Simulation method and device for wind, light and water resource elements based on improved land surface mode

Abstract

The invention discloses a method and a device for simulating wind, light and water resource elements based on an improved land surface mode. The method comprises the following steps: fusing a Fitch scheme into a Noah-MP land surface mode, and carrying out runoff production parameterization scheme optimization so as to obtain an optimized land surface process model; determining a land surface mode grid corresponding to each catchment area, and determining an area proportion of the land surface mode grid in the corresponding catchment area; driving the WRF model to obtain an output result of the wind, light and water resource elements; in each time step of operation of the optimized land surface process model, taking an area proportion as a weight, and carrying out weighted average on surface runoff and underground runoff output by the optimized land surface process model to obtain total runoff of the water collection area; and inputting the total runoff of the catchment area as lateral inflow of the corresponding river reach into the river network convergence model, operating the river network convergence model, and simulating to obtain an output result of the river hydrological process.

Description

Technical Field

[0001] The present application belongs to the technical field of water resource treatment, and specifically relates to a simulation method and device for wind and solar water resource elements based on an improved land surface model. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, the simulation and evaluation of wind, solar and hydropower complementarity has become a research hotspot. Wind, solar and hydropower complementarity refers to the complementary characteristics of wind, solar and hydropower resources in time and space. Through reasonable configuration and optimized scheduling, the stability and reliability of the energy system can be improved. However, the existing numerical weather models have certain limitations in simulating wind, solar and hydropower complementarity, which are mainly manifested in the following aspects: Limitations of numerical weather models. Numerical weather models (such as WRF, ECMWF, etc.) are mainly used to simulate atmospheric processes, including meteorological elements such as temperature, humidity, wind speed, and precipitation. These models have high accuracy in simulating wind and solar energy resources, but have obvious deficiencies when considering river network hydrodynamic processes. River network hydrodynamic processes involve complex hydrological, hydraulic and topographic factors, which require special models to accurately simulate.

[0003] The hydrodynamic process of a river network includes the calculation of key parameters such as river flow, water level, and flow velocity, which are affected by multiple factors such as precipitation, evaporation, soil moisture, and topography. Existing numerical weather models usually use simplified hydrological parameterization schemes, which are difficult to accurately simulate these complex hydrological processes. Existing numerical weather models often lack complete physical mechanisms when simulating the hydrodynamic process of a river network.

[0004] In summary, the existing numerical weather models have certain limitations in simulating the complementarity of wind, solar and water, which are mainly manifested in the insufficient consideration of the hydrodynamic processes of the river network, the lack of physical mechanisms, and the challenges of data assimilation. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a method and device for simulating wind, solar, and water resource elements based on an improved land surface model, so as to solve the problem that the current numerical weather model has limitations in simulating the complementarity of wind, solar, and water.

[0006] In a first aspect, an embodiment of the present application provides a method for simulating wind, solar and water resources elements based on an improved land surface model, the method comprising:

[0007] The Fitch scheme is integrated into the Noah-MP land surface model to optimize the runoff parameterization scheme to obtain an optimized land surface process model.

[0008] Conduct spatial overlay analysis of the land surface model grids and watersheds of the optimized land surface process model, determine the land surface model grid corresponding to each watershed, and determine the area proportion of the land surface model grid in the corresponding watershed;

[0009] Drive the WRF model to obtain the output results of wind and solar water resources elements, including surface runoff;

[0010] At each time step of the optimized land surface process model, the surface runoff and underground runoff output by the optimized land surface process model are weighted averaged with the area proportion as the weight to obtain the total runoff of the catchment area.

[0011] The total runoff of the catchment area is taken as the lateral inflow of the corresponding river section and input into the river network confluence model. The river network confluence model is run to simulate the output results of the river hydrological process.

[0012] In a second aspect, an embodiment of the present application provides a simulation device for wind, solar and water resources elements based on an improved land surface model, the device comprising:

[0013] Integration module is used to integrate the Fitch scheme into the Noah-MP land surface model and optimize the runoff parameterization scheme to obtain an optimized land surface process model;

[0014] A determination module is used to carry out spatial overlay analysis of the land surface model grid and the watershed of the optimized land surface process model, determine the land surface model grid corresponding to each watershed, and determine the area proportion of the land surface model grid in the corresponding watershed;

[0015] The driving module is used to drive the WRF model to obtain the output results of wind and solar water resources elements. The output results of wind and solar water resources elements include surface runoff;

[0016] The weighting module is used to take the area ratio as the weight at each time step of the optimized land surface process model to weighted average the surface runoff and underground runoff output by the optimized land surface process model to obtain the total runoff of the catchment area;

[0017] The input module is used to input the total flow of the catchment area as the lateral inflow of the corresponding river section into the river network confluence model, run the river network confluence model, and simulate the output results of the river hydrological process.

[0018] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0019] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0020] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0021] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the method described in the first aspect.

