This data set comprises pictures of geological sections and landscape of Nima Basin and Lunpola Basin in the north of Tibetan Plateau which produced on achievement of geological survey in these years. The process of section pictures drawing comprises: measurement of different stratas by hand; identify and description of stratas by experienced geological researcher; picture production with software, based on information collected above. Landscape pictures were drew from satellite maps as base map, then added texts with software. All the pictures are clear, detailed and comprehensive. They are very critical for research on geology, geomorphology of the important localities in the north of Tibetan Plateau, such as Nima Basin and Lunpola Basin, and necessary for paleo-altimetry and uplift of Tibetan Plateau.
The slope dataset of the Green Silk Road, which represent the tilts of the land surface units, is a quantitative description for land surface steepness. This dataset calculated from GMTED2010 (Multi-resolution Terrain Elevation Data 2010) which product by USGS (United States Geological Survey). First, the DEM (Digital Elevation Model) of Green Silk Road are obtained by the preprocessing including projection, clipping for the GMTED2010. Second, the slopes are calculated by using the degree method in ArcGIS software to generate the slope dataset of the green Silk Road. This dataset is of good quality and can accurately reflect the land surface tilt of the Green Silk Road areas. It can be used in the fields of Surveying and mapping, remote sensing, environmental resources, agricultural and forestry land planning, urban planning, disaster monitoring, hydropower engineering, military and other resources, environment and social economy.
Xiaohuan Yang
The Relief Degree of Land Surface is a comprehensive representation of regional altitude and surface cutting degree. Using the resampled Digital Elevation Model data and the Feng(2007) Model, the Relief Degree of Land Surface dataset of the Green Silk Road in 1km resolution was developed. The dataset includes the spatial distribution data of the Relief Degree of Land Surface, elevation, and flat area at 1km covering the Green Silk Road, as well as each country of the Green Silk Road.
XIAO Chiwei
Digital Elevation Model (DEM) is a kind of solid ground Model that represents the ground Elevation in the form of a set of ordered numerical arrays. The arctic region within 66 ° 34 'refers to the arctic regions and parts of Greenland in the arctic.Elevation data include arctic digital dem and hillshade data in tif format.Range of 66 ° ~ 90 ° N N, the spatial resolution of 0.008 ° x 0.008 °. The data is downloaded from NASA global elevation data DEM describes ground elevation information, which is widely used in surveying and mapping, hydrology, meteorology, geomorphology, geology, soil, engineering construction, communication, military and other fields of national economy and national defense as well as humanities and natural sciences.
National Aeronautics and Space Administration
This data set is a three-level classification map of Eurasian grassland remote sensing in 2009. The data is in TIF grid format, with a spatial resolution of 1km. The three-level grassland is classified as: temperate meadow grassland, temperate typical grassland, temperate desertification grassland, temperate grassland desertification, and temperate desert. The data is processed according to the ESA global cover 2009 Product global cover map, combined with the historical meteorological data (precipitation, annual accumulated temperature, humidity coefficient, evaporation) and DEM data of ECMWF website. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
This data set is a spatiotemporal variation map of temperate grassland types in Eurasia - three level classification of Inner Mongolia region of China (2009). The data is in TIF grid format with a spatial resolution of 1km. The data is processed on the basis of the existing grass type map of Inner Mongolia grassland. The grassland type map of Inner Mongolia grassland is based on the field survey data, neimengqi County as the unit, the grassland type classification system, on the basis of prediction, the field sample data, remote sensing image and other information data are superposed, and the local historical grassland survey data and relevant data are referred to, and the field plot is modified. We select 2000-2009 historical meteorological data, further analyze and modify the satellite data, and carry out spatial interpolation calculation. The classification of temperate grassland in Inner Mongolia was obtained. The data can be used to provide the basis for the distribution information and temporal and spatial variation analysis of warm grassland in Eurasia.
