CN106875032A - A kind of Optimal Configuration Method of Watershed Agricultural Non-point Sources Pollution control measure - Google Patents

Abstract

The invention discloses an optimal allocation method of agricultural non-point source pollution control measures in watersheds. From the perspective of improving the implementation efficiency of BMPs, this method identifies the distribution characteristics of agricultural non-point source pollution loads in the watershed, and provides a basis for the configuration of BMPs at different scales; builds a BMPs evaluation toolbox to provide standardized evaluation techniques for determining the efficiency of BMPs reduction; establishes Based on the NSGA‑II optimal allocation scheme simulation scenario of BMPs for agricultural non-point source pollution in the watershed, the optimal allocation scheme of BMPs for the control of agricultural non-point source pollution in the watershed is screened, and a BMPs combined spatial hierarchical allocation system suitable for the characteristics of the watershed is established. Analyze its cost-benefit from the perspective of the whole river basin, so as to lay a reliable foundation for effectively controlling non-point source pollution, promoting water source protection and river basin health. At the same time, it lays a reliable foundation for effectively controlling non-point source pollution, promoting water source protection and river basin health.

Description

An optimal configuration method of watershed agricultural non-point source pollution control measures

technical field

The invention relates to the field of environmental science, in particular to a method for controlling agricultural non-point source pollution in watersheds.

Background technique

With the closing of the Third Plenary Session of the 18th CPC Central Committee, the introduction of policies related to environment, land and agriculture will undoubtedly change the pace of China's agricultural development in the future and implement deeper rural reforms. In this process, the environmental problems brought about by agricultural modernization cannot be ignored. With the gradual control of urban domestic sewage and industrial wastewater, the problem of agricultural non-point source pollution has become prominent and has become an important factor threatening the safety of the water environment. Countries around the world have gradually realized the seriousness of the problem of agricultural non-point source pollution and the limitations of control forms. Due to the urgency, the research work on pollution prevention and control has also been carried out (Delgado and Scalenghe 2008). The promulgation of the "Water Pollution Prevention and Control Action Plan" in April 2015 requires agricultural non-point source pollution research to be carried out for a longer period of time in the future. Among them, the proposal and innovation of systematic non-point source pollution control technologies will be the focus of water environment protection. . But at present, my country is still in the reserve stage in terms of non-point source pollution control theory and technology application, and related practical work has just started (Shen et al.

In the seven major river basins in my country, due to long-term frequent traditional farming, large-scale reclamation of sloping land, continuous expansion of livestock and poultry breeding scale, surface erosion and gully erosion continue to intensify, and a large amount of nutrients flow into the receiving water body with runoff and sediment, resulting in A series of environmental problems such as the decline of river water environment quality and eutrophication of lakes. The water environment in these areas is facing increasing pressure from non-point source pollution from agricultural activities. How to effectively coordinate the contradiction between economic growth and water environment security has become an urgent problem to be solved.

Compared with point source pollution, non-point source pollution has the characteristics of randomness, pervasiveness, hysteresis, ambiguity and latentness (He Chansheng et al. Influenced by many factors such as soil, land use, and management methods, the spatial variability is strong, and the pollution load of different landscape units varies significantly (Rao et al.2009). It is relatively difficult to monitor and manage it, and it needs to consume a lot of manpower, material resources and financial resources. Therefore, how to carry out the management and control of non-point source pollution from the perspective of the river basin as a whole has become a hot and difficult point of concern at home and abroad (Chen Lei 2013). Promoting the implementation of "Best Management Practices" (BMPs) is an effective means of controlling agricultural non-point source pollution. As far as the river basin is concerned, the output of non-point source pollutants in a few regions usually accounts for the vast majority of the total load of the whole river basin. Non-point source pollution critical source areas (CSAs, Critical Source Areas) refer to pollution-sensitive areas that have a decisive impact on the overall status of the water environment in the basin (Tripathi et al. 2005). For non-point source pollution control at the watershed scale, the temporal and spatial distribution characteristics of pollution sources should be clarified first, and on this basis, the key source areas of non-point source pollution should be identified. This has become the core concept and key point of current non-point source pollution in watersheds. BMPs are a non-point source pollution control method commonly used at home and abroad, and there are mainly two types: engineering type and non-engineering type (Maringanti et al. 2009). The implementation of BMPs often needs to be closely combined with the law of pollutant generation and the characteristics of the underlying surface in the study area, and on this basis to accurately evaluate and optimize many engineering and management measures. From the perspective of the watershed as a whole, how to realize the reasonable and efficient allocation of BMPs under different spatial scales and limited capital conditions, and how to effectively control the transmission, migration and transformation of non-point source pollution, so as to provide decision-makers with A more comprehensive, objective, detailed and accurate reference basis for decision-making is the ultimate goal to ensure the scientific and efficient control of non-point source pollution in the watershed.

