108 results about "Catchment hydrology" patented technology

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 provides a freezing circle watershed hydrological prediction method and system based on multi-source data fusion, and relates to the technical field of hydrological monitoring and sediment monitoring, and the method comprises the steps: obtaining a vibration signal of the interaction of a water flow and a riverbed and meteorological data in a region, and carrying out the preprocessing; the preprocessed meteorological data and vibration signals are input into a multi-source data fusion model, sequential feature extraction is conducted on the meteorological data and the vibration signals through a multi-layer perceptron in the multi-source data fusion model, the extracted sequential features are fused through a gating circulation unit, and multi-source data fusion features are obtained; and carrying out empirical mode decomposition and Hilbert spectrum analysis on the preprocessed vibration signal to obtain a plurality of signal characteristics such as instantaneous frequency and instantaneous amplitude of the vibration signal, inputting the signal characteristics and the multi-source data fusion characteristics into a hydrological parameter prediction model, and outputting corresponding predicted values of the water flow velocity, the flow and the sediment transport capacity.

The invention provides a drainage basin mountain torrent debris flow physical process simulation method for risk early warning, which comprises specific research and development contents and corresponding problems and achieves the following purposes: constructing a drainage basin unit database, clarifying drainage basin and channel characteristics, and providing data support based on physical process refined simulation; and carrying out basin hydrological process simulation research on the basis of the SWAT distributed model for improving glacier melt water simulation. A mountain torrent and debris flow disaster whole-process coupling model is constructed, a dynamic data chain is used for connecting watershed hydrology and a channel water and sediment dynamic process, full-link simulation from rainfall to disaster modeling is achieved, a disaster forming mechanism is accurately revealed, a visualization system is integrated through the model, channel disaster dynamic evolution evaluation and risk zoning functions are provided, and the reliability of channel disaster dynamic evolution evaluation is improved. A forecasting system designed in an object-oriented development mode can be seamlessly accessed to a provincial early warning platform, and a systematic solution is provided for early warning of watershed or regional disasters.

The invention relates to the technical field of wetland ecological restoration, in particular to a beach wetland near-natural ecological restoration method based on a hydrological connectivity threshold value, which comprises the following steps of: firstly, collecting multi-source data such as river network boundary vector data and wetland topographic data of a drainage basin, and selecting a representative remote sensing image; analyzing a hydrological sequence change trend and water body information, and calculating a hydrological situation change degree; then constructing a hydrological and hydrodynamic model, and cooperatively observing space-time evolution of the wetland; the hydrological connectivity is quantified, and the evolution relation between the hydrological connectivity and the wetland is analyzed; and finally, according to the response of wetland evolution to hydrological communication, identifying a hydrological communication threshold value, and proposing water system communication and ecological channel restoration suggestions. The method is scientific, reasonable and high in practicability, can effectively reveal the wetland evolution mechanism, provides an important reference basis for basin hydrological regulation and control and wetland ecological restoration under the influence of human activities, and has important application value in the field of wetland ecological restoration.

The invention relates to the technical field of hydrological assessment and environmental science, in particular to a watershed hydrological assessment method and system under climate change, and the method comprises the steps: collecting multi-source heterogeneous data, and generating fusion feature data and causal path data through multi-modal fusion; optimizing a watershed hydrological network structure based on reinforcement learning, performing runoff simulation by using an enhanced hydrological model, and generating optimized runoff prediction data; combining runoff prediction data and a run-length theory to extract drought characteristic data, and generating scene data for describing an extreme scene through a path constraint type generative adversarial network; and constructing a dynamic joint distribution model of the drought characteristics based on the scene data, and carrying out multi-scene risk assessment. According to the method, accurate modeling and risk quantification of drainage basin drought characteristics in a complex climate scene are realized, the precision and applicability of hydrological assessment are remarkably improved, and a scientific basis is provided for drainage basin water resource management.

