124 results about "Climate variation" patented technology

The invention relates to the technical field of supplementing missing measurement data of an anemometer tower, in particular to a method for supplementing missing measurement data of an anemometer tower. In the data preparation stage, historical observation data of a target anemometer tower needs to be collected, and missing time periods and missing features are determined, so that subsequent method selection is linked; meanwhile, data of surrounding meteorological stations in the same period are obtained, the spatial correlation between the data and the anemometer tower is verified, and a basis is provided for probability distribution mapping and quantile matching in subsequent CDF-t and QDM methods. The nonlinear correction method based on cumulative distribution function transformation and quantile increment mapping shows unique advantages in the field of climate downscaling, the CDF-t can effectively eliminate system deviation by establishing a probability distribution mapping relation between observation data and mode output, and the system performance is improved. The QDM maintains statistical characteristics of historical sequences while retaining climatic change signals through quantile matching, and new possibility is provided for long-period data reconstruction through combination of the two methods.

The invention relates to an energy-saving control strategy for a medical building air-conditioning ventilation system based on climate change characteristics and load prediction. The energy-saving control strategy comprises the following steps of: acquiring all original data by utilizing various sensors, actuators and the like of a sensing layer; calculating, optimizing and deciding on an intelligent control layer (including building comprehensive cooling and heating load prediction, clean operation department area cooling and heating demand calculation, dynamic water supply temperature optimization, a particle swarm optimization (PSO) algorithm for domain optimization, constraint check and other algorithm modules) on a central management server; and an optimization result is issued to the execution layer and the executed equipment layer, so that energy-saving control is finally realized. The method overcomes the defects that the existing hospital building load prediction technology mostly adopts a macroscopic and rough prediction model, and the global optimization particle swarm optimization algorithm is slow in convergence and long in solving time when coping with a large medical building adopting multiple types of cold and heat source equipment. The device is suitable for large-scale hospital projects in hot-summer and warm-winter areas.

The invention discloses a glacier material balance simulation method and system based on feature interaction, and belongs to the technical field of glacier hydrology and climate change monitoring, and the method comprises the steps: sequentially carrying out the uniform format processing and slope feature enhancement of a multi-source data set of a glacier, and dividing the multi-source data set into a simulation data set and an observation data set; training a model based on a trans-attention Transform encoder framework by using the simulation data set to obtain a pre-training model; when one-glacier-leaving cross validation is carried out on the pre-trained model, multi-stage fine tuning training is carried out by using the observation data set to obtain a plurality of fine-tuned models; performing model integration based on performance index screening on the fine-tuned models, and constructing an integrated simulation model; and preprocessing the meteorological dynamic characteristics and the static topographic characteristics of the target glacier, inputting the preprocessed meteorological dynamic characteristics and static topographic characteristics into the integrated simulation model, and carrying out glacier material balance simulation to obtain a glacier material balance simulation result. According to the method, the simulation uncertainty is reduced, and the high-precision simulation of the glacier material balance is realized.

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.

A system and method for architecturally designing and manufacturing building structures customized for environments vulnerable to extreme weather events or other natural phenomena. The method includes determining an environmental susceptibility of a proposed building location, designing building components factoring in geographical and climate-related features, manufacturing the building components, and assembling the building structure in a manner better configured to resist extreme weather or natural events.

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 relates to the field of climate change influence assessment, and discloses a method for quantizing contribution of natural internal variability to drought intensity based on a circulation similarity method, and the method comprises the steps: data collection; evaluating the drought strength based on the difference between the rainfall distance and the potential evapotranspiration distance, and quantifying the relative contribution of rainfall deficit and evapotranspiration demand to the drought strength; based on a circulation similarity method, a regional large-scale atmospheric circulation background is obtained, and contribution of a natural internal variation rate to drought intensity, rainfall deficit and evapotranspiration requirements is evaluated; estimating long-term evolution trends of drought intensity, rainfall deficit and evapotranspiration demands under different carbon emission scenes in the future; the method has the advantages that the natural internal influence mechanism of drought intensity change is defined, the relative contribution of historical and future rainfall deficit and evapotranspiration requirements to the drought intensity is quantified, the reliability of future drought trend estimation is enhanced, theoretical support is provided for making disaster prevention and reduction policies, and the accuracy of drought risk prediction is improved.

