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3 results for “Industrial heat source”
Global remote industrial heat sources dataset
<p>Data content: Based on the VIIRS (Visible Infrared Imaging Radiometer Suite) sensor medium resolution 375mNPP-VIIRS active thermal anomaly data, field research, and other big data of the earth, we constructed the global continental region of high-energy-consuming industrial heat source product data set, totaling 25,544 data. After validation 23232 items are industrial heat source objects, and the recognition accuracy is 90.95%. The output format is shapefile.</p> <p>Time range of data:2012-2021<br> Spatial scope: Global continental area<br> Projection method: WGS84<br> Volume of data: The total volume of data is about 3346kb.<br> Type of data: Vector<br> </p>
Data of industrial heat source between 2012 and 2021 using long-term Active Fire/Hotspot data in China
<p>Industrial heat sources serve as crucial indicators of energy consumption levels and air pollution. Energy-intensive industries are facing substantial challenges in transforming and upgrading in China. Therefore, accurately identifying industrial heat source locations and monitoring their temporal patterns are becoming utmost importance. In this study, a long-term industrial heat source datasets between 2012 and 2021 in China using long-term Active Fire/Hotspots (ACF) data has been constructed to monitor and analyze large-scale industrial heat sources. Firstly, density segmentation method based on an improved k-means algorithm using long-term ACF data and spatial topological correlation analysis was conducted to build industrial heat sources. Then, 4410 industrial heat sources were obtained between 2012 and 2021 in China, with an identification accuracy of 95.08% by manual verification using high-resolution remote sensing images and point of interest (POI) data. Finally, the trend in the spatio-temporal variation of industrial heat sources was analyzed using long-term series. The results from 2012 to 2021 showed that the spatial distribution of industrial heat sources in China exhibits local aggregation and a gradual shift from east to west.And,the number of industrial heat sources in China has followed a trend of an initial increase from 2012 to 2014, followed by a decrease since 2014, consistent with national energy reform-related policies.The result of this study indicated the temporal variation of industrial heat sources, enhanced the accuracy of fire points category identification, and demonstrated potential for advancing energy efficiency, emission reduction, and sustainable development in China.</p>
Dataset: First comprehensive assessment of industrial-era land heat uptake from multiple sources
<p><strong>Dataset Overview</strong></p> <p>This Zenodo repository contains a comprehensive dataset of global mean yearly land heat uptake (LHU) estimates for the historical period (all data sources) and the SSP585 scenario (exclusively for CMIP6 models). The dataset includes estimates from multiple data sources: gridded observations (OBS, 5 sources), reanalyses (REA, 7 sources), and CMIP6 simulations (CMIP6, 37 models). These LHU estimates were developed and first analyzed in <a href="https://doi.org/10.5194/esd-15-547-2024" target="_blank" rel="noopener">García-Pereira et al. (2024)</a>.</p> <p>The estimates were obtained using the one-dimensional heat conduction forward model (ConForM; <a href="https://doi.org/10.5281/zenodo.10371439" target="_blank" rel="noopener">García-Pereira et al., 2023</a>), forced with yearly global mean ground surface temperature data from each source over various time periods. The time periods include the full available range (fromINIT), as well as specific periods starting in 1950, 1960, and 1971, extending to the most recent data available. For a detailed explanation of the methods, rationale, main findings, and comparisons with previous geothermal LHU estimates, refer to <a href="https://doi.org/10.5194/esd-15-547-2024" target="_blank" rel="noopener">García-Pereira et al. (2024)</a>.</p> <p><br><strong>Dataset Contents</strong></p> <p>The dataset is provided in NetCDF format and is organized by data source type (OBS, REA, CMIP6) and time period (fromINIT, from1950, from1960, from1971). Each file follows the naming convention:</p> <blockquote> <p><em><source_type>_LHU_ym_gb_sum_<period>.nc</em></p> </blockquote> <p>where <em><source_type></em> indicates the data source (OBS, REA, CMIP6) and <em><period></em> specifies the starting time (fromINIT, from1950, from1960, from1971). For example, <em>OBS_LHU_ym_gb_sum_from1971.nc</em> contains LHU estimates derived from observational data starting in 1971. For files corresponding to from1950, from1960, and from1971, the dataset also includes mean and standard deviation calculations. Additionally, raw LHU estimates derived from CMIP6 subsurface temperature data are provided in the file <em>CMIP6/CMIP6raw_LHU_ym_gb_sum.nc</em>.