TANG Jiakui
The dataset contains all individual glacial storage (unit: km3) over the Qinghai-Tibetan Plateau in 1970s and 2000s. It is sourced from the resultant data of the paper entitled "Consolidating the Randolph Glacier Inventory and the Glacier Inventory of China over the Qinghai-Tibetan Plateau and Investigating Glacier Changes Since the mid-20th Century". The first draft of this paper has been completed and is planned to be submitted to Earth System Science Data journal. The baseline glacier inventories in 1970s and 2000s are the Randolph Glacier Inventory 4.0 dataset, and the Glacier Inventory of China, respectively. Based on the individual glacial boundaries extracted from the above-mentioned two datasets, the grid-based bedrock elevation dataset (https://www.ngdc.noaa.gov/mgg/global/global.html, DOI: 10.7289/v5c8276m), and the glacier surface elevation obtained by a slope-dependent method, the individual glacier volumes in 1970s and 2000s are then calculated. In addition, the calculated results of individual glacier volumes in this study have been compared and verified with the existent results of several glacier volumes, relevant remote sensing datasets, and the global glacier thickness dataset based on the average of multiple glacier model outputs (https://www.research-collection.ethz.ch/handle/20.500.11850/315707, doi:10.3929/ethz-b-000315707), and the errors in the calculations have also been quantified. The established dataset in this study is expected to provide the data basis for the future regional water resources estimation and glacier ablation-involved researches. Moreover, the acquisition of the data also provides a new idea for the future glacier storage estimation.
HU Xiaofei, PAN Baotian
I. Overview This data set contains the terrain data, soil data, meteorological data, land use data, NDVI data, etc. required for the operation of the IWEMS model. All maps and relevant point coordinates (weather stations) use the isometric projection UTM / WGS94 coordinate system. Ⅱ. Data processing description All maps and related point coordinates (weather stations) use the isometric projection UTM / WGS84 coordinate system. Ⅲ. Data content description The data content mainly includes: The basic terrain data includes the Cuneiform Desert (DEM) and the river network. The river network is used as the boundary for wind and sand transmission. The size of the DEM grid is 250 * 250 m. The river network was extracted using the ASTER-GDEM terrain data with the river burning method. Soil data, including soil physics, chemistry, and spatial distribution of soil types. It is derived from 1: 1 million soil database of China and converted to ESRI-grid format with a grid size of 250 * 250 m. Meteorological data, including daily data from Baotou, Dongsheng and Linhe meteorological stations around the Kubuqi Desert, from 2002 to 2010. Includes precipitation, wind speed and wind direction data. Land use data, 2000 land use data, scale is 1: 100,000. Convert it to ESRI-grid format with a grid size of 250 * 250 m. Ⅳ. Data usage description Evaluate wind and sand hazards along the Yellow River, estimate the amount of wind and sand entering the upper reaches of the Yellow River, and provide data support for establishing an early warning system for wind and sand hazards in the region.
XUE Xian, DU Heqiang
ASTER Global Digital Elevation Model (ASTER GDEM) is a global digital elevation data product jointly released by National Aeronautics and Space Administration (NASA) and Japan's Ministry of Economy, Trade and Industry (METI) .The DEM data is based on the observation results of the new generation of Earth observation satellite TERRA Completed, it is produced by 1.3 million stereo pair data collected by ASTER (Advanced Space borne Thermal Emission and Reflection Radio meter) sensors, and its coverage area exceeds 99% of the earth's land surface. The data has a horizontal accuracy of 30 meters (95% confidence) and an elevation accuracy of 20 meters (95% confidence). This data is the third global elevation data, which is a significant improvement over the previous SRTM3 DEM and GTOPO30 data. ASTER GDEM released two versions. The first version was released in June 2009 and the second version was released in October 2011. Compared with the first version, the second version has make further progress in water coverage and deviation removal. The quality of the data has been greatly improved. This dataset is the second version of the ASTER GDEM dataset in the Shule River Basin, including DEM, mountain shadow, slope, and aspect data. Spatial resolution: 1 radian second (about 30 meters), accuracy: vertical accuracy of 20 meters, horizontal accuracy of 30 meters.
