The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
ZHANG Yu, ZHANG Guoqing
This dataset provides the in-situ lake water parameters of 124 closed lakes with a total lake area of 24,570 km2, occupying 53% of the total lake area of the TP.These in-situ water quality parameters include water temperature, salinity, pH,chlorophyll-a concentration, blue-green algae (BGA) concentration, turbidity, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), and water clarity of Secchi Depth (SD).
ZHU Liping
The data are from 2011 to 2012. A 30m×30m Picea crassifolia canopy interception sample plot was set up in the Picea crassifolia sample plot at an altitude of 2800m m. A siphon raingauge model DSJ2 (Tianjin Meteorological Instrument Factory) was set up on the open land of the river about 50m from the sample plot to observe the rainfall outside the forest and its characteristics. Penetrating rain in the forest adopts a combination of manual observation and automatic observation. Automatic observation is mainly realized through a penetrating rain collection system arranged in the interception sample plot, which consists of a water collecting tank and an automatic recorder. Two 400cm×20cm water collecting tanks are connected with DSJ2 siphon rain gauge, and the change characteristics of penetrating rain under the forest are continuously recorded by an automatic recorder. Due to the spatial variability of the canopy structure of Picea crassifolia forest in the sample plot, a standard rainfall tube for manual observation is also arranged in the sample plot to observe the penetrating rain in the forest. Ninety rainfall tubes with a diameter of 20cm are arranged in the sample plot at intervals of 3m. After each precipitation event ends and the penetrating rain in the forest stops, the amount of water in the rain barrel will be emptied and the penetrating rain in the barrel will be measured with the rain cup.
ZHAO Chuanyan, MA Wenying
From June 10, 2011 to September 2, 2011, the observation instrument of 3100m grassland weather station in Tianlaochi watershed of Qilian mountain was a 20cm evaporating pan, a round metal basin with a diameter of 20 cm and a height of 10 cm, and the mouth of the basin was blade-shaped. In order to prevent birds and animals from drinking water, a trumpet-shaped wire mesh ring was set on the upper part of the mouth of the vessel. During measurement, the instrument shall be placed on the shelf with the mouth 70cm from the ground, and quantitative clear water shall be put in every day. After 24 hours, the remaining water quantity shall be measured by the dosage cup, and the reduced water quantity shall be the evaporation capacity. Data are daily evaporation from June 10, 2011 to September 2, 2011.
ZHAO Chuanyan, MA Wenying
Canopy interception field is located in 2700m forest in Pailugou watershed of Qilian mountain, with 60 precipitation interception barrels arranged at equal intervals on the ground. The specifications of the interception barrel are: the radius of the bottom surface is 10cm and the height is 35cm. The observation period was from June to July 2012 and from July to September 2013, and 17 precipitation events (including each precipitation) were recorded. The unit is mm.
HE Zhibin
The output data of the distributed eco hydrological model in the upper reaches of Heihe River includes the spatial distribution data of 1-km grid and the discharge time series data of the outlet of the basin. (1) Spatial distribution data of 1-km grid, monthly average soil moisture, actual evapotranspiration, runoff depth and other spatial distribution data of 1-km resolution. (2) Runoff time series daily flow data of river basin outlet.
YANG Dawen
This data includes three parts of data, namely shrub water holding experiment, shrub interception experiment and shrub transpiration experiment data. Shrub water holding experiment: select the two shrub types of Caragana jubata and Potentilla fruticosa, respectively pick the branches and leaves of the two vegetation types, weigh their fresh weight, carry out water holding experiment, measure the saturated weight of branches and leaves, dry weight of branches and leaves, dry weight of branches and leaves after completion, and finally obtain the data of branches, leaves and total water holding capacity. Shrub interception experiment: two shrubs, Caragana jubata and Potentilla fruticosa, were also selected and investigated. 30 rain-bearing cups were respectively arranged under the two shrubs. after each rainfall, penetration rainfall was measured and observed from June 1, 2012 to September 10, 2012. Shrub Transpiration Experiment: Potentilla fruticosa on July 14, Caragana jubata on August 5, Salix gilashanica on August 15, 2012. The measurement is made every hour according to the daily weather conditions.
