124 results about "Future climate" patented technology

The invention relates to the technical field of land utilization change evaluation, in particular to a multi-scene land gradient utilization carbon emission prediction method and system and a readable storage medium. A multi-scenario land gradient utilization carbon emission prediction coupling model under three scenarios of historical development trend, future climate change and policy planning effect is considered to comprehensively evaluate the influence of land gradient utilization change on carbon emission under different development paths. And a decision basis is provided for formulating scientific and reasonable regional space planning, a carbon emission reduction policy and a sustainable development strategy. The invention aims to solve the problem of how to improve the simulation accuracy of the carbon emission management regulation simulation system.

The invention provides an atmosphere data assimilation method and device, equipment and a storage medium. Relates to the technical field of computer science and atmospheric science fusion. The method comprises the following steps: adding a cost function, priori estimation and the gradient of the cost function into a gradient-based descent algorithm, adding posteriori estimation, and constructing a four-dimensional variation and ensemble Kalman filtering combined model based on Bayesian priori information; and carrying out data assimilation on atmosphere data based on the four-dimensional variation and ensemble Kalman filtering combined model. According to the method, an artificial intelligence technology is combined, especially deep learning and a Bayesian optimization adaptive algorithm are adopted, a traditional data assimilation algorithm is enhanced and optimized, and the method can be widely applied to prediction of extreme weather events such as high temperature and heat waves. Through assimilation of historical data and real-time observation data, the prediction precision of the future climate change trend is significantly improved, and more powerful support is provided for extreme weather early warning and decision making.

The invention discloses a climate mode deviation correction method based on ensemble Kalman filtering and deep learning, and the method comprises the steps: obtaining first climate historical simulation data and future climate estimation data in a research region, and obtaining historical observation data corresponding to climate variables; performing deviation correction on the first climate history simulation data point by point by adopting an ensemble filtering method; carrying out lag processing on the first climate history simulation data; constructing a long short-term memory network model; performing feature engineering processing on future climate estimation data; and performing deviation correction on future climate estimation data by using the average deviation of the Kalman gain sequence and the historical sequence of each time period in the future. According to the method, the dynamic correction capability of the ensemble Kalman filtering and the nonlinear prediction advantage of the deep learning model are combined, the correction precision of climate mode simulation data is improved, and effective deviation correction is carried out on future climate estimation data.

The invention relates to the field of ecological environment and climate change research, and discloses a vegetation phenology change trend comprehensive evaluation method, which comprises the following steps: acquiring climate data and tree ring data of a target area, simulating a relationship between radial growth and phenology of a tree by using a Vaganov-Shashki n model based on the acquired data, and generating a time sequence of phenology change; extracting phenological key nodes such as growing season starting time, end time and growing season length; analyzing the driving effect of climatic factors on phenological key nodes, and determining main influence factors; on the basis of the climate model and different emission scenes, predicting the vegetation phenological trend under future climate change; the response of the regional ecosystem to climate changes is evaluated based on historical and future data. Through simulation and quantitative analysis, a scientific basis is provided for long-term trend prediction and regional ecological management of vegetation phenological change, and the method has high applicability and reliability.

The invention discloses a drainage basin seasonal runoff change prediction and attribution method and system and a medium, and the method comprises the steps: carrying out the seasonal division of a hydrological year based on the monthly scale rainfall, potential evapotranspiration and land water reserve change rules in a drainage basin; establishing a plurality of coupling models of the GCM and the ABCD so as to predict the runoff process of the drainage basin under the conditions that only future climate change scenes are considered and climate change and human activity influence scenes are considered at the same time; a seasonal-scale Buddyko model is considered, and the relative influence of seasonal-scale climate change and human activities on future runoff change is quantified. The method has a strong statistical basis, can objectively reflect the change rule of the watershed hydrologic runoff, can fully decouple the complex relationship between climate change and human activity and other factors on a seasonal time scale, and provides a new technology for the research of a water circulation process driving mechanism in a changing environment.

