109 results about "Hydrological modelling" patented technology

The invention relates to the technical field of coupling simulation, in particular to a river and underground water coupling simulation parameter generation method and system. The method comprises the following steps: collecting remote sensing and ground sensing monitoring data, and generating a multi-source space-time initial data set through time mark calibration, space resampling and signal-to-noise ratio weighted fusion; extracting features between the water level of the river and the water level of the underground water based on the data set, constructing a river-underground water space-time topological structure and identifying an interaction mode, and forming a space-time coupling feature map through significance screening and periodic enhancement; performing parameter inversion by adopting a hydrodynamic equation, and generating a physical inversion parameter set in combination with sensitivity analysis and local optimization; a parallelization hydrological simulation framework is constructed, error-driven optimization is executed, and dynamic optimization parameters are obtained; river and underground water exchange simulation is executed, and finally, coupling simulation parameter set updating is achieved through sliding window deviation evaluation and incremental parameter correction. Therefore, the precision and operability of a coupling simulation result are improved.

The invention discloses an urban ecological unit scale water conservation function evolution simulation system, relates to the technical field of ecological hydrological simulation, and is technically characterized in that the system combines an InVEST model, a water balance method, a geographic detector and a Moran index to construct a multi-source data fused water conservation evaluation method. According to the system, key factors are obtained through remote sensing data and geostatistical data, a mapping model between water yield and actually measured runoff depth is established based on a regression fitting relation, and a spatial heterogeneity analysis and factor interaction detection mechanism is introduced, so that time sequence dynamic simulation and spatial pattern recognition of a water conservation function are realized. The system can be widely applied to the fields of urban water resource regulation and control, ecological space planning, hydrological safety evaluation and the like.

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 distributed model-oriented medium and small watershed sub-watershed division method, which comprises the following steps of S1, collecting digital elevation model data, performing depression filling processing, and extracting preliminary river network information; s2, extracting an actual water system, and calculating a river network main stream grid elevation difference; s3, adjusting digital elevation model data according to the elevation difference, and optimizing a river network extraction result; s4, basin morphological parameters are calculated, and basin classification is carried out through a manifold learning method; s5, constructing a sub-basin optimal threshold calculation model, and dynamically adjusting a sub-basin division threshold; s6, flow direction calculation, confluence analysis and sub-basin division are carried out, and sub-basin boundaries are optimized; and S7, verification is carried out in combination with the distributed hydrological model, the influence of the division result is analyzed, and sub-basin division parameters are optimized. According to the method, multi-scale features, dynamic threshold optimization and hydrological network topology are fused, sub-basin division precision, boundary continuity and hydrological simulation stability are improved, and intelligent basin division is realized.

The invention discloses a method for dividing calculation units of a hydrological model in a plain water network area. The method comprises the following steps: acquiring and preprocessing multi-source data to form an integrated data set; extracting a river network skeleton based on the integrated data set and constructing a river network topological structure; identifying hydraulic engineering facilities and analyzing the connectivity of the hydraulic engineering facilities, and generating a regional connectivity feature map; and carrying out hydraulically-driven calculation unit adaptive division based on the river network topological structure and the regional connectivity feature map. According to the method, the plain water network area hydrological simulation precision can be effectively improved, and the calculation efficiency is optimized.

The invention discloses a dynamic DEM spatial interpolation method which comprises the following steps: performing depression filling, flow direction analysis and confluence cumulant calculation on DEM data, and extracting a natural sub-basin unit by adopting a minimum catchment area threshold method; constructing a topographic feature matrix, performing refined second-level classification on the first-level drainage basin through an improved self-organizing mapping network, generating a hydrological response unit through boundary processing, and establishing a hydrological attribute library; fusing multi-source data, supplementing attribute interpolation such as underlying surface and rainfall, and constructing an interpolation auxiliary parameter system; a drainage basin is divided into regular grids as neurons, a dynamic neural network containing dynamic states and static attributes is constructed, and nonlinear mapping of DEM correction parameters is achieved through optimization of a dynamic activation function and a loss function. The method overcomes the defects that a traditional interpolation algorithm does not consider hydrological boundary constraints, a neural network model topological structure is fixed and the like, and the DEM interpolation precision and the hydrological simulation effect of the complex terrain area are improved.

