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104 results for “Yangtze River”
Yangtze River Basin Water Reservoir (YWR) dataset
<p>The Yangtze River Basin Water Reservoir (YWR) dataset, developed by multi-source satellite remote sensing data, provides monthly time series data for 443 reservoirs (with a total storage capacity of 276.51 km³) in the Yangtze River Basin (YRB) from 1990 to 2023, including area and storage data for all 443 reservoirs and water level data for 175 reservoirs. This dataset is associated with the study: Wang et al., "Advanced monitoring of reservoirs in the Yangtze River Basin from 1990 to 2023 using multi-source satellite remote sensing", Journal of Remote Sensing, under review, 2025.</p>
30-m Spatial Resolution Bioclimatic Dataset of 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches
<p><strong>Brief Introduction of the Dataset</strong></p> <p>This bioclimatic dataset is the product of research article "Mapping 30-m Resolution Bioclimatic Variables During 1991-2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches." published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.</p> <p>The dataset contains 19 30-m resolution average bioclimatic variables during 1991-2020 Climate Normals for Hubei Province (108°21′42″—116°07′50″ E, 29°01′53″—33°6′47″ N), the core region of the Yangtze River middle reaches. The dataset was constructed by statistically downscaling the Climatic Research Unit (CRU) 1-km monthly climate variables (1440 in total), cablirating with ground observation data with 82 weather stations and aggregating based on the defination of 19 bioclimatic variables. The downscaling of four 1-km Climatic Research Unit monthly climate variables including monthly maximum, mean, minimum temperature and precipitation was firstly achieved by random forest model with 30-m resolution terrain and spatial data. Then the interpolation-based geographical differential analysis (GDA) was applied to improve the accuracy of downscaled products based on ground observation data. Finally, the bioclimatic variables were aggregated based on their definitions and averaged for the 30 years. The Yangtze River middle reaches is abundant of forestry, agriculture, biodiversity resources that requires finer bioclimatic data for better understands of these aspects. This dataset will provide higher spatial accuracy, more information and applicability in finer regional studies in the Yangtze River middle reaches.</p> <p> </p> <p><strong>Description of the 19 Bioclimatic Variables</strong></p> <p>The dataset contains 19 geotiff files in total. File names and the corresponding full name of bioclimatic variables are described as follows:</p> <p>Bio01 Mean annual air temperature (℃)<br>Bio02 Mean diurnal air temperature range (℃)<br>Bio03 Isothermality (%)<br>Bio04 Temperature seasonality (℃)<br>Bio05 Mean daily maximum air temperature of the warmest month (℃)<br>Bio06 Mean daily minimum air temperature of the coldest month (℃)<br>Bio07 Annual range of air temperature (℃)<br>Bio08 Mean daily mean air temperatures of the wettest quarter (℃)<br>Bio09 Mean daily mean air temperatures of the driest quarter (℃)<br>Bio10 Mean daily mean air temperatures of the warmest quarter (℃)<br>Bio11 Mean daily mean air temperatures of the coldest quarter (℃)<br>Bio12 Annual precipitation amount (mm)<br>Bio13 Precipitation amount of the wettest month (mm)<br>Bio14 Precipitation amount of the driest month (mm)<br>Bio15 Precipitation seasonality (%)<br>Bio16 Precipitation amount of the wettest quarter (mm)<br>Bio17 Precipitation amount of the driest quarter (mm)<br>Bio18 Precipitation amount of the warmest quarter (mm)<br>Bio19 Precipitation amount of the coldest quarter (mm)</p> <p> </p> <p><strong>Others</strong></p> <p>More information related to bioclimatic variables can be found on https://chelsa-climate.org/bioclim/</p>
Divergent geographic patterns and functional characteristics: Subtle mapping for ponds in the Yangtze River Delta Region
<p>Pond water surfaces (PWS) possess diverse functional types, such as aquaculture, agriculture-water supplement, and ecosystem regulation. However, existing research often treats PWS as a homogeneous aquatic ecosystem; the absence of a comprehensive PWS classification system hinders ours understanding of PWS background characteristics and is detrimental to watershed management. Here, a comprehensive classification system of PWS, including fish aquaculture ponds (FAP), shrimp and crab aquaculture ponds (SCAP), natural ponds (NP), and landscaping ponds (LP) was proposed from remote sensing perspectives. Additionally, interpretation rules were standardized from multi-features including spectrum, shape, topography, and surrounding geographical environments. Subsequently, refined spatiotemporal data product of PWS in the Yangtze River