281 results about "Evapotranspiration" patented technology

The invention relates to the technical field of data processing, and discloses a water quality and water quantity accurate simulation method and system based on an SWAT model and a storage medium. The method comprises the following steps: analyzing rainfall and evapotranspiration data through a two-parameter trend test, and generating standard hydrological data by adopting a triple configuration algorithm; dividing sub-basins by combining topographic information, and calculating a hydrological response unit; performing water balance operation and water conservation evaluation; pollutant diffusion is analyzed, and a diffusion equation is solved; running state monitoring is implemented, and an early warning system is designed; and comparing and correcting measured data, and constructing a hierarchical monitoring mechanism. According to the invention, the whole-process precise management from hydrological data acquisition, water quality evaluation to early warning monitoring is realized, and the accuracy and reliability of water quality and water quantity monitoring are remarkably improved.

The invention relates to the technical field of automatic irrigation control, in particular to a multi-sensor fusion agricultural precise irrigation system which comprises a leaf area analysis module, an environment evaluation module, a task arrangement module, an execution regulation and control module and a feedback regulation module. According to the method, the crop leaf area index is introduced to be compared with the growth stage target value, the dynamic expression of the group moisture demand is realized, the environmental evapotranspiration influence factor is generated in combination with illumination and temperature disturbance parameters, the timeliness of water demand driving force identification is enhanced, and the task execution sequence is judged by adopting regional water pressure disturbance data; a multi-region irrigation resource allocation process is optimized, a water quantity and pressure difference double-factor adjustment mechanism is utilized, the rationality and response accuracy of time length allocation are improved, water supply state analysis and task target deviation comparison are performed, dynamic supplementary irrigation regulation and control are realized, the closed-loop capability and supplementary irrigation accuracy of irrigation period control are improved, and the irrigation period control efficiency is improved. And the stable allocation capability and the irrigation response efficiency of the irrigation system under the influence of multiple variables are enhanced.

The invention discloses a daily-scale corn water stress diagnosis method based on an unmanned aerial vehicle technology, and relates to the technical field of agriculture, the method is based on the definition of a water stress coefficient, introduces a Jarvis stomatal conductance model, combines canopy and plant height data obtained by an unmanned aerial vehicle, accurately estimates the aerodynamic impedance and the canopy impedance of a canopy, and improves the accuracy of the canopy stress. The actual evapotranspiration and the potential evapotranspiration are obtained through a multispectral leaf area index inversion model, then a daily scale moisture stress index is calculated, a plurality of groups of unmanned aerial vehicle thermal images are obtained according to four important growth periods of corn, and the moisture stress index is calculated and counted. And comparing the characterization capabilities of the daily scale moisture stress index and the moisture stress index in different time periods in one day on the actually measured moisture index, so as to improve the problems that the instantaneous moisture stress coefficient is insufficient in time representativeness and is easily influenced by the environment, and more accurately characterize the crop moisture stress. A monitoring effect close to a daily-scale moisture stress index is obtained by a easier method, and the practicability of the index is improved.

The invention provides an ecological hydrological simulation method based on a machine learning model, and the method comprises the steps: obtaining ecological hydrological data, carrying out the preprocessing of the obtained ecological hydrological data, obtaining a data set, building an ecological hydrological model based on an improved ConvLSTM network structure, and training the ecological hydrological model based on the data set. And obtaining a trained ecological hydrological model, inputting to-be-measured data into the trained ecological hydrological model, and performing evapotranspiration prediction. According to the method, evapotranspiration prediction is realized based on various data, advantages of remote sensing data and ground meteorological data are fully utilized, evapotranspiration prediction precision is remarkably improved, real-time data can be processed, real-time performance is high, different applications can be performed according to different areas, flexible adjustment of model input can be realized, expandability is high, and the method is suitable for popularization and application. The method can be applied to multiple fields, and provides real-time support for agricultural and ecological monitoring.

