• Energy Research

AbstractOne important challenge facing the urbanization and global environmental change community is to understand the relation between urban form, energy use and carbon emissions. Missing from the current literature are scientific assessments that evaluate the impacts of different urban spatial units on energy fluxes; yet, this type of analysis is needed by urban planners, who recognize that local scale zoning affects energy consumption and local climate. Satellite-based estimation of urban energy fluxes at neighbourhood scale is still a challenge. Here we show the potential of the current satellite missions to retrieve urban energy budget fluxes, supported by meteorological observations and evaluated by direct flux measurements. We found an agreement within 5% between satellite and in-situ derived net all-wave radiation; and identified that wall facet fraction and urban materials type are the most important parameters for estimating heat storage of the urban canopy. The satellite approaches were found to underestimate measured turbulent heat fluxes, with sensible heat flux being most sensitive to surface temperature variation (−64.1, +69.3 W m−2 for ±2 K perturbation). They also underestimate anthropogenic heat fluxes. However, reasonable spatial patterns are obtained for the latter allowing hot-spots to be identified, therefore supporting both urban planning and urban climate modelling.

------------------------------------------------------------------------------------------------------------------------------------------- This version has been superseded. The latest version is at https://doi.org/10.5281/zenodo.5517550 ------------------------------------------------------------------------------------------------------------------------------------------- Eddy covariance flux tower datasets of all Urban-PLUMBER sites, associated with the manuscript: "Harmonized, gap-filled dataset from 20 urban flux tower sites" Use of any data must give credit through citation of the above manuscript and other sources as appropriate. We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant contacts are included in timeseries metadata. For site information and timeseries plots see https://urban-plumber.github.io/sites. For processing code see https://github.com/matlipson/urban-plumber_pipeline. Within each site folder: - `index.html`: A summary page with site characteristics and timeseries plots. - `SITENAME_sitedata_vX.csv`: comma seperated file for numerical site characteristics e.g. location, surface cover fraction etc. - `timeseries/` (following files available as netCDF and txt) - `SITENAME_raw_observations_vX`: site observed timeseries before project-wide quality control. - `SITENAME_clean_observations_vX`: site observed timeseries after project-wide quality control. - `SITENAME_metforcing_vX`: site observed timeseries after project-wide quality control and gap filling. - `SITENAME_era5_corrected_vX`: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset. - `log_processing_SITENAME_vX.txt`: a log of the print statements through running the create_dataset_SITENAME scripts. Authors Mathew Lipson, Sue Grimmond, Martin Best, Andreas Christen, Andrew Coutts, Ben Crawford, Bert Heusinkveld, Erik Velasco, Helen Claire Ward, Hirofumi Sugawara, Je-Woo Hong, Jinkyu Hong, Jonathan Evans, Joseph McFadden, Keunmin Lee, Krzysztof Fortuniak, Leena Järvi, Matthias Roth, Nektarios Chrysoulakis, Nigel Tapper, Oliver Michels, Simone Kotthaus, Stevan Earl, Sungsoo Jo, Valéry Masson, Winston Chow, Wlodzimierz Pawlak, Yeon-Hee Kim. Corresponding author: Mathew Lipson <m.lipson@unsw.edu.au>

