CN113837891B - Large-area agricultural irrigation area water resource balance efficient allocation method for coping with climate change - Google Patents
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Abstract
The invention discloses a large-area agricultural irrigation area water resource balance efficient allocation method for coping with climate change. The method comprises the following steps: s1: collecting basic data; s2: constructing a crop water utilization efficiency function, and quantifying crop water resource allocation in a research area; s3: constructing an economic benefit function of crops, and quantifying the fairness of economic benefit distribution based on the jealousness value of the users; s4: constructing a social welfare function, and building a multi-objective optimization model, and balancing the water resource allocation efficiency and the economic benefit fairness of the irrigation area; s5: setting a model boundary; s6, constructing a Bayesian network, and determining meteorological variables affecting runoff of a research area; s7: and constructing a multi-objective optimization model, and calculating water resource allocation data of the large-area agricultural irrigation area which is in response to the climate change. The method gives consideration to the comprehensive effect of multiple targets, processes the economic benefit relation from the subjective and objective angles, comprehensively considers the uncertainty of the influence of multiple factors on runoffs from the current situation and the future, and therefore realizes sustainable and efficient management of water resources in irrigation areas.
Description
Technical Field
The invention belongs to the technical field of agricultural water management, and particularly relates to a large-area agricultural irrigation area water resource balancing and efficient allocation method for coping with climate change.
Background
Agricultural water resources are important strategic resources for guaranteeing agricultural production, and currently, water resource shortage and uneven space-time distribution are important constraint factors for grain safety and ecological safety in China. With the increasing demands of the national people for grains, the contradiction between the supply and demand of agricultural water resources is increasingly highlighted. In addition, extreme weather conditions exacerbate the uncertainty of water resources and restrict sustainable development of agriculture. The agricultural irrigation area is taken as a center of agricultural production in China, is an important support for guaranteeing national grain safety, and the reasonable and efficient allocation of water resources in the agricultural irrigation area based on climate change is a hot spot problem to be solved in China.
At present, the conventional water resource allocation optimization method only considers the economic benefit or the decision target of the water resource utilization efficiency independently, and ignores the comprehensive effect and the social effect of the economic benefit or the water resource utilization efficiency on water resource management. The conventional water resource optimal allocation model only calculates economic income and output from an objective angle when processing the relation of economic benefits, and fails to consider subjective factors of clients and resource allocation fairness and appropriately adjust the actual conditions of each water unit in time, so that the model has no universality and flexibility. In addition, the existing water resource optimal allocation model rarely considers the coupling uncertainty among various hydrological meteorological elements, random uncertainty exists in water resource supply quantity due to natural condition changes such as rainfall, runoff and the like caused by climate change, and the calculation accuracy and the application range of the existing model are limited. Therefore, there is a need to develop a method for balancing and efficiently allocating water resources in a large-area agricultural irrigation area to cope with climate change by comprehensively considering the allocation of agricultural water resources.
Disclosure of Invention
The invention aims to solve the problems in the prior art, provides a balanced and efficient allocation method for water resources in a large-area agricultural irrigation area, which is used for coping with climate change, gives consideration to multi-objective comprehensive effects such as resource utilization efficiency, economic benefit, social effect and the like, processes the linkage relation between resource utilization and social economic effect from subjective and objective angles, comprehensively considers uncertainty of the influence of multiple factors on runoffs from current situation and future aspects, predicts future runoff amount, can meet the cooperative regulation and control among different water decision departments, and allocates agricultural water resources more reasonably, thereby realizing sustainable and efficient management of the water resources in the irrigation area.
(1) On the basis of considering economic benefit or water resource utilization efficiency decision targets, the comprehensive effect and social effect of multiple targets are considered, and the cooperative regulation and control among different decision departments are satisfied; (2) The economic benefit is comprehensively quantified by calculating the income and the output of the economy from the subjective angle through reflecting the jealoy value of the user and combining the objective angle, so that the agricultural resources can be allocated more reasonably; (3) And through the Bayesian network, the influence of multiple factors on the runoff is comprehensively considered from the current situation and the future, and the future runoff is predicted.