[0022] In the embodiments of the present application, the Fitch scheme is integrated into the Noah-MP land surface model to optimize the runoff parameterization scheme to obtain an optimized land surface process model; the Fitch scheme can more accurately describe the runoff process at the basin scale, and its integration into Noah-MP is conducive to improving the simulation accuracy. Carry out spatial overlay analysis of the land surface model grid and watershed of the optimized land surface process model, determine the land surface model grid corresponding to each watershed, and determine the area ratio of the land surface model grid in the corresponding watershed; drive the WRF model to obtain the output results of wind and solar water resources elements, and the output results of wind and solar water resources elements include surface runoff; surface runoff is a key hydrological element, which can provide a basis for subsequent watershed runoff calculation; at each time step of the optimized land surface process model, use the area ratio as the weight to weight the surface runoff and underground runoff output by the optimized land surface process model to obtain the total runoff of the watershed, so as to realize the accurate conversion of runoff information from grid scale to basin scale; take the total runoff of the watershed as the lateral inflow of the corresponding river section, input it into the river network confluence model, and run the river network confluence model, which can accurately simulate the hydrological process of each section of the river and provide support for water resources assessment and water environment analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a method for simulating wind, solar and water resources elements based on an improved land surface model provided in an embodiment of the present application;

[0024] Figure 2 It is a structural diagram of a simulation device for wind, solar and water resources elements based on an improved land surface model provided in an embodiment of the present application;

[0025] Figure 3 It is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings of the embodiments of the present application to clearly describe the technical solutions of the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present application belong to the scope of protection of this application.

[0027] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0028] The technical terms involved in the embodiments of the present application are explained below.

[0029] Runoff generation refers to the process by which rainfall flows through the surface and soil and eventually enters rivers, lakes or groundwater. This process involves the following main steps:

[0030] Rainfall is the beginning of the runoff process. The intensity, duration and spatial distribution of rainfall will affect the runoff process. Interception and evaporation: During rainfall, part of the water is intercepted by vegetation and the surface, and the other part returns to the atmosphere through evaporation and transpiration. This part of water does not participate in runoff. Surface runoff: Part of the water that is not intercepted or evaporated will form runoff on the surface and flow to low-lying areas. Surface runoff is affected by topography, vegetation cover and surface conditions. Infiltration: Part of the rainwater will infiltrate the soil and rocks to replenish groundwater. The infiltration rate is affected by soil properties and moisture. Intermediate flow: Part of the water that infiltrates the soil flows along the soil layer and eventually enters the river. This is intermediate flow. Underground runoff: The part that eventually infiltrates into the groundwater flows into rivers or lakes in the form of underground runoff driven by the groundwater level gradient. The runoff process is a complex water cycle process that requires comprehensive consideration of multiple factors.

[0031] The Fitch scheme is a physical-based runoff parameterization scheme that can be used in land surface process models. It takes into account complex land surface hydrological processes such as soil moisture and vegetation evapotranspiration.

[0032] Noah-MP is a widely used land surface model that can simulate complex land surface-atmosphere interactions. In land surface process simulation, the land surface model grid refers to the division of the earth's surface into a series of regular or irregular grid cells, so that physical processes can be calculated and simulated in each grid cell. These grid cells are usually called grid points or grid cells, which are the basic units for numerical calculations in land surface models.

[0033] The land surface model grid is the basis of land surface process simulation, involving grid division, grid resolution, grid properties, physical process simulation, data assimilation and model coupling. The accuracy and reliability of land surface model simulation can be improved by rationally selecting grid division methods, optimizing grid resolution, accurately simulating physical processes, and using data assimilation technology.

[0034] The WRF model (Weather Research and Forecasting Model) is a widely used mesoscale weather forecast model that can simulate meteorological elements such as wind speed and irradiance. Through parameter sensitivity analysis and optimization algorithms, the physical parameters of the WRF model can be adjusted to improve the simulation accuracy of wind speed and irradiance.

[0035] The river network confluence model refers to the process of water flow from the source to the estuary in a river system, involving the confluence and flow of multiple rivers and tributaries. This process is of great significance in the fields of hydrological simulation, flood forecasting, water resources management, etc. The following are the main components and related mechanisms of the river network confluence model:

[0036] The river network structure refers to the topological relationship of the river system, including the main river channel, tributaries, confluence points, etc. The river network structure is usually represented by a river network map or river network matrix, which reflects the distribution and connection relationship of the rivers.

[0037] Confluence mechanism refers to the process of water flow merging at the confluence point of different rivers or tributaries. The confluence mechanism is affected by factors such as river flow, flow velocity, river channel morphology, and riverbed material.

[0038] River network confluence models are mathematical models used to simulate and predict river network confluence processes. These models usually include hydrological modules (used to simulate runoff generation) and hydraulic modules (used to simulate runoff routing and confluence mechanisms).

[0039] The river network confluence process is affected by many factors, including meteorological conditions (such as precipitation and evaporation), terrain characteristics (such as slope and riverbed slope), soil properties (such as permeability and soil moisture), vegetation cover (such as vegetation type and root distribution), and human activities (such as reservoir operation and river regulation).

[0040] In response to the problems arising in the related art, the embodiments of the present application provide a method and device for simulating wind, solar, and water resource elements based on an improved land surface model, which can solve the problem in the related art that the current numerical weather model has limitations in simulating the complementarity of wind, solar, and water.

[0041] The simulation method of wind, solar and water resources elements based on the improved land surface model provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings through specific embodiments and their application scenarios.

[0042] Figure 1 A flowchart of a method for simulating wind, solar and water resource elements based on an improved land surface model provided in an embodiment of the present application.

