A natural-social coupled water trading prediction method, system and storage medium

Abstract

The present application discloses a natural-social coupled water trading prediction method, system and survival medium, including: obtaining hydrological and meteorological data, global climate model output data, social population-economic data and water resources data in the study area; determining relative humidity and specific humidity, and calibrating four hydrological model parameters based on hydrological and meteorological data; using the corrected future global climate model output data to train four hydrological models to obtain the future daily runoff series data; obtaining the probability distribution characteristics of the future natural runoff of each sub-basin in the study area; evaluating the water demand threshold of the ecosystem in each sub-basin; constructing a natural-social coupled water trading dynamic prediction optimization model; estimating the corrected future water shortage area, and finally obtaining the evolution trend of the water shortage area after future water trading. The present invention can accurately predict the response characteristics of the dynamic changes of water trading under future climate change and future economic trends, and provide a scientific basis for water trading decisions.

Description

Technical Field

[0001] The present invention relates to the field of water resources management, and in particular to a nature-society coupled water transaction prediction method, system and storage medium. Background Art

[0002] In response to the increasingly severe water shortage and supply-demand contradiction, there is no literature that applies the combination of nature and society to the derivation of water resources management. The main methods for derivation and design of water resources regulation process lines are flawed and do not analyze the inherent response mechanism of water resources allocation under future climate change and future economic trends.

[0003] Water trading is an important means of optimizing water resource allocation through market-based mechanisms. It aims to give full play to the role of the market in water resource allocation on the basis of reasonably defining and allocating water resource use rights, and can improve the efficiency of water resource utilization and the comprehensive management effectiveness. However, the water trading system is a system full of uncertainty. Uncertain characteristics such as the randomness of meteorology and runoff, the uncertainty of water demand, the error of statistical data, and the incompleteness of hydrological data affect the effectiveness of water trading decisions. On the one hand, due to the randomness of natural processes in real problems, the trading process is often limited by inherent uncertainty. For example, affected by uncertain temperature and precipitation, the amount of available water resources has a high degree of random uncertainty, which may aggravate the conflict of water resource allocation between multiple competing interests. In addition, it is difficult to obtain accurate parameters in real situations, such as production scale, water consumption and economic data. If the above uncertainty and complexity characteristics are ignored, the decision-making process will lose a lot of important information, affecting the scientificity and effectiveness of the trading decision-making plan, and easily leading to decision-making errors and causing losses to the trading system. On the other hand, water resource management systems involve different decision-making levels, with conflicting goals and preferences. For example, in water trading, it is necessary to pursue benefits to achieve industrial and economic development, and to reduce water resource usage to meet multiple goals such as ecological water consumption. Therefore, it is necessary to introduce uncertainty analysis technology in water trading planning to reflect system complexity information in decision-making and improve decision-making accuracy. However, there is currently a lot of uncertainty in the long-term trend of changes under various parameters and constraints, which restricts the identification of mutual feedback between nature and society in the system. Although scholars at home and abroad have gradually paid attention to the impact of the nature-society coupling system, there is no literature that comprehensively considers the response mechanism of quantitative analysis of water trading dynamic changes under the prediction of future climate change and future economic trends. At present, there is an urgent need for a nature-society coupled water trading prediction method, system and storage medium. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a nature-society coupled water transaction prediction method, system and storage medium to solve the defect of the prior art in not considering the nature-society coupled response under uncertain conditions in the dynamic prediction of water resources management.

[0005] In a first aspect, the present invention provides a nature-society coupled water trading prediction method, comprising the following steps:

[0006] Step 1: Obtain hydrometeorological data, global climate model output data, socio-demographic-economic data, and water resources data for the study area;

[0007] Step 2: Determine relative humidity and specific humidity based on hydrological and meteorological data, and calibrate four hydrological model parameters based on the hydrological and meteorological data;

[0008] Step 3: Based on the global climate model output data and hydrological and meteorological data, the corrected future global climate model output data is obtained. The corrected future global climate model output data is used to train four hydrological models to obtain the future daily runoff series data.

[0009] Step 4: Based on the future daily runoff series data, obtain the annual series data of natural runoff in the study area in the future, fit its distribution, and obtain the probability distribution characteristics of natural runoff in each sub-basin in the study area in the future;

[0010] Step 5: Calculate the standardized runoff index based on the probability distribution of natural runoff in the future period and evaluate the water demand threshold of the ecosystem in each sub-basin;

[0011] Step 6: Take socio-demographic-economic data, water resources data, probability distribution of natural runoff in the future period and water demand threshold of ecosystem as input data, and take economic benefits, food production, water trading volume and water-deficient area in the future period as output data to construct a dynamic prediction and optimization model of water trading for natural-social coupling;

[0012] Step 7: Based on the constructed water trading dynamic prediction optimization model, the evolution trend of the water-scarce area after future water trading is predicted.

[0013] Furthermore, the hydrological and meteorological data in step 1 include: daily runoff data of the control water level station in the selected study area in the historical period, and hydrological and meteorological observation data of the ERA5 reanalysis data set in the historical period;

[0014] The global climate model output data include historical global climate model output data and future global climate model output data; the historical global climate model output data are historical simulation data of 35 global climate models; the future global climate model output data are future prediction data of 35 global climate models under three SSPs;

[0015] Socio-demographic-economic data include: demographic and socio-economic index data for historical and future periods;

[0016] Water resources data include: historical social water use, water supply and water storage data, and future water storage, social water use and water withdrawal data under the ISIMIP3b framework.

[0017] Furthermore, the step 2 comprises:

[0018] Based on the hourly surface 2m air temperature, hourly dew point temperature and Clausius-Clapeyron thermodynamic equation of each grid point in the hydrological and meteorological observation data of the historical ERA5 reanalysis data set, the hourly relative humidity is obtained, and then the daily average relative humidity is calculated;

[0019] Based on the hourly dew point temperature and the near-surface air pressure in the hydrological and meteorological observation data of the historical ERA5 reanalysis data set, the hourly specific humidity is obtained, and then the daily average specific humidity is calculated;

[0020] Based on the daily precipitation, daily average surface 2m temperature, daily maximum temperature, daily minimum temperature, shortwave radiation, near-ground wind speed, longwave radiation and daily snowfall at each grid point in the hydrological and meteorological observation data of the historical ERA5 reanalysis data set obtained in step 1, as well as the calculated daily average relative humidity and daily average specific humidity, the four hydrological models XAJ, GR4J-6, HBV and SIMHYD were calibrated with the daily runoff data of the control water level station in the study area as the target variable, and the SCE-UA algorithm was used to estimate the parameters of the four hydrological models.

