A runoff simulation and prediction method suitable for bedrock mountainous area ecological hydrology

Abstract

This invention relates to the field of eco-hydrology technology and discloses a method for simulating and predicting runoff in bedrock mountainous areas. The method includes: acquiring an input dataset for an eco-hydrological model in bedrock mountainous areas; converting it to gridded data to unify spatial resolution; sequentially calculating the effective water content of the weathering crust, the vegetation-available water content, climate-underlying surface non-physical parameters, atmospheric potential evapotranspiration, cover layer potential evapotranspiration, evaporation coefficient, and simulated runoff; constructing a fitness function by combining deterministic coefficients and Nash efficiency coefficients; selecting parameters to be calibrated, and using precipitation and potential evapotranspiration for the simulation period as inputs to output simulated runoff. This invention extends the simulation depth to the weathering crust in bedrock mountainous areas, calculates water content layer by layer, and calculates vegetation-available water content segment by segment, reflecting the ability of deep-rooted vegetation to utilize deep water. The fitness function is optimized, and the simulated runoff is output, providing a basis for ecological and water resource management in bedrock mountainous areas.

Description

Technical Field

[0001] This invention relates to the field of eco-hydrology technology, specifically to a method for simulating and predicting runoff applicable to eco-hydrology in bedrock mountainous areas. Background Technology

[0002] Establishing eco-hydrological models is one of the important research methods for simulating the water cycle of vegetation growth processes in watersheds or regions. Eco-hydrological models simulate the mutual transformation processes between precipitation, evapotranspiration, runoff and water content in the root zone of plants in a watershed, providing a scientific basis for intervention programs such as watershed ecological restoration and irrigation management.

[0003] Existing ecohydrological models primarily simulate the soil layer shallower than 2 meters below the surface, suitable for simulating ecohydrological processes in crops in plains areas. However, in bedrock mountainous regions, the surface cover is diverse, including forests, shrubs, grasslands, and cultivated land. Different vegetation types have varying root depths and access to different water levels. Forest vegetation, in particular, has root systems extending to depths of over 2 meters. These roots are not only distributed in the soil but also in the residual colluvial deposits within the weathering crust, which encompasses the soil layer. Moisture in the weathering crust is a key factor limiting vegetation growth; its stratification, thickness, and water-holding capacity all constrain water transport within the root zone. Therefore, simulating the water cycle and runoff in bedrock mountainous areas becomes a pressing issue. Summary of the Invention

[0004] This invention provides a method for simulating and predicting runoff applicable to the eco-hydrology of bedrock mountainous areas, in order to solve the problem of how to simulate runoff.

[0005] In a first aspect, the present invention provides a method for simulating and predicting runoff applicable to eco-hydrology in bedrock mountainous areas, the method comprising: Obtain the input dataset for the bedrock mountain eco-hydrological model. The input dataset includes geographical data, geological data, meteorological data, and hydrological data. The meteorological data includes precipitation and potential evapotranspiration. Precipitation and potential evapotranspiration from different weather stations were converted into gridded data, and the spatial resolution of the gridded data was standardized. Geographic data, geological data, and meteorological data with unified spatial resolution were input into the bedrock mountain eco-hydrological model. The effective water content of the weathering crust, the available water content of the weathering crust vegetation, climate-underlying non-physical parameters, atmospheric potential evapotranspiration, potential evapotranspiration of the cover layer, evaporation coefficient, and simulated runoff were calculated in sequence. The climate-underlying non-physical parameters were determined based on the parameters to be calibrated, the available water content of the weathering crust vegetation, and precipitation. The fitness function is constructed by combining the deterministic coefficient and the Nash efficiency coefficient; The parameter to be calibrated that minimizes the fitness function value is selected as the optimal parameter. The precipitation and potential evapotranspiration for the time period to be predicted are used as inputs, and the simulated runoff is output.

[0006] This invention targets bedrock mountainous areas, extending the simulation depth to the weathering crust. By calculating water content in layers and segmented calculation of vegetation-available water content, it reflects the ability of deep-rooted vegetation to utilize deep water. It constructs a fitness function to optimize parameters and uses meteorological data for the time period to be predicted as input to output simulated runoff, providing a scientific basis for ecological restoration and water resource management in bedrock mountainous areas.

[0007] In one alternative implementation, the method further includes: Assign values ​​to the waiting rate calibrator parameters according to the preset step size; Calculate climate-underlying surface non-physical parameters using the assigned parameters to be calibrated; If the calculated climate-underlying non-physical parameters are within the preset parameter range, the parameter is deemed valid and retained. If the calculated climate-underlying non-physical parameters are not within the preset parameter range, the parameter is deemed invalid and removed.

