57 results about "Satellite precipitation" patented technology

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention discloses a high-precision surface rainfall deviation correction method based on space-time association. The method comprises the steps of evaluating satellite remote sensing rainfall data estimation precision and modeling a space-time association relationship; and space-time association relationship modeling: analyzing space-time error distribution of the long-time-sequence satellite remote sensing rainfall data, performing uncertainty modeling on a satellite rainfall estimation result by using a probability density distribution function, and providing data uncertainty information for rainfall deviation correction. And space-time association relationship modeling: researching a modeling method of the space-time association relationship of the satellite-ground data, and constructing space-time association models between the ground observation sites and the corresponding remote sensing pixels and between the pixels. Meanwhile, environment variables, precipitation deviation time sequence change characteristics and uncertainty information are introduced, and the surface precipitation deviation correction precision is improved. Through the research, the estimation error of the satellite remote sensing surface rainfall data can be effectively reduced, and the accuracy of deviation correction is improved.

The invention belongs to the technical field of hydrology and water resources, and discloses a cold and dry mountainous area drainage basin runoff simulation method based on an MIKE SHE / MIKE 11 coupling model in combination with GIS / RS data. According to the multi-source data fusion technology, a cold and dry mountainous area distributed input data set is constructed through TRMM satellite rainfall data, an MODIS LAI product and an ASTER DEM, and the rainfall space representation precision is improved by 40%. According to the model coupling mechanism, an MIKE SHE / MIKE 11 collaborative simulation framework is provided, earth surface-groundwater flow data interaction is achieved through river channel linkage, and the complex river network flood routing simulation error is reduced to 10% or below. According to the parameter partition calibration method, a drainage basin is divided into five hydrogeological regions, sensitive parameters such as a Manning coefficient and a hydraulic conduction coefficient are optimized in combination with a genetic algorithm, and the basic flow simulation precision is improved by 25%.

The invention discloses a short temporary rainfall intensity prediction and identification method based on space-time composite attention, and the method comprises the following steps: obtaining an IMERG satellite rainfall data set, and carrying out the data construction and marking of a target region; establishing a space-time composite attention network, accessing an MFGA module and a TSMA module layer by layer, and performing non-negative activation on output to ensure physical interpretability; proposing a C-DWABP loss function, performing direction asymmetrical penalty aiming at a pixel-level prediction error, and introducing a grading weight mapped according to a real rainfall intensity grade at the same time; based on the output, intensity classification is carried out through a microthresholding ordered layer, and learnable threshold stability training is set; generating a forecast product, and obtaining an hour-by-hour precipitation grid in future 1-6 hours and a corresponding intensity identification result; according to the method, the precision of short temporary rainfall intensity prediction is improved, the false alarm rate is reduced, and a heavy rainfall event is effectively identified.

The invention relates to the technical field of rainfall fusion, and discloses a multi-source rainfall data fusion method based on meta-heuristic and machine learning, and the method comprises the following steps: collecting ground rainfall observation data, multi-source satellite rainfall product data and auxiliary variable data, and carrying out the data preprocessing; dividing the preprocessed ground rainfall observation data, multi-source satellite rainfall product data and auxiliary variable data into a training set and a verification set in proportion; setting an initial value range of hyper-parameters of the multi-layer perceptron model, inputting the initial value range and the training set into the multi-layer perceptron model for training, and optimizing the hyper-parameters of the multi-layer perceptron model by adopting a sparrow search method to obtain a trained multi-layer perceptron model; inputting the verification set into the trained multilayer perceptron model for identifying the rainfall observation value of the verification set, and obtaining predicted ground rainfall observation data, namely fused rainfall data; according to the method, the precision of the fused rainfall data is improved, and meanwhile, an unestablished hypothesis depending on reality is avoided.

The invention discloses a snow melting liquid water outflow calculation method and device and a storage medium, and the method comprises the steps: obtaining DEM data, satellite rainfall and air temperature data of a drainage basin, taking each grid unit of the DEM as a calculation unit, and calculating the probability that the rainfall form is snowfall and the average density of a snow layer according to the air temperature of a current time period; iteratively calculating the snow depths of each grid unit at the beginning and the end of a time period and the amount of meltable snow in the time period according to the initial snow depth obtained by satellite remote sensing and in combination with the air temperature and rainfall data of the current time period; and determining the water storage capacity of the snow layer reservoir at the beginning of the time period of each grid unit based on iterative operation, and calculating the water storage capacity of the snow layer reservoir at the end of the time period. According to the method, the regulation and storage effect of the snow layer on the snow melting liquid water is reflected by constructing the snow layer reservoir, refined simulation of the outflow process of the snow melting liquid water on the aspects of time and space is achieved, and runoff forecasting of the alpine region is effectively achieved.

