Simulation method and device of wind, solar and water resources elements based on improved land surface model

Abstract

The present application discloses a method and device for simulating wind, solar and water resource elements based on an improved land surface model. The method includes: integrating the Fitch scheme into the Noah-MP land surface model to optimize the runoff parameterization scheme to obtain an optimized land surface process model; determining the land surface model grid corresponding to each watershed, and determining the area ratio of the land surface model grid in the corresponding watershed; driving the WRF model to obtain the output results of the wind, solar and water resource elements; at each time step of the optimized land surface process model, using the area ratio as a weight, taking the weighted average of the surface runoff and underground runoff output by the optimized land surface process model to obtain the total runoff of the watershed; taking the total runoff of the watershed as the lateral inflow of the corresponding river section, inputting it into the river network confluence model, running the river network confluence model, and simulating the output results of the river hydrological process.

Description

Technical Field

[0001] The present application belongs to the field of water resource treatment technology, and specifically relates to a simulation method and device for wind, solar and 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 the complementarity of wind, solar and hydropower has become a research hotspot. The complementarity of wind, solar and hydropower 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 the complementarity of wind, solar and hydropower, 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 shortcomings when considering the hydrodynamic processes of river networks. The hydrodynamic processes of river networks involve complex hydrological, hydraulic and topographic factors, and require special models to accurately simulate them.

[0003] River network hydrodynamics involve the calculation of key parameters such as flow, water level, and velocity. These parameters are influenced by a variety of factors, including precipitation, evaporation, soil moisture, and topography. Existing numerical weather models typically employ simplified hydrological parameterization schemes, making it difficult to accurately simulate these complex hydrological processes. Existing numerical weather models often lack a complete physical framework for simulating river network hydrodynamics.

[0004] In summary, existing numerical weather models have certain limitations in simulating the complementarity between wind, solar and water, mainly manifested in insufficient consideration of river network hydrodynamic processes, lack of physical mechanisms, and challenges in 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 resource elements based on an improved land surface model, the method comprising:

[0007] The Fitch scheme was 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 optimized land surface model grids and watersheds to 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 subsurface runoff output by the optimized land surface process model are weighted averaged using 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 device for simulating 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 ratio of the land surface model grid in the corresponding watershed;

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

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

[0017] The input module is used to input the total runoff 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 comprising a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the programs 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. 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, which includes a processor and a communication interface, the communication interface and the processor are coupled, and the processor is used to run programs or instructions 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 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 simulation accuracy. Carry out spatial overlay analysis of the land surface model grid and watershed of the optimized land surface process model to 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 and 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 take the weighted average of 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 to accurately simulate the hydrological process of each section of the river, providing support for water resources assessment and water environment analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This 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 This 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 accompanying drawings of the embodiments of the present application to clearly describe the technical solutions of the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0027] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

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

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

[0030] Rainfall marks the beginning of the runoff generation process. The intensity, duration, and spatial distribution of rainfall all influence this process. During rainfall, some water is intercepted by vegetation and the surface, while another portion returns to the atmosphere through evaporation and transpiration. This portion of water does not participate in runoff generation. Surface runoff, the portion of water that is not intercepted or evaporated, forms runoff on the surface and flows to low-lying areas. Surface runoff is influenced by topography, vegetation cover, and surface conditions. Infiltration, some rainwater seeps into soil and rock, replenishing groundwater. The infiltration rate is affected by soil properties and moisture. Intermediate flow, the portion of water that infiltrates the soil, flows along the soil layers and ultimately enters rivers. Subsurface runoff, the portion that ultimately infiltrates into groundwater, is driven by the groundwater level gradient and flows into rivers or lakes as subsurface runoff. Runoff generation 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 capable of simulating complex land-atmosphere interactions. In land surface process simulations, a land surface model grid divides the Earth's surface into a series of regular or irregular grid cells, allowing computation and simulation of physical processes within each grid cell. These grid cells, often referred to as grid points or cells, are the basic units of numerical computation in land surface models.

