A data-based multi-form feature runoff simulation method, system and medium

Abstract

The application discloses a kind of based on data's multi-form feature runoff simulation method, by carrying out data preprocessing, the method for establishing model, establishing comprehensive objective function, optimizing model and testing model runoff simulation effect, obtains basin data information, carries out data preprocessing;Runoff is simulated based on model;Different indexes are used to characterize the form feature of runoff, and a comprehensive objective function is constructed in combination with the form feature index of runoff;Model parameters library is established by optimizing the model using algorithm;The effect of runoff simulation of the model is tested, and after meeting the requirements, it can be used for different form feature runoff data interpolation extension or runoff prediction in the basin, considering the different form features of runoff, so that the model can more accurately simulate the runoff of different form features.

Description

Technical Field

[0001] This invention relates to the field of hydrology and water resources technology, specifically to a data-based multi-morphological runoff simulation method, system, and storage medium. Background Technology

[0002] Runoff simulation is one of the key and challenging areas in hydrology. Improving the accuracy of runoff simulation based on effective simulation methods is of great significance for reservoir operation, water resources planning, and flood emergency management. Because water resources systems are influenced by numerous factors, runoff sequences exhibit multi-timescale variation and multi-morphological stability characteristics, which complicates runoff sequence simulation. Especially against the backdrop of global climate change and increasingly intensified human activities, runoff sequences show greater volatility, necessitating greater emphasis on the identification and analysis of various runoff morphological characteristics to deepen our understanding of watershed runoff characteristics. Currently, there are many runoff models available, but many scholars focus more on the accuracy of the entire runoff process during model building, often neglecting the identification of runoff morphological characteristics. This results in less than ideal simulation performance for runoff with different morphological features.

[0003] The patent with patent number "CN202110258365.4" entitled "A Runoff Simulation Method and System Based on SOM-BPNN Model" includes the following steps: Step 1) Multi-source data acquisition and processing: Collect and download hydrological station flow data, meteorological station meteorological factor data, and related remote sensing data within the study area; the meteorological factors include rainfall, temperature, sunshine duration, relative humidity, and wind speed; the remote sensing products include evapotranspiration and soil moisture data; perform outlier processing and missing value imputation on the collected data; Step 2) Key influencing factor screening: Based on the random forest algorithm, screen out the simulation prediction variables (runoff) from the collected dataset. The key influencing factors are identified, and the model input sample dataset is obtained. Step 3) SOM neural network clustering model construction: A clustering model is constructed based on the Self-Organizing Map (SOM) model to cluster the sample dataset of the study area obtained in step 2) into subsample datasets that can represent different runoff characteristics. The input of the model is the key influencing factors, and the output of the model is the category of the influencing factors. Step 4) SOM-BPNN hybrid neural network model construction: The multiple subsample sets obtained in step 3) are trained separately based on the Backpropagation Neural Network (BPNN) model to obtain the number of layers and neurons of the BPNN model for each subsample, and a runoff simulation model corresponding to each subsample is constructed. The input of the hybrid model is the key influencing factors and type, and the output is the runoff volume. Step 5) Runoff simulation: Runoff simulation is performed based on the model constructed in step 4). When the runoff simulation accuracy of the watershed does not meet the preset accuracy, steps 3) and 4) are repeated to retrain the model until the model accuracy reaches the preset accuracy. However, the model generated by this method has a simple structure, is not suitable for various forms of runoff, cannot accurately predict the form of runoff, and cannot achieve the expected simulation effect. Summary of the Invention

[0004] The purpose of this invention is to provide a data-based multi-morphological runoff simulation method, system, and storage medium, which solves the problem that the constructed model does not perform well in simulating runoff with different morphological characteristics, and provides a method, system, and storage medium with smaller and more accurate simulation errors for runoff with different morphological characteristics.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: a data-based multi-morphological runoff simulation method, system, and storage medium, comprising the following steps:

[0006] S110. Perform data preprocessing to obtain watershed data within the region, including meteorological data, runoff data, and spatial attribute data.