[0021] Furthermore, the step 3 comprises:

[0022] Calculate the differences between the historical global climate model output data and the hydrological and meteorological observation data of the historical ERA5 reanalysis data set at each quantile, and remove the differences at each quantile of the future global climate model output data to obtain the corrected future global climate model output data;

[0023] The corrected output data of the future global climate model were input into the four calibrated hydrological models to simulate and obtain the future daily runoff series data.

[0024] Furthermore, the step 4 comprises:

[0025] Based on the hydrological and meteorological data and water resources data obtained in step 1, the future daily runoff series data obtained in step 3 are restored using the sub-item survey method to obtain the future natural runoff annual series data of the study area;

[0026] The Pearson III distribution is used to fit the probability density function and calculate the basin characteristic value; the frequency curve of the basin's natural runoff is fitted to determine the values ​​of the coefficient of variation Cv and the coefficient of skewness Cs; and based on the annual series data of natural runoff in the study area in the future, the probability distribution characteristics of natural runoff in each sub-basin in the study area in the future are obtained.

[0027] Furthermore, the step 5 comprises:

[0028] Calculate the standardized runoff index (SRI) based on the probability distribution of natural runoff in the future period of each sub-basin in the study area in step 4;

[0029] The duration and severity of drought were identified based on the travel theory, and the SWAT model was used to analyze the changes in water demand of the ecosystem in each sub-basin under the above-mentioned drought severity by adjusting the hydrological parameters and ecological parameters in the SWAT model, so as to determine the water demand threshold of the ecosystem.

[0030] Further, the step 6 comprises:

[0031] The multi-objective programming method, interval linear programming method, two-stage stochastic programming method and bilateral chance-constrained programming method are integrated, and the obtained socio-demographic-economic data, water resources data, probability distribution of natural runoff in the future period and water demand threshold of the ecosystem are used as input data, and the future economic benefits, grain output, water trading volume and water-deficient area are used as output data to construct a dynamic prediction optimization model of water trading with interval random multi-layer bilateral chance constraints and nature-society coupling.

[0032] The nature-society coupled water trading dynamic prediction optimization model includes an objective function and constraints, wherein the objective function includes:

[0033] Upper-level goal: maximize economic benefits;

[0034] Mid-level goals: maximize food production;

[0035] Lower level goals: maximize ecological water use;

[0036] Constraints: including water use trading rules, food production scale, and water resources management constraints;

[0037] The nature-society coupled water trading dynamic prediction optimization model is solved based on the fuzzy satisfaction algorithm.

[0038] Furthermore, the step 7 comprises:

[0039] Based on the historical global climate model output data and the hydrological and meteorological observation data of the historical ERA5 reanalysis data set, the spatial mean was calculated using the Thiessen polygon method to obtain the annual rainfall and snowfall in each sub-basin of the study area.

[0040] Based on the nature-society coupled water trading dynamic prediction optimization model, the future trend of water shortage area in each sub-basin of the study area is predicted, and an emergence constraint model is constructed; the least squares method is used to solve the parameters of the emergence constraint model;

[0041] Based on the annual rainfall and snowfall data of the hydrological and meteorological observation data of the ERA5 reanalysis data set in each sub-basin of the study area in the historical period, the change trend is deduced and substituted into the constructed emergence constraint model to obtain the corrected trend of water shortage area change in the future period.

[0042] The weighted average of the corrected future water-scarce area change trends under 35 global climate models under three SSPs was used to predict the evolution trend of the water-scarce area after water trading in the future.

[0043] In another aspect, the present invention provides a nature-society coupled water trading prediction system, comprising:

[0044] Data acquisition module: It is used to obtain hydrological and meteorological data, global climate model output data, socio-demographic-economic data and water resources data in the study area;

[0045] A hydrological model calibration module, which is used to determine relative humidity and specific humidity according to hydrological and meteorological data, and calibrate four hydrological model parameters based on the hydrological and meteorological data;

[0046] A hydrological model training module is used to obtain corrected future global climate model output data based on global climate model output data and hydrological and meteorological data, and to train four hydrological models using the corrected future global climate model output data to obtain future daily runoff series data;

[0047] A fitting module is used to obtain the annual series data of natural runoff in the future period of the study area based on the daily runoff series data in the future period, fit its distribution, and obtain the probability distribution characteristics of the natural runoff in each sub-basin in the study area in the future period;

[0048] A water demand threshold assessment module, which is used to calculate the standardized runoff index based on the probability distribution of natural runoff in the future period and assess the water demand threshold of the ecosystem in each sub-basin;

[0049] The optimization model building module is used to take social demographic-economic data, water resources data, probability distribution of natural runoff in the future period and water demand threshold of the ecosystem as input data, and take economic benefits, food production, water trading volume and water-deficient area in the future period as output data to build a dynamic prediction optimization model of water trading for natural-social coupling;

[0050] A prediction module, which is used to predict the evolution trend of water-scarce areas after future water transactions based on a constructed water transaction dynamic prediction optimization model;

[0051] In a third aspect, the present invention provides a computer storage medium, wherein the computer storage medium is a server workstation;

[0052] The server workstation stores a computer program executed by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps in a nature-society coupled water trading prediction method.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The water trading prediction method, system and storage medium of the nature-society coupling provided by the present invention can accurately predict the response characteristics of the dynamic changes of water trading under future climate change and future economic trends, provide a scientific basis for water trading decision-making, and comprehensively consider factors such as climate, water resources, social economy and policies to improve the pertinence and effectiveness of decision-making. It is helpful to achieve the optimal allocation and efficient utilization of water resources and provide reference value for promoting the sustainable development of social economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 : A schematic diagram of the method flow provided by an embodiment of the present invention;

[0057] Figure 2 : A schematic diagram of future population and GDP forecast results provided by an embodiment of the present invention;

[0058] Figure 3 : A schematic diagram of the Pearson III type natural runoff distribution provided by an embodiment of the present invention;

[0059] Figure 4 : A schematic diagram of the run theory provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0061] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The process steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved. The division of units in this application is a logical division. There may be other division methods in actual application. For example, multiple units may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection between units may be electrical or other similar forms, which are not limited in this application. Moreover, the units or sub-units described as separate components may or may not be physically separated, may or may not be physical units, or may be distributed in multiple circuit units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present application.

