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

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