流域非点源氮素流失空间分异特征的多时间尺度分析

流域非点源污染关键源区的定位是控制和治理流域非点源污染的重要问题，为进一步揭示时间尺度对流域非点源氮素流失空间分异特征的影响，通过构建山美水库流域SWAT模型，在对各个子流域的总氮(TN)流失强度进行模拟的基础上，从多年平均、多年月平均、场次暴雨洪水过程等3种时间尺度，对氮素流失的空间分异特征和关键源区进行分析，并通过多元线性相关分析，对自然和人为因素的影响进行研究.结果表明： 不同时间尺度下流域氮素流失空间差异均十分显著，氮素流失空间分异程度以多年月平均最高，场次暴雨洪水过程最低；桃溪亚流域氮素流失量最大，是非点源氮污染关键源区.不同时间尺度下，土地利用类型均是影响流域非点源氮素流失空间分布的主要因素，而降雨、径流等自然因子对氮素流失空间分异的影响仅表现在不施肥月份和部分不施肥场次的暴雨洪水过程，这种规律与土地利用及施肥具有显著空间变化、而降雨径流的空间变异程度低有关.

Multiple time scales analysis of spatial differentiation characteristics of non-point source nitrogen loss within watershed.

Identification of the critical source areas of non-point source pollution is an important means to control the non-point source pollution within the watershed. In order to further reveal the impact of multiple time scales on the spatial differentiation characteristics of non-point source nitrogen loss, a SWAT model of Shanmei Reservoir watershed was developed. Based on the simulation of total nitrogen (TN) loss intensity of all 38 subbasins, spatial distribution characteristics of nitrogen loss and critical source areas were analyzed at three time scales of yearly average, monthly average and rainstorms flood process, respectively. Furthermore, multiple linear correlation analysis was conducted to analyze the contribution of natural environment and anthropogenic disturbance on nitrogen loss. The results showed that there were significant spatial differences of TN loss in Shanmei Reservoir watershed at different time scales, and the spatial differentiation degree of nitrogen loss was in the order of monthly average > yearly average > rainstorms flood process. TN loss load mainly came from upland Taoxi subbasin, which was identified as the critical source area. At different time scales, land use types (such as farmland and forest) were always the dominant factor affecting the spatial distribution of nitrogen loss, while the effect of precipitation and runoff on the nitrogen loss was only taken in no fertilization month and several processes of storm flood at no fertilization date. This was mainly due to the significant spatial variation of land use and fertilization, as well as the low spatial variability of precipitation and runoff.