The invention discloses a cold region SWAT model construction method considering a glacier melting process. A calculation method comprises the following steps: step 1, obtaining corrected rainfall data; corrected air temperature data are obtained; evapotranspiration correction data is obtained; actually measured runoff is obtained; 2, obtaining a plurality of sub-basin boundaries and spatial distribution of each sub-basin glacier; 3, calculating the glacier melting amount of each sub-drainage basin in the research time period by adopting a day-to-day factor method to obtain the day-by-day glacier melting amount of each sub-drainage basin; step 4, performing double-element parameter calibration based on the actually measured runoff data and the evapotranspiration correction data to obtain parameters suitable for the watershed hydrological process; and 5, calculating to obtain the relative contribution rate of the glacier melting process to runoff and evapotranspiration. The simulation precision of hydrological elements such as runoff of the cold region hydrological model is improved, and a scientific basis can be provided for sustainable utilization and planning of water resources of a local drainage basin.

The invention relates to the technical field of agricultural irrigation, and particularly discloses a farmland irrigation control method and system, and the method comprises the steps: constructing a multi-sensor fusion monitoring network, and collecting soil, weather and plant physiological data in real time; transmitting data to an edge computing node through LoRa wireless communication; utilizing a lightweight neural network model deployed by edge computing nodes to calculate and predict crop water demand and accurate irrigation volume in combination with evapotranspiration; the irrigation strategy is executed through the intelligent irrigation execution system; and remote monitoring and abnormity alarm are realized by means of a remote monitoring and management platform based on WebGIS. Precise and intelligent management of farmland irrigation is realized, the utilization rate of water resources and fertilizers can be effectively improved, irrigation abnormity can be timely handled, the labor management cost is reduced, and the growth requirements of crops are guaranteed.

The invention provides a daily scale drought and flood sharp turn predictable index construction method and system, and relates to the technical field of drought and flood sharp turn prediction.The method comprises the steps that according to weather forecast data, an early-stage accumulated rainfall and evapotranspiration difference index is calculated and converted into a standardized index SAPEI; determining the optimal window size of the waterlogging period window and the drought period window; calculating a drought period average index and a waterlogging period average index; and judging whether a drought and flood sudden turning event occurs, and calculating the drought and flood sudden turning intensity and the drought and flood sudden turning speed. According to the method, by constructing a multi-dimensional index system, different change characteristics of drought and flood sudden change events in the aspects of conversion intensity and speed are reflected, and the characteristic that drought-to-flood is asymmetric in the time scale can be fully considered; the SAPEI is used as basic data, so that the ground moisture state instead of the rainfall condition can be better reflected; the sizes of T and U are calculated according to the drought and flood period window corresponding to the maximum value of T instead of fixing the size of the window, so that the method is suitable for the nationwide range.

The invention discloses a modularized distributed cold region hydrothermal coupling hydrological model which comprises the following steps: S1, dividing a drainage basin into a plurality of grids, and preparing a file required by a confluence process; s2, preparing driving data and grid feature description data of each grid; s3, calculating an energy balance process of the soil and vegetation surfaces by using an energy balance module; s4, calculating an evapotranspiration process by using an evapotranspiration module; s5, calculating an ice and snow melting process by using an ice and snow melting water module and adopting a degree-day factor method; s6, calculating soil moisture infiltration and an internal hydrothermal transmission process by utilizing a soil hydrothermal transmission and infiltration module; s7, calculating the runoff volume by using a runoff production calculation and confluence module; and S8, evaluating the simulation performance of the model through the model evaluation index. The device has a flexible modular structure, can automatically adjust the time step length, improves the simulation capability of the runoff process in the cold region, and can provide scientific guidance for water resource management in the river source region.

