The long-time series data set of extreme precipitation index in the arid region of Central Asia contains 10 extreme precipitation index long-time series data of 49 stations. Based on the daily precipitation data of the global daily climate historical data network (ghcn-d), the data quality control and outlier elimination were used to select the stations that meet the extreme precipitation index calculation. Ten extreme precipitation indexes (prcptot, SDII, rx1day, rx5day, r95ptot, r99ptot, R10, R20) defined by the joint expert group on climate change detection and index (etccdi) were calculated 、CWD、CDD）。 Among them, there are 15 time series from 1925 to 2005. This data set can be used to detect and analyze the frequency and trend of extreme precipitation events in the arid region of Central Asia under global climate change, and can also be used as basic data to explore the impact of extreme precipitation events on agricultural production and life and property losses.
YAO Junqiang CHEN Jing LI Jiangang
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.
JIN Rui KANG Jian
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 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, 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 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
The data set records the surface water quality assessment data set of the Yangtze River mainstream (2008.3-2020.6). The data are collected from Yushu ecological environment bureau. The data set contains 226 files, including: water quality assessment of surface water in June 2010, water quality assessment of surface water in July 2010, water quality assessment of surface water in August 2010, water quality assessment of surface water in August 2011, and water quality assessment of surface water in April 2012. Each data table has seven fields: Field 1: monitoring section Field 2: classification of water environment functional areas Field 3: water quality category Field 4: main pollution indicators Field 5: water quality status Field 6: water quality last month Field 7: water quality in the same period of last year
Department of Ecology and Environment of Qinghai Province
The data set records the water quality evaluation results of the monitoring sections of the Yangtze River, Yellow River and Huangshui (2010-2012). The data is collected from Yushu ecological environment bureau. The data set contains 18 files, which are: water quality assessment of national control section of Yangtze River in April 2010, water quality assessment of national control section of Yangtze River in May 2010, water quality assessment of national control section of Yangtze River in September 2010, water quality assessment of national control section of Yangtze River in October 2010, etc. the data table structure is the same. There are seven fields in each data table Field 1: monitoring section Field 2: classification of water environment functional areas Field 3: water quality category Field 4: main pollution indicators Field 5: water quality status Field 6: water quality last month Field 7: water quality in the same period of last year
Ecological Environment Bureau of Yushu Prefecture
The data set records the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in 2016. Data statistics from the Department of natural resources of Qinghai Province, the data set contains 16 data tables, which are: water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the first quarter of 2016, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second quarter of 2016, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the third quarter of 2016, and water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the fourth quarter of 2016 Water quality of drinking water sources In the second half of 2016, the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province, the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second half of 2016, the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the first quarter of 2017, and the water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second quarter of 2017 The quality of drinking water sources, the quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the third quarter of 2017, the quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the fourth quarter of 2017, the quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the first quarter of 2018, the quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second quarter of 2018, and the quality of centralized drinking water sources in Qinghai Province in the third quarter of 2018 Water quality of centralized drinking water sources in county-level cities and towns, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the fourth quarter of 2018, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the first quarter of 2020, water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the second quarter of 2020, and water quality of centralized drinking water sources in county-level cities and towns of Qinghai Province in the third quarter of 2020, The data table has the same structure. Each data table has six fields: Field 1: serial number Field 2: city name Field 3: water source name Field 4: water source type Field 5: compliance type
Department of Natural Resources of Qinghai Province
The data set records the information disclosure data (2018) of centralized drinking water quality monitoring and safety status in cities and towns at or above the county level in Xining city. The data statistics are from the Department of ecological environment of Qinghai Province, and the data set contains three documents, which are respectively: information disclosure form of centralized drinking water quality monitoring and safety status in cities and towns at or above the county level of Xining City in the first quarter of 2018, information disclosure form of centralized drinking water quality monitoring and safety status in cities and towns at or above the county level of Xining City in the second quarter of 2018, information disclosure form of centralized drinking water quality monitoring and safety status in cities and towns at or above the county level of Xining City in the second quarter of 2018 In the second half of 2018, the structure of the data sheet is the same. There are 10 fields in each data table Field 1: serial number Field 2: name of water source Field 3: water level Field 4: water source type Field 5: monitoring unit Field 6: number of monitoring indicators Field 7: monitoring frequency Field 8: evaluation criteria Field 9: pass rate Field 10: public period
Department of Ecology and Environment of Qinghai Province
The data set records the dynamic statistical data of groundwater level in the monitoring area of Ping'an district (Ping'an County) of Xining city from 2014 to 2018. The data is collected from the Department of natural resources of Qinghai Province, and the data set contains five data tables, which are: the groundwater level dynamic of Haidong monitoring area in 2014, the groundwater level dynamic statistical table of Ping'an monitoring area in 2015, the groundwater level dynamic statistical table of Ping'an monitoring area in 2016, the groundwater level dynamic statistical table of Ping'an monitoring area in 2017, and the groundwater level dynamic statistical table of Ping'an monitoring area in 2018 Sketch Map. The data table has the same structure and contains four fields Field 1: year Field 2: n16 Field 3: n34 Field 4: N46
