The long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
ZHANG Yu, ZHANG Guoqing
HBV hydrological model is one of the representatives of semi empirical hydrological model, which is widely used in watershed runoff simulation. Based on HBV model, the daily resolution runoff of six sub basins in the upper Indus River is simulated: 1) the daily resolution runoff data of 1980-2013 is calculated through the latest driving data; 2) HBV semi empirical hydrological model is more suitable for the simulation of alpine cold region ; 3) it is convenient to compare with the measured runoff data, so as to evaluate the applicability of the model and the reliability of the simulation results, make reasonable hydrological forecast in the downstream and prevent hydrological disasters. It plays an important role in the study of hydrological laws and practical production problems.
ZHANG Yinsheng
1) It is also called evapotranspiration, which is the sum of leaf emission (transpiration) of plants on the ground and soil evaporation between plants. That is, the water demand of crops in irrigation project. This data set is the monthly data of evapotranspiration in Central Asia; 2) MODIS data, which is calculated by energy balance method; 3) station disk evaporation verification; 4) evapotranspiration is the total water vapor flux transported to the atmosphere by vegetation and the ground as a whole, which mainly includes vegetation transpiration, soil water evaporation and the evaporation of intercepted water or dew. As an important part of energy balance and water cycle, evapotranspiration is not only a shadow The growth, development and yield of ring plants also affect the general circulation of the atmosphere and play a role in regulating the climate
LIU Tie
The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
The data set of supply of agricultural water resources in Central Asian adopts the water balance method to calculate the precipitation and runoff depth on grid scale in five central Asian countries, respectively, and estimate the agricultural water resources supply in five central Asian countries. The data source is mainly the precipitation and runoff data products of NOAH model in GLDAS. Each original raster data of 0.25 ° is resampled, starting from the upper-left corner of the original grid, and extending to the adjacent right and lower grids in turn, and every four grids (0.5 °) are merged into one grid, taking the median data as the center point value corresponding to four grid of geographic coordinates. The extreme values of the grids could be eliminated. The data sets includes three time periods of 2000s (2001-2005), 2010s (2006-2010) and 2015s (2011-2015) with a spatial resolution of 0.5°*0.5°; The data of demand of agricultural water resources in Central Asia include irrigation water requirement of cotton and winter wheat in 2006, 2010 and 2016 over Central Asia. This was calculated by the equation of irrigation water requirement presented by FAO. It is expected to provide basic data support for distributed water cycle simulation, water supply and demand, development and utilization analysis in five central Asian countries.
ZHANG Yongyong, YANG Peng, TIAN Jing, ZHANG Yongqiang
The data include the datasets of temporal changes in water level, water storage and area of the Aral sea (1911−2017), the inter-decadal change of ecosystem structure (NDVI—Normalized Difference Vegetation Index) of the Aral sea (1977−2017), and dust intensity (EDI—Enhanced Dust Index) in the Aral sea (2000−2018). Using data fusion technology in the construction of a lake basin terrain, terrain based on remote sensing monitoring and field investigation, on the basis of the analysis of the Aral sea terrain data, generalized analyses the water - area - the changes of water content, the formation of water - water - area of temporal variation data set, can clearly reflect the Aral sea water change process and the present situation, provide basic data for the Aral sea environmental change research. The NDVI was used to reflect the vegetation ecology in the receding area. Landsat satellite data, with a spatial resolution of 30 m, was used for NDVI analysis in 1977, 1987, 1997, 2007, and 2017. Based on ENVI and GIS software, remote sensing image fusion, index calculation, and water extraction were used to determine the lake surface and lakeshore line of the Aral sea. The lakeside line in the south of the Aral sea is taken as the starting point, and it extends for 3 km to the receding area. The variation characteristics of vegetation NDVI in the lakeside zone within 0-3 km are obtained to reflect the structural changes of the lakeside ecosystem. EDI was extracted from MODIS image data. This index is introduced into the dust optical density to enhance the dust information to form the enhanced dust index. Based on remote sensing monitoring, the use of EDI, established the Aral sea area-EDI index curve, the curve as the construction of the Aral sea dry lake bed dust release and meteorological factors, quantitative relationship laid the foundation of soil physical and chemical properties, in order to determine the control of sand/salt dust in the reasonable area of the lake.
