22 results about "Climate variation" patented technology

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 application belongs to the field of sub-seasonal climate prediction, and provides a sub-seasonal climate prediction method and system based on the combination of dynamic model downscaling and machine learning downscaling, which comprises the following steps: S1, generating initial field and model boundary field information required for dynamic downscaling based on global climate model output data; S2, driving regional climate model to perform dynamic downscaling by using the initial field and model boundary field information; S3, generating input field required for machine learning downscaling based on the output circulation field information of the regional climate model; S4, performing machine learning downscaling correction optimization on the output circulation field of dynamic downscaling based on a convolution model; S5, performing machine learning super-resolution based on the output data of machine learning downscaling correction; and S6, generating sub-seasonal prediction information of double downscaling of dynamic model and machine learning. The application utilizes the complementary advantages of dynamic downscaling and deep learning downscaling, improves the sub-seasonal prediction skill, and copes with new challenges brought by climate change.

This invention discloses a three-dimensional quantification method for subsurface lateral preferential flow in arid and hot valleys based on GPR (Geophysical Permeability Reduction) detection and SSIM (Spatial Infiltration Model) analysis. It integrates time-delayed GPR scanning with artificial infiltration experiments and applies SSIM analysis to depict LPF (Limited Placement Flow) path activation, network structure evolution, and humid zone distribution in a three-dimensional manner. This method achieves, for the first time, three-dimensional non-destructive quantification of the dynamics of the LPF network on arid and hot valley slopes. The established hydrogeophysical data acquisition and post-processing workflow provides a crucial and transferable research method for quantitatively analyzing the interaction between structural connectivity and process connectivity in slope runoff generation and confluence. This method overcomes the limitations of traditional two-dimensional slope LPF analysis by quantitatively characterizing the spatiotemporal dynamics of subsurface three-dimensional connectivity. It provides direct support for improving the water cycle mechanism in arid and hot valleys and improving hydrological models based on physical mechanisms, and provides a scientific basis for sustainable management decisions in water conservation areas of arid and hot valleys under climate change conditions.

This invention relates to a typhoon-wave-current hazard assessment method based on physical field coupled numerical simulation. The method includes: selecting historical or future typhoon events affecting a target area from historical typhoon trajectory data or artificially synthesized future typhoon trajectory data to generate a typhoon event set; modeling a two-dimensional typhoon mixed wind field based on the constructed typhoon event set, combined with typhoon trajectory and intensity parameters; performing a physical mechanism-based typhoon-wave-current coupled numerical simulation on the target area to generate corresponding wind, wave, and current evolution time history data; and extracting the event maximum and annual maximum values ​​from the simulated wind, wave, and current time history data, and using extreme value analysis to assess hazard risk. This invention is not limited to historical typhoon events; the entire assessment framework can be applied to the hazard assessment and analysis of future typhoon events considering the impact of climate change, improving the climate adaptability and resilience of coastal engineering infrastructure against typhoon-related complex disasters.

The application discloses a runoff process reconstruction and attribution analysis method based on coupling of a hydrological model and deep learning. The method is carried out by using a coupling hydrological model comprising a series of physical hydrological modules and a regulation simulation module, and comprises the following steps: obtaining measured hydro-meteorological data, measured runoff data and reservoir attribute data of a target basin; performing detrend processing on the measured hydro-meteorological data to obtain natural meteorological data, driving the coupling hydrological model to reconstruct a runoff process under natural climate conditions; driving the coupling hydrological model by using the measured hydro-meteorological data, and applying a no-regulation constraint condition to the regulation simulation module, to reconstruct a runoff process without reservoir regulation under climate change; and based on the measured runoff, the natural climate runoff and the runoff process without reservoir regulation, quantitatively analyzing the influence of climate change and reservoir regulation on runoff change. The application solves the technical problems of lack of physical constraints and inaccurate attribution separation of a data-driven model under the condition of lacking specific scheduling operation data of a reservoir.

The application discloses a kind of based on hour scale temperature sudden change event identification method and system, the method includes: obtaining hour scale temperature data and carrying out data preprocessing, and calculating temperature variation amplitude and temperature variation duration;Build the reference statistics background of temperature sudden change event, and carry out percentile statistics in combination with temperature variation amplitude, obtain sliding percentile relative threshold;Define temperature sudden change event discriminant function, and carry out event identification in combination with temperature variation duration, determine cold to warm event and warm to cold event;De-duplication processing and event multidimensional feature index extraction processing are carried out, and the spatial distribution of cold to warm sudden event and warm to cold event, seasonal distribution, interannual variability and variation characteristics are output.The application can improve the identification accuracy of hour scale rapid temperature mutation process.The application can be widely applied to the field of climate change detection as a kind of based on hour scale temperature sudden change event identification method and system.