[0007] S120. Establish a model. The model is established based on meteorological data, runoff data, and spatial attribute data. The model selects different attribute indicators according to actual engineering needs to describe the morphological characteristics of runoff under different working conditions, and obtains sequence data of measured and simulated values ​​of different indicators.

[0008] S130. Establish a comprehensive objective function, specifically, based on the above model and data, select model parameters related to runoff and determine the range of parameter values;

[0009] S140. Optimize the model and build a model parameter library. Specifically, optimize one or more morphological runoff models and build a model parameter library.

[0010] S150. Verify the model's runoff simulation effect. Once the simulation effect meets the requirements, it can be used for interpolation and extension of runoff data with different morphological characteristics in the watershed or for runoff forecasting.

[0011] In the aforementioned data-based multi-morphological runoff simulation method, in step S110, the meteorological data and spatial attribute data are measured data from meteorological stations. When measured data from meteorological stations is scarce, satellite meteorological data or reanalysis methods can be used to supplement meteorological data. Runoff data can be measured data from hydrological stations, and spatial attribute data includes spatial attribute data required for modeling, such as hydrological station location, DEM, land use data, and soil data.

[0012] In the aforementioned data-based multi-morphological runoff simulation method, in step S120, when establishing the model, the data of the model based on meteorological data, runoff data and spatial attribute data are first processed into the input format required by the runoff model, and then a watershed hydrological, hydrodynamic model or artificial neural network model is constructed to simulate the flow at the watershed outlet section.

[0013] In the aforementioned data-based multi-morphological runoff simulation method, step S120 uses different indicators to characterize the morphological characteristics of runoff. Different attribute indicators can be selected according to actual engineering needs to describe the morphological characteristics of runoff under different working conditions, and sequence data of measured and simulated values ​​of different indicators are obtained.

[0014] In the aforementioned data-based multi-morphological runoff simulation method, step S130 involves selecting watershed and runoff-related data within the region, analyzing and processing the data, and determining the parameter range in the model.

[0015] In the aforementioned data-based multi-morphological runoff simulation method, step S130 involves constructing an objective function for model optimization based on the sequence data of measured and simulated values ​​of runoff with different morphological characteristics within the region. The specific formula is as follows:

[0016]

[0017] In the formula, Indicates the calculation result; The objective function is... These are the measured values ​​of morphological characteristic indicators; These are simulated values ​​for morphological characteristic indicators.

[0018] In the aforementioned data-based multi-morphological runoff simulation method, step S140 involves using an objective function to calculate and continuously optimize the constructed model, thereby completing the optimization of one or more morphological runoff models and constructing a model parameter library.

[0019] In the aforementioned data-based multi-morphological runoff simulation method, in step S150, the simulation effect of the model on different morphological runoff characteristics is evaluated using accuracy evaluation indicators or hypothesis testing. If the accuracy evaluation indicators are met or the hypothesis testing is passed, the model can be applied to the interpolation extension or runoff forecasting of runoff data with different morphological characteristics. Otherwise, step S140 will continue to be executed.

[0020] This invention also provides a data-based multi-morphological runoff simulation system, characterized in that it specifically includes:

[0021] The data preprocessing module is used to acquire watershed data and perform data preprocessing.

[0022] The runoff model module is used to simulate runoff at the watershed outlet section;

[0023] The index calculation module is used to quantitatively describe the characteristics of different types of runoff;

[0024] The model parameter optimization module is used to determine the model parameters and their value range, determine the optimization objective, optimize the model parameters of runoff with different morphological characteristics, and build a model parameter library.

[0025] The evaluation module is used to assess the model's effectiveness in simulating runoff with different morphological characteristics.

[0026] The present invention also provides a computer medium, characterized in that the computer medium stores a computer program, which, when executed by a processor, implements the steps of the data-based multimorphic runoff simulation method according to any one of claims 1-8.

