CN103106347B - A kind of agricultural area source phosphorus based on soil attribute space distribution pollutes evaluation method - Google Patents
A kind of agricultural area source phosphorus based on soil attribute space distribution pollutes evaluation method Download PDFInfo
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- 229910052698 phosphorus Inorganic materials 0.000 title claims abstract description 43
- 239000011574 phosphorus Substances 0.000 title claims abstract description 43
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Abstract
一种基于土壤属性空间分布的农业面源磷污染估算方法，该方法有三大步骤：步骤一：土壤属性空间分布与面源污染负荷响应关系的建立，包含典型小研究区域选择、土壤属性空间分布、典型区面源污染SWAT模型模拟和土壤属性与面源污染负荷空间分布响应关系建立；步骤二：待估算区域土壤样品采集与检测；步骤三：农业面源污染负荷估算。根据本发明，在建立土壤属性空间分布与模型模拟的面源污染负荷响应关系的基础上，只需获取区域土壤属性空间分布情况，即可快速有效地预测估算该研究区农业面源污染的负荷。它在面源污染管理技术领域里具有较好的应用前景。
A method for estimating agricultural non-point source phosphorus pollution based on the spatial distribution of soil attributes. This method has three steps: Step 1: The establishment of the relationship between the spatial distribution of soil attributes and the response to non-point source pollution load, including the selection of typical small research areas and the spatial distribution of soil attributes. 1. Simulation of non-point source pollution SWAT model in typical areas and establishment of response relationship between soil properties and spatial distribution of non-point source pollution load; Step 2: collection and detection of soil samples in the area to be estimated; Step 3: Estimation of agricultural non-point source pollution load. According to the present invention, on the basis of establishing the response relationship between the spatial distribution of soil attributes and the non-point source pollution load simulated by the model, it is only necessary to obtain the spatial distribution of soil attributes in the region to quickly and effectively predict and estimate the load of agricultural non-point source pollution in the research area . It has a good application prospect in the technical field of non-point source pollution management.
Description
技术领域technical field
本发明属于面源污染管理技术领域，涉及一种简单快捷的农业面源污染的估算方法，尤其涉及一种基于土壤属性空间分布的农业面源磷污染估算方法。The invention belongs to the technical field of non-point source pollution management, and relates to a simple and quick method for estimating agricultural non-point source pollution, in particular to a method for estimating agricultural non-point source phosphorus pollution based on the spatial distribution of soil attributes.
背景技术Background technique
农业面源污染是指在农田耕作等农业生产活动中，化肥、农药、土壤流失与农业废弃物等，在降水（灌溉）过程中，随着地表径流和地下渗漏等水文过程，携带污染物质流入水体，进而发生污染。我们水资源的时空差异和高强度的农业生产加剧了农业面源污染的程度，在工业点源和生活污染源得到有效的控制下面源的贡献率达到40%-65%，迫切需要对农业面源污染进行有效的估算和管理。Agricultural non-point source pollution refers to the chemical fertilizers, pesticides, soil loss and agricultural wastes in agricultural production activities such as farmland farming. In the process of precipitation (irrigation), along with hydrological processes such as surface runoff and underground seepage, pollutants are carried into water bodies, causing pollution. The temporal and spatial differences of our water resources and high-intensity agricultural production have exacerbated the degree of agricultural non-point source pollution. Under the effective control of industrial point sources and domestic pollution sources, the contribution rate of source reaches 40%-65%. There is an urgent need for agricultural non-point source pollution. Effective pollution estimation and management.