The invention relates to a crop irrigation demand prediction method based on an intelligent integrated prefabricated pump station, and belongs to the technical field of artificial intelligence. The method comprises the following steps: collecting a crop image in a prefabricated pump station irrigation scene, obtaining auxiliary data, calculating a daily accumulated reference evapotranspiration value, and obtaining a time-synchronized multi-modal data set; labeling and dividing the multi-modal data set; constructing an irrigation demand prediction model, wherein the irrigation demand prediction model comprises a spectral reflectivity enhancement module, a double-flow separation convolution module, a cross gating fusion module, a region sensitive pyramid module and a coupling prediction head module; inputting data in the divided training set into the model, and training the model through weighted total loss to obtain a trained model; inputting a to-be-detected image into the trained model to obtain a soil water content and transpiration prediction result; and performing irrigation decision based on the soil water content and transpiration prediction result. The accuracy of irrigation prediction can be improved.

The invention discloses a flood lake wetland heat flux simulation method in a watershed hydrological model, and relates to the technical field of heat flux simulation, and the method comprises the following steps: dispersing a watershed space into a slope river channel and a lake wetland, simulating the water movement of a slope river channel unit, and simulating the water movement of the lake wetland; the method comprises the following steps: simulating surface energy exchange for a lake wetland unit on the basis of simulating a hydrological process, simulating land hydrology and lake inflow runoff of a sub-basin by adopting a grid-based distributed hydrological model, respectively dividing simulation grid units for a slope river channel and a lake wetland, establishing evapotranspiration, infiltration and surface runoff models, and establishing an evapotranspiration, infiltration and surface runoff model; a two-dimensional shallow water equation is used for processing slope confluence, a one-dimensional hydrodynamic model is used for calculating river channel confluence, a lake water quantity balance model is established for calculating the lake water level, the lake submerging process is simulated, and the lake wetland distributed heat flux process is simulated. And the technical blank of flooding wetland hydrothermal simulation is filled.

The invention provides a drought and flood sudden turning event identification method, device and system, and relates to the technical field of water resource management, and the method comprises the steps: determining a research region and time period, collecting rainfall, sunshine duration, wind speed, pressure, humidity and air temperature data, carrying out the linear difference of vacant data, and carrying out the data preprocessing; constructing a rainfall difference comparison index for judging the sudden change degree of the drought and flood sudden change event; calculating a standardized rainfall evapotranspiration index for judging and identifying drought and flood events; establishing a grading standard of drought and flood events, and classifying the severity of the identified drought and flood events; and combining the rainfall difference comparison index with a standardized rainfall evapotranspiration index to construct a drought and flood sudden turning index for analyzing the sudden turning intensity. According to the method, the influence of rainfall intensity change on the flood disaster event is fully considered, and the occurrence process and severity level of the drought and flood sudden change event can be effectively and accurately identified.

The invention relates to the technical field of ecology and hydrology, in particular to a terrestrial ecosystem water carbon flux estimation method considering water balance constraint, which comprises the following steps: estimating leaf scale photosynthetic rate and stomatal conductance by utilizing a photosynthesis model; the leaf scale photosynthetic rate and the stomatal conductance are upscaled into canopy vegetation total primary productivity and canopy conductance to construct an evapotranspiration model to estimate evapotranspiration data; constructing a water balance model according to the evapotranspiration data so as to estimate the available water amount of the soil; constructing a soil moisture stress function according to the available water amount of the soil so as to constrain the estimation process of the photosynthesis model and the evapotranspiration model; and acquiring historical water-carbon flux data and historical remote sensing observation data of a long sequence daily scale, and calibrating the model to obtain a water-balance-constraint-considered water-carbon flux estimation model of the terrestrial ecosystem capable of estimating the water-carbon flux data. Therefore, the problems that an existing water carbon flux prediction frame is difficult to adapt to climate changes, low in prediction accuracy and the like are solved.

