China meteorological forcing dataset (1979-2018)

The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.

0 2020-04-01

The atmospheric forcing data in the Heihe River basin (2000-2015)

The WRF model was used to prepare the near-surface atmospheric driving data of 2m air temperature, surface pressure, 2m water vapor mixing ratio, radiation, 10m wind field and cumulative precipitation at 0.05 degrees per hour from 2000 to 2015 in heihe river basin.The coordinate system is the longitude and latitude projection.Data is stored month by month, UTC time system, data format is netcdf, naming method: wrfout_heihe_yyyy_mm_01, where "wrfout_heihe_" is a fixed format, representing the result of the second layer of nesting, yyyy is the year, mm is the month, "01" represents every month from the 1st. Through daily and hourly verification with the observation data of 15 CMA sites and 7 WATER sites, the following conclusions are drawn: 2m surface temperature, surface pressure and relative humidity are relatively reliable, especially 2m surface temperature and surface pressure, the mean error is small and the correlation coefficient is above 0.96;The correlation between the downward short-wave radiation and the observed data at the WATER station was more than 0.9, and the correlation between the downward long-wave radiation was also 0.6.The wind speed of 10m differs greatly from the observed data, and the correlation is weak.The above verification results are for reference only, for specific reference, reference 1 and reference 2. The data of 2014-2015 were updated in April 2016. Data for 2016-2018 were updated in October 2019.

0 2020-03-06

The dataset of spatio-temporal water resources distribution in the source regions of Yangtze River and Yellow River (1998-2017)

This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff,soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.

0 2019-09-22

HiWATER: Dataset of GPS radiosonde sounding observations in the middle and upper reaches of the Heihe River Basin in 2012

The dataset generated from the radiosonde observations in middle basin of Heihe River during 2012. The instrument type are RS92-SGP (Vaisala inc., Finland) or CF-06-A (Changfeng Micro-Electroinics, CHINA). Radiosondes were released during aerospace experiment, such as CASI/SAI, TASI, WIDAS sensors. Atmospheric parameters: pressure, temperature, relative humidity, wind speed and wind direction are measured or calculated at different altitude. This atmospheric parameter profiles can back up atmospheric correction in remote sensing. It can support meteorology research. Observation Site: 1. Wuxing Village: Latitude: 38°51′11.9″N,Longitude: 100°21′48.8″E,Altitude: 1563 m 2. Gaoya Hydrological Station Latitude: 39°8′7.2″N,Longitude: 100°23′59.0″E,Altitude: 1418 m 3. A’Rou Super Station Latitude: 38°03′17.9″N,Longitude: 100°27′28.1″E,Altitude: 2991 m Observation Instrument Type: RS92-SGP manufacture by Vaisala inc., Finland CF-06-A manufacture by Beijing Changfeng Micro-Electronics Technology Co., LTD, CHINA. Observation Time: Simultaneous observation time from 29 June, 2012 to 29 July, 2012 (UTC+8). Accessory data: Pressure, temperature, relative humidity, wind speed and wind direction profiles data.

0 2019-09-15

The meteorological forcing dataset in Three-River Headwater Region (1979-2016)

This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.

0 2019-09-15

Meteorological observation data in an alpine steppe site of Shenzha Station (2015-018)

(1)This data set provides atmospheric temperature (2 meters above land surface), vapor content, precipitation, press, wind velocity and solar radiation (since 2015). (2)All data were generated using AWS (auto weather station), and been calculated their daily average. (3)All data are presented here are raw data, after being evaluated regarding their quality. (4)This data set could be used in background description for related studies.

0 2019-09-13