This dataset is derived from the paper: Xiaodan Wu, Kathrin Naegeli, Valentina Premier, Carlo Marin, Dujuan Ma, Jingping Wang, Stefan Wunderle. (2021). Evaluation of snow extent time series derived from AVHRR GAC data (1982-2018) in the Himalaya-Hindukush. The Cryosphere, 15,4261-4279. ln this paper, the performance of the AVHRR GAC snowpack product in the Hindu Kush Himalayas is comprehensively evaluated for the first time on a long time scale (1982-2018) based on ground station data, Landsat data, and MODIS snowpack product, respectively, including the consistency of the accuracy/precision of the product over a long time series, and the consistency of the product with Landsat and MODIS snowpack data in terms of spatial distribution. The main factors affecting the accuracy of the AVHRR GAC snowpack product are also revealed.

This dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. ELBARA-III horizontal and vertical brightness temperature are computed from measured radiometer voltages and calibrated internal noise temperatures. The data is reliable, and its quality is evaluated by 1) Perform ‘histogram test’ on the voltage samples (raw-data) of the detector output at sampling frequency of 800 Hz. Statistics of the histogram test showed no non-Gaussian Radio Frequency Interference (RFI) were found when ELBAR-III was operated. 2) Check the voltages at the antenna ports measured during sky measurements. Results showed close values. 3) Check the instrument internal temperature, active cold source temperature and ambient temperature. 3) Analysis the angular behaviour of the processed brightness temperatures. -Temporal resolution: 30 minutes -Spatial resolution: incident angle of observation ranges from 40° to 70° in step of 5°. The area of footprint ranges between 3.31 m^2 and 43.64 m^2 -Accuracy of Measurement: Brightness temperature, 1 K; Soil moisture, 0.001 m^3 m^-3; Soil temperature, 0.1 °C -Unit: Brightness temperature, K; Soil moisture, m^3 m^-3; Soil temperature, °C/K

This data set is the spatial distribution of soil POPs in the Tibetan Plateau, including OCPs, PCBs, PBDEs and PAHs. Fourty soil samples were taken from remote sites (i.e., away from towns, roads, or other human activity) in 8 soil zones of the Tibetan Plateau in 2007. The samples were collected using a stainless steel hand-held corer.Five cores (0-5 cm), taken over an area of ~100 m2, were bulked together to form one sample. The samples were wrapped in aluminum foil twice and sealed in two plastic bags to minimize the possibility for contamination. All the samples were analyzed at Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Chinese Academy of Sciences. The samples were Soxhlet-extracted, purified on an aluminium/silica column (i.d. 8 mm), a gel permeation chromatography (GPC) column subsequently, and were detected on a gas chromatograph with an ion-trap mass spectrometer (GC-MS, Finnigan Trace GC/PolarisQ) operating under MS–MS mode. A CP-Sil 8CB capillary column (50 m ×0.25 mm, film thickness 0.25 μm) was used for OCPs, PCBs and PBDEs, and a DB-5MS column (60 m ×0.25mm, film thickness 0.25 μm) was used for PAHs. Procedural blanks were prepared. The recoveries ranged from 53% to 130% for OCPs, and 58% to 92% for PAHs. The reported concentrations were not corrected for recoveries.

The global monthly all-sky land surface temperature (2000-2020) is produced by the method from Chen et al. 2017 JHM.

Glacier is the supply water source of rivers in the western mountainous area, and it is one of the most basic elements for people to survive and develop industry, agriculture and animal husbandry in the western region. Glaciers are not only valuable fresh water resources, but also the source of serious natural disasters in mountainous areas, such as sudden ice lake outburst flood, glacier debris flow and ice avalanche. Glacier hydrological monitoring is the basis for studying the characteristics of glacier melt water, the replenishment of glacier melt water to rivers, the relationship between glacier surface ablation and runoff, the process of ice runoff and confluence, and the calculation and prediction of floods and debris flows induced by glacier and seasonal snow melt water. Glacial hydrology refers to the water and heat conditions of glacial covered basins (i.e. glacial action areas), that is, the water and heat exchange between glaciers and their surrounding environment, the physical process of water accumulation and flow on the surface, inside and bottom of glaciers, the water balance of glaciers, the replenishment of glacial melt water to rivers, and the impact of water bodies in cold regions on climate change. At present, hydrological monitoring stations are mainly established at the outlet of the river basin to carry out field monitoring《 Glacial water resources of China (1991), hydrology of cold regions of China (2000) and glacial Hydrology (2001) summarize the early studies on glacial hydrology. China has carried out glacier hydrological monitoring on more than 20 glaciers in Tianshan, Karakorum, West Kunlun, Qilian, Tanggula, Nianqing Tanggula, gangrigab, Hengduan and Himalayas. This data set is the monthly runoff data of representative glaciers.

Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. The relative moisture index is the difference between the precipitation in a certain period of time and the potential evapotranspiration in the same period and then divided by the potential evapotranspiration in the same period.The precipitation data comes from the downscaling of the TRMM/GPM satellite precipitation data, and the potential evapotranspiration is estimated using the Thornthwaite method. For detailed algorithm, please refer to "National Standard for Meteorological Drought of China" (GB/T 20481-2017). The data only covers 34 key node areas along the Belt and Road.

The data was obtained from the 30-second global elevation dataset developed by the US Geological Survey (USGS) and completed in 1996. Downloaded the data from the NCAR and UCAR Joint Data Download Center (https://rda.ucar.edu/datasets/ds758.0/) and redistributed it through this data center. GTOPO30 divides the world into 33 blocks. The sampling interval is 30 arc seconds, which is 0.00833333333333333 degrees. The coordinate reference is WGS84. The DEM is the distance from the sea level in the vertical direction, ie the altitude, in m, the altitude range from -407 to 8752, the ocean depth information is not included here, the negative value is the altitude of the continental shelf; the ocean is marked as -9999, the elevation above the coastline is at least 1; the island less than 1 square kilometer is not considered. In order to facilitate the user's convenience, on the basis of the block data, splice 10 blocks in -10S-90N and 20W-180E without any resampling processing. This data file is DEM_ptpe_Gtopo30.nc

The data include raw sequencing result of plant DNA in surface sediments of 33 lakes in the Qinghai-Tibetan Plateau and arid northwestern China. We used PowerMax Soil Kit of Qiagen company in Germany to extract DNA, then used universal plant primer g-h (Taberlet et et al., 2007) to amplify P6 loop of chloroplast trnL (UAA) intron in the sample. The PCR products were then sent to Fasteris company in Switzerland for the next-generation paired-end sequencing. The sequencing instrument is Illumina Nextseq 550. The data quality score (Q30) is 81.97.

Thematic data on desertification (land desertification, salinization and vegetation degradation) in Central Asia, includes three parts: Distribution Map of Sandy Land in Central Asia, Distribution Map of Salinized Land in Central Asia and Distribution Map of Land Vegetation Degradation in Central Asia. The spatial resolution of the data is 1km, the time resolution is in 2015. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.

Simulation results of four cmip6 models in 2015-2100 under the scenario of shared socio-economic path (SSP) 5-8.5. The selection standard is that the resolution of the four modes is less than 1 °, and there are daily data. Eight variables representing extreme climate are extracted from the original simulation results, which are the extremely high value of daily maximum temperature (TXX), the extremely high value of daily minimum temperature (TNX), the extremely low value of daily maximum temperature (TxN), the extremely low value of daily minimum temperature (TNN), the number of continuous dry days (CDD), the number of continuous wet days (CWD), precipitation intensity (SDII) and the number of heavy precipitation days (r20mm). The time resolution of the data is years, the spatial range is the Qinghai Tibet Plateau, and the time range is 2015-2100.

Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm, and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation, mean and time trend derived from LZU had a higher correlation with that produced by the CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP) - Asian Monsoon Project, (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change.

This data set contains the temperature anomaly series for each quarter and month of the years from January, 1951 to December, 2006 on the Tibetan Plateau. Based on the “China Homogenized Historical Temperature Data Set (1951–2004) Version 1.0” and the daily average temperature data from 2005 to 2006, the monthly average temperature of 123 sites on the Tibetan Plateau and its neighboring areas were gridded using the Climate Anomaly Method (CAM). Further, the average monthly temperature anomaly sequences from 1951 to 2006 were established using the area weighting factor method. To maximize the use of the observation data, the method using the data at a nearby reference station to correct the short series of the climatic standard values of the air temperature data is emphatically discussed. Reference: Yu Ren, Xueqin Zhang, Lili Peng. Construction and Analysis of Mean Air Temperature Anomaly Series for the Qinghai-Xizang Plateau during 1951-2006. Plateau Meteorology, 2010. The “China Homogenized Historical Temperature Data Set (1951–2004) Version 1.0” and the daily average temperature data from 2005 to 2006 meet the relevant national standards. There are five fields in the monthly temperature anomaly data table. Field 1: Year Field 2: Month Field 3: Number of grids Number of grids included in the calculation Field 4: Number of sites Number of sites included in the calculation Field 5: Monthly Temperature Anomaly Unit °C There are five fields in the year and quarter temperature anomaly data table. Field 1: Year Field 2: Quarter Field 3: Number of grids Number of grids included in the calculation Field 4: Number of sites Explanation: Number of sites included in the calculation Field 5: Temperature anomaly °C In the quarter field: 1. If it is null, it is the annual temperature anomaly 2. DJF: Winter (Last December to this February) temperature anomaly °C 3. MAM: Spring (March-May) temperature anomaly °C 4. JJA: Summer (June-August) temperature anomaly °C 5. SON: Fall (September-November) temperature anomaly °C Data accuracy: the monthly average temperature anomaly to the third decimal places, the annual and quarterly average temperature anomaly to the second decimal places.