[0008] This invention assigns values ​​to the calibration parameters according to a preset step size, avoiding deviations caused by subjective or random assignment. It introduces physical constraints and ensures that the parameters are within a parameter space that conforms to the characteristics of the watershed by considering whether the climate-underlying surface non-physical parameters are within the preset parameter range, thus avoiding interference from invalid parameters.

[0009] In one alternative implementation, the fitness function is constructed by combining the deterministic coefficients and the Nash efficiency coefficients, including: Calculate the coefficient of determination, which is used to assess the consistency between simulated runoff and actual observed runoff in terms of their trends of change; Calculate the Nash efficiency coefficient, which is used to assess how close the simulated runoff is to the actual observed runoff in terms of numerical values; The sum of the squared deviations of the deterministic coefficients from their corresponding ideal values ​​and the squared deviations of the Nash efficiency coefficients from their corresponding ideal values ​​is calculated, and the square root of the sum is used as the fitness function.

[0010] This invention constructs a fitness function by combining deterministic coefficients and Nash efficiency coefficients, comprehensively evaluating the consistency in the changing trends and the degree of numerical similarity between simulated runoff and actual observed runoff, overcoming the limitations of single-index evaluation.

[0011] In one optional implementation, the determinism coefficient is calculated using the following formula:

[0012] in, For the deterministic coefficient, For the first t Actual observed runoff over the time period This represents the average of the actual observed runoff from time period 1 to time period T. For the first t Simulated runoff over a time period This represents the average simulated runoff from time period 1 to time period T; The formula for calculating the Nash efficiency coefficient is as follows:

[0013] in, NSE Nash efficiency coefficient; The fitness function formula is as follows:

[0014] in, This is the fitness function.

[0015] In one alternative implementation, calculating climate-underlying surface non-physical parameters includes: The ratio of available water content of weathered crust vegetation to precipitation is calculated, and the result of multiplying the parameter to be calibrated with the ratio of available water content of weathered crust vegetation to precipitation is determined as the climate-underlying surface non-physical parameter.

[0016] This invention simplifies the process to a single parameter to be calibrated, thereby reducing complexity. The climate-underlying surface non-physical parameters are determined by the parameter to be calibrated, the available water content of the weathered crust vegetation, and precipitation, thus giving them physical meaning.

[0017] In one alternative implementation, the evaporation coefficient is calculated according to the following formula:

[0018] in, The evaporation coefficient is... This represents the actual evaporation rate. For precipitation, This represents the potential evapotranspiration of the covering layer. This refers to climate-underlying non-physical parameters.

[0019] In one alternative implementation, the simulated runoff is calculated according to the following formula:

[0020] in, To simulate runoff, For precipitation, This represents the actual evaporation rate. For storing variables.

[0021] Secondly, this invention provides a runoff simulation and prediction system suitable for eco-hydrology in bedrock mountainous areas, the system comprising: The acquisition module is used to acquire the input dataset for the bedrock mountain eco-hydrological model. The input dataset includes geographical data, geological data, meteorological data, and hydrological data. The meteorological data includes precipitation and potential evapotranspiration. The conversion module is used to convert precipitation and potential evapotranspiration from different weather stations into gridded data and to unify the spatial resolution of the gridded data. The calculation module is used to input geographic data, geological data, and meteorological data with unified spatial resolution into the bedrock mountain eco-hydrological model, and calculate the effective water content of the weathering crust, the available water content of the weathering crust vegetation, climate-underlying surface non-physical parameters, atmospheric potential evapotranspiration, cover layer potential evapotranspiration, evaporation coefficient, and simulated runoff in sequence. The climate-underlying surface non-physical parameters are determined based on the parameters to be calibrated, the available water content of the weathering crust vegetation, and precipitation. The function building module is used to construct the fitness function by combining the deterministic coefficients and the Nash efficiency coefficients; The output module is used to select the parameter to be calibrated that minimizes the fitness function value as the optimal parameter. It takes the precipitation and potential evapotranspiration of the time period to be predicted as input and outputs the simulated runoff.

[0022] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the runoff simulation and prediction method applicable to the eco-hydrology of bedrock mountainous areas described in the first aspect or any corresponding embodiment.