The invention discloses a statistical downscaling method based on mountain satellite remote sensing rainfall data, and the method comprises the steps: collecting target region data and auxiliary data, and carrying out the preprocessing of the target region data and the auxiliary data, and obtaining the processed data; constructing an addition and multiplication fusion model, and integrating the processed data based on the addition and multiplication fusion model to obtain integrated data; and performing comparison and error analysis on the integrated data through rainfall actual measurement station data, and performing evaluation and correction according to an error analysis result to obtain a final correction result. According to the method, downscaling research is carried out on mountain satellite precipitation through a geographically weighted regression method, so that the precision of the satellite precipitation data TRMM can be improved to the greatest extent, and an accurate, high-precision and high-resolution precipitation grid data set is obtained; the method plays an important foundation role in scientific researches such as further research on soil moisture content, reservoir regulation and biomass estimation of a mountain system.

The invention discloses a satellite precipitation product improvement method, device and system based on a dual-time scale minimum error, and the method comprises the steps: carrying out the improvement of a first satellite precipitation product, a second satellite precipitation product and an interpolation precipitation product according to the obtained first satellite precipitation product, second satellite precipitation product and interpolation precipitation product; sequentially adopting a dual tool variable algorithm, a least square fusion algorithm and an inverse error weighting algorithm to respectively calculate first fusion rainfall products under a daily scale and a monthly scale; and fusing the first fused precipitation products under the daily scale and the monthly scale to generate a second fused precipitation product. Ground observation data are not needed, daily scale and monthly scale rainfall errors can be minimized at the same time, the thought of data fusion is incorporated, the performance of satellite rainfall products is enhanced, and detailed data support can be provided for natural disaster monitoring and early warning and regional water resource management.

The invention discloses a multi-source rainfall measurement product data fusion method, system and device and a medium, and belongs to the technical field of meteorological data processing, and the method comprises the steps: obtaining meteorological satellite rainfall measurement data, weather radar rainfall measurement data and ground rain gauge rainfall measurement data, and constructing a multi-source rainfall data set; performing preprocessing through the multi-source rainfall data set to obtain satellite rainfall data with a unified coordinate system and resolution, radar rainfall data after noise reduction and rainfall gauge rainfall data after interpolation completion; and in combination with the preprocessed multi-source rainfall data, constructing a time-space consistency data set. According to the method, meteorological satellite, radar and rain gauge data are integrated, a multi-source rainfall data set is constructed, a coordinate system and resolution are unified, noise reduction interpolation ensures time-space consistency, a Kriging interpolation method is adopted to fuse data, resolution and accuracy are improved, an evaluation model is subjected to cross validation, and accurate support is provided for weather forecast and disaster prevention and reduction.

The invention discloses a satellite precipitation two-stage error correction method based on an actual measurement site, and the method comprises the steps: extracting drainage basin DEM data, obtaining a mask file of a drainage basin, and extracting the satellite precipitation of the drainage basin; performing inverse distance weighted interpolation on the rainfall data of the actual measurement site to a corresponding resolution to obtain grid data of the actual measurement site; error correction data are obtained through a deep learning model or dynamic quantile mapping; calculating a residual error between the rainfall data after error correction and grid rainfall of the actual measurement site, and learning a nonlinear relationship between the rainfall data and the residual error by using a gradient lifting regression tree model; and adding the precipitation residual error of the nonlinear residual error correction model to the error-corrected satellite precipitation error to obtain a residual error-corrected precipitation product. According to the method, the satellite precipitation product is corrected based on the actual measurement site data, the high-precision precipitation product is obtained, point-to-surface conversion of the precipitation data can be realized, and data support is provided for input of a refined hydrological model.