Department of Natural Resources of Qinghai Province
The monitoring data set of surface water quality in Xining city of Qinghai Province was collected from July, 2015 to July, 2015. The data is collected from the Department of ecological environment of Qinghai Province. The data set contains 15 data tables, which are: surface water quality of Xining City in July 2015, surface water quality of Xining City in November 2015, surface water quality of Xining City in January 2016, and surface water quality of Xining City in February 2016. The data table structure is the same. There are six fields in each data table, such as the monitoring section water quality table of Xining surface water in July 2015 Field 1: serial number Field 2: section name Field 3: executive standard level Field 4: actual water quality grade Field 5: over standard items
Department of Ecology and Environment of Qinghai Province
The data set records the statistical data of groundwater levels in Beichuan, Xichuan and Nanchuan of Xining City (2012-2018). The data is collected from the Department of natural resources of Qinghai Province. The data set contains 31 data tables, including the groundwater level of Nanchuan in Xining City in 2011, the groundwater level of Beichuan in Xining City in 2011, the groundwater level of Xichuan and xinachuan in Xining City in 2011, and the groundwater level of Beichuan in Xining City in 2012. The data are grouped by year, and the unit is meter (m). The data table has the same structure and contains five fields Field 1: year G9103: Field Field 3: G31 Field 4: G23 Field 5: G27
Department of Natural Resources of Qinghai Province
The data set records the monthly water quality monitoring and evaluation data of Huangshui river monitoring section from January 2008 to June 2020. The data set consists of 146 Excel / PDF data files. They are water quality assessment.xls in January 2008, water quality assessment.xls in February 2008 Water quality assessment of national control section of Huangshui River in June 2020.xls. Data monitoring points include: Jintan and zhamalong section of Huangshui mainstream; Xiaoxia bridge section; Minhe bridge section. The detection indicators include: water environment function zoning category, water quality category, main pollution indicators, water quality status, water quality status in last month, and water quality status in the same period of last year. The data table has the same structure and contains 7 fields Field 1: section name Field 2: water environment function zoning category Field 3: water quality category Field 4: main pollution indicators Field 5: water quality status Field 6: water quality last month Field 7: water quality in the same period last year
Department of Ecology and Environment of Qinghai Province
The data set records the information disclosure form of surface water quality monitoring in Haixi Prefecture of Qinghai Province from January 2019 to June 2020. The data were collected from the ecological environment bureau of Haixi Prefecture. The data set contains 18 data tables, which are respectively the information disclosure table of surface water quality monitoring in January, February, 3, 4, 5, 6, 7, 8, 9, 10, 11 and 12 of 2019, and the information disclosure table of surface water quality monitoring in January, February, 3, 4, 5 and 6 of 2020. Each data table has 11 fields, such as the information disclosure table of surface water quality monitoring in January 2019 Field 1: serial number Field 2: Region Field 3: water body Field 4: section name Field 5: section level Field 6: monitoring unit Field 7: monitoring frequency Field 8: water quality objectives Field 9: is it up to standard Field 10: over standard factor Field 11: remarks
Ecological Environment Bureau of Haixi Prefecture Qinghai Province
The data set records the statistical table of groundwater level dynamic changes in various monitoring areas of Qinghai Province from 2015 to 2018. The data are recorded from the Department of natural resources of Qinghai Province, and the data set contains four data tables, which are: the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2015, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2016, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2017, and the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2018 The data table has the same structure and contains 7 fields Field 1: "geographic location" Field 2: "basic balance area (km2)" Field 3: "percentage of monitoring area (%)" Field 4: "weak descent area (km2)" Field 5: "percentage (%) of monitored area" Field 6: "strong uplift area (km2)" Field 7: "percentage (%) of monitored area"
Department of Natural Resources of Qinghai Province
The data set records the water quality of centralized drinking water sources in prefecture level cities of Qinghai Province from January 2016 to August 2020. The data is collected from the Department of natural resources of Qinghai Province. The data set contains 48 Excel data sheets, which are: water quality of centralized drinking water sources in prefecture level cities of Qinghai Province in January 2016, water quality of centralized drinking water sources in prefecture level cities of Qinghai Province in February 2016 In August 2020, the water quality of centralized drinking water sources in prefecture level cities of Qinghai Province has the same data table structure. There are six fields in each data table, such as the water quality of centralized drinking water sources in prefecture level cities of Qinghai Province in February 2016 Field 1: serial number Field 2: city name Field 3: water source name Field 4: water source type Field 5: compliance Field 6: over standard index and over standard multiple
Department of Natural Resources of Qinghai Province
The data set records the dynamic statistical data of groundwater level in the monitoring area around Qinghai Lake from 2014 to 2018. The data are collected from the Department of natural resources of Qinghai Province. The data set includes five data tables, which are: the dynamic diagram of groundwater level in the monitoring area around Qinghai Lake in 2014, the dynamic statistical table of groundwater level in the monitoring area around Qinghai Lake in 2015, the diagram of groundwater level in the monitoring area around Qinghai Lake in 2016, the diagram of groundwater level in the monitoring area around Qinghai Lake in 2017, and the diagram of groundwater level in the monitoring area around Qinghai Lake in 2017 The groundwater level monitoring data of the same area around Qinghai Lake in 18 years. The groundwater level dynamic diagram (water level dynamic statistical table) of the monitoring area around Qinghai Lake contains 8 fields Field 1: year Field 2: qing2 Field 3: qing3 Field 4: qing4 Field 5: qing6 Field 6: qing7 Field 7: qing8 Field 8: qing9
Department of Natural Resources of Qinghai Province