[0062] Example 1

[0063] In the description of the present invention, it should be noted that the professional terms used in the present invention are first explained and annotated as follows:

[0064] ERA5 reanalysis dataset (ECMWF Reanalysis v5): The ERA5 reanalysis dataset is the fifth generation of atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts.

[0065] Cobb-Douglas model: Cobb-Douglas model is an important production function model in economics. Specifically, it mainly describes the relationship between input and output, covers all other factors that affect economic growth except labor and capital, and is an economic mathematical model used to predict the production of national and regional industrial systems or large enterprises and analyze the way to develop production.

[0066] CMIP6 (International Coupled Model Intercomparison Project Phase 6): CMIP6 is a climate system model that simulates various complex processes of the Earth's climate system, including the interactions between multiple components such as the atmosphere, ocean, land surface, and sea ice. Specifically, the climate model is simulated by stipulating a series of standardized experimental schemes, such as shared socioeconomic pathways and representative concentration pathways for socioeconomic development. The simulation results can provide rich data for climate change research and help understand the process, mechanism, and possible future trends of climate change.

[0067] ISIMIP3b (The Inter-Sectoral Impact Model Intercomparison Project)

[0068] :ISIMIP3b is a model used to study the impact of climate change and is the 3b phase of the Inter-sectoral Impact Model Comparison Program. Specifically, ISIMIP3b is mainly used to evaluate the impact of different climate models on future climate change, especially in the fields of agriculture, water resources, health and energy.

[0069] XAJ (Xin'anjiang Model): The XAJ model is a conceptual rainfall-runoff model with 15 parameters. It uses mathematical equations with empirical relationships derived from observational data to estimate the various components of the water balance equation, including evapotranspiration calculation, runoff calculation, water source division, and confluence calculation. Specifically, the model divides the watershed into permeable and impermeable areas, and considers evaporation from three layers of soil; for impermeable areas, precipitation is directly converted into surface runoff after meeting evaporation; while for permeable areas, the total runoff consists of surface runoff, soil flow, and groundwater runoff.

[0070] GR4J-6 (Génie Rural à 4 paramètres Journalier-6): The GR4J-6 model relies on the simplification of natural processes. It is a lumped conceptual hydrological model with only six parameters. Specifically, it consists of two nonlinear reservoirs, a runoff-generating reservoir and a confluence reservoir. The model consists of two unit process lines to simulate the confluence process, and a snow module with two free parameters is added. The total rainfall involved in the confluence calculation is divided into two parts: 90% of the water enters the confluence reservoir through a unit line for regulation, and the remaining 10% of the water forms direct confluence through another unit line.

[0071] HBV (Hydrologiska Byråns Vattenbalansavdelning): The HBV model simulates hydrological processes at the basin scale and has a simple structure. Specifically, it mainly includes snow accumulation and snowmelt modules, soil moisture calculation modules and runoff generation and confluence modules. It has relatively good performance in capturing key hydrological processes at various time scales. Therefore, it is widely used in flood forecasting, water resources planning and management, and climate change impact assessment worldwide.

[0072] SIMHYD (Simple Hydrological Model): The SIMHYD model is a nine-parameter conceptual hydrological model with a simple and clear structure. It divides the watershed into three parts: interception reservoir, soil reservoir and linear reservoir. Specifically, in this model, infiltration capacity, soil water content and soil moisture are the main factors affecting runoff; full runoff is generated after the water deficit in the vadose zone is replenished, and rainfall exceeding the infiltration capacity will cause excess infiltration runoff; infiltration water is divided into soil flow, groundwater recharge and soil absorption parts, and the infiltration amount is a function of soil moisture. The unit process line is used for the confluence process.

[0073] SCE-UA algorithm (Shuffled Complex Evolution-University of Arizona): SCE-UA algorithm is a global optimization algorithm that can study the characteristics and construction of the global optimal solution of multivariable nonlinear functions in the constraint area to seek the global optimal solution. Specifically, it integrates the advantages of random search algorithm, simplex method, cluster analysis and biological competitive evolution, and can effectively deal with the problems of roughness, insensitive areas and non-convexity on the objective function reflection surface, and is not disturbed by local minimum points.

[0074] Pearson III distribution: Pearson III distribution is an important probability distribution, which contains location parameters, shape parameters and scale parameters, which respectively determine the starting position, shape and degree of dispersion of the distribution. This distribution is widely used in many fields, especially in hydrology to analyze the frequency of floods and low water. By assuming that historical hydrological data obeys this distribution, the flow under different recurrence periods is calculated. In meteorological statistics, it is also used to evaluate the probability distribution of meteorological elements such as precipitation and temperature.

[0075] Based on the defects in the background technology, the embodiment of the present invention proposes a natural-society coupled water transaction prediction method, system and storage medium. Figure 1 is a flow chart of a natural-society coupled water trading prediction method, system and storage medium provided by an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0076] Step 1: Obtain hydrometeorological data, global climate model output data, socio-demographic-economic data, and water resources data for the study area;

[0077] Step 2: Determine relative humidity and specific humidity based on hydrological and meteorological data, and calibrate four hydrological model parameters based on the hydrological and meteorological data;

[0078] Step 3: Based on the global climate model output data and hydrological and meteorological data, the corrected future global climate model output data is obtained. The corrected future global climate model output data is used to train four hydrological models to obtain the future daily runoff series data.