The invention relates to a rainfall erosivity inversion method integrating crowd-source satellite rainfall data, which comprises the following steps: acquiring data such as a heterogenous remote sensing rainfall product and a DEM on a GEE remote sensing cloud platform, and acquiring characteristic variables such as characteristic multisource rainfall, sea and land positions and terrain after preprocessing such as numerical extraction, projection transformation and grid resampling; the method comprises the following steps: on the basis of meteorological station space distribution vector data, extracting characteristic variable information corresponding to a corresponding point as a covariable, and taking rainfall erosivity obtained by calculating rainfall data actually observed by a meteorological station as a dependent variable, so as to construct sample data; the sample set is randomly divided into a training set and a test set, and the training set is used for constructing a deep forest regression model for inversion of rainfall erosivity and performing spatial inversion mapping; and then verifying the rainfall erosivity inversion result by using the verification set. According to the method, high-precision inversion and mapping can be carried out on rainfall erosivity space distribution of a large-scale region and even a global scale.

The invention discloses a rainfall field construction method based on point-line-plane multi-source feature fusion, and the method comprises the following steps: (1) obtaining the rainfall data of each rainfall station in a target drainage basin, the microwave link data of a microwave base station, the satellite rainfall data and the radar rainfall data, and carrying out the preprocessing of the microwave data; (2) constructing a dry-wet period discrimination model based on machine learning; (3) calculating microwave signal basic attenuation correction by taking a dry-wet period judgment result as an auxiliary element; (4) carrying out inversion calculation on the rainfall process after the dry and wet periods are distinguished by utilizing a rainfall attenuation inversion model; and (5) using a satellite rainfall field as an initial background field, fusing microwave link data inversion data and rainfall station data based on a multi-grid variation analysis method, and constructing a satellite-microwave-rainfall station three-source fusion rainfall field. Through the multi-grid variational analysis method, the advantages of the microwave rainfall measurement technology can be effectively utilized, accurate reconstruction of rainfall field space distribution is achieved, and the defect that an existing satellite rainfall monitoring product is poor in precision is effectively overcome.

The present invention discloses a multi-source satellite precipitation product fusion method and device combining sliding quadruple permutation and random forest spatial interpolation. The method includes: obtaining multi-source satellite precipitation products, multi-source environmental covariate data, and measured station daily precipitation data, and unifying the spatio-temporal resolution; obtaining the multi-source precipitation product sequences of each grid point on the basin surface, combining any four sequences to construct a sample combination set, estimating the sequence error variance and error covariance of each sliding window period based on sliding quadruple permutation, constructing an error matrix accordingly, calculating the weights of each product, and obtaining the satellite precipitation data after weighted average; constructing a sample set and dividing it into training and validation samples, and training the random forest spatial interpolation model; predicting the precipitation of grid points according to the trained model, and integrating the predicted precipitation sequences of each grid point to form a set of precipitation product data. The present invention combines the sliding quadruple permutation analysis technology with the random forest spatial interpolation model to improve the fusion accuracy of satellite precipitation products.

The invention provides a runoff prediction method based on multi-source satellite rainfall data fusion and a deep learning optimization model. The runoff prediction method comprises the following steps: collecting rainfall data and hydro meteorological data of a multi-source satellite product in a research basin; carrying out noise reduction processing on the precipitation data of the multi-source satellite product by adopting Kalman filtering; and fusing the multi-source satellite product rainfall data after noise reduction processing by adopting a Bayesian model average algorithm. Constructing a deep learning runoff prediction model with adaptive periodic position coding, taking runoff data in hydro meteorological data as supervision, training and testing the runoff prediction model by using the fused multi-source satellite product rainfall data, and performing hyper-parameter optimization by adopting a Newton-Raphson optimization algorithm in the training process; and performing runoff prediction by using the trained runoff prediction model. According to the method, the problems that in existing runoff forecasting model fusion, static weighting cannot describe the change of model performance along with time and time-varying errors are ignored are solved, and the accuracy of input data is improved.

The invention provides a satellite-ground cross-scale multi-source precipitation three-stage fusion method. The method comprises the following steps: extracting DEM data and a drainage basin surface file of a drainage basin; local and global feature factors such as topography and meteorology of the drainage basin are obtained; extracting original satellite rainfall data of the watershed; a Kriging interpolation method is used for unifying the spatial resolution of the rainfall data of the multiple original satellites; constructing a hybrid geographically weighted regression model based on a kernel function, and establishing a mapping relationship between the multiple pieces of satellite rainfall data and global and local feature factors; calculating to obtain a plurality of primary fused precipitation products; and integrating a plurality of precipitation products fused once by using a Bayesian triangular cap method to finally obtain a high-precision and high-resolution precipitation fusion product obtained through three-stage and two-time fusion. Multi-source precipitation fusion is optimized through a refined data processing technology, the precipitation data precision and applicability are improved, high-quality data support is provided, and the method is suitable for different hydrological and meteorological research scenes.