The invention discloses a reservoir group-lake system flood control risk adaptive analysis method and system. The method comprises the following steps: acquiring a basin hydrological sequence and a pre-configured physical causal constraint matrix, and constructing a VineCopula joint distribution and initial Bayesian network conforming to physical topology according to the basin hydrological sequence and the pre-configured physical causal constraint matrix; risk sensitive weights of the sample points are calculated based on the Bayesian network, and weighted sampling density inclining towards the high-risk area is constructed; generating a self-adaptive scene set based on the density, solving an optimal scheduling scheme, feeding back a scheduling result to the Bayesian network to update parameters, carrying out closed-loop iteration of perception-sampling-scheduling-learning until risk assessment is converged, and determining a comprehensive flood control risk assessment result of the system based on the converged risk factor Bayesian network. According to the method, the problems of poor physical interpretability and scarcity of extreme risk samples of a traditional model are solved, and accurate capture and quantification of extreme risks of a complex flood control system are realized.

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 relates to a drainage basin hydrological forecasting method and system based on big data, and the method comprises the following steps: collecting drainage basin hydrological related data through a big data platform, transmitting the data to an edge calculation node of a data collection end in real time, and carrying out the preprocessing and feature extraction; forming a data set by the data after feature extraction, and dividing the data set into a test set and a verification set through space-time cross validation; respectively constructing and training a random forest model and a long short-term memory network model LSTM by using the training set to obtain a trained random forest model and a trained long short-term memory network model LSTM; fusing the trained random forest model and the long short-term memory network model LSTM by using a Stacking method to obtain a watershed hydrological prediction model, and evaluating and optimizing the watershed hydrological prediction model by using test set data; newly-collected data are processed by the edge computing nodes and then fused into the original data set, and basin hydrological data are predicted in real time through the optimized basin hydrological prediction model.

The invention relates to a reservoir dispatching management method and system based on digital twinning, and the method comprises the following steps: carrying out the real-time parameter collection of a target reservoir, and obtaining a reservoir characteristic parameter matrix; performing three-dimensional space modeling based on the reservoir characteristic parameter matrix and the reservoir hydrogeological environment in the target reservoir to obtain a reservoir digital twinborn model; performing drainage basin rainfall-runoff dynamic response analysis on the reservoir digital twin model to obtain a drainage basin hydrological response characteristic curve group; performing prospective water balance calculation on the watershed hydrological response characteristic curve group to obtain a multi-period reservoir flow prediction sequence; and formulating an optimal scheduling instruction set of the target reservoir based on the multi-period reservoir inflow prediction sequence, and regulating and controlling a reservoir gate control system in the target reservoir based on the optimal scheduling instruction set. And the method is difficult to adapt to hydro meteorological conditions and multi-target scheduling requirements which change in real time.

The invention discloses a drainage basin hydrological prediction method based on digital twinning, and the method comprises the steps: obtaining multi-source heterogeneous data, carrying out the processing of the multi-source heterogeneous data, and obtaining a multi-source data set; constructing a multi-dimensional spatial feature set according to the multi-source data set; based on the multi-dimensional space feature set, a finite volume method is adopted to simulate a water circulation process, and a water circulation simulation result is obtained; obtaining an error value of the water circulation simulation result and actual observation data, and performing nonlinear relation mining on the multi-dimensional spatial feature set through a neural network algorithm based on the error value to obtain a data driving prediction result; fusing the water circulation simulation result and the data driving prediction result by adopting a weighted fusion algorithm to obtain a fused prediction result; and constructing a digital twinborn model according to the fusion prediction result, and predicting the hydrological state in real time based on the digital twinborn model. The method can provide more effective decision support for water resource management and flood control and disaster reduction.

The invention discloses a hydrological forecasting method based on multi-feature combination and a Transform model. The method comprises the following steps: acquiring measured data of a target watershed hydrological station, building a physical hydrological model, acquiring output data and derivative feature data of the physical hydrological model, and integrating to obtain basic hydrological data; creating enhanced hydrological physical features, and forming a multi-dimensional original feature pool; constructing a plurality of combination strategies based on the basic hydrological data and the multi-dimensional original feature pool; capturing a long-term dependency relationship of the hydrological time sequence by using an improved Transform model; and the model performance is improved through automatic hyper-parameter optimization. According to the method, the influence of different input feature combinations on the flood forecasting precision is highlighted, and effective technical support is provided for water resource management and flood control and disaster reduction.