The invention provides a three-dimensional triple configuration potential evapotranspiration fusion method and system considering neighborhood space-time non-stationary errors, and relates to the technical field of potential evapotranspiration estimation. The method comprises the following steps: obtaining a potential evapotranspiration observation value of a target watershed meteorological station and three sets of independent potential evapotranspiration data sets, and unifying temporal-spatial resolution; calculating a Gaussian kernel function matched with the predetermined space-time window, and performing data standardization by using the kernel function; constructing a variance calculation model of the three-dimensional triple configuration error, and obtaining a variance of an error of neighborhood dimension spatio-temporal information contained in each data set under a predetermined spatio-temporal window; and constructing a data fusion model based on a least square framework, determining an optimal fusion weight and realizing fusion of the multi-source potential evapotranspiration data. According to the method, the problems that local space-time non-stationary errors exist during fusion of the potential evapotranspiration data and the fusion precision is influenced by the scale effect of the space-time window are solved, and the precision of the potential evapotranspiration fusion data is effectively improved.

The invention discloses a drought and flood sudden change event process risk quantification method and device. The method comprises the following steps: acquiring precipitation evapotranspiration data of a plurality of grid points in each month in a rasterized drainage basin; determining a standardized rainfall evapotranspiration index of each grid point in each month according to the rainfall evapotranspiration data of each grid point in each month; determining a plurality of target months and a plurality of target drought and flood patches based on the standardized rainfall evapotranspiration index of each grid point in each month and each grid point; according to each target month and each target drought and flood patch, determining a plurality of drought and flood sudden change events, performing three-dimensional drought and flood feature extraction on each drought and flood sudden change event, and outputting a plurality of feature attributes of each drought and flood sudden change event; generating a risk quantification result of each drought and flood sudden change event according to the plurality of feature attributes of each drought and flood sudden change event; the technical problem that an existing drought and flood sudden turning event analysis method cannot integrally reflect the overall risk condition of drought and flood sudden turning disasters on drainage basin water resource safety is solved.

The invention discloses a drainage basin seasonal runoff change prediction and attribution method and system and a medium, and the method comprises the steps: carrying out the seasonal division of a hydrological year based on the monthly scale rainfall, potential evapotranspiration and land water reserve change rules in a drainage basin; establishing a plurality of coupling models of the GCM and the ABCD so as to predict the runoff process of the drainage basin under the conditions that only future climate change scenes are considered and climate change and human activity influence scenes are considered at the same time; a seasonal-scale Buddyko model is considered, and the relative influence of seasonal-scale climate change and human activities on future runoff change is quantified. The method has a strong statistical basis, can objectively reflect the change rule of the watershed hydrologic runoff, can fully decouple the complex relationship between climate change and human activity and other factors on a seasonal time scale, and provides a new technology for the research of a water circulation process driving mechanism in a changing environment.

The invention discloses a vegetation vulnerability constraint estimation method and system under dry-heat composite stress. The vegetation vulnerability constraint estimation method comprises the following steps: 1, acquiring observed vegetation total primary productivity data, meteorological data and similar meteorological data of each lattice point in a global climate mode; 2, determining a standardized rainfall evapotranspiration index time scale of each lattice point and determining dominant stress types of different lattice points in growing seasons; 3, respectively constructing two-dimensional Copula models of corresponding dominant stress and total primary productivity, and determining a stress threshold value corresponding to induced vegetation loss; 4, calculating future estimation of dominant stress based on a climate mode, and constructing an emergence constraint model; 5, correcting a future estimated mean value and uncertainty of the dry heat stress index based on the emergence constraint relation; and 6, inputting the corrected mean value and uncertainty range of the future dry heat stress indexes into a Copula model, and calculating a corresponding loss probability. According to the method, the response difference of different vegetation ecosystems to dry heat stress is considered while the prediction accuracy of the drought heat waves and the compound events thereof is improved.

An early warning method for debris flow disaster in a small watershed, and a disaster reduction method for debris flows in a small watershed is disclosed. An early warning method for debris flow disaster in a small watershed is provided, in which parameters of a hydrological model are calibrated by rainfall, evaporation and runoff data of the watershed based on water balance of the watershed, and an early monitoring and warning scheme for debris flows, which takes water storage in the watershed as a core monitoring and evaluation indicator, is established. The GR4J hydrological model is improved by taking into account a watershed area, specific yield of an aquifer and several geological parameters, as well as an evapotranspiration effect of vegetation. A disaster reduction method by means of regulation of a drainage channel can provide a basis for mathematical model simulation studies of disaster reduction in small watersheds.