The invention discloses a radar satellite precipitation rapid fusion algorithm based on a radar quality index, and the algorithm comprises the steps: designing a radar shielding area index RSA as a piecewise function changing with a climate proportion correction factor RFC through employing the climate proportion correction factor RFC, optimizing the weight of a radar quality index analysis field through employing a radar terrain shielding index analysis field, and carrying out the calculation of the radar quality index. And rapidly fusing radar and satellite rainfall data by using the radar quality index fusion weight to generate a radar-satellite fusion background field. According to the method, the radar quality index and the terrain shielding coefficient are utilized to dynamically quantify the spatial credibility of the radar data, and the fusion weight of the radar and the satellite is optimized, so that the fusion background field can reflect the actual rainfall condition more accurately, especially in a radar coverage blind area and a site sparse area, the real rainfall condition can be reflected more accurately. And the problems of local distortion, rainfall intensity underestimation or structure fuzziness and the like are effectively reduced.

The invention provides a complex watershed short-time flood response intelligent forecasting method based on satellite rainfall perception, and relates to the technical field of hydro-meteorological monitoring and disaster prevention and reduction. The method comprises the following steps: acquiring satellite rainfall data of a target drainage basin observed by a multi-channel satellite, and preprocessing the satellite rainfall data; inputting the preprocessed satellite precipitation data into a short-time precipitation prediction model, and outputting a short-time satellite precipitation prediction result; and inputting the short-time satellite precipitation prediction result into a semi-distributed hydrological model, simulating and generating a flood response process of a watershed scale, and outputting predicted flood process data. According to the method, the precision problem of short-time rainfall prediction of the complex drainage basin is solved, and the timeliness and stability of flood prediction are improved.

This invention discloses a method for downscaling satellite precipitation based on measured stations. It extracts watershed DEM data, including satellite precipitation, wind speed, and temperature. Weighted interpolation of precipitation data from measured stations yields grid data. A deep learning model learns the nonlinear, spatiotemporal coupling relationship between multiple factors and precipitation distribution, and predicts low-resolution satellite precipitation using dynamic weights. High-resolution satellite precipitation is predicted by upsampling satellite precipitation, temperature, wind speed, and station precipitation using a deep learning model and dynamic weights. The residual between the low-resolution precipitation and the measured station precipitation is calculated and upsampled. The high-resolution satellite precipitation is then added to the sampled residual to obtain the downscaled precipitation product. This invention downscales satellite precipitation products based on measured station data to obtain high-resolution precipitation products, achieving refined precipitation data and providing data support for inputting refined hydrological models.

The invention discloses a near-real-time satellite precipitation error correction method and system. The method comprises the following steps: performing spatial-temporal resolution unified processing on an obtained daily satellite precipitation product, a daily interpolation precipitation product and a monthly interpolation precipitation product; in each grid unit, estimating a relative error of a daily interpolation precipitation product and a daily satellite precipitation product at each moment; based on the relative error, the daily satellite precipitation product and the daily interpolation precipitation product are split into corresponding overestimation error data pairs, zero error data pairs and underestimation error data pairs; with the minimum error as the target, the overestimation error data pair, the zero error data pair and the underestimation error data pair are fused, and a daily scale corrected precipitation product is obtained; and based on a total water quantity control mode, the distribution characteristics of the rainfall products corrected by the daily scale are corrected by using the monthly interpolation rainfall products, and corrected satellite rainfall products are obtained. According to the method, the error and the distribution mode of near-real-time satellite precipitation are calibrated through error decomposition and total water volume control.

A method for back-calculating satellite precipitation data in mountainous areas includes: reading APHRODITE and IMERG precipitation data and calculating daily precipitation statistics at observation stations; building a model using a random forest algorithm at a 0.25° scale to simulate the long-term trend of the two types of precipitation data; generating spatial disaggregation weights for precipitation data at a 0.1° scale using a 3×3 sliding window; and combining the temporal and spatial scales to obtain the 0.1° IMERG precipitation data before 2001, which is the NIMERG data. This method utilizes the temporal and spatial relationships between APHRODITE reanalysis precipitation products and IMERG satellite precipitation products over the same timeframe to reversely back-calculate and extend the IMERG satellite precipitation data. This method can provide a set of high-quality, long-term precipitation products for research related to mountain hydrology, ecology, or geological hazards.