[0079] Step 4: Based on the future daily runoff series data, obtain the annual series data of natural runoff in the study area in the future, fit its distribution, and obtain the probability distribution characteristics of natural runoff in each sub-basin in the study area in the future;

[0080] Step 5: Calculate the standardized runoff index based on the probability distribution of natural runoff in the future period and evaluate the water demand threshold of the ecosystem in each sub-basin;

[0081] Step 6: Take socio-demographic-economic data, water resources data, probability distribution of natural runoff in the future period and water demand threshold of ecosystem as input data, and take economic benefits, food production, water trading volume and water-deficient area in the future period as output data to construct a dynamic prediction and optimization model of water trading for natural-social coupling;

[0082] Step 7: Based on the constructed water trading dynamic prediction optimization model, the evolution trend of the water-scarce area after future water trading is predicted.

[0083] Step 1 specifically includes the following:

[0084] The hydrometeorological data include: daily runoff series of the control water level stations in the study area during the historical period, and the hydrometeorological observation data of the ERA5 reanalysis dataset;

[0085] Specifically, hydrological and meteorological data such as precipitation, air pressure, 2m surface air temperature, 2m dew point temperature, shortwave radiation, near-ground wind speed, longwave radiation, and snowfall are obtained from the hydrological and meteorological observation data of the historical ERA5 reanalysis dataset. ERA5 is the fifth generation of atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts, with a spatial resolution of 0.25°, providing hourly meteorological data covering the world since 1940. For each grid point in the study area, the embodiment of the present invention obtains a daily series through time scale conversion.

[0086] The global climate model output data include: historical global climate model output data and future global climate model output data; the historical global climate model output data are historical simulation data of 35 global climate models; the future global climate model output data are future prediction data of 35 global climate models under three SSPs;

[0087] Specifically, the latest CMIP6 release includes 35 global climate models for historical and future periods. CMIP6 introduces three dual scenario frameworks, namely SSP126, SSP370 and SSP585, which focus on the shared socioeconomic pathway (SSP) for socioeconomic factors and representative concentration pathway (RCP) for environmental change in the future period. The embodiment of the present invention selects the output data of the global climate model for the historical and future periods, including the meteorological variables of daily precipitation, daily average surface temperature at 2m, daily maximum temperature, daily minimum temperature, specific humidity, relative humidity, shortwave radiation, near-ground wind speed, longwave radiation and daily snowfall data. The historical period is set to 1985-2014, and the future period is set to 2015-2100.

[0088] Socio-demographic-economic data include: demographic and socio-economic index data for historical and future periods;

[0089] Specifically, the embodiment of the present invention uses a population policy estimation data set released by a university evaluation collaborative innovation center. The data set takes into account the results of previous domestic population and economic censuses, and estimates the population and socioeconomic index of the domestic shared socioeconomic path from 2010 to 2100 by using the Cobb-Douglas model and the population-environment-development model. Figure 2 The dataset has been widely used to assess socio-economic impacts on water resource management, as shown by the results of population and economic projections for the future period.

[0090] Water resources data include: historical social water use, water supply and water storage data, and future water storage, social water use and water withdrawal data under the ISIMIP3b framework;

[0091] Specifically, historical water resources data were obtained from the water resources management departments, water conservancy bureaus, hydrological and water resources survey bureaus and reservoir water and rainfall monitoring stations in the study area, including data on crop production, forestry, animal husbandry and fishery, industry and urban life, as well as water resources supply and demand. Future water resources data were obtained from the future data of the H08 global hydrological model under the ISIMIP3b framework, using the output data of the SSP126, SSP370 and SSP585 matrix frameworks to obtain annual-scale urban life water consumption, industrial water consumption, forestry, animal husbandry and fishery water consumption and total water consumption of other departments in the future.

[0092] Step 2 specifically includes the following:

[0093] The latent heat of vaporization constant, the water vapor gas constant, the first integral constant and the second integral constant in the Clausius-Clapeyron thermodynamic equation are determined to obtain a nonlinear function of saturated water vapor pressure and air temperature; the hourly surface 2m air temperature and dew point temperature of each grid point in the hydrological and meteorological observation data of the historical ERA5 reanalysis data set are substituted into the nonlinear function of saturated water vapor pressure and air temperature to obtain the hourly relative humidity, and then the daily average relative humidity is calculated; the hourly dew point temperature is substituted into the nonlinear function of saturated water vapor pressure and air temperature, and the hydrological and meteorological observation data of the historical ERA5 reanalysis data set are substituted into the nonlinear function of saturated water vapor pressure and air temperature. The near-ground air pressure in the meteorological observation data is used to obtain the hourly specific humidity, and then the daily average specific humidity is calculated; based on the daily precipitation, daily average surface 2m temperature, daily maximum temperature, daily minimum temperature, shortwave radiation, near-ground wind speed, longwave radiation and daily snowfall of each grid point in the hydrological and meteorological observation data of the historical period ERA5 reanalysis data set obtained in step 1, and the derived daily average specific humidity and relative humidity are used as inputs, and the daily runoff data of the control water level station in the selected study area in the historical period is used as the target variable to calibrate the four hydrological models of XAJ, GR4J-6, HBV and SIMHYD;

[0094] Specifically, hourly relative humidity and specific humidity are first derived based on the hydrological and meteorological observation data of the historical ERA5 reanalysis data set. The Clausius-Clapeyron thermodynamic equation can quantitatively describe the nonlinear relationship between saturated water vapor pressure and temperature T:

[0095] (1)

[0096] Where: For temperature T Saturated water vapor pressure under and are the first and second integral constants, which are 273.16 K and 611 Pa respectively; is the latent heat of vaporization constant, taken as 2.5×106 J·kg-1; is the water vapor gas constant, which is taken as 461 J·kg-1·K-1;

[0097] The dew point temperature represents the temperature when the air is cooled to water vapor saturation under the condition of constant water vapor content and air pressure. Substituting it into the Clausius-Clapeyron equation can measure the actual water vapor pressure. ) and dew point temperature ( ) are substituted into formula (1) to deduce the near-ground relative humidity Specific humidity q is the ratio of water vapor mass to total air mass mass, using the near-surface pressure in the hydrological and meteorological observation data of the historical ERA5 reanalysis dataset p And dew point temperature, the formula is as follows:

[0098] (2)

[0099] After obtaining the hourly relative humidity and specific humidity of each grid point, the daily average relative humidity and specific humidity of each grid point were calculated. Then, the XAJ, GR4J-6, HBV and SIMHYD hydrological models were calibrated based on the daily runoff data of the control water level stations in the study area selected in the historical period and the daily data of each grid point in the hydrological and meteorological observation data of the ERA5 reanalysis data set in the historical period.