现阶段的面源污染研究中，野外实地监测和模型模拟是最为主要的管理评估手段。随着人们对面源污染认识的逐步深入，发现面源具有随机性、广泛性、滞后性、不确定性等特点。进行野外实地监测往往需要长时间尺度和大范围空间尺度的密集监测，劳动强度增大、效率降低、周期延长、成本耗费大，使得基础数据的获得较为困难。因此，野外实地监测在面源污染研究中多数情况下仅是作为一种辅助手段，主要用于各类面源模型的验证和模型参数的校正。模型模拟在进行面源污染的量化研究以及影响评价和污染治理中，是比较直接和有效的研究方法。模型的优势在于可以在不需要野外实地面源污染监测数据的情况下，借助气候、地形、土地利用、农田管理等现成数据，对面源污染进行时间和空间序列上的模拟，可以直观估算面源污染的负荷量及空间分布。模型模拟相较于野外实地监测，有工作量小、基础数据易获取、结果直观可信等优势，但模型模拟也存在自身的缺陷：首先，每一次的模型模拟仍然需要详细的气象、土地利用、水文数据做支撑，在无资料地区的面源污染模拟中，数据是制约模型模拟的瓶颈；2.模型是对现实的近似模拟，由于模型本身的设计和机理缺陷以及各种不确定性因素对模型模拟精度的影响，模型模拟的并不能做到对现实情况百分百的模拟。In the current research on non-point source pollution, field monitoring and model simulation are the most important means of management assessment. With the gradual deepening of people's understanding of non-point source pollution, it is found that non-point source has the characteristics of randomness, universality, hysteresis, and uncertainty. Field monitoring often requires intensive monitoring on a long-term scale and a wide range of spatial scales, which increases labor intensity, reduces efficiency, prolongs the cycle, and costs a lot, making it difficult to obtain basic data. Therefore, in most cases, field monitoring is only used as an auxiliary means in the research of non-point source pollution, and it is mainly used for the verification of various non-point source models and the correction of model parameters. Model simulation is a relatively direct and effective research method in the quantitative research of non-point source pollution, impact assessment and pollution control. The advantage of the model is that it can simulate non-point source pollution in time and space sequences with the help of ready-made data such as climate, topography, land use, and farmland management without the need for field surface source pollution monitoring data, and can intuitively estimate non-point source pollution. Pollution load and spatial distribution. Compared with field monitoring, model simulation has the advantages of small workload, easy access to basic data, and intuitive and credible results. However, model simulation also has its own defects: First, each model simulation still requires detailed meteorological and land use information. , hydrological data as support, in the simulation of non-point source pollution in areas without data, the data is the bottleneck restricting the simulation of the model; 2. The model is an approximate simulation of reality, due to the design and mechanism defects of the model itself and various uncertain factors The impact on the simulation accuracy of the model, the simulation of the model cannot achieve a 100% simulation of the real situation.
鉴于这两种面源污染评价方法有其各自的局限性，因此，为了更有效、快速地估算农业面源污染负荷，有必要在野外实地监测和模型模拟之外，建立一种行之有效的便捷的估算方法：在数据详实的典型区域模拟基础上，建立模型磷污染模拟结果与土壤属性空间分布的响应关系，在相似区域通过现有或者实地监测的土壤属性空间分布情况反推农业面源磷污染的负荷。In view of the limitations of these two evaluation methods of non-point source pollution, in order to estimate the agricultural non-point source pollution load more effectively and quickly, it is necessary to establish an effective method in addition to field monitoring and model simulation. Convenient estimation method: On the basis of typical regional simulations with detailed data, establish the response relationship between the simulation results of phosphorus pollution and the spatial distribution of soil attributes, and inversely infer agricultural non-point sources from the existing or field-monitored spatial distribution of soil attributes in similar areas Phosphorus pollution load.
发明内容Contents of the invention
1、目的：本发明的目的是提供一种基于土壤属性空间分布的农业面源磷污染估算方法，根据本发明，在建立土壤属性空间分布与模型模拟的面源污染负荷响应关系的基础上，只需获取区域土壤属性空间分布情况，即可快速有效地预测估算该研究区农业面源污染的负荷。1. Purpose: the purpose of the present invention is to provide a method for estimating agricultural non-point source phosphorus pollution based on the spatial distribution of soil attributes. According to the present invention, on the basis of establishing the response relationship between the spatial distribution of soil attributes and the non-point source pollution load of model simulation, Only by obtaining the spatial distribution of regional soil attributes, the load of agricultural non-point source pollution in the study area can be quickly and effectively predicted.