Delta from 2016 to 2022 was generated using Sentinel-2 images with 10 m spatial resolutions. The results indicate that: (1) The spatiotemporal changes exhibited three stages, i.e., “declining – stable – recovery.” The area of PWS decreased from 5186.52 km² to 4920.90 km²in 2016-2017, stabilized at approximately 4500 km² in 2019-2021, and then rebounded to 4834.12 km² in 2022. (2) Regarding different PWS functional types, significant differences were demonstrated in terms of area, surrounding environment, and spatiotemporal changes. Firstly, FAP dominated in terms of area, accounting for 47.81% of the total. Secondly, FAP was widely around rivers and lakes. At the same time, SCAP was concentrated around lakes or along the coast, LP was primarily found in urban areas, and NP was predominantly found in rural areas and mountainous regions; Thirdly, NP decreased as land remediation work progressed continuously, while LP increased due to policy support for urban renewal. Changes in aquaculture were more complex, experiencing a sharp decline from 2016 to 2020 due to reduced market demand but rebounded in 2021-2022 with supportive policies. In summary, the system and data products developed in this study reveal the diverse relationships of "functional type-geographical environment-driving factor" regarding PWS, implicating appropriate planning for aquatic ecosystem.</p>
Risk assessment (susceptibility) of thaw slumps and thermokarst lakes in the Yangtze River source region
<p>Due to the influence of climate warming, the degradation of permafrost on the Qinghai-Tibet Plateau (QTP) has become evident. The formation of thermokarst hazards induced by the degradation of ice-rich permafrost has a significant impact on infrastructure construction and local ecology; therefore, it is necessary to assess its risk. In this study, a novel multiple thermokarst hazards risk assessment framework was proposed by combining stacking machine learning and potential environmental factors (vegetation factors, terrain factors, climate factors, and soil factors) to assess the risk of thermokarst hazards in the Yangtze River source region (YRSR). The results show the risk assessment (susceptibility) of thermokarst hazards in the YRSR from 2000 to 2016 at 500 m spatial resolution. This study divided the risk into 5 levels: very low (0.0-0.2), low (0.2-0.4), moderate (0.4-0.6), high (0.6-0.8), and very high (0.8-1.0) </p>
lake dataset (area ≥ 1 km2) in the Yangtze River basin from 2000 to 2019
<p>All lakes larger than 1km<sup>2</sup> from 2000 to 2019 in the Yangtze River basin were obtained. Which are shapefile files, and the area of each lake was calculated (based on the Asia_North_Albers_Equal_Area_Conic projection). The data volume is 257MB, compressed into one file (65MB).</p>
Stable Water Isotopes and Nutrients in the Changjiang (Yangtze River) Estuary and adjacent East China Sea shelf in Winter
<p>The dataset presented here includes the temperature, salinity, stable water isotopes, and nutrients of seawater from the Changjiang Estuary and adjacent East China Sea shelf in March 2013. </p>
Fig. 6 in A new tintinnid ciliate (Ciliophora: Spirotrichea) from Yangtze River Estuary, with notes on its habitat
Fig. 6. Tintinnopsis estuariensis sp. nov. and its allied species. A. T. estuariensis; B. T. akkeshiensis; C. T. sufflata; D. T. kofoidi; E. T. radix; F. T. cylindrical. B and C after Hada (1937); D after Hada (1932a, b, 1937), Balech (1948), Alder (1999) and Zhang et al. (2012b); E after Kofoid and Campbell (1929), Xu and Song (2005); F after Kofoid and Campbell (1929) and Zhang et al. (2012a). Scale bar=50 μm.
Fig. 5 in A new tintinnid ciliate (Ciliophora: Spirotrichea) from Yangtze River Estuary, with notes on its habitat
Fig. 5. Surface water temperature (T, C) and salinity (S, ‰) in the sampling sites during four cruises in the estuary of Yangtze River in 2005. Different sizes of circles indicates different abundances (ind./ m3) of Tintinnopsis estuariensis Zhang, Feng & Yu, sp. nov. in the sampling site, and the solid dots means no individual were found in the according site.
Fig. 4 in A new tintinnid ciliate (Ciliophora: Spirotrichea) from Yangtze River Estuary, with notes on its habitat
Fig. 4. Distribution of surface temperature (T, C), salinity (S, ‰) and abundance (Abun, ind./ m3) in May, September and November of 2005.
Fig. 2 in A new tintinnid ciliate (Ciliophora: Spirotrichea) from Yangtze River Estuary, with notes on its habitat
Fig. 2. Tintinnopsis estuariensis Zhang, Feng & Yu, sp. nov., six different individuals with same scale. Scale bar=50 μm.
Fig. 3 in A new tintinnid ciliate (Ciliophora: Spirotrichea) from Yangtze River Estuary, with notes on its habitat
Fig. 3. SEM images of major axis in Tintinnopsis estuariensis Zhang, Feng & Yu, sp. nov. Scale bars: A=100 μm; B – C=10 μm.
