State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Beijing, 100038, China; China Institute of Water Resources and Hydropower Research, Beijing, 100038, China.
China Institute of Water Resources and Hydropower Research, Beijing, 100038, China; College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, China.
J Environ Manage. 2021 Feb 15;280:111826. doi: 10.1016/j.jenvman.2020.111826. Epub 2020 Dec 23.
The trophic state index (TSI) and trophic level index (TLI) are commonly used methods for evaluating the eutrophication state of lakes and reservoirs. However, they are unable to overcome uncertainties such as calculation errors and spatial heterogeneity of evaluation indicators. To comprehensively evaluate the eutrophication state of a region, we introduce a probability density function and propose the stochastic trophic level index model (STLI). The probability density function of each trophic level is derived through the principle of maximum entropy, and membership vector F (F1, F2, F3, F4, F5) for each trophic level is established to quantify the risk of regional eutrophication. We utilized STLI to evaluate the eutrophication status of Songhua Lake, China, and determined that the method can be used for uncertainty and risk assessment. Our results show that the Jiaohe River backwater area has the highest eutrophication level (light eutropher), with a 0.12 probability of further deterioration to middle eutropher. The eutrophication status of the Main Scenic Area of the Songhua Lake Scenic Resort was shown to be mesotropher, with 0.26 and 0.08 probabilities of further deterioration to light eutropher and middle eutropher, respectively. Finally, the eutrophication status of the Songhua River Three Lakes Reserve Experimental Area was shown to be mesotropher, with a 0.24 probability of further deterioration to light eutropher. Overall, the Songhua River Three Lakes Reserve Experimental Area is the most promising for the lowest level of eutrophication. We recommend that the management department take effective targeted measures against the Jiaohe River backwater area first. The probability density and membership vector of STLI can effectively solve the uncertainties presented by traditional methods for evaluating regional eutrophication status.
营养状态指数（TSI）和营养层次指数（TLI）是常用的评价湖泊和水库富营养化状态的方法。然而，它们无法克服计算误差和评价指标空间异质性等不确定性。为了全面评价区域富营养化状态，我们引入概率密度函数，提出了随机营养层次指数模型（STLI）。通过最大熵原理推导出每个营养层次的概率密度函数，并建立每个营养层次的隶属向量 F（F1、F2、F3、F4、F5），以量化区域富营养化的风险。我们利用 STLI 评价了中国松花湖的富营养化状况，确定该方法可用于不确定性和风险评估。结果表明，蛟河回水区域富营养化水平最高（轻度富营养化），进一步恶化到中度富营养化的概率为 0.12。松花湖风景名胜区主要景区富营养化程度为中营养化，进一步恶化到轻度富营养化和中度富营养化的概率分别为 0.26 和 0.08。最后，松花湖三湖保护区试验区富营养化程度为中营养化，进一步恶化到轻度富营养化的概率为 0.24。总体而言，松花湖三湖保护区试验区最有希望达到最低富营养化水平。建议管理部门首先对蛟河回水区域采取有效有针对性的措施。STLI 的概率密度和隶属向量可以有效地解决传统评价区域富营养化状态方法所存在的不确定性问题。
