This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2019. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the downscaling model include GLASS Albedo/LAI/FVC, Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST – Tibetan Plateau (TRIMS LST-TP) by Ji Zhou and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the Jingyangling station from January 1 to December 31, 2020. The site (101.116° E, 37.838° N) was located on a cold meadow surface in the Jingyangling, Qilian County, Qinghai Province. The elevation is 3750 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (5 m, north), wind speed and direction (10 m, north), air pressure (in the tamper box on the ground), rain gauge (10 m), four-component radiometer (6 m, south), two infrared temperature sensors (6 m, south, vertically downward), soil heat flux (3 duplicates, -0.06 m), soil temperature profile (0, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and soil moisture profile (-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_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/m^2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m^2), soil temperature (Ts_0 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), and soil moisture (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. Due to the snow cover the solar panel causing insufficient power supply, data during March 14-April 25 were missing; due to the sensor malfunction, there were some NAN invalid values of the wind speed and direction; incorrect data of upward shortwave radiation occasionally; (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: 2020-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei, REN Zhiguo, LI Xin
In this study, an algorithm that combines MODIS Terra and Aqua (500 m) and the Interactive Multisensor Snow and Ice Mapping System (IMS) (4 km) is presented to provide a daily cloud-free snow-cover product (500 m), namely Terra-Aqua-IMS (TAI). The overall accuracy of the new TAI is 92.3% as compared with ground stations in all-sky conditions; this value is significantly higher than the 63.1% of the blended MODIS Terra-Aqua product and the 54.6% and 49% of the original MODIS Terra and Aqua products, respectively. Without the IMS, the daily combination of MODIS Terra-Aqua over the Tibetan Plateau (TP) can only remove limited cloud contamination: 37.3% of the annual mean cloud coverage compared with the 46.6% (MODIS Terra) and 55.1% (MODIS Aqua). The resulting annual mean snow cover over the TP from the daily TAI data is 19.1%, which is similar to the 20.6% obtained from the 8-day MODIS Terra product (MOD10A2) but much larger than the 8.1% from the daily blended MODIS Terra-Aqua product due to the cloud blockage.
ZHANG Guoqing
Daily precipitation data was reconstructed for streamflow simulation in the entire UB by combining orographic and linear correction approaches based on 262 gauge observations. The reconstructed precipitation is used to drive the VIC hydrological model linked with a temperature-index model (VIC-Glacier) , and is inversely evaluated by comparing with observed discharge, glacier area changes, and MODIS-based snow cover faction (SCF) data in the upper Brahmaputra Basin.
SU Fengge, SUN He
These datasets fill the data gap between GRACE and GRACE-FO, they contain CSR RL06 Mascon and JPL RL06 Mascon. They take China as the study area, and the dataset includes "Decimal_time”, "lat”, "lon”, "time”, "time_bounds”, "TWSA_REC" and "Uncertainty" 7 parameters in total. Among them, "Decimal_time” corresponds to decimal time. There are 191 months from April 2002 to December 2019 (163 months for GRACE data, 17 months for GRACE-FO data, and 11 months for the gap between GRACE and GRACE-FO. We have not filled the missing data of individual months between GRACE or GRACE-FO data). "lat" corresponds to the latitude range of the data; "lon" corresponds to the longitude range of the data; "time" corresponds to the cumulative day of the data from January 1, 2002. And "time_bounds" corresponding to the cumulative day at the start date and end date of each month. “TWSA_REC" represents the monthly terrestrial water storage anomalies from April 2002 to December 2019 in China; "Uncertainty" is the uncertainty between the data and CSR RL06 Mascon products. We use GRACE satellite data from CSR GRACE/GRACE-FO RL06 Mascon solutions (version 02), China Gauge-based Daily Precipitation Analysis (CGDPA, version 1.0) data, and CN05.1 temperature dataset. The precipitation reconstruction model was established, and the seasonal and trend terms of CSR RL06 Mascon products were considered to obtain the dataset of terrestrial water storage anomalies in China. The data quality is good as a whole, and the uncertainty of most regions in China is within 5cm. This dataset complements the nearly one-year data gap between GRACE and GRACE-FO satellites, and provides a full time series for long-term land water storage change analysis in China. As the CSR RL06 Mascon product, the average value between 2004.0000 and 2009.999 is deducted from this dataset. Therefore, the 164-174 months (i.e., July 2017 to May 2018) of this dataset can be directly extracted as the estimation of terrestrial water storage anomalies during the gap period. The reconstruction method for the gap of JPL RL06 Mascon is consistent with that of CSR RL06 Mascon.
