The lower reaches of the Tarim River Basin serve as a crucial ecological barrier in China's extreme arid region,with its plant diversity playing a key role in maintaining regional ecological balance and addressing global climate change.This study combines field survey data with literature data to systematically construct a plant species inventory dataset for the lower reaches of the Tarim River.The field survey was conducted from July 30 to August 19,2024,utilizing remote sensing and GIS technologies.A 10 m x 10 m plot was established in the lower reaches of the Tarim River Basin(39°25′12″N-40°40′12″N,87°35′24″E-88°30′0″E).To ensure data quality and accuracy,the research team invited plant experts to identify plant information during the collection process.The authors meticulously documented information on 24 families,65 genera,and 81 species of plants,compiling them into the Dataset of plant species of the lower reaches of the Tarim River(2024).The dataset includes:(1)geo-locations of the sample sites;(2)plant list and statistics of families and life forms.The plan list is composed of family,genus,species,classification,life form,national protection status,common names,and collection locations;(3)plants' photos.The dataset is archived in.shp,.xlsx,and.jpg formats,and consists of 87 data files with data size of 1.63 GB(compressed into 4 files with 1.62 GB).
As a crucial ecological barrier and a major hydropower energy base in China,hydropower development on the Qinghai-Xizang Plateau exerts a profound influence on the regional ecological environment.To support the evaluation of ecological effects of major construction projects under the Second Comprehensive Scientific Expedition to the Qinghai-Xizang Plateau,this study systematically collected and organized data on large and medium-sized hydropower stations that have been built or are currently under construction across the plateau.The dataset encompasses 61 hydropower stations distributed across 6 major river basins-the Yarlung Zangbo River,Yellow River,Jinsha River,Lancang River,Yalong River,and Min River-and includes key attributes such as station name,geographical location,total reservoir capacity,installed capacity,annual power generation,regulation performance,development mode,and construction period.The data sources consist of field surveys and literature review.There was a"dense in the east and sparse in the west"spatial pattern of the large and medium-sized hydropower stations on the Qinghai-Xizang Plateau.Significant inter-basin differences were observed in both installed capacity and reservoir scale.Although the pace of hydropower construction has slowed,the scale of individual projects has increased,and dam-type development remains predominant.This dataset provides a solid scientific foundation for assessing ecological impacts of hydropower projects,managing regional water resources,and planning energy systems under the goal of carbon neutrality.
Total soil nitrogen(TN)content is a key indicator reflecting soil nutrient status and ecological functions.Based on the Google Earth Engine(GEE)cloud computing platform,we integrated multi-source remote sensing data and selected key environmental variables-including MODIS-derived NDVI,Sentinel-2 near-infrared reflectance(Band 8),surface soil moisture,precipitation,land surface temperature,and digital elevation model(DEM)—as input features.3 machine learning algorithms were employed for TN content prediction:Random Forest(RF),Classification and Regression Tree(CART),and Gradient Boosting Regression Tree(GBRT).Using these models,we generated a 2020 soil total nitrogen dataset for Taiyuan City,China.The SoilGrids global soil nitrogen dataset,provided by the International Soil Reference and Information Centre(ISRIC),was used as the reference data.Model performance was evaluated using root mean square error(RMSE)and coefficient of determination(R2)through cross-validation.The average RMSE values for RF,CART,and GBRT across different soil depths were 0.16 g/kg,0.21 g/kg,and 0.33 g/kg,respectively,with corresponding average R2 values of 0.62,0.64,and 0.85.The validation results indicate that the dataset exhibits high accuracy and reliability,providing robust scientific support for regional soil nutrient assessment,agricultural decision-making,and ecological-environmental management.The dataset includes soil total nitrogen content at 6 soil depths(0-5 cm,5-15 cm,15-30 cm,30-60 cm,60-100 cm,and 100-200 cm)for Taiyuan City in 2020,with a spatial resolution of 30 m.The dataset is archived in.tif format,and consists of 18 data files with data size of 1.52 GB(compressed to 1 file with 219 MB).
Qinghai-Xizang Plateau plays a crucial role in regional water cycles and ecosystem functioning through its surface soil moisture dynamics.This study developed a surface soil moisture dataset for the Qinghai-Xizang Plateau covering the period from 2015 to 2100 with a spatial resolution of 0.1°×0.1°.First,in situ measurements from the MAQU,NAQU,and NGARI networks were used to evaluate the accuracy of 21 CMIP6 soil moisture datasets,along with SMAP and ERA5-Land products,using bias,correlation coefficient(R),root mean square error(RMSE),and unbiased RMSE(ubRMSE).Meanwhile,the Enhanced Triple Collocation(ETC)method was employed to obtain random error standard deviation(RESD)and correlation coefficient(CC),based on which 4 Earth system models were selected for data fusion.Second,SMAP and ERA5-Land datasets were fused using differential weighting guided by the ETC evaluation results,and the optimal fusion result was identified.Finally,a Random Forest algorithm was used to integrate multiple sources of explanatory variables for monthly model training,and the model's prediction accuracy was validated against in situ observations.The resulting dataset includes:(1)monthly soil moisture data under 4 Shared Socioeconomic Pathways(SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5)from 2015 to 2100 at 0.1° spatial resolution;(2)monthly in situ measurements(0-0.1 m depth)from the MAQU,NAQU,and NGARI networks.The dataset is archived in.mdd,.tif,.shp,and.csv formats,consisting of 4,838 data files with data size of 0.99 GB(compressed into 1 file with data size of 315 MB).Results indicate that compared to the original CMIP6 model outputs,the fused product exhibits significantly higher accuracy and lower error,enhancing the characterization of soil moisture dynamics over the Qinghai-Xizang Plateau.
The Qinghai-Xizang Plateau is characterized by ecological fragility and high sensitivity to climate change,with soil erosion posing a major threat to the ecological security of the Pan-Third Pole region.To reduce discrepancies in current assessments of soil erosion on the Qinghai-Xizang Plateau and to provide a more reliable representation of the spatial distribution and temporal dynamics in soil erosion,the authors applied an ensemble RUSLE model.This model integrates multiple datasets,including the China Meteorological Forcing Dataset(CMFD),Climate Hazards group Infrared Precipitation with Stations(CHIRPS)and ERA-Interim,SoilGrids soil data,and a 90-m DEM,combined with diverse erosion factor schemes(9 R factors × 3 K factors × 3 LS factorsx3 C factors).The model was used to evaluate soil erosion on the Qinghai-Xizang Plateau from 1981 to 2018.The"RUSLE-IC-SDR"method was compared with measured sediment yield data to develop a soil erosion dataset for the Qinghai-Xizang Plateau.The dataset includes:(1)multi-year average water erosion data for the periods 1981-2018,1981-1998,and 1999-2018,corresponding to the median values from the ensemble model;and(2)rates of change in water erosion for 1981-2018,1981-1998,and 1999-2018.The spatial resolution of the data is 100 m.The dataset is archived in.tif format and consists of 26 data files,with a total size of 12.3 GB(compressed into 6 files,5.38 GB).