The invention discloses a flood season water environment pollution prevention and control method, and relates to the technical field of water pollution prevention and control, and the method comprises the following specific steps: data collection and preprocessing: collecting related data covering hydrology, water quality, land utilization, population distribution and industrial layout from each information department, according to the method, the influence of climate change on flood season water pollution is considered, historical meteorological data of a target area is collected and analyzed, a long-term climate change trend is accurately identified by using a meteorological analysis model, a climate background basis is provided for correlation analysis, and on the basis, a water pollution diffusion prediction model and a hydrological simulation model are combined, so that the flood season water pollution prediction accuracy is improved. The water regimen change characteristics and the water pollution risk degree of the flood season in the future specific prediction period are analyzed, the current flood season water pollution problem can be effectively solved, the adaptability to future climate change challenges can be improved, and a scientific basis is provided for making a long-term water pollution prevention and control strategy.

The invention relates to a species suitable habitat simulation method and device under future climate, and the method comprises the steps: obtaining remote sensing data, foundation observation data and species distribution data in a target region, and carrying out the unified preprocessing of the data, and obtaining processed data; constructing environment characteristic variables based on the processed data, and performing screening to obtain a training data set meeting species suitability modeling input conditions; and constructing a species suitability model under the current climate condition by using the training data set, predicting species suitable habitat distribution under a future climate scene by using the species suitability model, and analyzing a spatial change trend according to the species suitable habitat distribution. Therefore, the problems that remote sensing and foundation observation data are isomerism in the aspects of time, space, scale and the like, an effective data fusion and index conversion mechanism is lacked, registration errors and feature inconsistency are easily caused, and the accuracy and reliability of ecological parameter estimation are affected are solved.

The invention provides an urban rainfall area IDF curve establishment constraint method and system. The method comprises the following steps: collecting actually measured rainfall data and GCM simulated rainfall data; establishing a spatial downscaling model, and performing spatial downscaling on the grid monthly rainfall estimated by the GCM in the future period; calculating the condition transition probability of rainfall occurrence in each calendar month in the future period; generating a parameter set for driving a random rainfall generator, and inputting the parameter set into the random rainfall generator to obtain a daily rainfall sequence in a future period; performing time discretization from the daily scale to the hour scale on the daily rainfall sequence in the future time period; extracting an annual maximum value sequence from the hour drop sequence in the future time period to carry out regional frequency analysis, and constructing an urban rainfall region IDF curve; constructing a random forest model, and deducing alternative factors of IDF curve migration change; and an emergence constraint model is constructed, and the uncertainty of urban rainfall risk prediction under future climate change is reduced. The accuracy of rainfall frequency analysis is improved.

The invention relates to a water resource pressure assessment method integrating social economy and climate change. Relates to the technical field of drought basin water resource pressure assessment. The method comprises the following steps: acquiring a historical value and a predicted value of social economic data, and correcting the predicted value of the social economic data to obtain a corrected predicted value; calculating the potential evapotranspiration of the drainage basin in different scenes based on the meteorological data of the historical scenes and the corrected future climate mode data; calculating a future drainage basin water resource quantity based on a correlation between drainage basin rainfall and the water resource quantity; according to the variation trend of the drainage basin drought index in the historical scene and the future scene, drought intensity characteristics are extracted; constructing a drainage basin water resource pressure index calculation formula; and according to the calculated water resource pressure index of the water resource area, standardization is carried out to obtain a standardization index, and according to the standardization index, the water resource pressure change trend of different scene areas is evaluated. According to the method, the quantization and evaluation problem of the drainage basin water resource pressure under the complex condition is solved.