The technical scheme of the invention is realized as follows: a large-area agricultural irrigation area water resource balance efficient allocation method for coping with climate change is characterized by comprising the following steps: s1: basic data collection: the meteorological hydrologic data comprise effective rainfall, irrigation water utilization coefficient and crop water demand; the social economic data comprise crop price, planting cost, irrigation area, crop water productivity, complete rate of matched engineering and dosage of chemical fertilizer, pesticide, agricultural machinery and agricultural film in unit area; the environmental data includes temperature, transpiration, specific humidity and pressure;
s2: constructing a crop water utilization efficiency function, and quantifying crop water resource allocation in a research area; wherein, the moisture utilization efficiency function is:
in the method, in the process of the invention,and->The irrigation quota of surface water and underground water in a period of time of the irrigation k crops in the i irrigation areas is respectively, m 3 /hm 2 ；a ik Is the ratio of irrigation area to planting area; k represents crop index, k=1, 2, …, K; sur represents surface water superscript; gro represents ground water superscript; a is that ik Is the planting area of k crops in an i irrigation area, hm 2 ；Y ik Is the yield per unit area of k crops in the irrigation area, kg/hm 2 The method comprises the steps of carrying out a first treatment on the surface of the IWUE is a moisture utilization efficiency function.
S3: constructing an economic benefit function of crops, and quantifying the fairness of economic benefit distribution based on the user jealousness value: constructing economic benefit, engineering effect and ecological effect index data of a plurality of research areas, evaluating and scoring the economic benefit, engineering effect and ecological effect of each research area respectively through weight determination of a first-level index and weight calculation of a second-level index, and calculating the coupling degree, the co-schedule and the coupling co-schedule; the first-level index comprises economic benefit, engineering effect and ecological effect; wherein, the second-level index of the economic effect comprises a unit area yield value and a single water yield; the second-level indexes of the engineering effect comprise irrigation water utilization coefficient, water-saving potential and complete rate of matched engineering; the secondary indexes of the ecological effect comprise ecological water demand, groundwater supply and effective precipitation; evaluating the coupling coordination degree level and determining the coupling coordination degree level and the interval form; and (3) inputting the coupling cooperative control value as a jealoy value of the economic benefit into an economic benefit model, and evaluating the influence of each research area on the overall utility, wherein the weight of the primary index and the weight of the secondary index are calculated by an AHP method and an entropy weight method respectively.
Wherein, the economic benefit fairness model is:
wherein R is a benefit, a member; c (C) cos Is the cost, the element; d, d i Is the jealoy value of each i-irrigated area; alpha is the maximum acceptable jealousness value for the user; epsilon is a sufficiently small number. When alpha → +. In the case of infinity, the air conditioner is controlled,0, and the loss due to unfairness is 0; when α=0, ++>Individuals are totally unable to accept unfairness;
in PC k Representing the k price of crops, yuan/kg; YA ik Representation ofYield per unit area of crop k in irrigated area i, kg/hm 2 。
C cos ＝ECF+WCF
Wherein ECF is the total planting cost of crops, yuan/ha; WCF is the water cost of crops; delta k Representing the cost per unit area of crop k, yuan/hm 2 ；Representing the price of surface water, yuan/m 3 ；/>Representing the price of groundwater, yuan/m 3 。
S4: establishing a social benefit function, and establishing a multi-objective optimization model, and balancing the water resource allocation efficiency and the economic benefit fairness of the irrigation area, wherein the social benefit function is as follows:
W[f(U),f(F)]＝f(U) a ×f(F) 1-a
wherein F (U) is a water utilization efficiency function, F (F) is an economic benefit function, and a is a fairness parameter;
s5: setting a model boundary: model boundary constraints include surface water availability constraints, groundwater availability constraints, grain supply constraints, irrigation quota constraints, and decision variables should not be negative constraints; the surface water availability constraints:
wherein SWA is the total water quantity on the surface, m 3 ；η sur Representing surface water conservancyEfficiency of use;
the groundwater availability constraint:
in the formula, the total amount of TGWA groundwater, m 3 ；η gro Represents the efficiency of groundwater use;
the grain supply constraint:
in the formula, PO i Representing the population of the partition; FD represents food demand, kg/person;
the irrigation quota constraint is:
in IQ min And IQ max Is the minimum quota and the maximum quota of the crop allowed irrigation;
decision variables should not be negative constraints:
s6: constructing a Bayesian network, and determining meteorological variables affecting runoff of a research area; learning the Bayesian network structure by a climbing method, checking the relevant strength of each variable, and adjusting the Bayesian network structure by a black-and-white table; parameter learning is carried out by utilizing a maximum likelihood method; bayesian reasoning is carried out, posterior probability distribution of each parameter is determined, and probability density of influence of each variable on the runoff is obtained; predicting irrigation area water resource supply and demand through two scenes of RCP4.5 and RCP8.5, and carrying out uncertainty analysis on the predicted value;
s7: constructing a multi-objective optimization model, and calculating water resource balance and high-efficiency allocation data of a large-area agricultural irrigation area for coping with climate change:
maxW[f(U),f(F)]＝f(U) a ×f(F) 1-a ＝(IWUE) a ×(z) 1-a 。
compared with the prior art, the method has the advantages that the economic benefit function and the jealous value reflecting the fairness of the user are quantized in the same target, and the comprehensive targets of the social benefit function in the two aspects of economic benefit and water utilization efficiency are utilized to establish a multi-target optimal configuration model: (1) On the basis of considering economic benefit or water resource utilization efficiency decision targets, the comprehensive effect and social effect of multiple targets are considered, and the cooperative regulation and control among different decision departments are satisfied; (2) The economic benefit is comprehensively quantified by calculating the income and the output of the economy from the subjective angle through reflecting the jealoy value of the user and combining the objective angle, so that the water resources of the large-area agricultural irrigation area can be allocated more reasonably; (3) And through the Bayesian network, the influence of multiple factors on the runoff is comprehensively considered from the current situation and the future, and the future runoff is predicted.
Drawings
FIG. 1 is a schematic flow diagram of a method for balancing and efficiently allocating water resources in a large-area agricultural irrigation area for coping with climate change;
Detailed Description
A large-area agricultural irrigation area water resource balance efficient allocation method for coping with climate change is characterized by comprising the following steps: s1: basic data collection: the meteorological hydrologic data comprise effective rainfall, irrigation water utilization coefficient and crop water demand; the social economic data comprise crop price, planting cost, irrigation area, crop water productivity, complete rate of matched engineering and dosage of chemical fertilizer, pesticide, agricultural machinery and agricultural film in unit area; the environmental data includes temperature, transpiration, specific humidity and pressure;
s2: constructing a crop water utilization efficiency function, and quantifying crop water resource allocation in a research area; wherein, the moisture utilization efficiency function is:
in the method, in the process of the invention,and->The irrigation quota of surface water and underground water in a period of time of the irrigation k crops in the i irrigation areas is respectively, m 3 /hm 2 ；a ik Is the ratio of irrigation area to planting area; k represents crop index, k=1, 2, …, K; sur represents surface water superscript; gro represents ground water superscript; a is that ik Is the planting area of k crops in an i irrigation area, hm 2 ；Y ik Is the yield per unit area of k crops in the irrigation area, kg/hm 2 The method comprises the steps of carrying out a first treatment on the surface of the IWUE is a moisture utilization efficiency function.
S3: constructing an economic benefit function of crops, and quantifying the fairness of economic benefit distribution based on the user jealousness value: constructing economic benefit, social benefit and ecological benefit index data of a plurality of research areas, evaluating and scoring the economic benefit, the social benefit and the ecological benefit of each research area respectively through weight determination of a first-level index and weight calculation of a second-level index, and calculating the coupling degree, the co-scheduling and the coupling co-scheduling of the research areas; the first-level index comprises economic benefit, engineering effect and ecological effect; wherein, the second-level index of the economic effect comprises a unit area yield value and a single water yield; the second-level indexes of the engineering effect comprise irrigation water utilization coefficient, water-saving potential and complete rate of matched engineering; the secondary indexes of the ecological effect comprise ecological water demand, groundwater supply and effective precipitation; evaluating the coupling coordination degree level and determining the coupling coordination degree level and the interval form; and (3) inputting the coupling cooperative control value as a jealoy value of the economic benefit into an economic benefit model, and evaluating the influence of each research area on the overall utility, wherein the weight of the primary index and the weight of the secondary index are calculated by an AHP method and an entropy weight method respectively.