National Aeronautics and Space Administration
Ⅰ. Overview The SRTM (Space Shuttle Radar Topographic Mapping Mission) was performed by NASA, the Geospatial Intelligence Agency, and German and Italian space agencies in February 2002. A total of 222 hours and 23 minutes of data collection was performed by the US space shuttle Endeavour onboard the SRTM system, and 9.8 trillion bytes of radar images were collected between 60 degrees in North America and 56 degrees in south latitude with an area of more than 119 million km2 Data, Fei changed more than 80% of the earth's surface, this data set covers the entire territory of China. It took two years to process, and finally obtained a global digital elevation model (DEM) with a plane longitude of ± 20m and an elevation longitude of ± 16m. Ⅱ. Data processing description The processing of SRTM data is done by the Ground Data Processing System (GDPS). The GDPS consists of three parts: (1) an interferometric processor, which uses the interferometric processor to convert the data into elevation maps and radar image bands; (2) a mosaic processor, which is used to compile collected global airborne data Draw a mosaic map of continental elevation data and images; (3) Verification system is responsible for checking the quality of the mosaic map and providing accuracy maps. These processors are currently installed on JPL workstations, and the next step is to install them on a set of supercomputers for the systematic processing of real SRTM data. As this work progresses, JPL will release auxiliary data to the work. Ⅲ. Data content description SRTM data provides a file for each latitude and longitude grid. There are two types of longitude: 1 arc-second and 3 arc-second. Called SRTM1 and SRTM3, or 30m and 90m data. This dataset uses SRTM3 data with 90m resolution. Each file contains elevation data of 1201 × 1201 sampling points. The data format is DEM format. The spatial position of each picture frame is shown in the attached picture (1-25 thousand pictures in the country). Ⅳ. Data usage description SRTM data has computable and visual functions, and has broad application prospects in various fields, especially in the fields of surveying and mapping, surface deformation, and military. Specifically, it mainly includes the following aspects: In scientific research, SRTM data plays a very important role in geology, geophysics, seismic research, level modeling, volcano monitoring, and registration of remote sensing images. Using high-precision digital terrain elevation data to build a three-dimensional three-dimensional model of the ground, which is superimposed on the ground image, can observe slight changes in the earth's surface. In civil and industrial applications, SRTM data can be used for civil engineering calculations, reservoir dam site selection, land use planning, etc. In terms of communications, digital terrain data can help businesses build better broadcast towers and determine the best In terms of aviation safety, the use of SRTM digital elevation data can establish an enhanced aircraft landing alarm system, which greatly improves the aircraft landing safety factor. In the military, SRTM data is the basic information platform of C4ISR (Army Automatic Command System). It is necessary to study the structure of the battlefield, the direction of the battlefield, the presetting of the battlefield, the deployment of operations, the concentration of forces in the delivery, the protection conditions, and logistics support Essential.
XUE Xian, DU Heqiang
Adopt aster with 30 meter resolution provided by Heihe project data management center GDEM data and 90 meter resolution SRTM data are two sets of grid data, as well as multi-source point data. These point data include radar point cloud elevation data in the middle and upper reaches; elevation data extracted from soil sample points and vegetation sample in the data management center of Heihe plan; elevation data extracted from climate and hydrological stations; and elevation sample data measured by the research group. By using the HASM scaling up algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high-precision DEM data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final DEM surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
Two sets of grid data, aster GDEM data with a resolution of 30 meters and SRTM data with a resolution of 90 meters provided by the data management center of Heihe project, as well as point data from multiple sources, are used. By using the HASM scaling algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high precision slope data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final slope surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
Two sets of grid data, aster GDEM data with a resolution of 30 meters and SRTM data with a resolution of 90 meters provided by the data management center of Heihe project, as well as point data from multiple sources, are used. By using the HASM scaling up algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high precision slope direction data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final slope surface. The spatial resolution is 500 meters.