The invention relates to the technical field of water-saving irrigation, in particular to a drought risk management-based corn planting water-saving irrigation system, which comprises a crop stress state monitoring module for acquiring a corn canopy temperature and an environment temperature and calculating a temperature difference value between the canopy and the environment. According to the method, the actual stress state of the crop is quantified by acquiring the difference value between the corn canopy temperature and the environment temperature in real time, and the crop water stress index which is dynamically calculated is combined, so that the water stress identification precision and the response time efficiency are improved, and the problems of irrigation decision lag and blindness are avoided; further, the real evapotranspiration demand of crops is accurately judged by dynamically monitoring the change rate of latent heat flux, misjudgment possibly caused by single monitoring of temperature difference is avoided, and the irrigation demand judgment accuracy is ensured; meanwhile, the prices, the flows and the available amounts of different water sources are comprehensively optimized, the taking proportion of multiple water sources is configured with the lowest total irrigation cost as the standard, water source economical efficiency optimization becomes possible, and irrigation operation cost and resource consumption are effectively reduced.

The invention relates to the technical field of bamboo forest ecological environment monitoring, in particular to a bamboo forest ecological environment collaborative observation method based on multi-source Internet of Things sensing, and the method comprises the steps: synchronously collecting a multi-source collaborative observation parameter set of a bamboo forest slope through an Internet of Things sensor network, and then constructing a point-surface scale fusion bamboo forest moisture dynamic model; correcting evapotranspiration point cluster data by using a random forest gradient lifting algorithm to generate terrain weighted evapotranspiration, aggregating runoff data according to terrain weight to obtain a total amount of runoff sub-regions, then executing dynamic water balance constraint calculation, optimizing infiltration capacity in combination with a variation assimilation algorithm, redistributing infiltration capacity space proportion according to soil hydraulic conductivity when a residual error exceeds a threshold value, and calculating the total amount of the runoff sub-regions. Finally, according to the residual space distribution thermodynamic diagram, the evapotranspiration point cluster is migrated to the micro-topography representative area, and observation network adaptive optimization is achieved, the problems that in the prior art, the data scale is disjointed, the observation precision is insufficient, and the network is difficult to adapt to change are solved, and the bamboo forest ecological observation precision and reliability are improved.

The invention discloses a drainage farmland underground water burial depth prediction method and system, and belongs to the technical field of agricultural prediction. Obtaining daily actual evapotranspiration, lateral displacement and groundwater burial depth prediction values through physical process model simulation; determining an actual absolute error of a daily groundwater burial depth predicted value output by the physical model, determining a daily moisture balance intermediate variable, taking the daily moisture balance intermediate variable and the groundwater burial depth predicted value as input variables, and performing training according to the corresponding actual absolute error to obtain an absolute error prediction network; according to the absolute error prediction network, the predicted value of the absolute error is obtained and serves as the correction amount, the groundwater burial depth predicted value obtained through physical process model simulation is corrected, the corrected groundwater burial depth predicted value is obtained, and the method can more accurately and reliably simulate GWD dynamic changes under different drainage conditions.

The invention discloses a vegetation index time sequence prediction method and device based on deep learning, and the method comprises the following steps: obtaining and processing historical period vegetation indexes and environmental factor data of a preset time period, and obtaining NDVI, EVI and kNDVI data as vegetation indexes, environment factor data including temperature, rainfall, evapotranspiration, wind speed, relative humidity and soil humidity; by determining the intensity and direction of the linear relation between the environmental factors and the vegetation indexes, the environmental factors with high correlation with the vegetation indexes are selected; a CNN-BiLSTM-AM model comprising an input layer, a CNN layer, a BiLSTM layer, an AM layer and an output layer is constructed to predict a vegetation index time sequence, environmental factors with high correlation serve as input data, and NDVI, EVI and kNDVI vegetation indexes serve as output prediction targets. The technical process is simple and clear, and the accuracy of vegetation index time sequence prediction is greatly improved.

