56 results about "Hydrological modelling" patented technology

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 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 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.

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 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 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.

The present disclosure provides a permafrost mountainous hydrological simulation method and system based on space-ground-air integrated monitoring, relating to the technical field of hydro-meteorological monitoring and water resources forecasting. The method comprises: obtaining permafrost hydrological monitoring data of a permafrost region through space-ground-air multi-monitoring equipment; the permafrost hydrological monitoring data comprises permafrost state and precipitation data; assimilating the permafrost hydrological monitoring data into a distributed hydrological model coupled with a permafrost runoff generation process to obtain a first distributed hydrological model; simulating runoff processes under different permafrost states and permafrost underlying surface hydrological characteristics through the first distributed hydrological model to dynamically quantify precipitation-runoff conversion efficiency. The present scheme can accurately simulate the runoff process in the permafrost region, and then reliably dynamically quantify the precipitation-runoff conversion efficiency.

This invention discloses a multi-dimensional watershed similarity discrimination method based on geography, meteorology, and hydrology, relating to the fields of hydrological simulation and watershed management technology. By integrating a method for extracting underlying surface morphology features from multi-source data, combined with a meteorological time-series data feature selection method based on high-dimensional dynamic time distortion distance, and utilizing Sentinel-1 / 2 multi-source remote sensing images to invert flood hydrological process features, the method calculates the geographical morphological similarity, meteorological feature similarity, and hydrological process similarity between data-free and data-rich small watersheds. Weighted comprehensive similarity is then used to complete the small watershed similarity discrimination, achieving high-precision transfer of hydrological model parameters in data-free areas. This invention's method provides comprehensive underlying surface discrimination that closely aligns with hydrological mechanisms, effectively preserving dynamic meteorological and hydrological features and improving time-series classification accuracy. It directly matches flood processes through remote sensing inversion, avoiding misjudgments caused by traditional indirect inference. The process is standardized, adaptable to multiple scenarios, and suitable for hydrological simulation in data-free areas.

The application provides a lake surface temperature prediction method based on a neural network, applied to the field of hydrological simulation, and the method comprises the following steps: obtaining meteorological driving data sets of a selected region in a simulation period, and combining a physical mechanism model to obtain lake physics parameters; constructing a long short-term memory neural network model according to a lake surface temperature data set and the lake physics parameters, wherein the long short-term memory neural network model is used to predict the time step increment of the lake surface temperature; optimizing the hyperparameters of the long short-term memory neural network model based on a Bayesian optimization algorithm to obtain a long short-term memory neural network model based on Bayesian optimization; updating the physical mechanism model according to the long short-term memory neural network model based on Bayesian optimization to obtain an updated physical mechanism model; and combining the updated physical mechanism model and the meteorological driving data sets of the selected region in the simulation period to obtain a corrected lake surface temperature.

The invention discloses a soil water content correction method based on an XAJ and LSTM coupling model, and the method comprises the steps: extracting watershed DEM data, and constructing an input set; running XAJoptuna, outputting a time sequence containing three-layer evapotranspiration, runoff production and initial soil water content, and calculating a process file; constructing an LSTM (Long Short Term Memory) taking daily evaporation capacity, net rainfall and the current three-layer soil water content as input, outputting a next moment to correct the three-layer soil water content, and training by adopting a loss function; the corrected soil water content predicted by the LSTMoptuna is fed back in real time to replace an XAJopyuna internal state variable; performing three-water-source division and confluence calculation on the coupled runoff result; according to the method, the advantages of a physical mechanism and data driving are fused, the simulation precision of the soil water content is remarkably improved, dynamic correction from points to sites is achieved, and high-temporal-spatial-resolution state variable support is provided for drainage basin drought early warning, water resource scheduling and refined hydrological simulation.

The invention discloses a watershed multi-scale water network optimization scheduling model system based on ecological restoration, and relates to the technical field of ecological restoration, and the system comprises the steps: building a coupling model through data collection, hydrological simulation and hydraulic engineering digitization, simulating different scheduling scenes, and building a scheduling rule base with ecological flow as a target according to the data collection, hydrological simulation and hydraulic engineering digitization; and finally, a scheduling scheme is intelligently generated based on real-time flow monitoring and a rule base, and optimal scheduling and ecological guarantee of a drainage basin water network are realized. By constructing the ecological scheduling rule base and monitoring the flow in real time, the downstream ecological flow is guaranteed, and the aquatic ecosystem is restored; by means of hydrological simulation and engineering digitization, reservoir scheduling is optimized, and the utilization efficiency of water resources is improved; an intelligent decision-making module is integrated, so that quick response and automatic scheduling are realized; and through multi-scene deduction, a scientific basis is provided for drainage basin planning, and the test cost and uncertainty are reduced.

