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During the years 2006-2009, lakes in the Qinghai-Tibetan Plateau (QTP) were investigated using satellite remote sensing strategies. We report the results of this investigation as well as follow-up research and expanded work. For the investigation, we mainly focused on lakes whose areas are more than 1 km. The remote sensing data that we used included 408 scenes of CBERS CCD images and 5 scenes of Landsat ETM+ images in Qinghai Province and Tibet Autonomous Region. All these data were acquired around years 2005-2006. Besides remote sensing images, we also collected 1,259 topographic maps. Numbers and areas of lakes were analyzed statistically, which were then compared with those coming from the first lake investigation (implemented between the 1960s and 1980s). According to our investigation, up to and around year 2005-2006, the total number of lakes in the QTP was 1,055 (222 in Qinghai and 833 in Tibet), accounting for more than 30 % of that of China. Thirty newborn lakes with area >1 km were found, and 5 dead lakes with initial area >1 km were also found. Among those 13 big lakes (>500 km), Yamzhog Yumco had seriously shrunk, and it has continued to shrink in recent years; Qinghai Lake had shrunk during the period, but some new researches indicated that it has been expanding since the year 2004; Siling Co, Nam Co, and Chibuzhang Co had expanded in the period. We divided the newborn lakes into six categories according to their forming reasons, including river expansion, wetland conversion, etc. The changes of natural conditions led to the death of four lakes, and human exploitation was the main reason for the death of Dalianhai Lake in Qinghai. We picked out three regions which were sensitive to the change of climate and ecological environment: Nagqu Region, Kekexili Region, and the source area of the Yellow River (SAYR). Lakes in both Nagqu and Kekexili have been expanded; meanwhile, most lakes in the SAYR have obviously been shrunk. These regional patterns of lake changes were highly related to variations of temperature, glacier, precipitation, and evaporation. Our investigation and analysis will provide references for researches related to lake changes in the QTP and the response to climate fluctuations. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

Lake surface water temperature (LSWT) is of vital importance for hydrological and meteorological studies. The LSWT ground measurements in the Tibetan Plateau (TP) were quite scarce because of its harsh environment. Thermal infrared remote sensing is a reliable way to calculate historical LSWT. In this study, we present the first and longest 35-year (1981–2015) daytime lake-averaged LSWT data of 97 large lakes (>80 km2 each) in the TP using the 4-km Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data. The LSWT dataset, taking advantage of observations from NOAA's afternoon satellites, includes three time scales, i.e., daily, 8-day-averaged, and monthly-averaged. The AVHRR-derived LSWT has a similar accuracy (RMSE = 1.7 °C) to that from other data products such as MODIS (RMSE = 1.7 °C) and ARC-Lake (RMSE = 2.0 °C). An inter-comparison of different sensors indicates that for studies such as those considering long-term climate change, the relative bias of different AVHRR sensors cannot be ignored. The proposed dataset should be, to some extent, a valuable asset for better understanding the hydrologic/climatic property and its changes over the TP. Design Type(s) data collection and processing objective • statistical analysis and modeling objective • time series design Measurement Type(s) temperature of water Technology Type(s) satellite imaging Factor Type(s) geographic location • size • temporal_interval Sample Characteristic(s) Tibetan Plateau • lake Machine-accessible metadata file describing the reported data (ISA-Tab format) [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important hydrometeorological components of the Tibetan Plateau (TP). The relationships between precipitation and topography in the TP were investigated using 17 years of Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) data and 2 years of Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) data. First, the retrieval and total errors of the two radars were quantified in the TP using ground-based precipitation datasets. The TRMM PR and GPM DPR both significantly underestimate the precipitation, with total errors up to 40% and 53%, respectively. Then, precipitation of the two radars was corrected prior to effective application. The correction was separately performed for each river basin in the TP by adding total errors. Finally, precipitation–topography relationships were documented using corrected radar data. The main findings on precipitation–topography relationships are: (1) precipitation generally decreases with increasing elevation from 2 to 6 km in the TP. However, the trend is inverse in the Qaidam Basin which is characterized by extremely low precipitation in the interior basin and high precipitation along the windward slopes of surrounding mountains; (2) quantitative relationships between precipitation and elevation can be depicted for most exorheic basins in the TP using exponential fitting. The coefficients of determination for the fitting are higher than 0.9 in six basins; (3) the relationships based on GPM DPR data are less robust due to its fewer samples, whereas the GPM DPR probably resolves the problem of overestimating precipitation in some grid pixels with high elevation, compared to the TRMM PR; (4) three typical mountains (i.e., Himalaya, Hengduan, and Tianshan mountains) exhibit significantly negative correlation between precipitation and elevation. Particularly, there are two bands of precipitation maxima along the southern slopes of the central Himalayas (78°E–88°E), corresponding to the two-step topography, which affects the vegetation distribution. This study provides insights into the precipitation–topography relationships in the TP and its surrounding regions based on data from spaceborne precipitation radars. [ABSTRACT FROM AUTHOR]

Soil moisture is a key variable in the exchange of water and energy between the land surface and the atmosphere, especially over the Tibetan Plateau (TP) which is climatically and hydrologically sensitive as the Earth’s ‘third pole’. Large-scale spatially consistent and temporally continuous soil moisture datasets are of great importance to meteorological and hydrological applications, such as weather forecasting and drought monitoring. The Fengyun-3B Microwave Radiation Imager (FY-3B/MWRI) soil moisture product is a relatively new passive microwave product, with the satellite being launched on November 5, 2010. This study validates and reconstructs FY-3B/MWRI soil moisture across the TP. First, the validation is performed using in situ measurements within two in situ soil moisture measurement networks (1° × 1° and 0.25° × 0.25°), and also compared with the Essential Climate Variable (ECV) soil moisture product from multiple active and passive satellite soil moisture products using new merging procedures. Results show that the ascending FY-3B/MWRI product outperforms the descending product. The ascending FY-3B/MWRI product has almost the same correlation as the ECV product with the in situ measurements. The ascending FY-3B/MWRI product has better performance than the ECV product in the frozen season and under the lower NDVI condition. When the NDVI is higher in the unfrozen season, uncertainty in the ascending FY-3B/MWRI product increases with increasing NDVI, but it could still capture the variability in soil moisture. Second, the FY-3B/MWRI soil moisture product is subsequently reconstructed using the back-propagation neural network (BP-NN) based on reconstructed MODIS products, i.e., LST, NDVI, and albedo. The reconstruction method of generating the soil moisture product not only considers the relationship between the soil moisture and NDVI, LST, and albedo, but also the relationship between the soil moisture and four-dimensional variations using the longitude, latitude, DEM and day of year (DOY). Results show that the soil moisture could be well reconstructed with R 2 higher than 0.56, RMSE less than 0.1 cm 3 cm −3 , and Bias less than 0.07 cm 3 cm −3 for both frozen and unfrozen seasons, compared with the in situ measurements at the two networks. Third, the reconstruction method is applied to generate surface soil moisture over the TP. Both original and reconstructed FY-3B/MWRI soil moisture products could be valuable in studying meteorology, hydrology, and ecosystems over the TP. [ABSTRACT FROM AUTHOR]