摘要
长期以来,水环境的治理效果受到许多不确定性因素的影响,这些不确定性来源于监测资料的短缺以及人类认识水平的有限性。不确定性总是存在,不可能完全被消除,此时,传统的确定性研究方法表现出不适应性。量化污染物预测结果的不确定性并制定考虑不确定性的管理决策成为目前的研究热点。概率统计理论可以量化随机不确定性,是目前最主要的不确定性研究方法。基于概率统计理论的不确定性研究方法又分为基于机理模型的方法和基于统计模型的方法。传统的贝叶斯网络是一种统计模型,基于数据驱动,通过数据来建立变量间的统计关系。此外,还存在一种特殊的贝叶斯网络,它利用机理模型代表变量间的关系,是基于机理模型的方法和基于统计模型的方法的融合。
本文以钱塘江流域的子流域东阳江流域为案例,利用概率统计理论量化水质超标风险。先后建立了基于机理模型的贝叶斯网络BN1以及基于数据驱动的贝叶斯网络BN2。前者用于计算不同降雨和气温条件下的水质超标风险,后者用于计算考虑降雨和气温随机性时的水质超标风险。BN1的优势是能够基于少资料建模,BN2的优势是推理迅速,适用于大量的风险计算。本文对两种方法取长补短,实现了优势互补。此外,以2020s时期(2011-2030年)为预测期,进一步研究了气候变化对东阳江水质超标风险的可能影响,以便决策者对未来水质情况进行评估并做出预防性决策。论文的主要内容和成果如下：
(1)建立了基于降雨径流模型和水质模型的贝叶斯网络BN1,将本案例的主要控制变量、中间变量和响应变量相互连接,并用降雨径流模型、水质模型等机理模型(即子模型)来代表各变量之间的关系。利用一种新的MCMC算法—DRAM,对子模型进行贝叶斯参数估计(亦即对BN1的参数估计),然后通过对BN1进行MC随机模拟实现了不同降雨、气温、水库下泄流量和点污染源排放条件下的水质超标风险计算。并利用DRAM将点源与BN1的水质模型参数一同进行贝叶斯参数估计,既有效量化了不确定性,又突破了数据的限制实现了少资料地区建模。
(2)建立了基于数据驱动的贝叶斯网络BN2,选择6月和12月为典型月份,将月均降雨和月均气温的随机分布输入BN1进行随机模拟,利用基于概率分布的柔性离散方法对降雨、气温以及BNl模拟产生的随机数进行离散以用于训练BN2的参数,最后利用BN2推理计算了减排前后考虑降雨和气温随机性的水质超标风险。此外,还计算了增加水库下泄流量对水质超标风险的影响,结果显示,通过增加水库下泄流量可以有效降低水质超标风险。BN2的一个重要优势是推理计算迅速,当参数估计完毕,各类风险计算可以通过推理计算很快完成。尤其是当随机模拟次数比较多、风险计算量比较大、机理模型比较复杂时,BN2的效率要明显高于BN1。
(3)择2020s时期为预测期,利用大气环流模式HadCM3在A1B、A2和B1三种气候情景下的预测结果,并选择天气发生器对预测结果进行降尺度处理生成逐日降雨和气温数据。结果显示2020s时期东阳市的月均降雨量和月最高、最低气温没有明显变化,但强降雨和高温天气发生的概率增加。然后利用BN2计算了2020s时期考虑降雨和气温随机性时6月份和12月份的水质超标风险。最后将基准期和预测期的水质超标风险进行综合比较,为管理者从不同角度预防未来水质超标提供借鉴。
For a long time, water environmental treatment is affected by uncertainty, which comes from the shortage of monitoring data and the limited knowledge of human. Uncertainty will always exist and can not be completely avoided. Then, the traditional deterministic methods show their unadaptability. Studying on the uncertainty methods to quantify the uncertainty of water quality prediction and make risk-based descisions has become a focus recently. Probability and statistics theory can be used to quantify the statistical uncertainty, and is the most important uncertainty analysis method. Uncertainty methods based on probability and statistics theory can be further divided into method based on mechanism model and method based on statistical model. The traditional Bayesian Network is data-driven statistical model, that is, the statistical relationships between viariables are estimated by data. In addtion, there is a special kind of Bayesian Network, which uses mechanism models to represent relationships between variabes. It is a couple of methods based on mechanism model and that based on statistical model.
The subbasin of Qiantang River Basin—ongyang River Basin is taken as a study case, and the probability and statistics theory is used to quantify the risk of water quality exceeding standard values. Two Bayesian Networks—BN1and BN2are constructed. BN1is used to calculate the risks of water quality exceeding the standard values under different rainfall amount and temperature, and BN2is used to calculate the corresponding risks when considering the statistical distributions of rainfall and temperature. The main advantage of BN1is that it can be parameterized by little data, and that of BN2is that it can quickly calculate a great deal of risks. These two methods can be completment with each other's advantages. In addition, for the period2020s, the possible impact of climate change on the risks of water quality exceeding the standard values in Dongyang River is further studied in purpose of helping decision-makers to assess future water quality and make preventive descisions. The main contents and research results are as follows:
(1) A mechanism-model-based Bayesian Network—BN1, which connects control, intermediate and response viariables and uses mechanism modes (which are submodels) to stand for the relationships of viariables, is built. A new MCMC algorithm—RAM is applied on Bayesian parameter estimation of submodels(that is also parameter estimation of BN1). Then, MC simulations are carried out on BN1to calculate the risks of water quality exceeding standard values under different rainfall amount and temperature. DRAM can be used to make Bayesian estimation of point pollution dsicharge and water quality parameter at the same time. That can breakthrough the limitation of little data, and also quantify the uncertainty.
(2) A data-based Bayesian Network—BN2is built. June and December are chosen as typical months, and the distributions of monthly average rainfall and temperature of June and Decmber are inputed to BN1to carry out MC simulations. A probability-based soft discretization approach is used to discretize the random results of BN1, then the discretized data is used to train BN2. At last, by the inference algorithm of BN2, the water quality risks when considering the statistical distributions of rainfall and temperature are calculated. Furthermore, the risks under more reservoir discharge are also calculated. The important advantage of BN2is its rapid reasoning algorithm, which can qulickly calculate all kinds of risks once parameter estimation is finished. Especially, when there are many times of MC simulation, or many risks need to be calculated, or complex mechanism models, BN2is significantly more efficient than BN1.
(3) This paper applies the LARS-WG weather generator to simulate daily rainfall and temperature data of a single station under the A1B, A2and B1emission scenarios using the results of General Circulation Model HadCM3. The results show that the monthly average rainfall, monthly highest and lowest temperature will not change obviously, but the frequency of heavy rainfall and high temperature will possibly increase. Then, based on BN2, water quality risks when considering stochastic uncertainty of rainfall and temperature of June and December in2020s are calculated. Finally, a comparison of water quality risks between baseline period, A1B, A2and B1emission scenarios is made in order to provide a reference for decision-makers to prevent water quality from exceeding standard values.
引文
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