16 results about "Climate change scenario" patented technology

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 relates to the technical field of soil pollution monitoring, and discloses a farmland soil heavy metal ecological risk assessment method and system, and the system comprises a multi-source collaborative collection module, a dynamic risk assessment module, a climate-driven correction module and a risk prediction execution module. A multi-dimensional dynamic parameter set is generated by constructing a soil-crop-microorganism collaborative collection mechanism, and systematicness and integrity of pollution data are guaranteed; meanwhile, based on heavy metal spatial distribution modeling, a three-dimensional pollution pattern is analyzed in real time, and a combination mode of deep migration risk and table enrichment characteristics is identified, so that the system can overcome the defect of insufficient sampling depth in the prior art, the accuracy of pollution source identification and risk boundary is improved, and the reliability of an evaluation result is enhanced. A dual-drive model is constructed by fusing historical climate parameters and rotation parameters, and risk early warning and prevention strategy dynamic adjustment in a climate sudden change scene are realized.

The application discloses a kind of complex tide river network's multi-source compound disaster inundation risk simulation method, contains the construction method of astronomic tide-storm surge-flood-salinity-falling multi-scale dynamic process full coupling model, can analyze complex river network in climate change scenario mountain torrent, storm surge, salinity and rainstorm etc. extreme compound disaster's inundation and salinity upstream risk.Through potential inundation area determination, model grid optimization, numerical format setting and parallel acceleration etc. technical means, on the basis of guaranteeing momentum and material flux conservation in river network, maximum degree optimizes calculation efficiency, can realize in estuary-coastal cross-scale spatial region high-precision simulation of the purpose of giving priority to multi-source disaster inundation and saltwater stratification.Based on the application, the problem that water dynamics, salinity, relatively isolated nearshore complex topography cannot interact in traditional simulation can be solved, technical support is provided for scientific evaluation of estuary area multi-source disaster risk and return period, with outstanding social benefits and disaster prevention and mitigation economic benefits.

The invention discloses a space-time pattern prediction method based on forest carbon sink climate change. The method comprises the following steps: collecting vegetation data, soil data, meteorological data, topographic data and special interference factor data of a target forest area; preprocessing the collected multi-source data, and performing space-time matching on the preprocessed data; quantifying a climate index of the target forest region; taking a Biome-BGC ecological process model as a core, and integrating a graph neural network to construct a carbon sink-climate index-terrain coupling model; setting various climatic change scenes, and predicting a carbon sink spatial and temporal distribution pattern under different scenes based on a carbon sink-climatic index-terrain coupling model. The space-time pattern prediction method based on the forest carbon sink climate change is adopted, and the prediction precision is improved.

This invention discloses an extreme wind field discrimination method based on non-equilibrium Wasserstein distance, belonging to the field of meteorological wind energy prediction technology. The method includes the following steps: obtaining a set of wind field samples in the study area during the target period to construct a reference field; calculating the non-equilibrium Wasserstein distance between each wind field sample and the reference field as an extremity index; fitting the statistical distribution of the extremity index; calculating an extreme discrimination threshold based on the fitted GEV distribution model; and comparing the extreme index with the extreme discrimination threshold to determine whether the wind field sample is an extreme event or a non-extreme event. This invention, employing the aforementioned extreme wind field discrimination method based on non-equilibrium Wasserstein distance, can provide a high-precision foundation for identifying typical extreme wind fields and constructing scenarios for research such as climate change scenario analysis, extreme scenario risk assessment, renewable energy uncertainty quantification, and energy system resilience optimization.