Flux tower observations, model spin-up and site characteristics data for Urban-PLUMBER sites associated with the manuscript: "Harmonized, gap-filled dataset from 20 urban flux tower sites" https://doi.org/10.5194/essd-14-5157-2022 Use of any data must give credit through citation of the above manuscript and other site sources as appropriate (see below). We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant site contacts are included in site metadata. Data can be downloaded from the bottom of this page. Sitename City Country Observed period References AU-Preston Melbourne Australia Aug 2003 – Nov 2004 (Coutts et al., 2007a, b) AU-SurreyHills Melbourne Australia Feb 2004 – Jul 2004 (Coutts et al., 2007a, b) CA-Sunset Vancouver Canada Jan 2012 – Dec 2016 (Christen et al., 2011; Crawford and Christen, 2015) FI-Kumpula Helsinki Finland Dec 2010 – Dec 2013 (Karsisto et al., 2016) FI-Torni Helsinki Finland Dec 2010 – Dec 2013 (Järvi et al., 2018; Nordbo et al., 2013) FR-Capitole Toulouse France Feb 2004 – Mar 2005 (Masson et al., 2008; Goret et al., 2019) GR-HECKOR Heraklion Greece Jun 2019 – Jun 2020 (Stagakis et al., 2019) JP-Yoyogi Tokyo Japan Mar 2016 – Mar 2020 (Hirano et al., 2015; Ishidoya et al., 2020) KR-Jungnang Seoul South Korea Jan 2017 – Apr 2019 (Jo et al., n.d.; Hong et al., 2020) KR-Ochang Ochang South Korea Jun 2015 – Jul 2017 (Hong et al., 2019, 2020) MX-Escandon Mexico City Mexico Jun 2011 – Sep 2012 (Velasco et al., 2011, 2014) NL-Amsterdam Amsterdam Netherlands Jan 2019 – Oct 2020 (Steeneveld et al., 2020) PL-Lipowa Łódź Poland Jan 2008 – Dec 2012 (Fortuniak et al., 2013; Pawlak et al., 2011) PL-Narutowicza Łódź Poland Jan 2008 – Dec 2012 (Fortuniak et al., 2013, 2006) SG-TelokKurau Singapore Singapore Feb 2015 – Feb 2016 (Roth et al., 2017) UK-KingsCollege London UK Apr 2012 – Jan 2014 (Bjorkegren et al., 2015; Kotthaus and Grimmond, 2014a, b) UK-Swindon Swindon UK May 2011 – Apr 2013 (Ward et al., 2013) US-Baltimore Baltimore USA Jan 2002 – Jan 2007 (Crawford et al., 2011) US-Minneapolis Minneapolis USA Jun 2006 – May 2009 (Peters et al., 2011; Menzer and McFadden, 2017) US-WestPhoenix Phoenix USA Dec 2011 – Jan 2013 (Chow, 2017; Chow et al., 2014) For further site information and timeseries plots see https://urban-plumber.github.io/sites. For processing code see https://github.com/matlipson/urban-plumber_pipeline. Data Two data archives are available on this page. The full collection includes all observed, gap-filled, spin-up and site characteristic data, in both netcdf and text form. The "obs_only" archive includes a duplicate of site observation timeseries (after quality control) in a single netcdf file. Full collection The full archive includes site folders with: index.html: A summary page with site characteristics and timeseries plots. SITENAME_sitedata_v1.csv: comma separated file for numerical site characteristics e.g. location, surface cover fraction etc. timeseries/ (following files are available as netCDF and txt) SITENAME_raw_observations_v1: site observed timeseries before project-wide quality control. SITENAME_clean_observations_v1: site observed timeseries after project-wide quality control. SITENAME_metforcing_v1: gap-filled and prepended (10yr spinup) site observation forcing dataset for model evaluation. SITENAME_era5_corrected_v1: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset. "Obs Only" This archive contains duplicate data from the full collection (observations after QC): UP_all_clean_observations_UTC_v1.nc: in coordinated universal time (UTC) UP_all_clean_observations_localstandardtime_v1.nc: in local standard time Site references Bjorkegren, A. B., Grimmond, C. S. B., Kotthaus, S., and Malamud, B. D.: CO2 emission estimation in the urban environment: Measurement of the CO2 storage term, Atmospheric Environment, 122, 775–790, https://doi.org/10.1016/j.atmosenv.2015.10.012, 2015. Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through 2012-12-31, https://doi.org/10.6073/PASTA/FED17D67583EDA16C439216CA40B0669, 2017. Chow, W. T. 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Ishidoya, S., Sugawara, H., Terao, Y., Kaneyasu, N., Aoki, N., Tsuboi, K., and Kondo, H.: O2 : CO2 exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO2 emissions, Atmospheric Chemistry and Physics, 20, 5293–5308, https://doi.org/10.5194/acp-20-5293-2020, 2020. Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmospheric Measurement Techniques, 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018. Jo, S., Hong, J.-W., and Hong, J.: The observational flux measurement data of suburban and low-residential areas in Korea (in preparation), n.d. Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., W., O. K., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land‐surface models in the high‐latitude city Helsinki, Q.J.R. Meteorol. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016. Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Climate, 10, Part 2, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a. Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Climate, 10, Part 2, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b. Masson, V., Gomes, L., Pigeon, G., Liousse, C., Pont, V., Lagouarde, J.-P., Voogt, J., Salmond, J., Oke, T. R., Hidalgo, J., Legain, D., Garrouste, O., Lac, C., Connan, O., Briottet, X., Lachérade, S., and Tulet, P.: The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment, Meteorol Atmos Phys, 102, 135, https://doi.org/10.1007/s00703-008-0289-4, 2008. Menzer, O. and McFadden, J. P.: Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components, Atmospheric Environment, 170, 319–333, https://doi.org/10.1016/j.atmosenv.2017.09.049, 2017. Nordbo, A., Järvi, L., Haapanala, S., Moilanen, J., and Vesala, T.: Intra-City Variation in Urban Morphology and Turbulence Structure in Helsinki, Finland, Boundary-Layer Meteorol, 146, 469–496, https://doi.org/10.1007/s10546-012-9773-y, 2013. Pawlak, W., Fortuniak, K., and Siedlecki, M.: Carbon dioxide flux in the centre of Łódź, Poland—analysis of a 2-year eddy covariance measurement data set, International Journal of Climatology, 31, 232–243, https://doi.org/10.1002/joc.2247, 2011. Peters, E. B., Hiller, R. V., and McFadden, J. P.: Seasonal contributions of vegetation types to suburban evapotranspiration, Journal of Geophysical Research: Biogeosciences, 116, https://doi.org/10.1029/2010JG001463, 2011. Roth, M., Jansson, C., and Velasco, E.: Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city, Int. J. Climatol., 37, 2679–2698, https://doi.org/10.1002/joc.4873, 2017. Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., and Vogt, R.: Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece, Atmospheric Environment, 201, 278–292, https://doi.org/10.1016/j.atmosenv.2019.01.009, 2019. 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Deciding upon optimum planning actions in terms of sustainable urban planning involves the consideration of multiple environmental and socio-economic criteria. The transformation of natural landscapes to urban areas affects energy and material fluxes. An important aspect of the urban environment is the urban metabolism, and changes in such metabolism need to be considered for sustainable planning decisions. A spatial Decision Support System (DSS) prototyped within the European FP7-funded project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism), enables accounting for the urban metabolism of planning actions, by exploiting the current knowledge and technology of biophysical sciences. The main aim of the BRIDGE project was to bridge the knowledge and communication gap between urban planners and environmental scientists and to illustrate the advantages of considering detailed environmental information in urban planning processes. The developed DSS prototype integrates biophysical observations and simulation techniques with socio-economic aspects in five European cities, selected as case studies for the pilot application of the tool. This paper describes the design and implementation of the BRIDGE DSS prototype, illustrates some examples of use, and highlights the need for further research and development in the field.