[0023] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the runoff simulation and prediction method applicable to the eco-hydrology of bedrock mountainous areas as described in the first aspect or any corresponding embodiment. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1This is a schematic diagram of the first process of a runoff simulation and prediction method applicable to eco-hydrology in bedrock mountainous areas according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the atmospheric-vegetation-weathering layer water cycle process in a bedrock mountainous area according to an embodiment of the present invention; Figure 3 This is a structural block diagram of a runoff simulation and prediction system applicable to eco-hydrology in bedrock mountainous areas according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] According to an embodiment of the present invention, a method for simulating and predicting runoff applicable to the eco-hydrology of bedrock mountainous areas is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a method for simulating and predicting runoff applicable to eco-hydrology in bedrock mountainous areas. Figure 1 This is a flowchart of a runoff simulation and prediction method applicable to eco-hydrology in bedrock mountainous areas according to an embodiment of the present invention, as shown below. Figure 1 As shown, the process includes the following steps: Step S101: Obtain the input dataset for the bedrock mountain eco-hydrological model.

[0030] In this embodiment of the invention, the atmospheric-vegetation-weathering layer water cycle process in bedrock mountainous areas is as follows: Figure 2As shown, the vertical structure of the water cycle in bedrock mountainous areas, from top to bottom, includes: the atmosphere, vegetation layer, soil layer, colluvial layer, and bedrock layer. After precipitation reaches the surface, part of it is intercepted by the vegetation canopy and evaporates back to the atmosphere, while the rest infiltrates into the weathering layer (soil layer and colluvial layer). Some of the water in the weathering layer is absorbed by the roots of the vegetation and returns to the atmosphere through evaporation, while another part flows out of the watershed as runoff, and the remainder is stored in the weathering layer.

[0031] Unlike plains, vegetation roots in bedrock mountains can extend to the colluvial layer, allowing them to utilize the moisture in that layer. The eco-hydrological model for bedrock mountains extends the simulation depth from the traditional soil layer down to the entire weathering crust.

[0032] The input dataset for the bedrock mountain eco-hydrological model includes geographic data, geological data, meteorological data, and hydrological data. Geographic data includes land use patterns and vegetation root depth, while geological data includes weathering crust thickness, soil layer thickness, colluvial thickness, soil field water holding capacity, soil wilting water content, colluvial water holding capacity, and colluvial wilting water content. Meteorological data includes precipitation and potential evapotranspiration, and hydrological data includes actual observed runoff.

[0033] Specifically, if we simulate the daily runoff of a certain year (taking 2025 as an example), the time scale is the entire year of 365 days in 2025, the time step is 1 day, and the input meteorological data are the daily precipitation and potential evapotranspiration. The daily precipitation and potential evapotranspiration can be obtained through meteorological observation stations.

[0034] If we simulate the annual runoff over a period of several years (taking 2001-2025 as an example), the time scale is the total number of years (25 years in total from 2001 to 2025), the time step is 1 year, and the input meteorological data are annual precipitation and annual potential evapotranspiration, both of which can be obtained from meteorological observation stations.

[0035] Step S102: Convert the precipitation and potential evapotranspiration of different weather stations into grid data and unify the spatial resolution of the grid data.

[0036] In this embodiment of the invention, since the meteorological station's observation data is point-based, but the distributed eco-hydrological model needs to be calculated on a spatial grid, spatial interpolation methods such as the inverse distance weighting method and Kriging interpolation are used to extend the point-based observation data to grid points. Because the spatial resolution may differ depending on the source of the observation data, resampling methods such as the nearest neighbor method and bilinear interpolation are used to unify the grid point data to the same spatial resolution.

[0037] Step S103: Input the geographic data, geological data, and meteorological data after unifying the spatial resolution into the bedrock mountain eco-hydrological model, and calculate the effective water content of the weathering crust, the available water content of the weathering crust vegetation, the climate-underlying surface non-physical parameters, the atmospheric potential evapotranspiration, the potential evapotranspiration of the cover layer, the evaporation coefficient, and the simulated runoff in sequence.

[0038] In this embodiment of the invention, geographic data, geological data, and meteorological data with unified spatial resolution are used as inputs. A bedrock mountain eco-hydrological model is then used to sequentially calculate the weathering crust available water content (AWC), the weathering crust vegetation available water content (PAWC), and climate-underlying surface non-physical parameters. Potential atmospheric evapotranspiration Potential evapotranspiration of the cover layer Evaporation coefficient Simulated runoff Among them, the climate-underlying surface non-physical parameters are determined based on the parameters to be calibrated, the available water content of the weathered crust vegetation, and precipitation.