The invention discloses a coupling model nitrogen and phosphorus simulation method based on agricultural seasonal decomposition characteristics, provides an agricultural seasonal decomposition model SDA, and simulates the total nitrogen flux and the total phosphorus flux of a drainage basin in combination with a hydrological model and a multivariate deep learning model. The method not only can follow a watershed hydrological cycle process, considers migration and transformation processes of nitrogen and phosphorus along with hydrological processes such as rainfall, infiltration and evaporation, has a solid hydrological physical process, but also can deeply excavate a complex relationship between nitrogen and phosphorus and factors influencing migration and transformation of the nitrogen and phosphorus, so that the method has a good application prospect. According to the method, the features in the time series data can be efficiently extracted and fused, the interpretability of machine learning can be improved, the data requirement and parameter uncertainty of a traditional hydrological model can be reduced, and therefore the accuracy and robustness of total nitrogen flux and total phosphorus flux prediction are improved.

The invention belongs to the field of hydraulic structure safety monitoring and predictive analysis, and particularly relates to a method for estimating abnormal seepage flow of a concrete faced rockfill dam based on parameter transplantation and overflow weir monitoring flow. The scientificity and the precision of seepage flow estimation are improved by combining an upstream watershed hydrological model and dam rear micro-region runoff and confluence simulation; the overflow weir is adopted to monitor flow data, and is combined with the actual reservoir water level change to construct a more reliable water level-seepage flow relationship. The method is suitable for dam seepage flow estimation under the normal working condition, abnormal seepage can be recognized and quantified, and the early warning capacity for the hidden danger of seepage is improved. Dependence of a traditional method on a large number of monitoring devices is avoided, engineering operation and maintenance cost is reduced, and stability and reliability of data are improved.

The invention provides a large-scale watershed hydrological modeling method based on variable grids, and belongs to the field of large-scale distributed hydrological modeling. The large-scale watershed hydrological modeling method comprises the following steps: firstly, determining the size of a watershed basic grid according to the spatial resolution of available basic data and the calculation time consumption of a distributed hydrological model under different resolutions; secondly, determining a grid level according to a rainfall level and a mountain torrent prevention and control level of a flood event; and finally, according to the grid levels of the different partitions, the basic grids are aggregated into coarse grids of different levels, and basin variable grid hydrological modeling is completed. The method is suitable for a large-scale watershed flood forecasting system, and can meet the timeliness demand of flood control decision while guaranteeing the refined modeling of a key area.

The invention discloses a basin hydrological simulation method and system embedded with a canopy interception mechanism, and the method comprises the steps: embedding a revised Gash canopy interception model module in an SWAT model, dividing a rainfall event into a wetting stage, a saturation stage and a drying stage according to the parameters of rainfall, a leaf area index and a tree height, dynamically calculating the canopy interception amount and the interception evaporation amount, and carrying out the calculation of the canopy interception amount and the interception evaporation amount. The effective rainfall capacity is used for replacing original rainfall input in the SWAT model, the adjusting effect of the forest canopy on the rainfall process is reflected more truly, and the basin hydrological simulation precision is improved. According to the method, the response and simulation capability of the SWAT model to the hydrological process of the underlying surface of the forest is effectively improved, the dynamic response to effective rainfall and evaporation terms is enhanced, the method is suitable for watershed hydrological process modeling of various forest types, and the reliability of flood forecasting and water conservation evaluation is improved.