Wherein, the economic benefit fairness model is:
wherein R is a benefit, a member; c (C) cos Is the cost, the element; d, d i Is the jealoy value of each i-irrigated area; alpha is the maximum acceptable jealousness value for the user; epsilon is a sufficiently small number. When alpha → +. In the case of infinity, the air conditioner is controlled,0, and the loss due to unfairness is 0; when α=0, ++>Individuals are totally unable to accept unfairness;
in PC k Representing the k price of crops, yuan/kg; YA ik Indicating the yield per unit area of crop k in irrigated area i, kg/hm 2 。
C cos ＝ECF+WCF
Wherein ECF is the total planting cost of crops, yuan/ha; WCF is the water cost of crops; delta k Representing the cost per unit area of crop k, yuan/hm 2 ；Representing the price of surface water, yuan/m 3 ；/>Representing the price of groundwater, yuan/m 3 。
S4: establishing a social benefit function, and establishing a multi-objective optimization model, and balancing the water resource allocation efficiency and the economic benefit fairness of the irrigation area, wherein the social benefit function is as follows:
W[f(U),f(F)]＝f(U) a ×f(F) 1-a
wherein F (U) is a water utilization efficiency function, F (F) is an economic benefit function, and a is a fairness parameter;
s5: setting a model boundary: model boundary constraints include surface water availability constraints, groundwater availability constraints, grain supply constraints, irrigation quota constraints, and decision variables should not be negative constraints; the surface water availability constraints:
wherein SWA is the total water quantity on the surface, m 3 ；η sur Representing the surface water utilization efficiency;
the groundwater availability constraint:
in the formula, the total amount of TGWA groundwater, m 3 ；η gro Represents the efficiency of groundwater use;
the grain supply constraint:
in the formula, PO i Representing the population of the partition; FD represents food demand, kg/person;
the irrigation quota constraint is:
in IQ min And IQ max Is the minimum quota and the maximum quota of the crop allowed irrigation;
decision variables should not be negative constraints:
s6: constructing a Bayesian network, and determining meteorological variables affecting runoff of a research area; learning the Bayesian network structure by a climbing method, checking the relevant strength of each variable, and adjusting the Bayesian network structure by a black-and-white table; parameter learning is carried out by utilizing a maximum likelihood method; bayesian reasoning is carried out, posterior probability distribution of each parameter is determined, and probability density of influence of each variable on the runoff is obtained; predicting irrigation area water resource supply and demand through two scenes of RCP4.5 and RCP8.5, and carrying out uncertainty analysis on the predicted value;
s7: constructing a multi-objective optimization model, and calculating water resource balance and high-efficiency allocation data of a large-area agricultural irrigation area for coping with climate change:
maxW[f(U),f(F)]＝f(U) a ×f(F) 1-a ＝(IWUE) a ×(z) 1-a 。
Claims (6)
1. a large-area agricultural irrigation area water resource balance efficient allocation method for coping with climate change is characterized by comprising the following steps:
s1: basic data collection: the meteorological hydrologic data comprise effective rainfall, irrigation water utilization coefficient and crop water demand;
the social economic data comprise crop price, planting cost, irrigation area, crop water productivity, complete rate of matched engineering and dosage of chemical fertilizer, pesticide, agricultural machinery and agricultural film in unit area; the environmental data includes temperature, transpiration, specific humidity and pressure;
s2: constructing a crop water utilization efficiency function, and quantifying crop water resource allocation in a research area:
s3: constructing an economic benefit function of crops, and quantifying the fairness of economic benefit distribution based on the user jealousness value: constructing economic benefit, engineering effect and ecological effect index data of a plurality of research areas, evaluating and scoring the economic benefit, engineering effect and ecological effect of each research area respectively through weight determination of a first-level index and weight calculation of a second-level index, and calculating the coupling degree, the co-schedule and the coupling co-schedule; evaluating a coupling coordination degree level; the coupling cooperative scheduling value is used as a jealoy value of economic benefit and is input into an economic benefit model, and the influence of each research area on the whole effect is evaluated;
s4: establishing a social benefit function, and establishing a multi-objective optimization model, and balancing the water resource allocation efficiency and the economic benefit fairness of the irrigation area, wherein the social benefit function is as follows:
W[f(U),f(F)]＝f(U) a ×f(F) 1-a
wherein F (U) is a water utilization efficiency function, F (F) is an economic benefit function, and a is a fairness parameter;
s5: setting a model boundary: model boundary constraints include surface water availability constraints, groundwater availability constraints, grain supply constraints, irrigation quota constraints, and decision variables should not be negative constraints;
s6: constructing a Bayesian network, and determining meteorological variables affecting runoff of a research area; learning the Bayesian network structure by a climbing method, checking the relevant strength of each variable, and adjusting the Bayesian network structure by a black-and-white table; parameter learning is carried out by utilizing a maximum likelihood method; bayesian reasoning is carried out, posterior probability distribution of each parameter is determined, and probability density of influence of each variable on the runoff is obtained; predicting irrigation area water resource supply and demand through two scenes of RCP4.5 and RCP8.5, and carrying out uncertainty analysis on the predicted value;
s7: constructing a multi-objective optimization model, and calculating water resource balance and high-efficiency allocation data of a large-area agricultural irrigation area for coping with climate change:
maxW[f(U),f(F)]＝f(U) a ×f(F) 1-a ＝(IWUE) a ×(z) 1-a 。
2. the method for balanced and efficient allocation of water resources in a large-area agricultural irrigation area for coping with climate change according to claim 1, which is characterized by comprising the following steps: and S3, calculating the weight of the primary index and the weight of the secondary index through an Analytic Hierarchy Process (AHP) and an entropy weight method respectively.
3. The method for balanced and efficient allocation of water resources in a large-area agricultural irrigation area for coping with climate change according to claim 1, which is characterized by comprising the following steps: the moisture utilization efficiency function is:
in the method, in the process of the invention,and->The irrigation quota of surface water and underground water in a period of time of the irrigation k crops in the i irrigation areas is respectively, m 3 /hm 2 ；a ik Is the ratio of irrigation area to planting area; k represents crop index, k=1, 2, …, K; sur represents surface water superscript; gro represents ground water superscript; a is that ik Is the planting area of k crops in an i irrigation area, hm 2 ；Y ik Is the yield per unit area of k crops in the irrigation area, kg/hm 2 The method comprises the steps of carrying out a first treatment on the surface of the IWUE is a moisture utilization efficiency function.