The invention discloses a flora regulation and control method and system for soil micro-ecology, and belongs to the technical field of microbial flora regulation and control, and the method comprises the steps: continuously collecting real-time environment data based on a target soil region, combining future climate prediction data, and generating a predicted scene sequence through Monte Carlo simulation; constructing a microbial community composition table, screening core strains, and performing metagenome sequencing; taking the core strain as a game participant, constructing a participant strategy set, calculating an income matrix library, setting an environment correction coefficient for each scene, carrying out scene-based game solving, and generating a balanced summary table; a regulation response prediction model is constructed, multiple optimization objectives are set, robustness constraints and expert rules are introduced, candidate regulation schemes are generated, optimal scheme solving is carried out, it is ensured that regulation measures can achieve an expected effect by guiding inter-species competition, and original flora balance is prevented from being damaged.

The invention discloses a sudden snow disaster risk degree probability prediction method oriented to insufficient historical time sequences. The method comprises the following steps: step 1), constructing a multi-index risk index based on related data such as historical snow disasters; 2) performing time sequence modeling on the violent snow disaster risk index by adopting an autoregressive integral moving average model, and capturing trend and periodic characteristics of the violent snow disaster risk index; 3) discretizing a continuous risk index of modeling production into a plurality of risk levels, and constructing a state transition probability matrix; step 4), introducing space neighborhood influence and carrier exposure constraint, and constructing a Markov space weighted state transition model; 5) coupling CMIP6 climate scene data, and dynamically adjusting future state transition probability, and 6) fusing ARIMA trend prediction and Markov state transition probability, and outputting future multi-period snowstorm disaster risk level probability distribution.According to the method, the accuracy of snowstorm disaster risk prediction and the modeling ability and prediction adaptability of future climate change scenes are improved.

The invention discloses a hydrological model time-varying parameter prediction method based on ensemble Kalman filtering and a deep residual network. The hydrological model time-varying parameter prediction method comprises the following steps: acquiring watershed multi-source data; acquiring time-varying parameter historical time sequence data of the hydrological model by using an ensemble Kalman filtering method; constructing a deep residual network prediction model based on the convolutional neural network; and training the model, inputting the obtained hydrological and meteorological data in the future climate mode into the trained model, and outputting hydrological model time-varying parameters in the future scene. According to the method, the historical data of the time-varying parameters of the hydrological model are acquired by using a data assimilation method, the future change of the parameters of the hydrological model is predicted through the deep residual network and future climate mode data, and a brand new data driving method is provided for dynamic prediction of the parameters of the hydrological model; and powerful technical support is provided for water resource management and climate change challenges in the future.

The invention provides a drought and flood sudden change prediction method and system based on artificial intelligence, and the method comprises the steps: collecting the in-out reservoir runoff observation data of a reservoir in a target region, obtaining a restored natural reservoir runoff series based on a water balance method, calibrating a long-short-term memory model through a minimum batch gradient descent method, and carrying out the prediction of the drought and flood sudden change. Based on the natural reservoir runoff, the actual reservoir runoff and meteorological data, constructing a long-short-term memory model to simulate the influence of water conservancy project regulation and storage on the runoff; based on an earth system mode set and a multivariable deviation correction method, performing spatial downscaling on output data of earth system modes to obtain corrected meteorological variables; driving a multi-member set of an earth system mode, and separating by adopting a detection attribution technology to obtain a contribution rate of man-made climate compulsion to drought and flood sudden turning event evolution; and predicting a daily runoff process under future climate change, and predicting a future drought and flood sudden turning event by adopting deep learning and superposition of regulation and storage influence of a water conservancy project.

The invention belongs to the technical field of flood and drought disaster recognition and evaluation, and discloses a flood season anti-dry risk evaluation method based on a composite runoff index, and the method comprises the steps: obtaining geographic information data, hydro meteorological data and hydraulic engineering data of a target region; setting a drought threshold in combination with urban water supply requirements to select low-flow drought events in historical flood seasons; optimal probability distribution is selected, and marginal distribution characteristics of drought flow and duration of the low-flow drought event in the flood season are analyzed; selecting a Copula function, establishing joint distribution of drought flow and duration of the low-flow drought event in the flood season, and constructing a composite runoff index of the low-flow drought event in the flood season; and in consideration of different discharge scenes of future climate change, based on a drainage basin distributed hydrological model prediction result, analyzing occurrence risk response characteristics of future flood season anti-flood events by adopting a composite runoff index. The method can accurately evaluate the sudden rising characteristic of flood season anti-dry and the dry water characteristic of the flood season.

