The dataset of spatio-temporal water resources distribution in the source regions of Yangtze River and Yellow River (1998-2017)
  • 2019-09-22
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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.

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The lakes larger than 1k㎡ in Tibetan Plateau (V1.0) (1970s, 1990, 2000, 2010)
  • 2019-09-15
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The dataset includes vector map of the lakes larger than 1k㎡ on Tibetan Plateau in 1970s, 1990, 2000, 2010. The lake boundry data was extracted from remote sensing image like Landsat MSS, TM, ETM+, by means of visual interpretation. The data type is vector data, and it's attribute class includes Area (km²). The Projected Coordinate System is Albers Conical Equal Area. It is mainly used in the study of changes in lakes, hydrological and meteorological on the Tibetan Plateau.

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Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of meteorological elements gradient of Alpine meadow and grassland ecosystem superstation, 2018)
  • 2019-09-15
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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 August 31 to December 24, 2018. 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) (°), 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_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), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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.

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Spatial distribution maps of lakes over the Tibetan Plateau (1995-2015)
  • 2019-09-15
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This data set contains 5 data entities. The spatial distribution data of the lakes on the Tibetan Plateau extracted from Landsat images are used, and the time range is from September to November in 1995, 2000, 2005, 2010 and 2015. The data are made jointly by the Third Pole Environment Database and the Geospatial Data Cloud. The object-oriented method is used for lake extraction. (1) Data preprocessing occurs first. Because this paper only uses the first 7 wave bands of Landsat 8 data, band synthesis is carried out for 1-7 wave bands, and the influence of cloud cover on lake extraction is examined. The data are replaced if the influence is too great. (2) Multiscale segmentation of images is performed in eCognition. Because the spectral characteristics of the lake are uniform, spectral segmentation is used again on this basis. (3) The average characteristics of the 5, 6, and 7 bands (Brigh-567) are used for preliminary water extraction, and for some unextracted lakes, NDWI is used for supplementary extraction. (4) At this time, some shadows remain mistakenly extracted, and most of them can be excluded using NDWI<0.05. At the same time, based on the actual situation, the shadows can be eliminated using the value of the first band. (5) To ensure the accuracy, manually check the unextracted independent lake and the mistakenly extracted object and manually modify it. (6) On this basis, use NDWI>0 for the second extraction around the extracted water body and define the edge of the lake. (7) Check again, then merge the objects and export the results. The data effectively remove the influence of mountain shadows, clouds and cloud shadows, snow cover, glaciers and other non-water objects. The lake boundary is accurate and clear, and the error is controlled within a pixel. The accuracy requirement is 30 meters, which is a pixel.

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HiWATER: Dataset of hydrometeorological observation network (No.6 runoff observation system of Gaoya hydrological station, 2013)
  • 2019-09-15
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The No. 6 hydrological section is located at Gaoya Hydrological Station (100.433° E, 39.135° N, 1420 m a.s.l.) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. This hydrological section is for intercomparison of flow measurement between ADCP and manual method. The dataset contains recorded by the No. 6 hydrological section from 10 August, 2012 to 31 December, 2013. The width of this section is 58 meters. The water level was measured using an HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.

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HiWATER: Dataset of intensive runoff observations of No.7 in the midstream of the Heihe River Basin of the MUlti-Scale Observation EXperiment on Evapotranspiration over heterogeneous land surfaces 2012 (MUSOEXE-12)
  • 2019-09-15
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The No. 7 hydrological section is located at Pingchuan Heihe River Bridge (39 ° 20′2.03 ″ N, 100° 5′49.63″ E, 1375 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.7 hydrological section from 13 June, 2012, to 24 November, 2012. The width of this section is 130 meters. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.

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Water quality dataset of Gaacuo Lake (2017)
  • 2019-09-15
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This is the water quality data of the vertical profile of the observation point in Gaacuo Lake. The data is observed on July1, 2017. The data is stored as an excel file.

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Water index in the Qilian Mountain Area (1980-2015)
  • 2019-09-15
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This dataset contains the ground surface water (including liquid water, glacier and perennial snow) area in Qilian Mountain Area from 1980 to 2015. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected between 1978 and 2018 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 8 seasons, the dataset has a temporal resolution of 5 years and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the variation of water distribution within the Qilian Mountain in the past 35 years, and can be used for quantitative estimation of water resource.

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Water quality slope data of Co Ngoin Lake (2017)
  • 2019-09-15
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These are the water quality vertical slope data of Co Ngoin Lake obtained during the River and Lake Source Investigation from June to July in 2017. The main water quality observation data include dissolved oxygen, electrical conductivity, PH, water temperature and others.

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HiWATER: Dataset of investigation on channel flow and socio-economy in the midstream of the Heihe River Basin
  • 2019-09-15
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The dataset includes two parts that are: 1) channel flow, crop pattern, field management, and socio-economy data measured at super-station in 2008, 2010, 2011, 2012 (UTC+8), respectively. 2) irrigation data, crop pattern, and socio-economy data investigated at Daman irrigation district and Yingke irrigation district, respectively. 1.1 Objective of investigation Objectives of investigation for two parts data are to obtain crop pattern and irrigation water volume change with time, and to supply parameter for irrigation water optimal allocation model. 1.2 Investigation spots and items Investigation spots include six water management stations that are Dangzhai, Hua’er, Daman, Xiaoman, Jiantan, and Ershilidun, respectively, at Daman irrigation district. Investigation items comprise water allocation time, branch channel inflow, Dou channel inflow, irrigation area, channel water use efficiency, water price, and water fee. Investigation time is described as followed: 2012.03.16 to 2012.04.04, Spring irrigation; 2012.04.04 to 2012.05.14, Summer irrigation; 2012.05.20 to 2012.06.24, Summer irrigation; 2012.05.16 to 2012.07.06, Summer irrigation; 2012.07.15 to 2012.08.02, Autumn irrigation; 2012.08.10 to 2012.08.26, Autumn irrigation. Investigation spots include eight water management station that are Chang’an, Shangqin, Dangzhai, Liangjiadun, Shimiao, Xiaoman, Xindun, and Yangou, respectively, at Yingke irrigation district. Investigation time and items is described as followed: Year Data items Spots 2008, 2010, 2011 Irrigation data: Irrigation time, water level of Dou channel, channel flow, irrigation area Xiaoman county, Shangtouzha village 2012 Irrigation data: Irrigation time, water level of Dou channel, channel flow, irrigation area Chang’an, Shangqin, Dangzhai, Liangjiadun, Shimiao, Xiaoman, Xindun, Yangou 2012 Well data: Well deep, groundwater abstraction, irrigation area Chang’an, Liangjiadun, Shangqin 2012 Socio-economy data: population, agricultural income, un-agricultural income, water use for living, average residential area, education Chang’an, Xiaoman, Liangjiadun, Shangqin 2012 Field management: fertilizer name, fertilization time, fertilization rate, pesticide name, pesticide rate, time Chang’an, Xiaoman, Liangjiadun, Shangqin 2008, 2010, 2011, 2012 Crop pattern: crop name, seed time, harvest time, crop area, irrigation quota, field water use efficiency, crop yield, crop production value Xiaoman, Chang’an, Liangjiadun, Shangqin 1.3 Data collection Data was collected by cooperating with water management department of Yingke and Daman.

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