31 results about "Climate change scenario" patented technology

The invention discloses a method drawing up a multi-goal reservoir optimization scheduling graph capable of being self-adaptive to climate change. The method for drawing up the multi-goal reservoir optimization scheduling graph comprises the steps of: step 1. establishing a coupling model of a global climate model (GCM) and a variable infiltration capacity (VIC) hydrological model so as to forecast a run-off process under a future climate change scene; step two. establishing a multi-goal reservoir optimization scheduling graph model; and step 3. taking the forecast run-off process data under the future climate change scene as the input of the optimization scheduling graph model, and drawing up the multi-goal optimization scheduling graph model by adapting a self-adaptive genetic algorithm. The method for drawing up the multi-goal reservoir optimization scheduling graph has the advantages of balancing the social economy goal and ecological goal of reservoir scheduling, improving the comprehensive benefits of reservoir scheduling to the maximum degree on the condition of ensuring the flood control safety of a reservoir and being capable of being self-adaptive to future climate change and being widely applied to the production practice of multi-goal reservoir optimization scheduling.

The invention discloses a reservoir multi-variable design flood estimating method for achieving self-adaption to weather changes. A coupled model of a GCM and a VIC is built to predict the reservoir runoff process on the scene of future weather changes. A reservoir design flood peak volume most possible combination method calculation model is built through a Copula function, and reservoir predicted runoff data on the scene of the future weather changes serve as input of the design flood peak volume most possible combination method calculation model, and a combination design value of a reservoir at different recurrence period levels is calculated. The reservoir multi-variable design flood estimating method has a strong statistics basis, the characteristics of an actual measurement sample can be objectively reflected and the internal relatedness of the characteristic quantities is taken into full account. The design flood design value obtained under the condition that the weather changes are considered has higher practicability.

The invention discloses a staging design flood derivation method in an integrated climate mode. The staging design flood derivation method comprises the steps: establishing a coupling model of a GCM and a VIC so as to predict the reservoir runoff process under the future climate change scene; through an arithmetic average method, obtaining an average state runoff process under a climate change scene; analyzing the runoff process by adopting a probability variable point method to divide a main flood period and a non-main flood period; constructing a reservoir design flood peak quantity most possible combination method calculation model through a Copula function; and taking reservoir prediction runoff data under a future climate change scene as input of a designed flood peak quantity combination method calculation model, and meanwhile, calculating the united design values of the reservoir under different recurrence period levels by considering multivariate same-frequency combination and most possible combination. The staging design flood derivation method has a strong statistical basis, and provides a new way for staged design flood calculation under a climate change scene.

The invention discloses a design flood derivation method and system based on equal reliability in a climatic change scene. The invention belongs to the field of reservoir flood control safety design.A future climate change scene is obtained based on global climate mode output and a two-stage deviation correction method considering meteorological variable correlation; a VIC distributed hydrological model is driven to obtain a runoff situation considering the influence of climate change; a principal component analysis method is adopted to preferably select an extreme rainfall index influencingflood characteristics as a covariable; a time-varying hydrological frequency analysis model is constructed by considering inconsistency of a flood sequence, a flood design value combination under a climatic change scene is deduced based on an equal reliability method, a most possible value is adopted as a final design value, and a 95% confidence interval is selected to estimate uncertainty of thefinal design value. According to the method, the influence of climate change on flood characteristics can be fully considered, and an important reference basis with high operability can be provided for deducing adaptive design flood in a climate change scene.

The invention discloses a climatic change scenario revision method capable of reducing uncertainty. The method includes that rainfall and air temperature history observation data in an area are collected, climate scenario data, that is to say rainfall and air temperature under future scenarios, can be simulated, the rainfall and air temperature history observation data are interpolated to a grid which is matched with the climate scenario data by means of a distance inverse ratio interpolation algorithm, revision coefficients of air temperature and rainfall can be proposed and calculated by means of difference value between the rainfall and air temperature history observation data and rainfall and air temperature data obtained by simulation under climate scenarios in the same period, revision can be performed on climate scenario reference periods and future changes by means of the revision coefficients, and a revised climate scenario data set is established. According to the climatic change scenario revision method capable of reducing uncertainty, by combination with a database of history actual measurement hydro meteorology elements in the area, the revision method of climate scenarios is provided for revision, simulation capacity of original climate scenario data output in a climate mode within the area range can be evaluated, a climatic change scenario data set of a study area is established, and scientific reasonable basis can be provided for evaluation of climatic change influence.