[0039] Step S104: Construct the fitness function by combining the deterministic coefficient and the Nash efficiency coefficient.

[0040] In this embodiment of the invention, a fitness function composed of deterministic coefficients and Nash efficiency coefficients is constructed to evaluate the accuracy of simulated runoff. The smaller the fitness function, the more accurate the bedrock mountain eco-hydrological model.

[0041] Step S105: Select the parameter to be calibrated that minimizes the fitness function value as the optimal parameter, and take the precipitation and potential evapotranspiration of the time period to be predicted as input to output the simulated runoff.

[0042] In this embodiment of the invention, the optimal parameter is identified as the calibration parameter that minimizes the fitness function value. Using the precipitation and potential evapotranspiration for the period to be predicted or simulated as input data, the simulated and predicted runoff is generated.

[0043] The runoff simulation and prediction method provided in this embodiment, applicable to the eco-hydrology of bedrock mountainous areas, extends the simulation depth to the weathering crust. By calculating water content in layers and segmented calculation of vegetation-available water content, it reflects the ability of deep-rooted vegetation to utilize deep water. The fitness function is constructed to optimize the parameters and ensure optimal parameters. The simulated runoff is output using meteorological data for the time period to be predicted as input, providing a scientific basis for ecological restoration and water resource management in bedrock mountainous areas.

[0044] This embodiment provides a method for simulating and predicting runoff applicable to eco-hydrology in bedrock mountainous areas. The process includes the following steps: Step S301: Obtain the input dataset for the bedrock mountain eco-hydrological model.

[0045] Please see details Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0046] Step S302: Convert the precipitation and potential evapotranspiration of different weather stations into grid data and unify the spatial resolution of the grid data.

[0047] Please see details Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0048] Step S303: Input the geographic data, geological data, and meteorological data after unifying the spatial resolution into the bedrock mountain eco-hydrological model, and calculate the effective water content of the weathering crust, the available water content of the weathering crust vegetation, the climate-underlying surface non-physical parameters, the atmospheric potential evapotranspiration, the potential evapotranspiration of the cover layer, the evaporation coefficient, and the simulated runoff in sequence.

[0049] Specifically, the effective water content of the weathering crust is calculated according to the following steps: Step S3031: The difference between the field water holding capacity of the soil layer and the wilting water content of the soil layer is determined as the effective water content of the soil layer; Step S3032: The difference between the water holding capacity of the residual slope layer and the water content of the residual slope layer during wilting is determined as the effective water content of the residual slope layer.

[0050] In this embodiment of the invention, effective water content refers to the water content that can be absorbed and utilized by the roots of vegetation. The effective water content of the weathering crust includes the effective water content of the soil layer and the effective water content of the residual slope deposit.

[0051] The difference between the field water holding capacity and the wilting water content of the soil layer is the effective water content of the soil layer, and its calculation formula is as follows:

[0052] in, The effective water content of the soil layer. This refers to the field water holding capacity of the soil layer. This refers to the moisture content of the soil layer during wilting.

[0053] The difference between the water-holding capacity of the residual colluvial layer and its wilting water content is the effective water content of the residual colluvial layer, and its calculation formula is as follows:

[0054] in, The effective water content of the residual slope deposits. This refers to the water holding capacity of the residual slope deposits. This represents the water content of the residual slope deposits after wilting.

[0055] Specifically, the available water content of the weathered crust vegetation is calculated according to the following steps: Step S3033: If the depth of the vegetation root layer is less than or equal to the thickness of the soil layer, the effective water content of the soil layer is multiplied by the ratio of the vegetation root layer to the depth of the soil layer thickness and the result is determined as the available water content of the weathered crust vegetation. Step S3034: If the depth of the vegetation root layer is greater than the thickness of the soil layer and less than or equal to the thickness of the residual slope layer, then the sum of the effective water content of the soil layer and the effective water content of the residual slope layer is determined as the available water content of the weathered crust vegetation. The effective water content of the residual slope layer is the product of the effective water content of the residual slope layer and the ratio of the extension depth in the residual slope layer to the thickness of the residual slope layer. Step S3035: If the depth of the vegetation root layer is greater than the thickness of the residual slope layer, the sum of the effective water content of the soil layer and the effective water content of the residual slope layer is determined as the available water content of the weathered crust vegetation.

[0056] In this embodiment of the invention, the available water content of vegetation refers to the effective amount of water that the root system can actually absorb and utilize.

