79 results about "Future climate" patented technology

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 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 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 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 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 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 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.

The invention provides a crop irrigation and fertilization decision-making method based on probability constraint. The crop irrigation and fertilization decision-making method comprises the steps of obtaining multi-source input data, and collecting key growth index data of crops through field trials. A Markov chain is adopted to simulate distribution and correlation of different variables in meteorological historical observation data, a day-by-day meteorological sequence is generated, and the day-by-day meteorological sequence is used for reflecting the natural variation rate and uncertainty of future climate conditions. A to-be-optimized water and fertilizer management variable set is set, the crop DSSAT model is operated in each meteorological scene, and constraint conditions are constructed; and under the constraint condition, carrying out global optimization on the target function by adopting a multi-target optimization algorithm to obtain a Pareto optimal solution set. By introducing a multi-scene meteorological simulation method and combining a crop DSSAT model and a multi-objective optimization algorithm, the adaptive capacity and robustness of a crop management scheme under actual climate conditions are improved, and the scientificity and systematicness of agricultural management decisions are further improved.

This application discloses a method, apparatus, equipment, medium, and product for predicting regional crop water productivity, relating to the field of smart agriculture. The method includes inputting historical and future climate data into an ecosystem process model to obtain simulated net primary productivity and simulated evapotranspiration for historical and future times; using a bias mapping method based on the simulated evapotranspiration, actual evapotranspiration, and simulated evapotranspiration for future times to obtain predicted evapotranspiration for future times; training multiple machine learning models based on the simulated net primary productivity, agricultural management data, and grain yield per unit area for historical times to obtain a crop yield prediction model; obtaining the predicted grain yield per unit area for future times based on the crop yield prediction model; and obtaining the regional crop water productivity based on the predicted evapotranspiration and the predicted grain yield per unit area for future times. This application can achieve high-precision, high spatiotemporal resolution, and high-reliability prediction of regional crop water productivity.

The invention belongs to the technical field of protection biology, and particularly relates to a plant habitat suitability evaluation method. According to the specific technical scheme, survival rate and growth rate indexes under different precipitation amounts and temperature gradients are evaluated through field planting experiments, a model is established, and expression and potential adaptability of research species on different climate gradients are quantified and evaluated; mapping an optimal model result to a geographic space through a geographic information system or remote sensing data, and performing time regression on past climate suitability indexes to obtain the suitability change rate of the research species; and carrying out linear regression in the future time by utilizing the suitability change rate, which is an index showing the adaptability change trend of the research species, and predicting the future climate adaptability of the research species to obtain the suitable habitat area of the research species in the future space, thereby providing support for the protection work of the research species.

The application relates to a water resource pressure assessment method integrating social economy and climate change. It relates to the technical field of water resource pressure assessment in arid river basins. The method comprises the following steps: obtaining historical values and predicted values of social economic data, correcting the predicted values of the social economy to obtain corrected predicted values; calculating potential evapotranspiration in different scenarios based on historical scenario meteorological data and corrected future climate mode data; calculating future basin water resources based on the correlation between basin rainfall and water resources; extracting drought intensity characteristics according to the change trend of the historical scenario and the future scenario basin drought index; constructing a basin water resource pressure index calculation formula; and according to the calculated water resource pressure index of the water resource region, standardizing the index to obtain a standardized index, and evaluating the change trend of the water resource pressure of the region in different scenarios according to the standardized index. The method solves the problem of quantification and evaluation of basin water resource pressure under complex conditions.

The invention describes a method by which it is possible to predict past and future climate changes by measuring changes in local and global magnetic fields on the Earth's surface in time and space and comparing them with older measurements and effects.

This invention discloses a method for assessing ecosystem service losses due to regional climate change. The region includes counties, cities, or important ecological function zones. The steps are as follows: S1. Collect remote sensing data and preprocess the data through band combination, geometric correction, and projection transformation. Select a standard color composite scheme, establish interpretation markers, and perform visual interpretation. S2. Calculate and statistically analyze the unit area yield value and equivalent factor of grain crops. The equivalent factor is 1 / 7 of the average market value of grain in the current year. S3. Statistically analyze land use types, calculate the magnitude of land use change, reflecting the changes in the total amount of different land use types and the overall trend of land use type changes. S4. Select vegetation cover as an indicator, and adjust the ecosystem service value based on two parameters: net primary productivity and vegetation cover. This invention is of great significance for formulating ecosystem management strategies under the background of future climate change.