The invention relates to the technical field of environmental science, and discloses an SWAT-based BMP optimal configuration method adaptive to climate change, and the method comprises the steps: collecting pollution data of a target agricultural region, and constructing an NPS simulation model of the target agricultural region based on an SWAT model; collecting basic data of a target agricultural area, determining implementation data of a plurality of preset optimal management measures, and constructing an optimal management measure database; based on the optimal management measure database, performing parameter optimization on the NPS simulation model to form an NPS simulation optimization model; simulating climate change in future time according to the climate observation data and the historical meteorological data, and constructing a meteorological database; and based on the meteorological database, operating the NPS simulation optimization model, and performing optimal management measure optimization configuration analysis through a genetic algorithm to obtain an optimal management measure optimization result. According to the method, resources can be reasonably configured, and the flexibility, adaptability and actual effect of optimal management measures are improved.

The invention discloses an alpine grassland livestock capacity monitoring and predicting method and system based on climate change. The alpine grassland livestock capacity monitoring and predicting method based on climate change comprises the following steps: 1) modeling data preprocessing: acquiring grassland type data and meteorological factor data, and carrying out space-time mask fusion, year / month scale space-time alignment and dynamic standardization to obtain grassland type standardized data; 2) feature screening and XGBoost modeling: performing feature importance screening and multi-parameter grid search optimization on grassland type standardized data by using a random forest model, and establishing and verifying a model; 3) preprocessing future CMIP6 climate factor data; and (4) predicting the livestock carrying capacity by using the results of the steps (2) and (3). According to the method, the potential influence of the climate change on the alpine grassland ecosystem can be evaluated on the regional scale, the variation trend of the future livestock capacity is accurately predicted, and a scientific basis is provided for formulating pasturing area management, ecological restoration and climate adaptability strategies.

The invention discloses a forest carbon sink insurance method based on climate indexes. The forest carbon sink insurance method comprises the steps that a winter air temperature abnormal index, a summer air temperature abnormal index, a growing season rainfall abnormal index, a growing season double-month rainfall abnormal index and an annual sunlight abnormal index are designed; taking the climate index as an independent variable and the forest annual carbon sequestration amount as a dependent variable to construct a forest carbon sequestration amount-climate index model; and on the basis of a forest carbon sequestration loss rate calculated by a forest carbon sequestration quantity-climate index model, comprehensively considering historical occurrence probabilities of different grade values of climate indexes and risk extreme values of future climate changes, and determining insurance claim triggering critical conditions and insurance claim rules of the forest carbon sequestration climate index insurance model. The method has the advantages that forest carbon sink climate loss caused by abnormal climate conditions can be quickly and efficiently quantified only through observation data of a national basic weather station, the problems of man-made interference influence and model difference are solved, and the defect that data cannot be obtained through satellite remote sensing due to weather reasons is overcome.

The invention discloses a multi-source rainfall data fusion method for a regional landslide early warning threshold. The method comprises the following steps: improving the spatial resolution of long-sequence raster data by adopting a statistical downscaling technology; according to the landslide position, selecting an optimal neighborhood method or a Thiessen polygon method to generate a site and grid representative precipitation sequence pair; establishing a dry day / wet day discrimination model based on the characteristics of the rainfall event triggering the landslide; correcting daily precipitation based on a quantile mapping method; and constructing a regional rainfall landslide threshold curve and performing precision test. According to the method, the defects of low precision of long-sequence grid data, short site data time sequence and space-time difference between a site rainfall observation position and a landslide occurrence position can be improved, and an effective means for establishing a rainfall type landslide early warning system is provided for a data lacking area. Meanwhile, the method can be migrated to rainfall type landslide risk assessment in a global future climate situation mode, and scientific support is provided for constructing a climate adaptation type disaster reduction system.