LUO Yi, ZHENG Xinjun, HUANG Yue, JILILI Abuduwaili
PML_V2 terrestrial evapotranspiration and total primary productivity dataset, including gross primary product (GPP), vegetation transpiration (Ec), soil evaporation (Es), vaporization of intercepted rainfall , Ei) and water body, ice and snow evaporation (ET_water), a total of 5 elements. The data format is tiff, the space-time resolution is 8 days, 0.05°, and the time span is 2002.07-2019.08. Based on the Penman-Monteith-Leuning (PML) model, PML_V2 is coupled to the GPP process based on stomatal conductance theory. GPP and ET mutually restrict and restrict each other, which makes PML_V2 in ET simulation accuracy, which is greatly improved compared with the previous model. The parameters of PML_V2 are divided into different vegetation types and are determined on 95 vorticity-related flux stations around the world. The parameters were then migrated globally according to the MODIS MCD12Q2.006 IGBP classification. PML_V2 uses GLDAS 2.1 meteorological drive and MODIS leaf area index (LAI), reflectivity (Albedo), emissivity (Emissivity) as inputs, and finally obtains PML_V2 terrestrial evapotranspiration and total primary productivity data sets.
ZHANG Yongqiang
Terrestrial actual evapotranspiration (ET), including evaporation from soil and water surfaces, evaporation of rainfall interception, transpiration of vegetation canopy and sublimation of snow and glaciers, is an important component of the terrestrial water cycle and links the hydrological, energy, and carbon cycles. The dataset of ETMonitor-GlobalET-2013-2014 is obtained based on ETMonitor model, which combines parameterizations for different processes and land cover types, with multi-source satellite data as input. Several remote sensing based variables, e.g. net radiation flux and dynamic water body area, and meteorological variables from ERA5 reanalysis dataset, were used as input to estimate daily ET. The ET estimation is conducted at daily temporal step and 1km spatial resolution, and the generated global ET dataset is at 5km resolution and daily time step for publication. The data type is 16-bit signed integer, the scale factor is 0.1, and the unit is mm/day.
ZHENG Chaolei, JIA Li , HU Guangcheng
The spatial-temporal distribution map of topographic shadows in the upper reaches of Heihe River (2018), which is calculated based on the SRTM DEM and the solar position (http://www.esrl.noaa.gov/gmd/grad/solcalc/azel.html). The spatial resolution is 100 m and the time resolution is 15 min. The datased can be used in the fields of ecological hydrology and remote sensing research. Using the observed solar radiation at several automatic weather stations in the upper reaches of Heihe River, the accuracy of the calculation results is verified. Results show that the dataset can accurately capture the temporal and spatial changes of the topographic shadow at the stations, and the time error is within 20 minutes.
ZHANG Yanlin
This data set is the monthly runoff data of nijnii hydrological station, the main stream of the upper reaches of Amu Darya River in Central Asia from 1967 to 2017. The station is located on the main stream of the border between Tajikistan and Afghanistan. The data is from Tajikistan hydrometeorological Bureau. The data are processed according to the country's hydrological observation specifications and quality control process. The data period is 1967-2017. The hydrological station is located at 37.193121 ° n, 68.590218 ° e, 328m above sea level, and the unit of runoff is m3 / s. The data can be used for scientific research and water conservancy engineering services such as water resources assessment in Central Asia mountainous areas.