The invention discloses a hydrological analysis method and system based on SWAT model runoff reconstruction and a storage medium. The hydrological analysis method comprises the following steps: 1, obtaining research basin runoff data, meteorological data, digital elevation model data, land utilization data and soil data; 2, based on the data in the step 1, analyzing the evolution trend and potential mutation points in the runoff time sequence period by adopting a mutability test method; 3, dividing a natural period and a variation period based on the mutation points, and obtaining a natural runoff sequence by using an SWAT model; and 4, based on a SWAT model simulation result, extracting multiple hydrological indexes representing hydrological situation changes by adopting an IHA method, extracting ecologically-related hydrological indexes ERHIs from the hydrological indexes by adopting a PCA method, and calculating contribution rates of climate changes and human activities to the ecologically-related hydrological indexes by adopting a quantitative attribution method. The runoff driving mechanism of climate factors and human activities is disclosed, and the physical interpretability and decision support capability of the SWAT model are enhanced.

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.

This invention discloses a cluster analysis method for diagnosing the composite trend characteristics and intensity of climate change based on multi-source data. The method includes: selecting a study area, identifying the study objects, and acquiring multi-source data using big data technology; analyzing the changing trends of the study objects from each data source using the Sen slope estimation method; evaluating the reliability of the changing trends of the study objects from each data source using the Mann-Kendall test; selecting composite changing trend evaluation indicators for the study objects based on whether they pass the reliability test; distinguishing the changing trend characteristics of the study objects from each data source under different conditions, classifying and summarizing the composite changing trend characteristics of the study objects, calculating the ratio of composite changing trend characteristics of the study objects, and diagnosing the composite changing trend characteristics and intensity of the study objects. This method is based on cluster analysis theory and uses multiple data sources to determine the composite changing trends of hydrological and meteorological elements under specific conditions. It can be directly applied to the composite diagnosis of climate change in the hydrological and meteorological field.

The invention provides a surface water-underground water integrated simulation method and system coupled with a glacier module, the method is based on an extended SWAT-GL model, rainfall, accumulated snow melt water and glacier melt water are jointly used as daily effective rainfall input, infiltration supply and river water level are transmitted to MODFLOW through a daily interface, and the water level of the MODFLOW is effectively simulated. And simulating underground water head change and surface water-underground water exchange capacity by utilizing a three-dimensional underground water flow equation, and feeding back a result to SWAT-GL river water balance, thereby realizing bidirectional dynamic coupling of the glacier-accumulated snow-surface-underground water whole process. The system breaks through the limitation that glacier mass balance and groundwater dynamics are difficult to depict synchronously in cold region simulation, can reveal a supply and regulation mechanism of glacier melt water to a groundwater system under the driving of climate change under a fine time scale, and remarkably improves the simulation capability of a cold region water circulation key process.

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.

The invention discloses a method for estimating the photosynthetic efficiency of wetland vegetation under the influence of climate change, relates to the technical field of agricultural remote sensing, and aims to solve the problems that the photosynthetic efficiency of the wetland vegetation is inaccurately estimated and the future cannot be predicted in the conventional method. The method comprises the steps of 1, obtaining and preprocessing a data set; 2, collecting the month-by-month data into a growth season data set; 3, calculating to obtain a multi-year average LAI value in the research time period; 4, obtaining wetland vegetation distribution as a research area range; 5, obtaining a pixel-by-pixel growth season SIF value, a pixel-by-pixel growth season LAI value, a pixel-by-pixel growth season historical meteorological element value and a pixel-by-pixel growth season future meteorological element value in the research area range; 6, calculating the photosynthetic efficiency value of the wetland vegetation pixel by pixel in the range of the research area; 7, constructing a pixel-by-pixel wetland vegetation photosynthetic efficiency estimation model under the influence of climate change; and 8, predicting the photosynthetic efficiency of the future wetland vegetation in the range of the research area. According to the method, the future wetland vegetation photosynthetic efficiency can be quickly and accurately estimated and predicted on the regional scale.

