三峡库区黑沟流域AnnAGNPS参数空间聚合效应

农业非点源污染物是长江三峡库区主要污染源之一,已造成令人关注的生态、环境和健康等问题,流域模型(AnnAGNPS)与GIS结合,为空间数据组织和模型参数空间聚合提供技术基础,是其预测和流域规划与管理的有效途径.三峡库区小流域条件下,基于临界源面积(CSA)和最小初始沟道长度(MSCL)值域设定,形成不同流域划分方案,空间离散单元(SDU)水平,即SDU大小及数量,影响输入参数空间聚合效应及模型输出结果.在黑沟小流域(144.4 hm~2)应用已校准AnnAGNPS模型,设定CSA和 MSCL值域为0.5～15hm~2及7.5～200m,10种SDU水平、流域尺度和条件下,结果表明:空间参数聚合程度和模型输出结果均随SDU尺度改变而发生变化.土地利用与土壤类型等参数具有明显的聚合效应,径流、泥沙和养分输出具有不同的SDU适宜水平和范围.SDU尺度聚合效应对径流量影响较小,而对泥沙、总N、总P模拟影响较大;径流量、泥沙、总N、总P模拟输出误差可接受SDU尺度范围分别为0.5～18、2～6、0.5～6 hm~2.因此,应用AnnAGNPS模型,更需要注意不同子模型所需要适宜的SDU尺度水平.

Impact of parameter spatial aggregation on AnnAGNPS predictions for Heigou watershed in Three Gorges Reservoir Area conditions

Agriculture has been identified as a leading source of Nonpoint Source (NPS) pollution as a result of its intensive fertilizer applications and crop management practices, and has led to ecological and human health concerns in the Three Gorges Reservoir Area (TGRA), China. Watershed models, Annualized Agricultural Nonpoint Source (AnnAGNPS), are considered a cost-effective and time-efficient approach for the assessment of pollutant loads and management practices, are coupled with GIS to facilitate watershed planning and management, which offer an unprecedented flexibility in the organization of spatial data, the spatial extent of input parameter aggregation has previously been shown to have a substantial impact on model output. However, the aggregation of spatial information in the GIS, the effects of data aggregation on model input parameters, such as land use and cover and soil type, are not well studied in watershed of TGRA condition. For the study watershed, values of critical source area (CSA) and minimum source channel length (MSCL) was set from 0.5 to 15 hm2 and the MSCL from 7.5 to 200 m. The study focused on the impact of the size or the number of cells and spatial discretization unit (SDU) used to partition a watershed (10 watershed delineations) on the input parameter spatial aggregation and output of model, the results show that across levels of watershed partitioning, there are different extent of parameter spatial aggregation of land use and soil types. The results indicate that input parameter spatial aggregation had little effect on runoff yields predictions, and more effect on sediment, total nitrogen and phosphorous loads; within the range of acceptable accuracy, in this paper, the applicability and predictive capacity of AnnAGNPS model in TGRA conditions is examined. The optimal threshold values of SDU, relative to the scale of Heigou watershed and TGRA conditions, required to adequately predicting runoff, sediment yields and nitrogen, phosphorous were found to be around 0.5-18, 2-6, 0.5-6 hm2, respectively. The model can be applied for estimation of runoff and sediment losses with acceptable accuracy in TGRA, the CSA and MSCL values determine the hydrographic segmentation of the watershed and the cell size, especially in heterogeneous areas of the watershed, which can increase the accuracy of model results, which indicates that greater attention should be made to partition a watershed to match the underlying assumptions of sub-models within AnnAGNPS.