The invention discloses a lake water resource quantity assessment and prediction method. The method comprises the following steps: firstly carrying out ground surface-underground water joint simulation to obtain each water balance item of the lake, and then evaluating the water quantity of the lake; and then predicting the lake water quantity in the future scene by combining climate mode output results in different climate change scenes. Compared with a traditional method, the method has the advantages of being low in investment, high in precision and high in physical mechanism.

The invention relates to a regional future vegetation index estimation method applied to a climatic change scene, which comprises the following steps: firstly, preliminarily selecting characteristic variables, then generating a sample, and setting key parameters of a model; calling a random forest model in a Sklearn machine learning library through Python, and inputting sample data S for model training; secondly, performing random forest model prediction performance evaluation; sDSM forecasting factor screening is carried out; calibrating and verifying the SDSM model; constructing a forecasting factor set of the random forest model; and finally, inputting the generated forecasting factor set into a trained random forest model to carry out future vegetation index estimation under the influence of climate change. According to the method, the future vegetation index of the region can be estimated according to different weather change scenes in the future.

The invention discloses a method for predicting the suitable habitat of a ginkgo fruit forest based on climate and soil factors, and belongs to the technical field of species distribution prediction. The method comprises the steps of firstly, establishing a climate ecological niche model and a soil ecological niche model; predicting the habitat types of the ginkgo fruit forest by using the climate ecological niche model and the soil ecological niche model, and classifying the habitat types of the ginkgo fruit forest; carrying out verification test on the model and a prediction result through a seed field test; filtering the climate-suitable habitat by using the soil-suitable habitat; and predicting the change of the suitable habitat of the ginkgo fruit forest under the future climate scene. The two models are combined, the current and future under different climate change scenes can be predicted, the reliability of model output is verified through experimental data, the prediction process is more efficient, the result is more accurate, prediction of the habitat suitability classification model conforms to the knowledge of phytophysiology and economics, and the habitat suitability classification model has good application prospects. The method has important significance for guiding the production practice of the ginkgo fruit forest.

The invention relates to a comprehensive assessment technology for influences and risks of future climate changes on biodiversity and belongs to the technical field of ecological environment protection and climate change. Based on the comprehensive investigation of the characteristics of biodiversity in China, the technology, by using climate change scenario pre-estimation technology, a Monte Carlo method, computer simulation, GIS and risk assessment technology, discipline theories and practices such as climatology and probability theory, establishes an influence and risk assessment index of climate change on biodiversity, a species distribution loss rule, a randomized climate scenario, and a combination of niche models and climate scenarios, simulates a climate change influence, and assesses the influences and risks of future climate changes on biodiversity. The technology is a technical measure for establishing biodiversity protection acclimatization change, protects the biodiversity, and has important social, economical and environmental benefits. The technology is good in operability, low in cost, low in maintenance cost, wide in application range, and especially suitable for the assessment of influences and risks of the climate changes on biodiversity in China.

The invention discloses a water resource planning method based on simulation and considering climate change used for supporting regional water resource management among different water departments under complex and uncertain conditions. According to the method, a plurality of prediction models is coupled to reasonably predict the surface water resource quantity in a research area and the water demand of each large water consumption department in the area, and compared with a single prediction model, the coupled prediction model can improve the prediction precision to a certain extent; and different climate change scenes are constructed to drive the coupling prediction model, so that the limitation of influence of future climate change on water resource allocation is effectively solved. Onthis basis, a double-layer multi-objective planning model considering multiple uncertainty is established, the constructed optimization model is effectively solved through a hybrid algorithm, and finally a reasonable water resource optimal configuration scheme for selection under consideration of climate change and scene analysis is obtained.