[0027] Compared with the prior art, the advantages of this invention are that it uses processed data for simulation and echoing to simulate runoff, adopts different indicators to characterize the morphological characteristics of runoff, and combines runoff morphological characteristic indicators to construct a comprehensive objective function, optimize the model, establish a model parameter library, and then verify the simulation results. It satisfies runoff with different morphological characteristics, takes into account more of the various morphological characteristics of runoff, and enables the model to simulate runoff with different morphological characteristics more accurately, making it more suitable for practical applications. Attached Figure Description

[0028] Figure 1 This is a flowchart of the data-based multi-morphological runoff simulation method of the present invention;

[0029] Figure 2 This is a flowchart of an embodiment of the present invention;

[0030] Figure 3 This is a flowchart of an embodiment of the present invention;

[0031] Figure 4 This is a schematic diagram of the Thiessen polygon in the watershed above Daluo Station, according to an embodiment of the present invention;

[0032] Figure 5 This is a schematic diagram of the extreme values ​​and extreme times of annual precipitation and runoff in this invention;

[0033] Figure 6 This is a simulation result diagram of the continuous runoff process under the working condition of the present invention;

[0034] Figure 7 This is a simulation result diagram of the continuous flow process under two operating conditions according to the present invention;

[0035] Figure 8 These are simulation results of runoff extreme state indices under two operating conditions according to the present invention;

[0036] Figure 9 This is a data-based multi-morphological feature runoff simulation structure diagram of the present invention;

[0037] Figure 10 This is a table showing the physical meaning and value range of the parameters in this invention;

[0038] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0039] Embodiment 1 of the present invention: A data-based multi-morphological runoff simulation method, comprising the following steps:

[0040] S110. Perform data preprocessing to obtain watershed data within the region, including meteorological data, runoff data, and spatial attribute data.

[0041] S120. Establish a model. The model is established based on meteorological data, runoff data, and spatial attribute data. The model selects different attribute indicators according to actual engineering needs to describe the morphological characteristics of runoff under different working conditions, and obtains sequence data of measured and simulated values ​​of different indicators.

[0042] S130. Establish a comprehensive objective function, specifically, based on the above model and data, select model parameters related to runoff and determine the range of parameter values;

[0043] S140. Optimize the model and build a model parameter library. Specifically, optimize one or more morphological runoff models and build a model parameter library.

[0044] S150. Verify the model's runoff simulation effect. Once the simulation effect meets the requirements, it can be used for interpolation and extension of runoff data with different morphological characteristics in the watershed or for runoff forecasting.

[0045] like Figure 1 As shown, due to the influence of numerous factors on the water resource system, runoff sequences exhibit multi-timescale variation and multi-morphological stability characteristics. Therefore, it is necessary to obtain specific and detailed data and verify the data before predicting and simulating runoff to achieve accurate simulation results. To this end, in this embodiment, watershed data is first acquired and preprocessed; secondly, runoff is simulated based on a model; then, different indicators are used to characterize the morphological characteristics of runoff, and a comprehensive objective function is constructed by combining runoff morphological characteristic indicators; then, the model is optimized using an algorithm to establish a model parameter library; finally, the model's runoff simulation effect is verified, and once it meets the requirements, it can be used for interpolation and extension of runoff data with different morphological characteristics in the watershed or for runoff forecasting. This approach considers more of the different morphological characteristics of runoff, enabling the model to more accurately simulate runoff with different morphological characteristics.

[0046] Embodiment 2 of the present invention: A data-based multi-morphological runoff simulation method, such as... Figure 1 and Figure 2 As shown, it includes the following:

[0047] In step S110, the meteorological data and spatial attribute data are measured data from meteorological stations. When measured data from meteorological stations is scarce, satellite meteorological data or reanalysis methods can be used to supplement the meteorological data. The runoff data can be measured data from hydrological stations. The spatial attribute data includes the spatial attribute data required for modeling, such as the location of hydrological stations, DEM, land use data, and soil data.