2、技术方案：本发明可通过下述技术方案实现：2. Technical solution: the present invention can be realized through the following technical solutions:
本发明一种基于土壤属性空间分布的农业面源磷污染估算方法，该方法具体步骤如下：The present invention is a method for estimating agricultural non-point source phosphorus pollution based on the spatial distribution of soil attributes. The specific steps of the method are as follows:
步骤一：土壤属性空间分布与面源污染负荷响应关系的建立Step 1: Establishment of the relationship between the spatial distribution of soil properties and the response to non-point source pollution load
（1）典型小研究区域选择(1) Selection of typical small research areas
典型小研究区域的选择是本发明实施方案的第一步，也是关键的一步。典型的小研究区应具备以下几项基本特征：①区域较为典型，具备所研究大区域的地形、气候、水文等基本特征，涵盖大区域所有的土壤种类、土地利用种类；②区域气象、地形地貌、水文数据完备，模型模拟精度高；③现有土壤属性数据齐全或交通便利土壤样品容易获取。同时具有以上三种特征的小区域方能被选为建立土壤属性与面源污染负荷空间分布响应的典型研究区。The selection of a typical small research area is the first step in the embodiment of the present invention, and it is also a key step. A typical small research area should have the following basic characteristics: ①The area is relatively typical, with basic characteristics such as topography, climate, and hydrology of the large area studied, covering all soil types and land use types in the large area; ②Regional meteorology, topography The landform and hydrological data are complete, and the model simulation accuracy is high; ③The existing soil attribute data is complete or the transportation is convenient, and the soil samples are easy to obtain. A small area with the above three characteristics at the same time can be selected as a typical research area to establish the response of soil properties and spatial distribution of non-point source pollution load.
（2）土壤属性空间分布(2) Spatial distribution of soil properties
小研究区选定之后，需要对研究区内土壤属性的空间分布做详细的分析。数据的收集工作是这一步骤中的核心步骤。一般情况下，在当地的农业部门保存有大量的土壤基础属性数据，这些数据足够做土壤属性的空间分布分析。如果现有资料不全，需要实地对研究区内的土壤进行取样分析。土样的采集方法采用网格布点，在网格内选定干扰较小的典型田块，记录其经纬度、周围地貌、去年种植作物种类和坡度等，并采用S路线进行耕作层土壤的采集。对土壤的基本理化性质及土壤有机质、氮磷等营养元素及重金属元素等土壤属性相关指标进行实验测定。土壤数据测得之后，采用GIS中空间插值方法对土壤属性进行空间插值，获取空间分布信息。空间插值是根据已知的空间数据估计未知空间数据值的数学方法，已经应用于土壤养分空间分异研究的插值方法主要是Kriging插值和BP神经网络法。本研究推荐选用Kriging方法对区域土壤的有机质及氮、磷等属性进行空间插值，得到具有空间连续数据的土壤属性数据层。After the small study area is selected, a detailed analysis of the spatial distribution of soil properties in the study area is required. Data collection is the core step in this step. In general, there are a large amount of basic soil attribute data stored in the local agricultural sector, which is sufficient for spatial distribution analysis of soil attributes. If the existing data are incomplete, it is necessary to conduct on-site sampling and analysis of the soil in the study area. The soil sample collection method adopts grid layout, selects typical fields with less interference in the grid, and records its latitude and longitude, surrounding landforms, crop types and slopes planted last year, etc., and uses the S route to collect soil in the plow layer. The basic physical and chemical properties of the soil and related indicators of soil properties such as soil organic matter, nutrients such as nitrogen and phosphorus, and heavy metal elements were experimentally determined. After the soil data is measured, the spatial interpolation method in GIS is used to interpolate the soil attributes to obtain the spatial distribution information. Spatial interpolation is a mathematical method for estimating unknown spatial data values based on known spatial data. The interpolation methods that have been applied to the study of soil nutrient spatial differentiation are mainly Kriging interpolation and BP neural network method. This study recommends the use of the Kriging method for spatial interpolation of the organic matter, nitrogen, and phosphorus attributes of the regional soil to obtain a soil attribute data layer with spatially continuous data.