The invention discloses a grassland vegetation coverage estimation and prediction method based on remote sensing, and relates to a method for estimating and predicting grassland vegetation coverage by using remote sensing and meteorological data. The problems that an existing grassland vegetation coverage observation method is long in period and high in cost, and the future grassland vegetation coverage cannot be accurately predicted are solved. The method comprises the steps of obtaining and preprocessing a remote sensing and meteorological data set; performing spatial interpolation processing on the historical observation meteorological data set; extracting unchanged grassland vegetation distribution as a research area; calculating grassland vegetation coverage per pixel by using a pixel bipartite model; extracting grassland vegetation coverage and meteorological element values of pixel-by-pixel in the research area; constructing a grassland vegetation coverage estimation model under the influence of climate change by using multiple stepwise regression; and predicting the future grassland vegetation coverage in combination with the meteorological data under the future climate change scene. The characteristics that remote sensing data are easy to obtain and large in spatial scale are utilized, and a new method is provided for grassland vegetation coverage estimation and prediction.

The invention discloses a camptotheca acuminate distribution prediction method based on MaxEnt and ArcGIS. The camptotheca acuminate distribution prediction method comprises the steps of screening outimportant ecological factors from climate variables and soil variables; utilizing a MaxEnt model to predict potential distribution of the camptotheca acuminate only under the climate simulation condition and only under the soil simulation condition under the current and future climate scenes, and obtaining a climate and soil dual-adaptive region through layer superposition; and finally, predicting and quantifying changes of the camptotheca acuminate in a climate and soil double-height suitable habitat range under different climate change scenes. The construction is performed for the climate and soil environment of the camptotheca acuminate, and high-correlation variables are moved again, so that the influence of colinearity on the model precision is reduced. And layers of the two models are superposed, so that the influence of colinearity of climate and soil ecological factor variables on potential distribution of the camptotheca acuminate is avoided. Therefore, the limiting effect ofthe soil factor and the climate factor can be expressed more accurately.

The invention discloses an evaluation method for evaluating water-induced landslide disaster loss caused by climate change decline, and belongs to the technical field of geological disaster prevention and control. According to the method, a physical process model is utilized, spatial heterogeneity of surface features of all grid points on a region is considered, and rainfall threshold values corresponding to all space grid points in a region range with spatial heterogeneity are obtained; the method comprises the following steps of: selecting a landslide model, optimizing a climate mode by combining historical and climate mode data, simulating an easily-occurring area and a possibly-rushing-out and influence area of the landslide by utilizing a model, and better matching an influence range simulated by utilizing the method with disaster loss lattice point data. The problem that the climate change scene and the landslide influence in landslide disaster assessment are difficult to assess is solved.

The invention provides a flood disaster social economic exposure degree evaluation method based on a climate mode set. The method comprises the steps: collecting hydrological, meteorological and underlying surface data of a research region, calibrating a watershed distributed hydrological model, and extracting the output data of a global climate mode; performing deviation correction on the output data of the global climate mode by adopting a multivariable deviation correction method to obtain a corrected global climate mode meteorological series; using the corrected global climate mode meteorological series to drive the distributed hydrological model and the hydrodynamic model to simulate the river submerging depth under the climate change scene; and evaluating the population and GDP exposure degree of the flood disaster under the influence of the climate change based on the simulation result of the step 3 by considering the dynamic population sharing the social economic path and the GDP scene. According to the method, the influence of the flood events on the social economic system under various climate change scenes is evaluated, and the method has important scientific significance on flood risk prediction and loss evaluation in the future.

The invention discloses a method drawing up a multi-goal reservoir optimization scheduling graph capable of being self-adaptive to climate change. The method for drawing up the multi-goal reservoir optimization scheduling graph comprises the steps of: step 1. establishing a coupling model of a global climate model (GCM) and a variable infiltration capacity (VIC) hydrological model so as to forecast a run-off process under a future climate change scene; step two. establishing a multi-goal reservoir optimization scheduling graph model; and step 3. taking the forecast run-off process data under the future climate change scene as the input of the optimization scheduling graph model, and drawing up the multi-goal optimization scheduling graph model by adapting a self-adaptive genetic algorithm. The method for drawing up the multi-goal reservoir optimization scheduling graph has the advantages of balancing the social economy goal and ecological goal of reservoir scheduling, improving the comprehensive benefits of reservoir scheduling to the maximum degree on the condition of ensuring the flood control safety of a reservoir and being capable of being self-adaptive to future climate change and being widely applied to the production practice of multi-goal reservoir optimization scheduling.

