This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.
WANG Lei, LIU Hu
In the context of global change, the spatio-temporal continuous high-quality high-resolution long time series precipitation data set is of great significance for understanding the global "water carbon energy" and biogeochemical cycle mechanism. The daily total volume controlled merging and disaggregation algorithm (DTVCMDA) proposed in this study effectively considers the characteristics of continuous space-time and high spatial and temporal resolution of reanalysed precipitation data, as well as the high quality of ground analysis data, A set of AERA5 Asia (0.1 °, hourly, 1951-2015, Asia) precipitation data set with high quality and high spatial and temporal resolution for more than 70 years of long time series in Asia has been produced. The main features of the dataset are as follows: (1) AERA5 Asia is a set of data sets with high resolution, high quality, space-time continuity and long time series; (2) AERA5 Asia is significantly better than IMERG Final and ERA5 Land precipitation data, especially in terms of system deviation. In general, the deviation of AERA5 Asia, IMERG Final and ERA5 Land compared with ground observation is~5%,~11% and~20% respectively; (3) In extreme heavy rainfall (such as typhoons "Tamei" and "Tiantu"), the quality of AERA5 Asia is also significantly better than ERA5 Land and IMERG Final. AERA5 Asia will provide stable and reliable precipitation data support for relevant research in the weather, climate, hydrology and other fields in Asia, especially in China.
MA Ziqiang, MA Yaoming, MA Weiqiang*, 许金涛 XU Jintao
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 31 in 2021. The site (115.7880° E, 40.3491°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC) (class 1 to 9). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from April 13 to December 31 in 2021. The site (115.7923° E, 40.3574° N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 3.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&EC150) was 0 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC) (class1-9). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. There were lots of negative values of H2O density in winter where filling by -6999. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua
This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2021. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs) (W/m^2), soil temperature (Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua
This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2021. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and average soil temperature (TCAV, ℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This data is generated based on meteorological observation data, hydrological station data, combined with various assimilation data and remote sensing data, through the preparation of the Qinghai Tibet Plateau multi-level hydrological model system WEB-DHM (distributed hydrological model based on water and energy balance) coupling snow, glacier and frozen soil physical processes. The time resolution is monthly, the spatial resolution is 5km, and the original data format is ASCII text format, Data types include grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation in the month). If the asc cannot be opened normally in arcmap, please top the first 5 lines of the asc file.
WANG Lei, CHAI Chenhao
Meteorological forcing dataset for Arctic River Basins includes five elements: daily maximum, minimum and average temperature, daily precipitation and daily average wind speed. The data is in NetCDF format with a horizontal spatial resolution of 0.083°, covering Yenisy, Lena, ob, Yukon and Mackenzie catchments. The data can be used to dirve hydrolodical model (VIC model) for hydrological process simulation of the Arctic River Basins. The further quality control were made for daily observation data from Global Historical Climatology Network Daily database(GHCN-D), Global Summary of the Day (GSPD),The U.S. Historical Climatology Network (USHCN),Adjusted and homogenized Canadian climate data (AHCCD) and USSR / Russia climate data set (USSR / Russia). The thin plate spline interpolating method, which similar to the method used in PNWNAmet datasets (Werner et al., 2019), was employed to interpolate daily station data to 5min spatial resolution daily gridded forcing data using WorldClim and ClimateNA monthly climate normal data as a predictor.
ZHAO Qiudong, WU Yuwei
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.
WANG Lei
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation). This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation).
WANG Lei
The data set includes annual mass balance of Naimona’nyi glacier (northern branch) from 2008 to 2018, daily meteorological data at two automatic meteorological stations (AWSs) near the glacier from 2011 to 2018 and monthly air temperature and relative humidity on the glacier from 2018 to 2019. In the end of September or early October for each year , the stake heights and snow-pit features (snow layer density and stratigraphy) are manually measured to derive the annual point mass balance. Then the glacier-wide mass balance was then calculated （Please to see the reference). Two automatic weather stations (AWSs, Campbell company) were installed near the Naimona’nyi Glacier. AWS1, at 5543 m a. s.l., recorded meteorological variables from October 2011 at half hourly resolution, including air temperature (℃), relative humidity (%), and downward shortwave radiation (W m-2) . AWS2 was installed at 5950 m a.s.l. in October 2010 at hourly resolution and recorded wind speed (m/s), air pressure (hPa), precipitation (mm). Data quality: the quality of the original data is better, less missing. Firstly, the abnormal data in the original records are removed, and then the daily values of these parameters are calculated. Two probes (Hobo MX2301) which record air temperature and relative humidity was installed on the glacier at half hour resolution since October 2018. The observed meteorological data was calculated as monthly values. The data is stored in Excel file. It can be used by researchers for studying the changes in climate, hydrology, glaciers, etc.
ZHAO Huabiao
The water resources simulation data of Southeast Asian countries and the Lancang Mekong River Basin (1980-2019) is the result of using the meteorological data output from the WRF model as the driving data and simulation through the ways model. The data includes evapotranspiration, surface runoff, underground runoff, total runoff, groundwater, infiltration and soil moisture data of Southeast Asia land area from 1980 to 2019. The temporal resolution is daily and the spatial resolution is 3km. The data is generally good, but due to the limitations of the model, there are certain errors in the simulation results of a few variables. It is not recommended to use the research with high requirements for data accuracy. The data can reflect the situation of water resources in Southeast Asia to a certain extent, and provide data support for relevant research.
