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7 results for “thermal index”
2-meter Universal Thermal Climate Index (UTCI) and Human Heat Health Index (H3I) hazard for Austin, Texas
<p>Universal Thermal Climate Index (UTCI) is a physiological temperature that is widely used in biometeorological studies to assess the heat stress felt by humans. UTCI considers the shortwave and longwave radiation incident on humans from the six cubical directions as well as air temperature, humidity, wind speed and clothing. As a part of NOAA National Integrated Heat Health Information System (NIHHIS) and NASA Interdisciplinary Research in Earth Science (IDS) project, we have generated the UTCI data for Austin, Texas and surrounding peri-urban area at 2-meters spatial resolution for the year 2017. Details on data generation and methodology can be found in Kamath et al., (2023) but are summarized here. </p> <p><strong>1. Datasets and model used</strong></p> <p>The solar and longwave environmental irradiance geometry (SOLWEIG) model was used to simulate shadows, mean radiant temperature (T<sub>MRT</sub>) and the UTCI (Lindberg et al., 2008). T<sub>MRT</sub> is the equivalent temperature due to exposure to absorbed shortwave and longwave radiation from all directions in a standing position. SOLWEIG was forced using near-surface ERA-5 data available at a spatial resolution of 0.25°x 0.25°. Building, vegetation heights, and digital terrain model were again derived from 3DEP LiDAR point cloud data. SOLWEIG was run using the urban multi-scale environment predictor (UMEP) (Lindberg et al., 2018) plug-in with QGIS. </p> <p><strong>2. Data availability</strong></p> <p>Diurnal UTCI data were calculated for typical meteorological clear sky days corresponding to Summer and Fall. The typical clear sky day was selected using the 10-year Typical meteorological Year (TMY) for Austin, Texas (30.2672° N, 97.7431° W) provided by National Solar Radiation Database (NSRDB). More details on TMY files can be found at: https://nsrdb.nrel.gov/data-sets/tmy</p> <p>Additionally, data is developed for heat hazard for daytime Human Heat Health Index (H3I) calculation as defined by Kamath et al., (2023). Briefly, this heat hazard is defined as the fraction of the day when the UTCI exceeds certain threshold. The threshold used to calculate heat hazard for Summer and Fall were 35° C and 32°C, respectively that imply strong heat stress (Jendritzky et al., 2012). Note that UTCI is on a different scale compared to air temperature, and could yield different heat stress levels.</p> <p><strong>3. Data format</strong></p> <p>The georeferenced UTCI and heat hazard data are available in the geoTIFF file format. The files can be readily visualized using GIS software such as QGIS and ArcGIS, as well as programing languages such as Python.</p> <p> <strong>4. Companion dataset</strong></p> <p>Based on the calculated UTCI here, the potential locations for tree planting were calculated to increase the shade to reduce heat vulnerability for Austin, Texas. [https://doi.org/10.5281/zenodo.6363494]</p> <p><strong>References</strong></p> <ol> <li>Kamath, H. G., Martilli, A., Singh, M., Brooks, T., Lanza, K., Bixler, R. P., ... & Niyogi, D. (2023). Human heat health index (H3I) for holistic assessment of heat hazard and mitigation strategies beyond urban heat islands. Urban Climate, 52, 101675.</li> <li>Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0–Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. <em>International journal of biometeorology</em>, <em>52</em>, 697-713.</li> <li>Lindberg, F., Grimmond, C. S. B., Gabey, A., Huang, B., Kent, C. W., Sun, T., ... & Zhang, Z. (2018). Urban Multi-scale Environmental Predictor (UMEP): An integrated tool for city-based climate services. <em>Environmental modelling & software</em>, <em>99</em>, 70-87.</li> <li>Jendritzky, G., de Dear, R., & Havenith, G. (2012). UTCI—why another thermal index?. <em>International journal of biometeorology</em>, <em>56</em>, 421-428.</li> <li>Bixler, R. P., Coudert, M., Richter, S. M., Jones, J. M., Llanes Pulido, C., Akhavan, N., ... & Niyogi, D. (2022). Reflexive co-production for urban resilience: Guiding framework and experiences from Austin, Texas. Frontiers in Sustainable Cities, 4, 1015630.</li> <li>Lanza, K., Jones, J., Acuña, F., Coudert, M., Bixler, R. P., Kamath, H., & Niyogi, D. (2023). Heat vulnerability of Latino and Black residents in a low-income community and their recommended adaptation strategies: A qualitative study. <em>Urban Climate</em>, <em>51</em>, 101656.</li> </ol>
HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020
<p>The monthly <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection (<strong>HiTIC-Monthly</strong>) includes <strong><em>near-surface air temperature</em> </strong>(SAT) and 11 commonly used<strong> <em>human-perceived temperature</em> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is stacked by year, and each stack is composed of 12 monthly images in the <strong>NetCDF format</strong>. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”.</p> <p>If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via <a href="mailto:zhangh573@mail2.sysu.edu.cn">zhangh573@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:yqzhao@link.cuhk.edu.hk">zhaoyq66@mail.sysu.edu.cn</a>. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in:</p> <p>Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.</p>
Spatial data for creating a thermal inertia index and incorporating it for conservation applications
<p>This repository contains supporting material for a journal article being submitted to one of the journals published by the American Geophysical Union, titled Earth's Future. The repository contains the following items:</p> <p>1. README file of what is in the repository including methods associated with the geodatabase</p> <p>2. File Geodatabase</p> <p><strong>1. README file</strong></p> <p>The files collected here relate to a study being submitted to the American Geophysical Union's journal, Earth's Future. The title of the paper being submitted is, "The contribution of Microrefugia to landscape thermal inertia for climate-adaptive conservation and adaptation strategies."</p> <p>The study was conducted across 40,250 km<sup>2</sup> of complex mountainous terrain in Northern California. The objective of the study was to consider whether it was possible to identify the relative strength of microrefugia systematically in order to provide conservation and climate-adaptation strategies with information that could help with prioritizing actions. We selected an operational scale of 10 ha (25 acres) as a scale that is suitable for various types of landscape planning exercises, and created a hexagon grid for the region. We calculated the mean value for multiple variables and appended them into the hexagons. For thermal inertia, we calculated the mean elevation per hexagon and then its coolest (highest) point using an environmental lapse rate. We also calculated solar energy loading, calculated the mean solar load per hexagon, and calculated its effect on air temperature. We combined these two temperature metrics to identify how much thermal buffering capacity each hexagon contains, as measured by how much warming it could experience before the mean temperature, as determined from a baseline time period, is no longer found anywhere within the hexagon. We tied the mean annual temperature from 1981–2010 to the mean elevation in each hexagon, as well as a temperature from an earlier period, and from several future periods, based on global circulation models.</p> <p>The study shows how long current (baseline) climate conditions found in each hexagon may persist and shows how the resulting map of landscape thermal inertia can be used when considering natural vegetation types for conservation, identifying which parts of high-priority wildlife corridors have the greatest capacity to retain their current climate conditions, and what the potential for retaining baseline climate conditions is for areas with late-seral forest conditions as represented by forest canopy height.