The invention discloses a river sediment scouring and silting simulation method, and belongs to the technical field of hydraulic engineering and hydrological simulation. In order to solve the problem that it is difficult to accurately simulate the river sediment erosion and deposition evolution trend in the prior art, the method comprises the steps that on the basis of actually measured topographic data and channel chart data of a target river reach, a water-sediment coupling mathematical model is constructed by adopting a non-uniform sediment unbalance sediment transportation theory, and the model interacts with a water power module and a wave module in real time through an MCT coupler; actually measuring a flood and water and sediment series data calibration verification model; and obtaining future water and sediment process data predicted by a plurality of climate modes, inputting the future water and sediment process data into the verified model, dividing river reaches according to the characteristics such as riverbed gradient and river channel curvature, and calculating the erosion and deposition amount of each sub-river reach in the future 50 years. The method can simulate water flow and sediment movement of rivers, reservoirs and lakes, can be applied to the fields of riverway regulation planning, flood control and disaster reduction decision making, reasonable development and utilization of water resources and the like, and provides important data support and scientific basis for construction and management of water conservancy projects.

The invention discloses a runoff change attribution nonlinear contribution identification method based on segmented hydrological simulation. The method comprises the following steps: dividing an annual runoff sequence into a natural period and a human activity influence period; selecting a watershed hydrological model, and respectively calibrating parameters of the watershed hydrological model in a natural period and a human activity influence period by using hydrometeorological data; analyzing the influence of climate change on runoff change; analyzing the influence of human activities on runoff changes; combining the influence of the climate change and the human activity on the runoff change, and analyzing the synergistic effect of the climate change and the human activity on the runoff change; and quantifying the contribution rate of climatic change, human activity and synergistic effect of the climatic change and the human activity to runoff change. According to the method, on the basis of runoff change attribution linear analysis, the synergistic effect of climatic changes and human activities is recognized in an emphasized manner, the recognition result can objectively reflect the nonlinear response of the hydrological process to environmental elements, and the method is effectively applied to the specific work of watershed water resource evaluation and management.

The invention provides a watershed artificial precipitation enhancement operation effect evaluation method based on hydrological simulation, and belongs to the technical field of artificial influence weather, and the method comprises the steps: obtaining first data and second data; based on the first data, settling a rainfall-runoff process, selecting a preset hydrological model to carry out a drainage basin hydrological batch simulation test, calibrating hydrological parameters, and constructing a drainage basin hydrological simulation forecast model; based on the second data, calculating the natural precipitation of the non-operation area and the operation precipitation of the operation area, and constructing a natural precipitation-operation precipitation data sequence; based on the natural precipitation-operation precipitation data sequence, inputting the natural precipitation into the watershed hydrological simulation forecasting model to obtain runoff before precipitation enhancement operation, and inputting the operation precipitation into the watershed hydrological simulation forecasting model to obtain runoff after precipitation enhancement operation; and based on the runoff before the rainfall enhancement operation and the runoff after the rainfall enhancement operation, carrying out quantitative evaluation on the artificial rainfall enhancement operation effect of the drainage basin. Scientific quantitative evaluation of the artificial precipitation enhancement operation effect of the watershed is realized.

The invention discloses an intelligent simulation method for a runoff process in a region lacking data, and belongs to the technical field of hydrological simulation. The method comprises the following steps: step 1, collecting basic data; step 2, constructing an SWAT hydrological model and outputting physical variables; step 3, an SWAT-LSTM-Transform three-level coupling framework is constructed, and the SWAT-LSTM-Transform three-level coupling framework 4, carrying out transfer learning on the SWAT-LSTM-Transform coupling model, and carrying out transfer learning on the SWAT-LSTM-Transform coupling model; and step 5, runoff simulation and performance evaluation. According to the method, the Transform model and the SWAT-LSTM coupling model are creatively linked together, the adaptability and reliability under the condition of data scarcity are remarkably improved, compared with the prior art, the runoff simulation precision of the method in a region lacking data is improved by 20%, and an innovative solution is provided for hydrological research of the region lacking data.

