基于灰水足迹的洞庭湖区粮食生产环境效应评价

灰水足迹从稀释水量的角度评价水污染的总体程度,直观反映了粮食生产对区域水环境的影响。为评估粮食生产对区域环境的负面影响,该文以洞庭湖粮食主产区35个县(市、区)为案例区,应用灰水足迹理论,分析了1994-2012年粮食生产灰水足迹的时空变化特征,以水环境压力(water environment pressure,WEP)和离散型灰色(discrete grey model)DGM(1,1)模型为支撑建立了多年平均径流量情景下粮食生产对水环境负面影响的评价方法,评价了2015年和2020年县域尺度粮食生产灰水足迹的环境效应。结果表明:1)1994-2012年洞庭湖区粮食生产部门灰水足迹为273.00~438.08亿m3,19a平均值为347.65亿m3。总体上,洞庭湖粮食生产部门灰水足迹经历了先下降后回升的过程;2)洞庭湖各县(市、区)粮食生产部门灰水足迹为1.19~23.61亿m3,粮食生产灰水足迹为1.06~5.58 m3/kg;3)未来几年洞庭湖区大多数县(市、区)粮食生产部门灰水足迹呈逐步增高的态势,2015年和2020年全区粮食生产部门灰水足迹总量分别达到431.16和498.54亿m3;4)在多年平均径流量情景下,洞庭湖区因粮食生产导致的水环境压力由1994-2012年均值0.60提高至2015年0.76和2020年0.87,该区域水环境受粮食生产的负面影响持续增强。该文评价结果可为制定农业可持续发展规划及农业产业政策提供参考。

Environmental effects evaluation for grain production based on grey water footprint in Dongting Lake area

The environmental effect of grain production never fails to attract high attention of researchers and policy makers, especially in the main grain production area in China. The grey water footprint (GWF), as an indicator of aquatic pollution, is defined as the volume of freshwater required to assimilate the load of pollutants based on existing ambient water quality standards. The theories and methods of the GWF provide a new sight to evaluate environmental effects of grain production on water pollution level. By using the statistics methods, this study presented the past and the future changes of GWFs related to total nitrogen (TN), total phosphorus (TP) and pesticide inputs of grain production at the regional scale. Evaluation method of environmental sustainability of grain production for the ambient water quality under annual average discharge scenario was proposed using regional water environment pressure (WEP) and the discrete grey model DGM(1, 1). To test the approach, The Dongting Lake area was chosen as the case study area. As the main grain production area in China, high inputs of nitrogen fertilizer, phosphate fertilizer and pesticide have been broadly adopted to increase grain yield in this area, and caused serious agricultural non-point source pollution which has been recognized as a major contributor to water quality degradation. In this area excessive nitrogen and phosphorus have become important risk factors. The GWFs of the grain production showed a large variation among different counties from 1994 to 2012. Regional annual average GWF of grain production industry was 1.19-23.61×108 m3 at the county level, and the minimum and maximum GWF occurred in Xiangtan City and Xiangtan County, respectively. The GWF of grain production was 1.06-5.58 m3/kg in the period from 1994 to 2012. The counties which had medium value of the GWF were mostly located around the Dongting Lake. The annual total grey water volume for grain production industry was 273.00-438.08×108 m3 from 1994 to 2012, and the average value was 347.65×108 m3. The GWF trends of grain production industry were declined first, and then increased during 1994-2012. For analyzing the future trends, 2015 and 2020 were selected as the predicted years. The results showed that the grain production industry's GWFs were 431.16×108 and 498.54×108 m3, respectively, in 2015 and 2020. We illustrated spatial trends for WEP in 2015 and 2020 under annual average discharge scenario. Compared with the period from 1994 to 2012, the value of WEP of grain production industry increased from 0.60 to 0.76 in 2015 (the ratio that evaluation units whose water quality deteriorated accounted for the total units increased to 37.14%) and 0.87 in 2020 (increased to 42.86%). Although the WEPs in some counties were over 1 during 1994-2012, the evaluation results suggested that the level of WEP will show an increase trend in the future, which meant the negative environmental effect of grain production would increase continuously. From the spatial aspect, the counties with high WEPs were distributed near the Dongting Lake, for TN or TP. The results indicated that many counties' water resources would be polluted by TN or TP in the future. In addition, the TP has become a major type of agricultural non-point source pollution in the Dongting Lake area. Overall, the GWF as a useful indicator for water environmental quality assessment has been proved, and the managers are facing great challenges of environmental sustainability of grain production. However, the GWF also needs to be further discussed. For example, the calculation model of the GWF should take the effects of water quality, water quantity and time into consideration to assess environmental effects more accurately. In conclusion, the method in the paper can be employed as a novel approach to evaluate environmental effects of grain production on water pollution level. This paper provides a reference for other regions to assess the environmental risk of grain production, and the results also provide good suggests for sustainable agricultural planning and agricultural policy making.