The application provides a kind of urban rainfall area IDF curve establishment constraint method and system, comprising: collecting measured rainfall data, GCM simulation rainfall data;Establish spatial downscaling model, carry out spatial downscaling to the grid monthly rainfall of future period estimated by GCM;Calculate the conditional transition probability of each calendar month rainfall occurrence in future period;Generate the parameter set of driving random rainfall generator, input it into random rainfall generator to obtain future period daily rainfall sequence;Carry out time discretization of daily scale to hour scale to future period daily rainfall sequence;Extract annual maximum value sequence from future period hourly rainfall sequence to carry out regional frequency analysis, and construct urban rainfall regional IDF curve;Construct random forest model, and deduce the alternative factor of IDF curve migration change;Emergent constraint model is constructed, and the uncertainty of urban rainfall risk prediction under future climate change is reduced.The application improves the accuracy of rainfall frequency analysis.

The invention discloses a man-made forest ecological function monitoring method based on meteorology and hydrology, and relates to the technical field of forestry ecological monitoring, and the method comprises the steps: obtaining the soil penetration resistance time sequence data of different soil layers of a target man-made forest area, calculating the soil physical structure toughness index of the soil maintenance pore structure and the compaction resistance through combining with soil auxiliary parameters, and calculating the soil physical structure toughness index; analyzing the historical evolution trend; collecting short-term meteorological data and long-term climate change data of the target artificial forest area, coupling the short-term meteorological data and the long-term climate change data with the soil physical structure toughness index, and establishing a meteorological-hydrological-soil structure coupling process model; quantitatively evaluating the water source conservation capability, the soil erosion risk and the climate adaptability of the artificial forest area in the target artificial forest area, and simulating the future evolution trajectory of the soil physical structure and the ecological function of the target artificial forest area by taking different climate scenes and management intervention scenes in the future as compensation factors; meteorological and hydrological dynamic monitoring and early warning of the ecological function of the artificial forest are realized. According to the invention, the accuracy of ecological function evolution prediction is improved.

The present application belongs to the technical field of geochemistry, and particularly relates to the estimation of the carbon flux of the earth system, and especially relates to a method and system for estimating the carbon flux of the deep-time earth system. The method for estimating the carbon flux of the deep-time earth system provided by the present application solves the four defects in the traditional technology caused by the fixed carbon pool assumption, the neglect of the atmospheric / biosphere carbon content, the isotopic error, and the failure to consider the fractionation effect through dynamic carbon pool segmentation, the inclusion of multi-sphere isotopic composition, and the correction of fractionation effect. The high-precision and verifiable model framework not only improves the scientificity of the deep-time carbon cycle research, but also provides a key tool for analyzing the climate evolution mechanism in the geological history period and predicting the future climate change trend, and has important theoretical innovation and practical application value.

The invention provides an agricultural water safety risk assessment method based on climate change, and the method comprises the steps: sampling the crop distribution data of a target region according to a 0.1-degree spatial resolution, and determining an agricultural risk region affecting the water safety based on a target grid comprising at least one effective pixel; in a set future climate scene, the disastrous property, the exposure degree and the vulnerability of the agricultural risk area are quantified to obtain respective corresponding index data; wherein the disastrous property is used for representing a natural disaster event influencing agricultural water safety; the exposure degree is used for representing the production potential of land and climate affected by natural disaster events; the vulnerability is used for representing the sensitivity and toughness of the agricultural system to natural disaster events; and based on the quantified index data of disastrous, exposure and vulnerability, evaluating the agricultural water safety risk under the future climate scene. According to the method, transferable insights are provided for agricultural water safety risk assessment and sustainable development strategies of similar agricultural ecological regions in the world.

The invention discloses an interactive platform for simulating climate warming and rainfall pattern change, which comprises a rainfall increasing area, a rainfall decreasing area and a current rainfall area, the rainfall increasing area is divided into a warming rainfall increasing area and a current temperature rainfall increasing area, and the rainfall decreasing area is divided into a warming rainfall decreasing area and a current temperature rainfall decreasing area. The current precipitation area is divided into a temperature increasing current precipitation area and a current temperature current precipitation area, and a precipitation changing device is arranged between the precipitation increasing area and the precipitation decreasing area; all-soil profile warming devices are respectively arranged in the rainfall increasing area, the rainfall decreasing area and the current rainfall area, each all-soil profile warming device comprises a warming processing mechanism and a current temperature processing mechanism, and the warming processing mechanisms and the current temperature processing mechanisms are respectively and electrically connected with the temperature controller. According to the method, climate warming and rainfall pattern change are combined, the limitation of a traditional single climate change factor is broken through, and a key technical support is provided for simulating future climate scenes.