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

The invention discloses a hydrological model parameter attribution method based on a deep learning interpretability technology. The hydrological model parameter attribution method comprises the following steps: collecting data; constructing a distributed hydrological model, performing optimization parameter calibration by adopting an ant colony algorithm, and selecting three indexes to construct a threshold constraint function so as to perform model precision screening; using a deep residual network model to learn a correlation between the parameters of the region with good precision and meteorological factors and underlying surface feature factors, and complementing parameter distribution on the region lacking data or the region with unqualified hydrological simulation precision; a machine learning feature importance interpretability technology is applied, feature factors are selected to calculate arrangement importance, and finally an attribution analysis result is output. The intervention of the interpretability technology is beneficial to weakening the uncertainty influence of original machine learning'black box ', and well explains the decision process of the hydrological model, so that model parameters have more physical practical significance, and a more accurate prediction tool is provided for the fields of water resource management, environmental protection and the like.

The invention relates to a construction method and application of a pollutant non-point source migration prediction model based on a gated circulation unit neural network, and belongs to the urban non-point source pollution prediction technology, and the construction method is characterized by comprising the following steps: S1, collecting historical data of a to-be-detected region, dividing the historical data into a plurality of sub-basin data, and carrying out the standardized integration to form a feature data set; s2, establishing a pollutant non-point source migration prediction model through the feature data set; the pollutant non-point source migration prediction model comprises a hydrological simulation module, a hydraulic simulation module and a pollutant fate module; and S3, training the hydraulic simulation module and the pollutant fate module, and optimizing module parameters.

The invention belongs to the field of hydrology, and particularly provides a multi-source rainfall data fusion and hydrological applicability analysis method, which comprises the following steps: preprocessing multi-source rainfall data; constructing a multi-source data point-surface fusion model to obtain a fused precipitation product; drought identification application analysis of various rainfall products; hydrological simulation application analysis of various rainfall products; uncertainty calculation of hydrological simulation of various rainfall products; and evaluating the hydrological applicability of the rainfall product by adopting the hydrological applicability index. According to the method, multi-source precipitation data are fused, specific quantitative evaluation indexes of precipitation products are provided, and important reference is provided for guiding effective utilization of the multi-source precipitation products in different regions and different hydrological applications.

The invention relates to the technical field of urban hydrological simulation and flood forecasting, and particularly discloses a comprehensive unit line improvement method applicable to an urbanized region, which comprises the following steps: S1, constructing an urban rainstorm runoff model, and simulating the flow process of a drainage basin outlet section under rainstorm in different recurrence periods; s2, calculating the flow process of the drainage basin outlet section under the same design rainstorm, and reconstructing and converting the flow process into a centralized flow process; s3, distributing the centralized flow process into sub-flows of a water outlet and a river channel section; s4, on the basis of the actual maximum drainage capacity of the drainage port, storage and drainage response correction is carried out on the sub-flow process of the drainage port, and a time sequence flow process considering drainage capacity constraint is formed; s5, calculating the segmented delay of the propagation time lag to the corrected flow, and obtaining an improved total flow process; the improved urban rainstorm basin outlet flow simulation is realized by performing space-time reconstruction, space distribution, drainage capacity constraint correction and propagation time delay delay on the flow process.

The invention relates to the technical field of hydrological simulation and flood forecasting, and particularly discloses a watershed runoff simulation method and device with a graph neural network fused with hydrological priori knowledge, and the method comprises the steps: carrying out the grid division of a target watershed, and building a watershed graph structure in combination with the hydrological priori knowledge; introducing a trainable weight into the graph neural network model, and performing time delay weighting based on a spatial distance to obtain a fusion network model; the meteorological driving data, the rainfall data and the drainage basin attributes serve as input, the convergence process is simulated through the fusion network model, and the predicted downstream runoff volume is obtained; and comparing the predicted downstream runoff volume with the actually measured downstream runoff volume, and optimizing all learnable parameters of the fusion network model. According to the method, the defects that a traditional physical model is large in calculation amount and long in consumed time are overcome, rapid simulation of the runoff process is achieved, and the accuracy and reliability of a data driving model in a runoff simulation task are improved by fusing hydrological priori knowledge and introducing physical constraints.