[0100] Furthermore, the SCE-UA algorithm is used to estimate the parameters of the above four hydrological models, and the Kling-Gupta efficiency coefficient (KGE) is selected as the runoff simulation accuracy evaluation index. The calculation method of this index is as follows:

[0101] (3)

[0102] Where: Characterize the Pearson linear correlation coefficient, The ratio of the measured flow rate to the simulated flow rate. Characterizes the ratio of the measured flow rate to the average simulated flow rate. The range of is (-∞,1], where When it is equal to 1, the simulation effect of the characterization model is optimal.

[0103] Step 3 specifically includes the following:

[0104] The difference between the historical global climate model output data and the hydrological and meteorological observation data of the historical ERA5 reanalysis data set at each quantile (0.01-0.99) is removed from the future global climate model output data at each quantile to obtain the corrected future global climate model output data;

[0105] Specifically, the corrections for air temperature (as well as specific humidity, relative humidity, wind speed, shortwave radiation and longwave radiation) are as follows:

[0106] (4)

[0107] The correction for precipitation (and snowfall) is as follows:

[0108] (5)

[0109] Where: T and P Respectively represent air temperature (specific humidity, relative humidity, wind speed, shortwave radiation and longwave radiation) and precipitation (snowfall), adj represents the corrected series, obs represents the observed data, GCM, ref and GCM, fut Represent the historical period and the future period respectively. d Represents daily data, Q Represents each quantile.

[0110] Furthermore, the calibrated output data of the global climate model for the future period (including daily precipitation, daily average surface temperature at 2 m, daily maximum temperature, daily minimum temperature, specific humidity, relative humidity, shortwave radiation, near-surface wind speed, longwave radiation and daily snowfall) are used to drive the four hydrological models calibrated in step 2 to simulate the hydrological process under the future scenario. The calibrated output data of the global climate model for the future period are input into the four calibrated hydrological models to simulate the daily runoff series data for the future period.

[0111] Step 4 specifically includes the following:

[0112] Due to the influence of human activities, simulated runoff cannot fully reflect the law of river runoff. It is necessary to conduct runoff reduction investigation and analysis and calculation to restore the simulated runoff series to the natural runoff series. Based on the hydrological and meteorological data and water resources data obtained in step 1, the present invention uses the sub-item survey method to restore the future period daily runoff series data obtained in step 3 to obtain the future period natural runoff annual series data of the study area;

[0113] Specifically, the future daily runoff series data is converted into the future annual runoff series data for restoration calculation, and the water balance equation is as follows:

[0114] (6)

[0115] In the formula: is the annual series data of natural runoff in the future period (10 6 m 3 / year); is the annual runoff series data in the future period (10 6 m 3 / year); is the water consumption for planting (10 6 m 3 / year); is the water consumption for industry (10 6 m 3 / year); is the water consumption for forestry, animal husbandry and fishery (10 6 m 3 / year); is the water consumption for urban domestic use (10 6 m 3 / year); is the water consumption for other sectors (10 6 m 3 / year).

[0116] Furthermore, on this basis, as Figure 3 shown, the Pearson type III distribution is used to fit the probability density function, and the basin characteristic values are calculated; and the frequency curve of the basin natural runoff is fitted to determine the values of the variation coefficient Cv and the skewness coefficient Cs; and based on the annual series data of the natural runoff in the future period of the study area, the probability distribution characteristics of the natural runoff in the future period of each sub-basin in the study area are obtained, providing the natural runoff in the future period with randomness.

[0117] Step 5 specifically includes the following contents:

[0118] The Standardized Runoff Index (SRI) is an indicator that measures the runoff variation within a given duration based on the cumulative runoff. The hydrological drought classification and range indicated by SRI show that there is no drought: drought index (DI) > -0.5; mild drought: -1.0 < DI ≤ -0.5; moderate drought: -1.5 < DI ≤ -1.0; severe drought: -2.0 < DI ≤ -1.5; extreme drought: DI ≤ -2.0. In order to calculate SRI in the present invention, we use the probability distribution of the natural runoff in the future period of each sub-basin in the study area obtained in Step 4, and the calculation is as follows:

[0119] (7)

[0120] In the formula: , F(x) is the cumulative probability density of SRI. , , , , and are empirical constants, which are taken as 2.516, 0.803, 0.010, 1.433, 0.189 and 0.001 respectively.

[0121] Furthermore, the duration and severity of drought are identified based on the run theory, such as Figure 4 The schematic diagram of the run theory is shown in Figure 1. The run theory is usually used to identify disaster events. When the disaster index is lower than a certain threshold and the duration exceeds a certain length, a disaster event is considered to have occurred. The SWAT model is used to analyze the changes in the water demand of the ecosystem under the above-mentioned drought severity by adjusting the hydrological parameters and ecological parameters in the model, so as to determine the water demand threshold.

[0122] Step 6 specifically includes the following:

[0123] Mathematical programming methods and uncertainty methods, including multi-objective programming (MOP) methods, can achieve trade-offs between different stakeholders in a hierarchical order. In addition, the interval linear programming (ILP) method can be used to deal with uncertainties expressed as interval values; the two-stage stochastic programming (TSP) method can establish an effective connection between economic benefits and related water shortage penalties; the double-sided chance-constrained programming (DCP) handles the random uncertainties on the left and right sides of the constraints and verifies the risk of violation of system constraints. The present invention integrates MOP with ILP, TSP and DCP to establish the interval stochastic multi-level double-sided chance-constrained programming (IMDCP) method, which is specifically expressed in the following form:

[0124] Objective function:

[0125] (8)

[0126] (9)

[0127] (10)

[0128] Constraints:

[0129] (11)

[0130] (12)

[0131] (13)

[0132] (14)

[0133] in, , and They are the goals of different decision-making levels; k and j They are random events and specific sub-events respectively; K and J is the maximum number of random events and specific sub-events; and represent the decision variables of the first and second stages respectively; Represents random events k probability of occurrence, and In random events k The sum of the occurrence probabilities is 1, that is ; , , Represents a random variable that takes on discrete values. and , and , and Respectively represent and coefficients in multiple objective functions and constraints; , , Represent the coefficients in the constraints respectively. In the model, and is constrained in equation (12). Constraint (13) makes and becomes a non-negative variable. On both sides of the constraint, and is a normally distributed random variable; It is a violation of constraints i Probability and yes and Expected value; and yes and The standard deviation of the interval. (The superscript "+" indicates the upper limit of the interval value; the superscript "-" indicates the lower limit of the interval value).

