Mapping spatio-temporal dynamics of main water parameters and understanding their relationships with driving factors using GF-1 images in a clear reservoir

Due to eutrophication and water quality deterioration in clear reservoirs, it is necessary to monitor and manage the main water parameters: concentration of total phosphorus (C_TP), chemical oxygen demand (C_COD), chlorophyll-a (C_Chla), total suspended matter (C_TSM), and Secchi disk depth (SDD). Five random forest (RF) models are developed to estimate these parameters in Xin'anjiang Reservoir, which is a clear drinking water resource in Zhejiang, China. Then, the spatio-temporal distributions of the parameters over 7 years (2013-2019) are mapped using GaoFen-1 (GF-1) images and the relationships with driving factors are analyzed. Our study demonstrates that the parameters' distributions exhibited a significant spatio-temporal difference in Xin'anjiang Reservoir. Spatially, relatively high C_TP, C_COD, C_Chla, and C_TSM but low SDD appear in riverine areas, showing strong evidence of impact from the incoming rivers. Temporally, C_Chla and C_TSM reached high values in summer and winter, whereas SDD and C_TP were higher in the summer and autumn, respectively. In contrast, no significant seasonal variations of C_COD could be observed. This may be why C_COD is not sensitive to hydrological or meteorological factors. However, precipitation had a significant impact on C_Chla, C_TP, SDD, and C_TSM in riverine areas, though these parameters were less sensitive to meteorological factors. Moreover, the geomorphology of the reservoir and anthropogenic interference (e.g., tourism activities) also have a significant impact on the water quality parameters. This study demonstrates that coupling long-term GF-1 images and RF models could provide strong evidence and new insights to understand long-term dynamics in water quality and therefore support the development of corresponding management strategies for freshwater reservoirs.

Keywords: Clear reservoir; Driving factors; Eutrophication; GF-1 image; Random forest model; Water quality parameters.