（3）典型区面源污染SWAT模型模拟(3) SWAT model simulation of non-point source pollution in typical areas
通过对有关当地部门和农户的调查，补充整理有关农业生产资料，包括区域农业生产状况、灌排方式、施肥方式以及社会经济条件等；收集研究区农业气象方面的资料，为数据分析提供背景气象资料；运用环境遥感技术，解译LandsatTM数据获得研究区土地利用图，分析区域内各种土地利用的空间分布特征，建立模型所需的土地利用数据库；以中科院南京土壤所提供的1:100万土壤类型分布图为基础，结合现场土壤样品试验，建立模型所需土壤数据库。Through the investigation of relevant local departments and farmers, supplement and collate relevant agricultural production data, including regional agricultural production status, irrigation and drainage methods, fertilization methods, and socio-economic conditions; collect agricultural meteorological data in the study area to provide background meteorology for data analysis Data; using environmental remote sensing technology, interpreting LandsatTM data to obtain the land use map of the study area, analyzing the spatial distribution characteristics of various land uses in the area, and establishing the land use database required for the model; using the 1:1 million data provided by the Nanjing Soil Institute of the Chinese Academy of Sciences Based on the soil type distribution map, combined with field soil sample tests, the soil database required for the model was established.
在模型数据库建立之后，运用分布式水文模型SWAT作为面源模拟工具，将土壤属性、土地利用和气象等数据输入模型系统，进行适当的参数率定和调整之后，对研究区的面源磷污染负荷进行时空分布模拟。After the model database is established, the distributed hydrological model SWAT is used as a non-point source simulation tool, and the data of soil properties, land use and meteorology are input into the model system. After proper parameter calibration and adjustment, the non-point source phosphorus pollution in the study area The load is simulated in time and space distribution.
（4）土壤属性与面源污染负荷空间分布响应关系建立(4) Establishment of response relationship between soil properties and spatial distribution of non-point source pollution load
由于农业面源污染的主要来源是水田和旱田，将这两种土地利用类型占主要优势的子流域的各土壤属性与模型模拟的面源污染结果相对应，利用主成分分析的方法找出可以能为估算面源磷污染提供最多信息量的几种属性，以此为依据建立土壤属性空间分布与面源污染之间的响应关系。Since the main sources of agricultural non-point source pollution are paddy fields and dry fields, the soil properties of the sub-watersheds where these two land use types dominate are compared with the results of non-point source pollution simulated by the model, and the method of principal component analysis is used to find out the Several attributes that can provide the most information for estimating non-point source phosphorus pollution are used as a basis to establish the response relationship between the spatial distribution of soil attributes and non-point source pollution.
步骤二：待估算区域土壤样品采集与检测Step 2: Collect and test soil samples in the area to be estimated
同步骤一中的（2）相似，通过历史数据收集和现场实验，得出步骤一（4）中筛选出的土壤属性的空间分布情况。Similar to (2) in step 1, the spatial distribution of the soil properties screened in step 1 (4) is obtained through historical data collection and field experiments.
步骤三：农业面源污染负荷估算Step 3: Estimation of agricultural non-point source pollution load
根据筛选出的土壤属性的空间分布与响应关系估算出研究区域的面源磷污染负荷。步骤一的（4）中筛选出了提供面源污染信息量最多的几种土壤属性，通过步骤二中测得的土壤属性分布规律，可以估算出该区域面源污染的状况。According to the spatial distribution and response relationship of the selected soil properties, the non-point source phosphorus pollution load in the study area was estimated. In (4) of step 1, several soil properties that provide the most information on non-point source pollution are screened out, and the distribution of soil properties measured in step 2 can be used to estimate the status of non-point source pollution in this area.
3、优点及功效：本发明一种基于土壤属性空间分布的农业面源磷污染估算方法，其优点是：其一，这种方法只需建立响应关系，即可将之推广到相似区域的面源污染估算中，简单方便；其二，这种方法只需现场采集并检测土壤属性即可快速估算面源污染的负荷；其三，这种估算方法不完全基于简单的数字模拟，有更深层的机理分析，结果较为准确。3. Advantages and effects: a method for estimating agricultural non-point source phosphorus pollution based on the spatial distribution of soil attributes of the present invention has the following advantages: First, this method only needs to establish a response relationship, and it can be extended to areas in similar areas. In the estimation of source pollution, it is simple and convenient; second, this method can quickly estimate the load of non-point source pollution only by collecting and testing soil properties on site; third, this estimation method is not entirely based on simple digital simulation, and has deeper Mechanism analysis, the results are more accurate.