[0048] In step S120, when establishing the model, the data based on meteorological data, runoff data, and spatial attribute data are first processed into the input format required by the runoff model. Then, a watershed hydrological, hydrodynamic, or artificial neural network model is constructed to simulate the flow at the watershed outlet section. Different indicators are used to characterize the morphological characteristics of runoff. Different attribute indicators can be selected according to actual engineering needs to describe the morphological characteristics of runoff under different working conditions, obtaining sequence data of measured and simulated values ​​of different indicators.

[0049] In step S130, data related to watershed runoff within the region are selected, analyzed, and processed to determine the parameter range in the model. In step S130, for the sequence data of measured and simulated values ​​related to runoff with different morphological characteristics within the region, an objective function for model optimization is constructed, with the specific formula as follows:

[0050]

[0051] In the formula, Indicates the calculation result; The objective function is... These are the measured values ​​of morphological characteristic indicators; These are simulated values ​​for morphological characteristic indicators.

[0052] In step S140, the objective function is used to calculate and continuously optimize the constructed model, thereby completing the optimization of one or more morphological runoff models and constructing a model parameter library.

[0053] In step S150, the simulation effect of the model on runoff with different morphological characteristics is evaluated by using accuracy evaluation indicators or hypothesis testing. If the accuracy evaluation indicators are met or the hypothesis testing is passed, the model can be applied to the interpolation extension or runoff forecasting of runoff data with different morphological characteristics. Otherwise, step S140 will continue to be executed.

[0054] Specifically, in this embodiment, such as Figure 3 As shown, this method is applied to the watershed above the Daluo hydrological station section of the Yalong River. A comparison is made between conventional runoff simulation methods and data-based multi-morphological runoff simulation methods, with two operating conditions established:

[0055] Operating Condition 1: Conventional runoff simulation method;

[0056] Working condition 2: Runoff simulation method considering extreme value patterns.

[0057] The steps for the conventional runoff simulation method in Case 1 are as follows:

[0058] Step 1: Collect DEM data, extracting the watershed using the Daluo hydrological station section as the watershed outlet to obtain basic watershed information; collect meteorological data from meteorological stations in the Yalong River basin, including average temperature and precipitation, and interpolate missing data; calculate the isochronous series data of average temperature and precipitation in the watershed using the Thiessen polygon method, referring to [reference needed]. Figure 4 Collect runoff data from the Daluo Hydrological Station and compile it into time-series data such as runoff.

[0059] Step 2: Select the MISDc model as the runoff model, input the average temperature and precipitation data and basic watershed information obtained in Step 1 into the model, and establish the runoff model of the Yalong River Basin.

[0060] Step 3: Select the runoff-related parameters in the MISDc model and determine the range of parameter values. See [link / reference]. Figure 10 Construct the objective function , and Specifically:

[0061]

[0062]

[0063]

[0064] In the formula: These are measured values; These are simulated values; This represents the average of the measured values; This represents the average of the simulated values; The number of data points; ; Pearson correlation coefficient; This is the ratio of the standard deviation of the measured value to the simulated value; The ratio of the measured value to the average of the simulated value; the closer NSE, KGE, and CC are to 1, the better the simulation effect.

[0065] Step 4: Based on the objective function constructed in Step 3 , and The MISDc model established in step three was optimized using a multi-objective optimization algorithm—a non-dominated sorting genetic algorithm with an elitist strategy (NSGA-II)—to build a runoff model database.

[0066] Step 5: Utilize the accuracy evaluation index - correlation coefficient ( The runoff simulation results were evaluated, and the extreme state model established in step four achieved satisfactory results in both the calibration period (1999-2005) and the validation period (2006-2012) for runoff simulation. All are greater than 0.9; among them, The calculation formula is:

[0067]

[0068] In the formula: m represents the length of the runoff data series; ; This represents the measured value of the i-th runoff; This represents the average of the measured runoff values. This represents the simulated value of the i-th runoff; This represents the mean of the simulated runoff values.

