Articles | Volume 14, issue 8
https://doi.org/10.5194/essd-14-3835-2022
https://doi.org/10.5194/essd-14-3835-2022
Data description paper
 | 
29 Aug 2022
Data description paper |  | 29 Aug 2022

A global map of local climate zones to support earth system modelling and urban-scale environmental science

Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel

Related authors

U-Surf: A Global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
Yifan Cheng, Lei Zhao, Tirthankar Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-416,https://doi.org/10.5194/essd-2024-416, 2024
Preprint under review for ESSD
Short summary
Implementation of global soil databases in NOAH-MP model and the effects on simulated mean and extreme soil hydrothermal changes
Kazeem Ishola, Gerald Mills, Ankur Sati, Benjamin Obe, Matthias Demuzere, Deepak Upreti, Gourav Misra, Paul Lewis, Daire Walsh, Tim McCarthy, and Rowan Fealy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-304,https://doi.org/10.5194/hess-2023-304, 2024
Preprint under review for HESS
Short summary
Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium
Jorn Van de Velde, Matthias Demuzere, Bernard De Baets, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 26, 2319–2344, https://doi.org/10.5194/hess-26-2319-2022,https://doi.org/10.5194/hess-26-2319-2022, 2022
Short summary
Comparison of occurrence-bias-adjusting methods for hydrological impact modelling
Jorn Van de Velde, Bernard De Baets, Matthias Demuzere, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-83,https://doi.org/10.5194/hess-2020-83, 2020
Revised manuscript not accepted
Short summary
Global biosphere–climate interaction: a causal appraisal of observations and models over multiple temporal scales
Jeroen Claessen, Annalisa Molini, Brecht Martens, Matteo Detto, Matthias Demuzere, and Diego G. Miralles
Biogeosciences, 16, 4851–4874, https://doi.org/10.5194/bg-16-4851-2019,https://doi.org/10.5194/bg-16-4851-2019, 2019
Short summary

Related subject area

Domain: ESSD – Land | Subject: Land Cover and Land Use
Enhancing high-resolution forest stand mean height mapping in China through an individual tree-based approach with close-range lidar data
Yuling Chen, Haitao Yang, Zekun Yang, Qiuli Yang, Weiyan Liu, Guoran Huang, Yu Ren, Kai Cheng, Tianyu Xiang, Mengxi Chen, Danyang Lin, Zhiyong Qi, Jiachen Xu, Yixuan Zhang, Guangcai Xu, and Qinghua Guo
Earth Syst. Sci. Data, 16, 5267–5285, https://doi.org/10.5194/essd-16-5267-2024,https://doi.org/10.5194/essd-16-5267-2024, 2024
Short summary
Annual high-resolution grazing-intensity maps on the Qinghai–Tibet Plateau from 1990 to 2020
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu
Earth Syst. Sci. Data, 16, 5171–5189, https://doi.org/10.5194/essd-16-5171-2024,https://doi.org/10.5194/essd-16-5171-2024, 2024
Short summary
Global mapping of oil palm planting year from 1990 to 2021
Adrià Descals, David L. A. Gaveau, Serge Wich, Zoltan Szantoi, and Erik Meijaard
Earth Syst. Sci. Data, 16, 5111–5129, https://doi.org/10.5194/essd-16-5111-2024,https://doi.org/10.5194/essd-16-5111-2024, 2024
Short summary
A 28-time-point cropland area change dataset in Northeast China from 1000 to 2020
Ran Jia, Xiuqi Fang, Yundi Yang, Masayuki Yokozawa, and Yu Ye
Earth Syst. Sci. Data, 16, 4971–4994, https://doi.org/10.5194/essd-16-4971-2024,https://doi.org/10.5194/essd-16-4971-2024, 2024
Short summary
Mapping sugarcane globally at 10 m resolution using Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-2
Stefania Di Tommaso, Sherrie Wang, Rob Strey, and David B. Lobell
Earth Syst. Sci. Data, 16, 4931–4947, https://doi.org/10.5194/essd-16-4931-2024,https://doi.org/10.5194/essd-16-4931-2024, 2024
Short summary

Cited articles

Abascal, A., Rothwell, N., Shonowo, A., Thomson, D. R., Elias, P., Elsey, H., Yeboah, G., and Kuffer, M.: “Domains of deprivation framework” for mapping slums, informal settlements, and other deprived areas in LMICs to improve urban planning and policy: A scoping review, Comput. Enviro. Urban, 93, 101770, https://doi.org/10.1016/j.compenvurbsys.2022.101770, 2022. a
Alexander, P., Bechtel, B., Chow, W., Fealy, R., and Mills, G.: Linking urban climate classification with an urban energy and water budget model: Multi-site and multi-seasonal evaluation, Urban Climate, 17, 196–215, https://doi.org/10.1016/j.uclim.2016.08.003, 2016. a
Alexander, P. J., Mills, G., and Fealy, R.: Using LCZ data to run an urban energy balance model, Urban Climate, 13, 14–37, https://doi.org/10.1016/j.uclim.2015.05.001, 2015. a
Aminipouri, M., Knudby, A. J., Krayenhoff, E. S., Zickfeld, K., and Middel, A.: Modelling the impact of increased street tree cover on mean radiant temperature across Vancouver's local climate zones, Urban For. Urban Gree., 39, 9–17, https://doi.org/10.1016/j.ufug.2019.01.016, 2019. a
Assarkhaniki, Z., Sabri, S., and Rajabifard, A.: Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs' achievement, Big Earth Data, 5, 497–526, https://doi.org/10.1080/20964471.2021.1948178, 2021. a
Download
Short summary
Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Altmetrics
Final-revised paper
Preprint