[0043] like Figure 1 As shown, the simulation method of wind, solar and water resource elements based on the improved land surface model may include steps 110 to 150. The method is applied to a simulation device of wind, solar and water resource elements based on the improved land surface model, as shown below:

[0044] Step 110, integrating the Fitch scheme into the Noah-MP land surface model, optimizing the runoff parameterization scheme, and obtaining an optimized land surface process model;

[0045] In order to optimize the runoff simulation capability of the land surface process model and improve the simulation accuracy, the integration of the Fitch scheme can enhance the Noah-MP model's simulation of key runoff processes, such as the impact of soil moisture and vegetation on runoff. Through parameter optimization, the most suitable combination of runoff parameters for the target basin can be found, which can further improve the simulation accuracy and obtain an optimized land surface process model.

[0046] Step 120, performing spatial overlay analysis of the land surface pattern grids and watersheds of the optimized land surface process model, determining the land surface pattern grids corresponding to each watershed, and determining the area proportion of the land surface pattern grids in the corresponding watershed;

[0047] In order to establish the correspondence between the catchment area and the land surface model grid, and lay the foundation for the subsequent runoff weighting, the catchment area boundary is extracted through the digital elevation model data, and spatial matching and area calculation are performed with the land surface model grid boundary to determine which land surface model grids each catchment area contains and the area proportion of each grid in the catchment area, which can provide the necessary spatial relationship information for the runoff weighting calculation.

[0048] Step 130, driving the WRF model to obtain the output results of the wind and solar water resources elements, where the output results of the wind and solar water resources elements include surface runoff;

[0049] Simulation of wind, solar and water resources elements, including:

[0050] Wind resource simulation:

[0051] Wind speed and direction: Analyze the spatial and temporal distribution of wind speed and direction and assess the potential of wind resources.

[0052] Wind power output: Calculate the output power of the wind farm based on wind speed and wind turbine power curve.

[0053] Light resource simulation:

[0054] Solar radiation: Analyze the temporal and spatial distribution of solar radiation and evaluate the potential of light resources.

[0055] Photovoltaic output: Calculate the output power of the photovoltaic power station based on solar radiation and photovoltaic module characteristics.

[0056] Water resources simulation:

[0057] Precipitation and runoff: Analyze the spatial and temporal distribution of precipitation and runoff and assess water resource potential.

[0058] Hydropower: Calculate the power output of a hydropower station based on runoff and reservoir characteristics.

[0059] In order to obtain the meteorological boundary conditions and initial fields required to drive the land surface process model, by setting appropriate WRF model parameters, such as grid resolution and physical process scheme, the wind, solar and water resource elements of the target basin, such as radiation and precipitation, can be obtained. These elements will provide necessary data support for subsequent land surface process simulation and runoff calculation.

[0060] Step 140, at each time step of the optimized land surface process model, taking the area ratio as a weight, weighted average the surface runoff and underground runoff output by the optimized land surface process model to obtain the total runoff of the catchment area;

[0061] In order to convert the runoff results of the refined land surface model grid into the total runoff at the catchment scale, the surface runoff and groundwater runoff of each grid are converted to the catchment scale by area weighting, so that the distributed land surface simulation results can be corresponded to the runoff demand at the catchment scale.

[0062] Step 150, the total runoff of the catchment area is taken as the lateral inflow of the corresponding river section, input into the river network confluence model, and the river network confluence model is run to simulate and obtain the output result of the river hydrological process.

[0063] In order to use the total runoff of the catchment area obtained in the previous steps as input to drive the river network confluence model and simulate the river hydrological process, the simulation accuracy is improved by calibrating the river network model parameters, such as river channel geometry, river network structure, etc., and the runoff of the catchment area is allocated to the corresponding river network units and input into the river network confluence model as lateral inflow. This step can couple the land surface process with the river network process to obtain a comprehensive output result including the river hydrological process.

[0064] The runoff process parameters in the runoff parameterization scheme optimization include:

[0065] Soil parameters, vegetation parameters, terrain parameters, calculation method of shortwave radiation, calculation method of longwave radiation, and calculation method of radiation temporal and spatial distribution.

[0066] Specifically, the flow generation process parameters are:

[0067] Soil parameters: such as soil type, soil moisture content, soil permeability.

[0068] Vegetation parameters: such as leaf area index (LAI), vegetation type, root depth.

[0069] Topographic parameters: such as slope and watershed area.

[0070] Radiative transfer scheme:

[0071] Shortwave radiation: calculation methods for direct radiation and scattered radiation.

[0072] Long-wave radiation: calculation methods of surface radiation and sky radiation.

[0073] Spatial and temporal distribution of radiation: The spatial and temporal resolution of the radiative transfer model.

[0074] Collect and process input data, including meteorological data, soil data, vegetation data, and terrain data.

[0075] In a possible embodiment, step 110 includes:

[0076] Obtain measured data, including soil, vegetation and terrain data of the target watershed;

[0077] Analyze soil, vegetation and topographic data of the target watershed to identify key runoff process parameters for land surface process simulation;

[0078] Calibrate and optimize the key runoff process parameters of the Noah-MP model based on measured data;

[0079] Integrate the relevant algorithms of the Fitch scheme into the Noah-MP model to obtain the fused model;

[0080] The fused model is calibrated and key parameters are adjusted until the simulation accuracy reaches the preset accuracy to obtain an optimized land surface process model.

[0081] First, determine the research objectives, optimize the Fitch parameterization scheme in the WRF model, and couple it with the improved land surface model to conduct a comprehensive simulation of wind energy, solar energy and water resources. Select a specific area for research and ensure that there are sufficient observational data for verification and calibration. WRF model preparation, ensure that the WRF model has been correctly installed and configured. Input data: Prepare meteorological data to drive the WRF model. The Fitch parameterization scheme is used to simulate the impact of wind farms on the atmospheric boundary layer. It takes into account the momentum suction and turbulence generation of wind turbines.

