Long-term change of total suspended matter in a deep-valley reservoir with HJ-1A/B: implications for reservoir management

摘要

The valley reservoirs service as a critical resource for society by providing drinking water, power generation, recreation, and maintaining biodiversity. Management and assessment of the water environment in valley reservoirs are urgent due to the recent eutrophication and water quality deterioration. As an essential component of the water body, total suspended matter (TSM) hinder the light availability to underwater and then affect the photosynthesis of aquatic ecosystem. We used long-term HJ-1A/B dataset to track TSM variation and elucidating the driving mechanism of valley reservoirs. Taking a typical deep-valley reservoir (Xin'anjing Reservoir) as our case study, we constructed a TSM model with satisfactory performance (R-2, NRMSE, and MRE values are 0.85, 18.57%, and 20%) and further derived the spatial-temporal variation from 2009 to 2017. On an intra-annual scale, the TSM concentration exhibited a significant increase from 2.13 +/- 1.10mgL(-1) in 2009 to 3.94 +/- 0.82mgL(-1) in 2017. On a seasonal scale, the TSM concentration in the entire reservoir was higher in the summer (3.36 +/- 1.54mgL(-1)) and autumn (2.74 +/- 0.82mgL(-1)) than in the spring (1.84 +/- 1.27mgL(-1)) and winter (1.44 +/- 2.12mgL(-1)). On a monthly scale, the highest and lowest mean TSM value occurred in June (4.66 +/- 0.45mgL(-1)) and January (0.67 +/- 1.50mgL(-1)), and the monthly mean TSM value increased from January to June, then dropped from June to December. Combing HJ-1A/B-derived TSM, climatological data, basin dynamic, and morphology of the reservoir, we elucidated the driving mechanism of TSM variation. The annual increase of TSM from long-term HJ-1A/B data indicated that the water quality of Xin'anjiang Reservoir was decreasing. The annual increase of phytoplankton jointed with an increase of built-up land and decrease of forest land in the basin may partially be responsible for the increasing trend in TSM. This study suggested that combining the long-term remote sensing data and in situ data could provide insight into the driving mechanism of water quality dynamic and improve current management efforts for local environmental management.