The invention discloses a rainfall type landslide early warning method based on multi-source environment threshold and nonlinear fusion. The method comprises the following steps: constructing a landslide disaster-inducing factor set; introducing soil humidity, vegetation indexes and evapotranspiration, and calculating a threshold parameter corresponding to the rainfall type landslide event to obtain an environment threshold point set; according to the landslide disaster-inducing factor set, constructing and training a landslide susceptibility model based on progressive learning; adopting a double-layer nonlinear fusion model based on multi-source data fusion and quality perception gating, and combining a landslide susceptibility model to obtain comprehensive risk probability distribution; and performing graded early warning based on the comprehensive risk probability distribution to complete rainfall type landslide early warning. According to the method, on the basis of traditional rainfall parameters, early-stage disaster-pregnant environment factors are introduced, and a multi-source environment threshold value is formed; fuzzy logic reasoning is adopted to realize nonlinear fusion of the threshold information and the landslide susceptibility base map; and dynamically reflecting a regional environmental condition evolution process in combination with an annual iterative updated susceptibility layer, and realizing finer and more adaptive space grading early warning.

A mobile, modular shading and solar generation platform and system that provides adjustable shade for crops sensitive to excessive sunlight or as mobile paddock system for animals while optimizing power generation. The system has a platform with solar panels and shade panels that is supported by motorized mobile towers with batteries for electrical storage and transfer. The system can be equipped with different environmental sensors to detect biotic and abiotic stress in plants and control shading needs. Use of mobile, adjustable shading will reduce plant water use by decreasing the evapotranspiration rate. The platform may also include an irrigation system for controlled watering and growth lights to improve crop growth rate and quality. The system may also include a master controller for controlled movement of the apparatus structure, irrigation, sensor data and electricity transfer.

The invention discloses an irrigation method and system based on a crop growth model, and relates to the technical field of farmland irrigation, and the method comprises the steps: training a growth model based on a time sequence neural network through employing the obtained historical meteorological data, the crop growth process data of a corresponding time period, and the moisture state of a soil root zone; calculating the actual evapotranspiration of the crops by combining the actual moisture state of the soil and the moisture stress coefficient predicted by the growth model, and determining the irrigation amount according to the water deficit and the root depth by combining the current moisture state of the root zone with the stage target moisture content. And the irrigation frequency adaptive to the current environmental condition is calculated by using the meteorological prediction data and the actual evapotranspiration. According to the invention, by introducing the crop growth model based on time sequence modeling, correlation learning among meteorological conditions, soil moisture dynamic changes and crop moisture stress states is realized, so that irrigation decisions can reflect periodic physiological needs of crops on the basis of real-time data.

The invention discloses a comprehensive evaluation method for drought resistance of corn varieties, which relates to the technical field of corn variety evaluation, and comprises the following steps: obtaining multi-temporal multi-band reflection spectrum data under the condition of continuous drought of corn, and performing structure coupling modeling on the spectrum data in combination with the structural characteristics of corn leaf epidermis wax, so as to obtain the drought resistance of the corn varieties; constructing a time sequence curve capable of representing the dynamic change of leaf wax distribution as a dynamic spectrum monitoring baseline; differential analysis between continuous time points is carried out based on the dynamic spectrum monitoring base line, spectrum abrupt change points caused by leaf wax migration are identified, and space consistency inspection is combined. According to the method, a dynamic spectrum monitoring baseline and an energy migration fingerprint are constructed, wax migration interference is accurately identified, a spectrum stability hypothesis is broken through, high precision, self-adaption and consistency of evapotranspiration rate inversion under a drought condition are realized through multi-level correction and a closed-loop updating strategy, and scientificity and comparability of drought resistance evaluation are improved.