YUE Tianxiang, ZHAO Na
This data is based on the DEM data generated by 1:250,000 digital contour lines and elevation points in China released by national basic geographic information center, and the DEM data set of heihe river basin is generated by the nearest neighbor method resampling method of ARCGIS spatial analysis module with a spatial resolution of 30 SEC.
National Basic Geographic Information Center
A total of 137 soil samples of different vegetation types, different altitudes and different terrains were collected from June 2012 to August 2012. The soil layer of each sample point was divided into three layers of 0-10cm, 10-20cm and 20-30cm, with an altitude of 2700-3500m m. The vegetation types were divided into five types: Picea crassifolia forest, Sabina przewalskii, subalpine scrub meadow, grassland and dry grassland. At the same time of sampling, hand-held GPS is used to record the location information and environmental information of each sampling point, including longitude, latitude, altitude, slope, aspect, terrain curvature, vegetation type, soil thickness, maximum root depth, etc. Soil bulk density: The measurement method of soil bulk density is to put the sample into an envelope and dry it in an oven at 105℃ for 24 hours, then take it out and place it for 30 minutes to weigh. The ratio of the weighing result to the volume of the ring cutter is the soil bulk density, and the unit is g/cm3. Soil mechanical composition: hydrometer method is used to measure the soil mechanical composition, which includes the content of soil sand, silt and clay.
ZHAO Chuanyan, MA Wenying
The Shiyang River Basin Information System thematic data set is one of the results of the technical assistance project “Optimization of Desertification Control in Gansu Province” assisted by the Asian Development Bank, including 5 folders including document, investigation_point, maps, photo, and spatial. Each file The folder contains several files. The document folder includes the target design, data processing, thematic summary report, and projection information.The gpspoint folder includes files recorded in shapefile point format sampled by gps according to different purposes.The maps folder contains Chinese, english, and fonts files. Folder, the first two folders represent 14 Chinese and English maps stored in A4 format and pdf format, and fonts contain some special fonts: the photo folder contains field survey digital photos stored in bmp format: spatial The folder contains the dem folder of the digital elevation model, the gansu folder of the outline map of Gansu Province and the Hexi Corridor, the generate folder of the site data file shapefile, the grid folder of the raster data of various geographic features, and the remote sensing image. image folder, meteoHydro folder for original site text data, and vector folder for vector data for various geographic features. The data includes: 1. DEM folder: 100m dem, hillshade, divided into GRID and geotif formats 2. Gansu folder: Gansu border, Hexi border 3. Grid folder: NDVI (vegetation index), lndchange (land transfer matrix), landscape86 (land landscape map in 86 years), landscape2k (land landscape map in 2000), Desertiftype (desert type landscape map), Desersevrt (desert type map ), Annprecip 4. Meteohydro folder: Minqin, Wuwei, Yongchang meteorological data (1) daily daily observation items: Airpress (humidity), Precipitation (radiation), Sunlight (sunlight), Temperature (temperature) ), Wind (wind speed) (2) Months (monthly): Airpress (air pressure), Humidity (humidity), Rain (precipitation), Sunlight (sunlight), Temperature (temperature), Wind (wind speed) (3) tendays: Airpress, Humidity, Rain, Sunlight, Temperature, Wind (4) years (year by year): Precipitation, Temperature 5. Vectro folder: (1) Admwhole (county boundary map), (2) Lake (lake), (3) Hydrasta (hydrological site), (4) Basin (watershed boundary), (5) Landscape2000 (land use 200 (Year), (6) landscape86 (land use 1986), (7) Meteosta (meteorological station), (8) Lakep (reservoir point), (9) Place (residential point), (10) Rainfallcontour (railway), ( 11) Rainfallcontour (rainfall contour map), (12) Road (highway), (13) Stream (water system map), (14) Town (county name), (15) Township (county township boundary), (16) Vegetation (vegetation map) Data projection information: PROJCS ["Albers", GEOGCS ["GCS_Krasovsky_1940", DATUM ["Not_specified_based_on_Krassowsky_1940_ellipsoid", SPHEROID ["Krasovsky_1940", 6378245.0,298.3]], PRIMEM ["Greenwich", 0.0], UNIT ["Degree", 0.0174532925199433]], PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["False_Easting", 0.0], PARAMETER ["False_Northing", 0.0], PARAMETER ["longitude_of_center", 105.0], PARAMETER ["Standard_Parallel_1", 25.0], PARAMETER ["Standard_Parallel_2", 47.0], PARAMETER ["latitude_of_center", 0.0], UNIT ["Meter", 1.0]] For detailed data description, please refer to the data file
LI Xin
This data set provides a 1 km resolution Digital Elevation Model (DEM) of Antarctica. The DEM combines measurements from the European Remote Sensing Satellite-1 (ERS-1) Satellite Radar Altimeter (SRA) and the Ice, Cloud, and land Elevation Satellite (ICESat) Geosciences Laser Altimeter System (GLAS). The ERS-1 data are from two long repeat cycles of 168 days initiated in March 1994, and the GLAS data are from 20 February 2003 through 21 March 2008. The data set is approximately 240 MB comprised of two gridded binary files and two Environment for Visualizing Images (ENVI) header files viewable using ENVI or other similar software packages. The data are available via FTP.