The invention relates to the technical field of hydrological prediction and water resource management, in particular to a multi-time-scale runoff predictability measurement method and device, equipment and a medium, and the method comprises the steps: obtaining runoff time sequence data meeting a first preset processing condition; target classification processing is carried out on the runoff time sequence data to obtain a target classification result meeting a second preset processing condition, then the internal predictability and the implementation predictability of the runoff time sequence data are determined respectively, and the target correlation between the internal predictability and the implementation predictability is analyzed; and determining a multi-time scale runoff predictability measurement result of the runoff time sequence data. Therefore, the problems that only runoff forecasting can be carried out, the predictable degree of the runoff sequence cannot be systematically quantified, the prediction effect is difficult to form systematic intuitive understanding, the complexity and the regularity of a watershed hydrological system are difficult to comprehensively reveal, the predictability of a research object and the applicability and the reliability of a hydrological model are reduced and the like in related technologies are solved.

The invention discloses a space intelligent watershed hydrology and water quality multivariable prediction method based on quaternion time-frequency multi-scale, and the method comprises the steps: obtaining the hydrology and water quality data of a space-time heterogeneous watershed, and representing the space-time coupling characteristics of the interior of the watershed and cross-site; carrying out missing value restoration on the hydrology and water quality data; constructing a time domain-frequency domain composite feature tensor; sequentially mapping the constructed time domain-frequency domain composite feature tensor to a real part and a three-imaginary part of a quaternion to generate coupled quaternion feature representation; summarizing multi-head output through a quaternion fusion layer to obtain high-dimensional coupling feature representation; and inputting the high-dimensional coupling characteristics into a quaternion Transformer decoder, and after nonlinear mapping and channel-by-channel regression processing, outputting a hydrology and water quality multivariable predicted value in a future time period. The method can be quickly migrated to a new drainage basin or a monitoring station and maintain high-precision output, and provides a reliable and extensible intelligent decision basis for real-time water resource scheduling, pollution load management and control and ecological flow guarantee.

The invention discloses an intelligent water conservancy algorithm development system and method based on artificial intelligence, and relates to the technical field of intelligent water conservancy, and the method comprises the steps: constructing a cross-reservoir federated learning network, initializing local model parameters of each node, building communication connection between an edge layer and a cloud end, and receiving hydrological sensor data and meteorological early warning information in real time; recognizing a flood event through a space-time convolutional network, generating a dynamic gradient mask to perform sparse processing on a local model parameter, and reserving a gradient parameter of which the event association degree is higher than a dynamic gradient mask threshold value; and converting the non-dominated solution into a dynamic scheduling strategy, and generating real-time operation instructions of flood discharge, generator set control and irrigation water distribution. According to the method, federated learning and a space-time attention mechanism are combined, and the space-time correlation characteristics of the cross-basin hydrological data are identified more accurately, so that the accuracy of flood prediction is improved.

The invention relates to a flood and drought disaster risk assessment and deduction system and method based on slope multi-level regulation and storage, and belongs to the technical field of flood and drought disaster risk prevention and control and watershed hydrological regulation and control. The method comprises the following steps: acquiring multi-source data of a target drainage basin, performing water source refined analysis through coupling of water transfer coefficients (K1 and K2) based on a hierarchical synergistic effect of'upper closure-middle retention-lower dissipation 'of a slope, and calculating a controllable, effective and renewable water source amount; in combination with the regulation and storage capacity difference of each level, carrying out flood and drought disaster chain risk collaborative assessment, and outputting hierarchical risks and 0-10 level set risk indexes; a physical mechanism and data driving coupled scene deduction model is driven, and historical extreme event redisk and future climate scene prediction are completed; and after rendering by a visual engine, through hierarchical slope regulation and storage threshold comparison, outputting a regulation and storage measure layout optimization scheme. The system adopts a micro-service architecture and comprises a data management module, a multi-level regulation and storage collaborative analysis module, a water source analysis module, a risk evaluation module, a scene deduction module, a decision support module and a threshold comparison module. According to the method, high-resolution accurate positioning, low-error simulation and efficiency improvement of flood and drought disaster risks are realized, a decision closed loop from risk identification to optimization regulation is formed, and direct and quantitative technical support can be provided for flood and drought prevention and control engineering practice of drainage basins.