[0057] If the depth of the vegetation root layer is less than or equal to the thickness of the soil layer, and the roots are completely distributed in the soil layer without touching the residual slope layer, then the available water content of the weathering crust vegetation is determined by multiplying the effective water content of the soil layer by the proportion of the vegetation root layer to the depth of the soil layer. The calculation formula is as follows:

[0058] in, The available water content of vegetation in the weathered crust. This refers to the depth of the vegetation root system. This refers to the thickness of the soil layer.

[0059] If the root layer depth is greater than the soil layer thickness, but less than or equal to the sum of the soil layer thickness and the residual colluvial layer thickness, and the roots penetrate the soil layer and enter the residual colluvial layer, but do not exceed the bottom boundary of the residual colluvial layer, then the usable water content of the weathered crust vegetation can be calculated by taking the effective water content of the entire soil layer and the proportion of the root depth in the residual colluvial layer, and then converting this into the effective water content of the residual colluvial layer. The calculation formula is as follows:

[0060] in, This represents the thickness of the residual slope deposit.

[0061] If the root system depth is greater than the sum of the soil layer thickness and the residual slope layer thickness, and the roots penetrate the entire weathered crust, extending to the lower bedrock fissures or deeper, then the water content available to the roots is the sum of the effective water content of the soil layer and the effective water content of the residual slope layer. The calculation formula is as follows:

[0062] Specifically, the climate-underlying surface non-physical parameters are calculated according to the following steps: Step S3036: Calculate the ratio of available water content of weathered crust vegetation to precipitation, and determine the result of multiplying the parameter to be calibrated with the ratio of available water content of weathered crust vegetation to precipitation as the climate-underlying surface non-physical parameter.

[0063] In this embodiment of the invention, the input is the Z coefficient, which is a constant characterizing the seasonality of precipitation and is the only parameter that needs to be calibrated in the bedrock mountain eco-hydrological model.

[0064] The formulas for calculating climate-underlying non-physical parameters are as follows:

[0065] in, Climate-underlying surface non-physical parameters This refers to precipitation.

[0066] Specifically, the potential atmospheric evapotranspiration is calculated according to the following steps: Step S3037: Calculate the daily atmospheric potential evapotranspiration based on empirical coefficients, radiation factors, temperature factors, and temperature difference factors.

[0067] In this embodiment of the invention, the daily potential evapotranspiration is determined by an empirical coefficient, a radiation factor, a temperature factor, and a temperature difference factor. Using an empirical coefficient of 0.0023, the formula for calculating the daily potential atmospheric evapotranspiration is as follows:

[0068] in, Daily atmospheric potential evapotranspiration This refers to daily solar radiation. The highest temperature of the day, This is the lowest temperature of the day. This is the daily average temperature; 17.8 is a temperature correction value.

[0069] It should be noted that the daily potential evapotranspiration can also be calculated using the above-mentioned formula for calculating daily atmospheric potential evapotranspiration by inputting the daily maximum temperature, daily minimum temperature, and daily solar radiation. The annual potential evapotranspiration can be calculated by inputting the daily maximum temperature, daily minimum temperature, and daily solar radiation using the same formula, and then summed to obtain the annual potential evapotranspiration.

[0070] Specifically, the potential evapotranspiration of the overlying layer is calculated according to the following steps: Step S3038: Calculate the potential evapotranspiration of the cover layer based on the evapotranspiration coefficient of the cover layer and the potential atmospheric evapotranspiration.

[0071] In this embodiment of the invention, the potential evapotranspiration of the cover layer is determined by the cover layer evapotranspiration coefficient and the atmospheric potential evapotranspiration. The formula for calculating the potential evapotranspiration of the cover layer is as follows:

[0072] in, This represents the potential evapotranspiration of the covering layer. is the evapotranspiration coefficient of the covering layer.

[0073] Specifically, the evaporation coefficient is calculated according to the following steps: Step S3039: Calculate the evaporation coefficient based on the potential evapotranspiration of the cover layer, precipitation, and climate-underlying surface non-physical parameters.

[0074] In this embodiment of the invention, the evaporation coefficient is calculated based on the Budyko hydrothermal coupling equilibrium. The formula for calculating the evaporation coefficient is as follows:

[0075] in, The evaporation coefficient is... This represents the actual evaporation rate. For precipitation, This represents the potential evapotranspiration of the covering layer. This refers to climate-underlying non-physical parameters.

[0076] Specifically, the simulated runoff is calculated according to the following steps: Step S30310: Calculate the simulated runoff based on precipitation, actual evapotranspiration, and stored variables.