The invention relates to the technical field of ridge construction, and discloses an ecological ridge construction method for slowing down lateral seepage and intercepting non-point source pollution, which comprises the following steps of: extracting soil texture, elevation and area information of a target area, and scientifically delimiting a ridge and a farmland boundary by combining a spatial analysis and function partitioning algorithm; dynamically determining the height of the buffer zone according to the height difference relation and the flood and drought stress characteristics of plants; hydraulics characteristics are obtained through hydrological simulation, the width of the buffer zone and the filler amount are calculated based on the length-width ratio and hydrodynamic force coupling mechanism, optimal design of water flow regulation and control and pollution interception is achieved, and the stability and purification efficiency of the ridge system in different environments are ensured. According to the invention, the ecological ridge system with strong adaptability is constructed through cooperative analysis of multi-source terrain environment and ecological parameters.

The invention discloses a load balancing task scheduling method for grid hydrological model parallel computing. The method comprises the following steps: (1) analyzing a model based on component characteristics: analyzing four sub-model processing flows of a grid Xinanjiang model, and determining a model input file, related parameters, a calling relationship among the models and a model output result; the model is divided into three types of components, namely an independent calculation component, a time sequence dependence component and a space logic dependence component, and the processing mode of the components under a distributed parallel calculation framework is analyzed. And (2) Flink cluster construction and parallel algorithm design: according to the analysis, dividing a task into a plurality of sub-tasks, distributing the sub-tasks to different computing nodes, and processing the sub-tasks by using the parallel computing capability of the Flink. And (3) proposing a load balancing task scheduling algorithm based on resource feedback: aiming at the condition of non-uniform load in the distributed system, through real-time resource monitoring, distributing tasks in a waiting state in machines with heavy load to machines with light load so as to realize load balancing of the cluster. And (4) result integration: integrating calculation results of the models, and outputting a final hydrological simulation result. According to the load balancing task scheduling method for grid hydrological model parallel computing, distributed processing is carried out on subtasks of a grid Xinanjiang model through an Flink parallel computing framework, and the hydrological simulation computing efficiency is improved through load balancing.

The invention provides an urban green infrastructure comprehensive benefit applicability evaluation method based on subjective and objective weights. The urban green infrastructure comprehensive benefit applicability evaluation method comprises the steps of establishing a basic evaluation system, screening indexes, calculating index weights, calculating quantitative indexes, calculating qualitative indexes, performing grading evaluation, performing standardized score assignment and performing comprehensive evaluation. The basic evaluation system is of a three-layer structure and covers four criteria of runoff regulation and storage benefits, environmental benefits, economic benefits and social benefits and 19 indexes; a subjective weight is determined through an analytic hierarchy process, an objective weight is determined through an entropy weight method, and a comprehensive weight is obtained in combination with a D-S evidence theory fusion method; quantitative indexes are calculated through methods such as hydrological simulation, and qualitative indexes are rated by public; the indexes are unified into dimensionless scores through standardized score assignment, and finally an evaluation result is obtained through weighted summation. According to the method, the applicability quantitative evaluation of matching of the typical green infrastructure and the site condition construction target is realized, and a scientific and quantitative decision support tool is provided for sponge city planning.

The invention relates to the technical field of urban hydrological simulation. The invention discloses an urban community surface ponding simulation method considering a roof structure, and the method comprises the steps: obtaining a regional multi-angle picture through an oblique photography unmanned plane, generating an oblique photography model through image processing, and reconstructing a digital surface model according to the oblique photography model; ground data are separated from the DSM through a PMF method, roof data are extracted in combination with building features, mask data are output, other ground features are removed, and building roof elevation grid data are obtained; calculating the gradient of the roof, dividing the gradient into flat and sloping roofs, building drainage structures into roof drainage models, integrating the roof drainage models with the urban community hydrological model considering the roofs, and running to realize surface ponding simulation. The method comprises the following steps: building a high-precision model through an oblique photography unmanned aerial vehicle and a GIS, separating ground data through a PMF, and accurately dividing a flat / slope roof through median filtering and an SVM; special drainage structures are arranged for the two types of roofs; and integrating the roof drainage model and a two-dimensional coupling hydrological model, and outputting an accumulated water thematic map.