[0069] The steps for simulating runoff considering extreme value patterns in Case 2 are as follows:

[0070] Step 1: Collect DEM data, extracting the watershed using the Daluo hydrological station section as the watershed outlet to obtain basic watershed information; collect meteorological data from meteorological stations in the Yalong River basin, including average temperature and precipitation, and interpolate missing data; calculate the isochronous series data of average temperature and precipitation in the watershed using the Thiessen polygon method, referring to [reference needed]. Figure 4 Collect runoff data from the Daluo Hydrological Station and compile it into time-series data such as runoff.

[0071] Step Two: Select the MISDc model as the runoff model. Input the average temperature and precipitation data and basic watershed information obtained in Step One into the model to establish a runoff model for the Yalong River basin. Select three runoff extreme state indices—runoff extreme value, runoff extreme value time, and runoff extreme value intensity—to characterize the extreme value morphology of runoff and construct an extreme state characteristic runoff model. The specific calculation methods for the three runoff extreme state indices are as follows:

[0072] Runoff extremes represent the maximum daily runoff volume within a year, and runoff extreme time represents the time corresponding to the occurrence of the maximum daily runoff volume within a year. Figure 5 As shown. The extreme runoff intensity is defined as the ratio of extreme runoff volume to extreme runoff frequency; the greater the extreme runoff intensity, the greater the potential harm. The threshold for extreme runoff events is determined using the internationally accepted 95th percentile method. Extreme runoff volume is the total water volume of extreme runoff events each year, representing the amount of extreme runoff; extreme runoff frequency is the sum of the number of days on which extreme runoff events occur, representing the frequency of extreme events.

[0073] Step 3: Select the runoff-related parameters in the MISDc model and determine the range of parameter values. See [link / reference]. Figure 10A comprehensive evaluation objective function is constructed by combining runoff extreme state indicators. , and Specifically:

[0074]

[0075]

[0076]

[0077] In the formula: This represents the Nash efficiency coefficient; This represents the Kling efficiency coefficient; denoted by the coefficient of determination; n represents the number of morphological characteristic indicators. This represents the weight of the i-th morphological feature index; This represents the simulated value of the i-th morphological feature index; Represents the measured value of the i-th morphological feature index; where , and The specific calculation formula is as follows:

[0078]

[0079]

[0080]

[0081] In the formula: These are measured values; These are simulated values; This represents the average of the measured values; This represents the average of the simulated values; The number of data points; ; Pearson correlation coefficient; This is the ratio of the standard deviation of the measured value to the simulated value; The ratio of the measured value to the average of the simulated value; the closer NSE, KGE, and CC are to 1, the better the simulation effect.

[0082] Step 4: Based on the comprehensive evaluation objective function constructed in Step 3 , and The MISDc model established in step three was optimized using a multi-objective optimization algorithm—a non-dominated sorting genetic algorithm with an elitist strategy (NSGA-II)—to build an extreme state runoff model database.

[0083] Step 5: Utilize the accuracy evaluation index - correlation coefficient ( The runoff simulation results were evaluated, and the extreme state model established in step four achieved satisfactory results in both the calibration period (1999-2005) and the validation period (2006-2012) for runoff simulation. All are greater than 0.9; among them, The calculation formula is:

[0084]

[0085] In the formula: m represents the length of the runoff data series; ; This represents the measured value of the i-th runoff; This represents the average of the measured runoff values. This represents the simulated value of the i-th runoff; This represents the mean of the simulated runoff values.

[0086] Finally, the simulation results of the continuous runoff process under the two operating conditions are compared as follows: Figure 6 and Figure 7 It is evident that conventional runoff simulation models, prioritizing the improvement of global accuracy in continuous runoff processes at the expense of optimizing local runoff characteristics, tend to underestimate high runoff flow values. However, in scenario two, the extreme value morphology of high runoff flow values ​​is simulated more effectively.