[0082] Adjust key parameters in the Fitch parameterization scheme (such as wind turbine height, power curve, momentum extraction coefficient, etc.) to improve simulation accuracy. The optimization process includes: Run the WRF model using the default Fitch parameterization scheme to generate benchmark simulation results. Parameter sensitivity analysis: By adjusting different parameters, analyze their impact on the simulation results and determine sensitive parameters. Use optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to optimize sensitive parameters. Run the optimized WRF model to generate optimized simulation results.

[0083] The coupling process includes: using the output of the WRF model as the input of the land surface model. Coupling the optimized WRF model with the land surface model to ensure the consistency of data transfer and time step. Running the coupling model to conduct a comprehensive simulation of wind, solar and water resources.

[0084] The runoff formula and parameters of the Fitch scheme were introduced. Then the runoff calculation process of the Noah-MP land surface model was modified to integrate the runoff calculation of the Fitch scheme. A model evaluation index was defined and parameter optimization was performed. The three key parameters of the Fitch scheme (smax, ksat and b) were traversed, the total runoff was calculated, and compared with the observed values, and the parameter combination with the smallest RMSE was selected as the optimal scheme. Finally, the optimal Fitch scheme parameters were obtained, which can be used for runoff calculation of the Noah-MP land surface model.

[0085] The Fitch parameterization scheme is a runoff model based on physical processes, which can provide WRF with more accurate surface boundary conditions, thereby improving the simulation of wind speed and irradiance. Specific preferred methods may include:

[0086] Determine key physical parameters, such as surface roughness length, atmospheric stability parameters, etc.; construct objective functions, such as minimizing simulation errors of wind speed and irradiance; use optimization methods such as sensitivity analysis, genetic algorithms, and machine learning to find the optimal parameter combination; use observational data to verify simulation results and continuously iterate and optimize.

[0087] Integrating the Fitch scheme into the Noah-MP model can optimize the parameter scheme through parameter sensitivity analysis, multi-objective optimization and other methods to improve the accuracy and reliability of runoff simulation. This requires complex numerical calculations and a large amount of observational data support.

[0088] Therefore, the key parameters of the runoff process are analyzed, the parameters that have a greater impact on the simulation accuracy are identified, and the key parameters of the Noah-MP model are calibrated and optimized based on the measured data. The relevant algorithms of the Fitch scheme are integrated into the Noah-MP model to obtain the optimized model, and the parameters are adjusted to make the simulation accuracy meet the preset requirements to obtain the optimized land surface process model. The key parameters of Noah-MP are calibrated and optimized to improve the simulation accuracy and reliability of the model for the actual conditions of the target basin. This lays the foundation for the subsequent spatial matching analysis and runoff calculation.

[0089] In a possible embodiment, step 120 includes:

[0090] Obtain digital elevation model data of the target watershed;

[0091] Based on the digital elevation model data of the target watershed, the catchment boundaries of the watershed were extracted using geographic information system software;

[0092] The land surface model grid of the optimized land surface process model and the catchment area are overlaid and analyzed to obtain the intersection area, in which the boundary of the land surface model grid matches the boundary of the catchment area;

[0093] Determine the land surface model grid corresponding to each catchment area based on the intersection area;

[0094] The area proportion of the land surface model grid in the corresponding catchment area is determined based on the intersection area.

[0095] The spatial overlay analysis of land surface model grids and watersheds and the calculation of the area proportion of each watershed to the land surface model grid can be achieved through the following steps. This process involves geographic information system (GIS) technology and programming tools.

[0096] A watershed is a geographical area where surface water and groundwater flow to the same outlet. A watershed is the basic unit of the hydrological cycle and is of great significance for water resource management, flood control, ecological protection, etc. The following are the main characteristics and related mechanisms of a watershed:

[0097] The catchment boundary is determined by the topography and geological structure, usually along the ridgeline or watershed. The catchment boundary separates the water flow from different catchments, ensuring that the water flow from each catchment eventually flows to the same outlet. The catchment structure includes the main river channel, tributaries, confluence points, etc. The catchment structure reflects the distribution and connection of rivers, which is crucial for hydrological simulation and flood forecasting.

[0098] The hydrological processes in a catchment area are affected by many factors, including meteorological conditions (such as precipitation and evaporation), terrain characteristics (such as slope and riverbed slope), soil properties (such as permeability and soil moisture), vegetation cover (such as vegetation type and root distribution), and human activities (such as reservoir operation and river regulation).

[0099] The catchment is the basic unit of the hydrological cycle, involving multiple physical mechanisms and influencing factors. Accurate simulation and prediction of the hydrological process in the catchment is of great significance for water resources management, flood forecasting, ecological protection, etc. The accuracy and reliability of the simulation of the hydrological process in the catchment can be improved by integrating hydrological models, optimizing parameterization schemes, and using data assimilation technology.

[0100] Thus, we can understand the spatial distribution and area proportion of each grid in each catchment area. These area proportion data can be used for subsequent hydrological model calculations, such as weighted average precipitation, evapotranspiration, etc.

