湖泊科学2017，Vol.29Issue(4)：836-847,12.DOI:10.18307/2017.0407
基于WASP模型的太湖流域上游茅山地区典型乡村流域水质模拟
Water quality modeling for typical rural watershed based on the WASP model in Mountain Mao Region, upper Taihu Basin
摘要
Abstract
Ponds,rivers and reservoirs are the basic elements of water environment in a rural watershed,especially in the humid regions of Southeastern China.The water quality model for Lita-chenzhuang rural watershed in Mountain Mao Region is developed based on the WASP model.It is a combination among field investigation,GIS spatial analysis,pollution loads estimation and so on.The result shows that the order of contamination level of the main water quality indicators are total nitrogen (TN),total phosphorus (TP),permanganate index,and ammonia nitrogen,respectively.For TN concentrations of the year,52％-100％ of different water bodies are worse than Grade V of Environmental Quality Standards for Surface Water of China(GB 3838-2002),while the ponds are relatively close to the rivers on the contamination.During summer and winter,2％-6％ of rivers and 8％-14％ of ponds have exceeded the upper bound of Grade V in TP concentrations.Ponds in the center of the watershed and near the village are obviously more contaminated.The pollution loads are the main uncertainty factor of the water quality model.So the refinement on planting patterns could improve the simulation results on the whole.The pollutants from unfenced livestock and poultry have more effect on the ponds,while the water quality of the rivers is more vulnerable to sewage and garbage.This study establishes the links of water quality in different water bodies,which are affected by non-point source pollution in a typical rural watershed.It is also useful to formulate mitigation measures on rural water environment.关键词
WASP/乡村流域/茅山地区/面源污染/池塘/河渠Key words
WASP/rural watershed/Mountain Mao Region/non-point source pollution/ponds/rivers引用本文复制引用
陈文君,段伟利,贺斌,陈雯..基于WASP模型的太湖流域上游茅山地区典型乡村流域水质模拟[J].湖泊科学,2017,29(4):836-847,12.基金项目
中国博士后科学基金(2017M611938)、江苏省博士后科研资助计划(1601038B)、国家自然科学基金项目(41471460,41130750)和中国科学院科技服务网络计划(KFJ-SW-STS-174)联合资助. （2017M611938）