[0087] Continue to refer to Figure 8 It can be seen that the simulation in Condition 1 significantly underestimated the runoff extremes, while Condition 2 showed a significant improvement. Compared to Condition 1, Condition 2 greatly improved the simulation of the runoff extreme timing; for example, the simulation of the runoff extreme timings in 2004 and 2010 better fitted the measured values. In summary, compared to conventional runoff simulation methods, the method proposed in this invention provides a more accurate and less erroneous model for simulating runoff with different morphological characteristics, thus completing the simulation of...

[0088] Finally, the physical meaning and value range of each of the above parameters are as follows: Figure 10 As shown.

[0089] Embodiment 3 of the present invention also provides a data-based multi-morphological runoff simulation system, such as... Figure 9 As shown, it specifically includes:

[0090] The data preprocessing module is used to acquire watershed data and perform data preprocessing.

[0091] The runoff model module is used to simulate runoff at the watershed outlet section;

[0092] The index calculation module is used to quantitatively describe the characteristics of different types of runoff;

[0093] The model parameter optimization module is used to determine the model parameters and their value range, determine the optimization objective, optimize the model parameters of runoff with different morphological characteristics, and build a model parameter library.

[0094] The evaluation module is used to assess the model's effectiveness in simulating runoff with different morphological characteristics.

[0095] In this embodiment, the data preprocessing module acquires watershed data and preprocesses it. The runoff modeling module establishes a runoff model based on the processed watershed data, enabling the model to characterize runoff morphology based on different indicators and simulate runoff at the watershed outlet section. The indicator calculation module quantitatively describes the characteristics of different runoff morphologies, making the model more consistent with reality. The model parameter optimization module verifies the data and model, determines the model parameters and their value ranges, identifies optimization objectives, optimizes the model parameters for different runoff morphology characteristics, and constructs a model parameter library. Finally, the verification and evaluation module evaluates the model's effectiveness in simulating different runoff morphology characteristics. Once the verification requirements are met, the model can be used for interpolation and extension of runoff data with different runoff morphology characteristics or for runoff forecasting. This achieves smaller and more accurate simulation errors for different runoff morphology characteristics.

[0096] Embodiment 4 of the present invention also provides a computer medium on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the above-described data-based multimorphic runoff simulation method.

[0097] The working principle of one embodiment of the present invention is as follows: First, watershed data is acquired and preprocessed; second, runoff is simulated based on a model; then, different indicators are used to characterize the morphological characteristics of the runoff, and a comprehensive objective function is constructed by combining the runoff morphological characteristic indicators; then, the model is optimized using an algorithm to establish a model parameter library; finally, the model's runoff simulation effect is verified, and if it meets the requirements, it can be used for interpolation and extension of runoff data with different morphological characteristics in the watershed or for runoff forecasting. The method of the present invention takes into greater consideration the different morphological characteristics of runoff, enabling the model to more accurately simulate runoff with different morphological characteristics.

Claims

1. A data-driven multi-morphological runoff simulation method, characterized in that, Includes the following steps: S110. Perform data preprocessing to obtain watershed data within the region, including meteorological data, runoff data, and spatial attribute data. S120. Establish a model. The model is established based on meteorological data, runoff data, and spatial attribute data. The model selects different attribute indicators according to actual engineering needs to describe the morphological characteristics of runoff under different working conditions, and obtains sequence data of measured and simulated values ​​of different indicators. This includes selecting three runoff extreme state indicators—runoff extreme value, runoff extreme value time, and runoff extreme value intensity—to characterize the extreme morphological characteristics of runoff and constructing an extreme state characteristic runoff model. S130. Establish a comprehensive objective function. Specifically, based on the above model and data, select model parameters related to runoff and determine the range of parameter values; this includes constructing a comprehensive evaluation objective function by combining runoff extreme state indicators. , and Specifically: In the formula: This represents the Nash efficiency coefficient; This represents the Kling efficiency coefficient; denoted by the coefficient of determination; n represents the number of morphological characteristic indicators. Indicates the first The weight of each morphological feature indicator; Indicates the first Simulated values ​​of morphological characteristic indicators; Indicates the first Measured values ​​of each morphological characteristic index; S140. Optimize the model and build a model parameter library. Specifically, optimize one or more morphological runoff models and build a model parameter library. S150. Verify the model's runoff simulation effect. Once the simulation effect meets the requirements, it can be used for interpolation and extension of runoff data with different morphological characteristics in the watershed or for runoff forecasting.