This invention provides a simulation method based on the functional attributes of forest and grassland communities, comprising: acquiring community survey data and individual plant functional trait data of the target forest and grassland vegetation; calculating the functional attributes of the plant community; acquiring the actual values ​​of key hydrological parameters corresponding to the target forest and grassland vegetation; training the model using a random forest algorithm to construct a nonlinear mapping model; integrating the nonlinear mapping model into a parameterized process and outputting the simulation results. The hydrological simulation method based on the functional attributes of forest and grassland communities provided by this invention establishes a nonlinear mapping relationship between these ecological attributes and key hydrological parameters using a random forest algorithm, thereby dynamically driving the simulation of regional hydrological processes. This achieves a refined characterization of the mechanism by which vegetation community structure and function affect hydrological processes, significantly improving the simulation accuracy of the model in scenarios such as vegetation restoration and configuration optimization.

The invention relates to a process-by-process multi-model optimization fusion water resource allocation method and system. The method comprises the steps of obtaining historical water demand and historical water saving potential of a target area based on historical water resource monitoring data; inputting the historical water demand and the historical water-saving potential into a pre-constructed prediction model to obtain a comprehensive water demand and a comprehensive water-saving potential; constructing a three-dimensional joint distribution model based on the historical water resource monitoring data and the water resource management information data, and generating a plurality of water supply scenes based on the three-dimensional joint distribution model; and inputting the water supply scene, the comprehensive water demand and the comprehensive water-saving potential into a pre-constructed optimal configuration model to obtain a water resource configuration optimization scheme. By integrating water regimen, work regimen and other multi-element elements and coupling feedback of a water-saving regulation and control mechanism, a water engineering cluster multi-element joint real-time feedback type scheduling scheme is formulated, and quantitative assessment of water resource utilization and functional perfection of a model in the aspects of hydrological simulation, scheduling, configuration, management and the like are achieved.

The application provides an oasis ecological hydrology simulation method, device, equipment and storage medium. It relates to the technical field of ecological hydrology under geophysics. The method comprises the following steps: based on ecological hydrology basic data, using ecological hydrology equation to calculate oasis evapotranspiration, oasis area carrying capacity of groundwater and vegetation coverage carrying capacity of groundwater, as input features of machine learning modeling, and based on machine learning model suitable for regression modeling, respectively constructing lake area module, groundwater depth module, oasis area module and vegetation coverage module; integrating each module to construct an oasis ecological hydrology model, and realizing oasis ecological hydrology simulation based on the oasis ecological hydrology model. The method can provide an effective tool for the total water delivery amount and water delivery process optimization design of ecological water delivery engineering, and help to improve the ecological benefit of oasis recovery.

This invention discloses a method, device, equipment, medium, and product for urban flood peak identification, belonging to the field of urban hydrological technology. The method includes: obtaining the hydrological vector of each sub-catchment at time t; determining the first and second effective areas of each sub-catchment at time t based on the dynamic impermeable area ratio of each sub-catchment at time t; performing hydrological simulation on the first effective area using a dynamic depression-filling loss-deduction model to obtain the first flow rate of each sub-catchment, and performing hydrological simulation on the second effective area using an improved Green-Ampt model to obtain the second flow rate of each sub-catchment; determining the outlet flow rate of each sub-catchment based on the first and second flow rates and the dynamic confluence time; and identifying the flood peak of the target city based on the outlet flow rate of each sub-catchment. This method can improve the accuracy and real-time performance of urban flood peak identification.

The invention provides an oasis ecological hydrological simulation method, device and equipment and a storage medium. Relates to the technical field of ecological hydrological branches under geophysics. The method comprises the following steps: calculating oasis phreatic evapotranspiration, oasis area bearing capacity of groundwater and vegetation coverage bearing capacity of groundwater by applying an ecological hydrological equation based on ecological hydrological basic data as input characteristics of machine learning modeling, and calculating the vegetation coverage bearing capacity of the groundwater based on a machine learning model suitable for regression modeling; respectively constructing a lake area module, an underground water burial depth module, an oasis area module and a vegetation coverage module; all the modules are integrated to construct an oasis eco-hydrological model, and oasis eco-hydrological simulation is achieved based on the oasis eco-hydrological model. The method can provide an effective tool for optimal design of the total water delivery amount and the water delivery process of an ecological water delivery project, and facilitates improvement of oasis resuscitation ecological benefits.