ZHONG Yulong, FENG Wei, ZHONG Min, MING Zutao
Soil moisture is one of the core variables in the water cycle. Although its variation is very small, for a precipitation process, soil moisture directly determines the transformation of precipitation into evaporation, runoff and groundwater, which is very important to finely simulate spatial-temporal dynamics of various variables in hydrological process and to accurately estimate water inflow in the upper reaches of Heihe River. This dataset includes soil moisture and temperature data observed by 40 nodes from July 2013 to December 2017. Each node in Babao River Basin has soil moisture observation at depth of 4cm and 20cm; some nodes also include observations at depth of 10 cm. The data observation frequency is 1 hour. The dataset can provide ground -based observations for hydrological simulation, data assimilation and remote sensing verification.
KANG Jian
This project is based on the gsflow model of USGS to simulate the surface groundwater coupling in Zhangye basin in the middle reaches of Heihe River. The space-time range and accuracy of the simulation are as follows: Simulation period: 1990-2012; Simulation step: day by day; The spatial scope of simulation: Zhangye basin; The spatial accuracy of simulation: the underground part is 1km × 1km grid (5 layers, the total number of grids in each layer is 150 × 172 = 25800, among which the active grid 9106); the surface part is based on the hydrological response unit (HRU) (588 in total, each HRU covers an area of several square kilometers to dozens of square kilometers). The data include: surface infiltration, actual evapotranspiration, average soil moisture content, surface groundwater exchange, shallow groundwater level, simulated daily flow of Zhengyi gorge, simulated monthly flow of Zhengyi gorge, groundwater extraction and river diversion
ZHENG Yi
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2019. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) 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. 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 dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2019. 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 (3 duplicates, -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_5 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_5 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), 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_1, Gs_2, and Gs_3) (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: 2019-6-10 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 obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2018. 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 (3 duplicates, -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_5 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_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 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_1, Gs_2, and Gs_3) (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: 2018-6-10 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 contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2018. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) 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. 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 dataset includes the observation data from 01 Jan. 2019 through 31 Dec. 2018, collected by lysimeters, which are located at 115.788 E, 40.349 N and 480 m above sea level, near the Huailai Station in East Garden Town, Huailai County, Hebei Province. The land cover around the station was maize crop. The weighable lysimeter was built by UMS GmbH (Germany), with a surface area of 1m2, and a soil column of 1.5 m high. The original data sampling frequency was 1 Hz, and then averaged to 10min for distribution. The precision of the weighing data is 10g (equivalent to 0.01mm). During the crop growth period, a lysimeter is covered by bare soil and another one is covered by planted maize. The soil moisture, temperature and soil water potential sensors are installed both inside and outside of the lysimeter to ensure that the water cycle in the soil column is consistent with that of the field. Different sensors are located at different depths: 5, 50, 100 cm for soil temperature sensors, and 5, 10, 30, 50, 100 cm for soil moisture sensors, and 30 and 140cm for soil water potential sensors (the tensionmeter here can also measure soil temperature at 30, 140 cm). The soil heat flux plates in both lysimeters are buried at 10cm depth. The data processes and quality control according to: 1) ensuring there were 144 data every day, the lost data were replaced by -6999; 2) deleting the abnormal data; 3) deleting the outlier data; 4) keeping the consistent date and time format (e.g.2018-6-10 10:30). The distributed data include the following variables: Date-Time, Weight (I.L_1_WAG_L_000(Kg), I.L_2_WAG_L_000(Kg)), Drainage Weight (I.L_1_WAG_D_000(Kg), I.L_2_WAG_D_000(Kg)), Soil Heat Flux (Gs_1_10cm, Gs_2_10cm) (W/m2), Soil Moisture (Ms_1_5cm, Ms_1_10cm, Ms_1_30cm, Ms_1_50cm, Ms_1_100cm, Ms_2_5cm, Ms_2_10cm, Ms_2_30cm, Ms_2_50cm, Ms_2_100cm) (%), Soil Temperature (Ts_1_5cm , Ts_1_30cm, Ts_1_50cm, Ts_1_100cm, Ts_1_140cm, Ts_2_5cm , Ts_2_30cm, Ts_2_50cm, Ts_2_100cm, Ts_2_140cm) (C), Soil Water Potential (TS_1_30(hPa), TS_1_140(hPa), TS_2_30(hPa), TS_2_140(hPa)). The format of datasets was *.xls.
