Meteorological elements of the dataset include the near-surface land-air exchange parameters, such as downward/upward longwave/shortwave radiation flux, momentum flux, sensible heat flux, latent heat flux, etc. In addition, the vertical distributions of 3-dimensional wind, temperature, humidity, and pressure from the surface to the tropopause are also included. Independent evaluations were conducted for the dataset by comparison between the observational data and the most recent ERA5 reanalysis data. The results demonstrate the accuracy and superiority of this dataset against reanalysis data, which provides great potential for future climate change research.
LI Fei, Ma Shupo, ZHU Jinhuan, ZOU Han , LI Peng , ZHOU Libo
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
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
This data set is the conventional meteorological observation data of the Ngoring Lake Grassland Observation site (GS) in the source region of the Yellow River from 2017 to 2020, obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments, with a time resolution of half an hour. Mainly include wind speed, wind direction, temperature, relative humidity（specific humidity in 2020）, air pressure, downward short-wave radiation, downward long-wave radiation, precipitation.
MENG Xianhong, LI Zhaoguo
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from January 1 to October 9 in 2021. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). 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_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from January 1 to October 13, 2021. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient from Janurary 1 to October 13 in 2021. The site (100°14'8.99"E, 37°14'49.00"N) was located in Sanjiaocheng sheep breeding farm, Gangcha County, Qinghai Province. The elevation is 3210m.The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; towards north), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m; RH_3 m, RH_5 m, RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, 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: 2018/8/31 10:30.
Li Xiaoyan
This dataset provides the monitoring data of runoff, precipitation and temperature of the Duodigou Runoff Experimental Station located in the northern suburbs of Lhasa city. Among the dataset, there are two runoff monitoring stations, which provide discharge data from June to December 2019, with a data step of 10 minutes. There are five precipitation monitoring stations, which provide precipitation data from 2018 to 2021, with a data step of 1 day. There are eight air temperature monitoring stations, which provide air temperature data from 2018 to 2021 in 30 minute steps. The discharge, the precipitation and the temperature data are the measured values. The dataset can provide data support for the study of hydrological and meteorological processes in the Tibet Plateau.
LIU Jintao
The multi-scale dataset of environment and element-at-risk for the Qinghai-Tibet Plateau includes geomorphic data, normalized vegetation index data, annual temperature and rainfall data, and disaster bearing value grade data, covering an area of 6.56 million square kilometers. The data set is mainly prepared for disaster and risk assessment. Due to the huge coverage, the geomorphic data adopts 150m spatial resolution and other data adopts 1000m spatial resolution. Geomorphology, vegetation index, temperature and rainfall data are mainly produced by processing open source data, and disaster bearing value grade data are produced by superposition calculation, comprehensively considering population data, night light index, buildings and surface cover types.
TANG Chenxiao
The preparation of this data set is based on the proposed downscaling method of all-weather surface temperature data for the glacier area in Southeast Tibet. By analyzing the relationship between all-weather surface temperature and its spatio-temporal influence factor elevation, surface coverage type, vegetation index, snow cover index, surface reflectance and other data, a downscaling model of all-weather surface temperature is constructed, which increase the spatial resolution of all-weather surface temperature products from 1 km to 250 m. The validation results show that the RMSE of downscaling surface temperature at the site is about 2.25 K and 2.16 K in the daytime and at night, respectively, which is about 0.5 K higher than that of the original 1 km surface temperature product. The results of image quality index show that the downscaling surface temperature not only obtains a lot of detailed thermal information, but also maintains a high consistency with the original 1 km surface temperature in spatial pattern and amplitude. This data set has certain significance for high resolution all-weather surface temperature generation and disaster monitoring in glacier area of Southeast Tibet.
ZHOU Ji, HUANG Zhiming , ZHONG Hailing , TANG Wenbin
The surface meteorological data of tianmogou in Bomi county are collected from the meteorological monitoring points arranged in the middle reaches of tianmogou in PALONG Zangbu basin. The data collection time is 2020. The main content of the data includes the observation data of rainfall and temperature in tianmogou. The rainfall data is collected by hobo rain gauge. Hobo rain gauge is a tipping bucket rain gauge. Every 0.2mm rainfall is recorded as an event, and the number of recorded events is output. The number of events multiplied by 0.2mm is the rainfall value; The air temperature is measured by a built-in 10 bit resolution temperature sensor in the data recorder. The acquisition method is to collect and store once every hour, and the hourly average value of air temperature can be obtained. The data is reliable in quality and high in accuracy. It can be used to reflect the real-time changes of rainfall and temperature in Tianmo gully, monitor the critical conditions of debris flow start-up, and predict the possibility of future debris flow events in this area.
HOU Weipeng
The data were collected from the sample plot of Haibei Alpine Meadow Ecosystem Research Station (101°19′E，37°36′N，3250m above sea level), which is located in the east section of Lenglongling, the North Branch of Qilian Mountain in the northeast corner of Qinghai Tibet Plateau. Alpine meadow is the main vegetation type in this area. The data recorded the light, air temperature and humidity, wind temperature and wind speed above the alpine plant canopy. The radiation intensity above the alpine plant canopy was recorded by LI-190R photosynthetic effective radiation sensor (LI-COR, Lincoln NE, USA) and LR8515 data collector (Hioki E. E. Co., Nagano, Japan), and the recording interval was once per second. S580-EX temperature and humidity recorder (Shenzhen Huatu) and universal anemometer are used (Beijing Tianjianhuayi) record the daily dynamics of air temperature and humidity, wind temperature and wind speed every three seconds. The recording time is from 10:00 on July 13 to 21:00 on August 17, Beijing time. Due to the need to use USB storage time and replace the battery every day, 3-5min of data is missing every day, and the missing time period is not fixed. At present, the data has not been published. Through research on the data The data can further explore the microenvironment of alpine plant leaves and its possible impact on leaf physiological response.
