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The management and conservation of lakes should be conducted in the context of catchment because lakes collect water and materials from their upstream catchments. So the datasets of catchment-level characteristics are essential for limnology studies. Lakes are widely spread on the Tibetan Plateau (TP) with a total lake area exceeding 50 000 km2, accounting for more than half of the total lake area in China. However, there has been no dataset of lake-catchment characteristics in this region to date. This study constructed the first dataset of lake-catchment characteristics for 1525 lakes with area from 0.2 to 4503 km2 on the TP. Considering that large lakes block the transport of materials from upstream to downstream, lake catchments are delineated in two ways: the full catchment, which refers to the full upstream contributing area of each lake, and the inter-lake catchments which are obtained by excluding the contributing areas of upstream lakes larger than 0.2 km2 from the full catchment. There are six categories (i.e. lake body, topographic, climatic, land cover/use, soil & geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the daily time series of climatic variables are also extracted, which can be used to drive lumped hydrological models or machine learning models for hydrological simulation. The LCC-TP dataset contains fundamental information for analysing the impact of catchment-level characteristics on lake properties, which on the one hand can deepen our understanding on the drivers of lake environment change, and on the other hand, can be used to predict the water and sediment properties in unsampled lakes based on limited samples. This provides exciting opportunities for lake studies in a spatially-explicit context and promotes the development of landscape limnology on the TP. The dataset of lake-catchment characteristics for the Tibetan Plateau (LCC-TP v1.0) is accessible at the National Tibetan Plateau/Third Pole Environment Data Center https://doi.org/10.11888/Terre.tpdc.272026, Liu, 2022). [ABSTRACT FROM AUTHOR]

Water clarity provides a sensitive tool to examine spatial pattern and historical trend in lakes trophic status. Yet, this metric has insufficiently been explored despite the availability of remotely-sensed data. We used three Secchi disk depth (SDD) datasets for model calibration and validation from different field campaigns mainly conducted during 2004-2018. The red/blue band ratio algorithm was applied to map SDD for lakes (> 1 ha) based on the first SDD dataset, where R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9 %, MAE = 57.7 cm. The other two datasets were used to validate the SDD estimation model, which were indicated the model had a stable performance of temporal transferability. The annual mean SDD of lakes were retrieved across China using Landsat top of air reflectance products in GEE from 1984 to 2018. The spatiotemporal dynamics of SDD were analysed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found that the lakes with SDDs < 2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total area of lakes with SDD between 0-0.5 m and > 4 m were the largest, accounting for 48.28 % of the total lakes. During 1984-2018, lakes in the Tibetan-Qinghai Plateau lake region (TQR) had the clearest water with an average value of 3.32 ± 0.38 m, while that in the Northeastern lake region (NLR) exhibited the lowest SDD (mean: 0.60 ± 0.09 m). Among the 10,814 lakes with SDD results more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia-Xinjiang lake region (MXR), more than half of the total lakes in every other lake region exhibited significant increasing trends. In the Eastern lake region (ELR), NLR and Yungui Plateau lake region (YGR), almost more than 50 % of the lakes that displayed an increase or decrease in SDD were mainly distributed in an area of 0.01-1 km2, whereas that in the TQR and MXR were primarily concentrated in large lakes (> 10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset can now be accessed through the website of the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571. [ABSTRACT FROM AUTHOR]