</p> <p><br><strong>Citation instructions</strong></p> <p>If you use this dataset, please cite the following references:</p> <blockquote> <p>Garcia-Pereira, F. and González-Rouco, J. F. : "ConForM: a one-dimensional heat Conduction Forward Model", Zenodo, <a href="https://doi.org/10.5281/zenodo.10371439" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10371439</a>, 2023.</p> <p>García-Pereira, F., González-Rouco, J. F., Melo-Aguilar, C., Steinert, N. J., García-Bustamante, E., de Vrese, P., Jungclaus, J., Lorenz, S., Hagemann, S., Cuesta-Valero, F. J., García-García, A., and Beltrami, H.: "First comprehensive assessment of industrial-era land heat uptake from multiple sources", Earth Syst. Dynam., 15, 547–564, <a href="https://doi.org/10.5194/esd-15-547-2024" target="_blank" rel="noopener">https://doi.org/10.5194/esd-15-547-2024</a>, 2024.</p> </blockquote> <p><br><strong>Additional Resources</strong></p> <p>For further insight into the evolution of LHU, its role in terrestrial energy partitioning, and the limitations of state-of-the-art Earth System Models in representing it, we recommend exploring these additional publications:</p> <blockquote> <p>Cuesta-Valero, F. J., García-García, A., Beltrami, H., Smerdon J. E.: "First assessment of continental energy storage in CMIP5 simulations", Geophys. Res. Lett., 43, 5326–5335, <a href="https://doi.org/10.1002/2016GL068496" target="_blank" rel="noopener">https://doi.org/10.1002/2016GL068496</a>, 2016.</p> <p>Cuesta-Valero, F. J., García-García, A., Beltrami, H., González-Rouco, J. F., and García-Bustamante, E.: "Long-term global ground heat flux and continental heat storage from geothermal data", Clim. Past, 17, 451–468, <a href="https://doi.org/10.5194/cp-17-451-2021" target="_blank" rel="noopener">https://doi.org/10.5194/cp-17-451-2021</a>, 2021.</p> <p>Cuesta-Valero, F. J., Beltrami, H., García-García, A., Krinner, G., Langer, M., MacDougall, A. H., Nitzbon, J., Peng, J., von Schuckmann, K., Seneviratne, S. I., Thiery, W., Vanderkelen, I., and Wu, T.: "Continental heat storage: contributions from the ground, inland waters, and permafrost thawing", Earth Syst. Dynam., 14, 609–627, <a href="https://doi.org/10.5194/esd-14-609-2023" target="_blank" rel="noopener">https://doi.org/10.5194/esd-14-609-2023</a>, 2023.</p> <p>González-Rouco, J. F., Steinert, N. J., García-Bustamante, E., Hagemann, S., de Vrese, P., Jungclaus, J. H., Lorenz, S. J., Melo-Aguilar, C., García-Pereira, F., and Navarro, J.: "Increasing the depth of a Land Surface Model. Part I: Impacts on the soil thermal regime and energy storage", Journal of Hydrometeorology, 22(12), 3211-3230, <a href="https://doi.org/10.1175/JHM-D-21-0024.1" target="_blank" rel="noopener">https://doi.org/10.1175/JHM-D-21-0024.1</a>, 2021.</p> <p>Steinert N. J., González-Rouco, J. F., Melo Aguilar, C. A., García Pereira, F., García-Bustamante, E., de Vrese, P. Alexeev, V., Jungclaus, J. H., Lorenz, S. J., and Hagemann, S.: "Agreement of analytical and simulation-based estimates of the required land depth in climate models", Geophysical Research Letters, 48, e2021GL094273, <a href="https://doi.org/10.1029/2021GL094273" target="_blank" rel="noopener">https://doi.org/10.1029/2021GL094273</a>, 2021.</p> <p>Steinert, N. J., Cuesta-Valero, F. J., García-Pereira, F., de Vrese, P., Melo Aguilar, C. A., García-Bustamante, E., Jungclaus, J., González-Rouco, J. F.: "Underestimated land heat uptake alters the global energy distribution in CMIP6 climate models", Geophysical Research Letters, 51, e2023GL107613, <a href="https://doi.org/10.1029/2023GL107613" target="_blank" rel="noopener">https://doi.org/10.1029/2023GL107613</a>, 2024.</p> <p>von Schuckmann, K., Minière, A., Gues, F., Cuesta-Valero, F. J., Kirchengast, G., Adusumilli, S., Straneo, F., Ablain, M., Allan, R. P., Barker, P. M., Beltrami, H., Blazquez, A., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C. M., García-García, A., Giglio, D., Gilson, J. E., Gorfer, M., Haimberger, L., Hakuba, M. Z., Hendricks, S., Hosoda, S., Johnson, G. C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Landerer, F. W., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marti, F., Marzeion, B., Mayer, M., MacDougall, A. H., McDougall, T., Monselesan, D. P., Nitzbon, J., Otosaka, I., Peng, J., Purkey, S., Roemmich, D., Sato, K., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S. I., Simons, L., Slater, D. A., Slater, T., Steiner, A. K., Suga, T., Szekely, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S. E., Wu, T., and Zemp, M.: "Heat stored in the Earth system 1960–2020: where does the energy go?", Earth Syst. Sci. Data, 15, 1675–1709, <a href="https://doi.org/10.5194/essd-15-1675-2023" target="_blank" rel="noopener">https://doi.org/10.5194/essd-15-1675-2023</a>, 2023.</p> </blockquote>
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