The invention relates to a machine learning and multi-factor coupled concrete carbonization depth prediction model establishment method. The method specifically comprises the steps of obtaining future global climate data; building a dynamic temperature and humidity coupling carbonization model; building a diffusion-reaction equation of CO2, Ca (OH) 2 and relative humidity, and correcting a CO2 diffusion coefficient; setting initial and boundary conditions of the equation; future climate data is dynamically mapped to concrete surface concentration calculation under boundary conditions, and a surface mass transfer correction coefficient equation driven by wind speed is introduced; performing space-time discretization on the equation, introducing Taylor expansion to construct a third-order precision difference format, and solving a nonlinear coupling equation set; future global climate data are input into the dynamic temperature and humidity coupling carbonization model, the surface boundary condition of the coupling carbonization model in each time and space step length is updated, the internal diffusion coefficient is corrected in real time, and the change of the carbonization depth along with time is calculated. The method provides a scientific basis for the durability design of the infrastructure, and has remarkable economic benefits and social values.

The present invention provides a method and device for simulating wind erosion at a construction site, the method comprising the following steps: Step 1: Collect 0-5 cm surface soil samples of the riverside slope to be studied using a base with a detachable baffle on one side; Step 2: Combine a cover with an adjustable baffle with a base filled with topsoil to form an airtight space; Step 3: Connect an adjustable-speed blower to the cover to ensure airtightness at the interface, adjust the wind speed to a preset value, and simulate the normal wind speed of the research point; Step 4: Install a particle capture device at the opening on the opposite side of the cover connected to the blower to receive particles generated by wind erosion, and complete the measurement of wind erosion amount and particle size composition. This method fills the data gap caused by the incomplete collection of wind erosion products by the existing wind erosion monitoring method. The technical solution of the adjustable wind speed and slope windward angle of the present application has the technical effect of restoring the current and simulating the acquisition of wind erosion parameters under different degrees of future climate change conditions.

The invention provides a historical observation constraint-based climate prediction method and device, a medium and equipment, and the method comprises the steps: obtaining a climate prediction result of a climate prediction model for a target region in a target time period of a target year; a climate prediction result of a target region in a target time period of a target year is converted into a prediction time sequence corresponding to the target year, the prediction time sequence corresponding to the target year is corrected to obtain a corrected time sequence, and then the climate prediction result of the target region in a target time period of the target year is calculated according to the corrected time sequence and an observation space base. And the corrected climate prediction result is determined, so that the accuracy of the corrected climate prediction result is ensured.

The invention discloses a nature-society coupled water transaction prediction method and system, and a continuation medium. The method comprises the steps of obtaining hydro meteorological data, global climate mode output data, social population-economy data and water resource data in a research area; relative humidity and specific humidity are determined, and four hydrological model parameters are calibrated based on hydrometeorological data; training four hydrological models by adopting the corrected future-period global climate mode output data to obtain future-period daily runoff series data; obtaining probability distribution characteristics of natural runoff volume of each sub-basin in a future period in the research area; evaluating a water demand threshold value of an ecological system in each sub-basin; constructing a natural-social coupled water transaction dynamic prediction optimization model; and estimating the corrected water shortage area in the future period, and finally obtaining the evolution trend of the water shortage area after the water transaction in the future period. The method can accurately predict the response characteristics of the dynamic change of the water transaction under the future climate change and the future economic trend, and provides a scientific basis for the decision making of the water transaction.