[0101] Ensure that the land surface model grid data and the catchment data use the same projection coordinate system to avoid errors in spatial overlay analysis. High-resolution grid data and detailed catchment boundary data can improve the accuracy of the analysis. For large areas and high-resolution data, the calculation process may be time-consuming, and you can consider using parallel computing or optimization algorithms to improve efficiency. Through the above steps, you can systematically perform spatial overlay analysis of land surface model grids and catchments, and calculate the area share of each grid in each catchment, providing basic data for subsequent hydrological simulation and analysis.

[0102] Thus, the spatial correspondence between the grid unit of the land surface process model and the watershed catchment area was established, which provided the necessary spatial support for the subsequent conversion of runoff information from the grid scale to the catchment scale. This spatial matching analysis helps to improve the physical significance and interpretability of the simulation results.

[0103] In a possible embodiment, step 130 includes:

[0104] Collect boundary conditions and initial field data required for the WRF model;

[0105] According to the characteristics of the target watershed, the WRF model parameters are set, including: grid resolution and physical process scheme;

[0106] Run the WRF model and output the results of wind, solar and water resources elements.

[0107] Collect the boundary conditions and initial field data required by the WRF model, usually including global or regional reanalysis data, satellite observation data, etc. According to the characteristics of the target basin's topography, land use, vegetation cover, etc., set the WRF model's grid resolution (such as 5km×5km) and physical process parameter schemes, such as turbulence scheme, microphysical process scheme, land surface process scheme, etc. Run the WRF model to simulate numerical weather forecasts and output the required meteorological elements such as wind, radiation, and precipitation.

[0108] Therefore, by running the WRF model, the meteorological forcing data of the target basin were obtained, including wind, light, water resources and other factors. These data provided the necessary input conditions for land surface process simulation and river network simulation, ensuring the coherence of the entire hydrological simulation.

[0109] In a possible embodiment, step 140 includes:

[0110] From the output of the optimized land surface process model, the surface runoff and subsurface runoff of each grid cell are extracted;

[0111] Traverse each catchment area, and for each grid unit in the catchment area, multiply the surface runoff and underground runoff of the grid by the area proportion to obtain the weighted runoff value;

[0112] The weighted runoff values ​​of all grid cells in the catchment are accumulated to obtain the total surface runoff and total groundwater runoff of the catchment;

[0113] The total surface runoff and total ground runoff of a catchment are added together to obtain the total runoff generation of the catchment.

[0114] Surface runoff and underground runoff are two key processes in the hydrological cycle, which describe the process of precipitation forming runoff on the land surface and underground respectively. These two processes are of great significance for water resources management, flood forecasting, ecological protection, etc. The following are the main characteristics and related mechanisms of surface runoff and underground runoff:

[0115] Surface runoff refers to the process in which precipitation flows along the surface to form runoff without being absorbed by the soil or intercepted by vegetation. Surface runoff usually occurs when the precipitation intensity exceeds the soil infiltration capacity.

[0116] Underground runoff refers to the process in which precipitation infiltrates into the soil, flows through the soil, and eventually replenishes rivers, lakes, and other water bodies. Underground runoff usually occurs when the precipitation intensity is lower than the soil infiltration capacity.

[0117] Lateral inflow refers to the process in which water from lateral tributaries or groundwater flows into the main river channel in a river or channel. This process is of great significance for hydrological simulation, flood forecasting, water resources management, etc. The following are the main characteristics and related mechanisms of lateral inflow:

[0118] Urban drainage, in urban areas, lateral inflows may include runoff from urban drainage systems (such as storm sewers, combined sewer systems). These runoffs are usually more significant during heavy rain events and may cause urban flooding. Agricultural drainage, in agricultural areas, lateral inflows may include runoff from farmland drainage systems (such as drainage ditches, drainage pipes). These runoffs usually carry nutrients and pollutants generated by agricultural activities, affecting the water quality of the river.

[0119] From the output of the optimized land surface process model, extract the surface runoff and underground runoff of each grid cell. Traverse each catchment area, and for each grid cell in the catchment area, weightedly accumulate its surface runoff and underground runoff according to the area proportion of the grid cell in the catchment area. Add the weighted surface runoff and underground runoff of all grid cells in the catchment area to obtain the total runoff of the catchment area. Repeat the above steps to obtain the total runoff information of all catchments.

[0120] Therefore, the unit runoff information of the optimized land surface process model is weighted and summarized according to the spatial matching relationship to obtain the total runoff information at the basin scale. This provides accurate inflow boundary conditions for subsequent river network simulation and helps to improve the accuracy of river hydrological process simulation.

[0121] In a possible embodiment, step 150 includes:

[0122] Collect hydrological observation data of the target basin;

[0123] Calibrate the parameters of the river network confluence model by minimizing the deviation between the simulation results and the observed data; the parameters of the river network confluence model include: river channel geometry, river network structure and river inflow boundary conditions;

[0124] The total flow of the catchment area is distributed to the corresponding river network units by spatial matching, and is input into the river network confluence model as the lateral inflow of the corresponding river section;

[0125] Run the river network confluence model to simulate and obtain the output results of the river hydrological process. The output results of the river hydrological process include: the hydrological process of each section of the river, the flow of the main river and the change of water level over time.

[0126] Collect hydrological observation data of the target basin, including flow and water level of the river section. Calibrate the parameters of the river network confluence model, such as river geometry, river network topology, river inflow boundary conditions, etc., by minimizing the deviation between the simulation results and the observation data. The total flow of the catchment area obtained in step 140 is distributed to the corresponding river network units by spatial matching and input into the river network confluence model as lateral inflow. Run the calibrated river network confluence model to simulate the hydrological process of each section of the river, including the change process of flow and water level over time.