The invention provides a flood frequency calculation method and device under the climate change situation. The flood frequency calculation method comprises the steps that flood monitoring historical data of a target section of a river in a preset historical period is obtained, and climate change parameters in an estimated period is obtained; a non-stationary GEV model is determined according to the flood monitoring historical data; the climate change parameters are input into a distributed hydrological model to calculate the daily runoff volume of the target section of the river within the estimated period; and the flood frequency of the target section of the river in the estimated period is determined according to the non-stationary GEV model and daily runoff volume. In the embodiment ofthe invention, the accuracy of the flood frequency determined according to the non-stationary GEV model and daily runoff volume is relatively high, and thus the accuracy of the flood frequency in thesimulation estimated period is improved.

The invention provides a multi-objective optimization scheduling method for improving the elasticity of a power system, which aims at overcoming the defects of the prior art in power system optimization scheduling, and comprises the following steps of: predicting sequence wind speed in a region and output of a wind power plant in combination with a future climate change scene; establishing a powersystem elasticity quantitative evaluation method containing wind power integration; establishing an opportunity constraint-based multi-objective optimization model for improving the elasticity of thepower system under extreme-period climate conditions; and solving the multi-objective optimization model, and actively carrying out power grid topology transformation. According to the invention, firstly, the elasticity of the power system containing wind power integration is evaluated, then opportunity constraint and power transmission line opening and closing constraint-based multi-objective optimization scheduling for improving the elasticity of the power system is carried out, and power grid topology transformation of power transmission line opening and closing operation is actively combined; and theoretical suggestions and guidance can be provided for an electric power operator to make an operation scheme in extreme weather.

The invention discloses a drought social economic exposure degree evaluation method and system under a climate change scene, and belongs to the field of water conservancy projects. Firstly, a plurality of global climate mode output data are extracted; then, obtaining a multi-mode meteorological series under a climate change scene by adopting a deviation correction method; calculating an evapotranspiration series of each grid based on a Penman formula, deducing a standardized rainfall evapotranspiration index, and extracting a corresponding drought characteristic value through a run-length theory; constructing a joint distribution function of drought duration and intensity of each grid in different periods based on a Copula function, and deducing duration and intensity corresponding to a given return period based on a most probable combination method; and finally, considering the dynamic population sharing the social economic path and the GDP scene, and evaluating the social economic exposure degree of drought risk increase under the influence of climate change. The method has important scientific significance for future two-dimensional drought disaster prediction and loss evaluation, and can provide important reference basis for scientifically evaluating the social economic exposure degree of multi-dimensional climate disasters.

The invention discloses an evaluation method for evaluating landslide disaster loss induced by climate change and falling water, and belongs to the technical field of geological disaster prevention and control. This method uses the physical process model, considers the spatial heterogeneity of the surface characteristics of each grid point in the region, and obtains the precipitation threshold corresponding to each spatial grid point in the region with spatial heterogeneity; combined with historical and climate model data, the optimal The climate model was established, and then the landslide-prone area and the possible rush-out and impact area were simulated by the model. The impact range simulated by this method can better match the disaster loss grid data, which solves the problem of climate in landslide disaster assessment. Change scenarios and landslide impacts are difficult to assess.

The present invention provides a flood frequency calculation method and device under a climate change scenario, the method comprising: acquiring historical flood monitoring data of a target section of a river within a preset historical time period, and acquiring climate change parameters within an estimated period; Determine the non-stationary GEV model based on the historical flood monitoring data; input the climate change parameters into the distributed hydrological model, and calculate the daily runoff of the target section of the river within the forecast period; according to the non-stationary GEV model and the daily runoff , to determine the flood frequency of the target section of the river during the projected period. In the embodiment of the present invention, the accuracy of the flood frequency determined according to the non-stationary GEV model and the daily runoff is relatively high, thereby improving the accuracy of the flood frequency within the simulation prediction period.