The invention relates to the field of hydrological disaster risk assessment, and discloses a man-made signal identification and future estimation method for hydrological drought risks, and the method comprises the steps: collecting data; hydrological drought identification; hydrological drought occurrence probability trend quantification; quantification of contribution of human climate change and human water taking and land management change; human activity signal detection of hydrological drought occurrence probability; hydrological drought attribution risk score quantification; identifying human activity signals in hydrological drought based on a signal-to-noise ratio criterion; the uncertainty source of the future hydrological drought occurrence probability trend is quantified; according to the method, the global hydrological drought occurrence risk change in historical and future periods under the influence of human activities is evaluated, and the human activity signals in the global hydrological drought occurrence risk change are identified, so that the understanding of the global hydrological drought risk change and influence mechanism is enhanced, and a scientific basis is provided for making a hydrological drought disaster prevention and reduction decision.

This invention relates to the field of remote sensing technology, specifically to remote sensing image classification methods, systems, computer equipment, and storage media. The remote sensing image classification method includes the following steps: based on raw remote sensing data, using satellite cloud image analysis and surface temperature mapping techniques to perform comprehensive analysis of microclimate characteristics; using K-means clustering analysis algorithm to classify the data; and generating a comprehensive climate characteristic dataset. The beneficial effects of this invention are that by integrating K-means clustering, adaptive histogram equalization, convolutional neural networks, long short-term memory networks, and fully convolutional network algorithms, it can effectively identify geographic patterns and monitor abnormal climate events. Simultaneously, it optimizes surface classification, combines Gaussian filtering, MODIS cloud detection algorithms, and data assimilation techniques to deeply preprocess raw data, improve data quality, enrich information content, and effectively predict and respond to climate change using long short-term memory networks and time series analysis techniques.

A technological improvement to a system for vehicle navigation is provided. The improvement diminishes a contribution to global climate change, e.g., global warming, by generating a flight path (generated flight path) or modifying the flight path (modified flight path) of a vehicle to avoid an atmospheric region in which the vehicle would create a contrail if the vehicle travelled through the atmospheric region.

The invention is suitable for the field of marine meteorology, and provides a three-dimensional sea temperature space-time prediction method, which comprises the following steps: obtaining reanalysis data and numerical mode prediction data; and inputting the reanalysis data and the numerical mode prediction data into a pre-trained three-dimensional sea temperature intelligent prediction model to obtain a three-dimensional sea temperature space-time prediction result. According to the method, the spatial-temporal characteristics of the sea temperature change can be effectively captured, so that the prediction precision is improved, a high-precision sea temperature prediction result can be provided, good interpretability is achieved, and more powerful support is provided for scientific research and climate change response.

The invention discloses a method for quantitatively evaluating the dynamic hydrological effect of vegetation, and belongs to the technical field of climate change evaluation. Aiming at the defect that vegetation and hydrological effects cannot be independently and quantitatively separated due to the fact that underlying surface parameters confuse vegetation and non-vegetation factors in an existing Budyko hydrothermal balance attribution model, meteorological, hydrological, vegetation and environmental data are collected and processed; inverting underlying surface parameters based on a Budyko framework; calculating elastic coefficients of runoff to rainfall, potential evapotranspiration and parameters; constructing a linear model taking the drought index and the soil water-holding capacity as variables to obtain vegetation sensitivity parameters; finally, the runoff component caused by vegetation change is quantitatively separated in combination with the elastic coefficient and the vegetation sensitivity parameter. According to the method, the influence of vegetation dynamics on the hydrological effect is independently and accurately quantified, and the transparency of runoff attribution and the application value in ecological hydrological management are improved.