The application relates to the field of road engineering quality detection, in particular to a high embankment slope structure and a deformation monitoring device thereof, which comprises an embankment slope main body and an embankment solidification structure arranged on the embankment slope main body, the embankment solidification structure comprises a transverse support structure and a vertical support structure which cooperate with each other, the transverse support structure comprises a bottom transverse protection net and a top transverse protection net, the bottom transverse protection net is composed of a plurality of transverse telescopic support rods and a bottom plate support seat, each transverse telescopic support rod is perpendicular to the bottom plate support seat, each transverse telescopic support rod is movably connected with the bottom plate support seat, and the bottom plate support seat is fixedly arranged at the outer side edge of the embankment slope main body, so that the technical problem that the existing embankment slope is prone to landslides due to climate changes such as rain and causes road collapse or deformation and no timely monitoring alarm is solved.

The application provides a kind of angle consistent global MODIS ground surface temperature product development method, comprising: according to the initial global scale MODIS remote sensing image data and initial reanalysis data, the database of MODIS remote sensing data and ERA5-Land reanalysis data is constructed;The MODIS zenith observation direction remote sensing data and reanalysis data in the database of MODIS remote sensing data and ERA5-Land reanalysis data are extracted using MODIS observation zenith angle information, and the training data set is constructed according to the MODIS zenith observation direction remote sensing data and reanalysis data;Based on the training data set, a global scale vertical ground surface temperature estimation model is established using a random forest machine learning algorithm;Based on the global scale vertical ground surface temperature estimation model, the corresponding MODIS vertical ground surface temperature data is estimated using MODIS non-vertical observation time remote sensing data and ERA5-Land reanalysis data.The application helps to improve the application value of ground surface temperature product in climate change, resource environment monitoring, ground surface energy balance and the like.

This invention provides a method for assessing the regulation of grassland biomass by non-growing season precipitation in the Northern Hemisphere, including: data collection and screening, classification of regulatory factors, statistical analysis, and result output. This method retrieves 571 sets of comparative data from in-situ controlled experiments conducted between 1990 and 2025, identifying five categories of regulatory factors, including climate change and environmental geographical conditions. Statistical analysis is performed using a logarithmic response ratio combined with a multi-level mixed-effects model, and publication bias verification ensures the reliability of the results. The results show that increased non-growing season precipitation significantly enhances grassland biomass, with the impact being more pronounced in drier climates; the water gain is concentrated during the vigorous growth period, while the adverse effects of drought begin in the early stages and persist; aboveground biomass is more sensitive to precipitation fluctuations, and differences in wet and dry conditions drive different biomass allocation strategies. This invention's method is standardized and universally applicable, providing a scientific basis for grassland ecological management and climate change risk prediction.

A method for tracking a four-dimensional soil moisture drought event and identifying a warming signal, includes following steps: data acquisition; identification of four-dimensional soil moisture drought events; characteristic quantification of four-dimensional soil moisture drought events; anthropogenic warming signal detection based on temporal evolution of four-dimensional soil moisture drought characteristics in a historical period; estimation of future evolution of four-dimensional soil moisture drought; and attribution of four-dimensional soil moisture drought events to driving factors. The future spatial-temporal evolution characteristics of four-dimensional soil moisture drought are explored, and the driving factors and physical mechanisms of four-dimensional soil moisture drought are analyzed. A strong scientific basis is provided for tackling global climate change, safeguarding water resource security, and formulating disaster prevention and mitigation strategies.

This invention discloses a method and system for assessing the risk of typhoon-induced coastal flooding considering climate change, belonging to the field of flood risk assessment technology. The method includes: acquiring a synthetic typhoon track dataset for the study area; calculating the pressure field and wind field data for the study area; simulating storm surge levels and flood distribution data under baseline and future scenarios using a dry-wet algorithm; and importing the flood distribution data and land use data into a geographic information system to quantify flood risk indicators for the study area under different scenarios. This invention assesses the typhoon-induced flood risk in the study area from a long-term perspective. Based on synthetic typhoon tracks and the Holland model, it calculates sea level levels under baseline and future scenarios, and compares and analyzes the impact of floods on different cities, landform types, and land use categories under the two scenarios. The research results reveal the high-impact types and potentially rapidly increasing risks that storm surges and climate change continue to pose to Hainan Island.