TANG Yanhong, ZHENG Tianyu
1) Data content (including elements and significance): the data includes the daily values of air temperature (℃), precipitation (mm), relative humidity (%), wind speed (M / s) and radiation (w / m2) 2) Data source and processing method; Air temperature, relative humidity, radiation and wind speed are daily mean values, and precipitation is daily cumulative value; Data collection location: 29 ° 39 ′ 25.2 ″ n near the forest line on the east slope of Sejila Mountain; 94°42′25.62″E; 4390m； The underlying surface is natural grassland; Collector model Campbell Co CR1000, acquisition time: 10 minutes. Digital automatic data acquisition. The temperature and relative humidity instrument probe is hmp155a; The wind speed sensor is 05103; The precipitation is te525mm; The radiation is li200x; 3) Data quality description; The original data of air temperature, relative humidity and wind speed are the average value of 10 minutes, and the precipitation is the cumulative value of 10 minutes; The daily average temperature, relative humidity, precipitation and wind speed are obtained by arithmetic average or summation. Due to the limitation of the sensor, the precipitation in winter may have a certain error. 4) Data application achievements and prospects: this data is the update of the existing data "Sejila Mountain meteorological data (2007-2017)" and "basic meteorological data of Sejila east slope forest line of South Tibet station of Chinese Academy of Sciences (2018)". The data time scale span is large, which is convenient for scientists or graduate students in Atmospheric Physics, ecology and atmospheric environment. This data will be updated from time to time every year.
Luo Lun
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 dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from Janurary 1to December 31, 2020. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, 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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.
Li Xiaoyan
Based on China's daily meteorological elements data set and National Geographic basic data, the extreme precipitation, extreme temperature, drought intensity, drought frequency and other indicators in Hengduan Mountain area were calculated by using rclimdex, nspei and bilinear interpolation methods. The data set includes basic data set of disaster pregnant environment, basic data set of extreme precipitation index, basic data set of extreme temperature index, basic data set of drought intensity and frequency. The data set can provide a basic index system for regional extreme high temperature, precipitation and drought risk assessment.
SUN Peng
This data is the data of automatic weather station (AWS, Campbell company) set up at the top of the mountain in the west slope of Sejila by the comprehensive observation and research station of Southeast Tibet alpine environment of Chinese Academy of Sciences in 2016. The geographical coordinates are 29.5919 n, 94.6102 e, with an altitude of 4640 m, and the underlying surface is alpine grassland. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s) and air pressure (MB) and daily accumulated value of precipitation. The original data is an average of 30 minutes before October 2018, and an average of 10 minutes after that. The temperature and humidity are measured by hmp155a temperature and humidity probe. The rainfall instrument model is rg3-m, the atmospheric pressure sensor probe is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. In terms of data quality: the obvious abnormal values are eliminated, the battery is damaged due to snow in the first half of 2019, and the data is missing. The missing temperature data is corrected by using the temperature fitting regression of 43900 m at nearby stations, and the data is yellow. Please pay attention when using it; the monitoring of precipitation starts from August 2019. The data station is a high altitude meteorological station in Southeast Tibet, which will be updated from time to time. It can be used by scientific researchers studying ecology, climate, hydrology, glaciers, etc.
Luo Lun
This data is the data of the automatic weather station (AWS, Campbell company) set up in Yigong Zangbu basin by the Southeast Tibet alpine environment comprehensive observation and research station of Chinese Academy of Sciences in 2018. The geographic coordinates are 30.1741 n, 94.9334 e, and the altitude is 2282m. The underlying surface is grassland. The data include daily arithmetic mean data of air temperature (℃), relative humidity (%), wind speed (M / s), water vapor pressure (kPa) and air pressure (MB) and daily accumulated value of precipitation. The original data is an average value recorded in 10 minutes. The temperature and humidity are measured by hmp155a temperature and humidity probe. The rainfall instrument is tb4, the atmospheric pressure sensor is ptb210, and the wind speed sensor is 05103. These probes are 2 m above the ground. Data quality: the quality of the original data is better, less missing. The data station is a meteorological station in the lower altitude of the Qinghai Tibet Plateau, which will be updated from time to time in the future. It can be used by researchers studying climate, hydrology, glaciers, etc.
Luo Lun
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 stable isotopes (2H and 18O) are adequately understood on their climate controls in the Tibetan Plateau, especially the north of Himalayas via about 30 years’ studies. However, knowledge of controls on precipitation stable isotopes in Nepal (the south of Himalayas), is still far from sufficient. This study described the intra-seasonal and annual variations of precipitation stable isotopes at Kathmandu, Nepal from 10 May 2016 to 21 September 2018 and analysed the possible controls on precipitation stable isotopes. All samples are located in Kathmandu, the capital of Nepal (27 degrees north latitude, 85 degrees east longitude), with an average altitude of about 1400 m. Combined with the meteorological data from January 1, 2001 to September 21, 2018, the values of precipitation (P), temperature (T) and relative humidity (RH) are given.
GAO Jing
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