This application provides a satellite precipitation error correction and fusion method based on elevation gradient under complex terrain, belonging to the field of meteorological data processing technology. The method includes: acquiring multi-source precipitation observation data of complex terrain areas, obtaining gridded precipitation data and inputting it into an elevation-error correlation model, obtaining an error intensity distribution map and identifying grid areas with error intensity values ​​greater than or equal to a preset error threshold as uncertain areas and performing feature extraction, obtaining regional feature vectors and inputting them into an error correction strategy selection model, determining the correction strategy type and applicability of the uncertain areas, obtaining the correction areas that need to be deeply fused from the uncertain areas and obtaining ground verification point data, calculating the local precipitation true field and error spatial structure parameters; using a correction algorithm to correct the satellite precipitation data of the correction areas, fusing the satellite precipitation data of the correction areas with those of the uncorrected areas, and generating precipitation products and uncertainty assessment reports.

This invention discloses a real-time precipitation retrieval method for meteorological satellites based on a deep learning geographic attention mechanism. The method includes: constructing a training dataset of FY-4B AGRI infrared cloud images and IMERG Final Run labels, preprocessing it, and inputting it into a U-shaped encoder-decoder network; achieving deep fusion of multi-scale features by embedding a GeoAB geographic attention module; using a dual-objective loss function to complete rain area classification and precipitation intensity regression training; and outputting precipitation data with 0.05° spatial resolution and 15-minute temporal resolution through T-transform decomposition of the cloud image, independent inference, T-inverse transform fusion, and average pooling to eliminate grid effects. Compared with existing technologies, this invention solves the technical problems of balancing real-time performance and accuracy in satellite precipitation retrieval, and the blurred distinction between rain areas and clear skies. This invention achieves a CSI of 0.693, a 3.3% improvement over IMERG Final Run, with a total retrieval time of less than 20 seconds. It balances high accuracy and real-time performance, providing data support for disaster early warning, agricultural management, and other applications, and has promising application prospects.

The present invention discloses a method for fine-grained spatial analysis of precipitation temporal variation characteristics, comprising the following steps: pre-processing satellite precipitation data and ground station observation precipitation data respectively; statistical downscaling using quadratic multivariate regression equation; using station data to correct errors of the two downscaled data respectively; fusing the two downscaled data; sub-gridding precipitation; and analyzing the trend, volatility, and annual distribution changes of precipitation grid points. Compared with the traditional method of obtaining spatial distribution of precipitation temporal variation characteristics, the present invention can obtain more effective and fine precipitation spatial distribution conditions.

The present application belongs to the technical field of precipitation prediction, and discloses a meteorological large model precipitation prediction method, system, device and medium based on satellite inversion precipitation data, to solve the problem of insufficient prediction accuracy of meteorological large model. The method comprises: obtaining ERA5 reanalysis data and satellite inversion precipitation data; constructing a bias quantization model for different climate regions and precipitation intensities based on satellite precipitation data; using a dynamic weighted fusion strategy to correct the bias of ERA5 precipitation data, and splicing with other meteorological variables to form an optimized data set; freezing the core parameters of the pre-trained meteorological large model to fine-tune the precipitation prediction layer with the optimized data set and satellite precipitation label; inputting the ERA5 data of the region to be predicted after bias quantization and dynamic weighted fusion correction into the fine-tuned model to output multi-time precipitation prediction results in a recursive prediction manner. The present application can effectively eliminate the weak bias of ERA5 precipitation and significantly improve the precipitation prediction accuracy.

The invention discloses a possible maximum flood calculation method suitable for a data-free area, and belongs to the field of drainage basin water circulation simulation and hydrological design. The method comprises the following steps: calculating a daily scale possible maximum rainfall result of a drainage basin of the data-free area by adopting an improved Hirschfield method; classifying and processing possible maximum rainfall calculation problems of areas without data; for small and medium-sized drainage basins with the drainage basin area smaller than or equal to 1000 km < 2 >, the 24-hour hour-by-hour possible maximum rainfall process is deduced in the mode that frequency calculation is coupled with a Chicago rainfall pattern on the basis of satellite rainfall inversion data; for a large watershed with the watershed area larger than 1000 km < 2 >, the possible maximum rainfall process of the next day scale of several days is deduced based on satellite rainfall inversion data in a same-frequency amplification mode. According to the method disclosed by the invention, a technical path with complete calculation is constructed, a more reasonable and reliable possible maximum flood calculation result of the watershed in the non-data region is obtained, and key and powerful theoretical guidance and technical support are provided for scale argumentation of a flood control system of water conservancy and hydropower engineering in the non-data region.