附图说明Description of drawings
图1为基于土壤属性空间分布的农业面源污染估算方法的流程框图Figure 1 is a flow chart of the estimation method of agricultural non-point source pollution based on the spatial distribution of soil attributes
图2为水田子流域面源磷污染负荷与土壤属性关系示意图Figure 2 is a schematic diagram of the relationship between the non-point source phosphorus pollution load and soil properties in the paddy field sub-watershed
图3为旱田子流域面源磷污染负荷与土壤属性关系示意图Figure 3 is a schematic diagram of the relationship between non-point source phosphorus pollution load and soil properties in the upland sub-watershed
图4为面源磷污染负荷与土壤0-20cm总磷相关性示意图Figure 4 is a schematic diagram of the correlation between non-point source phosphorus pollution load and soil 0-20cm total phosphorus
图5为面源磷污染负荷与土壤0-20cm锌相关性示意图Figure 5 is a schematic diagram of the correlation between non-point source phosphorus pollution load and soil 0-20cm zinc
图6为面源磷污染负荷与土壤20-40cm铬相关性示意图Figure 6 is a schematic diagram of the correlation between non-point source phosphorus pollution load and soil 20-40cm chromium
图7为面源磷污染负荷与土壤20-40cm铜相关性示意图Figure 7 is a schematic diagram of the correlation between non-point source phosphorus pollution load and soil 20-40cm copper
具体实施方式detailed description
本发明提出的面源污染估算方法，是一种从当前土壤属性出发，通过结合土壤属性与模型模拟的污染负荷结果的响应关系，快速估算面源污染负荷的方法。The non-point source pollution estimation method proposed by the present invention is a method for rapidly estimating the non-point source pollution load starting from the current soil properties and combining the response relationship between the soil properties and the pollution load results simulated by the model.
见图1，本发明一种基于土壤属性空间分布的农业面源磷污染估算方法，该方法具体步骤如下：See Fig. 1, a kind of method for estimating agricultural non-point source phosphorus pollution based on the spatial distribution of soil attributes of the present invention, the specific steps of the method are as follows:
步骤一：step one:
本案例选择东北地区典型的商品粮生产基地八五九农场内阿布胶河小流域作为实例分析。在本实例中土壤属性的获取主要来自农场土壤的历史资料整理和现场土壤样品采集。现场样品采集以农场范围内1.5km的网格布点，一共采集了30个点各两层（0-20cm,20-40cm）的土壤样品，分析检测有效磷（Availablephosphorus,AP）、总磷（Totalphosphorus,TP）、总氮（Totalnitrogen,TN）、总钾(TotalK,TK)等八个物理属性。通过结合农场历史土壤数据，对土壤属性进行空间插值，得到土壤属性的空间分布特征。This case selects the small watershed of the Abujiao River in the Bawujiu Farm, a typical commercial grain production base in Northeast China, as a case study. In this example, the acquisition of soil properties mainly comes from the collation of historical data of farm soil and the collection of on-site soil samples. On-site sample collection is based on a grid of 1.5km within the farm. A total of 30 points of soil samples in two layers (0-20cm, 20-40cm) were collected for analysis and detection of available phosphorus (Available phosphorus, AP) and total phosphorus (Total phosphorous ,TP), total nitrogen (Totalnitrogen, TN), total potassium (TotalK, TK) and other eight physical properties. By combining the historical soil data of the farm, the spatial interpolation of the soil attributes is carried out to obtain the spatial distribution characteristics of the soil attributes.
分别通过遥感解译，资料收集及农户调查，建立SWAT模型模拟所需的土壤、土地利用、气象、农田管理数据库，在参数率定与验证的基础上运用模型对该区域的面源磷污染负荷进行时空分布模拟，分别输出矿物质磷（SedimentP）、有机磷(OrganicP)、总磷(TP)的模拟结果，得到此三种形态磷污染负荷的空间分布情况。Through remote sensing interpretation, data collection and farmer investigation, establish the soil, land use, meteorology, and farmland management databases required for SWAT model simulation, and use the model on the basis of parameter calibration and verification to determine the non-point source phosphorus pollution load in the area The spatio-temporal distribution simulation is carried out, and the simulation results of mineral phosphorus (SedimentP), organic phosphorus (OrganicP) and total phosphorus (TP) are respectively output to obtain the spatial distribution of the three forms of phosphorus pollution load.