[0134] Furthermore, a dynamic prediction and optimization model of water trading for natural-social coupling is constructed, which includes objective functions and constraints. Specifically, the content is as follows:

[0135] Upper-level goal: maximize economic benefits, taking into account future economic development and its impact on water use efficiency while ensuring economic development and efficiently regulating water resource allocation;

[0136] (15)

[0137] in, t and u The future period and the user respectively; T and U are the maximum number of future periods and the number of users respectively; For the future t user u water resource allocation target; For the future t user u The economic benefits brought by the unit target water allocation; For the future t user u The amount of water shortage; For the future t user u The economic loss caused by the unit water shortage; For the future t user u The amount of water rights purchased; For the future t user u The transaction cost of the unit water right purchase; the superscript "±" indicates the upper and lower limits of the interval value. Specifically, it involves the economic benefits brought by the unit target water allocation, the economic losses brought by the unit water shortage and the transaction cost of the unit water right purchase, based on the socio-demographic-economic data in step 1;

[0138] The middle-level goal: maximizing food production controls the amount of water allocated to cereal crop production in each future period;

[0139] (16)

[0140] in, For the future t Cereal crop production output;

[0141] Lower level goal: maximizing ecological water use, which controls water allocation to the ecological sector;

[0142] (17)

[0143] in, r is the sub-basin within the study area; R is the maximum number of sub-basins that can be divided in the study area; for t future period r water demand of sub-catchment ecosystems;

[0144] Water trading rules and regulations:

[0145] (18)

[0146] (19)

[0147] in, For the future t user u The amount of water rights held; For the future t user u The initial water rights; For the future t user u The amount of water rights purchased (water trading volume); For the future t user u The water rights sold (water trading volume) is used to ensure that the total amount of water rights sold is equal to the total amount of water rights purchased;

[0148] Constraints on food production scale:

[0149] (20)

[0150] (twenty one)

[0151] in, For the future t Cereal crop demand yield; For the future t population; For the future t per capita consumption of cereal crops; For the future t user u Actual water consumption for irrigation (for crop users only); For the future t user uThe yield of high-quality cereal crops (rice, wheat and corn) per unit of actual water consumption (for crop users only). Specifically, in order to ensure food security in the face of population growth, it is hoped that the yield of cereal crops (rice, wheat and corn) will be increased to meet the demand. The future population data is estimated based on the socio-demographic-economic data in step 1;

[0152] Water management constraints:

[0153] (twenty two)

[0154] (twenty three)

[0155] (twenty four)

[0156] (25)

[0157] in, For the future t user u Actual water consumption; For the future t user u water resource allocation target; For the future t user u The amount of water shortage; For the future t Sub-basin r Water shortage (the water shortage area of ​​the sub-basin in the future period is sub-basin area); Sub-basin r With users u The corresponding relationship between users u Located in sub-basin r ,but =1, otherwise =0; For the future t Total water shortage For the future t Sub-basin r The water demand threshold of the ecosystem. Specifically, in order to alleviate the problem of ecological degradation during droughts, it is necessary to reasonably adjust the water distribution layout in future droughts to cope with the trend of ecological degradation during water shortages and maximize ecological water use. To this end, in step 5, by adjusting the hydrological parameters and ecological parameters in the SWAT model, the changes in the water demand of the ecosystem under the above-mentioned drought severity are analyzed to determine the water demand threshold of the ecosystem. In addition, water resource management constraints also contain water balance, and based on the water resource data obtained in step 1, step 3 obtains the annual series data of natural runoff in the study area in the future, and divides the study area into different sub-basins.r to ensure that basin water trading is based on an accurate water balance.

[0158] Furthermore, the above multi-level planning problem is solved based on the fuzzy satisfaction algorithm. Specifically, the upper-level goal, the middle-level goal and the lower-level goal are solved independently to obtain the corresponding solutions, namely , and , respectively for the upper, middle and lower decision variables x, y, z Set a tolerance threshold and establish the following triangular membership function:

[0159] (26)

[0160] (27)

[0161] (28)

[0162] In the formula, , and Represent the upper, middle and lower decision variables respectively x, y, z satisfaction; and Represent the upper-level decision variables x The left and right tolerance thresholds of and Represent the upper-level decision variables y The left and right tolerance thresholds, and Represent the upper-level decision variables z The left and right tolerance thresholds;

[0163] Assuming that the upper, middle, and lower level goals in the three-level plan are mutually contradictory, and By respectively solving the solutions for the middle and lower level objectives , Substitute it into the upper objective function and calculate it: and ; and represents the tolerance value of the upper and lower objective functions, that is, and ; Similarly, and represents the tolerance value of the upper and lower objective functions, that is, and , and establish the following membership function:

[0164] The upper objective function also needs to set a tolerance threshold, and its membership function is shown as follows:

[0165] (29)

[0166] The middle-level objective function also needs to set a tolerance threshold, and its membership function is shown as follows:

[0167] (30)

[0168] The lower objective function also needs to set a tolerance threshold, and its membership function is shown as follows:

[0169] (31)

[0170] In the formula, , and Represents the satisfaction of the upper objective function.