SHANG Huaming
This data provides 38 key parameters in the land surface model CLM4.0, involving hydrology, soil and vegetation. The detailed information about key parameters can be found in the attached documents. This data has about 1 degree resolution (f09 grid) globally, and also provides high resolution product (0.1 degree) in the Heihe region. Three objectives: evapotranspiration (ET), volumetric soil moisture (VSM) and freeze/thaw (FT) have been used to calibrate the 38 parameters. Each objective was used individually to calibrate parameters for each grid. The ET, VSM and FT datasets were provided by other groups in the save project. According to our assessment, the error of ET can be reduced 23%, VSM 52% and FT 34%. But since this is single objective optimization, the three improvements cannot be obtained simultaneously. The distribution of optimal parameters can be used for improving the structure of land surface model. The optimal parameters can be used directly by just replacing the corresponding number in the CLM source code.
GONG Wei
The basic data set of water resources research of Southeast Asian countries and Lancang Mekong basin (1901-2010) collected and sorted out the main hydrometeorological data of Southeast Asian countries and Lancang Mekong basin, including precipitation, average temperature, maximum temperature, minimum temperature, water vapor pressure, etc. the data came from CRU TS v. 4.03 (clinical research unit time series version 4.03), which is widely used in the whole world The format is NC, the time resolution is month by month, and the time length is from January 1901 to December 2018. Hydrological data includes surface runoff and underground runoff simulated by the hydrological model. The data comes from GLDAS (Global Land Data Assimilation System). The data format is NC, the time resolution is month by month, and the time length is from January 1979 to February 2019.
Climatic Research Unit CRU, Global Land Data Assimilation System GLDAS
There is a lack of a set of high-resolution precipitation gridded data with long time series in the main basin of the Arctic. This dataset provides daily precipitation in the main basin of the Arctic. The range of data set is from 45 ° N to 76.15 °N. The metadata used includes: the meteorological station data during 1980-2015 obtained from GSOD and the reanalysis data of ERA-interim during 1980-2018. This dataset was obtained by bias correction of ERA-interim data with the improved quantile mapping method, and the background data used for bias correction is the interpolation gridded precipitation, in interpolation process, we not only take into account the effect of elevation, but fully consider the influence of wind on gauge measurements, the gauge used in interpolation all undergo bias adjustment. This dataset performs well both in region scale and cell grid scale, with high accuracy, which providing a set of reliable precipitation gridded data for the hydrological research of the Arctic.
LEI Huajin, LI Hongyi
HOBO water temperature loggers (U22-001, Onset Corp., USA) were used to monitor changes in water temperature with an accuracy of ±0.2 oC. Two water temperature profiles were installed in Paiku Co’s southern (0-42 m in depth) and northern (0-72 m in depth) basins (Fig. 1). In the southern basin, water temperature was monitored at the depths of 0.4 m, 5m, 10 m, 15 m, 20 m, 30 m and 40 m. In the northern basin, water temperature was monitored at the depths of 0.4 m, 10 m, 20 m, 40 m, 50 m, 60 m and 70 m. To investigate local hydro-meteorology at Paiku Co, air temperature and specific humidity over the lake were monitored since June 2015 by using HOBO air temperature and humidity loggers (U12-012, Onset Corp., USA). The logger was installed in an outcrop ~2 m above the lake surface at the north part of the lake (Fig. 2). Lake evaporation was calculated using the energy budget (Bowen-ratio) method。
LEI Yanbin
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation). This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation).
WANG Lei
Precipitation and temperature are essential input variables for hydrological models. There are few meteorological stations in the big Naryn Basin of the Syr Darya, which cannot meet the needs of hydrological simulation. Precipitation data in the Syr Darya were collected through online resources and field research. The precipitation gradient in the study area is obtained. Based on the precipitation gradient, the precipitation and temperature grid products (PGMFD) (http://hydrology.princeton.edu/data.pgf.php)were then corrected to get this set of data sets. The year covered by this data is 1951-2016, the spatial precision is 10km, and the time resolution is daily. The more detail information about the correction method can be found in (Generation of High Mountain Precipitation and Temperature Data for a Quantitative Assessment of Flow Regime in the Upper Yarkant Basin in the Karakoram, Kan et al., 2018)
SU Fengge
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.
