Software development and application of key source areas identification of agricultural non-point source pollution based on multi-scenario analysis
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摘要:
为解决农业面源污染关键源区识别过程中情景单一、数据量大、涉及环节多，以及模型复杂、操作繁琐和治理效果不明确等问题，采用GIS技术结合InVEST的产水量与营养物传输率模型，构建可进行农业面污染关键源区识别与治理模拟的信息系统。设计了基于入河负荷、潜在径流浓度、负荷与产水量比值3种不同情景下的关键源区识别功能，模拟了3种情景下关键源区的分布状况；整合了InVEST的产水量、营养物传输率和流失负荷等模型；设计了面源污染关键源区治理模拟功能，可直观展示3种情景下识别出的关键源区的治理效果；并以海河流域为例，应用该软件对农田总氮（TN）面源关键源区进行了识别和模拟治理。结果表明：1）基于入河负荷情景下识别出的关键源区较为分散，分布在除永定河与子牙河水系外的大部分水系；基于潜在径流浓度情景下的关键源区分布较为集中，多分布在流域中部至南部，其余分布在东南部；基于负荷与产水量比值情景下识别出的关键源区分布高度集中，分布在流域中部至南部。2）基于潜在径流浓度、负荷与产水量比值情景在TN、总磷（TP）模拟治理中的效果接近，且明显优于基于入河负荷情景；结合软件中设定的基于入河负荷、潜在径流浓度、负荷与产水量比值3种情景进行分析，能降低复杂地理环境带来的影响，提高面源污染关键源区的识别效率，提升面源污染关键源区治理决策的科学性，具有实用性。
Abstract:There exists the problems of single scenario, large amount of data, multiple links involved, complex models, cumbersome operations and unclear governance effects in the process of identifying the key source areas of agricultural non-point source pollution (ANSP). To solve these problems, an information system that could identify and simulate the treatment of the key source areas of ANSP was constructed by using GIS technology combined with InVEST water yield and nutrient transfer rate model. The key source areas identification function was designed under three scenarios, based on the river inflow load, potential runoff concentration and load to water yield ratio, respectively, and the distribution of key source areas under the three scenarios was simulated. The water yield, nutrient transfer rate and loss load sub-models of InVEST were integrated. A simulation function for the treatment of key source areas of ANSP was designed, which could visually display the treatment effects of the key source areas identified under the three scenarios. Taking Haihe River Basin as an example, the software was used to identify and simulate the treatment of the key source areas of non-point source of total nitrogen (TN) in farmland. The results showed that: 1) The key source areas identified based on the river inflow load scenario were relatively scattered, and distributed in most of the water systems except Yongding River and Ziya River. Based on the potential runoff concentration scenario, the distribution of key source areas was relatively concentrated, mostly distributed in the areas from the middle to the south, and the rest were distributed in the southeast. The distribution of key source areas identified based on the load-to-yield ratio scenario of the river basin was highly concentrated, ranging from central to southern of the area. 2) The scenarios based on potential runoff concentration and the ratio of load to water yield had similar effects in the simulation of TN and total phosphorus (TP) governance, and were significantly better than the scenario based on the inflow load. Combined with the above three scenarios set in the software, it could reduce the impact of complex geographic environment, improve the identification efficiency of key source areas of ANSP, and enhance the scientific nature of governance decision-making for the areas. The software is practical.
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图 1 ANSP关键源区识别与治理模拟统一建模语言
注：（）为开发语言中函数的结尾；数字表示各类关系中对象的数量，如0..1表示允许0~1个对象；+表示含有子项的菜单，−为菜单展开的子项。
Figure 1. Unified modelling language for the identification and control simulation of ANSP key source areas
图 2 ANSP关键源区识别与治理模拟软件结构
Figure 2. Software structure diagram for identification and simulation of the treatment of ANSP key source areas
图 4 关键源区识别流程
注：T表示累加后子流域入河负荷；B表示子流域顺位序号；n表示子流域总个数；Per表示子流域累计入河负荷与流域总负荷之比；Th为设定的阈值。
Figure 4. Identification process for key source area
图 5 农业面源污染关键源区识别软件用户界面
Figure 5. User interface of the key source area identification software for ANSP
图 6 3种情景下的TN污染区域分布情况
Figure 6. Regional distribution of total nitrogen pollution under three scenarios
图 8 原始径流浓度与3种情景下治理效果对比
Figure 8. Comparison chart of original runoff concentration and that of three methods after treatment
表 1 3种情景下关键源区模拟治理效果
Table 1. Simulation of governance effects in key source areas under three scenarios
情景 河段TN负荷下降率/% 河段TP负荷下降率/% 基于入河负荷 19.06 26.91 基于潜在径流浓度 32.05 36.89 基于负荷与产水量比值 33.19 37.34 
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