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 invention discloses a drainage basin ecological hydrological process simulation and prediction system based on a mechanism hydrological model and AI coupling, and relates to the technical field of drainage basin ecological hydrological simulation and prediction. Multi-source data integration and preprocessing are carried out through a data input layer, and a standardized data set is output and provided for a model coupling layer; the model coupling layer directly receives the standardized data set of the data input layer and outputs an optimized simulation result and a predicted value to the simulation and prediction layer; the simulation prediction layer receives a simulation result and a prediction value of the model coupling layer, performs multi-dimensional ecological hydrological element collaborative simulation and short-term-medium and long-term scene adaptive prediction, and outputs a simulation prediction result to the result output layer; and the result output layer receives the simulation prediction result of the simulation prediction layer and provides visual interaction and decision suggestion generation functions. The invention provides an innovative scheme of deep fusion of a mechanism model and an AI technology, and comprehensive requirements of physical rationality, data fitting precision, multi-element coverage and efficient response are covered.

The application discloses a rainfall correction runoff simulation method, comprising the following steps: obtaining observed precipitation data, measured runoff data and simulated runoff data generated by a hydrological simulation model of a target basin; constructing a multi-year average runoff coefficient based on the observed precipitation data and the simulated runoff data; using the multi-year average runoff coefficient and the measured runoff data to back-calculate the theoretical precipitation, and comparing and quantifying the total precipitation gap of the basin with the observed precipitation data; constructing a spatial feature weight coupled with the area and elevation of an observation site, distributing the total precipitation gap of the basin to each observation site based on the spatial feature weight to generate a rainfall correction coefficient; correcting the observed precipitation data by using the rainfall correction coefficient, and inputting the hydrological simulation model to output a re-simulated runoff process. The application promotes the distribution of the spatial precipitation gap and improves the runoff simulation precision under the premise of satisfying the hydrological mechanism and water mass conservation.

The invention discloses a high-precision mountain area river course roughness extraction method based on a multi-source remote sensing image. Comprising the steps of S1, multi-source remote sensing image data acquisition and data preprocessing, S2, riverway center line extraction and riverway range determination based on remote sensing images, S3, vegetation coverage calculation and riverway vegetation region identification, 4, riverway vegetation height calculation, S5, hydraulic radius and water depth calculation, and S6, riverway roughness inversion based on vegetation-hydraulic parameters. The method is low in cost and high in timeliness, large-scale field actual measurement is not needed, multi-source remote sensing images are mainly adopted, and the data acquisition cost is obviously reduced; mountain area river channel parameter extraction can be realized, the period is shortened to 1-2 weeks, and the dynamic monitoring requirement is met; reliable parameters are provided for high-precision hydrological simulation; the method is wide in applicability and high in expansibility, and is suitable for mountainous river channels with different terrains and different vegetation types (herbaceous plants, shrubs and arbors); inSAR deformation data can be added subsequently, and a river channel erosion and deposition height change monitoring function is expanded.

The invention discloses a rainfall data scarce coastal port-oriented flood risk assessment method and system, and relates to the technical field of port flood control engineering and hydrological simulation, and the method comprises the steps: obtaining rainfall data, basic data, other data and a port facility distribution diagram of a target port; obtaining a corrected design rainstorm hydrograph based on the rainfall data; based on the designed rainstorm hydrograph and the basic data, rainfall-runoff simulation is carried out, and a flood flow hydrograph is obtained; obtaining a port inundation simulation result based on the flood flow hydrograph and other data; and based on the port inundation simulation result, inundation indexes are extracted and combined with the port facility distribution map to generate a flood risk assessment report. The whole process simulation from rainfall to port submerging is realized, so that the comprehensiveness and accuracy of flood risk assessment are improved.

