The multi-decadal lake number and area changes in China during 1960s–2020 are derived from historical topographic maps and >42151 Landsat satellite images, including lakes as fine as ≥1 km^2 in size for the past 60 years (1960s, 1970s, 1990, 1995, 2000, 2005, 2010, 2015, 2020). From the 1960s to 2020, the total number of lakes (≥ 1 km ^ 2) in China increased from 2127 to 2621, and the area expanded from 68537 km ^ 2 to 82302 km ^ 2.
ZHANG Guoqing
The long-term sequence data set of lake areas on the Tibetan Plateau contains area data of 364 lakes with areas greater than 10 square kilometers from 1970s to 2013. Based on Landsat images, Landsat data in October are mainly used, and one data is taken every three years to reduce seasonal variation and make the available data reach the maximum. The data set is extracted by the NDWI Water Index, and each lake undergoes manual visual inspection and edition. The data set can be used to study lake change, lake water balance and climate change on the Tibetan Plateau. Data type: Vector data. Projection: WGS84.
ZHANG Guoqing
The dataset of runoff plot observations was obtained in the Binggou watershed foci experimental area from Jun. 19 to Oct. 17, 2008. The runoff plot (38°03′, 100°13′, 3472m, with a slope of 20.16°) was 10m long, 5m wide and 80cm deep, with soil depth about 50cm and sandy clay and gravels beneath (50-80cm). The main vegetation type is scrub (about 20cm high) and grass (about 3cm high). Observation items included the surface flow, interflow (80cm down the land surface), and precipitation at a fixed point at the right of the runoff plot. One subfolder and two data files (directions on data observations and raw data) were archived.
LI Hongyi, LI Zhe, BAI Yunjie, XIN Bingjie
In the mid-latitude region of Asia, the southeastern region is humid and affected by monsoon circulation (thus, it is referred to as the monsoon region), and the inland region is arid and controlled by the other circulation patterns (these areas include the cold and arid regions in the northern Tibetan Plateau, referred to as the westerly region). Based on the generalization of the climate change records published in recent years, the westerly region was humid in the mid-late Holocene, which was significantly different from the pattern of the Asian monsoon in the early-middle Holocene. In the past few millennia, the westerly region was arid during the Medieval Warm Period but relatively humid during the Little Ice Age. In contrast, the oxygen isotope records derived from a stalagmite in the Wanxiang Karst Cave showed that the monsoon precipitation was high in the Medieval Warm Period and low during the Little Ice Age. In the last century, especially in the last 50 years, the humidity of the arid regions in the northwest has increased, while the eastern areas of northwestern and northern China affected by the monsoon have become more arid. Moreover, in the northern and southern parts of the Tibetan Plateau, which are affected by the westerlies and the monsoon, respectively, the precipitation changes on the interdecadal and century scales have also shown an inverse phase. Based on these findings, we propose that the control zone of the westerly belt in central Asia has different humidity (precipitation) variation patterns than the monsoon region on every time scale (from millennial to interdecadal) in the modern interglacial period. The integrated research project on Holocene climate change in the arid and semi-arid regions of western China was a major research component of the project Environmental and Ecological Science for West China, which was funded by the National Natural Science Foundation of China. The leading executive of the project was Professor Fahu Chen from Lanzhou University. The project ran from January 2006 to December 2009. The data collected by the project include the following: 1. The integrate humidity data over the Holocene in the arid regions of Central-East Asia and 12 lakes (11000-0 cal yr BP): including Lake Van, Aral Sea, Issyk-Kul, Ulunguhai Lake, Bosten Lake, Barkol Lake, Bayan Nuur, Telmen Lake, Hovsgol Nuur, Juyan Lake, Gun Nuur and Hulun Nuur. 2. The integrated humidity data over the past millennium in the arid regions of Central-East Asia and at five research sites (1000-2000): including Aral Sea, Guliya, Bosten Lake, Sugan Lake, and the Badain Juran desert. Data format: excel table.
