China GIS data

**ADAM ID: **1001305\_46 Tropical storm during the period Aug 18 2026-Sep 02 2026 in Japan, China, Northern Mariana Islands. The center of the storm was located near latitude 23.4 longitude 118.4. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

**ADAM ID: **sm\_us7000tdw6 Magnitude 5.4 earthquake at 10.0 depth occurred on Sep 03 2026 26km N of Beichuan Qiang Autonomous County. The epicentre of the earthquake was located at latitude 31.8458 longitude 104.5066. It impacted 1280732 people within 50km. WFP’s Automated Disaster Analysis and Mapping (ADAM) system is an operational system for collecting, analysing and mapping geospatial and socio-economic information following sudden onset humanitarian emergencies.

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons. Fields available: SID: A unique storm identifier (SID) assigned by IBTrACS algorithm. ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss) BASIN: Basin of the current storm position SUBBASIN: Sub-basin of the current storm position NATURE: Type of storm (a combination of the various types from the available sources) NUMBER: Number of the storm for the year (restarts at 1 for each year LAT: Mean position - latitude (a combination of the available positions) LON: Mean position - longitude (a combination of the available positions) WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

The Soil and Terrain database for China primary data (version 1.0), at scale 1:1 million (SOTER_China), was compiled of enhanced soil information within the framework of the FAO's program of Land Degradation Assessment in Drylands (LADA). The primary database was compiled using the SOTER methodology. The SOTER unit delineation was based on a raster format of the soil map of China, correlated and converted to FAO’s Revised Legend (1988), in combination with a SOTER landform characterization derived from Shuttle Radar Topographic Mission (SRTM) 90 m digital elevation model (DEM). Reference profiles for the dominant soil of the SOTER units has been directly linked to the polygons. SOTER forms a part of the ongoing activities of ISRIC, FAO and UNEP to update the world's baseline information on natural resources.The project involved collaboration with national soil institutes from the countries in the region as well as individual experts.

Dates of Images: 4/28/2024, 4/29/2024, 4/30/2024, 5/1/2024, 5/2/2024, 5/5/2024, 5/6/2024 Summary: LHASA was created at Goddard Space Flight Center to identify the potential for rainfall-triggered landslides in real time. LHASA version 2 adopts machine learning to estimate the probability of landslide occurrence at a 30-arcsecond (~1 km) daily resolution. In addition, it estimates the potential exposure of human population and roads to landslide hazard and maps the basins likely to experience post-fire debris flows. Suggested Use: The global landslide nowcast shows where landslide hazard is highest on a given date. Resolution: ~1km Credits: Please cite: Stanley T. A., J. R. Sutton, R. S. Vershel, P. M. Amatya. 2025. "Better Satellite Precipitation Algorithms Slightly Improved Landslide Hazard Assessment." Journal of Applied Meteorology and Climatology 64 (10): 1379-1394 10.1175/jamc-d-25-0021.1 Esri REST Endpoint: See URL on the right side of the page.