[0171] In order to satisfy the satisfaction of both upper-level and lower-level goals, global satisfaction is introduced. , and establish the relationship , then the above three-level planning problem can be transformed into solving the maximum The specific formula is as follows:

[0172] (32)

[0173] constraint:

[0174] (33)

[0175] Step 7 specifically includes the following:

[0176] Based on the output data of the global climate model in the historical period and the hydrological and meteorological observation data of the ERA5 reanalysis data set in the historical period, the spatial mean was calculated using the Thiessen polygon method to obtain the annual-scale rainfall and snowfall data of each sub-basin in the study area in the historical period; further, the dynamic prediction optimization model of water trading was used to predict the trend of water shortage area changes in each sub-basin in the study area in the future period, and an emergence constraint model was constructed. Specifically, for the output data of the global climate model in the historical period, the linear relationship between the annual-scale rainfall and snowfall change trend of the sub-basin in the historical period (35 global climate models; 1985-2014) and the dynamic prediction optimization model of water trading coupled with nature and society predicted the trend of water shortage area changes in each sub-basin in the study area in the future period (35 global climate models under 3 SSPs; 2015-2100) was constructed, specifically:

[0177] (34)

[0178] Where: RF and SF Indicates the annual rainfall and snowfall in the historical period; and They represent the changing trend of water-deficient area in the future and the changing trend of annual rainfall and snowfall in the basin in the historical period respectively; a and b represent model parameters;

[0179] The least squares method is used to solve the parameters a and b of the emergence constraint model. Based on the annual rainfall and annual snowfall data of the hydrological and meteorological observation data of the historical ERA5 reanalysis data set of each sub-basin in the study area, the change trend during 1985-2014 is deduced, and the trend term is substituted into the constructed emergence constraint model. Specifically, the formula is as follows:

[0180] (35)

[0181] Where: and They represent the trend of the corrected water-deficit area change in the future period and the trend of the annual-scale rainfall and snowfall in the hydrological and meteorological observation data of the ERA5 reanalysis dataset in the historical period, respectively;

[0182] Therefore, the changing trends of annual rainfall and snowfall in the hydrological and meteorological observation data of the historical ERA5 reanalysis dataset were substituted into the constructed emergence constraint model to obtain the corrected changing trend of water-scarce area in the future; finally, the weighted average of the corrected changing trends of water-scarce area in the future under 35 global climate models under three SSPs was performed to predict the evolution trend of water-scarce areas after water trading in the future.

[0183] Example 2

[0184] This embodiment provides a nature-society coupled water trading prediction system, including:

[0185] Data acquisition module: It is used to obtain hydrological and meteorological data, global climate model output data, socio-demographic-economic data and water resources data in the study area;

[0186] A hydrological model calibration module, which is used to determine relative humidity and specific humidity according to hydrological and meteorological data, and calibrate four hydrological model parameters based on the hydrological and meteorological data;

[0187] A hydrological model training module is used to obtain corrected future global climate model output data based on global climate model output data and hydrological and meteorological data, and to train four hydrological models using the corrected future global climate model output data to obtain future daily runoff series data;

[0188] A fitting module is used to obtain the annual series data of natural runoff in the future period of the study area based on the daily runoff series data in the future period, fit its distribution, and obtain the probability distribution characteristics of the natural runoff in each sub-basin in the study area in the future period;

[0189] A water demand threshold assessment module, which is used to calculate the standardized runoff index based on the probability distribution of natural runoff in the future period and assess the water demand threshold of the ecosystem in each sub-basin;

[0190] The optimization model building module is used to take social demographic-economic data, water resources data, probability distribution of natural runoff in the future period and water demand threshold of the ecosystem as input data, and take economic benefits, food production, water trading volume and water-deficient area in the future period as output data to build a dynamic prediction optimization model of water trading for natural-social coupling;

[0191] The prediction module is used to predict the evolution trend of water-scarce areas after future water transactions based on the constructed water transaction dynamic prediction optimization model.

[0192] Example 3

[0193] A specific embodiment of the present invention also provides a computer storage medium.

[0194] The computer storage medium is a server workstation;

[0195] The server workstation stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of an embodiment of the present invention involving a method for predicting water trading under natural-social coupling conditions under uncertainty.

[0196] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.

[0197] It should be understood that parts not elaborated in detail in this specification belong to the prior art.

[0198] It should be understood that the above description of the preferred embodiment is relatively detailed and cannot be regarded as limiting the scope of patent protection of the present invention. Under the enlightenment of the present invention, ordinary technicians in this field can also make substitutions or modifications without departing from the scope of protection of the claims of the present invention, which all fall within the scope of protection of the present invention. The scope of protection requested for the present invention shall be based on the attached claims.

Claims

1. A nature-society coupled water trading prediction method, characterized in that: include: Step 1: Obtain hydrometeorological data, global climate model output data, socio-demographic-economic data, and water resources data for the study area; Step 2: Determine relative humidity and specific humidity based on hydrological and meteorological data, and calibrate four hydrological model parameters based on the hydrological and meteorological data; Step 3: Based on the global climate model output data and hydrological and meteorological data, the corrected future global climate model output data is obtained. The corrected future global climate model output data is used to train four hydrological models to obtain the future daily runoff series data. Step 4: Based on the future daily runoff series data, obtain the annual series data of natural runoff in the study area in the future, fit its distribution, and obtain the probability distribution characteristics of natural runoff in each sub-basin in the study area in the future; Step 5: Calculate the standardized runoff index based on the probability distribution of natural runoff in the future period and evaluate the water demand threshold of the ecosystem in each sub-basin; Step 6: Take socio-demographic-economic data, water resources data, probability distribution of natural runoff in the future period and water demand threshold of ecosystem as input data, and take economic benefits, food production, water trading volume and water-deficient area in the future period as output data to construct a dynamic prediction and optimization model of water trading for natural-social coupling; Step 7: Based on the constructed water trading dynamic prediction optimization model, the evolution trend of water-scarce areas after future water trading is predicted.

2. A nature-society coupled water trading prediction method according to claim 1, characterized in that: The hydrological and meteorological data in step 1 include: daily runoff data of the control water level station in the selected study area in the historical period, and hydrological and meteorological observation data of the ERA5 reanalysis data set in the historical period; The global climate model output data include historical global climate model output data and future global climate model output data; the historical global climate model output data are historical simulation data of 35 global climate models; the future global climate model output data are future prediction data of 35 global climate models under three SSPs; Socio-demographic-economic data include: demographic and socio-economic index data for historical and future periods; Water resources data include: social water use, water supply and water storage data in historical periods, and water storage, social water use and water withdrawal data in the future under the ISIMIP3b framework.