The invention discloses a basin sand reduction efficiency evaluation and regulation method based on a reservoir group topological structure, and belongs to the field of hydraulic engineering, and the method comprises the steps: S1, inputting basin hydrological data; s2, calculating accumulated runoff sediment transport and drawing a double-accumulation curve; s3, recognizing turning years of human activities on the water-sediment relationship through the double-accumulation curve mutation points, and dividing a natural period and a human influence period by combining the mutation points; s4, analyzing a water-sediment relationship, and calculating a drainage basin sediment reduction amount; s5, determining the topology type of the reservoir; s6, calculating an effective control drainage basin area; s7, establishing a relationship between the effective control basin area and the sand reduction rate; and S8, nonlinear regression equation modeling is carried out. The topology cutting effect theory is applied to reservoir group sand reduction efficiency evaluation, and the error problem of a traditional linear superposition model is solved; dynamic prediction of the long-term sand reduction cumulative effect of the cascade reservoir group under the watershed scale is realized; quantitative decision support is provided for newly-built reservoir site selection and built reservoir group optimization scheduling, and the river channel siltation treatment cost is reduced.

The present invention provides a large-scale watershed hydrological modeling method based on a variable grid, which belongs to the field of large-scale distributed hydrological modeling. The large-scale watershed hydrological modeling method is as follows: first, the size of the basin basic grid is determined based on the spatial resolution of the available basic data and weighing the calculation time of the distributed hydrological model at different resolutions; second, the grid level is determined according to the rainfall level of a flood event and the flash flood prevention level; finally, according to the grid level of different partitions, the basic grid is aggregated into coarse grids of different levels to complete the variable grid hydrological modeling of the watershed. The present invention is applicable to large-scale watershed flood forecasting systems, and can meet the timeliness requirements of flood control decisions while ensuring the refined modeling of key areas.

The application relates to the technical field of hydrology and water resources simulation, and discloses a SWAT watershed hydrology model task planning system, method and product, which comprises a knowledge graph construction module, an embedded index construction module, a query coding and two-stage retrieval module, a context construction module and a large language model task planning module. Through the complete planning process of knowledge graph modeling, vector index construction, two-stage retrieval, context generation and structured task output, the whole-process intelligentization from a user's natural language demand to SWAT watershed hydrology model executable task planning is realized, the SWAT file / parameter structure can be accurately matched, the professionalism, accuracy and executability of task planning are improved, and the problems of high operation threshold, large artificial workload, unstable and unexecutable output of a general large model of a traditional SWAT watershed hydrology model are solved.

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 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.

The application introduces a kind of aquatic organism protection threshold identification method based on four water coupling models, comprising: S1, constructing a watershed hydrological model, then inputting rainfall data, obtaining runoff output data;S2, constructing a river water power and water quality model, then inputting water quantity data, water level data and water quality concentration data, obtaining water quantity output data, flow velocity output data, water level output data, flow direction output data and water quality concentration output data;S3, determine the aquatic organism sampling point, and obtain the hydrology, hydrodynamic, water quality data of the corresponding aquatic organism sampling point through the watershed hydrological model and the river water power and water quality model;S4, extract the hydrology, hydrodynamic, water quality data matched with the aquatic organism sampling point from the model, construct a multi-factor coupling model based on random forest, and identify the aquatic organism protection threshold. Through the above design, the technical effect of solving the data matching in the aquatic organism protection threshold identification process is realized.

The application provides a hydrological simulation method and system based on one-way driving of a nested model in the Yellow River Basin, and belongs to the technical field of hydrological simulation; the method comprises the following steps: taking a SHUD model as a core layer for simulating the hydrological cycle of the basin, taking a MIKE hydrodynamic model as a river channel hydrodynamic simulation layer, coupling the two models to construct a nested model, and performing one-way driving based on a spatial nesting technology; a two-way data interface is constructed, data exchange between the SHUD model and the MIKE hydrodynamic model is realized, a data assimilation algorithm is introduced, historical observation data are used to calibrate the parameters of the nested model, and key parameters are optimized; and then, the hydrological process of the basin is simulated based on the optimized nested model. The application can realize high-precision simulation of the hydrological process of the basin, and further provides scientific guidance for water resource management in the Yellow River Basin.

