鄱阳湖流域泥沙流失及吸附态氮磷输出负荷评估

Evaluation of soil loss and transportation load of adsorption N and P in Poyang Lake watershed

水土流失是吸附态氮磷输出负荷的主要方式,也是非点源污染评估的重要环节。鄱阳湖流域水土流失及其所带来的泥沙和吸附态氮磷等营养盐将直接影响到鄱阳湖的生态功能,进而影响着长江中下游水环境安全。科学估算鄱阳湖赣江、抚河、信江、饶河、修河等五大流域的土壤侵蚀量和吸附态氮、磷的输出负荷,将为鄱阳湖流域农业非点源污染控制及鄱阳湖生态建设和环境保护提供理论依据。以2007年全国第一次农业污染源普查数据和2007年江西省土壤质量调查数据为基础,利用RUSLE方程和GIS的空间统计功能,对江西省境内鄱阳湖流域的土壤侵蚀量和吸附态氮、吸附态磷的输出负荷进行估算。结果表明:基于RUSLE得到的2007年鄱阳湖五大流域输沙模数比较可信,鄱阳湖流域内泥沙、吸附态氮、吸附态磷的年输出负荷分别为1245183t、3383t和73t,其中赣江流域吸附态氮磷的年输出负荷最大,占鄱阳湖全流域的58.1%,抚河、饶河、信江、修河等分别占11.2%、7.2%、11.3%和12.2%。与流域内农业污染源总氮、总磷排放量相比较,尽管流域尺度内泥沙的输出负荷相对较大,但吸附态氮、磷的输出负荷较小,应该不是鄱阳湖水污染中总氮和总磷等营养盐的主要来源。

As China′s largest freshwater lake and the international important wetland,the Poyang Lake has extremely valuable ecological function and plays an important part in maintaining the safety of regional and national ecological security.Due to the main transportation way of the particle Nitrogen（N） and Phosphorus（P）,sediment and nutrients caused by soil erosion of the Poyang Lake watershed not only directly affect ecological functions of the Poyang Lake but also pose risk potential on mid-and down stream of the Yangtze River.Therefore the purpose of this study was to make an assessment of soil erosion and output load of the particle N and P in five sub-watersheds of the Poyang Lake,including Ganjiang river,Fuhe river,Raohe river,Xinjiang river,Raohe river and Xiuhe river in order to provide theoretical information for agricultural non-point source pollution management,ecological construction,and environmental protection in Poyang lake watershed.Based on data of the First National Agriculture Pollution Source Census and Soil Quality Survey of Jiangxi province in the Watershed in 2007,RUSLE model and spatial statistic function of GIS were used for assessing soil erosion and output nutrients（particle N and P） load in the Poyang Lake watershed.Results showed that the sediment transport modulus of the five sub-watersheds based on RUSLE in 2007 is reliable.The soil erosion load of Poyang Lake watershed amounted to 9913921t/a,in which Ganjiang,Fuhe,Xinjiang,Raohe,and Xiuhe Rivers accounted for 63.3%、9.9%、9.9%、6.2% and 10.7% of the total load,respectively,and the sediment transport modulus of each watershed above mentioned was 9.09、8.92、9.76、7.59 and 10.61 t·km-2·a-1,respectively.Compared with that published in Changjiang Sediment Bulletin（CSB）,as for sediment transport modulus errors of the five sub-watersheds,the maximum relative error is that from Ganjiang river sub-watershed as high as 66.7%,the minimum relative error is that from Raohe river sub-watershed as low as 11%,and the average error of these five sub-watersheds was as much as 42.3%.Furthermore,output loads of erosion sand,particle N and particle P transportation were 12451831,33831 and 731 t/a in the Poyang Lake watershed,respectively.Among these output loads,the adsored N and P from the Ganjiang river sub-watershed was the biggest,more than any of other sub-watersheds,accounting for 58.1% of the total load of the Poyang Lake watershed.The output nutrients load from the Fuhe river,Raohe river,Xinjiang river,and Xiuhe river accounted for 11.2%,7.2%,11.3% and 12.2% of the total load of the Poyang Lake watershed,respectively.Compared with output loads of TN and TP from agricultural pollution sources,the erosion sand load from the Poyang Lake watershed was relative high,but the output loads of the adsored N and P from the watershed were low,which were proved not the primary source of nutrients in the Poyang Lake.Due to difficulty of getting spatial distribution data of the nutrient background contents in the whole Poyang Lake watershed,thus the relationship between pollutant background contents and land use,soil and topography could not be reflected properly,which affected the precision and reliability of pollution load assessment in the watershed scale to some degree.Therefore,it is crucial for non-point source pollution assessment in the Poyang Lake watershed in the future through strengthening the fundamental research related to soil erosion and reducing evaluation model cumulative deviation.