4. The method for balanced and efficient allocation of water resources in a large-area agricultural irrigation area for coping with climate change according to claim 1, which is characterized by comprising the following steps: the economic benefit fairness model described in S3 is:
wherein R is a benefit, a member; c (C) cos Is the cost, the element; d, d i Is the jealoy value of each i-irrigated area; alpha is the maximum acceptable jealousness value for the user; epsilon is a sufficiently small number; when alpha → +. In the case of infinity, the air conditioner is controlled,0, and the loss due to unfairness is 0; when α=0, ++>Individuals are totally unable to accept unfairness;
in PC k Representing the k price of crops, yuan/kg; YA ik Indicating the yield per unit area of crop k in irrigated area i, kg/hm 2，
C cos ＝ECF+WCF
Wherein ECF is the total planting cost of crops, yuan/ha; WCF is the water cost of crops; delta k Representing the cost per unit area of crop k, yuan/hm 2 ；WP i sur Representing the price of surface water, yuan/m 3 ；WP i gro Representing the price of groundwater, yuan/m 3 。
5. The method for balanced and efficient allocation of water resources in a large-area agricultural irrigation area for coping with climate change according to claim 1, which is characterized by comprising the following steps: surface water availability constraints:
wherein SWA is the total water quantity on the surface, m 3 ；η sur Representing the surface water utilization efficiency;
groundwater availability constraints:
in the formula, the total amount of TGWA groundwater, m 3 ；η gro Represents the efficiency of groundwater use;
grain supply constraint:
in the formula, PO i Representing the population of the partition; FD represents food demand, kg/person;
irrigation quota constraints are:
in IQ min And IQ max Is the minimum quota and the maximum quota of the crop allowed irrigation;
decision variables should not be negative constraints:
6. the method for balanced and efficient allocation of water resources in a large-area agricultural irrigation area for coping with climate change according to claim 1, which is characterized by comprising the following steps: the first-level indexes in the S3 comprise economic benefit, engineering effect and ecological effect; wherein, the second-level index of the economic effect comprises a unit area yield value and a single water yield; the second-level indexes of the engineering effect comprise irrigation water utilization coefficient, water-saving potential and complete rate of matched engineering; the secondary indexes of the ecological effect comprise ecological water demand, groundwater supply and effective precipitation.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106557658A (en) * | 2016-11-10 | 2017-04-05 | 水利部交通运输部国家能源局南京水利科学研究院 | Irrigation requirement computing system and its method under a kind of climate change background |
| CN110992201A (en) * | 2019-12-17 | 2020-04-10 | 黄河勘测规划设计研究院有限公司 | Comprehensive measure configuration method for realizing water saving and diving in ecological irrigation area |
| EA202090498A1 (en) * | 2018-05-08 | 2020-07-06 | Рубикон Ресёрч Пти Лтд | METHOD AND SYSTEM OF WATER DISTRIBUTION AND DETERMINATION OF SOIL MOISTURE |
| CN112241607A (en) * | 2020-08-31 | 2021-01-19 | 广东工业大学 | Water resource planning method based on simulation and considering climate change |
| CN112465206A (en) * | 2020-11-20 | 2021-03-09 | 广东工业大学 | Water resource optimization configuration and optimization result evaluation feedback method |
| KR102277856B1 (en) * | 2020-04-29 | 2021-07-16 | 경희대학교 산학협력단 | Socio-hydrological system for supporting decisions on water resources policy and operating method thereof |
-
2021
- 2021-08-09 CN CN202110906820.7A patent/CN113837891B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106557658A (en) * | 2016-11-10 | 2017-04-05 | 水利部交通运输部国家能源局南京水利科学研究院 | Irrigation requirement computing system and its method under a kind of climate change background |
| EA202090498A1 (en) * | 2018-05-08 | 2020-07-06 | Рубикон Ресёрч Пти Лтд | METHOD AND SYSTEM OF WATER DISTRIBUTION AND DETERMINATION OF SOIL MOISTURE |
| CN110992201A (en) * | 2019-12-17 | 2020-04-10 | 黄河勘测规划设计研究院有限公司 | Comprehensive measure configuration method for realizing water saving and diving in ecological irrigation area |
| KR102277856B1 (en) * | 2020-04-29 | 2021-07-16 | 경희대학교 산학협력단 | Socio-hydrological system for supporting decisions on water resources policy and operating method thereof |
| CN112241607A (en) * | 2020-08-31 | 2021-01-19 | 广东工业大学 | Water resource planning method based on simulation and considering climate change |
| CN112465206A (en) * | 2020-11-20 | 2021-03-09 | 广东工业大学 | Water resource optimization configuration and optimization result evaluation feedback method |
Non-Patent Citations (2)
| Title |
|---|
| 基于混合遗传算法的红兴隆分局水资源优化配置;刘姣;刘东;;水土保持研究(第06期);183-187 * |
| 灌区广义水资源不确定性多目标优化配置;岳琼;郭萍;唐毅宽;赵敏;;干旱地区农业研究(第04期);174-180 * |
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