The invention relates to a flood and drought disaster risk assessment and deduction system and method based on slope multi-level regulation and storage, and belongs to the technical field of flood and drought disaster risk prevention and control and watershed hydrological regulation and control. The method comprises the following steps: acquiring multi-source data of a target drainage basin, performing water source refined analysis through coupling of water transfer coefficients (K1 and K2) based on a hierarchical synergistic effect of'upper closure-middle retention-lower dissipation 'of a slope, and calculating a controllable, effective and renewable water source amount; in combination with the regulation and storage capacity difference of each level, carrying out flood and drought disaster chain risk collaborative assessment, and outputting hierarchical risks and 0-10 level set risk indexes; a physical mechanism and data driving coupled scene deduction model is driven, and historical extreme event redisk and future climate scene prediction are completed; and after rendering by a visual engine, through hierarchical slope regulation and storage threshold comparison, outputting a regulation and storage measure layout optimization scheme. The system adopts a micro-service architecture and comprises a data management module, a multi-level regulation and storage collaborative analysis module, a water source analysis module, a risk evaluation module, a scene deduction module, a decision support module and a threshold comparison module. According to the method, high-resolution accurate positioning, low-error simulation and efficiency improvement of flood and drought disaster risks are realized, a decision closed loop from risk identification to optimization regulation is formed, and direct and quantitative technical support can be provided for flood and drought prevention and control engineering practice of drainage basins.

The invention provides a reservoir scheduling method based on rainfall forecast and remote sensing monitoring. The reservoir scheduling method comprises the following steps: step 1, collecting basic data of a research area and constructing a plurality of basic databases; 2, the basic data of the basic database are preprocessed; 3, constructing a weather forecast model taking the reservoir catchment range as a core, and forecasting weather data in a forecast period; step 4, constructing a distributed hydrological model considering future climate and land utilization or cover change, and calculating a reservoir incoming flow process in a prediction period; 5, calculating irrigation area water deficit state data based on the irrigation area crop remote sensing image; 6, the incoming flow process and irrigation area water deficit state data of the reservoir in the future period in the prediction period serve as input conditions, boundary conditions and constraint conditions are coupled, and a reservoir adaptability scheduling model is constructed; and 7, solving the reservoir adaptability scheduling model. According to the invention, through comprehensive analysis of multi-source information, a multi-level scheduling model is established, and accurate scheduling of the reservoir is realized.

The invention discloses a method for simulating spatial and temporal change of drainage basin runoff under climate change based on an RCCC-WBM model. The method comprises the following steps: acquiring actually measured rainfall, runoff, air temperature, evapotranspiration data and future climate data of a research area; performing cutting, interpolation, downscaling and multi-mode averaging processing on the corresponding future climate data, and calculating corresponding future evapotranspiration data; constructing a model, and performing parameter calibration and verification; 4 climate estimation scenes are classified and input into the model, and future runoff is simulated; and performing time change analysis on the monthly future runoff sequence. According to the method, the problems that a hydrological model adopted by an existing method is complex in structure, multiple in parameter, difficult to calibrate, insufficient in resolution ratio, low in precision and different in regional adaptability are fully solved; according to the method, a linear trend analysis method and a nonlinear trend analysis method are combined, the future runoff trend is analyzed, and the problem that an existing prediction method is not comprehensive enough in time change characteristic analysis is solved.

The application discloses a simulation method and system for engineering geological survey in alpine permafrost regions based on digital twinning, and relates to the technical field of engineering geological survey.The method comprises the following steps: obtaining multi-source heterogeneous data to construct a digital base; constructing a thermal-water-force-fracture multi-field coupling physical model based on the phase field theory; constructing a generative adversarial network as a time reversal engine to learn the mapping from the current state to the past state; inputting the current measured state into the time reversal engine to generate a historical evolution sequence, and comparing the historical evolution sequence with actual geological relics to dynamically calibrate model parameters; loading future climate scenarios to perform forward prediction, and fusing monitoring data through Kalman filtering to form a closed-loop corrected digital twin; and outputting a risk thermodynamic map, a fracture connectivity atlas and an engineering warning level based on the prediction result.The method can realize dynamic calibration and real-time early warning of the engineering geological model in the permafrost region, and improve the accuracy and calculation efficiency of the prediction of engineering geological disasters in cold regions.