[0127] The above steps obtain an optimized land surface process model, conduct offline land surface process simulation experiments, and use the simulation results as input to the river network confluence model for confluence calculation, which involves the following main steps:

[0128] Model preparation: Configure and initialize the Noah-MP model and river network confluence model.

[0129] Collect and prepare the required meteorological data, terrain data, land cover data, etc. Meteorological data: precipitation, temperature, wind speed, relative humidity, solar radiation, etc. Terrain data: digital elevation model (DEM). Land cover data: vegetation type, land use type. Soil data: soil type, soil hydrological characteristics.

[0130] Configure the parameters and input files of the land surface process model; configure the parameters and input files of the river network confluence model; run the Noah-MP model; set the initial conditions and time step; run the Noah-MP model to simulate the land surface process and generate output data such as soil moisture, evapotranspiration, and surface runoff.

[0131] Confluence calculation includes: input data conversion, converting the output results of the Noah-MP model into the input format of the confluence model. It should be noted that surface runoff and groundwater runoff are key inputs for confluence calculation, ensuring that the data format and spatial resolution are consistent with the requirements of the confluence model.

[0132] Run the confluence model, including: setting up the river network, initial flow and boundary conditions, running the confluence model, and calculating the flow changes and flood peak propagation within the river network.

[0133] The results analysis includes: analyzing the total runoff, peak flow and its temporal and spatial distribution; analyzing the flow changes within the river network to determine the peak arrival time and flow size.

[0134] Therefore, by calibrating the parameters of the river network model and using the total flow of the catchment area obtained in the above steps as the lateral inflow, the simulation of the hydrological process of the target river basin is realized. This provides important support for water resources assessment, water environment analysis, etc., and provides a basis for subsequent water resources management and planning decisions.

[0135] In the embodiments of the present application, the Fitch scheme is integrated into the Noah-MP land surface model to optimize the runoff parameterization scheme to obtain an optimized land surface process model; the Fitch scheme can more accurately describe the runoff process at the basin scale, and its integration into Noah-MP is conducive to improving the simulation accuracy. Carry out spatial overlay analysis of the land surface model grid and watershed of the optimized land surface process model, determine the land surface model grid corresponding to each watershed, and determine the area ratio of the land surface model grid in the corresponding watershed; drive the WRF model to obtain the output results of wind and solar water resources elements, and the output results of wind and solar water resources elements include surface runoff; surface runoff is a key hydrological element, which can provide a basis for subsequent watershed runoff calculation; at each time step of the optimized land surface process model, use the area ratio as the weight to weight the surface runoff and underground runoff output by the optimized land surface process model to obtain the total runoff of the watershed, so as to realize the accurate conversion of runoff information from grid scale to basin scale; take the total runoff of the watershed as the lateral inflow of the corresponding river section, input it into the river network confluence model, and run the river network confluence model, which can accurately simulate the hydrological process of each section of the river and provide support for water resources assessment and water environment analysis.

[0136] The simulation method of wind-solar water resource elements based on the improved land surface model provided in the embodiment of the present application can be executed by a simulation device of wind-solar water resource elements based on the improved land surface model. In the embodiment of the present application, the simulation method of wind-solar water resource elements based on the improved land surface model is executed by a simulation device of wind-solar water resource elements based on the improved land surface model as an example to illustrate the simulation device of wind-solar water resource elements based on the improved land surface model provided in the embodiment of the present application.

[0137] Figure 2 200 is a block diagram of a simulation device for wind, solar and water resources elements based on an improved land surface model provided in an embodiment of the present application. The device 200 includes:

[0138] The integration module 210 is used to integrate the Fitch scheme into the Noah-MP land surface model to optimize the runoff parameterization scheme to obtain an optimized land surface process model;

[0139] A determination module 220 is used to perform spatial overlay analysis of the land surface pattern grid and the watershed of the optimized land surface process model, determine the land surface pattern grid corresponding to each watershed, and determine the area proportion of the land surface pattern grid in the corresponding watershed;

[0140] A driving module 230 is used to drive the WRF model to obtain the output results of wind and solar water resources elements, and the output results of wind and solar water resources elements include surface runoff;

[0141] The weighting module 240 is used to take the area ratio as a weight at each time step of the optimized land surface process model to perform a weighted average of the surface runoff and underground runoff output by the optimized land surface process model to obtain the total runoff of the catchment area;

[0142] The input module 250 is used to input the total flow of the catchment area as the lateral inflow of the corresponding river section into the river network confluence model, run the river network confluence model, and simulate the output results of the river hydrological process.

[0143] In a possible embodiment, the flow generation process parameters in the flow generation parameterization scheme include:

[0144] Soil parameters, vegetation parameters, terrain parameters, calculation method of shortwave radiation, calculation method of longwave radiation, and calculation method of radiation temporal and spatial distribution.

[0145] In a possible embodiment, the integration module 210 is specifically configured to:

[0146] Obtain measured data, including soil, vegetation and terrain data of the target watershed;

[0147] Analyze soil, vegetation and topographic data of the target watershed to identify key runoff process parameters for land surface process simulation;

[0148] Calibrate and optimize the key runoff process parameters of the Noah-MP model based on measured data;

[0149] Integrate the relevant algorithms of the Fitch scheme into the Noah-MP model to obtain the fused model;

[0150] The fused model is calibrated and key parameters are adjusted until the simulation accuracy reaches the preset accuracy to obtain an optimized land surface process model.