WANG Lei
This dataset contains daily land surface evapotranspiration products of 2018 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products and China Meteorological Forcing Dataset (He Jie, Yang Kun. China Meteorological Forcing Dataset. Cold and Arid Regions Science Data Center at Lanzhou, 2011. doi:10.3972/westdc.002.2014.db).
YAO Yunjun, LIU Shaomin, SHANG Ke
This dataset contains monthly land surface evapotranspiration products in Qilian Mountain area every 5 years from 1985 to 2015. It has 0.05 degree spatial resolution from 1985 to 1995 and 0.01 degree spatial resolution from 2000 to 2015. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products, GIMMS AVHRR NDVI and China Meteorological Forcing Dataset (He Jie, Yang Kun. China Meteorological Forcing Dataset. Cold and Arid Regions Science Data Center at Lanzhou, 2011. doi:10.3972/westdc.002.2014.db).
YAO Yunjun, LIU Shaomin, SHANG Ke
Surface evapotranspiration (ET) is an important link of water cycle and energy transmission in the earth system. The accurate acquisition of ET is helpful to the study of global climate change, crop yield estimation, drought monitoring, and has important guiding significance for regional and even global water resources planning and management. With the development of remote sensing technology, remote sensing estimation of surface evapotranspiration has become an effective way to obtain regional and global evapotranspiration. At present, a variety of low and medium resolution surface evapotranspiration products have been produced and released in business. However, there are still many uncertainties in the model mechanism, input data, parameterization scheme of remote sensing estimation of surface evapotranspiration model. Therefore, it is necessary to use the real method. The accuracy of remote sensing estimation of evapotranspiration products was quantitatively evaluated by sex test. However, in the process of authenticity test, there is a problem of spatial scale mismatch between the remote sensing estimation value of surface evapotranspiration and the site observation value, so the key is to obtain the relative truth value of satellite pixel scale surface evapotranspiration. Based on the flux observation matrix of "multi-scale observation experiment of non-uniform underlying surface evaporation" in the middle reaches of Heihe River Basin from June to September 2012, the stations 4 (Village), 5 (corn), 6 (corn), 7 (corn), 8 (corn), 11 (corn), 12 (corn), 13 (corn), 14 (corn), 15 (corn), 17 (orchard) and the lower reaches of January to December 2014 Oasis Populus euphratica forest station (Populus euphratica forest), mixed forest station (Tamarix / Populus euphratica), bare land station (bare land), farmland station (melon), sidaoqiao station (Tamarix) observation data (automatic meteorological station, eddy correlator, large aperture scintillation meter, etc.) are used as auxiliary data, and the high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) are used as auxiliary data. See Fig. 1 for the distribution map. Considering the land Through direct test and cross test, six scale expansion methods (area weight method, scale expansion method based on Priestley Taylor formula, unequal weight surface to surface regression Kriging method, artificial neural network, random forest, depth belief network) were compared and analyzed, and finally a comprehensive method (on the underlying surface) was optimized. The area weight method is used when the underlying surface is moderately inhomogeneous; the unequal weight surface to surface regression Kriging method is used when the underlying surface is moderately inhomogeneous; the random forest method is used when the underlying surface is highly inhomogeneous) to obtain the relative true value (spatial resolution of 1km) of the surface evapotranspiration pixel scale of MODIS satellite transit instantaneous / day in the middle and lower reaches of the flux observation matrix area respectively, and to observe through the scintillation with large aperture. The results show that the overall accuracy of the data set is good. The average absolute percentage error (MAPE) of the pixel scale relative truth instantaneous and day-to-day is 2.6% and 4.5% for the midstream satellite, and 9.7% and 12.7% for the downstream satellite, respectively. It can be used to verify other remote sensing products. The evapotranspiration data of the pixel can not only solve the problem of spatial mismatch between the remote sensing estimation value and the station observation value, but also represent the uncertainty of the verification process. For all site information and scale expansion methods, please refer to Li et al. (2018) and Liu et al. (2016), and for observation data processing, please refer to Liu et al. (2016).
LIU Shaomin, LI Xiang , XU Ziwei
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