The invention is called future climate change impact on biodiversity and comprehensive risk assessment technology, which belongs to the technical field of ecological environment protection and climate change response. Based on the comprehensive investigation of the characteristics of biodiversity in my country, the climate change scenario prediction technology, Monte Carlo method, computer simulation, GIS and risk assessment technology, as well as theories and practices of climatology, probability theory, etc., are used to establish climate change response. Biodiversity impact and risk assessment indicators and species distribution loss rules, randomized climate scenarios, combination of niche models and climate scenarios, simulation of climate change impacts, and assessment of climate change impacts and risks on biodiversity. In order to establish technical countermeasures for biodiversity conservation and adaptation to climate change, effective conservation of biodiversity can play important social, economic and environmental benefits. These technologies are highly operable, low in cost, low in maintenance, and widely applicable, especially for assessing the impact and risk of climate change on my country's biodiversity.

The invention discloses a reservoir flood control risk calculation method and system based on an ARIMA model in a future scene, and belongs to the technical field of reservoir flood control risk analysis. A climate scene and a hydrological simulation technology are combined, an ARIMA random model is established for a future flood sequence, statistical characteristics of future flood are simulated, meanwhile, it is assumed that an initial value in the model obeys probability distribution deduced by historical flood data, and then a random model capable of reflecting past, current and future flood statistical characteristics at the same time is constructed; and the reservoir flood control risk calculation method and system in the future scene aim at achieving reservoir flood control risk calculation in the future climate change scene, and a scientific basis is provided for design and operation of a reservoir.

The invention provides a data processing method for water-vapor fluxes under climatic change scenes. The method comprises the following steps of: obtaining environment data, comprising a wind direction, a wind speed and specific humidity data of a vertical layer, of a to-be-researched area; and determining a water-vapor flux by utilizing the environment data and a preset water-vapor flux corresponding relationship. According to the method, the wind direction, wind speed and specific humidity data of the vertical layer are considered in the environment data, so that the error problems caused byinsufficient reference quantity are decreased and the water-vapor flux processing results are more correct and reliable. The invention furthermore provides a data processing device for water-vapor fluxes under climatic change scenes.

The invention discloses a method for predicting the productivity of industrial timber forests based on the coupling of species distribution and productivity. The maximum entropy model is used to simulate the suitability of industrial timber forests, and the coupling relationship between the predicted environmental suitability and the productivity of industrial timber forests is established to obtain the environmental suitability Degree-productivity conversion equation. Then carry out the simulation of the suitable habitat area under a variety of climate change scenarios with different intensities in the future, and use the obtained global environmental suitability layer for further grid calculation through the conversion equation between productivity and environmental suitability. Finally, the suitable habitat area and production potential of industrial timber forests in the world are obtained. The species distribution model of the present invention combines the published ground survey biomass data to predict future forest productivity, not only realizes high-precision simulation on a global scale, but also can simulate future biomass distribution on a large scale in combination with the characteristics of future climate change. It is of great significance to the selection of industrial timber forest afforestation area and the maintenance or improvement of future productivity.

The invention discloses a method for predicting the net primary productivity of vegetation in a regional marsh wetland, and relates to a method for predicting the net primary productivity of future regional marsh wetland vegetation under the influence of climate change. The invention aims to solve the problem that the prior art means cannot accurately predict the net primary productivity of future marsh wetland vegetation. The method comprises the following steps: selecting unchanged marsh wetland distribution within a certain research time period as a research area; acquiring and preprocessing remote sensing data and meteorological data of marsh wetland vegetation in the research area; interpolating the meteorological data, and resampling; extracting net primary productivity and meteorological element values corresponding to all marsh vegetation pixels in the research area; constructing a prediction model; and predicting the net primary productivity of the marsh wetland vegetation in the future by using the prediction model in combination with meteorological data in a future climate change scene. The net primary productivity of the marsh vegetation under the influence of future climate change is predicted by utilizing the advantages that the remote sensing data is large in spatial scale and easy to obtain and combining the existing meteorological data.

The invention relates to an insect-borne infectious disease transmission risk prediction method and device, and the method comprises the steps: obtaining historical case data of insect-borne infectious diseases throughout the country and local climate environment data matched with the historical case data; taking the historical case data as dependent variables, taking local climate environment data as independent variables, performing modeling analysis based on a generalized additive hybrid model, and verifying model precision under different strategies to obtain an optimal fitting model; obtaining predicted meteorological data of future preset time; and according to the meteorological data of the future preset time and the optimal fitting model, in combination with different climatic change scenes, predicting the spatial and temporal distribution of the insect-borne infectious diseases of the future preset time.