ZHAO Chuanyan, MA Wenying
From 1947 to 1948, the Hexi Water Conservancy Project Corps of the Ministry of Water Resources of the Republic of China compiled the Heihe Mainstream Water Conservancy Project (15 items). This is the first comprehensive engineering plan compiled by the whole basin based on modern hydraulic engineering principles. This batch of planning mainly focus on irrigation projects, taking into account inter-basin water transfer and flood control projects. Most of these projects achieved varying degrees of realization after 1949, but the plan to introduce the Datong River into the Heihe River has never been implemented. The collection of hydrological and socioeconomic data in these documents was mostly completed during the Anti-Japanese War, and was completed by the Gansu Irrigation Works, Plantation and Pasturage Company. It is the earliest and systematic data of the basin. It has irreplaceable value for analyzing and understanding the water conservancy development and socio-economic situation of the Heihe River mainstream during the Republic of China. The main contents of this data include Zhangye, Shandan, Minle, Linze, Gaotai reservoir projects, groundwater interception and irrigation projects, surface runoff irrigation projects, irrigation canal system consolidation projects and other plans.
WANG Zhongjing
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
1. Data overview: This data set is the scale artificial evaporation dish and precipitation data of qilian station from January 1, 2012 to December 31, 2012. The artificial evaporator is a 20cm standard evaporator, and the precipitation is a 20cm standard rain gauge. 2. Data content: (1) the evaporation capacity is measured at 20:00 every day with 20 special measuring cups;It is before a day commonly 20 when measure clear water 20 millimeter with special measure cup (original quantity) pour into implement inside, 24 hours hind namely in the same day 20 hour, again measure the water inside implement (allowance), its reduce quantity is evaporation quantity.Namely: evaporation = original quantity - residual quantity.If there is precipitation between 20:00 of the previous day and 20:00 of the same day, the calculation formula is: evaporation = original quantity + precipitation - residual quantity. (2) precipitation is generally observed in two stages, namely once at 8 o 'clock and once at 20 o 'clock each day. In the rainy season, observation periods are increased, and additional measurements are needed when the rainfall is large.The daily rainfall is divided into 8 a.m. of each day, and the precipitation from 8 a.m. to 8 a.m. of the next day is the precipitation of the current day.If it is rain, measure it with 20 special measuring cups. When it snows, only use the outer tube as snow bearing equipment, and then weigh it with an electronic balance (shenyang longteng es30k-12 type electronic balance, the minimum sensible amount is 0.2g). 3. Space and time range: Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2981.0 m
CHEN Rensheng, SONG Yaoxuan, LIU Junfeng, YANG Yong, LIU Zhangwen, HAN Chuntan
Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.
SONG Peilin, ZHANG Yongqiang
The monthly precipitation data set of China's alpine mountains includes the qilian mountains (1960-2013), tianshan mountains (1954-2013) and Yangtze river source (1957-2014). The distributed hydrological model needs high-precision spatial distribution information of precipitation as input.Because of the scarcity of stations, the precipitation interpolation at stations cannot reflect the spatial distribution of precipitation in the alpine mountainous areas.Generation method of this dataset: (1) collect precipitation data of national meteorological stations and hydrological stations in various regions, and add precipitation observation data of field stations of Chinese academy of sciences above an altitude of 4000m; (2) use the temperature data of each station to correct the collected precipitation data of different precipitation types; (3) establish the relationship between precipitation data and altitude, longitude and latitude, and fit monthly to generate monthly precipitation data set of 1km scale. The interpolation year of this data is 1954-2014. The data projection method is Albers projection. The spatial interpolation precision is 1-km, and the time precision is monthly data.The results show that the interpolation precipitation is reliable. The data is stored in ASCII files. The file names of the monthly precipitation data files of tianshan mountain and Yangtze river source are in the form yyyymm.txt. YYYY is the year and MM is the month.The monthly precipitation data of qilian mountain is named as: month_10001.txt, this file is the precipitation data of January 1960, successively month_10002.txt is the precipitation of February 1960, and month_10013.txt is the precipitation data of January 1961,......Month_10648.txt represents the precipitation data for December 2013.Each ASCII file represents the grid precipitation data of the day in mm.