2. The data-based multi-morphological runoff simulation method according to claim 1, characterized in that, In step S110, the meteorological data and spatial attribute data are measured data from meteorological stations. When measured data from meteorological stations is scarce, satellite meteorological data or reanalysis methods can be used to supplement meteorological data. Runoff data can be measured data from hydrological stations. Spatial attribute data includes the location of hydrological stations, DEM, land use data, and spatial attribute data required for soil data modeling.

3. The data-based multi-morphological runoff simulation method according to claim 1, characterized in that, In step S120, when establishing the model, the data of the model based on meteorological data, runoff data and spatial attribute data are first processed into the input format required by the runoff model, and then a watershed hydrological, hydrodynamic model or artificial neural network model is constructed to simulate the flow at the watershed outlet section.

4. The data-based multi-morphological runoff simulation method according to claim 3, characterized in that, In step S120, different indicators are used to characterize the morphological characteristics of runoff. Different attribute indicators can be selected according to actual engineering needs to describe the morphological characteristics of runoff under different working conditions, and sequence data of measured and simulated values ​​of different indicators are obtained.

5. The data-based multi-morphological runoff simulation method according to claim 1, characterized in that, In step S130, data related to watershed and runoff within the region are selected, and the data is analyzed and processed to determine the parameter range in the model.

6. The data-based multi-morphological runoff simulation method according to claim 5, characterized in that, In step S130, a target function for model optimization is constructed from the sequence data of measured and simulated values ​​of runoff with different morphological characteristics within the region. The specific formula is as follows: In the formula, Indicates the calculation result; The objective function is... These are the measured values ​​of morphological characteristic indicators; These are simulated values ​​for morphological characteristic indicators.

7. The data-based multi-morphological runoff simulation method according to claim 6, characterized in that, In step S140, the objective function is used to calculate and continuously optimize the constructed model, thereby completing the optimization of one or more morphological runoff models and constructing a model parameter library.

8. The data-based multi-morphological runoff simulation method according to claim 7, characterized in that, In step S150, the simulation effect of the model on runoff with different morphological characteristics is evaluated using accuracy evaluation indicators or hypothesis testing methods. If the accuracy evaluation indicators are met or the hypothesis testing is passed, the model can be applied to the interpolation extension or runoff forecasting of runoff data with different morphological characteristics. Otherwise, step S140 will continue to be executed.

9. A data-based multi-morphological runoff simulation system, characterized in that, The data-based multimorphic runoff simulation system implements the steps of the data-based multimorphic runoff simulation method described in any one of claims 1-8; specifically including: The data preprocessing module is used to acquire watershed data and perform data preprocessing. The runoff model module is used to simulate runoff at the watershed outlet section; The index calculation module is used to quantitatively describe the characteristics of different types of runoff; The model parameter optimization module is used to determine the model parameters and their value range, determine the optimization objective, optimize the model parameters of runoff with different morphological characteristics, and build a model parameter library. The evaluation module is used to assess the model's effectiveness in simulating runoff with different morphological characteristics.

10. A computer medium, characterized in that, The computer medium stores a computer program, which, when executed by a processor, implements the steps of the data-based multimorphic runoff simulation method according to any one of claims 1-8.

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