李琪云, 李继峰, 李泽利, 沈彦军, 刘夏. 基于流域模型与遥感解译方法的农业面源污染精细化源解析技术研究[J]. 中国生态农业学报 (中英文), 2024, 32(0): 1−10. DOI: 10.12357/cjea.20240178
引用本文: 李琪云, 李继峰, 李泽利, 沈彦军, 刘夏. 基于流域模型与遥感解译方法的农业面源污染精细化源解析技术研究[J]. 中国生态农业学报 (中英文), 2024, 32(0): 1−10. DOI: 10.12357/cjea.20240178
LI Q Y, LI J F, LI Z L, SHEN Y J, LIU X. The research of agricultural non-point sources apportionment based on hydrology model and remote sensing technology[J]. Chinese Journal of Eco-Agriculture, 2024, 32(0): 1−10. DOI: 10.12357/cjea.20240178
Citation: LI Q Y, LI J F, LI Z L, SHEN Y J, LIU X. The research of agricultural non-point sources apportionment based on hydrology model and remote sensing technology[J]. Chinese Journal of Eco-Agriculture, 2024, 32(0): 1−10. DOI: 10.12357/cjea.20240178

基于流域模型与遥感解译方法的农业面源污染精细化源解析技术研究

The research of agricultural non-point sources apportionment based on hydrology model and remote sensing technology

  • 摘要: 农业面源污染源解析是面源污染防治的基础。农作物种植是农业面源污染源的重要组成, 然而传统的面源污染源解析方法不能量化不同农作物对农业面源污染负荷的贡献, 其解析精度已难以满足环境管理部门精细化管理的客观需求。本研究以天津于桥水库上游沙河流域为研究区, 采用流域模型与遥感解译的方法, 解析流域总磷负荷的来源与贡献, 旨在建立一种农业面源污染源精细化源解析技术。研究结果显示: 基于 Google Earth Engine (GEE) 平台的农作物遥感总体分类精度在88%以上, kappa系数均大于0.81, 整体分类结果可信。沙河流域主要作物类型包括冬小麦—夏玉米、板栗、果树与其它作物, 其中冬小麦-夏玉米的种植面积最大, 占比介于44%—67%之间, 板栗种植面积次之, 占比介于11%—29%。冬小麦—夏玉米种植面积总体呈现下降趋势, 板栗种植面积则呈现快速上升趋势。沙河流域Generalized Watershed Loading Function (GWLF)模型河道径流量与总磷负荷的模拟表现良好, 其模型校准期与验证期NSE在0.59以上, R2在0.79以上。农业种植为沙河流域最大的面源总磷负荷来源, 占总磷负荷总量的61%, 在农业种植源中, 小麦-玉米占比最大(52%), 板栗次之(20%), 然而考虑到板栗这一经济作物种植面积近年来不断上升, 未来沙河流域面源总磷负荷仍有升高的风险。

     

    Abstract: Non-points sources pollution is caused by rainfall or snowmelt moving over and through the ground. As the runoff moves, it picks up and carries away natural and human-made pollutants, finally depositing them into lakes, rivers, wetlands, coastal waters and ground waters. The agricultural non-point sources apportionment is the premise of non-point sources pollution prevention. Crop planting is an important part of agricultural non-point source. However, traditional non-point sources apportionment methods can’t quantify nutrient load originating from different kind of crops. The apportionment accuracy of traditional methods can’t meet the demand of more precise environment management. This study selected Shahe River Basin as the study area and used remote sensing and hydrology model to apportion the total phosphorus (TP) load aiming at establish a high-precision agricultural non-point sources apportionment method. The results indicate that classification accuracy of the Google Earth Engine (GEE) is above the 88%, the kappa coefficients are above 0.81, and classification results of different crops are credible. The main crops of Shahe River Basin include wheat-maize, chestnut, fruit, and other crops. The planting area of wheat-maize is the biggest and accounts for the 44%−67% cropland area. The planting area of fruit is the secondary and accounts for the 11%−29% cropland area. The area of wheat-maize shows a decreasing trend and the area of fruit shows an increasing trend during 2006—2012. The increased planting area of chestnut and fruit are manly from wheat-maize cropland. The farmlands of wheat-maize are mainly distributed in the south of the Shahe River Basin and the farmland of the chestnut are mainly distributed in the north of the Shahe River Basin. The Generalized Watershed Loading Function (GWLF) can simulate the streamflow and total phosphorus load of Shahe River Basin and the Nash-Sutcliffe efficiency and Correlation coefficient of simulated VS observed streamflow in calibration period is above 0.59 and 0.79 and the Nash-Sutcliffe efficiency and Correlation coefficient of simulated VS observed streamflow in validation period is above 0.92 and 0.96. The Nash-Sutcliffe efficiency and Correlation coefficient of simulated VS observed total phosphorus in calibration period is above 0.61 and 0.89 and the Nash-Sutcliffe efficiency and Correlation coefficient of simulated VS observed total phosphorus in validation period is above 0.81 and 0.94. Agricultural planting is the largest non-point source total phosphorus load in Shahe River Basin, accounting for 61% of the total phosphorus load. The resident is the secondary non-point source total phosphorus load in Shahe River Basin, accounting for 29% of the total phosphorus load. Among agricultural planting sources total phosphorus load, wheat-maize accounted for the largest proportion (52%) in total phosphorus load coming from agricultural planting activities and chestnut accounted for the second proportion (20%) in total phosphorus load coming from agricultural planting activities. Although the planting of chestnut is not the biggest the non-point source total phosphorus load, considering the increasing trend of chestnut area in recent years, there is still a risk in agricultural non-point sources pollution in Shahe River Basin. As a consequence, we also should pay enough attention for the planting of chestnut and other cash crops, which may pose a pollution risk into Yuqiao Reservoir.

     

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