The application discloses an intelligent climate downscaling method and system based on deep learning, and relates to the technical field of climate downscaling data processing.The intelligent climate downscaling method based on deep learning comprises the following steps: auxiliary feature redundancy monitoring, climate downscaling data load monitoring, model adaptability monitoring and scenario adaptability monitoring.The application determines whether auxiliary features are needed for redundancy processing by performing climate downscaling preprocessing data redundancy discrimination, then determines whether light preprocessing is needed by performing climate downscaling preprocessing data load analysis, and finally determines whether scenario commonality analysis is needed by performing model adaptability analysis, so that the timeliness of high-resolution climate change scenario estimation data is improved, and the problem of low timeliness of high-resolution climate change scenario estimation data in the prior art is solved.

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.

The invention relates to the technical field of photovoltaic generating capacity prediction. The invention provides a regional photovoltaic annual generating capacity prediction method and system fused with a climate change trend. The method comprises the following steps: constructing and preprocessing a multi-source data set including historical power generation, historical meteorological observation, climate system mode output and photovoltaic module performance parameters; extracting trend components and volatility components of climate key factors in a future year, and generating a multi-path climate change scene; establishing a component performance attenuation-climatic factor dynamic response model, and outputting a photovoltaic component equivalent power generation efficiency sequence considering a climatic change cumulative effect; based on photovoltaic power station space distribution and landform data, a region-level power generation capacity space-time aggregation model is constructed, and through scale conversion and probability aggregation, a prediction result and a confidence interval of photovoltaic annual power generation capacity of a target region are output. The problem that the long-term prediction deviation is large due to the fact that an existing photovoltaic generating capacity prediction method does not fully consider the future climate change trend influence is solved.

The invention provides a rape planting simulation method and device responding to and adapting to climate changes and a storage medium. The method comprises the steps that multi-source data of a target area is acquired, the multi-source data comprises climate data, soil data and historical rape crop data, and the climate data comprises historical observation data and future climate scene data; according to the soil data and historical rape crop data, optimizing model parameters of the WOFOST model by adopting a Bayesian optimization algorithm to obtain optimized model parameters; based on the climate data, obtaining changed climate data, and according to the changed climate data and the optimized model parameters, simulating the response of the rape in a climate change scene through a WOFOST model to obtain a simulation result; according to the simulation result and the soil data, an adaptive measure optimization scheme of the rape is determined, and the adaptive measure optimization scheme comprises the sowing time adjustment days and variety characteristic parameters. Therefore, the uncertainty of climate change can be captured, and the accuracy of a simulation result is improved.

The invention discloses an intelligent climate downscaling method and system based on deep learning, and relates to the technical field of climate downscaling data processing. The intelligent climate downscaling method based on deep learning comprises the following steps: carrying out auxiliary feature redundancy monitoring; monitoring climate downscaling data load; monitoring the adaptability of the model; and monitoring scene adaptability. According to the method, climate downscaling preprocessing data redundancy judgment is carried out to determine whether auxiliary feature redundancy removal processing is needed or not, climate downscaling preprocessing data load analysis is carried out to determine whether lightweight preprocessing is needed or not, and finally model suitability analysis is carried out to determine whether scene generality analysis is needed or not. The effect of improving the timeliness of the high-resolution climate change scene estimation data is achieved, and the problem that the timeliness of the high-resolution climate change scene estimation data is low in the prior art is solved.

The invention relates to the technical field of ecological environment monitoring, in particular to a glacier permafrost region multi-underlying surface carbon flux dynamic monitoring and environmental factor collaborative analysis method, which realizes long-term continuous monitoring of underlying surface carbon flux of bare rock permafrost, alpine meadow and the like through coupling of a vortex related system, a microclimate sensor network and an ecological model. And combining multivariate statistics and a machine learning algorithm to analyze a driving mechanism of the environmental factors to the carbon flux. By constructing a distributed monitoring network, a multi-source data fusion model and a climatic change scene simulation module, the technical problems of lack of carbon flux monitoring data and unclear environment response mechanism in the cold and cold mountainous area are solved. The method has the advantages of high monitoring precision, wide space-time coverage, high prediction reliability and the like.

