Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; School of Tourism and City Management, Zhejiang Gongshang University, Hangzhou 310018, China.
Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
Sci Total Environ. 2017 Dec 1;599-600:1705-1717. doi: 10.1016/j.scitotenv.2017.05.075. Epub 2017 May 19.
Nutrient enrichment is a major cause of water eutrophication, and variations in nutrient enrichment are influenced by environmental changes and anthropogenic activities. Accurately estimating nutrient concentrations and understanding their relationships with environmental factors are vital to develop nutrient management strategies to mitigate eutrophication. Landsat 8 Operational Land Imager (OLI) data is used to estimate nutrient concentrations and analyze their responses to hydrological and meteorological conditions. Two well-accepted empirical models are developed and validated to estimate the total nitrogen (TN) and total phosphorus (TP) concentrations (C and C) in the Xin'anjiang Reservoir using Landsat 8 OLI data from 2013 to 2016. Spatially, C decreased from the transition zone to the riverine zone and the lacustrine zone. On the other hand, C decreased from the riverine zone to the transition zone and the lacustrine zone. Temporally, C displayed elevated values during the late fall and winter and had lower values during the summer and early fall, whereas C was higher during the spring and lower during the winter. Among the environmental factors, the rainfall and the inflow rate have strong positive correlations with the nutrient concentrations. TN is more sensitive to meteorological factors (wind speed, temperature, sunshine duration), and the spatial driving forces vary among the different sections of the reservoir. However, TP is more easily influenced by human activities, such as fishery and agricultural activities. Current results would improve our understanding of the drivers of nutrients spatiotemporal variability and the approach in this study can be applicable to other similar reservoir to develop related strategies to mitigate eutrophication.
营养盐富化是水体富营养化的主要原因，营养盐的变化受到环境变化和人为活动的影响。准确估计营养盐浓度并了解其与环境因素的关系对于制定营养盐管理策略以减轻富营养化至关重要。陆地卫星 8 操作陆地成像仪（OLI）数据用于估计营养盐浓度，并分析它们对水文和气象条件的响应。使用 2013 年至 2016 年的陆地卫星 8 OLI 数据，开发并验证了两个经过充分验证的经验模型，以估算新安县水库中的总氮（TN）和总磷（TP）浓度（C 和 C）。空间上，C 从过渡带向河流带和湖泊带减少。另一方面，C 从河流带向过渡带和湖泊带减少。时间上，C 在深秋和冬季表现出较高的值，而在夏季和初秋则较低，而 C 在春季较高，冬季较低。在环境因素中，降雨量和流入率与营养盐浓度呈强正相关。TN 对气象因素（风速、温度、日照时间）更为敏感，且在水库不同地段的空间驱动力不同。然而，TP 更容易受到人类活动的影响，如渔业和农业活动的影响。当前的结果将提高我们对营养盐时空变化驱动因素的理解，本研究中的方法可适用于其他类似水库，以制定相关策略来减轻富营养化。