In the control of non-point source pollution, selecting BMPs suitable for regional characteristics is the key to improving the effectiveness of non-point source pollution control. Some foreign scholars have initially summarized the applicable conditions and cost-effectiveness of different measures from the review studies of BMPs in some countries (Cuttle et al. 2007), providing selection basis and data support for further research. Empirical studies on the application of BMPs mostly use engineering measures or management measures (Schumans et al. Although such studies can prove the control effect of BMPs on non-point source pollution, the implementation effect of BMPs is greatly affected by factors such as the environmental background of the research area, farming practices, and pollutant types (Jha et al.2009). In the case of changing or complex regional hydrological conditions, the implementation of a single BMP often cannot meet the needs of water quality control goals (Allan 2004). There are few studies on the comprehensive effect of BMPs combination on non-point source pollution, and it is not clear whether there is synergy or adverse effect between different BMPs. Based on the establishment of a comprehensive database of BMPs efficiency in different regions and different sources of pollutants, understanding the application effects and applicable conditions of various BMPs, and exploring the combination methods of different BMPs and the relationship between measures will help to choose the best one according to local conditions. management measures.

It is very necessary to optimize the combination of BMPs according to the regional characteristics, and to conduct economic analysis and evaluation of its cost and benefit. Early studies mostly used field monitoring methods at the scale of plots or farmland plots to explore the cost-effectiveness of BMPs implementation (Dillaha et al. Material resources, and its results have obvious regional characteristics, and the portability is limited. In recent years, model simulation has been widely used to quantify the long-term benefits of watershed-scale BMPs due to its low cost and short evaluation period (Srivastava et al. 2002, Hsieh and Yang 2007). Most of these studies use mechanism models to simulate the pollution load reduction effects under various BMPs scenarios, and combine certain economic methods for cost-benefit evaluation. However, since the occurrence of non-point source pollution is affected by many factors such as soil, topography, climate, hydrology, land use, and management methods, there are significant spatial differences in the output of pollution loads (Marsh, 1998), which makes the implementation effect of BMPs It is "site-specific", and its reduction efficiency on pollutant loads varies greatly in different regions and at different scales (Rao et al. 2009). In addition, the implementation effect of BMPs also depends to some extent on the impact of pollutant types. This makes the optimal allocation of watershed BMPs a complex multi-objective problem, and the trade-off among multiple objectives such as social, economic, environmental and ecological benefits should be considered.

The non-point source pollution process has a high degree of spatio-temporal heterogeneity, so that the implementation effect of BMPs is also affected by the scale effect (Rao et al.2009). Since 2004, the Canadian Department of Agriculture has launched the Best Management Measures Watershed Evaluation Project (The Watershed Evaluation of BMPs, WEBs) to evaluate the environmental and economic benefits of implementing BMPs in nine small watersheds across the country. Studies have found that the effect of a single BMP on pollutant reduction is often more obvious at the plot scale, but it is difficult to determine the comprehensive impact of multiple BMPs on water quality at the watershed scale. Some foreign scholars have conducted research on the implementation efficiency of BMPs at different scales. Gitau et al. applied the SWAT model and GA algorithm to the farmland and watershed scales to evaluate the cost-benefits of BMPs. The study concluded that due to the impact of scale effects, it is difficult to apply small-scale research results such as farmland to large-scale watersheds (Gitau et al. .2006). Panagopoulos et al. (2011) pointed out that the previous model evaluation studies on watershed-scale BMPs ignored the differences between plots, and it was difficult to evaluate the reduction effect of BMPs on plot-scale pollution loads (Panagopoulos et al.2011). Although the above studies pointed out the scale effect of the implementation effect of BMPs, they lacked an in-depth discussion on the comparison of the results. Therefore, research on the BMPs framework system at different spatial scales of hydrological response units (HRUs), sub-watersheds, and watersheds should be combined with the actual conditions of the study area to construct non-point source pollution control measures that integrate BMPs combination optimization, cost-benefit evaluation, and spatial allocation. system.

