The dataset of spatio-temporal water resources distribution in the source regions of Yangtze River and Yellow River (1998-2017)

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.

0 2020-10-01

Hydrogen and oxygen stable isotope data set of Kathmandu precipitation (2016-2018)

Precipitation stable isotopes (2H and 18O) are adequately understood on their climate controls in the Tibetan Plateau, especially the north of Himalayas via about 30 years’ studies. However, knowledge of controls on precipitation stable isotopes in Nepal (the south of Himalayas), is still far from sufficient. This study described the intra-seasonal and annual variations of precipitation stable isotopes at Kathmandu, Nepal from 10 May 2016 to 21 September 2018 and analysed the possible controls on precipitation stable isotopes. All samples are located in Kathmandu, the capital of Nepal (27 degrees north latitude, 85 degrees east longitude), with an average altitude of about 1400 m. Combined with the meteorological data from January 1, 2001 to September 21, 2018, the values of precipitation (P), temperature (T) and relative humidity (RH) are given.

0 2020-09-22

Freezing/ thawing index for air in the Heihe River Basin

According to the monthly temperature observation data of each conventional meteorological station in Heihe River basin set up by China Meteorological Administration, the annual air freeze-thaw index of each meteorological station is calculated, and then the annual average value of 1960-2004 is obtained. Finally, based on the regression relationship between the multi-year mean value of air freeze-thaw index and altitude of each meteorological station, and with the aid of 1 km DEM data, the spatial distribution map of air freeze-thaw index in Heihe River Basin is constructed.

0 2020-09-15

The mechanism of vegetation degradation in Yuanjiang dry hot valley of Yunnan Province

The experimental project of vegetation degradation mechanism and reconstruction in Yuanjiang dry-hot valley in Yunnan belongs to the major research program of "Environmental and Ecological Science in Western China" of the National Natural Science Foundation. The principal is researcher Cao Kunfang of Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences. The project runs from January 2004 to December 2007. Data collected for this project include: 1. Excel table of multi-year average temperature and rainfall in Yuanjiang dry-hot valley (1961-2004), with attribute fields including monthly average temperature and monthly average rainfall. 2. excel table of annual average temperature (1750-2006) in the middle of Hengduan Mountain in China based on tree ring, with attribute fields including year and reconstructed average temperature. 3. excel table of summer temperatures (1750-2006) in the central Hengduan Mountains in southern China based on tree rings. The attribute fields include the year and the reconstructed average temperature in summer (April-September). 4. excel table of drought index (1655-2005) in central Hengduan Mountains of China based on tree rotation, with attribute fields including year and reconstruction of drought index in spring (March-May). 5. pdf file of growth dynamic graph of leaves and branches. it records the growth dynamic trend line and leaf dynamic trend graph of plants with s-type, f-type, intermediate-type and S+SD-type branches from March 22, 2004 to April 8, 2005. 6.32 Phenological Summary Tables of Woody Plants (word Document: Specific Name, Number of Observed Plants/Branches, Type of Branch Extension, Leaf Phenology, Length of Current Year Branches (cm), Total Leaves on Branches, Leaf Area (cm2), Non-leaf Period (Months), Flowering Period, Fruit Ripening Period and Fruit Type) 7. Seasonal Changes of Relative Water Content of Plant Leaves in Yuanjiang Dry-hot Valley (March 2003-February 2004) Excel Table 8. Seasonal Changes of Photosynthesis of 6 Representative Plants in Yuanjiang Dry-hot Valley (Maximum Photosynthetic Rate, Stomatal Conductance, Water Use Efficiency, Maximum Subefficiency of photosystem II) excle Table (2003-2005) 9. excle Table of Long-term Water Use Efficiency (Isotope) Data of Representative Plants in Yuanjiang Dry-hot Valley (Water Use Efficiency in Dry and Wet Seasons of Shrimp Flower, Red-skin Water Brocade Tree, Three-leaf Lacquer, Phyllanthus emblica, Pearl Tree, Dried Sky Fruit, Cyclobalanopsis glauca, West China Small Stone Accumulation, Geranium, Tiger thorn, Willow and Pigexcrement Bean) 10. word Document of List of Plants in Mandan Qianshan, Yuanjiang

0 2020-06-08

The East Asian summer monsoon index (1851-2018)

The East Asian summer monsoon (EASM) and its variability involve circulation systems in both the tropics and midlatitudes as well as in both the lower and upper troposphere. Considering this fact, a new EASM index (NEWI) is proposed based on 200-hPa zonal wind, which takes into account wind anomalies in the southern (about 5°N), middle (about 20°N), and northern areas (about 35°N) of East Asia. NEWI = Nor[u(2.5°–10°N, 105°– 140°E) - u(17.5°–22.5°N, 105°– 140E) + u(30°– 37.5°N, 105°– 140°E)] where Nor represents standardization and u is JJA-mean 200-hPa zonal wind. When easterly anomalies appear around 20°N and westerly anomalies appear around 5° and 35°N, the index is positive, and the EASM is stronger. The NEWI can capture the interannual EASM-related climate anomalies and the interdecadal variability well. Compared to previous indices, the NEWI shows a better performance in describing precipitation and air temperature variations over East Asia. It can also show distinct climate anomalous features in early and late summer. The NEWI is tightly associated with the East Asian–Pacific or the Pacific–Japan teleconnection, suggesting a possible role of internal dynamics in the EASM variability. Meanwhile, the NEWI is significantly linked to El Niño–Southern Oscillation and tropical Indian Ocean sea surface temperature anomalies. Furthermore, the NEWI is highly predictable in the ENSEMBLES models, indicating its advantage for operational prediction of the EASM. The physical mechanism of the EASM variability as represented by the NEWI is also explicit. Both warm advection anomalies of temperature by anomalous westerly winds and the advection of anomalous positive relative vorticity by northerly basic winds cause anomalous ascending motion over the mei-yu–changma–baiu rainfall area, and vice versa over the South China Sea area. Hence, this NEWI would be a good choice to study, monitor, and predict the EASM (Zhao et al,2015,J Clim).

0 2019-09-11