org/10.5285/da5d7028-2aec-4da2-96ff-f347a0dfa77e

This dataset is derived from modelled changes to the distributions of >12,700 terrestrial mammal and bird species under four different climate scenarios, projected to 2070. It contains national-level projections of species richness change under each climate scenario, based on species' modelled climatic niches, as well as projected range shifts in relation to political borders globally. Full details about this dataset can be found at https://doi.org/10.5285/5bf972a8-c9a3-4721-8089-552dfe3ff124

The QUEST-GSI WP-I5 "Aquatic Ecosystems" project provided an analysis of global fisheries vulnerability across a range of global climate models, emissions scenarios, fixed degree scenarios and alternative impact metrics. This dataset contains model output data from the emission, fixed degree, Cheung potential analysis, Allison socio-economic comparison and freshwater run-off analysis scenarios. -Emission Scenarios- These results are from the analysis using the SRES emissions scenarios from the IPCC AR4 - A1b, A2, B1 and B2. -Fixed Degree- This analysis was driven by the fixed degree rise scenarios, corresponding to a fixed increase in global temperature by 2050. These are 1 to 4 degrees C, in half degree increments, with each fishery impact equally weighted across freshwater, EEZ and High Seas (see report). They are also carried out for a variety of GCMs and socio-economic scenarios. -Cheung Potential Catch Analysis- These results were generated for marine fisheries using an alternative metric to temperature change in calculating potential impact- that of predicted change in potential catch from the study carried out by W.W.L. Cheung et al. (2009 Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology 16, 24-35). This was carried out for the A1b SRES scenario using the GFDL CM2.1 global climate model. -Allison Socio-economic comparison- A comparison study using the adaptive capacity metric developed in Allison et al. (2009 Vulnerability of national economies to the impacts of climate change on fisheries. Fish and Fisheries 10, 173–196). This was undertaken for the A1b Emission Scenario using HadCM3. -Freshwater Runoff Analysis- Using predicted changes in freshwater availability from the outputs of QUEST-GSI WP-I1 global water resources project, an alternative analysis for freshwater fisheries vulnerability was carried out. This was under the 2 degrees fixed increase scenario using HadCM3.

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

This dataset represent hydrological statistics calculated over a 30‐year period, at a spatial resolution (over land) of 0.5x0.5o across the global domain. The simulations were made using the global hydrological model Mac‐PDM.09. The data files represent runoff simulated with the baseline (1961‐1990) climate, together with runoff simulated by climate change scenarios derived from CMIP3 global climate model output (i) based on specific IPCC SRES emissions scenarios (“SRES”) and (ii) scaled to represent prescribed changes in global mean temperature (“PRESC”), and from CMIP5 global climate model output based on RCP scenarios. The simulations were run at the University of Reading between 2009 and 2013. See Gosling & Arnell (2011)mfor a description and validation of Mac‐PDM.09, and Arnell & Gosling (2013) for details of the CMIP3 climate change scenarios and their application to the simulation of river runoff. Arnell & Lloyd‐Hughes (2013) describe the application of the model with CMIP5 scenarios.

QUEST Fish was led by Dr Manuel Barange (PML) with 18 co-investigators from POL, PML, CEFAS, University of Plymouth, University of Portsmouth, CSIC (Spain), UEA, WorldFish Centre, IPSL, ICES (Denmark), Met Office, IRD (Paris) and University of North Carolina, as part of QUEST (Quantifying and Understanding the Earth System) This dataset collection contains global fish biomass estimates from the Global Coastal-Ocean Modelling System. QUEST-Fish has delivered a near-global assessment of consequences of climate change for fisheries, demonstrating excellent and innovative bridging of marine biogeochemistry models and socio-economics. QUEST-Fish specifically focused on the added impacts that climate change is likely to cause on global fish production, and on the subsequent additional risks and vulnerabilities to human societies. The team have demonstrated the broad capability of an integrated regional coastal/shelf seas model system. The physical-ecological POLCOMS-ERSEM model that underpinned the research was developed for Europe’s regional seas. Its application to 20 Large Marine Ecosystems (coastal bioregions) worldwide, covering two-thirds of the world’s fish catch, has been critically evaluated and found adequate for most regions (the physical and biogeochemical differences of the upwelling region off Peru presents challenges, with the climate impact likely to be over-expressed in the fisheries projection output).

The the CNRM-CERFACS team team consisted of the following agencies: Centre National de Recherches Météorologiques (CNRM) and Centre Européen de Recherche et Formation Avancée en Calcul Scientifique (CERFACS).World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the the CNRM-CERFACS team CNRM-ESM2-1 model. The official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.