Contents of the invention

In order to overcome the shortcomings of our country's existing watershed agricultural non-point source pollution control technology system, which is single and the method is relatively uncertain, the present invention provides a method for optimizing the configuration of watershed agricultural non-point source pollution control measures to realize the water environment of the watershed. The purpose of overall improvement, which will provide strong scientific and technological support for the scientific selection of water and water conservation measures.

In order to solve the problems of the technologies described above, the technical solution adopted in the present invention is:

A method for optimal configuration of watershed agricultural non-point source pollution control measures, the steps of which are:

1) Construct a spatial and attribute database of non-point source pollution in the target research area and a BMPs reduction efficiency evaluation toolbox;

2) Perform parameter calibration on the SWAT model and IFSM model based on the measured hydrological and water quality data at the outlet of the target study area, and then perform parameter sensitivity analysis, model calibration and verification on the SWAT model and IFSM model after parameter calibration;

3) Use the non-point source pollution model to comprehensively analyze the non-point source pollution under the conditions of each underlying surface in the watershed in the area to be studied, and evaluate the underlying surface factors, and screen out the underlying surface factors that have the most significant impact;

4) According to the underlying surface factors obtained in step 4), identify CSAs with the same underlying surface characteristics at the field scale in the target study area; Soil type and slope information of the underlying surface plot in the high-risk area;

5) Taking the soil type and slope information as the evaluation basis, using the BMPs reduction efficiency evaluation toolbox to pre-evaluate the pollutant reduction efficiency of each proposed BMPs, and then according to the reduction efficiency of different measures in the target plot, Obtain the scenario scheme of the corresponding BMPs configuration;

6) Using the SWAT model and the IFSM model to evaluate the BMPs reduction efficiency of the scenario plan, and obtain the efficiency value of the BMPs to be implemented at the plot scale;

7) Use the database construction method to construct the basic database of optimization algorithms under different scenario scenarios for the plot-scale high-risk areas in the target research area, the proposed BMPs in the scenario scenarios of different BMPs configurations, and the cost of BMPs implementation, and then analyze the different scenario scenarios. The optimization simulation is carried out to obtain the optimal BMPs spatial configuration combination for the control of watershed agricultural non-point source pollution in the target research area.

Further, first input the land use, soil type and meteorological data in the spatial and attribute database into the IFSM model, and then input the simulation results of the IFSM model into the SWAT model for operation, and output the quantitative analysis of non-point source pollution in the watershed closed feedback loop simulation system.

Further, combined with SWAT-CUP 2012, parameter sensitivity analysis and model calibration and verification were carried out.

Furthermore, the underlying surface factors were judged by multi-factor analysis of variance, and the underlying surface factors with the most significant influence were screened out.

Further, the non-dominated sorting genetic algorithm is used to optimize the simulation of different scenarios, and obtain the optimal BMPs spatial configuration combination for watershed agricultural non-point source pollution control.

Further, based on the monthly-scale continuous monitoring water quality data of each water system in the target study area and combined with non-parametric testing methods, quantitatively analyze the probability of statistically significant changes in water quality under different load reduction ratios, and then use the regression fitting method , to reveal the nonlinear response relationship between the probability of significant improvement in water quality and the reduction ratio of watershed pollution load, and thus set the environmental benefit index of the non-dominated sorting genetic algorithm.

