Characterization of lake-catchments leveraging remote sensing data

The management and conservation of lakes should be conducted in the context of catchments because lakes collect water and materials from their upstream catchments. Thus, the datasets of catchment-level characteristics are essential for limnology studies. Remote sensing is an important data source for the characterization of lake-catchments. Leveraging remote sensing data, we constructed the first dataset of lake-catchment characteristics for 1525 lakes with areas 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, topography, climate, land cover/use, soil and geology, and anthropogenic activity) and a total of 721 attributes in the dataset. Besides multi-year average attributes, the time series of 16 hydrological and meteorological variables are extracted, which can be used to drive or validate lumped hydrological models and machine learning models for hydrological simulation. The dataset contains fundamental information for analyzing the impact of catchment-level characteristics on lake properties, which on the one hand, can deepen our understanding of 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 details of this dataset can be found in our paper published in Earth Syst. Sci. Data (https://doi.org/10.5194/essd-14-3791-2022).