[0033] Land surface model grids are the foundation of land surface process simulations, involving multiple aspects such as grid division, grid resolution, grid properties, physical process simulation, data assimilation, and model coupling. The accuracy and reliability of land surface model simulations can be improved by rationally selecting grid division methods, optimizing grid resolution, accurately simulating physical processes, and employing data assimilation techniques.

[0034] The WRF (Weather Research and Forecasting Model) is a widely used mesoscale weather forecast model that simulates 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 accuracy of wind speed and irradiance simulations.

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

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

[0037] Confluence refers to the process by which water flows from different rivers or tributaries merge at a confluence point. This process is influenced by factors such as river flow, flow velocity, channel morphology, and riverbed material.

[0038] River network confluence models are mathematical models used to simulate and predict river network confluence processes. These models typically include a hydrological module (for simulating runoff generation) and a hydraulic module (for simulating 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 channel regulation).

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

[0041] The following describes in detail the method for simulating wind, solar and water resources based on the improved land surface model provided in the embodiment of the present application through specific embodiments and their application scenarios in conjunction with the accompanying drawings.

[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 to optimize the runoff parameterization scheme to obtain an optimized land surface process model;

[0045] In order to optimize the runoff simulation capability of the land surface process model and improve 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 runoff parameter combination for the target basin can be found, further improving simulation accuracy and obtaining an optimized land surface process model.

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

[0047] To establish the correspondence between catchments and land surface model grids, laying the foundation for subsequent runoff weighting, the catchment boundaries are extracted from digital elevation model data. Spatially matching and calculating the area with the land surface model grid boundaries, the land surface model grids within each catchment are determined, and the area proportion of each grid within the catchment is determined. This provides the necessary spatial relationship information for runoff weighting calculations.

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

[0049] Wind, solar and water resource element simulation, including:

[0050] Wind resource simulation:

[0051] Wind speed and direction: Analyze the spatiotemporal distribution of wind speed and direction and assess wind resource potential.

[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: Calculates the output power of a photovoltaic power station based on solar radiation and photovoltaic module characteristics.

[0056] Water resource simulation:

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

[0058] Hydropower: Calculate the output power 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 the 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, using the area ratio as a weight, weighted averages the surface runoff and subsurface 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 groundrunoff of each grid are converted to the catchment scale by weighting them by area, so that the distributed land surface simulation results can be matched with the runoff demand at the catchment scale.

[0062] In step 150 , the total runoff of the catchment area is input into a river network confluence model as the lateral inflow of the corresponding river section, and the river network confluence model is run to simulate and obtain output results 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 and river network structure. 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 spatial and temporal distribution of radiation.

[0066] Specifically, the parameters of the runoff process are:

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

[0068] Vegetation parameters: such as leaf area index (LAI), vegetation type, and 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] Radiative spatiotemporal distribution: The spatiotemporal resolution of the radiative transfer model.

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

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

[0076] Obtaining measured data, including soil, vegetation and topographic data of the target watershed;

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

[0078] Calibrate and optimize the key runoff 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, we define the research objectives, optimize the Fitch parameterization scheme in the WRF model, and couple it with the improved land surface model to conduct comprehensive simulations of wind, solar, and water resources. We select a specific region for the study and ensure sufficient observational data for validation and calibration. We then prepare the WRF model to ensure it is 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 accounts for momentum extraction and turbulence generation by wind turbines.

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

[0083] The coupling process involves using the WRF model output as input to the land surface model. The optimized WRF model is coupled with the land surface model to ensure consistency in data transfer and time steps. The coupled model is then run to perform a comprehensive simulation of wind, solar, and water resources.

[0084] The runoff formula and parameters of the Fitch scheme were introduced. The runoff calculation process of the Noah-MP land surface model was then modified to incorporate the Fitch scheme's runoff calculations. A model evaluation metric was defined, and parameter optimization was performed. The three key parameters of the Fitch scheme (smax, ksat, and b) were traversed, total runoff was calculated, and compared with the observed values. The parameter combination that minimized the RMSE was selected as the optimal scheme. Ultimately, the optimal Fitch scheme parameters were obtained, which can be used for runoff calculations in the Noah-MP land surface model.

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

[0086] Determine key physical parameters, such as surface roughness length and atmospheric stability parameters; 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 allows for the optimization of parameter schemes through parameter sensitivity analysis and multi-objective optimization, thereby improving the accuracy and reliability of runoff simulations. This requires complex numerical calculations and extensive observational data.

[0088] By analyzing key runoff process parameters and identifying those that significantly impact simulation accuracy, the Noah-MP model's key parameters were calibrated and optimized based on measured data. The Fitch scheme's algorithms were then integrated into the Noah-MP model to create an optimized model. Parameters were then adjusted to achieve the desired simulation accuracy, resulting in an optimized land surface process model. Calibration and optimization of key Noah-MP parameters improved the model's accuracy and reliability in simulating the actual conditions of the target watershed. This laid the foundation for subsequent spatial matching analysis and runoff calculations.

[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 area boundary of the watershed is extracted using geographic information system software;

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

[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 following steps can be used to perform a spatial overlay analysis of land surface model grids and watersheds, and calculate the area proportion of each watershed corresponding to the land surface model grid within the watershed. 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. It is the basic unit of the hydrological cycle and is crucial for water resource management, flood control, and ecological conservation. The following are the main characteristics and mechanisms of a watershed:

[0097] Catchment boundaries are determined by topography and geology, typically along ridgelines or watersheds. They separate flows from different catchments, ensuring that water from each ultimately flows to the same outlet. Catchment structure, including main river channels, tributaries, and confluences, reflects the distribution and connectivity of rivers and is crucial for hydrological modeling and flood forecasting.

[0098] The hydrological processes in a catchment area are affected by many factors, including meteorological conditions (such as precipitation and evaporation), topographic 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 channel regulation).

[0099] A watershed is the fundamental unit of the hydrological cycle, involving multiple physical mechanisms and influencing factors. Accurately simulating and predicting watershed hydrological processes is crucial for water resource management, flood forecasting, and ecological protection. The accuracy and reliability of watershed hydrological simulations can be improved through methods such as integrating hydrological models, optimizing parameterization schemes, and employing data assimilation techniques.

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

[0101] Ensure that the land model grid data and 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. Consider using parallel computing or optimization algorithms to improve efficiency. By following these steps, you can systematically perform spatial overlay analysis of the land model grid and catchment, and calculate the area contribution of each grid within each catchment, providing basic data for subsequent hydrological simulation and analysis.

[0102] This established a spatial correspondence between the grid cells of the land surface process model and the catchment area of the watershed, providing the necessary spatial support for the subsequent conversion of runoff information from the grid scale to the catchment scale. This spatial matching analysis helps 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. The WRF model parameters include: grid resolution and physical process scheme;

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

[0107] Collect the boundary conditions and initial field data required for the WRF model, typically including global or regional reanalysis data and satellite observation data. Based on the topography, land use, and vegetation cover characteristics of the target watershed, set the WRF model's grid resolution (e.g., 5 km × 5 km) and physical process parameter schemes, such as turbulence, microphysical processes, and land surface processes. Run the WRF model to perform numerical weather forecast simulations and output the required meteorological elements, such as wind, radiation, and precipitation.

[0108] Thus, by running the WRF model, we obtained meteorological forcing data for the target basin, including wind, sunlight, water resources, and other factors. This data provided the necessary input conditions for land surface process simulation and river network simulation, ensuring the consistency of the entire hydrological simulation.

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

[0110] Extract surface runoff and subsurface runoff for each grid cell from the output of the optimized land surface process model;

[0111] Traverse each catchment area, and for each grid cell 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 ground runoff of the catchment;

[0113] The total surface runoff and total subsurface runoff of a catchment are added together to obtain the total runoff generated by the catchment.

