三峡库区消落带农用坡地氮素流失特征及其环境效应

三峡库区消落带坡地的自发农用较为常见，消落带的这种利用方式可能加剧养分流失并对库区水环境造成影响。通过对库区2011~2013年3个落干期消落带农用坡地的地表径流、壤中流中氮素形态与浓度进行定位监测，研究消落带农用坡地氮素流失特征及其环境效应。结果表明：常规施肥下，消落带农用坡地侵蚀模数为1 443 kg/（hm2·a），落干期内坡地平均径流量为230 mm，径流系数为0.58，其中壤中流流量占总径流量的77%。历次降雨产流事件中常规施肥处理时，地表径流、壤中流中TN平均浓度分别是4.85±0.85、20.73±2.05 mg/L，落干期地表径流（泥沙）和壤中流的TN流失量分别为6.63±1.19、35.22±3.38 kg/hm2，分别占当季施肥量的2.2%、11.7%。可见，随壤中流流失是三峡库区消落带农用坡地氮素流失的主要途径。与常规施肥处理相比，减量施肥处理使地表径流（泥沙）、壤中流TN流失通量分别显著降低了25%、48%，表明减少氮肥用量可以显著降低消落带农用带来的环境风险，建议消落带农用地氮肥进行减量施肥，使其既不影响作物产量，也显著降低氮流失。 关键词： 三峡水库；消落带；地表径流；壤中流；氮负荷；减量施肥

SIMULATION OF DISTRIBUTION OF SOIL ORGANIC MATTER BASED ON AUXILIARY VARIABLES AND NEURAL NETWORK MODEL

Accurate spatial information about soil organic matter (SOM) is critical for farmland use and soil environmental protection.In order to find the best interpolation method of SOM at the provincial scale,here we proposed there methods,back propagation neural network combined with ordinary kriging (BPNN_ OK,based on geographic coordinates,environmental factors and neighbor information as auxiliary variables),radial basis function neural network with ordinary kriging (RBFNN_ OK,based on geographic coordinates,environmental factors and neighbor information as auxiliary variables) and ordinary kriging (OK),to predict the distribution of SOM.Environmental factors were extracted by digital terrain and remote sensing image analysis technique.The four-direction search method was applied to get the neighbor information.To establish and validate this method,16 109 soil samples were collected during the project of soil-test-based formulated fertilization in Jiangxi Province in 2012 and randomly divided into two groups,as modeling points (13 693) and validation points (2 416).The results show that three methods produced the similar SOM maps.The error analyses indicated:Based on auxiliary variables and neural network model has greatly improved than OK method.Compare to OK,the root mean square errors (RMSE),mean absolute errors (MAE) and mean relative errors (MRE) of BPNN_ OK were reduced 2.76 g/kg,2.34 g/kg,9.83％,RBFNN_ OK were reduced 2.70 g/kg,2.29 g/kg,9.61％.This result suggested that it is helpful for improving the prediction accuracy to employ artificial neural network model in spatial prediction of SOM,and this model provides guidance how to select the model to predict soil nutrient at provincial scale,but could be improved in the future.