[0077] In this embodiment of the invention, the formula for calculating simulated runoff is as follows:

[0078] in, To simulate runoff, For precipitation, This represents the actual evaporation rate. For storing variables.

[0079] In some alternative implementations, the method further includes: Step Sa: Assign the timing parameters according to the preset step size; Step Sb: Calculate the climate-underlying surface non-physical parameters using the assigned parameters to be calibrated; In step Sc, if the calculated climate-underlying surface non-physical parameters are within the preset parameter range, then the parameter is deemed valid and retained. In step Sd, if the calculated climate-underlying surface non-physical parameters are not within the preset parameter range, the parameter is determined to be invalid and removed.

[0080] In this embodiment of the invention, a preset step size, such as 0.05, 0.1, 0.2, etc., is used to determine the calibration parameter Z according to the preset step size amplitude. The climate-underlying surface non-physical parameters are calculated according to the following formula:

[0081] Check whether the climate-underlying surface non-physical parameters are within the preset parameter range. For example, the preset parameter range is [1.25, 5]. If the climate-underlying surface non-physical parameters are within the preset parameter range, the parameter is considered valid and retained. If the climate-underlying surface non-physical parameters are not within the preset parameter range, the parameter is invalid and removed.

[0082] The calibration parameters are assigned values ​​according to a preset step size to avoid deviations caused by subjective or random assignment. Physical constraints are introduced to ensure that the parameters are within the preset parameter range based on whether the climate-underlying surface non-physical parameters are within the range of the preset parameters, thus avoiding interference from invalid parameters.

[0083] Step S304: Construct the fitness function by combining the deterministic coefficients and the Nash efficiency coefficients.

[0084] Specifically, step S304 includes: Step S3041: Calculate the coefficient of determination; Step S3042: Calculate the Nash efficiency coefficient; Step S3043: Calculate the sum of the squared deviations of the deterministic coefficients from the corresponding ideal values ​​and the squared deviations of the Nash efficiency coefficients from the corresponding ideal values, and use the square root of the sum as the fitness function.

[0085] In this embodiment of the invention, the determinism coefficient is calculated. The coefficient of determination is used to assess the consistency of the changing trends between simulated runoff and actual observed runoff.

[0086] Specifically, the formula for calculating the coefficient of determination is as follows:

[0087] in, For the deterministic coefficient, For the first t Actual observed runoff over the time period This represents the average of the actual observed runoff from time period 1 to time period T. For the first t Simulated runoff over a time period This represents the average simulated runoff from time period 1 to time period T.

[0088] Calculate the Nash efficiency coefficient NSE The Nash-Sutcliffe efficiency coefficient is used to assess how closely simulated runoff and actual observed runoff are numerically similar.

[0089] Specifically, the formula for calculating the Nash efficiency coefficient is as follows:

[0090] in, NSE This is the Nash efficiency coefficient.

[0091] Combining the deterministic coefficient and the Nash efficiency coefficient, with the ideal value of the deterministic coefficient being 1 and the ideal value of the Nash efficiency coefficient being 1, a fitness function is constructed. Specifically, the fitness function as follows:

[0092] in, This is the fitness function.

[0093] By constructing a fitness function by combining the deterministic coefficient and the Nash efficiency coefficient, the consistency of the changing trends and the degree of numerical similarity between simulated runoff and actual observed runoff can be comprehensively evaluated, overcoming the limitations of evaluation by a single indicator.

[0094] Step S305: Select the parameter to be calibrated that minimizes the fitness function value as the optimal parameter, and take the precipitation and potential evapotranspiration of the time period to be predicted as input to output the simulated runoff.

[0095] Please see details Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0096] This embodiment also provides a runoff simulation and prediction system suitable for eco-hydrology in bedrock mountainous areas. This system is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0097] This embodiment provides a runoff simulation and prediction system suitable for eco-hydrology in bedrock mountainous areas, such as... Figure 3 As shown, it includes: The acquisition module 301 is used to acquire the input dataset of the bedrock mountain eco-hydrological model. The input dataset includes geographical data, geological data, meteorological data, and hydrological data. The meteorological data includes precipitation and potential evapotranspiration. The conversion module 302 is used to convert precipitation and potential evapotranspiration from different meteorological stations into grid data and to unify the spatial resolution of the grid data. The calculation module 303 is used to input geographic data, geological data, and meteorological data after unified spatial resolution into the bedrock mountain eco-hydrological model, and calculate the effective water content of the weathering crust, the available water content of the weathering crust vegetation, climate-underlying surface non-physical parameters, atmospheric potential evapotranspiration, cover layer potential evapotranspiration, evaporation coefficient, and simulated runoff in sequence. The climate-underlying surface non-physical parameters are determined based on the parameters to be calibrated, the available water content of the weathering crust vegetation, and precipitation. Function construction module 304 is used to construct a fitness function by combining deterministic coefficients and Nash efficiency coefficients; The output module 305 is used to select the parameter to be calibrated that minimizes the fitness function value as the optimal parameter. It takes the precipitation and potential evapotranspiration of the time period to be predicted as input and outputs the simulated runoff.