National Aeronautics and Space Administration
The section data of the upper reaches of Heihe River mainly show the structure and cross section distribution characteristics of the terrace of Heihe River. These data are mainly obtained through field investigation and measurement. The data include the forest farm section and raft section near Qilian County in the upper reaches of Heihe River, and the Heihekou section in Yingluoxia.
HU Xiaofei, PAN Baotian
Based on the field survey results of this project, the previous hydrogeological survey results and the prediction and judgment of desert depressions, we obtained more than 600 known water level points in badain jaran desert and its surrounding areas, and drew a first-order approximate contour map of the groundwater level in badan jaran desert by using the measured or predicted groundwater level data.This isometric chart fills a gap in the study of groundwater in badain jaran desert. The so-called first-order approximation is the distribution of the macroscopic groundwater level, which reaches a resolution of 1 km on the spatial scale, and it is assumed that the groundwater level in the shallow and deep layers is the same, and the groundwater in the quaternary and bedrock distribution areas remains continuous.The error level of the first-order approximate contour is ± 10 m, which mainly comes from the uncertainty of ground elevation data. This data set contains a vector diagram of the groundwater level contour line and a raster data file.
WANG Xusheng, HU Xiaonong
This dataset contains three basic remote sensing data of digital topography (DEM), TM remote sensing image and NDVI vegetation index of badan jilin desert. 1. DEM, digital terrain data, from the SRTM1 data set released by NASA in the United States, was cropped in the desert area.The resolution is 30 m.The data is stored in the DEM folder, and the dm.ovr file can be opened by ArcGIS. 2. TM image data.The composite data of Landsat TM/ETM + 543 band released by NASA were cropped in the desert lake group distribution area.The resolution is 30 m.From 1990 to 2010, one scene was selected in summer and one scene in autumn every five years to analyze the long-term changes of the lake.In 2002, there was a scene for each quarter to analyze the changes of the lake during the year.The data is stored in TM folder, TIFF format, can be opened by ArcGIS or ENVI software.The file naming rule is yyyymm.tif, where yyyy refers to the year and mm to the month. For example, 199009 refers to the time corresponding to the impact data of September 1990. 3. NDVI, vegetation index.The modis-ndvi product MOD13Q1, released by NASA, was cropped in desert areas.The NDVI data of every ten days of the growing season (June, July, August and September) from 2000 to 2012 are included. The spatial resolution is 250 m and the temporal resolution is 16 days.Stored in NDVI folder, TIFF format, can be opened by ArcGIS or ENVI software.Mosaic_tmp_yyyyddd.hdfout.250m_16_days_ndvi_roi.tif, Where yyyy represents the year and DDD represents the day of DDD of the year.