CHEN Fahu
The dataset of runoff measuring weir observations at the hydrological section was obtained in the Pailugou watershed foci experiment area during two full years from Oct. 2006 to Oct. 2008. The ice flow measurement and the container aet were used manually. The measurement was carried out every five days from Oct. 2006 to Apr. 2007, three times a day from May to Sep. 2007, every five days from Oct. 2007 to Apr. 2008, once a day from May to Sep. 2008, and every five days during Oct. 2008. Data were archived in Excel format.
WANG Shunli, LUO Longfa, JING Wenmao, WANG Rongxin, ZHANG Xuelong, NIU Yun
The dataset includes channel flow measured at the second irrigation stage in spring (22 May, 2012), the third irrigation stage in spring (18 June, 2012) and the first irrigation stage in autumn (16 July, 2012). The time used in this dataset is in UTC+8 Time. 1.1 Objective of measurement Objective of measuring channel flow are to provide the conference data for irrigation water optimal allocation model according to obtain reality water volume measured at Dou channel and Mao channel. Data set also is used to reference data for other observations such as eddy, biophysical parameters. 1.2 Observation measures and principle Measures: flow meter named Flowatch, which is made in Switzerland, observation precision: 0.1m/s; and rule, observation of which is 1cm. Principle: Flowatch, which is mechanical-based, is used to compute the velocity of the fluid according to vanes speed. The flow of channels is computed by using observed flow velocity and channel sectional area calculated on the basis of channel engineer sectional parameters and water level. 1.3 Observation location and items Observation spots include Yingyi branch San dou (Liu She, Shang’er She, and Xia’er She of Shiqiao village), Si Dou (Qi She, Ba She, and Jiu She of Shiqiao village), and Wu Dou (Yi She of Shiqiao village) at Yingke irrigation district, and seven Mao channels branched from five star branch channel Si Dou San Nong. Observation time is described as followed: Second stage irrigation in summer: 2012-5-22: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-5-23: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-5-24 to 2012-5-25: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-5-26 to 2012-5-28: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-5-28 to 2012-5-29: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-5-29 to 2012-5-30: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-5-30 to 2012-6-2: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-6-6: Yi Mao, Er Mao, San Mao, Si Mao, and Wu Mao branched from Five star branch channel Si Dou San Nong: Five star village 2012-6-7: Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Third stage irrigation in summer: 2012-6-18 to 2012-6-19: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-6-19 to 2012-6-20: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-6-20 to 2012-6-21: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-6-22 to 2012-6-24: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-6-24 to 2012-6-26: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-6-26 to 2012-6-27: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-6-27 to 2012-6-30: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-7-1 to 2012-7-2: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village First stage irrigation in Autumn: 2012-7-16 to 2012-7-18: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-7-18 to 2012-7-19: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-7-19 to 2012-7-21: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-7-21 to 2012-7-24: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-7-24 to 2012-7-25: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-7-25 to 2012-7-27: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-7-27 to 2012-7-31: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-7-27 to 2012-7-28: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Second stage irrigation in Autumn: 2012-8-8 to 2012-8-9: Si Dou, Yingyi branch channel: Jiu She (Shiqiao village) 2012-8-9 to 2012-8-10: Si Dou, Yingyi branch channel: Ba She (Shiqiao village) 2012-8-10 to 2012-8-12: Si Dou, Yingyi branch channel: Qi She (Shiqiao village) 2012-8-13 to 2012-8-15: Wu Dou, Yingyi branch channel: Yi She (Shiqiao village) 2012-8-15 to 2012-8-17: San Dou, Yingyi branch channel: Xia’er She (Shiqiao village) 2012-8-17 to 2012-8-19: San Dou, Yingyi branch channel: Shang’er She (Shiqiao village) 2012-8-19 to 2012-8-22: San Dou, Yingyi branch channel: Liu She (Shiqiao village) 2012-8-24 to 2012-8-25: Yi Mao, Er Mao, San Mao, Si Mao, Wu Mao, Liu Mao, and Qi Mao branched from Five star branch channel Si Dou San Nong: Five stars village Observed items: average flow velocity of channel (m/s), water level of channel (m), water temperature (℃), engineer sectional parameters of channel (investigation). Average flow velocity and water level of channel are measured one time per hour when channel flow is stable. However, the two items are measured two times or more times when channel flow is unstable. 1.4 Data process Observed data is saved in excel sheet, types of which include channel flow velocity, channel sectional area, water level, and water temperature. Channel flow and irrigation water volume are calculated by using observed data according to data per-process approach.