The application provides a kind of coupling ice module surface water-groundwater integrated simulation method and system, which is based on the extended SWAT-GL model, and inputs rainfall, snowmelt and glacial melt water as daily effective rainfall, transfers the infiltration recharge and river level to MODFLOW through daily interface, simulates the change of groundwater head and the exchange amount of surface water-groundwater by using three-dimensional groundwater flow equation, and feeds back the results to SWAT-GL river water balance, realizes the two-way dynamic coupling of the whole process of glacier-snow-surface-groundwater. The system breaks through the limitation that it is difficult to simultaneously depict the glacier mass balance and groundwater dynamics in cold region simulation, and can reveal the recharge and regulation mechanism of glacial melt water to groundwater system under the driving of climate change at a fine time scale, significantly improving the simulation capability of key processes of water cycle in cold regions.

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 application discloses a land water storage prediction method and system based on artificial intelligence and electronic equipment. The prediction method comprises the following steps: collecting atmospheric reanalysis data sets and gravity satellite land water storage data, and obtaining various global climate model output data; a long short-term memory model for simulating long series of land water storage is constructed; the long short-term memory model is driven by the corrected grid global climate model output data to simulate the first prediction result of the land water storage in the future prediction period; a hook-shaped response function of the land water storage and the dew point temperature is fitted, and the second prediction result of the land water storage in the future prediction period is obtained; and an emergent constraint model for correcting the prediction result is constructed. The application can fully reflect the spatio-temporal evolution law of the land water storage under the influence of climate change, significantly prolong the prediction period of the land water storage, improve the prediction effect, and solve the defect that the prediction of the land water storage has great uncertainty in the prior art.

The invention discloses a mountain species protection system based on a microclimate and ecological niche model in the technical field of biodiversity protection, which comprises a microclimate sensor network module, a data preprocessing module, an ecological niche prediction module, a protection priority evaluation module and a decision output platform module, the microclimate sensor network module comprises a multi-type sensor unit, a gradient layout unit and a dual-mode transmission unit, the mountain species protection system based on the microclimate and ecological niche model is reasonable in structural design, the data precision is improved, the deviation problem of a traditional constant LRT hypothesis is solved through dynamic LRT monitoring, the protection pertinence is enhanced, and the reliability of the mountain species protection system is improved. A first-level protection area of a high-risk mountainous area is accurately identified, dynamic adaptability and a sensor network support real-time data updating, sudden climate changes can be dealt with, the practical value is remarkable, and the method can be applied to research concerned arid areas and high-humidity low-direct-reduction-rate areas.

The present invention comprises a method for synthesizing a culture of psychrophilic (cold-adapted) algae, enhanced with growth-optimized accelerators into a product for deployment in extreme cold environments. This solution integrates industrial-scale microalgal cultivation with a novel form of microalgal “afforestation” targeted at uninhabited cold geozones. The system functions as a nature-based, low-cost, and scalable carbon sequestration “vaccine,” designed to remove atmospheric carbon dioxide at gigaton scales.The method aims to restore global CO2 levels to pre-industrial concentrations within a decade through repeated deployments. Carbon captured through this process is intended to be sequestered over centuries with minimal or no adverse environmental impacts.This invention represents a significant advancement in Nature-based Climate Solutions (NbCS), offering a feasible pathway toward climate change reversal rather than mere mitigation.

The invention discloses methods and a composition for cloud seeding utilizing magnesium carbonate (MgCO3) particles as ice nucleating agents to enhance precipitation. The sub-micron to nano-sized MgCO3 particles, with lattice parameters approximating a ≈ 4.64 Å, b ≈ 4.64 Å, and c ≈ 14.93 Å, are introduced into the atmosphere via aircraft, generators, or rockets. Preferably bio-engineered through a green CO2 conversion process, these particles reduce carbon footprint, pose no environmental hazards per SDS regulations, and serve as plant nutrients. The invention extends to ice nucleation in artificial rain and ice recovery methods, offering a sustainable alternative for drought mitigation and climate change efforts.