CHEN Rensheng, LIU Junfeng
The data set collects the long-term monitoring data on atmosphere, hydrology and soil from the Integrated Observation and Research Station of Multisphere in Namco, the Integrated Observation and Research Station of Atmosphere and Environment in Mt. Qomolangma, and the Integrated Observation and Research Station of the Alpine Environment in Southeast Tibet. The data have three resolutions, which include 0.1 seconds, 10 minutes, 30 minutes, and 24 hours. The temperature, humidity and pressure sensors used in the field atmospheric boundary layer tower (PBL) were provided by Vaisala of Finland. The wind speed and direction sensor was provided by MetOne of the United States. The radiation sensor was provided by APPLEY of the United States and EKO of Japan. Gas analysis instrument was provided by Licor of the United States, and the soil moisture content, ultrasonic anemometer and data collector were provided by CAMPBELL of the United States. The observing system is maintained by professionals on a regular basis (2-3 times a year), the sensors are calibrated and replaced, and the collected data are downloaded and reorganized to meet the meteorological observation specifications of the National Weather Service and the World Meteorological Organization (WMO). The data set was processed by forming a time continuous sequence after the raw data were quality-controlled, and the quality control included eliminating the systematic error caused by missing data and sensor failure.
MA Yaoming
Three artificial rainfall events were performed on the shady grassland at the altitude of 2700m in the Pailugou watershed of the Qilian Mountains. The times were July 15, 2011, July 16, and July 22, 2011, respectively. Runoff rate, data is recorded every half an hour. Two rainfall simulations were also performed on the sun-slope grassland at the same altitude. As a comparative experiment, the time was July 24 and 25, 2011.
HE Zhibin
The output data of the distributed eco hydrological model (gbehm) in the upper reaches of Heihe River includes the spatial distribution data series of 1-km grid. Region: Heihe River (Yingluo gorge), Beida River (Binggou new land), temporal resolution: Monthly Scale, spatial resolution: 1km, period: 1960-2014. Data include precipitation, evapotranspiration, runoff depth, soil volume water content (0-100cm). All data are in ASCII format. Please refer to the basin.asc file in the reference directory for the spatial range of the basin. Projection parameters of model results: sphere_Arc_Info_Lambert_Azimuthal_Equal_Area
YANG Dawen
The Tibet-Obs established in 2008 consists of three regional-scale soil moisture (SM) monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected at a depth of 5 cm by multiple SM monitoring sites of all the networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.
ZHANG Pei, ZHENG Donghai, WEN Jun, ZENG Yijian, WANG Xin, WANG Zuoliang, MA Yaoming, SU Zhongbo
This data set describes the temporal and spatial distribution of precipitation in the Upper Brahmaputra River Basin. We integrate (CMA, GLDAS, ITP-Forcing, MERRA2, TRMM) five sets of reanalysis precipitation products and satellite precipitation products, and combine the observation precipitation of 9 national meteorological stations from China Meteorological Administration (CMA) and 166 rain gauges of the Ministry of Water Resources (MWR) in the basin. The time range is 1981-2016, the time resolution is 3 hours, the spatial resolution is 5 km, and the unit is mm/h. The data will provide better data support for the study of Upper Brahmaputra River Basin, and can be used to study the response of hydrological process to climate change. Please refer to the instruction document uploaded with the data for specific usage information.
WANG Yuanwei, WANG Lei, LI Xiuping, ZHOU Jing
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 matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
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
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