LIU Junguo
This product provides the data set of key variables of the water cycle of major Arctic rivers (North America: Mackenzie, Eurasia: Lena from 1971 to 2017, including 7 variables: precipitation, evapotranspiration, surface runoff, underground runoff, glacier runoff, snow water equivalent and three-layer soil humidity, which are numerically simulated by the land surface model vic-cas developed by the project team. The spatial resolution of the data set is 0.1degree and the temporal resolution is month. This data set can be used to analyze the change of water balance in the Arctic River Basin under long-term climate change, and can also be used to compare and verify remote sensing data products and the simulation results of other models.
ZHAO Qiudong, WANG Ninglian, WU Yuwei
The evapotranspiration (ET) is an important variable connecting land energy balance, water cycle and carbon cycle. Accurate monitoring and estimations of ET are essential not only for water resources management but also for simulating regional, global climate, and hydrological cycles. Remote sensing technology is an effective method to monitor ET. At present, a variety of ET remote sensing products have been produced and released. However, in the process of validation, there is a problem of spatial scale mismatch between ET remote sensing estimation value and station observation value, especially on heterogeneous surface. Therefore, it is very important to obtain the ground truth ET values at the satellite pixel scale by upscaling method on heterogeneous surface. In this study, using the station observation data and multi-source remote sensing information, the ET observed at a single ground station is upscaled to the satellite pixel scale, and the ground truth ET values at the satellite pixel scale in Heihe River Basin is obtained. Based on the ET data observed by the eddy covariance (EC) at 15 stations (3 superstations and 12 ordinary stations) in the Heihe integrated observatory network, combined with the fused high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) and atmospheric reanalysis data, the upscaling is carried out to obtain the ground truth ET at the satellite pixel scale. The distribution diagram is shown in Figure 1. Specifically, firstly, the spatial heterogeneity of the spatial heterogeneity of the land surface hydrothermal conditions was evaluated; Secondly, nine upscaling methods (the integrated Priestley-Taylor equation method, the Penman-Monteith equation combined with EnKF method, the Penman-Monteith equation combined with SCE_UA method, EC observation value, artificial neural network, Bayesian linear regression, deep belief network, Gaussian process regression, and random fores and directly taking the EC observation value as the ground truth ET) were compared and analyzed through direct validation and cross-validation; Finally, a comprehensive method (directly using the EC observation value on the homogeneous underlying surface; using the Gaussian process regression method for upscaling on the moderately heterogeneous underlying surface and highly heterogeneous underlying surface) was optimized to obtain the groud truth ET at the satellite pixel scale at 15 typical underlying surfaces in Heihe River Basin (2010-2016, spatial resolution of 1km). The results showed that the ground truth ET at the satellite pixel scale is relatively reliable. Compared with the pixel scale reference value (LAS observation value), the MAPE of the ground turth ET at the satellite pixel scale at the three superstations are 1.57%, 3.23% and 4.59% respectively, which can meet the needs of the validation of ET remote sensing products. For all site information and data processing, please refer to Liu et al. (2018), and for upscaling methods, please refer to Li et al. (2021).
LIU Shaomin, LI Xiang , XU Ziwei
The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.
PAN Xiaoduo
The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
MA Yaoming
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The long-time series data set of extreme precipitation index in the arid region of Central Asia contains 10 extreme precipitation index long-time series data of 49 stations. Based on the daily precipitation data of the global daily climate historical data network (ghcn-d), the data quality control and outlier elimination were used to select the stations that meet the extreme precipitation index calculation. Ten extreme precipitation indexes (prcptot, SDII, rx1day, rx5day, r95ptot, r99ptot, R10, R20) defined by the joint expert group on climate change detection and index (etccdi) were calculated 、CWD、CDD）。 Among them, there are 15 time series from 1925 to 2005. This data set can be used to detect and analyze the frequency and trend of extreme precipitation events in the arid region of Central Asia under global climate change, and can also be used as basic data to explore the impact of extreme precipitation events on agricultural production and life and property losses.
YAO Junqiang, CHEN Jing, LI Jiangang
Precipitation estimates with fine quality and spatio-temporal resolutions play significant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at fine resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. Focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (finished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0◦/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1◦/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25◦/daily) at the daily scale for the Asian applications. Considering the advantages from both satellite-based precipitation estimates and the ground observations, AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China.
MA Ziqiang
As an important part of global semi-arid grassland, adequately understanding the spatio-temporal variability of evapotranspiration (ET) over the temperate semi-arid grassland of China (TSGC) could advance our understanding of climate, hydrological and ecological processes over global semi-arid areas. Based on the largest number of in-situ ET measurements (13 flux towers) within the TSGC, we applied the support vector regression method to develop a high-quality ET dataset at 1 km spatial resolution and 8-day timescale for the TSGC from 1982 to 2015. The model performed well in validation against flux tower‐measured data and comparison with water-balance derived ET.
LEI Huimin
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