[0098] In some alternative implementations, the system further includes: The assignment module is used to assign values ​​to the calibration parameters according to a preset step size; The Climate-Underlying Surface Non-Physical Parameters module is used to calculate climate-underlying surface non-physical parameters using the assigned parameters to be calibrated. The retention module is used to determine that a parameter is valid and retain it if the calculated climate-underlying non-physical parameter is within the preset parameter range. The elimination module is used to determine that a calculated climate-underlying non-physical parameter is invalid and eliminate it if the calculated parameter is not within the preset parameter range.

[0099] In some alternative implementations, function construction module 304 includes: The first calculation unit is used to calculate the deterministic coefficient, which is used to evaluate the consistency between the simulated runoff and the actual observed runoff in terms of the trend of change. The second calculation unit is used to calculate the Nash efficiency coefficient, which is used to evaluate the numerical similarity between simulated runoff and actual observed runoff. The third calculation unit is used to calculate the sum of the squared deviations of the deterministic coefficients from their corresponding ideal values ​​and the squared deviations of the Nash efficiency coefficients from their corresponding ideal values, and the result of taking the square root of the sum is used as the fitness function.

[0100] In some optional implementations, the determinism coefficient is calculated using the following formula:

[0101] in, For the deterministic coefficient, For the first t Actual observed runoff over the time period This represents the average of the actual observed runoff from time period 1 to time period T. For the first t Simulated runoff over a time period This represents the average simulated runoff from time period 1 to time period T; The formula for calculating the Nash efficiency coefficient is as follows:

[0102] in, NSE Nash efficiency coefficient; The fitness function formula is as follows:

[0103] in, This is the fitness function.

[0104] In some alternative implementations, the computing module 303 includes: The fourth calculation unit is used to calculate the ratio of available water content of weathered crust vegetation to precipitation. The result of multiplying the parameter to be calibrated with the ratio of available water content of weathered crust vegetation to precipitation is determined as the climate-underlying surface non-physical parameter.

[0105] In some alternative implementations, the evaporation coefficient is calculated according to the following formula:

[0106] in, The evaporation coefficient is... This represents the actual evaporation rate. For precipitation, This represents the potential evapotranspiration of the covering layer. This refers to climate-underlying non-physical parameters.

[0107] In some alternative implementations, the simulated runoff is calculated according to the following formula:

[0108] in, To simulate runoff, For precipitation, This represents the actual evaporation rate. For storing variables.

[0109] The runoff simulation and prediction system for bedrock mountain eco-hydrology provided in this invention can execute the runoff simulation and prediction method for bedrock mountain eco-hydrology provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0110] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0111] The following is a detailed reference. Figure 4 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 402 or a program loaded from memory 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the electronic device. The processor 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0112] Typically, the following devices can be connected to I / O interface 405: input devices 406 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 407 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 408 including, for example, magnetic tapes, hard disks, etc.; and communication devices 409. Communication device 409 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 4 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0113] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 409, or installed from a memory 408, or installed from a ROM 402. When the computer program is executed by the processor 401, it performs the functions defined in the runoff simulation and prediction method for eco-hydrology in bedrock mountainous areas according to embodiments of the present invention.

[0114] Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0115] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the runoff simulation and prediction method applicable to eco-hydrology in bedrock mountainous areas shown in the above embodiments is implemented.

[0116] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0117] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended invention.