The application discloses a flood risk area personnel emergency transfer planning method and device, and belongs to the technical field of emergency transfer path planning, which comprises the following steps: constructing a road network map of a target area based on three-dimensional geographic data, and obtaining the relative elevation index of the roadbed of each path in the road network map; determining a sensitive path set based on the road network map and historical disaster data, wherein the sensitive path set comprises a river-crossing bridge and a wading path; performing hydrological simulation on the target area by using a hydrological model; calculating the passing cost of each path in the road network map by using a cost function; and determining the optimal transfer path of a first user to be transferred at time t according to the passing cost of each path in the road network map; wherein if a path in the sensitive path set is flooded at time t, the passing cost of the flooded sensitive path at time t is set to infinity. The method makes the planned personnel transfer route executable under extreme rainstorm working conditions.

The invention discloses an urban hydrological situation awareness and identification method and system and a readable medium, and the method comprises the steps: constructing an urban hydrological emergency knowledge system, which comprises the construction of an urban hydrological emergency plan library, an urban hydrological event library and an urban hydrological simulation model library; natural language query input by a user is analyzed, retrieval and fusion are carried out based on the urban hydrological emergency plan library and the urban hydrological event library, and a specific hydrological event scene knowledge sub-library is constructed; performing adaptive matching and simulation on the specific hydrological event scene knowledge sub-library and the urban hydrological simulation model library; and fusing the simulation result with the knowledge rule to generate an early warning rule related to the specific hydrological event scene. According to the method, the unification of hydrological situation dynamic perception and accurate identification is realized, the emergency response timeliness is improved, and scientific and intelligent support can be provided for urban hydrological disaster control decision. The method disclosed by the invention is subsidized by complex space scene situation identification and virtual-real mutual feedback (2022YFB3904104) of national key research and development projects.

This invention discloses a deep learning simulation method for extreme precipitation in southeastern Tibet based on topographic and water vapor coupling characteristics, belonging to the field of meteorological and hydrological simulation technology. The method includes: acquiring multi-source raw data covering the target area of ​​southeastern Tibet and performing collaborative preprocessing to obtain a multi-source data array; extracting topographic dynamic features and atmospheric water vapor features based on the multi-source data array to generate a multi-dimensional comprehensive feature tensor; constructing directional connections for spatial grid points based on the real-time wind field in the multi-dimensional comprehensive feature tensor to generate a physically guided dynamic directed graph; inputting the structure of the physically guided dynamic directed graph into a graph neural operator network, simulating the propagation and superposition of coupled information through multi-layer message passing, and mapping it to a precipitation intensity function. After statistical post-processing and information extraction, this invention outputs extreme precipitation area forecast maps and gridded forecast data.

The invention provides a data-deficient reservoir hydrological forecasting method based on remote sensing data. The data-deficient reservoir hydrological forecasting method comprises the steps of model construction, data-free reservoir water level-area-storage variable relation identification, reservoir dispatching rule training and parametric modeling, basin storage variable process simulation and reservoir dispatching coupling and the like. According to the method, the remote sensing inversion technology, reservoir dispatching behavior analysis and hydrological model construction are fused, and integrated combination of remote sensing information and hydrological dispatching simulation is achieved. The method solves the problem of forecast deviation caused by lack of dispatching data in a traditional method, has the advantages of low data dependency, high adaptability, high precision and good expandability, is particularly suitable for hydrological simulation and drainage basin management of typical areas lacking data, and provides reliable technical support for water resource dispatching and flood early warning.

The invention provides a reservoir thermocline feature forecasting method considering climate and hydrological condition changes, belongs to the technical field of hydrological simulation and hydraulic engineering, and is suitable for a reservoir area without main branch convergence. The method comprises the following steps: collecting and preprocessing a multi-source data set; constructing a drainage basin runoff yield model and a facade two-dimensional water temperature model; main influence factors of the vertical water temperature are screened, and a data set is constructed; on the basis of a production and convergence model, a reservoir water balance equation model and a Copula model, 27 working conditions are designed by considering climate and hydrological elements and a reservoir dispatching coupling relation, and an expansion data set is generated by utilizing a facade two-dimensional water temperature model; an LSTM-BNN neural network model is established to carry out vertical water temperature probability forecasting; the model performance is evaluated and the occurrence time, location, thickness and strength of the quantified thermocline are identified. According to the method, effective expansion of the data set is realized through physical and reasonable working condition combination, meanwhile, redundancy of the training set is avoided, and the problem that the generalization ability of the model is insufficient under a short-series monitoring data scene is solved.