Compared with prior art, positive effect of the present invention is:

The beneficial effects of the present invention are: from the perspective of improving the implementation efficiency of BMPs, identify the distribution characteristics of non-point source pollution loads in watershed agriculture, and provide a basis for the configuration of BMPs at different scales; build a BMPs evaluation toolbox to provide standardization for the determination of BMPs reduction efficiency assessment technology; establish NSGA-II-based BMPs optimal allocation scheme simulation scenarios for agricultural non-point source pollution in the watershed, screen the optimal allocation scheme for BMPs for agricultural non-point source pollution control in the watershed, and establish a BMPs combined spatial hierarchical allocation system suitable for the characteristics of the watershed. Analyze its cost-benefit from the perspective of the whole river basin, so as to lay a reliable foundation for effectively controlling non-point source pollution, promoting water source protection and river basin health. At the same time, it will lay a solid foundation for effective control of non-point source pollution, promotion of water source protection and watershed health.

Description of drawings

Figure 1 is a technical system diagram of optimal allocation of agricultural non-point source pollution control measures in the watershed.

detailed description

The present invention will be described in further detail below in conjunction with the accompanying drawings.

The optimal configuration method of the watershed agricultural non-point source pollution control measures of the present invention is as shown in Figure 1, and it comprises the following steps:

(1) Analysis of non-point source pollution characteristics

1) With the support of RS and GIS technology, construct the non-point source pollution space and attribute database (Table 1) in the study area, which will serve as the basis for various data calls in the next step of model application;

2) Using SWAT model (Arnold J G, Moriasi D N, Gassman P W, et al.SWAT: Modeluse, calibration, and validation[J].Transactions of the ASABE,2012,55(4):1491-1508.) and IFSM model (Rotz C A, Coiner C U. Integrated Farm System Model (IFSM): Reference Manual Version 2.0 [J]. Agricultural Research Service, USDA. http://www.ars.usda.gov/Main/docs.htm, 2006) Combined multi-scale model combination method, input the land use, soil type and meteorological data in the spatial database and attribute database constructed in step 1) into the model, and construct a comprehensive simulation technology for quantitative analysis of non-point source pollution in the watershed In the scheme, the SWAT model is used as a tool to identify key source areas of non-point source pollution, and the IFSM model is used as a tool for economic benefit analysis of BMPs at the plot scale;

3) Use the measured hydrological and water quality data at the outlet of the basin to calibrate the parameters of the SWAT model and the IFSM model, and combine SWAT-CUP 2012 (Abbaspour K C.SWAT-CUP 2012[J].SWAT Calibration and Uncertainty program—A User Manual. Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, 2012) conducts sensitivity analysis, model calibration and verification of the parameters of the SWAT model to ensure the accuracy and rationality of the simulation results of the SWAT and IFSM models;

4) On this basis, the SWAT model is used to comprehensively analyze the non-point source pollution under the conditions of the underlying surfaces of the watershed in the research area, and the multi-factor analysis of variance is used to evaluate the factors of each underlying surface, and the most significant impact is screened out. The underlying surface factor is used as a method to identify non-point source pollution critical source areas (CSAs, Critical Source Areas) with the same underlying surface characteristics at the field scale and to simulate the measures taken in the CSAs area through the IFSM model.

(2) Evaluation of BMPs reduction efficiency

5) By collecting 280 documents related to BMPs efficiency research at home and abroad, extracting information about BMPs reduction efficiency, using SQL and VBA technology, building a BMPs reduction efficiency evaluation toolbox based on Microsoft ACCESS 2010 platform, and obtaining non-point source pollution through step 4) Based on the results of characteristic analysis, the soil type, slope and other attribute information of the underlying land plots in different high-risk areas were obtained as the evaluation basis, and the BMPs reduction efficiency evaluation toolbox was used to pre-evaluate the pollutant reduction efficiency of each proposed BMPs. According to the reduction efficiency of different measures in the target plot, according to the water environment quality requirements and the empirical pollutant reduction efficiency of the BMPs configuration plan, a scenario plan for BMPs configuration is proposed;

6) Use the method of combining SWAT model and IFSM model to evaluate the reduction efficiency of BMPs to be implemented at the field scale corresponding to the scenario scheme of BMPs allocation proposed in step 5), so as to obtain the efficiency of BMPs to be implemented at the plot scale value;

7) On this basis, the results of the relevant parameters of the block-scale BMPs assessment (step 6)) are fed back and simulated in the watershed-scale model SWAT to solve the problem of evaluating the efficiency of BMPs reduction under multi-scale conditions;