The application discloses a kind of based on hydrological hydrodynamic coupling model's small and medium-sized reservoir scheduling optimization method, obtains the basic information of reservoir basin and carries out data processing;Set boundary condition, adopt SCS runoff generation model to carry out runoff generation calculation to reservoir basin grid;Based on two-dimensional N-S equation, model is constructed, two-dimensional hydrodynamic calculation is realized;And according to actual data, using different reservoir area calculation method, construct reservoir basin hydrological hydrodynamic coupling model;Carry out reservoir basin multi-source water resources allocation, for scheduling rule optimization boundary inflow condition and water demand upper limit;According to coupling model, in combination with the objective function of reservoir, constraint condition and water demand upper limit, scheduling rule is optimized, obtains optimal scheduling parameter and scheduling scheme.Optimal scheduling strategy of real-time and forecast period reservoir can be given, and the problem that real-time or forecast optimization scheduling simulation is difficult due to the lack of hydrological data in the basin where small and medium-sized reservoir is located is preferably overcome.

The invention relates to the technical field of watershed hydrological model parameter calibration, in particular to a hydrological model parameter calibration method for a watershed lacking hydrological data, which comprises the following steps: determining a watershed A lacking data and a watershed B controlled by an adjacent hydrological station (reference station) with data, and collecting hydrometeorological and underlying surface feature data of the two watersheds; calculating the design flood of the drainage basin A and the drainage basin B by using a rainstorm runoff survey algorithm; comparing the design flood calculated by the rainstorm runoff of the watershed B close to the data with the design flood calculated by the actually measured flow data, and determining a flood correction coefficient kappa for correcting the design flood calculated by the watershed A lacking the data by using the rainstorm runoff calculation algorithm; inputting the model parameters of the data-lacking watershed A and the design surface rainstorm into a hydrological model; simulating an outlet section flood; and comparing a simulation result with a corrected rainstorm runoff checking result, analyzing parameter sensitivity, and adjusting and optimizing model parameters. The method is suitable for parameter calibration of the data-lacking small-watershed mountain torrent forecasting model, and a new means is provided for flood control and disaster reduction.

The invention discloses a stage-by-stage dynamic flood control water level regulation and control method suitable for a hydropower station, and the method comprises the steps: obtaining watershed hydro meteorology, engineering operation and topographic geology multi-source basic data, and carrying out the Spark distributed cleaning, wavelet denoising and GAN missing value complementation preprocessing, thereby forming a standardized data set; an optimal flood season staging scheme is screened through coupling of qualitative analysis and a quantitative method; then, the safe pre-discharge amount and the effective pre-discharge time serve as sensitive factors, a flood control water level dynamic control model is built in combination with fuzzy mathematics and Copula joint distribution, and flood control water level process lines and regulation and control domain values of all flood season stages are generated; the safety of a regulation and control threshold value is verified through finite element simulation and a two-dimensional hydrodynamic model, a regulation and control scheme meeting flood control safety and efficient utilization of water resources is output, dynamic and accurate regulation and control of the flood control water level of the hydropower station are achieved, and the problems that in traditional static flood control water level dispatching, the contradiction between flood control and benefit making is prominent, the staging precision is low, and safety verification is insufficient are solved.

The application relates to a two-dimensional flood risk prediction method, device, electronic equipment, medium and product, which comprises the following steps: acquiring multi-modal data in a target river basin; obtaining simulated runoff results based on a plurality of hydrological models pre-established according to the multi-modal data in the target river basin, and establishing a hydrological-machine learning coupled model based on the simulated runoff results; simulating the river basin hydrological process under a future scenario through the hydrological-machine learning coupled model to obtain a river basin hydrological process simulation result under the future scenario, and extracting flood duration and flood volume characteristic values under a plurality of warming level time windows by using a preset optimal model based on the river basin hydrological process simulation result, obtaining a two-dimensional flood risk prediction result based on the flood duration and flood volume characteristic values under the plurality of warming level time windows, thereby solving the problems of low flood risk prediction accuracy and poor applicability in the related art, and improving the flood risk prediction accuracy and applicability.