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(9): 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(9): 1−10. DOI: 10.12357/cjea.20240178
LI Q Y, LI J F, LI Z L, SHEN Y J, LIU X. Refined apportionment of agricultural non-point sources based on hydrology model and remote sensing technology[J]. Chinese Journal of Eco-Agriculture, 2024, 32(9): 1−10. DOI: 10.12357/cjea.20240178
Citation: LI Q Y, LI J F, LI Z L, SHEN Y J, LIU X. Refined apportionment of agricultural non-point sources based on hydrology model and remote sensing technology[J]. Chinese Journal of Eco-Agriculture, 2024, 32(9): 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

Refined apportionment of agricultural non-point sources 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, 其解析精度也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、果树与其他作物, 其中冬小麦-夏玉米的种植面积最大, 占比为44%~67%, 板栗种植面积次之, 占比为11%~29%。冬小麦-夏玉米种植面积总体呈下降趋势, 板栗种植面积则呈快速上升趋势。沙河流域Generalized Watershed Loading Function (GWLF)模型对溪流量与总磷负荷的模拟表现良好, 其模型校准期与验证期的NSE在0.59以上, R2在0.79以上。耕地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%; 在耕地中, 冬小麦-夏玉米总磷负荷占比最大(52%), 板栗次之(20%); 考虑到板栗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-point source pollution is caused by rainfall or snowmelt moving over and through the ground. As runoff moves, it picks up and carries away natural and human-made pollutants, finally depositing them in lakes, rivers, wetlands, coastal waters, and groundwater. Agricultural non-point source apportionment is the premise for preventing non-point source pollution. Crop planting is an important source of agricultural non-point sources. However, traditional non-point source apportionment methods cannot quantify the nutrient loads originating from different types of crops. The apportionment accuracy of the traditional methods does not satisfy the demand for more precise environmental management. This study selected the Shahe River Basin as the study area, and used remote sensing and hydrological models to apportion the total phosphorus (TP) load to establish a high-precision agricultural non-point source apportionment method. The results indicated that the classification accuracy based on the Google Earth Engine (GEE) was higher than 88%, the Kappa coefficients were higher than 0.81, and the classification results for different crops were credible. The major crops in the Shahe River Basin include winter wheat-summer maize, chestnuts, fruit, and other crops. The winter wheat-summer maize system has the largest planting area, accounting for 44%–67%, and the planting area of chestnut was the second largest, accounting for 11%–29%. Winter wheat-summer maize planting area is generally declining, chestnut planting area is rapidly rising trend. The Generalized Watershed Loading Function (GWLF) model of the Shahe River Basin performed well in the simulation of river runoff and total phosphorus loading, with the NSE of the model calibration and validation periods above 0.59 and the R2 above 0.79. Farmland was the largest non-point source of TP load in the Shahe River Basin, accounting for 61% of the TP load. Among the farmland winter wheat-summer maize system accounted for 52% of the TP load from agricultural sources, while chestnuts planting contributed to the second largest share (20%), but considering that the planting area of chestnut has been increasing in recent years, the TP load from surface sources in the Shahe River Basin is still at risk of increasing in the future.

     

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