基于Markov状态切换理论的水环境系统随机模拟与风险评估

水质时序随机模拟是水环境系统风险分析与评价的有效途径，针对水质时间序列一般具有结构复杂性和演变过程非平稳性的特征，提出将马尔可夫状态切换-自回归模型（Markov Switching-Auto Regressive Model）用于水质时序的随机模拟和水环境系统的风险分析，并探讨了运用MS-AR模型进行水质时序随机模拟与分析的关键步骤。最后运用该模型和蒙特卡罗（Monte Carlo）方法，对天津果河桥断面的总磷浓度时序（1999年11月～2007年12月）进行了随机模拟和污染风险评价。结果表明，MS-AR模型有效识别出了该水质时序在演变过程中的不同结构模式，模拟出的总磷浓度序列能从总体上反映实测总磷时序的演变特征和动态变化趋势，克服了利用单纯统计理论进行水环境风险分析无外延性的不足。

Water environment system random simulation and risk assessment based on Markov Switching theory

The random simulation of water quality time series is a good method for the environmental risk assessment of water system. Focusing on the non-stationary and structural complexity of water quality time series, Markov Switching-Auto Regressive Model (MS-AR) was introduced for water quality time series random simulating, and then discussed the main procedures of environmental risk assessment for water system by using MS-AR model. Finally, by means of this model and Monte Carlo method, the total phosphorus (TP) concentration time series in the Guohe bridge section of Tianjin was random simulated, and the risk of TP exceeding national standard limits was assessment. The result shows that MS-AR model identified two evolution patterns of the series, the simulate series reflected the evolution features and the changing trend of the actual time series, which overcame the lack of extensionality and prediction capability of using statistical method only to the environmental risk analysis of water system.