[0114] Surface runoff and subsurface runoff are two key processes in the hydrological cycle, describing how precipitation forms runoff on the land surface and underground, respectively. These two processes are crucial for water resource management, flood forecasting, and ecological protection. The following are the main characteristics and mechanisms of surface and subsurface runoff:

[0115] Surface runoff is the process by which precipitation flows along the surface as runoff without being absorbed by the soil or intercepted by vegetation. Surface runoff typically occurs when precipitation intensity exceeds the soil's infiltration capacity.

[0116] Underground runoff refers to the process by which precipitation infiltrates the soil, flows through it, and ultimately replenishes rivers, lakes, and other water bodies. Underground runoff typically occurs when precipitation intensity is lower than the soil's infiltration capacity.

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

[0118] Urban drainage: In urban areas, lateral inflow may include runoff from urban drainage systems (e.g., storm sewers and combined sewer systems). This runoff is often significant during heavy rain events and may lead to urban flooding. Agricultural drainage: In agricultural areas, lateral inflow may include runoff from farmland drainage systems (e.g., drains and sewers). This runoff often carries nutrients and pollutants from agricultural activities, impacting river water quality.

[0119] Extract surface runoff and subsurface runoff for each grid cell from the output of the optimized land surface process model. Traverse each catchment and, for each grid cell within it, weightedly add up the surface runoff and subsurface runoff, based on the grid cell's proportion of the catchment area. Add the weighted surface runoff and subsurface runoff for all grid cells within the catchment to obtain the total runoff for the catchment. Repeat these steps to obtain total runoff information for all catchments.

[0120] The optimized land surface process model's unit runoff information is then weighted and aggregated based on spatial matching relationships to obtain basin-scale total runoff information. This provides accurate inflow boundary conditions for subsequent river network simulations, helping to improve the accuracy of river hydrological process simulations.

[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 runoff 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 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 for the target watershed, including flow and water levels at river sections. By minimizing the deviation between simulation results and observed data, calibrate the parameters of the river network confluence model, such as river geometry, river network topology, and river inflow boundary conditions. The total runoff generated in the catchment area in step 140 is distributed to the corresponding river network units through spatial matching and input into the river network confluence model as lateral inflow. Run the calibrated river network confluence model to simulate the hydrological processes at each river section, including how flow and water levels change over time.

[0127] The above steps result in an optimized land surface process model. Offline land surface process simulation experiments are conducted and the simulation results are used as input for the river network confluence model to calculate the confluence. The following main steps are involved:

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

[0129] Collect and prepare the required meteorological data, topographic data, land cover data, etc. Meteorological data: precipitation, temperature, wind speed, relative humidity, solar radiation, etc. Topographic data: Digital Elevation Model (DEM). Land cover data: vegetation type, land use type. Soil data: soil type, soil hydrological properties.

[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] Runoff calculations involve converting input data, converting the Noah-MP model output into the runoff model input format. It is important to note that surface runoff and subsurface runoff are key inputs to runoff calculations, ensuring that the data format and spatial resolution are consistent with the runoff model requirements.

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

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

[0134] Thus, by calibrating the river network model parameters and using the total catchment runoff generated in the previous steps as lateral inflow, we achieved simulation of the hydrological processes of the target river basin. This provides important support for water resource assessment and water environment analysis, and provides a basis for subsequent water resource 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 simulation accuracy. Carry out spatial overlay analysis of the land surface model grid and watershed of the optimized land surface process model to 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 and 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 take the weighted average of 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 to accurately simulate the hydrological process of each section of the river, providing support for water resources assessment and water environment analysis.

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

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

[0138] 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] Determination module 220, for performing spatial overlay analysis of the land surface model grids and watersheds of the optimized land surface process model, determining the land surface model grid corresponding to each watershed, and determining the area ratio of the land surface model grid in the corresponding watershed;

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

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

[0142] The input module 250 is used to input the total runoff 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 optimal 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 spatial and temporal distribution of radiation.