The application discloses a kind of salinization wetland vegetation recovery method based on gradient moisture control, first, according to moisture gradient, degraded wetland is divided into three levels of moisture zone, namely low moisture zone, medium moisture zone and high moisture zone, and according to moisture zoning, vegetation recovery scheme is formulated, based on the quantitative relationship between soil moisture content and vegetation aboveground biomass, water supplement measures are formulated, and soil is improved through salt-nutrient synergistic control.The application innovatively integrates hydrological engineering and ecological process control, breaks through the differentiated water supply mode, realizes the synergistic effect of increasing soil and salt inhibition, provides a replicable recovery paradigm for semi-arid region wetlands, and provides a quantifiable natural solution for coping with land desertification and climate change.

This invention relates to the field of meteorological data analysis technology, specifically to a method for plotting the temporal distribution of heat waves, comprising the following steps: data acquisition, analysis, and temperature sequence construction; identification of heat wave intervals; calculation of heat wave intensity index based on the temperature deviation and duration, obtaining a heat wave intensity index sequence corresponding to each heat wave interval; adjustment of the temperature level interval distribution using an arctangent function to generate a corresponding color mapping relationship; generation of heat wave marker boxes on the heat map based on the final heat wave interval sequence; and plotting a stacked bar chart of high-temperature days. This invention can simultaneously reflect the duration of heat waves and the degree of temperature exceeding the threshold, effectively distinguishing the intensity differences between different years and different heat wave events, and providing more refined indicator support for subsequent heat wave risk assessment, urban thermal environment analysis, and climate change research, thereby improving the depth and comparability of heat wave event analysis.

The application provides a reservoir desilting scheduling method and related device under water-sand relationship abnormal conditions, aiming at the problems of lack of reservoir sediment observation data in the Qinghai-Tibet Plateau, low efficiency of traditional scheduling mode caused by water-sand relationship abnormality (such as increase of sand amount and "zero storage and whole taking"), and realizes the method through the following steps: 1. Based on the flow and air temperature data of the upstream monitoring point, the sand content is calculated by using the formula; 2. The control target of the current deposition amount is determined according to the design deposition allowable value, and the required storage capacity is calculated by combining the desilting ratio formula; 3. The desilting water level is obtained by converting the storage capacity curve, and the desilting starting time is predicted by using the sediment transport time model. The application does not need to rely on online sediment concentration monitoring, can dynamically adapt to the water-sand abnormal conditions caused by climate change, accurately controls the deposition amount, reduces the waste of water resources, and is suitable for the plateau reservoirs with poor sediment data and complex water-sand relationship.

This invention discloses a method for optimizing vegetation photosynthesis models, belonging to the field of remote sensing inversion and ecosystem productivity simulation technology. This method addresses the problem that existing vegetation photosynthesis models often have fixed optimal temperature parameters for photosynthesis, failing to reflect spatiotemporal dynamic changes and thus limiting the accuracy of total primary productivity (TPP) estimation. The key points are: obtaining a dynamic optimal temperature parameter at the site scale based on the response relationship between light energy utilization efficiency and temperature; using a machine learning model to integrate multi-source feature variables and extrapolate this parameter to a global scale, generating a spatiotemporally continuous dynamic optimal temperature dataset; and replacing the original fixed optimal temperature parameter based on biome settings in the vegetation photosynthesis model with this dataset, thereby optimizing the model's representation of temperature response. This invention improves the accuracy of remote sensing estimation of TPP, and the generated optimal temperature dataset can be used for global carbon cycle simulation and climate change impact assessment.

The present application belongs to the technical field of numerical simulation, and discloses a method for predicting the seawater intrusion of shallow groundwater under the influence of different climate changes, comprising the following steps: (1) establishing the hydrogeological concept of the study area, determining the source and sink items, the initial conditions of the groundwater flow field and the salinity field; (2) using the groundwater model GMS to establish the groundwater numerical model and the groundwater solute transport model of the study area and performing identification verification; (3) inputting the climate data after the Cmhyd downscaling into the groundwater solute transport model for simulation, obtaining the predicted groundwater salinity distribution and dynamic change, and analyzing the influence of different climate factors on the seawater intrusion of groundwater. The present application considers the influence of global warming, and the precipitation and temperature change with time instead of being a fixed value, so that the future climate can be better predicted; when simulating the seawater intrusion of shallow groundwater, the changing precipitation and temperature can also comprehensively understand how the climate change influences the degree of the seawater intrusion of shallow groundwater.

This invention discloses a method for quantifying the coupling degree of drought-heat wave and decomposing its driving forces based on a time-varying moment model (PMOE). It constructs a time-varying moment-constrained maximum entropy distribution (POME) model for heat wave and drought sequences, expressing the key Lagrange multipliers of the PMOE model as a linear function of climate indices and urbanization factors to characterize the time-varying characteristics of univariate marginal distributions. Based on pseudo-observations, a time-varying Copula model considering physical driving factors is constructed. Through the optimal combination of various parameter structures, the dynamic evolution of the interdependent structure between drought and heat waves is characterized. By calculating and comparing risk impact indicators under multiple models, the influence of climate change and human activities on the coupling degree of drought-heat waves is quantified and separated. This invention achieves simultaneous characterization of the dynamic evolution and non-stationary characteristics of complex extreme events, and effectively identifies and decomposes the contributions of key physical driving factors, providing a scientific basis for dynamic risk assessment and adaptive countermeasure formulation for regional complex events.