GE Yingchun, MA Chunfeng, Xu Fengying, LI Xin
I. Overview The Yellow River is the second longest river in our country. The problem of the Yellow River's sediment has attracted the attention of people all over the world. Based on the vector map of the 14 million rivers in China as a base map, the upper reaches of the Yellow River basin were cut out. The vector map of the river is a key element for extracting the boundary of the basin by using the topographic map, and it is also a key element for flood evolution and sediment evolution. Ⅱ. Data processing description Using the national vector map of the 14 million rivers as the data source, it is cut out by using the boundary of the upper reaches of the Yellow River. Ⅲ. Data content description The map is stored in ArcGIS, .shp files, including vector diagrams of the main and tributaries from the source area of the Yellow River to Toudaoguai. Ⅳ. Data usage description The vector map of the river is a key element for extracting the boundary of the watershed by using the topographic map, and it is also a key element for flood evolution and sediment evolution.
XUE Xian, DU Heqiang
Based on the meteorological data of 105 meteorological stations in and around the Qinghai Tibet Plateau from 1980 to 2019, the National Meteorological Science Data Center of China Meteorological Administration (CMA) was established. By calculating the oxygen content, it is found that there is a significant linear correlation between oxygen content and altitude, y = - 0.0263x + 283.8, R2 = 0.9819. Therefore, the oxygen content distribution map can be calculated based on DEM data grid. Due to the limitation of the natural environment in the Qinghai Tibet Plateau, there are few related fixed-point observation institutions. This data can reflect the distribution of oxygen content in the Qinghai Tibet Plateau to a certain extent, and has certain reference significance for the research of human living environment in the Qinghai Tibet Plateau.
HE Xiaobo, ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The dataset of regimen change statistics was obtained at the hydrological section of the Dayekou watershed reservoir from Jan. 1, 2007 to May 23, 2008. Ten days observations were carried out from Oct. 21, 2007 to Apr. 11, 2008, and diurnal observations from Apr. 15 to Oct. 21, 2007, and from Apr. 16 to May 23, 2008. Data record fields included: inflow (m^3/s), water level (m), impoundment (ten thousand m^3/s), outflow (m^3/s), ten days mean inflow (m^3/s), ten days mean outflow (m^3/s), monthly mean inflow (m^3/s), and monthly mean outflow (m^3/s).
MA Mingguo
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.
GE Yingchun, Xu Fengying, LI Xin
Data overview: This set of data mainly includes perennial River, seasonal river, river trunk, surface main channel, surface branch channel and other water system conditions in the Heihe River Basin. The data base year is 2009. Data preparation process: obtained from 1:100000 topographic map and 2009 TM remote sensing image digitization. Data content description: the data mainly has three important attributes, namely, grade, GB and name. The river classification is based on the Strahler classification method, and the final level of the main stream reaches seven levels. River coding is based on the national basic geographic information element dictionary. The standard of basic geographic information element data dictionary is adopted.
National Basic Geographic Information Center
The data set includes the observation data of river water level and velocity at No. 4 point in the dense observation of runoff in the middle reaches of Heihe River from January 1 to June 25, 2014. The observation point is located in Heihe bridge, Shangbao village, Jing'an Township, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n39 ° 03'53.23 ", E100 ° 25'59.31", with an altitude of 1431m and a width of 58m. In 2012, hobo pressure type water level gauge was used for water level observation with acquisition frequency of 30 minutes; since 2013, sr50 ultrasonic distance meter was used with acquisition frequency of 30 minutes. The data description includes the following parts: For water level observation, the observation frequency is 30 minutes, unit (CM); the data covers the period from January 1, 2014 to June 25, 2014; for flow observation, unit (M3); for flow monitoring according to different water levels, the water level flow curve is obtained, and the runoff change process is obtained based on the observation of water level process. The missing data is uniformly represented by string-6999. Refer to Li et al. (2013) for hydrometeorological network or station information and he et al. (2016) for observation data processing.