[0151] In a possible embodiment, the determination module 220 is specifically configured to:

[0152] Obtain digital elevation model data of the target watershed;

[0153] Based on the digital elevation model data of the target watershed, the catchment boundaries of the watershed were extracted using geographic information system software;

[0154] The land surface model grid of the optimized land surface process model and the catchment area are overlaid and analyzed to obtain the intersection area, in which the boundary of the land surface model grid matches the boundary of the catchment area;

[0155] Determine the land surface model grid corresponding to each catchment area based on the intersection area;

[0156] The area proportion of the land surface model grid in the corresponding catchment area is determined based on the intersection area.

[0157] In a possible embodiment, the driving module 230 is specifically configured to:

[0158] Collect boundary conditions and initial field data required for the WRF model;

[0159] According to the characteristics of the target watershed, the WRF model parameters are set, including: grid resolution and physical process scheme;

[0160] Run the WRF model and output the results of wind, solar and water resources elements.

[0161] In a possible embodiment, the weighting module 240 is specifically configured to:

[0162] From the output of the optimized land surface process model, the surface runoff and subsurface runoff of each grid cell are extracted;

[0163] Traverse each catchment area, and for each grid unit in the catchment area, multiply the surface runoff and underground runoff of the grid by the area proportion to obtain the weighted runoff value;

[0164] The weighted runoff values ​​of all grid cells in the catchment are accumulated to obtain the total surface runoff and total groundwater runoff of the catchment;

[0165] The total surface runoff and total ground runoff of a catchment are added together to obtain the total runoff generation of the catchment.

[0166] In a possible embodiment, the input module 250 is specifically configured to:

[0167] Collect hydrological observation data of the target basin;

[0168] Calibrate the parameters of the river network confluence model by minimizing the deviation between the simulation results and the observed data; the parameters of the river network confluence model include: river channel geometry, river network structure and river inflow boundary conditions;

[0169] The total flow of the catchment area is distributed to the corresponding river network units by spatial matching, and is input into the river network confluence model as the lateral inflow of the corresponding river section;

[0170] Run the river network confluence model to simulate and obtain the output results of the river hydrological process. The output results of the river hydrological process include: the hydrological process of each section of the river, the flow of the main river and the change of water level over time.

[0171] In the embodiments of the present application, the Fitch scheme is integrated into the Noah-MP land surface model to optimize the runoff parameterization scheme to obtain an optimized land surface process model; the Fitch scheme can more accurately describe the runoff process at the basin scale, and its integration into Noah-MP is conducive to improving the simulation accuracy. Carry out spatial overlay analysis of the land surface model grid and watershed of the optimized land surface process model, determine the land surface model grid corresponding to each watershed, and determine the area ratio of the land surface model grid in the corresponding watershed; drive the WRF model to obtain the output results of wind and solar water resources elements, and the output results of wind and solar water resources elements include surface runoff; surface runoff is a key hydrological element, which can provide a basis for subsequent watershed runoff calculation; at each time step of the optimized land surface process model, use the area ratio as the weight to weight the surface runoff and underground runoff output by the optimized land surface process model to obtain the total runoff of the watershed, so as to realize the accurate conversion of runoff information from grid scale to basin scale; take the total runoff of the watershed as the lateral inflow of the corresponding river section, input it into the river network confluence model, and run the river network confluence model, which can accurately simulate the hydrological process of each section of the river and provide support for water resources assessment and water environment analysis.

[0172] The device provided in the embodiment of the present application can implement each process implemented in the above method embodiment. To avoid repetition, it will not be described again here.

[0173] Optionally, Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0174] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0175] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0176] The memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 402 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 402 is a non-volatile solid-state memory. In a specific embodiment, the memory 402 includes a read-only memory (ROM). In appropriate cases, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM) or a flash memory or a combination of two or more of these.

[0177] The processor 401 implements any one of the methods in the embodiments shown in the figure by reading and executing the computer program instructions stored in the memory 402.

[0178] In one example, the electronic device may further include a communication interface 404 and a bus 410. Figure 3 As shown, the processor 401, the memory 402, and the communication interface 404 are connected via a bus 410 and communicate with each other.

[0179] The communication interface 404 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0180] Bus 410 includes hardware, software or both, and the parts of electronic equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0181] The electronic device can execute the method in the embodiment of the present application, thereby realizing the combination Figure 1 Describe the method.

[0182] In addition, in combination with the method in the above embodiment, the embodiment of the present application can provide a computer-readable storage medium to implement. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the Figure 1 method.

[0183] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0184] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0185] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0186] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for simulating wind, solar and water resources elements based on an improved land surface model, characterized in that: The method comprises: The Fitch scheme is integrated into the Noah-MP land surface model to optimize the runoff parameterization scheme to obtain an optimized land surface process model. Conduct spatial overlay analysis of the land surface model grids and watersheds of the optimized land surface process model, determine the land surface model grid corresponding to each watershed, and determine the area proportion of the land surface model grid in the corresponding watershed; Drive the WRF model to obtain the output results of wind and solar water resources elements, including surface runoff; At each time step of the optimized land surface process model, the surface runoff and underground runoff output by the optimized land surface process model are weighted averaged with the area proportion as the weight to obtain the total runoff of the catchment area. The total runoff of the catchment area is taken as the lateral inflow of the corresponding river section and input into the river network confluence model. The river network confluence model is run to simulate the output results of the river hydrological process.