3. A nature-society coupled water trading prediction method according to claim 2, characterized in that: The step 2 comprises: Based on the hourly surface 2m air temperature, hourly dew point temperature and Clausius-Clapeyron thermodynamic equation of each grid point in the hydrological and meteorological observation data of the historical ERA5 reanalysis data set, the hourly relative humidity is obtained, and then the daily average relative humidity is calculated; Based on the hourly dew point temperature and the near-surface air pressure in the hydrological and meteorological observation data of the historical period ERA5 reanalysis data set, the hourly specific humidity is obtained, and then the daily average specific humidity is calculated; Based on the daily precipitation, daily average surface 2m temperature, daily maximum temperature, daily minimum temperature, shortwave radiation, near-ground wind speed, longwave radiation and daily snowfall at each grid point in the hydrological and meteorological observation data of the historical ERA5 reanalysis data set obtained in step 1, as well as the calculated daily average relative humidity and daily average specific humidity, the four hydrological models XAJ, GR4J-6, HBV and SIMHYD were calibrated with the daily runoff data of the control water level station in the study area as the target variable, and the SCE-UA algorithm was used to estimate the parameters of the four hydrological models.

4. The method for dynamic prediction of water trading in nature-society coupling according to claim 2 is characterized in that: The step 3 comprises: Calculate the differences between the historical global climate model output data and the hydrological and meteorological observation data of the historical ERA5 reanalysis data set at each quantile, and remove the differences at each quantile of the future global climate model output data to obtain the corrected future global climate model output data; The corrected output data of the future global climate model were input into the four calibrated hydrological models to simulate and obtain the future daily runoff series data.

5. The method for predicting water trading based on natural-social coupling according to claim 2, characterized in that: The step 4 comprises: Based on the hydrological and meteorological data and water resources data obtained in step 1, the future daily runoff series data obtained in step 3 are restored using the sub-item survey method to obtain the future natural runoff annual series data of the study area; The Pearson III distribution is used to fit the probability density function and calculate the basin characteristic value; the frequency curve of the basin's natural runoff is fitted to determine the values ​​of the coefficient of variation Cv and the coefficient of skewness Cs; and based on the annual series data of natural runoff in the study area in the future, the probability distribution characteristics of natural runoff in each sub-basin in the study area in the future are obtained.

6. The method for predicting water trading based on natural-social coupling according to claim 2, characterized in that: The step 5 comprises: Calculate the standardized runoff index (SRI) based on the probability distribution of natural runoff in the future period of each sub-basin in the study area in step 4; The duration and severity of drought were identified based on the travel theory, and the SWAT model was used to analyze the changes in water demand of the ecosystem in each sub-basin under the above-mentioned drought severity by adjusting the hydrological parameters and ecological parameters in the SWAT model, so as to determine the water demand threshold of the ecosystem.

7. The method for predicting water trading based on natural-social coupling according to claim 2, characterized in that: The step 6 comprises: The multi-objective programming method, interval linear programming method, two-stage stochastic programming method and bilateral chance-constrained programming method are integrated, and the obtained socio-demographic-economic data, water resources data, probability distribution of natural runoff in the future period and water demand threshold of the ecosystem are used as input data, and the future economic benefits, grain output, water trading volume and water-deficient area are used as output data to construct a dynamic prediction optimization model of water trading with interval random multi-layer bilateral chance constraints and nature-society coupling. The nature-society coupled water trading dynamic prediction optimization model includes an objective function and constraints, wherein the objective function includes: Upper-level goal: maximize economic benefits; Mid-level goals: maximize food production; Lower level goals: maximize ecological water use; Constraints: including water use trading rules, food production scale, and water resources management constraints; The nature-society coupled water trading dynamic prediction optimization model is solved based on the fuzzy satisfaction algorithm.

8. The method for predicting water trading based on natural-social coupling according to claim 2, characterized in that: The step 7 comprises: Based on the historical global climate model output data and the hydrological and meteorological observation data of the historical ERA5 reanalysis data set, the spatial mean was calculated using the Thiessen polygon method to obtain the annual rainfall and snowfall in each sub-basin of the study area. Based on the nature-society coupled water trading dynamic prediction optimization model, the future trend of water shortage area in each sub-basin of the study area is predicted, and an emergence constraint model is constructed; the least squares method is used to solve the parameters of the emergence constraint model; Based on the annual rainfall and snowfall data of the hydrological and meteorological observation data of the ERA5 reanalysis data set in each sub-basin of the study area in the historical period, the change trend is deduced and substituted into the constructed emergence constraint model to obtain the corrected trend of water shortage area change in the future period. The weighted average of the corrected future water-scarce area change trends under 35 global climate models under three SSPs was used to predict the evolution trend of the water-scarce area after water trading in the future.

9. A nature-society coupled water trading prediction system, characterized in that: include: Data acquisition module: It is used to obtain hydrological and meteorological data, global climate model output data, socio-demographic-economic data and water resources data in the study area; A hydrological model calibration module, which is used to determine relative humidity and specific humidity according to hydrological and meteorological data, and calibrate four hydrological model parameters based on the hydrological and meteorological data; A hydrological model training module is used to obtain corrected future global climate model output data based on global climate model output data and hydrological and meteorological data, and to train four hydrological models using the corrected future global climate model output data to obtain future daily runoff series data; A fitting module is used to obtain the annual series data of natural runoff in the future period of the study area based on the daily runoff series data in the future period, fit its distribution, and obtain the probability distribution characteristics of the natural runoff in each sub-basin in the study area in the future period; A water demand threshold assessment module, which is used to calculate the standardized runoff index based on the probability distribution of natural runoff in the future period and assess the water demand threshold of the ecosystem in each sub-basin; The optimization model building module is used to take social demographic-economic data, water resources data, probability distribution of natural runoff in the future period and water demand threshold of the ecosystem as input data, and take economic benefits, food production, water trading volume and water-deficient area in the future period as output data to build a dynamic prediction optimization model of water trading for natural-social coupling; A prediction module, which is used to predict the evolution trend of water-scarce areas after future water transactions based on a constructed water transaction dynamic prediction optimization model; The nature-society coupled water trading prediction system is used to execute the steps in the nature-society coupled water trading prediction method according to any one of claims 1-8.

10. A computer storage medium, characterized in that: The computer storage medium is a server workstation; The server workstation stores a computer program executed by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps in the nature-society coupled water trading prediction method according to any one of claims 1 to 8.

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