JIN Xiaomei, HU Xiaonong
Digital Elevation Model (DEM) is a kind of solid ground Model that represents the ground Elevation in the form of a set of ordered numerical arrays. The third pole region of40°1′52″N~23°11′59″N、105°43′45″E~61°28′45″E of the roof of the world ecological geographic area,These include the qinghai-tibet plateau, the hengduan mountains, the Himalayas, the Hindu kush mountains and the pamirs plateau.Classified according to：At 4000 m altitude as a benchmark, the fusion of slope, reference mountain integrity and ecological system integrity, the spatial resolution of 0.008 ° x 0.008 °
National Aeronautics and Space Administration
The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .
JIANG Zijie, JIANG Weiguo, WU Jianjun, ZHOU Hongmin
DEM is the English abbreviation of Digital Elevation Model, which is the important original data of watershed topography and feature recognition.DEM is based on the principle that the watershed is divided into cells of m rows and n columns, the average elevation of each quadrilateral is calculated, and then the elevation is stored in a two-dimensional matrix.Since DEM data can reflect local topographic features with a certain resolution, a large amount of surface morphology information can be extracted through DEM, which includes slope, slope direction and relationship between cells of watershed grid cells, etc..At the same time, the surface flow path, river network and watershed boundary can be determined according to certain algorithm.Therefore, to extract watershed features from DEM, a good watershed structure pattern is the premise and key of the design algorithm. Elevation data map 1km data formed according to 1:250,000 contour lines and elevation points in China, including DEM, hillshade, Slope and Aspect maps. Data set projection: Two projection methods: Equal Area projection Albers Conical Equal Area (105, 25, 47) Geodetic coordinates WGS84 coordinate system
TANG Guoan
This dataset is the 2018 Digital Elevation Model (DEM) of the key area of Qilian Mountain,spatial resolution 5m. This dataset is based on the high-definition survey satellite ZY-3. Through the three-lines CCD with spatial resolution of 2.1m and 3.5m, combined with the basic data such as high-precision topographic maps. The dem is generated by the front-view stereo relative and adjustment model. Finally, the 5m×5m Qilian Mountain key area DEM data set was spliced by the Mosaic tool of GIS software. The data can be applied to three-dimensional spatial data processing, hydrological analysis, terrain analysis, disaster monitoring, and human activity monitoring in key areas of Qilian Mountain.
QI Yuan, ZHANG Jinlong, ZHOU Shengming, WANG Hongwei
Soil bulk density, porosity, water content, water characteristic curve, saturated hydraulic conductivity, particle analysis, infiltration rate, and sampling point location information in the upper reaches of the Heihe River Basin. 1. The data is for 2014 supplementary sampling for 2012, using the ring knife to take the original soil; 2. The soil bulk density is the dry bulk density of the soil and is measured by the drying method. The original ring-shaped soil sample collected in the field was thermostated at 105 ° C for 24 hours in an oven, and the soil dry weight was divided by the soil volume (100 cubic centimeters) , unit: g/cm 3 . 3. Soil porosity is obtained according to the relationship between soil bulk density and soil porosity; 4. Soil infiltration analysis data set, the data is the field experimental measurement data from 2013 to 2014. 5. The infiltration data is measured by “MINI DISK PORTABLE TENSION INFILTROMETER”, and the approximate saturated hydraulic conductivity under a certain negative pressure is obtained. 6. Soil particle size data was measured at the Grain Granulation Laboratory of the Key Laboratory of the Ministry of Education of Lanzhou University. The measuring instrument is a Malvern laser particle size analyzer MS2000. 7. The saturated hydraulic conductivity is measured according to the enamel hair self-made instrument of Yi Yanli (2009). The Marioot bottle was used to maintain the head during the experiment; at the same time, the Ks measured at the time was converted to the Ks value at 10 °C for analysis and calculation. 8. Soil water content data is measured using ECH2O, including 5 layers of soil water content and soil temperature. 9. The water characteristic curve is measured by the centrifuge method: the undisturbed soil of the ring cutter collected in the field is placed in a centrifuge, and each of the speeds is measured at 0, 310, 980, 1700, 2190, 2770, 3100, 5370, 6930, 8200, 11600. The secondary rotor weight is obtained.
HE Chansheng
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