2. The method according to claim 1, characterized in that The flow generation process parameters in the preferred flow generation parameterization scheme include: Soil parameters, vegetation parameters, terrain parameters, calculation method of shortwave radiation, calculation method of longwave radiation, and calculation method of radiation temporal and spatial distribution.

3. The method according to claim 1, characterized in that The method of integrating the Fitch scheme into the Noah-MP land surface model and optimizing the runoff parameterization scheme to obtain an optimized land surface process model includes: Obtain measured data, including soil, vegetation and terrain data of the target watershed; Analyze soil, vegetation and topographic data of the target watershed to identify key runoff process parameters for land surface process simulation; Calibrate and optimize the key runoff process parameters of the Noah-MP model based on measured data; Integrate the relevant algorithms of the Fitch scheme into the Noah-MP model to obtain the fused model; The fused model is calibrated and key parameters are adjusted until the simulation accuracy reaches the preset accuracy to obtain an optimized land surface process model.

4. The method according to claim 1, characterized in that: The performing of the spatial overlay analysis of the land surface pattern grid and the watershed of the optimized land surface process model, determining the land surface pattern grid corresponding to each watershed, and determining the area proportion of the land surface pattern grid in the corresponding watershed, includes: Obtain digital elevation model data of the target watershed; Based on the digital elevation model data of the target watershed, the catchment boundaries of the watershed were extracted using geographic information system software; The land surface model grid of the optimized land surface process model and the catchment area are overlaid and analyzed to obtain the intersection area, in which the boundary of the land surface model grid matches the boundary of the catchment area; Determine the land surface model grid corresponding to each catchment area based on the intersection area; The area proportion of the land surface model grid in the corresponding catchment area is determined based on the intersection area.

5. The method according to claim 1, characterized in that The WRF model is driven to obtain the output results of wind, solar and water resources elements, including: Collect boundary conditions and initial field data required for the WRF model; According to the characteristics of the target watershed, the WRF model parameters are set, including: grid resolution and physical process scheme; Run the WRF model and output the results of wind, solar and water resources elements.

6. The method according to claim 1, characterized in that At each time step of the optimized land surface process model, the surface runoff and underground runoff output by the optimized land surface process model are weighted averaged with the area ratio as the weight to obtain the total runoff of the catchment area, including: From the output of the optimized land surface process model, the surface runoff and subsurface runoff of each grid cell are extracted; Traverse each catchment area, and for each grid unit in the catchment area, multiply the surface runoff and underground runoff of the grid by the area proportion to obtain the weighted runoff value; The weighted runoff values ​​of all grid cells in the catchment are accumulated to obtain the total surface runoff and total groundwater runoff of the catchment; The total surface runoff and total ground runoff of a catchment are added together to obtain the total runoff generation of the catchment.

7. The method according to claim 1, characterized in that The total runoff of the catchment area is used as the lateral inflow of the corresponding river section, input into the river network confluence model, and the river network confluence model is run to simulate the output results of the river hydrological process, including: Collect hydrological observation data of the target basin; Calibrate the parameters of the river network confluence model by minimizing the deviation between the simulation results and the observed data; the parameters of the river network confluence model include: river channel geometry, river network structure and river inflow boundary conditions; The total flow of the catchment area is distributed to the corresponding river network units by spatial matching, and is input into the river network confluence model as the lateral inflow of the corresponding river section; Run the river network confluence model to simulate and obtain the output results of the river hydrological process. The output results of the river hydrological process include: the hydrological process of each section of the river, the flow of the main river and the change of water level over time.

8. A simulation device for wind, solar and water resources elements based on an improved land surface model, characterized in that: The device comprises: Integration module is used to integrate the Fitch scheme into the Noah-MP land surface model and optimize the runoff parameterization scheme to obtain an optimized land surface process model; A determination module is used to carry out spatial overlay analysis of the land surface model grid and the watershed of the optimized land surface process model, determine the land surface model grid corresponding to each watershed, and determine the area proportion of the land surface model grid in the corresponding watershed; The driving module is used to drive the WRF model to obtain the output results of wind and solar water resources elements. The output results of wind and solar water resources elements include surface runoff; The weighting module is used to take the area ratio as the weight at each time step of the optimized land surface process model to weighted average the surface runoff and underground runoff output by the optimized land surface process model to obtain the total runoff of the catchment area; The input module is used to input the total flow of the catchment area as the lateral inflow of the corresponding river section into the river network confluence model, run the river network confluence model, and simulate the output results of the river hydrological process.

9. The device according to claim 8, characterized in that The integration module is specifically used for: Obtain measured data, including soil, vegetation and terrain data of the target watershed; Analyze soil, vegetation and topographic data of the target watershed to identify key runoff process parameters for land surface process simulation; Calibrate and optimize the key runoff process parameters of the Noah-MP model based on measured data; Integrate the relevant algorithms of the Fitch scheme into the Noah-MP model to obtain the fused model; The fused model is calibrated and key parameters are adjusted until the simulation accuracy reaches the preset accuracy to obtain an optimized land surface process model.

10. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the simulation method of wind, light and water resources elements based on the improved land surface model as described in any one of claims 1-7 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement a method for simulating wind, solar and water resources elements based on an improved land surface model as described in any one of claims 1 to 7.

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