The present invention comprises the deployment of a product known as RubisCO Climate Vaccine® or simply RCV which contains a specialised adapted culture of psychrophile algae and accelerators. This RCV is inoculated into targeted tropospheric clouds. The cumulatively large air-liquid optical interface area of clouds enables conditions for potential exponential photosynthetic algal growth and replication during which carbon dioxide is processed and oxygen released directly in the troposphere. Optimal inoculation sites are determined by altitude, humidity, temperature, weather patterns and predicted direction and duration of travel for precipitation, The biomass produced in this process is intended to be precipitated over mid-ocean where it is sequestered within natural ocean food chains or sedimented as detritus essentially permanently. This nature-based solution through repeated deployments offers a feasible scalable pathway toward potential climate change reversal within a decade with minimal or no adverse environmental impact.

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 invention discloses a method for determining the contribution rate of the influence of climate and land cover change on runoff and carbon sequestration, and relates to the technical field of carbon neutralizer.The method comprises the steps that benchmark period meteorological data, benchmark period land cover data, change period meteorological data and change period land cover data of a target drainage basin are utilized; calculating a first runoff variable quantity and a first total primary productivity variable quantity of the target drainage basin in a climate change scene, and a second runoff variable quantity and a second total primary productivity variable quantity of the target drainage basin in a land cover change scene; according to the first runoff variation and the second runoff variation, determining the contribution rate of climate and land cover change to the runoff variation; according to the first total primary productivity variable quantity and the second total primary productivity variable quantity, determining the contribution rate of climate and land cover change to the total primary productivity variable quantity, and determining the contribution rate of the climate and land cover change to the runoff and carbon sequestration influence of the target drainage basin; and the reliability of determining the contribution rate of climate and land cover change to runoff and carbon sequestration is improved.

The invention relates to the field of hydrological disaster risk assessment, and discloses an extreme flood demarcation point identification, influence factor quantification and future estimation method based on drainage basin characteristic factors, and the method comprises the steps: collecting data; dividing different grades of dry and wet areas of the watershed; spatial distribution and recurrence period identification of global flood demarcation points; analyzing flood magnitude characteristics of different dry and wet areas; constructing a theoretical relationship between the flood demarcation point magnitude and the drainage basin characteristic factors; analyzing the relationship between the flood demarcation point and the hydrological decline index as well as the daily flow variation coefficient; analyzing the change of the flood demarcation point under different climatic changes and human activity scenes; quantifying spatial distribution of flood demarcation points and recurrence period characteristics under different carbon emission scenes in the future; according to the method, reliable identification of the flood demarcation points of different dry and wet areas is realized, the relationship between the flood demarcation points and the drainage basin characteristic factors is quantified, the evolution trends under different carbon emission scenes in the future are estimated, and a scientific basis is provided for optimal setting of a flood early warning threshold value.

The invention provides a rain and snow separation calculation method and device, equipment and a storage medium. Relates to the technical field of meteorological and hydrological data processing. The method comprises the following steps: acquiring daily meteorological data of a target area, wherein the daily meteorological data comprises the daily maximum temperature Tmax, the daily minimum temperature Tmin, the daily average temperature Tmean, the daily temperature difference Tdr, the relative humidity RH and the daily precipitation P; inputting daily meteorological data of the target area into the rain and snow separation model, and calculating to obtain snow-melting runoff DSnow; wherein the rain and snow separation model is a multiple regression equation based on heat condition parameters and water vapor condition parameters, the heat condition parameters comprise at least three of Tmax, Tmin, Tmean and gas Tdr, and the water vapor condition parameters comprise RH and P. Rain and snow separation discrimination is carried out based on the relation of climate condition factors. When the region and the climate change, the judgment basis is only related to the climate condition, so that obvious misjudgment does not occur along with the change of the region.