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

[0146] Obtaining measured data, including soil, vegetation and topographic data of the target watershed;

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

[0148] Calibrate and optimize the key runoff 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 area boundary of the watershed is extracted using geographic information system software;

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

[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. The WRF model parameters include: grid resolution and physical process scheme;

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

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

[0162] Extract surface runoff and subsurface runoff for each grid cell from the output of the optimized land surface process model;

[0163] Traverse each catchment area, and for each grid cell 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 ground runoff of the catchment;

[0165] The total surface runoff and total subsurface runoff of a catchment are added together to obtain the total runoff generated by 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 runoff 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 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 simulation accuracy. Carry out spatial overlay analysis of the land surface model grid and watershed of the optimized land surface process model to 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 and 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 take the weighted average of 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 to accurately simulate the hydrological process of each section of the river, providing 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 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. Where appropriate, the memory 402 may include a removable or non-removable (or fixed) medium. Where appropriate, 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). Where appropriate, 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 figures by reading and executing 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 comprises hardware, software or both, couples the parts of electronic equipment to each other.For example, and not limitation, bus can comprise accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry 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 suitable cases, bus 410 can comprise one or more buses.Although the present application embodiment describes and shows 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 Described 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 understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of 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, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, 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 that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), 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. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0186] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity 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 scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A method for simulating wind, solar and water resources based on an improved land surface model, characterized in that: The method comprises: Obtaining measured data, including soil, vegetation and topographic data of the target watershed; Analyze soil, vegetation, and topographic data of the target watershed to identify key runoff parameters for land surface process simulation; Calibrate and optimize the key runoff 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; Calibrate the fused model and adjust key parameters until the simulation accuracy reaches the preset accuracy to obtain an optimized land surface process model; Conduct spatial overlay analysis of the optimized land surface model grids and watersheds to 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 subsurface runoff output by the optimized land surface process model are weighted averaged using 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 key flow generation process parameters include: Soil parameters, vegetation parameters, terrain parameters, calculation method of shortwave radiation, calculation method of longwave radiation, and calculation method of spatial and temporal distribution of radiation.

3. The method according to claim 1, characterized in that The performing of the spatial overlay analysis of the land surface model grids and the watershed of the optimized land surface process model, determining the land surface model grid corresponding to each watershed, and determining the area proportion of the land surface model 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 area boundary of the watershed is extracted using geographic information system software; The optimized land surface process model grid and the catchment area are overlaid to obtain the intersection area, in which the land surface model grid boundary matches the catchment area boundary. 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.

4. The method according to claim 1, wherein 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. The WRF model parameters include: grid resolution and physical process scheme; Run the WRF model and output the output results of wind, solar and water resources elements.

5. The method according to claim 1, wherein 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 using the area ratio as the weight to obtain the total runoff of the catchment area, including: Extract surface runoff and subsurface runoff for each grid cell from the output of the optimized land surface process model; Traverse each catchment area, and for each grid cell 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 ground runoff of the catchment; The total surface runoff and total subsurface runoff of a catchment are added together to obtain the total runoff generated by the catchment.

6. 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 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, 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 runoff 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 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.

7. A simulation device for wind, solar and water resources based on an improved land surface model, characterized in that: The device comprises: The integration module is used to obtain measured data, including soil, vegetation, and topographic data of the target watershed; analyze the 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 the measured data; integrate the relevant algorithms of the Fitch scheme into the Noah-MP model to obtain a fused model; calibrate the fused model and adjust key parameters until the simulation accuracy reaches the preset accuracy, thereby obtaining 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 ratio of the land surface model grid in the corresponding watershed; The driver module is used to drive the WRF model to obtain the output results of wind, solar and water resources elements, including surface runoff; The weighting module is used to calculate the weighted average of the surface runoff and subsurface runoff output by the optimized land surface process model at each time step of the optimized land surface process model, using the area proportion as the weight, to obtain the total runoff of the catchment area; The input module is used to input the total runoff 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.

8. An electronic device, characterized in that: The electronic device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the simulation method of wind, light and water resources elements based on the improved land surface model as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the 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 6.

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