</p> <p class="MsoNormal">The methods section below describes the data used in the study to create the data in the geodatabase that is posted here. The Geodatabase itself provides all the data needed to replicate the various results presented in the paper. Further information can be found in Thorne et al. 2020. That report is more extensive than the results in our associated paper, but it contains more information on the calculation of various metrics associated with and was the foundation from which we developed this study. The report is provided here in order to keep all the relevant materials compiled for potential use by others. </p> <p><strong>2. File Geodatabase</strong></p> <p>The geodatabase is provided as a separate file.</p> <p>Name: ThermalInertiaIndex.gdb</p> <p>Contents:</p> <ul> <li>AllHexagons <ul> <li>A feature class containing all 408,948 hexagon grids used in this study</li> <li>Fields within the feature class:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>Id</p> </td> <td> <p>A unique ID for each hexagon</p> </td> </tr> <tr> <td> <p>Watershed</p> </td> <td> <p>Watershed the hexagon falls within</p> </td> </tr> <tr> <td> <p>DomWHR</p> </td> <td> <p>Habitat type (WHR) that had the majority coverage within the hexagon</p> </td> </tr> <tr> <td> <p>WHR_Name</p> </td> <td> <p>Descriptive name of the habitat type</p> </td> </tr> <tr> <td> <p>WHR_GroupName</p> </td> <td> <p>Major vegetation type</p> </td> </tr> <tr> <td> <p>CanopyHt_Score</p> </td> <td> <p>Canopy Height Score ranging from 1 (under 1m) to 5 (over 25m)</p> </td> </tr> <tr> <td> <p>CanopyHt_m</p> </td> <td> <p>Average canopy height within the hexagon (m)</p> </td> </tr> <tr> <td> <p>Conn_Score</p> </td> <td> <p>Connectivity Score ranging from 1 (low) to 5 (high)</p> </td> </tr> <tr> <td> <p>dem10m</p> </td> <td> <p>Average elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_min</p> </td> <td> <p>Minimum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>dem10m_max</p> </td> <td> <p>Maximum elevation within the hexagon (m)</p> </td> </tr> <tr> <td> <p>SRtemp_min</p> </td> <td> <p>The lowest Solar Radiation load within the hexagon (degree C)</p> </td> </tr> <tr> <td> <p>ElevLR_NegEff2</p> </td> <td> <p>Effect of elevation on air temperature (degree C)</p> </td> </tr> <tr> <td> <p>Thermal_Inertia</p> </td> <td> <p>Hexagon buffering capacity (degree C)</p> </td> </tr> <tr> <td> <p>tave_5180</p> </td> <td> <p>Average temperature 1951-1980</p> </td> </tr> <tr> <td> <p>tave_8110</p> </td> <td> <p>Average temperature 1981-2010</p> </td> </tr> <tr> <td> <p>tave_1039mi8</p> </td> <td> <p>Average temperature 2010-2039 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069mi8</p> </td> <td> <p>Average temperature 2040-2069 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099mi8</p> </td> <td> <p>Average temperature 2070-2099 (MIROC-ESM RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_1039cn8</p> </td> <td> <p>Average temperature 2010-2039 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_4069cn8</p> </td> <td> <p>Average temperature 2040-2069 (CNRM-CM5 RCP 8.5)</p> </td> </tr> <tr> <td> <p>tave_7099cn8</p> </td> <td> <p>Average temperature 2070-2099 (CNRM-CM5 RCP 8.5)</p> </td> </tr> </tbody> </table> </div> <p> </p> <ul> <li>Connectivity_Scores <ul> <li>90m raster containing all 3 connectivity scores</li> <li>Fields within the raster:</li> </ul> </li> </ul> <div> <table> <tbody> <tr> <td> <p>TNC_Conn_Score</p> </td> <td> <p>Connectivity Score from reclassed TNC/Omniscape</p> </td> </tr> <tr> <td> <p>CEHC_Score</p> </td> <td> <p>Connectivity Score from reclassed California Essential Habitat Connectivity</p> </td> </tr> <tr> <td> <p>Combined_Score</p> </td> <td> <p>Overall Connectivity Score</p> </td> </tr> </tbody> </table> </div> <p> </p>
A Daily High-Resolution (1 km) Human Thermal Index Collection over the North China Plain from 2003 to 2020
<p>The daily <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection over the <strong>North China Plain </strong>(<strong>HiTIC-NCP</strong>) includes <em><strong>near-surface air temperature</strong></em><strong> </strong>(SAT) and 11 commonly used<strong> </strong><em><strong>human-perceived temperature</strong></em><strong> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), Wet-Bulb Temperature (WBT), simplified Wet-Bulb Globe Temperature (sWBGT), and Wind Chill Temperature (WCT). This daily dataset has a high spatial resolution of 1 km × 1 km and covers the North China Plain<strong> </strong>from January 2003 to December 2020. It has high accuracy with averaged determination coefficient, mean absolute error, and root mean squared error of 0.987, 0.970 °C, and 1.292 °C, respectively. The dataset is stacked by year and each stack consists of 365 daily images in NetCDF format by day of the year. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) to save storage space, and thus need to be divided by 100 to get the values in degree Celcius when in use. The geographic coordinate system of the dataset is World Geodetic System (WGS) 1984 Coordinate System. Naming rules and other details can be found in "README.pdf".</p> <p>If you have any questions when using the HiTIC-NCP dataset, please feel free to contact Mr. Xiang Li via <a href="mailto:lixiang97@mail2.sysu.edu.cn">lixiang97@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:zhaoyq66@mail.sysu.edu.cn">yqzhao@link.cuhk.edu.hk</a>. More details on the procedure of producing the HiTIC-NCP dataset and its accuracy assessment can be found in:</p> <p>Li, X., Luo, M*., Zhao, Y*., Zhang, H., Ge, E., Huang, Z., Wu, S., Wang, P., Wang X., Tang Y. (2023). A daily high-resolution (1 km) human thermal index collection over the North China Plain from 2003 to 2020. <em>Scientific Data</em>, 10, 634. https://doi.org/10.1038/s41597-023-02535-y</p>
Spatial data for creating a thermal inertia index and incorporating it for conservation applications
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HiTIC-Monthly: A Monthly High Spatial Resolution (1 km) Human Thermal Index Collection over China during 2003–2020 [GeoTIFF]
<p>The monthly <strong>Hi</strong>gh spatial resolution human <strong>T</strong>hermal <strong>I</strong>ndex <strong>C</strong>ollection (<strong>HiTIC-Monthly</strong>) includes <strong><em>near-surface air temperature</em> </strong>(SAT) and 11 commonly used<strong> <em>human-perceived temperature</em> </strong>indices: indoor Apparent Temperature (AT<sub>in</sub>), outdoor shaded Apparent Temperature (AT<sub>out</sub>), Discomfort Index (DI), Effective Temperature (ET), Heat Index (HI), Humidex (HMI), Modified Discomfort Index (MDI), Net Effective Temperature (NET), simplified Wet Bulb Globe Temperature (sWBGT), Wet-Bulb Temperature (WBT), and Wind Chill Temperature (WCT). This dataset has a high spatial resolution of 1 km × 1 km and covers mainland China from January 2003 to December 2020. The overall R-square, root mean square error, and mean absolute error of the 12 thermal indices in the HiTIC dataset are 0.996, 0.693°C, and 0.512°C, respectively. It is compressed by year, and each zip package is composed of 12 monthly images in the<strong> GeoTIFF format</strong>. The unit of the dataset is 0.01 degree Celsius (°C), and the values are stored in an integer type (Int16) for saving storage space, and need to be divided by 100 to get the values in degree Celcius when in use. The projection coordinate system of the dataset is Albers Equal Area Conic Projection. The naming rule and other detailed information can be found in “README.pdf”.</p> <p>If you have any questions when using the HiTIC-Monthly dataset, please feel free to contact Miss Hui Zhang via <a href="mailto:zhangh573@mail2.sysu.edu.cn">zhangh573@mail2.sysu.edu.cn</a>, Dr. Ming Luo via <a href="mailto:luom38@mail.sysu.edu.cn">luom38@mail.sysu.edu.cn</a>, or Dr. Yongquan Zhao via <a href="mailto:yqzhao@link.cuhk.edu.hk">zhaoyq66@mail.sysu.edu.cn</a>. More details on the procedure of producing the HiTIC-Monthly dataset and its accuracy assessment can be found in:</p> <p>Zhang, H., Luo, M., Zhao, Y., Lin, L., Ge, E., Yang, Y., Ning, G., Cong, J., Zeng, Z., Gui, K., Li, J., Chan, T. O., Li, X., Wu, S., Wang, P., and Wang, X.: HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020, Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, 2023.</p>
GloUTCI-M: A Global Monthly 1 km Universal Thermal Climate Index Dataset from 2000 to 2022
<p>The GloUTCI-M comprises global monthly UTCI data at a spatial resolution of 1km, spanning from March 2000 to October 2022. The dataset is expressed in degrees Celsius (°C) and is stored as an integer type (Int16). To utilize it appropriately, one must divide the values by 100.</p>
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