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基于河流分段与分季节的涑水河水环境容量计算及模型预测 被引量:1

Sectional and seasonal calculation and model prediction of water environmental capacity of Sushui River
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摘要 【目的】建立适合于干旱和半干旱地区季节性河流的水环境容量计算模式及预测模型。【方法】以山西省涑水河为例,基于河流分段(上游(横水铁路桥断面以上)、中游(吕庄水库断面至横水铁路桥断面)、下游(吕庄水库断面以下))和分季节(丰水期、平水期、枯水期)的原则,在传统水环境容量计算模型的基础上,利用涑水河流域现有的水文资料、环境资料及生态、经济等资料,探索影响干旱和半干旱地区河流生态环境容量的主要因素,构建涑水河水环境容量计算模式与自适应调整粒子群-RBF神经网络预测模型,并用涑水河流域监测数据、历史资料进行验证。【结果】水体原污染物质量浓度、当前水环境的质量标准、河流距离、河流流量、排放污水量和排放时间是影响干旱和半干旱地区河流生态环境容量的主要因素。在丰水期、平水期、枯水期涑水河3个断面之间水环境容量计算值与预测值均非常接近,计算值相对误差为0.12%~1.47%,预测值相对误差为0.09%~2.02%,可见计算方法和预测方法的精度均较高。【结论】涑水河水环境容量预测值可用于指导涑水河水资源优化配置和水环境污染控制,对推动干旱和半干旱地区流域的可持续发展具有积极意义。 【Objective】This study aimed to establish seasonal calculation model and prediction model for water environment capacity suitable for arid and semi-arid watersheds.【Method】Based on traditional water environmental capacity calculation model,factors influencing the ecological environment capacity of river in arid and semi-arid basins were investigated.Sectional(upstream,midstream,and downstream)and seasonal(plentiful,flat,and withered)water environment capacity calculation model and neural network model with adaptive adjustment of particle swarm-RBF were built using hydrological,environmental,ecological,and economic data of Sushui River Basin.Water monitoring data and historical data of Sushui River were also used to validate the established models.【Result】Main factors influencing ecological environmentcapacity in arid and semi-arid basins included the original concentration of pollutants in water,the current water environmental quality standards,distance to river,river flow,sewage emission and discharge time.The calculated and predicted water environmental capacity values of Sushui River at the three sections and three seasons were very close with calculation relative error and prediction relative error of 0.12%-1.47%and 0.09%-2.02%,respectively.【Conclusion】The predicted water environmental capacity of Sushui River can be used to guide water resources optimal allocation of Sushui River and water environmental pollution control.The established calculation model and prediction model have positive significance for the sustainable development of arid and semi-arid basins.
出处 《西北农林科技大学学报(自然科学版)》 CSCD 北大核心 2014年第11期175-182,共8页 Journal of Northwest A&F University(Natural Science Edition)
基金 国家科技部创新基金项目(12C26213714122) 高等学校博士学科点专项科研基金项目(20126118110015)
关键词 水环境容量 河流分段 季节性河流 粒子群算法 RBF神经网络 涑水河 water environment capacity river section seasonal rivers particle swarm algorithm RBF neural network Sushui River
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