Precipitation estimates with ﬁne quality and spatio-temporal resolutions play signiﬁcant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at ﬁne resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. Focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (ﬁnished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0◦/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1◦/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25◦/daily) at the daily scale for the Asian applications. Considering the advantages from both satellite-based precipitation estimates and the ground observations, AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China.
The stable oxygen isotope ratio (δ 18O) in precipitation is a comprehensive tracer of global atmospheric processes. Since the 1990s, efforts have been made to study the isotopic composition of precipitation at more than 20 stations located on the TP of the Tibetan Plateau, which are located at the air mass intersection between westerlies and monsoons. In this paper, we establish a database of monthly precipitation δ 18O over the Tibetan Plateau and use different models to evaluate the climate control of precipitation δ 18O over TP. The spatiotemporal pattern of precipitation δ 18O and its relationship with temperature and precipitation reveal three different domains, which are respectively related to westerly wind (North TP), Indian monsoon (South TP) and their transition.
This dataset contains 18 years (2002-2019) global spatio-temporal consistent surface soil moisture . The resolution is 36 km at daily scale, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017). This study transfers the merits of SMAP to AMSR-E/2 through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with AMSR-E/2 brightness temperature (TB) as input. Finally, long term soil moisture data are output. The accuracy is about 5% volumetric water content. (evaluation accuracy of 14 dense ground network globally.)
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.
Daily precipitation data was reconstructed for streamflow simulation in the entire UB by combining orographic and linear correction approaches based on 262 gauge observations. The reconstructed precipitation is used to drive the VIC hydrological model linked with a temperature-index model (VIC-Glacier) , and is inversely evaluated by comparing with observed discharge, glacier area changes, and MODIS-based snow cover faction (SCF) data in the upper Brahmaputra Basin.
"One belt, one road" delineation of the key Asian regional watershed boundaries is based on the following principles: Principle 1: along the Silk Road Principle 2: located in arid and semi-arid areas Principle 3: high water risk Principle 4: watershed integrity 1. Division basis of arid area Food and Agriculture Organization of the United Nations. FAO GEONETWORK. Global map of aridity - 10 arc minutes (GeoLayer). (Latest update: 04 Jun 2015) Accessed (6 Mar 2018). URI: http://data.fao.org/ref/221072ae-2090-48a1-be6f-5a88f061431a.html?version=1.0 2. Water resources risk data: Gassert, F., M. Landis, M. Luck, P. Reig, and T. Shiao. 2014. Aqueduct Global Maps 2.1. Working Paper. Washington, DC: World Resources Institute. 3. Poverty index data: Elvidge C D, Sutton P C, Ghosh T, et al. A global poverty map derived from satellite data. Computers & Geosciences, 2009, 35(8): 1652-1660. https://www.ngdc.noaa.gov/eog/dmsp/download_ poverty.html 4. Basic basin boundary data: (1) Watershed boundaries were derived from HydroSHEDS drainage basins data (Lehner and Grill 2013) based on a grid resolution of 15 arc-seconds (approximately 500 m at the equator), which can be free download via https://hydrosheds.cr.usgs.gov/hydro.php (2) AQUASTAT Hydrological basins: This dataset is developed as part of a GIS-based information system on water resources. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations. The map is also available in the SOLAW Report 15: “Sustainable options for addressing land and water problems – A problem tree and case studies”. Data can be free download via http://www.fao.org/nr/water/aquamaps/ (3) HydroBASINS: https://www.hydrosheds.org/downloads 5. The GloRiC provides a database of river types and sub-classifications for all river reaches globally. https://www.hydrosheds.org/page/gloric 6. HydroATLAS offers a global compendium of hydro-environmental sub-basin and river reach characteristics at 15 arc-second resolution. https://www.hydrosheds.org/page/hydroatlas It covers an area of 1469400 square kilometers, including the following areas: Nujiang River Basin, Dead Sea basin, Sistan River Basin, Yellow River Basin, Jordan Syria eastern basin, Indus River Basin, Iran inland flow area, urmiya Lake Basin, Shiyang River Basin, hallelud mulgarb River Basin, Lianghe River Basin, Shule River Basin, Heihe River Basin, issekkor Lake Basin, Tata River Basin Limu River Basin, Turpan Hami basin, Ebinur Lake Basin, Junggar basin, Amu Darya River Basin, Manas River Basin, ulungu River Basin, Emin River Basin, Chu River Talas River Basin, Xil River Basin, Ili River Basin, Caspian Sea basin, Lancang River Basin, Yangtze River Basin, Qinghai lake water system, Eastern Qaidam Basin, western Qaidam Basin and Qiangtang plateau District, Yarlung Zangbo River Basin
The data includes the daily mean value of stable isotope δ18O in precipitation, the air temperature and precipitation amounts in Bomi in 2018; the precipitation samples are collected by Bomi meteorological station, and the stable isotope of precipitation is measured at the Laboratoire des Sciences du Climat et de l’Environnement, France., The δ18O amounts were measured by equilibration on a MAT-252 mass spectrometer, with an analytical precision of 0.05‰. The air temperatures and precipitation amounts were recorded for each precipitation events at Bomi meteorological stations, through the average of the observed temperature before and after the precipitation event, and through the total precipitation amount for each event. The data study has been published in the Journal of Climate, entitled Precipitation Water Stable Isotopes in the South Tibetan Plateau: Observations and Modeling.
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.
This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the observation system of Heihe remote sensing station from January 1 to December 31, 2019. The site (100.4756° E, 38.8270° N) was located on artificial grassland in Dangzhai Town of Zhangye, Gansu Province. The elevation is 1560 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (1.5 m, north), wind speed and direction (10 m, north), air pressure (2 m), rain gauge (0.7 m), four-component radiometer (1.5 m, south), two infrared temperature sensors (1.5 m, south, vertically downward), soil heat flux (3 duplicates, -0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, -1.6 m), and two photosynthetically active radiation (1.5 m, south, one vertically downward and one vertically upward). The observations included the following: air temperature and humidity (Ta_1.5, RH_1.5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, Ms_160 cm) (℃),on the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m^-2)). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2019-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation). This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation).