CN112700098A - Water environment quality and economic risk evaluation method based on random dominance theory - Google Patents

Water environment quality and economic risk evaluation method based on random dominance theory Download PDF

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CN112700098A
CN112700098A CN202011549277.1A CN202011549277A CN112700098A CN 112700098 A CN112700098 A CN 112700098A CN 202011549277 A CN202011549277 A CN 202011549277A CN 112700098 A CN112700098 A CN 112700098A
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crop
future
land utilization
pollution load
watershed
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樊敏
帅建英
刘云峰
杜奇林
谌书
姚婧
赵丽
刘静
陈雯
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Chengdu Tuojiang River Basin Investment Development Group Co ltd
Southwest University of Science and Technology
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Southwest University of Science and Technology
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention provides a water environment quality and economic risk evaluation method based on a random dominance theory, which is based on a land utilization prediction model, a global climate prediction model, a watershed hydrological model, a system protection planning model and a spatial analysis technology, takes a watershed as a research area, takes water circulation, nutrient substance circulation and crop growth process as research objects, selects pollution load generation amount and crop yield time sequence from farmlands, quantifies watershed water environment quality and economic risk caused by land utilization type change and climate change based on the random dominance theory of environmental economics, realizes the quantitative expression of watershed water environment quality and economic income caused by land utilization type change and climate change, and provides a win-win management new mode of watershed quality protection and economic development under the land utilization type change and climate change, provides a new method for watershed water environment management and planning mainly based on agricultural production activities.

Description

Water environment quality and economic risk evaluation method based on random dominance theory
Technical Field
The invention relates to the field of water resource management, in particular to a water environment quality and economic risk evaluation method based on a random dominance theory
Background
Water resources are important support and guarantee for economic and social development, Chinese researchers and governments pay high attention to solving the water resource problem, a series of keys are provided in time, a series of policy measures are taken, stable grain yield increase and continuous income increase of farmers are promoted, and powerful guarantee is provided for ecological environment construction. However, the situation of water resources in China is still severe, and the shortage of water resources and the serious pollution of water bodies coexist, so that the economic development of agricultural production and the water safety of residents are influenced.
With the development of agriculture in China in this year, farmers change forests into forest lands and change the planting types of crops in order to pursue short-term economic benefits, and soil erosion and excessive nitrogen and phosphorus loss along with surface runoff caused by improper land utilization modes and management modes under global climate change seriously affect the water quality of water bodies. In the complex watershed ecosystem mainly based on agricultural production activities with the characteristics of nonlinearity, openness and dynamic evolution, the research on the relation between the behavior of farmers seeking economic benefits based on crop yield and the water environment is a difficult and extremely challenging task, and the task urgently requires environmental decision makers and farmers to seek economic and ecological symbiotic watershed environmental management methods and technologies under the influence of changes in land utilization types and climate changes.
The traditional watershed water environment quality and economic risk assessment mainly based on agricultural production activities mostly adopts a watershed hydrological water quality model and a crop growth model to simulate the influence of land utilization type change and climate change on farmland pollution load generation quantity and an average value of crop yield, and cannot integrate the pollution load generation quantity from a farmland, the fluctuation of economic income from the crop yield and the risk attitude of an environment decision maker and a farmer into the water environment quality and economic risk assessment.
Aiming at the problems, the invention mainly solves the problems that: (1) determining the influence of the land utilization type change and the climate change on the pollution load generation amount and the crop yield of the farmland from the watershed by adopting which method; (2) how to evaluate the water environment quality and economic risk caused by land use type change and climate change based on a random dominance theory; (3) how to couple the watershed hydrological model with the risk assessment model and seek the optimal land use type change and climate change scene combination which are win-win for both water environment protection and agricultural production economic activity development.
At present, the influence of quantitative land utilization change and climate change on the watershed water quantity and the water quality and the economic income of farmers is mainly used for national watershed water environment quality and agricultural land management, in the practical application process, the water environment quality and the economic risk caused by the land utilization change and the climate change are generally evaluated by researching the average value of the pollution load generation amount from farmlands and the economic income from crop yield, the fluctuation of the pollution load generation amount from farmlands and the economic income from crop yield is ignored, the risk attitude of pursuing low pollution and high income of environment decision makers and farmers is not considered, and the contradiction between the water environment protection and the economic activity development of agricultural production is solved.
Disclosure of Invention
In order to solve the problems, the invention discusses how to seek the optimal land utilization type change and climate change scene combination under the win-win mode of water environment quality protection and agricultural production economic activity development from quantifying the watershed water environment quality and the economic risk so as to provide a thought for the water environment protection of other watersheds, improve the water environment protection system of China, promote the reasonable development of water resources of China and the effective protection of the ecological environment, and provide a water environment quality and economic risk evaluation method based on the random dominance theory
The technical scheme adopted by the invention is as follows: a water environment quality and economic risk evaluation method based on a random dominance theory comprises the following steps:
s1, simulating a hydrological cycle process, a nutrient substance cycle process and a crop growth process by using a watershed hydrological model according to the terrain, weather, land utilization and soil basic information of the current watershed, comparing and analyzing the simulated flow value, water quality value and crop yield value with the actual measurement flow value and water quality value of the known monitoring section and the crop yield yearly-identified statistic value of the watershed, and calibrating and correcting the model to obtain the time sequence of the pollution load yield and the crop yield of the watershed from farmlands;
s2, based on the historical land utilization type spatial distribution map layer and the historical meteorological data, simulating to obtain a future land utilization type spatial distribution map layer and a meteorological change trend by adopting a land utilization type prediction model and a global climate prediction model, and combining different future land utilization types and meteorological conditions;
s3, simulating hydrologic cycle process, nutrient substance cycle process and crop growth process of each scene combination by using a calibrated and corrected watershed hydrological model based on scene combinations of different future land utilization types and meteorological conditions to obtain farmland pollution load production and crop yield time sequence under each scene combination condition;
s4, simulating the scene of the farmland pollution load generation amount and the crop yield time sequence based on the change of each land use type and the change of meteorological conditions; according to a random dominance theory, a watershed water environment quality and economic risk assessment model is constructed, and the influence of land use type change and climate change on the watershed water environment quality and economic income is quantified.
Further, in S1, the whole research watershed is divided into a plurality of sub-watersheds by a watershed hydrological model, then each sub-watershed is divided into a plurality of hydrological response units HRU, a watershed hydrological cycle process, a nutrient cycle process and a crop growth process are simulated on each HRU, the simulated flow values, water quality values and crop yield values are compared with the actual measurement flow values and water quality values of known monitoring sections, and the crop yield of the watershed is compared with the agricultural annual identification statistical values, and the model is calibrated and corrected to obtain the pollution load yield and the crop yield in the HRU using the farmland as the main land utilization type, and the pollution load yield and the crop yield are subjected to the whole-watershed statistics to obtain the pollution load yield and the crop yield time sequence of the unit farmland area.
Further, the concrete steps of obtaining the future land use type spatial distribution map layer by using the land use type prediction model in S2 are as follows:
s21, counting the change trend of the total area of each land utilization type based on the historical land utilization type space distribution map layer in the drainage basin, predicting the total area of each future land utilization type, and obtaining the requirements of each future land utilization type;
s22, establishing a relation between land use space distribution and driving force based on the current land use type data and multiple driving factors by adopting a two-classification regression model, and obtaining suitability probability of each land use type, so as to obtain driving and limiting factors of land use, and the driving and limiting factors are used for establishing a future land use type space distribution map.
S23, determining land utilization units allowing conversion and calculating the conversion possibility of each grid unit to each land utilization type to form an initial land utilization distribution map; and carrying out space distribution of the land utilization area according to the matching of the land utilization distribution map and the land utilization type requirements, and establishing a future land utilization type space distribution map layer.
Further, in S21, a multivariate statistical method or a system dynamics method is used to predict the total area of each land use type in the future.
Further, in S23, the land use unit determination method allowing conversion is: the land of the natural protection area and the farmland protection area is a unit which is not allowed to be converted, and the rest is a unit which is allowed to be converted for land utilization; the conversion probability is determined by a conversion coefficient, the size of the conversion coefficient is between 0 and 1, the difference in value between 0 and 1 indicates the size of the conversion probability, and the larger the value, the larger the conversion probability.
Further, the acquiring process of the trend of the meteorological changes in S2 is as follows: based on historical data of air temperature and precipitation in the drainage basin, a global climate prediction model is adopted to calculate and obtain air temperature and precipitation data of the scale of the coming month, and the air temperature and precipitation data are input into a weather generator in a drainage basin hydrological model to generate weather data with the time step of day.
Further, the calculation method of the air temperature and the cooling data of the future month scale comprises the following steps:
calculating the future air temperature:
μ′mT＝μmT+(μmT，future-μmT，current)
wherein, mumTAnd mu'mTCurrent and future monthly average temperatures, respectively; mu.smT，currentAnd mumT，futureThe simulated monthly average air temperature under the current meteorological scene and the future meteorological scene respectively;
calculating future precipitation:
μ′mP＝μmP*(μmP，future/μmP，current)
wherein, mumPAnd mu'mPCurrent and future monthly average precipitation respectively; mu.smP，currentAnd mumP，futureThe simulated monthly average precipitation is respectively under the current and future meteorological scenes.
Further, the specific process of S3 is as follows: the method comprises the steps of simulating the services of the watershed hydrological ecosystem based on different future land utilization types and meteorological combination conditions, inputting future land utilization type spatial distribution image layers and meteorological conditions into a watershed hydrological model after calibration and correction respectively under the condition that other input data are not changed, obtaining pollution load generation amount and crop yield in HRUs with farmlands as main land utilization types, carrying out whole-watershed statistics on the pollution load amount and the crop yield, and further obtaining pollution load generation amount and crop yield time sequences of unit farmland areas under different future land utilization type and meteorological condition scene combinations.
Further, the specific process of S4 is as follows: the method comprises the following steps of quantifying the total profit and net profit of crop yield based on farmland pollution load production and crop yield time sequences under different future land utilization types and meteorological conditions, market values of crops derived from agricultural statistical yearbook and cost time sequences for planting the crops, respectively generating time sequences of the total profit and the net profit of the crop yield, and evaluating watershed water environment quality and economic risk caused by land utilization type change and climate change based on a random dominance theory, wherein the method comprises the following specific steps:
s41, lengthening the time sequence of farmland pollution load generation amount and crop yield, the market value of crops and the cost of planting the crops to obtain the farmland pollution load generation amount, the crop yield, the market value of the crops and the cost of planting the crops in a long-time sequence;
s42, calculating the pollution load generation amount of the unit farmland area of the whole drainage basin and the total profit and net profit of the crop yield by using the farmland pollution load generation amount, the crop yield, the market value of the crops and the cost for planting the crops in the long-time sequence:
Figure BDA0002856573760000041
Figure BDA0002856573760000042
Figure BDA0002856573760000043
Figure BDA0002856573760000044
wherein GM is the total profit from all crop yields; NR is net profit from all crop yields; i is the crop type; priceiMarket price for the ith crop; yield (y)iYield for the ith crop; costiThe cultivation cost for the ith crop; areaiThe planting area of the ith crop is shown; TN (twisted nematic)iTotal nitrogen pollution load production per unit field area from planting of the ith crop; TPiIs the total phosphorus pollution load production per unit area of farmland from planting of the ith crop;
s43, selecting a performance function according to a random dominance theory, and setting a risk aversion coefficient range of a farmer pursuing economic income from crops and a risk aversion coefficient range of an environment protection decision maker pursuing pollution load from farmlands; calculating the determined equivalent of the farmland pollution load production and the economic income of the crop yield; selecting the land use type, the farmland pollution load generation amount under the meteorological condition and the determination equivalent of the economic income of the crop yield in the S1 as a reference, calculating the difference between the determination equivalent of each farmland pollution load generation amount and the economic income of the crop yield under different future land use type changes and climatic change scene combinations and the determination equivalent of the reference, and arranging the difference sequence, thereby determining the water environment quality and the economic risk; the calculation method is as follows:
U(w)＝-exp(-ra(w)w)
CE(w,ra(w))＝U-1(w,ra(w))
RP(A,B,ra(w))＝CE(A,ra(w))-CE(B,ra(w))
wherein, U is the performance of the economic income of crops and the pollution load generation amountA function; w is the crop economic income and the pollution load generation amount; r isα(w) is a risk aversion coefficient of the farmer to the economic income of the crops and a risk aversion coefficient of the environmental decision maker to the pollution load generation amount; CE is the determined equivalent of crop economic income and pollution load production; RP is the risk-added expense for crop economic income and pollution load production; a and B are different scene combinations of two future land use types and climate change.
Further, the length-adding processing method in S41 includes: determining farmland pollution load production and crop yield time sequences under different future land utilization types and meteorological conditions, and crop market values and crop planting cost time sequences to obey multivariate empirical distribution, and generating long-time-sequence farmland pollution load production, crop yield, crop market values and crop planting costs by adopting a Monte Carlo random sampling method; and verifying whether the newly generated time sequence and the original time sequence obey the same distribution by adopting t test and F test, if obeying, retaining, and if not, sampling by adopting a Monte Carlo random sampling method again.
Compared with the prior art, the beneficial effects of adopting the technical scheme are as follows:
(1) the invention analyzes the water environment quality and economic risk of the whole watershed based on the random domination theory by means of the watershed hydrological model, so that the watershed water environment quality management method has the environmental and economic significance, and can effectively integrate the attitude of pursuing environmental protection and agricultural economic development of multi-decision makers into the watershed water environment quality management, so that the watershed water environment protection and the economic development win-win.
(2) The method can provide scientific basis for watershed land planning and climate countermeasure measures by quantifying the water environment quality and economic risk under different land utilization changes and meteorological changes.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a map layer of future land use type prediction spatial distribution of the drainage basin of the present invention.
Fig. 3 shows the future change trend of the climate conditions (temperature and precipitation) of the watershed of the invention.
Fig. 4 is a scenario combination of different future land use type changes and climate change conditions of the present invention.
Fig. 5 is a risk ranking chart of the pollution load production from farmland in the basin under different future land use type changes and climate changes according to the present invention.
FIG. 6 is a risk ranking graph of the total profit from crop production for a watershed with different future land use type changes and climate changes according to the present invention.
FIG. 7 is a risk ranking graph of the watershed net profit from crop yield for different future land use type changes and climate changes according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the technical solution adopted by the present invention is as follows: a water environment quality and economic risk evaluation method based on a random dominance theory comprises the following steps:
s1, simulating a hydrological cycle process, a nutrient substance cycle process and a crop growth process by using a watershed hydrological model according to the terrain, weather, land utilization and soil basic information of the current watershed, comparing and analyzing the simulated flow value, water quality value and crop yield value with the actual measurement flow value and water quality value of the known monitoring section and the crop yield of the watershed and the agricultural annual identification statistic value, and calibrating and correcting the model to obtain a time sequence of the watershed pollution load yield from farmland and the crop yield;
s2, based on the historical land utilization type spatial distribution map layer and the historical meteorological data, simulating to obtain a future land utilization type spatial distribution map layer and a meteorological change trend by adopting a land utilization type prediction model and a global climate prediction model, and combining different future land utilization types and meteorological conditions;
s3, simulating hydrologic cycle process, nutrient substance cycle process and crop growth process of each scene combination by using a calibrated and corrected watershed hydrological model based on scene combinations of different future land utilization types and meteorological conditions to obtain farmland pollution load production and crop yield time sequence under each scene combination condition;
s4, simulating the scene of the farmland pollution load generation amount and the crop yield time sequence based on the change of each land use type and the change of meteorological conditions; according to a random dominance theory, a watershed water environment quality and economic risk assessment model is constructed, and the influence of land use type change and climate change on the watershed water environment quality and economic income is quantified.
Example 2
On the basis of the example 1, in the S1, the whole research basin is divided into a plurality of sub-basins through the basin hydrological model, then dividing each sub-drainage basin into a plurality of Hydrological Response Units (HRUs), simulating a drainage basin hydrological cycle process, a nutrient substance cycle process and a crop growth process on each HRU, comparing and analyzing the simulated flow value, the simulated water quality value and the simulated crop yield value with the actually measured flow value and water quality value of the known monitoring section and the crop yield of the drainage basin and the agricultural yearbook statistical value, and carrying out calibration and correction on the model to obtain the pollution load generation amount and the crop yield in the HRU taking the farmland as the main land utilization type, and carrying out full watershed statistics on the pollution load amount and the crop yield to further obtain the pollution load generation amount and the crop yield time sequence of the unit farmland area. The HRU is the most basic unit of the sub-watershed and is also the basic computing unit of the watershed hydrological model, and the HRU represents that the same ground cover, soil type and crop cultivation management mode exist in the same sub-watershed.
Example 3
Based on embodiment 2, as shown in fig. 2, the specific steps of obtaining the future land use type spatial distribution map layer by using the land use type prediction model in S2 include:
s21, counting the change trend of the total area of each land utilization type based on the historical land utilization type space distribution map layer in the drainage basin, predicting the total area of each future land utilization type by adopting a multivariate statistical method or a system dynamics method, and obtaining the requirement of each future land utilization type;
s22, establishing a relation between land use space distribution and driving force based on the current land use type data and various driving factors (temperature, precipitation, soil, terrain, traffic, location, policy and the like) by adopting a two-classification regression model, and obtaining suitability probability of each land use type, so as to obtain driving and limiting factors of land use, and the driving and limiting factors are used for establishing a future land use type space distribution map.
S23, determining land utilization units allowing conversion and calculating the conversion possibility of each grid unit to each land utilization type to form an initial land utilization distribution map; the method comprises the steps of carrying out space distribution of land utilization areas according to matching of a land utilization distribution diagram and land utilization type requirements, and establishing a future land utilization type space distribution layer.
Example 4
On the basis of embodiment 3, in S23, the land use unit determination method allowing conversion is: the land of the natural protection area and the farmland protection area is a unit which is not allowed to be converted, and the rest is a unit which is allowed to be converted for land utilization; the conversion probability is determined by a conversion coefficient, the size of the conversion coefficient is between 0 and 1, the difference in value between 0 and 1 indicates the size of the conversion probability, and the larger the value, the larger the conversion probability.
Example 5
On the basis of the example 4, the acquiring process of the trend of the meteorological changes in the S2 is as follows: the global Climate model provides a feasible method for predicting future Climate Change of the drainage basin, an evaluation report provided by an inter-government Climate Change committee (IPCC) of the united nations contains simulation results of a series of global Climate models on future air temperature and rainfall Change, and the time scale is monthly; meanwhile, based on historical data of air temperature and precipitation in the watershed, a global climate prediction model is adopted to calculate and obtain air temperature and precipitation data of the scale of the coming month, and the air temperature and precipitation data are input into a weather generator in the watershed hydrological model to generate weather data with the time step of day, as shown in fig. 3.
Example 6
On the basis of the embodiment 5, the calculation method of the air temperature and the precipitation data of the future month scale comprises the following steps:
calculating the future air temperature:
μ′mT＝μmT+(μmT，future-μmT，current)
wherein, mumTAnd mu'mTCurrent and future monthly average temperatures, respectively; mu.smT，currentAnd mumT，futureThe simulated monthly average air temperature under the current meteorological scene and the future meteorological scene respectively;
calculating future precipitation:
μ′mP＝μmP*(μmP，future/μmP，current)
wherein, mumPAnd mu'mPCurrent and future monthly average precipitation respectively; mu.smP，currentAnd mumP，futureThe simulated monthly average precipitation is respectively under the current and future meteorological scenes.
Example 7
On the basis of embodiment 3, the specific process of S3 is as follows: as shown in fig. 4, the simulation of the watershed hydrological ecosystem service is performed based on different future land use types and meteorological combination conditions, the future land use type spatial distribution map layer and the meteorological conditions are respectively input into the watershed hydrological model after calibration and correction under the condition of not changing other input data, the pollution load generation amount and the crop yield in each HRU with the farmland as the main land use type are obtained, the pollution load generation amount and the crop yield are subjected to the whole-watershed statistics, and further the pollution load generation amount and the crop yield time sequence of the unit farmland area under different future land use type and meteorological condition scene combinations are obtained.
Example 8
On the basis of embodiment 7, as shown in fig. 5, 6, and 7, the specific process of S4 is as follows: the method comprises the following steps of quantifying the total profit and net profit of the crop yield based on farmland pollution load production and crop yield time sequences under different future land utilization types and meteorological conditions, market values of crops and cost time sequences for planting the crops based on source agricultural statistics yearbook, respectively generating time sequences of the total profit and the net profit of the crop yield, and evaluating watershed water environment quality and economic risk caused by land utilization type change and climate change based on a random dominance theory, wherein the method comprises the following specific steps:
s41, lengthening the time sequence of farmland pollution load generation amount and crop yield, the market value of crops and the cost of planting the crops to obtain the farmland pollution load generation amount, the crop yield, the market value of the crops and the cost of planting the crops in a long-time sequence;
s42, calculating the pollution load generation amount of the unit farmland area of the whole drainage basin and the total profit and net profit of the crop yield by using the farmland pollution load generation amount, the crop yield, the market value of the crops and the cost for planting the crops in the long-time sequence:
Figure BDA0002856573760000081
Figure BDA0002856573760000091
Figure BDA0002856573760000092
Figure BDA0002856573760000093
wherein GM is the total profit from all crop yields; NR is net profit from all crop yields; i is the crop type; priceiMarket price for the ith crop; yield (y)iYield for the ith crop; costiThe cultivation cost for the ith crop; areaiThe planting area of the ith crop is shown; TN (twisted nematic)iTotal nitrogen pollution load production per unit field area from planting of the ith crop; TPiIs the total phosphorus pollution load production per unit area of farmland from planting of the ith crop;
s43, selecting a performance function according to a random dominance theory, and setting a risk aversion coefficient range of a farmer pursuing economic income from crops and a risk aversion coefficient range of an environment protection decision maker pursuing pollution load from farmlands; calculating the determined equivalent of the farmland pollution load production and the economic income of the crop yield; selecting a certain specific land utilization type and the determination equivalent of farmland pollution load generation amount and economic income of crop yield under meteorological conditions as a reference, in the embodiment, calculating the difference value between the determination equivalent of each farmland pollution load generation amount and economic income of crop yield under different future land utilization type changes and climatic change scene combinations and the determination equivalent of the reference by adopting the land utilization type and the meteorological conditions under the S1 condition, and arranging the difference value sequence, thereby determining the water environment quality and the economic risk; the calculation method is as follows:
U(w)＝-exp(-ra(w)w)
CE(w，ra(w))＝U-1(w，ra(w))
RP(A，B，ra(w))＝CE(A，ra(w))-CE(B，ra(w))
wherein, U is a performance function of the economic income of crops and the generation amount of pollution load; w is the crop economic income and the pollution load generation amount; r isa(w) is a risk aversion coefficient of the farmer to the economic income of the crops and a risk aversion coefficient of the environmental decision maker to the pollution load generation amount; CE is the determined equivalent of crop economic income and pollution load production; RP is the risk-added expense for crop economic income and pollution load production; a and B are different scene combinations of two future land use types and climate change. In the embodiment, a negative index function is selected as the performance of risk analysisAnd assuming that the farmer not only pursues high income from crop production but also desires a small fluctuation range of the time series of income from crop production, i.e., pursues stable high income, i.e., setting the risk aversion coefficient range of the farmer pursues income from crop to be: 0 to 0.04. Similarly, assuming that the environmental protection decision maker not only seeks a low amount of production from the farm pollution load but also seeks to have a small time-series fluctuation range of the low amount of production from the farm pollution load, i.e., seeks a stable low pollution load yield, i.e., the environmental protection decision maker sets the risk aversion coefficient range of pursuing the load from the farm pollution to be: 0 to 0.04.
Example 9
On the basis of the embodiment 8, the processing method of the length increase in S41 is as follows: determining farmland pollution load production and crop yield time sequences under different future land utilization types and meteorological conditions, and crop market values and crop planting cost time sequences to obey multivariate empirical distribution, and generating long-time-sequence farmland pollution load production, crop yield, crop market values and crop planting costs by adopting a Monte Carlo random sampling method; and verifying whether the newly generated time sequence and the original time sequence obey the same distribution by adopting t test and F test, if obeying, retaining, and if not, sampling by adopting a Monte Carlo random sampling method again.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed. Those skilled in the art to which the invention pertains will appreciate that insubstantial changes or modifications can be made without departing from the spirit of the invention as defined by the appended claims.
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.

Claims (10)

1. A water environment quality and economic risk evaluation method based on a random dominance theory is characterized by comprising the following steps:
s1, simulating a hydrological cycle process, a nutrient substance cycle process and a crop growth process by using a watershed hydrological model according to the terrain, weather, land utilization and soil basic information of the current watershed, comparing and analyzing the simulated flow value, water quality value and crop yield value with the actual measurement flow value and water quality value of the known monitoring section and the crop yield of the watershed and the agricultural annual identification statistic value, and calibrating and correcting the model to obtain a time sequence of the watershed pollution load yield from farmland and the crop yield;
s2, based on the historical land utilization type spatial distribution map layer and the historical meteorological data, simulating to obtain a future land utilization type spatial distribution map layer and a meteorological change trend by adopting a land utilization type prediction model and a global climate prediction model, and combining different future land utilization types and meteorological conditions;
s3, simulating hydrologic cycle process, nutrient substance cycle process and crop growth process of each scene combination by using a calibrated and corrected watershed hydrological model based on scene combinations of different future land utilization types and meteorological conditions to obtain farmland pollution load production and crop yield time sequence under each scene combination condition;
s4, simulating the scene of the farmland pollution load generation amount and the crop yield time sequence based on the change of each land use type and the change of meteorological conditions; according to a random dominance theory, a watershed water environment quality and economic risk assessment model is constructed, and the influence of land use type change and climate change on the watershed water environment quality and economic income is quantified.
2. The method for evaluating the quality of the water environment and the economic risk based on the random dominance theory according to claim 1, characterized in that in S1, the whole research basin is divided into a plurality of sub-basins by the basin hydrological model, then dividing each sub-basin into a plurality of hydrological response units HRUs, simulating a basin hydrological cycle process, a nutrient substance cycle process and a crop growth process on each HRU, comparing and analyzing the simulated flow value, the simulated water quality value and the simulated crop yield value with the actual measurement flow value and the water quality value of the known monitoring section and the crop yield of the basin and the agricultural yearbook statistic value, and carrying out calibration and correction on the model to obtain the pollution load generation amount and the crop yield in the HRU taking the farmland as the main land utilization type, and carrying out full watershed statistics on the pollution load amount and the crop yield to further obtain the pollution load generation amount and the crop yield time sequence of the unit farmland area.
3. The method for evaluating the water environment quality and the economic risk based on the random dominance theory according to claim 2, wherein the concrete steps of obtaining the future land utilization type spatial distribution map layer by using the land utilization type prediction model in the step S2 are as follows:
s21, counting the change trend of the total area of each land utilization type based on the historical land utilization type space distribution map layer in the drainage basin, predicting the total area of each future land utilization type, and obtaining the requirements of each future land utilization type;
s22, establishing a relation between land use space distribution and driving force based on the current land use type data and multiple driving factors by adopting a two-classification regression model, and obtaining suitability probability of each land use type, so as to obtain driving and limiting factors of land use, and the driving and limiting factors are used for establishing a future land use type space distribution map.
S23, determining land utilization units allowing conversion and calculating the conversion possibility of each grid unit to each land utilization type to form an initial land utilization distribution map; and carrying out space distribution of the land utilization area according to the matching of the land utilization distribution map and the land utilization type requirements, and establishing a future land utilization type space distribution map layer.
4. The method for evaluating the quality of the water environment and the economic risk based on the stochastic dominance theory as claimed in claim 3, wherein in the step S21, a multivariate statistical method or a systematic kinetic method is adopted to predict the total area of each land use type in the future.
5. The method for evaluating the quality of the water environment and the economic risk based on the stochastic dominance theory as claimed in claim 4, wherein in the step S23, the land use unit judgment method allowing conversion is as follows: the land of the natural protection area and the farmland protection area is a unit which is not allowed to be converted, and the rest is a unit which is allowed to be converted for land utilization; the conversion probability is determined by a conversion coefficient, the size of the conversion coefficient is between 0 and 1, the difference in value between 0 and 1 indicates the size of the conversion probability, and the larger the value, the larger the conversion probability.
6. The method for evaluating the quality of the water environment and the economic risk based on the random dominance theory according to claim 5, wherein the acquiring process of the meteorological change trend in the S2 is as follows: based on historical data of air temperature and precipitation in the drainage basin, a global climate prediction model is adopted to calculate and obtain air temperature and precipitation data of the scale of the coming month, and the air temperature and precipitation data are input into a weather generator in a drainage basin hydrological model to generate weather data with the time step of day.
7. The method for evaluating the water environment quality and the economic risk based on the random dominance theory according to claim 6, wherein the method for calculating the air temperature and the water fall data in the future month scale comprises the following steps:
calculating the future air temperature:
μ'mT＝μmT+(μmT,future-μmT,current)
wherein, mumTAnd mu'mTCurrent and future monthly average temperatures, respectively; mu.smT,currentAnd mumT,futureThe simulated monthly average air temperature under the current meteorological scene and the future meteorological scene respectively;
calculating future precipitation:
μ'mP＝μmP*(μmP,future/μmP,current)
wherein, mumPAnd mu'mPCurrent and future monthly average precipitation respectively; mu.smP,currentAnd mumP,futureThe simulated monthly average precipitation is respectively under the current and future meteorological scenes.
8. The method for evaluating the quality of the water environment and the economic risk based on the random dominance theory according to claim 7, wherein the specific process of S3 is as follows: the method comprises the steps of simulating the services of the watershed hydrological ecosystem based on different future land utilization types and meteorological combination conditions, inputting future land utilization type spatial distribution image layers and meteorological conditions into a watershed hydrological model after calibration and correction respectively under the condition that other input data are not changed, obtaining pollution load generation amount and crop yield in HRUs with farmlands as main land utilization types, carrying out whole-watershed statistics on the pollution load amount and the crop yield, and further obtaining pollution load generation amount and crop yield time sequences of unit farmland areas under different future land utilization type and meteorological condition scene combinations.
9. The method for evaluating the quality of the water environment and the economic risk based on the random dominance theory according to claim 8, wherein the specific process of S4 is as follows: the method comprises the following steps of quantifying the total profit and net profit of crop yield based on farmland pollution load production and crop yield time sequences under different future land utilization types and meteorological conditions, market values of crops derived from agricultural statistical yearbook and cost time sequences of planted crops, generating time sequences of the total profit and the net profit of the crop yield respectively, and establishing a watershed water environment quality and economic risk assessment model to assess watershed water environment quality and economic risk caused by land utilization type change and climate change based on a random dominance theory, wherein the method specifically comprises the following steps:
s41, lengthening the time sequence of farmland pollution load generation amount and crop yield, the market value of crops and the cost of planting the crops to obtain the farmland pollution load generation amount, the crop yield, the market value of the crops and the cost of planting the crops in a long-time sequence;
s42, calculating the pollution load generation amount of the unit farmland area of the whole drainage basin and the total profit and net profit of the crop yield by using the farmland pollution load generation amount, the crop yield, the market value of the crops and the cost for planting the crops in the long-time sequence:
Figure FDA0002856573750000031
Figure FDA0002856573750000032
Figure FDA0002856573750000033
Figure FDA0002856573750000034
wherein GM is the total profit from all crop yields; NR is net profit from all crop yields; i is the crop type; priceiMarket price for the ith crop; yield (y)iYield for the ith crop; costiThe cultivation cost for the ith crop; areaiThe planting area of the ith crop is shown; TN (twisted nematic)iTotal nitrogen pollution load production per unit field area from planting of the ith crop; TPiIs the total phosphorus pollution load production per unit area of farmland from planting of the ith crop;
s43, selecting a performance function according to a random dominance theory, and setting a risk aversion coefficient range of a farmer pursuing economic income from crops and a risk aversion coefficient range of an environment protection decision maker pursuing pollution load from farmlands; calculating the determined equivalent of the farmland pollution load production and the economic income of the crop yield; selecting the land use type, the farmland pollution load generation amount under the meteorological condition and the determination equivalent of the economic income of the crop yield in the S1 as a reference, calculating the difference between the determination equivalent of each farmland pollution load generation amount and the economic income of the crop yield under different future land use type changes and climatic change scene combinations and the determination equivalent of the reference, and arranging the difference sequence, thereby determining the water environment quality and the economic risk; the calculation method is as follows:
U(w)＝-exp(-ra(w)w)
CE(w，ra(w))＝U-1(w，ra(w))
RP(A，B，ra(w))＝CE(A，ra(w))-CE(B，ra(w))
wherein, U is a performance function of the economic income of crops and the generation amount of pollution load; w is the crop economic income and the pollution load generation amount; r isa(w) is a risk aversion coefficient of the farmer to the economic income of the crops and a risk aversion coefficient of the environmental decision maker to the pollution load generation amount; CE is the determined equivalent of crop economic income and pollution load production; RP is the risk-added expense for crop economic income and pollution load production; a and B are different scene combinations of two future land use types and climate change.
10. The method for evaluating the quality of the water environment and the economic risk based on the random dominance theory according to claim 9, wherein the processing method in the step S41 is as follows: determining farmland pollution load production and crop yield time sequences under different future land utilization types and meteorological conditions, and crop market values and crop planting cost time sequences to obey multivariate empirical distribution, and generating long-time-sequence farmland pollution load production, crop yield, crop market values and crop planting costs by adopting a Monte Carlo random sampling method; and verifying whether the newly generated time sequence and the original time sequence obey the same distribution by adopting t test and F test, if obeying, retaining, and if not, sampling by adopting a Monte Carlo random sampling method again.
CN202011549277.1A 2020-12-24 2020-12-24 Water environment quality and economic risk evaluation method based on random dominance theory Pending CN112700098A (en)

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