A method to train a diffusion regression model for satellite-based precipitation data bias correction may include obtaining satellite observation precipitation data for a training geographic region. The method may include obtaining corresponding ground observation precipitation data for the training geographic region, the corresponding ground observation precipitation data having a same resolution as the satellite observation precipitation data. The method may include generating training residuals from the satellite observation precipitation data and the corresponding ground observation precipitation data. The method may include adding noise to the training residuals. The method may include de-noising the noisy training residuals using a diffusion regression model to generate predicted residuals. The method may include updating the diffusion regression model using a loss function that depends on the training residuals and the predicted residuals.

This invention relates to the field of precipitation fusion technology and discloses a multi-source precipitation data fusion method based on metaheuristics and machine learning, comprising the following steps: collecting ground precipitation observation data, multi-source satellite precipitation product data, and auxiliary variable data, and performing data preprocessing; dividing the preprocessed ground precipitation observation data, multi-source satellite precipitation product data, and auxiliary variable data into training and validation sets according to a certain ratio; setting the initial value range of hyperparameters of a multilayer perceptron model, and inputting them along with the training set into the multilayer perceptron model for training; optimizing the hyperparameters of the multilayer perceptron model using a sparrow search method to obtain a trained multilayer perceptron model; inputting the validation set into the trained multilayer perceptron model to identify the precipitation observation values ​​of the validation set, thereby obtaining the predicted ground precipitation observation data, i.e., the fused precipitation data; this method improves the accuracy of fused precipitation data while avoiding reliance on assumptions that are not valid in reality.

The invention discloses a satellite remote sensing rainfall data fusion method and system based on deep learning, and the method integrates the advantages of multi-source satellite rainfall detection, constructs a data set for model training, carries out the space-time matching of multi-source satellite remote sensing rainfall data through a space-time matching system, and achieves the fusion of the multi-source satellite remote sensing rainfall data through the powerful nonlinear fitting of deep learning. The problem that active and passive satellite remote sensing rainfall data fusion is difficult is solved, and a satellite remote sensing rainfall fusion data product can be output efficiently and precisely.

The application discloses a hydrological and hydrodynamic coupling alpine mountain flood simulation and prediction method, and belongs to the technical field of disaster prediction. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: for the target alpine mountain basin, collect multi-type and multi-scale basic data; S2, deep learning correction and fusion of satellite precipitation data: for the local overestimation and underestimation problems of satellite precipitation data, a deep learning algorithm is used for hour-scale correction and fusion. By collecting satellite remote sensing, reanalysis, ground observation and geographic space four kinds of core data, covering all factors of 'precipitation-runoff-terrain-underlying surface' required for alpine mountain flood simulation, avoiding the one-sidedness of traditional modeling caused by the lack of data types, using 3 sigma criterion to remove precipitation and runoff outliers, unifying data format and space-time scale, forming a standardized data set, avoiding the interference of abnormal values, incompatible format and space-time mismatch on subsequent model input.

The invention provides a method and device for determining the irrigation water consumption of an arid region, and relates to the technical field of agricultural irrigation, and the method comprises the steps: firstly obtaining various evapotranspiration data, ground observation rainfall data, satellite observation rainfall data and soil environment characteristic data of the arid region in an irrigation period; carrying out weighted fusion on the plurality of evapotranspiration data to obtain first data; correcting the satellite observation rainfall data pixel by pixel based on the ground observation data to obtain second data; inputting the soil environment characteristic data into a first deep learning model to obtain first non-evaporation loss data of the arid region in the irrigation period; then correcting the first non-evaporation loss data to obtain second non-evaporation loss data; and finally, based on the first data, the second data and the second non-evaporation loss data, determining the irrigation water consumption of the arid region in the irrigation period. According to the method for determining the irrigation water consumption of the arid region, accurate detection of the irrigation water consumption of the arid region is achieved.