7 results about "Climate change scenario" patented technology

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.

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 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 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.