Claims

1. A method for simulating and predicting runoff applicable to eco-hydrology in bedrock mountainous areas, characterized in that, The method includes: Obtain the input dataset for the bedrock mountain eco-hydrological model. The input dataset includes geographical data, geological data, meteorological data, and hydrological data. The meteorological data includes precipitation and potential evapotranspiration. Precipitation and potential evapotranspiration from different weather stations were converted into gridded data, and the spatial resolution of the gridded data was standardized. The geographic data, the geological data, and the meteorological data after unified spatial resolution are input into the bedrock mountain eco-hydrological model. The effective water content of the weathering crust, the available water content of the weathering crust vegetation, the climate-underlying surface non-physical parameters, the atmospheric potential evapotranspiration, the potential evapotranspiration of the cover layer, the evaporation coefficient, and the simulated runoff are calculated in sequence. The climate-underlying surface non-physical parameters are determined based on the parameters to be calibrated, the available water content of the weathering crust vegetation, and the precipitation. The fitness function is constructed by combining the deterministic coefficient and the Nash efficiency coefficient; The parameter to be calibrated that minimizes the fitness function value is selected as the optimal parameter. The precipitation and potential evapotranspiration for the time period to be predicted are used as inputs, and the simulated runoff is output.

2. The method according to claim 1, characterized in that, The method further includes: Assign values ​​to the waiting-to-rate parameters according to the preset step size; Calculate climate-underlying surface non-physical parameters using the assigned parameters to be calibrated; If the calculated climate-underlying non-physical parameters are within the preset parameter range, the parameter is deemed valid and retained. If the calculated climate-underlying non-physical parameters are not within the preset parameter range, the parameter is deemed invalid and removed.

3. The method according to claim 1, characterized in that, The construction of the fitness function by combining the deterministic coefficient and the Nash efficiency coefficient includes: Calculate the coefficient of determination, which is used to evaluate the consistency of the variation trend between simulated runoff and actual observed runoff; Calculate the Nash efficiency coefficient, which is used to assess the numerical similarity between simulated runoff and actual observed runoff; The sum of the squared deviations of the deterministic coefficients from their corresponding ideal values ​​and the squared deviations of the Nash efficiency coefficients from their corresponding ideal values ​​is calculated, and the square root of the sum is used as the fitness function.

4. The method according to claim 1, characterized in that, The formula for calculating the coefficient of determination is as follows: in, For the deterministic coefficient, For the first t Actual observed runoff over the time period This represents the average of the actual observed runoff from time period 1 to time period T. For the first t Simulated runoff over a time period This represents the average simulated runoff from time period 1 to time period T; The formula for calculating the Nash efficiency coefficient is as follows: in, NSE Nash efficiency coefficient; The fitness function formula is as follows: in, This is the fitness function.

5. The method according to claim 1, characterized in that, Calculation of climate-underlying nonphysical parameters, including: The ratio of available water content to precipitation in the weathered crust vegetation is calculated, and the result of multiplying the parameter to be calibrated with the ratio of available water content to precipitation in the weathered crust vegetation is determined as a climate-underlying non-physical parameter.

6. The method according to claim 5, characterized in that, Calculate the evaporation coefficient using the following formula: in, The evaporation coefficient is... This represents the actual evaporation rate. For precipitation, This represents the potential evapotranspiration of the covering layer. This refers to climate-underlying non-physical parameters.

7. The method according to claim 6, characterized in that, Calculate the simulated runoff using the following formula: in, To simulate runoff, For precipitation, This represents the actual evaporation rate. For storing variables.

8. A runoff simulation and prediction system suitable for eco-hydrology in bedrock mountainous areas, characterized in that, The system includes: The acquisition module is used to acquire the input dataset of the bedrock mountain eco-hydrological model. The input dataset includes geographical data, geological data, meteorological data, and hydrological data. The meteorological data includes precipitation and potential evapotranspiration. The conversion module is used to convert precipitation and potential evapotranspiration from different weather stations into gridded data and to unify the spatial resolution of the gridded data. The calculation module is used to input the geographic data, the geological data, and the meteorological data after unified spatial resolution into the bedrock mountain eco-hydrological model, and to calculate the effective water content of the weathering crust, the available water content of the weathering crust vegetation, the climate-underlying surface non-physical parameters, the atmospheric potential evapotranspiration, the potential evapotranspiration of the cover layer, the evaporation coefficient, and the simulated runoff in sequence. The climate-underlying surface non-physical parameters are determined based on the parameters to be calibrated, the available water content of the weathering crust vegetation, and the precipitation. The function building module is used to construct the fitness function by combining the deterministic coefficients and the Nash efficiency coefficients; The output module is used to select the parameter to be calibrated that minimizes the fitness function value as the optimal parameter. It takes the precipitation and potential evapotranspiration of the time period to be predicted as input and outputs the simulated runoff.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the runoff simulation and prediction method applicable to the eco-hydrology of bedrock mountainous areas as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the runoff simulation and prediction method applicable to the eco-hydrology of bedrock mountainous areas as described in any one of claims 1 to 7.

Images