鉴于分两层的8种土壤属性的统计过程较繁复，需要从中筛选出最能影响面源磷污染的因子；又加之面源污染的主要来源是水田和旱田。最终以模型划分的子流域作为研究的最小单元，将这两种土地利用类型占主要优势的各子流域的模型模拟结果与土壤属性空间分布数据进行主成分性分析，以此为基础，挖掘出影响磷污染的主要土壤属性。分析结果表明：前两个主成分在水田和旱田的表层总共贡献了86.3%、87.2%的信息量，在次表层（20-40cm）分别贡献了64.5%和73.4%的信息量（见图2、图3）。在水田部分，表层的AP、TP、SOC、Zn属性能用以估算面源磷污染，在次表层Cu、Cr、SOC可作为可靠的估算因子；在旱田部分，TP、Zn可以作为表层的估算因子，Cu、Cr、AP是次表层的主要贡献因子。总体来看，表层的TP和Zn，次表层的Cu和Cr可以作为快速估算该区域面源污染的土壤属性。In view of the complicated statistical process of the eight soil properties divided into two layers, it is necessary to screen out the factors that can most affect non-point source phosphorus pollution; in addition, the main sources of non-point source pollution are paddy fields and dry fields. Finally, the sub-basin divided by the model is taken as the smallest unit of research, and the model simulation results and the spatial distribution data of soil attributes of each sub-basin in which the two land use types dominate are subjected to principal component analysis. Key soil properties affecting phosphorus pollution. The analysis results show that the first two principal components contribute 86.3% and 87.2% of the information in the surface layer of paddy field and dry field, and contribute 64.5% and 73.4% of the information in the subsurface (20-40cm) respectively (see Figure 2 ,image 3). In paddy fields, surface AP, TP, SOC, and Zn attributes can be used to estimate non-point source phosphorus pollution, and in subsurface Cu, Cr, and SOC can be used as reliable estimation factors; in dry fields, TP, Zn can be used as surface estimates Factors, Cu, Cr, AP are the main contributing factors of the subsurface. Overall, TP and Zn in the surface layer, and Cu and Cr in the subsurface layer can be used as soil properties for quickly estimating non-point source pollution in this area.
通过对筛选出来的4种属性与面源磷污染两种形态的相关关系可以验证主成分分析的结果，这4种属性均与面源磷污染有着较高的相关性（见图4-图7），也就是说在该区域及相似区域只需要获取表层的TP和Zn，次表层的Cu和Cr的数据就能大致地估算出该区域的面源磷污染负荷。The results of principal component analysis can be verified through the correlation between the four selected attributes and the two forms of non-point source phosphorus pollution. These four attributes have a high correlation with non-point source phosphorus pollution (see Figure 4-Figure 7 ), that is to say, in this area and similar areas, it is only necessary to obtain the data of TP and Zn in the surface layer, and the data of Cu and Cr in the subsurface layer can roughly estimate the non-point source phosphorus pollution load in this area.
步骤二：待估算区域土壤样品采集与检测Step 2: Collect and test soil samples in the area to be estimated
同步骤一中的（2）相似，通过历史数据收集和现场实验，得出步骤一（4）中筛选出的土壤属性的空间分布情况。Similar to (2) in step 1, the spatial distribution of the soil properties screened in step 1 (4) is obtained through historical data collection and field experiments.
步骤三：农业面源污染负荷估算Step 3: Estimation of agricultural non-point source pollution load
根据筛选出的土壤属性的空间分布与响应关系估算出研究区域的面源磷污染负荷。步骤一的（4）中筛选出了提供面源污染信息量最多的几种土壤属性，通过步骤二中测得的土壤属性分布规律，可以估算出该区域面源污染的状况。这里，只需得到如步骤一中获得的某研究区的表层的TP和Zn，次表层的Cu和Cr的分布数据，即可获取该区域面源污染磷负荷的空间分布情况。According to the spatial distribution and response relationship of the selected soil properties, the non-point source phosphorus pollution load in the study area was estimated. In (4) of step 1, several soil properties that provide the most information on non-point source pollution are screened out, and the distribution of soil properties measured in step 2 can be used to estimate the status of non-point source pollution in this area. Here, only the distribution data of TP and Zn in the surface layer and Cu and Cr in the subsurface layer of a certain research area obtained in step 1 can be obtained to obtain the spatial distribution of phosphorus load of non-point source pollution in this area.
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