The invention discloses a multi-lake system low-carbon-ecology collaborative scheduling method and system adapting to extreme climate. The method comprises the following steps: constructing a future extreme climate scene set; constructing an urban-natural drainage basin coupling hydrological model, and obtaining multiple groups of lake inflow runoff sequences driven by various future extreme climate scenes according to the urban-natural drainage basin coupling hydrological model; taking minimization of annual total diversion carbon emission as a single target, and constructing a low-carbon-ecological collaborative optimization model according to a future extreme climate scene set and an urban-natural drainage basin coupling hydrological model; inputting each group of into-lake runoff sequences into a low-carbon-ecological collaborative optimization model for solving to obtain a plurality of groups of candidate scheduling schemes; and screening the candidate scheduling schemes to obtain an optimal scheduling scheme. The method can fully consider the uncertainty of future climate, improves the reliability of a scheduling scheme, solves the problem of difficulty in decision making of a multi-target model, and can be widely applied to the technical field of water resource scheduling.

The present application discloses a wetland vegetation carbon sequestration potential prediction method and system under the influence of future climate change, belongs to the technical field of wetland vegetation carbon sequestration potential prediction, and comprises the following steps: obtaining the net primary productivity of vegetation, the vegetation coverage, the monthly temperature, the precipitation and the atmospheric carbon dioxide concentration in the historical period and the future period; calculating the annual average net primary productivity of vegetation, the coverage and the multi-year average vegetation coverage; calculating the annual average temperature, the precipitation and the carbon dioxide concentration; extracting the wetland vegetation pixel distribution as the research area range; calculating the ratio of the annual average net primary productivity to the vegetation coverage to obtain the wetland vegetation carbon sequestration potential value in the historical period; adopting the multiple stepwise regression analysis method to build a per-pixel wetland vegetation carbon sequestration potential estimation model; and utilizing the annual temperature, the precipitation and the carbon dioxide concentration in the future period to realize the accurate prediction of the wetland vegetation carbon sequestration potential in the research area range in the future period.

The invention discloses an artificial wetland space optimization arrangement method and system, a non-point source pollution model is constructed based on a hydrological response unit, target meteorological data, target pollution data and an SWAT model, and the simulation precision of the non-point source pollution process can be improved. Climate scene data is constructed through future climate data, a preset path rule and a simple quantile algorithm, and the influence of climate change on pollution load can be reflected. Furthermore, a wetland optimization model is constructed based on a non-point source pollution model and a target function, the scientificity of an optimization strategy is enhanced, candidate wetland areas are identified by a spatial analysis algorithm based on a complex terrain index CTI, and the maximum potential area of each candidate wetland area is counted, so that efficient identification of potential arrangement areas can be realized. The constructed wetland optimization regulation and control scheme is generated based on the preset genetic algorithm, and the constructed wetland space optimization arrangement is performed according to the scheme, so that the optimization and practicability of the constructed wetland layout can be realized.

The invention discloses an ecological system productivity estimation method for predicting the optimal vegetation temperature based on LSTM, and relates to the technical field of ecological information, and the method comprises the steps: obtaining historical weather, terrain and future climate data; constructing a spatial neighborhood of a target pixel, quantifying climate connectivity cost based on terrain and a wind field, and generating anisotropic spatial features; resolving a dynamic space-time confidence factor representing the microclimate complexity; inputting the anisotropic spatial features and the historical temperature into a long short-term memory network, regulating and controlling fusion weight through a dynamic gating mechanism by using confidence factors, and predicting the optimal vegetation temperature; constructing a decoupling model containing thermal kinetic energy and nonlinear thermal stress terms, and calculating productivity based on a predicted value and future temperature; according to the method, the microclimate heterogeneity and the vegetation thermal adaptation mechanism can be accurately depicted, and the estimation precision of the productivity of the ecological system under the complex climate scene is effectively improved.