LIU Shaomin, ZHU Zhongli, XU Ziwei
This dataset includes the observation data from 01 Jan. 2019 through 31 Dec. 2019, collected by lysimeters, which are located at 115.788E, 40.349N and 480 m above sea level, near the Huailai Station in East Garden Town, Huailai County, Hebei Province. The land cover around the station was maize crop. The weighable lysimeter was built by UMS GmbH (Germany), with a surface area of 1m2, and a soil column of 1.5 m high. The original data sampling frequency was 1 Hz, and then averaged to 10min for distribution. The precision of the weighing data is 10g (equivalent to 0.01mm). During the crop growth period, a lysimeter is covered by bare soil and another one is covered by planted maize. The soil moisture, temperature and soil water potential sensors are installed both inside and outside of the lysimeter to ensure that the water cycle in the soil column is consistent with that of the field. Different sensors are located at different depths: 5, 50, 100 cm for soil temperature sensors, and 5, 10, 30, 50, 100 cm for soil moisture sensors, and 30 and 140cm for soil water potential sensors (the tensionmeter here can also measure soil temperature at 30, 140 cm). The soil heat flux plates in both lysimeters are buried at 10cm depth. The data processes and quality control according to: 1) ensuring there were 144 data every day, the lost data were replaced by -6999; 2) deleting the abnormal data; 3) deleting the outlier data; 4) keeping the consistent date and time format (e.g. 2019-01-01 10:30). The distributed data include the following variables: Date-Time, Weight (I.L_1_WAG_L_000(Kg), I.L_2_WAG_L_000(Kg)), Drainage Weight (I.L_1_WAG_D_000(Kg), I.L_2_WAG_D_000(Kg)), Soil Heat Flux (Gs_1_10cm, Gs_2_10cm) (W/m2), Soil Moisture (Ms_1_5cm, Ms_1_10cm, Ms_1_30cm, Ms_1_50cm, Ms_1_100cm, Ms_2_5cm, Ms_2_10cm, Ms_2_30cm, Ms_2_50cm, Ms_2_100cm) (%), Soil Temperature (Ts_1_5cm , Ts_1_30cm, Ts_1_50cm, Ts_1_100cm, Ts_1_140cm, Ts_2_5cm , Ts_2_30cm, Ts_2_50cm, Ts_2_100cm, Ts_2_140cm) (C), Soil Water Potential (TS_1_30(hPa), TS_1_140(hPa), TS_2_30(hPa), TS_2_140(hPa)). The format of datasets was *.xls.
LIU Shaomin, ZHU Zhongli, XU Ziwei
This data set mainly includes daily surface evapotranspiration products in Heihe River Basin (HRB) from 2010 to 2016, with a resolution of 100 meters. Based on multi-source remote sensing data (MODIS Landsat TM/ETM+ data) and regional meteorological data (China meteorological forcing dataset, CMFD), sensitivity parameters of the theoretically robust surface energy balance system (SEBS) model were determined through global sensitivity analysis, and then the parameterization scheme of the model was optimized to improve the estimation accuracy. At the same time, combined with spatial and temporal data fusion algorithm of remote sensing image. Finally, the High-Temporal and Landsat-Like surface evapotranspiration (ET) (HiTLL ET) was obtained over the Heihe Basin. It was validation by the EC measurements from the flux observation stations and ETMap, and the estimation results are consistent with the observation and the spatial and temporal distribution pattern of ETMap. This data set can provide data support for the study of water consumption law and scientific effective management of watershed water resources within HRB, especially for woodland and grassland in the upper stream regions, oasis farmland and desert vegetation in the midstream and downstream regions.