This invention discloses a method for sub-basin delineation in small and medium-sized watersheds based on a distributed model, comprising the following steps: S1, collecting digital elevation model (DEM) data, performing depression filling processing, and extracting preliminary river network information; S2, extracting the actual river system and calculating the elevation difference of the main stream grid; S3, adjusting the DEM data based on the elevation difference to optimize the river network extraction results; S4, calculating watershed morphological parameters and classifying the watershed using a manifold learning method; S5, constructing an optimal threshold calculation model for sub-basins and dynamically adjusting the sub-basin delineation threshold; S6, performing flow direction calculation, confluence analysis, and sub-basin delineation, and optimizing the sub-basin boundaries; S7, validating the method using a distributed hydrological model, analyzing the impact of the delineation results, and optimizing the sub-basin delineation parameters. This invention integrates multi-scale features, dynamic threshold optimization, and hydrological network topology to improve the accuracy of sub-basin delineation, boundary continuity, and hydrological simulation stability, achieving intelligent watershed delineation.

The invention discloses a variable-gradient experimental system for researching production and confluence of an underlying surface of a tailing pond, and belongs to the technical field of hydrological simulation and water and soil conservation experimental equipment. The system comprises an experimental simulation area, a runoff collection area and an observation recording area. The experimental simulation area is provided with six rectangular runoff plots which are arranged in rows, the gradients are respectively 5 degrees, 10 degrees and 15 degrees, and the periphery of each plot is provided with a retaining wall and an isolation structure with a specific height. The artificial rainfall simulation device is fixed through the horizontal iron stand and the vertical iron stand and simulates different rainfall scenes. The runoff collecting area comprises a flow collecting groove and a multi-stage runoff collecting pool and is used for collecting and measuring surface runoff and interflow. The observation recording area is used for installing various sensors. The underlying surface condition of the tailing pond can be accurately simulated, and a reliable physical experiment platform is provided for researching the production and confluence mechanism.

The invention relates to the field of electric digital data processing, in particular to a flood prediction method, device and equipment based on a rain detection radar. Performing quality evaluation and dynamic calibration according to the rain measurement radar data to obtain a calibrated radar precipitation field; according to the calibrated radar precipitation field, in combination with a radar echo motion field, generating a short and temporary quantitative precipitation forecast QPF sequence in the future N hours; driving a preset macroscopic hydrological model and a preset microscopic hydrological model to carry out hydrological simulation respectively to obtain a macroscopic hydrological state variable and a microscopic hydrological state variable; performing real-time exchange and coupling of state variables to obtain a coupled multi-scale model state; according to the multi-scale model state and ground observation data obtained in real time, data assimilation is carried out through a gradient optimization nonlinear least square method, and an updated hydrological forecasting result is obtained; and according to the updated hydrological forecasting result, flood early warning information is generated, and the flood forecasting precision is improved.

The invention provides a Yellow River basin hydrological simulation method and system based on nested model one-way driving, and belongs to the technical field of hydrological simulation. Comprising the steps that an SHUD model serves as a core layer of watershed hydrological cycle simulation, an MIKE hydrodynamic model serves as a river channel hydrodynamic simulation layer, a nested model is constructed through coupling between the two models, and one-way driving is conducted based on the space nested technology; a bidirectional data interface is constructed, data exchange between the SHUD model and the MIKE hydrodynamic model is achieved, meanwhile, a data assimilation algorithm is introduced, historical observation data is utilized to calibrate nested model parameters, and key parameters are optimized; and then simulating the watershed hydrological process based on the optimized nested model. According to the invention, high-precision simulation of the watershed hydrological process can be realized, and scientific guidance is provided for water resource management of the Yellow River watershed.

The invention discloses an evapotranspiration model parameter calibration method for a rice field and a reed wetland, and belongs to the technical field of ecological hydrological simulation. Comprising the following steps: S1, collecting multi-source data of a rice field and a reed wetland observation station, and processing the collected data; s2, performing sensitivity analysis on each parameter of the PML model and the PT-JPL model; s3, dividing growth stages according to different growth periods of the rice and the reed, setting a dynamic parameter optimization range, and calibrating key parameters of the PML model and the PT-JPL model in stages based on multi-year actually measured evapotranspiration data and a Bayesian optimization algorithm; s4, the calibrated optimal parameter combination is substituted into the corresponding PML model or PT-JPL model, and wetland evapotranspiration is simulated; and S5, verifying a model simulation effect through multiple reference indexes, and screening an optimal model and parameter combination. By the adoption of the method, the parameters can be accurately calibrated, the wetland evapotranspiration simulation accuracy and adaptability are improved, and ecological hydrological research is supported.