The application provides a method and system for predicting total primary productivity of an ecosystem under drought stress, comprising: coupling nonlinear response under drought stress and hydrothermal carbon physical mechanism to provide important and operable reference for response mechanism of future drought evolution to carbon sink function of an ecosystem under global and regional prediction of climate change scenarios.

The application discloses a regional surface water resource prediction simulation and optimization configuration method, which comprises the following steps: step one, collecting data for a research area, and constructing a watershed hydrological simulation model, namely, a SWAT model; step two, collecting future meteorological data, and inputting the data into the calibrated and verified SWAT model to predict and simulate the regional surface water resource quantity under the background of climate change in the research area; step three, integrating the regional surface water resource prediction simulation model under the background of climate change into an interval multi-objective optimization model; and step four, solving the interval multi-objective optimization configuration model of the regional surface water resource under the background of climate change; the beneficial effect is that, on the basis of predicting the future regional surface water resource quantity, the water resource configuration scheme of a reservoir under different climate change scenarios, which meets the power generation, water supply and water abandonment targets, can be obtained.

The invention discloses an urban ecosystem carbon reserve prediction method and device and a medium, and relates to the technical field of carbon reserve prediction, and the method comprises the steps: carrying out the scene simulation through a carbon reserve prediction model based on a fusion ecological data set, evaluating the different influences of a climate change scene and a land utilization change scene on the carbon reserve, and obtaining a prediction result; generating a multi-scenario carbon reserve prediction result; performing spatial division on the multi-scenario carbon reserve prediction result, identifying the trend and difference of carbon reserve changes in different regions, and generating carbon reserve spatial distribution; according to the carbon reserve space distribution, optimizing weight factors and parameter setting in the carbon reserve prediction model, and generating an optimized carbon reserve prediction model; and simulating carbon reserve changes in different scenes through the optimized carbon reserve prediction model, extracting a key trend of the carbon reserve changes, and generating a carbon reserve prediction report. According to the method, adaptive parameter adjustment is realized, the prediction precision is improved, and differentiated carbon management decisions are supported.

The invention relates to a single-ditch debris flow disaster-causing intensity prediction method considering climate change, which adapts to the change trend of historical climate by moving a time window, gradually changes the time window from small to large, and performs stationarity test on rainfall sequence characteristics in different historical periods, thereby determining rainfall time periods with different statistical characteristics. The span length of the rainfall time period determines how many years of rainfall sequence analysis and debris flow disaster-causing intensity analysis can be carried out in the future. And then, calculating and fitting rainfall extreme values of different time periods, and extrapolating the rainfall extreme value of a future time period. According to the specific geological conditions of the gully where the debris flow is located, the influence of the gully on the surface runoff is combined, and the disaster-causing intensity of the debris flow is predicted through FLO-2D simulation. According to the invention, dynamic coupling of water power change and a slope response mechanism under a climate change scene can be realized, and an influence rule of climate change on triggering and movement of debris flow disasters is revealed.

The invention relates to the technical field of infrastructure operation and maintenance, in particular to a pavement frozen soil foundation temperature field change influence evaluation method and device and a storage medium. According to meteorological, engineering and geological frozen soil data obtained in advance, a three-dimensional unsteady-state finite element model containing pavement plates and joints is established; setting a joint hot infiltration effect mathematical model; the unfrozen water content parameters are verified by using monitoring data, and the model simulation is sufficient. Setting a future climate boundary condition according to the climate change scene; setting pavement dynamic boundary conditions according to the pavement operation and maintenance data; respectively inputting the boundary conditions into the calibrated model, carrying out long-term simulation, and predicting the evolution of a foundation temperature field; calculating a frozen soil thermal stability index; finally, a mechanical model is coupled, pavement risk early warning is achieved, and the accuracy of the prediction result is high. The method realizes full-chain, refined and quantitative evaluation of the thermal stability of the frozen soil foundation under the pavement from mechanism to decision, and has practicability.