The application provides a land use and cover change local and non-local precipitation effect separation method and system, and relates to the technical field of climate change influence analysis and land-air interaction research. The application is based on observation and reanalysis data, identifies a processing grid that changes, and constructs a control sample library, and keeps the climate background consistent through boundary layer stability and seasonal stratification and circulation screening. A fan-shaped weighted window is established in the main water vapor transport direction to synthesize counterfactual precipitation time series, and precipitation feedback is extracted. In combination with a kernel weighted spatial weight matrix, non-local influence is represented, a hierarchical statistical model introducing a spatial correlation random field is established, local and non-local precipitation effects are quantitatively separated, and the scientificity and reliability of effect attribution are improved.

This invention provides a method for identifying the year of open-pit coal mine disturbance by integrating multiple land cover features, belonging to the field of mine ecological monitoring technology. It involves acquiring long-term remote sensing images of open-pit coal mines, performing radiometric calibration and cloud removal preprocessing to establish a high-quality annual dataset; calculating the spectral indices of vegetation, bare soil, and bare coal; and constructing a novel open-pit coal mine disturbance index, MDI_NBC, by comprehensively considering the changes in the spectral indices of these three land cover features during open-pit coal mining; comparing the index changes in undisturbed areas to determine the MDI_NBC threshold for distinguishing mining disturbances; and applying a time-series segmentation algorithm with optimized parameters to conduct long-term monitoring of open-pit coal mine disturbances in different vegetation gradient zones, identifying the year of open-pit coal mine disturbances. This invention achieves universal monitoring of open-pit coal mine disturbances across vegetation gradient zones, effectively distinguishing vegetation disturbances caused by climate change, and improving the efficiency and accuracy of identifying the year of open-pit coal mine disturbances.

The application discloses a runoff change attribution analysis method based on underlying surface spatial characteristics, and belongs to the technical field of runoff attribution analysis. First, in the constructed underlying surface spatial characteristic factor set, the key underlying surface spatial characteristic factors capable of effectively explaining the change of Budyko parameters are screened through collinearity test and stepwise regression. Second, an optimal parameter relationship model between the Budyko parameters and the key underlying surface spatial characteristic factors is established. Finally, combined with a water-heat coupling balance equation, the runoff change in different analysis periods is attributed and decomposed, so that the contribution amount of climate change, the contribution amount of underlying surface spatial characteristic change and the absolute contribution proportion are obtained. Through the relationship between the parameters and the underlying surface spatial characteristics, the underlying surface spatial heterogeneity is allowed to participate in the runoff change attribution analysis in a parameterized manner, so that the expression capability of the attribution result to the comprehensive influence of the underlying surface is improved, and guidance is provided for the basin water resource regulation and ecological protection management.

This invention provides a method, apparatus, electronic device, and storage medium for monitoring farmland drainage capacity, relating to the field of image data processing technology. The method includes: acquiring parameter information for each ditch within a target farmland area based on multispectral images of that area; acquiring drainage capacity monitoring results for each ditch based on its parameter information; and acquiring overall drainage capacity monitoring results for the target farmland area based on these results. This invention enables automated and highly accurate quantitative monitoring of drainage capacity in large-area farmland, significantly reducing the labor and time costs required for monitoring farmland drainage capacity, and substantially improving its efficiency and flexibility. It provides technical support for real-time and high-frequency monitoring of farmland drainage capacity and allows for rapid response to climate change and extreme weather events.

The application discloses a kind of space differentiation characteristics analysis method and system that reservoir water storage influences local climate in reservoir area, comprising the following steps: determining target reservoir;Collect the meteorological station data in the target area where the target reservoir is located, obtain the environmental factors of target reservoir and each meteorological index before and after water storage of target reservoir;Identify the dominant environmental factor that causes the spatial differentiation of the influence of water storage on local climate;Based on the dominant environmental factor, the improved Theil-Sen trend analysis method is used to determine the influence property of the dominant environmental factor in different partitions;Based on the LSTM model, the specific influence degree of water storage on climate in different partitions is quantified.The application quantifies the change effect of water storage on temperature, precipitation, wind speed, humidity and other climate elements under the premise of stripping the influence of global climate change, and further reveals the spatial differentiation mechanism behind the influence difference.