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The 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
The No. 1 hydrological section is located at 213 Heihe River Bridge (38°54′43.55″ N, 100° 20′41.05″ E, 1546 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.1 hydrological section from 13 June, 2012, to 24 November, 2012. This section consists of two river sections, i.e., the east section is marked as No. 1 and the west section is marked as No. 2. The width of this section is 330 meters. This section consists of a gravel bed; the cross-sectional area is unstable because of human factors. 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.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
The No. 2 hydrological section is located at 312 Heihe River Bridge (38°59′51.71″ N, 100° 24′38.76″ E, 1485 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.2 hydrological section from 19 June, 2012, to 24 November, 2012. This section consists of two river sections, i.e., the east section is marked as No. 1 and the west section is marked as No. 2. The width of this section is 90 meters. This section consists of a gravel bed; the cross-sectional area is unstable because of human factors. 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.
ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
Lakes on the Tibetan Plateau (TP) are an indicator and sentinel of climatic changes. We extended lake area changes on the TP from 2010 to 2021, and provided a long and dense lake observations between the 1970s and 2021. We found that the number of lakes, with area larger than 1 k㎡ , has increased to ~1400 in 2021 from ~1000 in the 1970s. The total area of these lakes decreased between the 1970s and ~1995, and then showed a robust increase, with the exception of a slight decrease in 2015. This expansion of the lakes on the highest plateau in the world is a response to a hydrological cycle intensified by recent climate changes.
ZHANG Guoqing
The data set includes the river level observation data of point 2 in the dense runoff observation of the middle reaches of Heihe River from January 1, 2015 to December 31, 2015. The observation point is located in Heihe bridge, 312 National Road, Zhangye City, Gansu Province. The riverbed is sandy gravel with unstable section. The longitude and latitude of the observation point are n38.996667 °, e100.427222 °, altitude 1485m, river width 70m and 20m. Sr50 ultrasonic range finder is used for water level observation, with acquisition frequency of 30 minutes. The data includes the following parts: Water level observation, observation frequency 30 minutes, unit (CM); In 2015, the section of bridge no.2-312 was frequently disturbed by human beings. The dam was built within 1km of the upstream and downstream of the section. The unstable area of the hydrological section led to the disorder of the water level and flow curve. During the measurement, the stable flow and water level curve could not be obtained. The observation of water level is based on the manual observation of water level at 0:00 on January 1, 2015. In the later stage, the hydrological section of river undercut changes. The result is that the datum water level changes and negative value appears; Refer to Li et al. (2013) for hydrometeorological network or station information, and he et al. (2016) for observation data processing
HE Xiaobo, LIU Shaomin, LI Xin, XU Ziwei
The dataset of dewfall measurements was obtained in the Linze station foci experimental area from 6 am to 7am and 7pm to 10pm. Two containers were used. One was the unsealed rectangle plastic condensate drain pan from May 26 to Jul. 28, 2008 (one time-continuous observation from Jun. 25 to 27 at intervals of 2 hours), and the other was the sealed and unsealed aluminum cases from Jun. 24 to Jul. 29, 2008 (two time-continuous observations from Jun. 25 to 27 and Jul. 19 to 20, respectively, both at intervals of 2 hours). Dewfall was weighed by G&G TC30K- H scales (accuracy: 1g) for the condensate drain pan and by electronic scales (accuracy: 0.1g) for the aluminum case.
BAI Yanfen, DING Songchuang, HAO Xiaohua, Qian Jinbo, SHU Lele, SONG Yi, WANG Yang, XU Zhen, ZHU Shijie
The No. 8 hydrological section is located at Gaotai Heihe River Bridge (39 ° 23′22 .93 ″ N, 99 ° 49′37 .29″ E, 1347 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.8 hydrological section from 17 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.
HE Xiaobo, ZHANG Jian, NING Tianxiang, HUANG Xiaoming, JIANG Heng, LIU Shaomin, LI Xin
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