The invention relates to the technical field of hydro meteorology, in particular to a method and a system for predicting and tracing extreme hydrological events in a reservoir area. The method comprises the following steps of S1, collecting meteorological and hydrological data, a global climate mode data set, geographic space data and stable isotope data of a reservoir area, and unifying a multi-source data scale through a time sequence fusion algorithm; according to the method, isotope data is adopted as an independent evidence constraint model, and compared with a traditional single model method, the method can more comprehensively capture a complex mechanism of occurrence of an extreme event, so that a simulation result is closer to an actual situation, and countermeasures can be taken in the face of an extreme climate event; the climate prediction and the isotope traceability technology are coupled for the first time, how causes of the extreme events change under different future climate scenes can be pre-judged in advance, and the research method and the model framework are not only suitable for specific areas, but also can be widely applied to research of the extreme events of large reservoirs and drainage basins.

The invention relates to a method for predicting influence of climate change on plant distribution based on an artificial intelligence algorithm, and belongs to the technical field of plant distribution prediction. Global plant distribution data is obtained from GBIF, cleaned and then subjected to spatial matching with WWF ecological region vector data, species and ecological region IDs are associated, woody and herbaceous plants are distinguished, species names are standardized, the number of species in each ecological region is counted, and the ratio of woody and herbaceous plants is calculated and visualized; according to the method, current and future climate data are fused, a climate characteristic median of each ecological region is extracted, a species-climate relationship model is constructed by using a traditional linear regression and artificial intelligence algorithm, the influence of temperature, rainfall, human footprint and temperature seasonality is analyzed, and finally the distribution pattern of two types of plants in a future climate scene is predicted. Through multi-scale data coupling and nonlinear modeling means, a scientific basis is provided for biodiversity protection and adaptive management under a global change background.

The invention discloses a building demand response strategy evaluation and optimization method considering a future climate scene. The method comprises the following steps: dynamically constructing meteorological data under the future climate scene of an evaluation region; acquiring load change characteristics of the building under different climate conditions; constructing a demand response scene parameter space, and forming a demand response'variable-performance index 'data set by adopting a Latin hypercube sampling and batch simulation method; constructing a machine learning agent model to quickly predict the building load response, and optimizing the hyper-parameters of the building load response by using an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm; the load adjustable capacity of the building demand response strategy in different future climate scenes is evaluated, the relative change rate is calculated, and the future climate adaptability of the building load flexible regulation and control strategy is determined; and establishing a sensitivity analysis method to identify key variables, and providing a decision basis for early-stage design and later-stage operation optimization of a building demand response strategy. The method provides important support for planning design and operation regulation and control of flexible buildings and novel energy systems.

The present invention discloses a field open-type warming test device and an installation method. The device includes multiple wind poles, each of which is provided with a first clamping plate, on which a combination bracket is supported, and multiple infrared radiation heating components are provided below the combination bracket. The combination bracket is also provided with a detachable connecting rod assembly, one end of which is connected to a canopy temperature sensor via a second clamping plate. The canopy temperature sensor is arranged at an angle, and the sensor probe of the canopy temperature sensor is oriented toward the infrared radiation heating component. The field open-type warming test device is based on a movable and adjustable infrared radiation heater, which can achieve timed warming of the plant canopy, regulate the warming amplitude in real time according to the target temperature difference, and automatically record data. The heat source height can also be adjusted according to changes in plant height caused by changes in the crop growth period. It is particularly suitable for conducting field experiments related to crop growth responses under simulated future climate warming conditions.

This invention discloses a method for hydropower prediction under climate change that integrates machine learning, belonging to the field of hydropower data management technology. It includes: simulating the hydropower response under future climate change using a finite number of representative samples in a target region through a physical process-based hydrological-reservoir-hydropower coupling model; constructing a training dataset containing complex nonlinear response characteristics while ensuring the reliability of the model's physical principles and results; then, using machine learning algorithms to establish an efficient generalization model between key climate change features and hydropower generation; finally, applying this model to a larger sample of future climate prediction data covering a wider range of climate change scenarios to achieve rapid and robust hydropower output prediction and uncertainty quantification. This provides more robust and reliable technical support for hydropower prediction under climate change scenarios, as well as for uncertainty quantification and risk decision-making.