MA Yanfei, LIU Shaomin
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
Estimate of the thermal state of the ocean is of vital importance to understand the process of air-sea interaction and footprint of climate change in the Indian Ocean. However, the insufficiency and poor coverage of subsurface observation brings quite a large challenge to estimate the subsurface temperature changes accurately. Moreover, surface observation from satellite contains well sample and could also reflect the subsurface information partly. We derived the ‘synthetic profiles’ based on the ‘surface-subsurface regression’ method in order to enrich the subsurface profiles. Then the 3-dimentional gridded temperature dataset are established by combining both the in-situ and synthetic profiles through objective analysis technique. Inter-comparison between the previous released datasets, such as IAP, EN4 and Ishii, this dataset could capture main thermal signal among the Indian Ocean and shows more mesoscale signal because of its higher resolution. This dataset provides monthly mean gridded subsurface temperature estimate among 30E-105E, 45S-30N, ranging from 2005 to 2018. We provide with dataset which has horizontal resolution in quarter degree and 42 vertical levels from surface to 2000-m depth in netCDF format.
WANG Gongjie, ZHAO Liang
Precipitation and temperature are essential input variables for hydrological models. There are few meteorological stations in the big Naryn Basin of the Syr Darya, which cannot meet the needs of hydrological simulation. Precipitation data in the Syr Darya were collected through online resources and field research. The precipitation gradient in the study area is obtained. Based on the precipitation gradient, the precipitation and temperature grid products (PGMFD) (http://hydrology.princeton.edu/data.pgf.php)were then corrected to get this set of data sets. The year covered by this data is 1951-2016, the spatial precision is 10km, and the time resolution is daily. The more detail information about the correction method can be found in (Generation of High Mountain Precipitation and Temperature Data for a Quantitative Assessment of Flow Regime in the Upper Yarkant Basin in the Karakoram, Kan et al., 2018)
SU Fengge
HOBO water temperature loggers (U22-001, Onset Corp., USA) were used to monitor changes in water temperature with an accuracy of ±0.2 oC. Two water temperature profiles were installed in Paiku Co’s southern (0-42 m in depth) and northern (0-72 m in depth) basins (Fig. 1). In the southern basin, water temperature was monitored at the depths of 0.4 m, 5m, 10 m, 15 m, 20 m, 30 m and 40 m. In the northern basin, water temperature was monitored at the depths of 0.4 m, 10 m, 20 m, 40 m, 50 m, 60 m and 70 m. To investigate local hydro-meteorology at Paiku Co, air temperature and specific humidity over the lake were monitored since June 2015 by using HOBO air temperature and humidity loggers (U12-012, Onset Corp., USA). The logger was installed in an outcrop ~2 m above the lake surface at the north part of the lake (Fig. 2). Lake evaporation was calculated using the energy budget (Bowen-ratio) method。
LEI Yanbin
The main idea of water resource estimation is to build a machine learning model using runoff coefficients and runoff influencing factors (including climate, topography, land use, soil, etc.), and then calculate the runoff coefficients based on the runoff depth and precipitation data estimated by the model. First, based on global public data, we build various machine learning models for runoff depth and topography, climate, soil, and land use, evaluate the simulation accuracy and validity of different models, and select the optimal model for runoff depth estimation. Finally, the optimal model is used to estimate and generate the runoff depth distribution in the Belt and Road region, and the runoff coefficient distribution is calculated based on the precipitation distribution data in 2015.
The restrictive classification/zonation of water resource carrying capacity of the countries along the "Belt and Road" is one of the important achievements in water resource carrying capacity evaluation. Restrictive classification/zonation method of water resource carrying capacity: Based on multi-source data such as remote sensing, statistics and research, combined with water resource availability and per capita comprehensive water use evaluation study, to quantitatively evaluate the water resource carrying capacity of countries and regions along the Green Silk Road from the perspective of water and soil balance and human water balance according to the relationship between resource supply and demand balance, to study the threshold system of water resource carrying capacity evaluation, to evaluate the water resource carrying capacity of countries and regions along the Green Silk Road from the perspective of water and soil balance and human water balance. The restrictive classification/zonation of water resource carrying capacity provides a basis for water security early warning, water resource carrying capacity enhancement and control strategies.
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