The compiled 8-year dataset (2012-2019) consisting of weekly river water quality indicators (CODMn, DO, NH3-N and PH ) in majors 10 sub-basin of Yangtze river based on imputation of machine learning
<p>Water quality is significantly affected by global climate change and human activities, with diverse critical factors shaping its state in rivers and lakes. In the study, we utilized four indicators to characterize water quality: the physical water quality parameters included dissolved oxygen (DO, mg/L) and PH, while the chemical water quality parameters encompassed chemical oxygen demand (CODMn, mg/L) and ammonia nitrogen (NH3-N, mg/L). This study establishes weekly water quality models for typical 10 sub-basins along the Yangtze River using machine learning methods, which incorporate the impacts of hydro-meteorological and anthropogenic factors.These 10 sub-basins represent the principal tributaries of the Yangtze River basin and include Dongting Lake, the upper Han River, the lower Han River, the Jialing River, the Jinsha River, the Li River, the Min River, Poyang Lake, the Xiang River, and the Yuan River. This data collection was performed by National Environmental Monitoring Centre (http://www.cnemc.cn/sssj/szzdjczb/index_1.shtml). The water quality indicators discussed in this study are assessed in accordance with the national standard GB 3838-2002. Please refer to the paper for details.</p>
Fig. 1 in Sinibrama Longianalis, A New Cyprinid Species (Pisces: Teleostei) From The Upper Yangtze River Basin In Guizhou, China
Fig. 1. Measurements taken on Sinibrama species. A: 1-13; B: 14-31: 1: standard length (SL); 2: body depth; 3: head length (HL); 4: snout length; 5: eye diameter; 6: interorbital width (impossible to be shown here); 7: length of caudal peduncle; 8; depth of caudal peduncle; 9: dorsal-fin length; 10: pectoral-fin length; 11: pelvic-fin length; 12: anal-fin length; 13: anal-fin basal length; 14: distance from anterior tip of snout to posterior point of neurocranium; 15: distance from anterior tip of snout to pectoral-fin insertion; 16; distance from posterior point of neurocranium to pectoral-fin insertion; 17: distance from posterior point of neurocranium to dorsal-fin origin; 18: distance from posterior point of neurocranium to pelvic-fin insertion; 19: distance from pectoral-fin insertion to dorsal-fin origin; 20: distance from pectoral- to pelvic-fin insertion; 21: distance from pelvic-fin insertion to dorsal-fin origin; 22: dorsal-fin basal length; 23: distance from dorsal- to anal-fin origin; 24: distance from pelvic-fin insertion to posterior end of dorsal-fin base; 25: distance from pelvic-fin insertion to anal-fin origin; 26: distance from posterior end of dorsal-fin base to anal-fin origin; 27: distance from posterior end of dorsal-fin base to dorsal origin of caudal fin; 28: distance from posterior end of dorsal-fin base to ventral origin of caudal fin; 29: distance from anal-fin origin to dorsal origin of caudal fin; 30: distance anal-fin origin to ventral origin of caudal fin; 31: distance from dorsal to ventral origin of caudal fin.
Fig. 4 in Sinibrama Longianalis, A New Cyprinid Species (Pisces: Teleostei) From The Upper Yangtze River Basin In Guizhou, China
Fig. 4. Three-dimensional distribution of the numbers of lateral line scales, scale rows above lateral line and branched rays in the anal fin of three species in Sinibrama. S. longianalis (Δ); S. macrops (D); S. wui (O).
Fig. 5 in Sinibrama Longianalis, A New Cyprinid Species (Pisces: Teleostei) From The Upper Yangtze River Basin In Guizhou, China
Fig. 5. Scatterplots on scores of the first and second principal component for all specimens of three species in Sinibrama: S. longianalis (Δ); S. macrops (·); S. wui (O). Arrow indicating the specimens from Taiwan.
Monthly area series of 1632 reservoirs in the Yangtze River Basin from 1984 to 2020
<p>The four compressed files contain the monthly area series of 1632 reservoirs in the upper, middle and upper, middle and lower, and lower reaches of the Yangtze River Basin, respectively, from April 1984 to December 2020. This dataset comprehensively includes 1632 reservoirs with an area greater than 1km2 (a total area of 9712 km2) in Yangtze River Basin based on the shapefiles provided by China Reservoirs Dataset. Combined the most complete reservoir database and advanced water enhance algorithm, the reconstructed monthly area time series can provide a data basis for the evaluation of future reservoir ecological benefits.</p>
A dynamic von Mises-based model to evaluate the impact of urbanization and climate change on flood timing in Yangtze and Huaihe River Basins, China
<p>The daily streamflow data extracted from 8 selected stations from the Huaihe and Yangtze River Basins, China.</p>
Data and code from: River network connectivity reductions dominate declines in the richness of plateau fish species under climate change in the upper Yangtze River Basin
Open the record for dataset details and reuse information.
Data for "Impacts of ozone-vegetation interactions on ozone pollution episodes in North China and the Yangtze River Delta"
<p>Data for "Impacts of ozone-vegetation interactions on ozone pollution episodes in North China and the Yangtze River Delta"</p>
Data for "Statistical characteristics of thunderstorm activity in the middle reaches of the Yangtze River Basin based on a five-year cloud-to-ground lighting dataset"
<p>Data for "Statistical characteristics of thunderstorm activity in the middle reaches of the Yangtze River Basin based on a five-year"</p>
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