The application discloses a large-scale basin deep learning flood forecasting method based on runoff lag information and relates to the technical field of hydrological simulation and flood forecasting of machine learning, and comprises the following steps: collecting basin hydrological observation site information data, basin characteristic data, basin historical hydrological data and meteorological forecasting characteristic data; dividing a large-scale basin into a plurality of sub-basins; preprocessing data, extracting runoff lag information obtained by using a convolutional neural network model, and constructing a runoff lag information database; adopting a deep learning long short-term memory model to couple the runoff lag information database, obtaining a plurality of sub-basin runoff lag information models; obtaining optimal runoff lag information parameters of the plurality of sub-basins according to a model precision evaluation index, obtaining an optimal hydrological forecasting model, and realizing accurate flood forecasting of the large-scale basin. The application makes up for the deficiency of flood forecasting under the condition of lack of hydrological data of a large-scale basin and has a strong application prospect in areas with little or no data.

The invention discloses a reservoir scheduling method and system based on hydrological simulation, and belongs to the field of reservoir optimal scheduling method.The method comprises the steps that a hydrological environment simulation model is built based on hydrological environment data and a preset water and soil conservation model; obtaining initial drainage basin runoff data based on the hydrological environment simulation model; based on the initial drainage basin runoff data and a preset drought evaluation model, obtaining initial drainage basin drought characteristics; constructing a reservoir balance model based on the reservoir feature information; constructing a multi-target reservoir scheduling model based on the initial drainage basin runoff data, the initial drainage basin drought characteristics and a reservoir balance model; and solving the multi-target reservoir scheduling model to obtain a reservoir scheduling optimization scheme so as to realize the scheduling of the multi-target reservoir. Therefore, the problem that in the prior art, a reservoir dispatching optimization scheme is limited to a certain extent in practical application is solved, flexible dispatching and real-time regulation and control of the multi-target reservoir are achieved by building the model, and the hydrological drought problem is avoided.

The invention discloses a river nitrogen pollution source analysis method based on process simulation and isotopic tracing, particularly relates to the technical field of river sewage treatment, and comprises hydrological simulation, nitrogen isotope analysis, nitrogen source analysis, model analysis and data processing and analysis. According to the method, the source and flux of nitrate nitrogen in a river system are simulated by utilizing a hydrological model, when long-term hydrogeological chemical data of a river are fully measured, a standard verification and simulation method is adopted, and for the river without measurement data, the emission amount of a field sample and nitrate nitrogen concentration data are used for helping to verify the hydrological model, so that the accuracy of the verification is improved. According to the method, a hydrological model is used for simulating river runoff and nitrate nitrogen flux so as to improve simulation accuracy, on the contrary, the hydrological model simulates river runoff and nitrate nitrogen flux so as to support calculation of isotope source flux, meanwhile, the hydrological model can also be used for verifying isotope-based results, and simulation results of the hydrological model and the isotope model are systematically integrated so as to realize reliable nitrate nitrogen space-time source tracking.

The present disclosure relates to the technical field of data processing, in particular to a DEM data filling method, a DEM data filling device, a storage medium and an electronic device. The DEM data filling method comprises: obtaining original DEM data, and detecting the original DEM data to obtain a depression point and depression point information; collecting real-time hydrological data, and predicting hydrological prediction information of the depression point based on the real-time hydrological data and the depression point information; determining a filling path of the depression point according to the hydrological prediction information and the depression point information; and filling the depression point in the original DEM data according to the filling path to obtain target DEM data. The DEM data filling method provided by the present disclosure can improve the accuracy of hydrological simulation.

The invention relates to the field of hydrological data analysis and model correction, in particular to a digital twin basin correction method based on non-stationary reservoir hydrology, which comprises the following steps: acquiring hydrological data of a basin in a predetermined season period to extract corresponding hydrological motion features; in combination with hydrological motion characteristics and the number of branch runoff paths, analyzing characteristic parameters representing hydrological connectivity stability of the watershed to divide hydrological fluctuation categories of the watershed; adaptively evaluating and correcting twinborn parameters of the drainage basin based on hydrological fluctuation categories; and determining a corresponding prediction deviation level based on the corrected prediction deviation value of the simulation prediction result and the actual result. According to the method, the adaptability and robustness of the digital twin basin model are improved while the accuracy of hydrological simulation and prediction is improved, and then the practicability and efficiency of engineering practical application are improved.