The invention relates to a water resource pressure assessment method integrating social economy and climate change. Relates to the technical field of drought basin water resource pressure assessment. The method comprises the following steps: acquiring a historical value and a predicted value of social economic data, and correcting the predicted value of the social economic data to obtain a corrected predicted value; calculating the potential evapotranspiration of the drainage basin in different scenes based on the meteorological data of the historical scenes and the corrected future climate mode data; calculating a future drainage basin water resource quantity based on a correlation between drainage basin rainfall and the water resource quantity; according to the variation trend of the drainage basin drought index in the historical scene and the future scene, drought intensity characteristics are extracted; constructing a drainage basin water resource pressure index calculation formula; and according to the calculated water resource pressure index of the water resource area, standardization is carried out to obtain a standardization index, and according to the standardization index, the water resource pressure change trend of different scene areas is evaluated. According to the method, the quantization and evaluation problem of the drainage basin water resource pressure under the complex condition is solved.

The invention discloses a drought influence prediction method and system based on hydrothermal anomaly characteristics, and the method comprises the steps: extracting a time sequence for calculating meteorological data of a research region, and carrying out the standardization processing of precipitation and evapotranspiration; calculating a quantile of soil water content to identify drought, completing drought time division, and obtaining a hydrothermal abnormal characteristic value and a drought formation rate; constructing a soil water restoring force index; simulating a drought rate by using a random forest; and evaluating the drought influence. According to the invention, the accuracy of estimating the occurrence and influence of sudden drought events can be improved.

The invention discloses a method for predicting reservoir inflow and under-dam water resource change trend in a future change environment. The method comprises the steps of obtaining future monthly rainfall data, future monthly temperature data, future monthly evapotranspiration data and future land utilization data based on a GCM climate mode; the future runoff process of the reservoir control section is obtained through simulation in combination with an RCCC-WBM model; based on a data mining technology and an LSTM deep learning coupling model, mining relation characteristics of future reservoir runoff and the under-dam water resource quantity, and obtaining the under-dam water resource quantity by inputting the reservoir runoff; and a nonlinear trend analysis method and the like are adopted to analyze change trend characteristics of water resources under the dam in the future. The method can fully solve the problem of insufficient consideration of climate change and land utilization in the existing method, and can significantly improve the prediction precision of the water resource under the dam in the future.

The invention discloses an actual evapotranspiration calculation method based on multi-source remote sensing data. Comprising the following steps: S1, acquiring multi-source remote sensing data; S2, based on an energy balance principle; S3, adopting a dynamic data fusion algorithm based on machine learning; S4, constructing an actual evapotranspiration calculation model adapted by regions; the method can accurately estimate the actual evapotranspiration of the heterogeneous region through multi-source remote sensing data fusion and a dynamic calculation model, is suitable for scenes such as agricultural areas, wetlands, urban green lands and forests, keeps high calculation precision and adaptability under various weather conditions, and has a wide application prospect. The problems of low resolution, data missing, lack of regional adaptability, low calculation efficiency and the like in the prior art are effectively solved, the precision, adaptability and efficiency of evapotranspiration calculation are improved, and more efficient technical support is provided for large-scale water resource management, ecological assessment and agricultural production.

The invention discloses a quantitative analysis method for influence of environmental factors on vegetation resistance and restoring force. The influence of the environmental factors on the vegetation resistance and restoring force is quantitatively analyzed by collecting tree ring samples and meteorological data, establishing a composite drought evaluation index and constructing a mixed linear effect model. The method comprises the following steps: firstly, obtaining annual ring width standardized data, calculating monthly vapor pressure deficit and potential evapotranspiration, determining a composite drought year, then calculating vegetation resistance and restoring force indexes, and finally analyzing the influence degree of environmental factors through a mixed linear effect model. According to the method, the evaluation precision is improved through the high-resolution tree ring data, the analysis robustness is improved by adopting the mixed effect model, the key environmental factors can be effectively identified, and a scientific basis is provided for ecological system management.