The combined effects of global warming, urbanization, and demographic change influence climate risk for urban populations, particularly in metropolitan areas with developing economies. To inform climate change adaptation and spatial planning, it is important to study urban climatic hazards and populations at risk in relation to urban growth trends and development patterns. However, this relationship has not been adequately investigated in studies dedicated to climate vulnerability. This study identifies the typologies of development patterns within Lahore, Pakistan, investigates the heat vulnerability of residents at a neighborhood scale, and establishes a relationship between both of these factors. We identified urban clusters with diverse development patterns. Fourteen context- and site-specific indicators were selected to construct a human heat vulnerability index. Weighted sum, cluster analysis, and ANOVA test of variance were conducted to analyze the data. Our results demonstrate that development patterns significantly influence human vulnerability to heat stress, e.g., vulnerability is higher in older cities and undeveloped neighborhoods with less diverse land uses. These findings are essential for informing policy-makers, decision-makers and spatial planners about proactive adaptation planning in dynamic urban environments.

Cooling degree-days (CDD) are a practical method for assessing the effect ambient air temperature has on the energy performance of buildings. In this study, the relationship between midday land surface temperatures derived from NOAA-AVHRR data and mean daily air temperature observations recorded at standard meteorological stations is defined and statistically validated. The relationship is further used for the calculation of CDD. The benefit of this approach is the direct application of daily satellite data for the definition of CDD in urban areas at a spatial resolution of 1.1 km.