(3) Optimization of the spatial configuration scheme of BMPs at the plot scale

9) Based on the monthly-scale continuous monitoring water quality data of each river system in the basin and combined with non-parametric testing methods, quantitatively analyze the probability of statistically significant changes in water quality under different load reduction ratios. On this basis, use the regression fitting method to reveal The non-linear response relationship between the probability of significant improvement of water quality and the reduction ratio of watershed pollution load, thus setting the environmental benefit index of non-dominated sorting genetic algorithm;

10) Use the database construction method to construct the basic database of optimization algorithms under different scenarios based on the proposed implementation of BMPs and BMPs implementation costs in high-risk areas at the plot scale and scenario scenarios of different BMPs configurations, and use this as a basis to combine non-dominated sorting genetic algorithms (NSGA-II) optimizes and simulates different scenario scenarios to obtain the optimal BMPs spatial configuration combination for watershed agricultural non-point source pollution control.

Claims (6)

1. a kind of Optimal Configuration Method of Watershed Agricultural Non-point Sources Pollution control measure, its step is：

1) space and attribute database and the BMPs reduction efficiency evaluation instruments of a goals research area non-point pollution are built Case；

2) the actual measurement hydrology according to goals research area basin outlet, water quality data enter line parameter to SWAT models and IFSM models Calibration, then carries out parameters sensitivity analysis and model calibration and checking to the SWAT models and IFSM models after parameter calibration；

3) using the non-point pollution feelings under each land surface condition in basin in the Non-point Source Pollution Model comprehensive analysis area to be studied Condition, and each Factors of Underlying Surface is judged, filter out influence Factors of Underlying Surface the most significant；

4) according to step 4) Factors of Underlying Surface that obtains recognize field plots yardstick in the goals research area with identical underlay The CSAs regions of region feature；Then the measure taken the CSAs regions by IFSM models is simulated, and obtains different height The soil types and grade information in risk area underlying surface plot；

5) using the soil types, grade information as Appreciation gist, efficiency evaluation tool box is cut down to each using the BMPs The pollutant fluxes efficiency that item intends implementing BMPs carries out Pre-Evaluation, then according to the reduction efficiency high of target plot different measure It is low, obtain the scene scheme of corresponding BMPs configurations；

6) cut down efficiency to the BMPs of the scene scheme using SWAT models and IFSM models to be estimated, obtain plot yardstick Intend implementing the efficiency value of BMPs；

7) the scene side that maintenance data base construction method configures the plot yardstick high risk area in the goals research area, difference BMPs Plan in case implements the optimized algorithm basic database that BMPs, BMPs implementation cost are built under different situation schemes, then to not Simulation is optimized with situation scheme, the Watershed Agricultural Non-point Sources Pollution for obtaining the goals research area controls optimal BMPs spaces to match somebody with somebody Put combination.

2. the method that such as claim 1 is stated, it is characterised in that first by the Land_use change in the space and attribute database, soil , then be input into IFSM model simulation results in SWAT models and run by earth type and meteorological data input IFSM models, output Basin non-point pollution quantitative analysis closed type backfeed loop simulation system.

3. method as claimed in claim 1 or 2, it is characterised in that the step 2), joined with reference to SWAT-CUP 2012 Number sensitivity analysis and model calibration and checking.

4. method as claimed in claim 1 or 2, it is characterised in that entered to each Factors of Underlying Surface by the multifactor analysis of variance Row is judged, and filters out influence Factors of Underlying Surface the most significant.

5. method as claimed in claim 1 or 2, it is characterised in that using non-dominated sorted genetic algorithm to different situation sides Case optimizes simulation, obtains Watershed Agricultural Non-point Sources Pollution and controls optimal BMPs space configurations combination.

6. the method that such as claim 5 is stated, it is characterised in that with each water system month yardstick continuous monitoring in the basin in the goals research area Non-parametric test method is combined based on water quality data, under quantitative analysis different load reduction ratio, water quality occurs statistics meaning The probability of justice change, then with the method for regression fit, discloses water quality and significantly improves probability with basin pollutional load reduction ratio Nonlinear response relation between example, so as to set the environmental benefit index of the non-dominated sorted genetic algorithm.