CN117036099A - Spatially fine accounting method and spatially fine accounting system suitable for vegetation flood regulation - Google Patents

Abstract

The present invention provides a spatially refined accounting method and system adapted to vegetation flood storage volume. The method includes: obtaining basic data of a target area; performing numerical division based on the basic data to obtain the annual target rainfall; The annual target rainfall is combined with the early soil moisture level and the soil hydrology grouping level to divide the ecological type regions to obtain the annual target runoff under different ecological types; based on the annual target rainfall and the annual target runoff based on The preset formula calculates the annual vegetation flood storage volume in the target area. This invention breaks through the previous traditional technology method of relying on monitoring data to obtain the rainstorm runoff coefficient to calculate the vegetation flood storage volume, and calculates the annual heavy rain runoff volume and the annual vegetation flood storage volume under different ecosystems by combining open source remote sensing data and hydrological models. It provides accurate and spatial accounting of flood storage volume in different ecosystem types.

Description

Spatial fine accounting method and system adapted to vegetation flood storage volume

Technical field

The invention relates to the technical fields of flood regulation and data processing, and in particular to a spatially refined accounting method and system adapted to vegetation flood regulation and storage amounts.

Background technique

The realization of the value of ecological products provides a solution for the transformation of ecological advantages into economic and social development advantages, and the Gross Ecosystem Product (GEP) is a measure of the value of "lucid waters and green mountains" and the evaluation of ecological benefits in the process of realizing the value of ecological products. The specific indicators, and the scientificity and accuracy of its accounting methods have also received widespread attention. Among them, the flood regulation and storage indicator is an essential indicator in the accounting of GEP regulation services.

At present, due to practical factors such as difficulty in monitoring and obtaining heavy rain runoff coefficient, the scientificity and accuracy of vegetation flood storage under different ecosystem types need to be improved. Therefore, vegetation flood storage is carried out based on differences in land use types. Spatial and precise accounting is particularly important. Traditional on-site monitoring is not refined and has low accuracy.

Contents of the invention

The object of the present invention is to provide a method and system for spatially fine accounting of vegetation flood storage volume, which is used to solve the problem of spatial fine accounting of vegetation flood storage volume.

In the first aspect, this application provides a spatially refined accounting method adapted to vegetation flood storage volume. The method includes:

Obtain basic data of the target area, where the basic data includes monitoring station data and soil data;

Perform numerical division based on the basic data to obtain the annual target rainfall;

Based on the annual target rainfall combined with the early soil moisture level and soil hydrology grouping level, ecological type regions are divided to obtain the annual target runoff under different ecological types;

Based on the annual target rainfall and the annual target runoff, the annual vegetation flood storage amount of the target area is calculated based on a preset formula.

In a possible implementation of this application, obtaining basic data of the target area specifically includes:

The monitoring station data is obtained based on the monitoring station installed in the target area, and the monitoring station data includes the longitude and latitude coordinates of the monitoring station and the daily rainfall data of the monitoring station;

The soil data is obtained based on the world soil database and the target area. The soil data includes land use type remote sensing raster data, soil clay particles, soil sand particles and soil organic matter percentage content remote sensing raster data.

In a possible implementation of this application, numerical division is performed based on the basic data to obtain the annual target rainfall, specifically including:

Numerical division is performed based on the daily rainfall data of the monitoring station, where,

If the day's rainfall data is greater than or equal to the preset threshold, the corresponding rainfall data is extracted as the day's target rainfall;

If the rainfall data for that day is less than the preset threshold value, the rainfall data is reset to zero and used as the target rainfall for that day;

The target rainfall is screened day by day and summed to obtain the annual target rainfall corresponding to each monitoring station.

In a possible implementation of this application, the annual target runoff volume under different ecological types is obtained by dividing the ecological type regions based on the annual target rainfall combined with the early soil moisture level and the soil hydrology grouping level, specifically including :

Determine the early soil moisture level and soil hydrological grouping level;

Based on the early soil moisture level and the soil hydrological grouping level, the runoff curve values under different ecosystem types are determined using the hypothesis method in combination with different ecological types;

Calculate the potential maximum water storage load corresponding to the soil based on the runoff curve value;

Calculate the target stormwater runoff volume based on the potential maximum water storage load and the target rainfall amount;

The annual target runoff is calculated based on the target stormwater runoff.

In a possible implementation of this application, determining the early soil moisture level and soil hydrology grouping level specifically includes:

If the rainfall data of the monitoring station on that day is greater than or equal to the preset threshold value, filter and sum to obtain the cumulative rainfall of the preset days before that day to determine the early soil moisture level;

The soil hydrology grouping level corresponding to the monitoring station is calculated based on the saturated hydraulic conductivity formula.

In a possible implementation of this application, the potential maximum water storage load corresponding to the soil is calculated based on the first formula. The first formula is as follows:

Among them, S is the potential maximum water storage load, and CN is the runoff curve value; the target stormwater runoff is calculated based on the second formula, and the second formula is as follows:

Among them, Q is the target heavy rain runoff, and P is the target rainfall; the annual target runoff is calculated based on the third formula, and the third formula is as follows:

Among them, R _fi is the annual target runoff volume, and n is the number of natural days.

In a possible implementation of this application, the annual vegetation flood storage amount of the target area is calculated based on the annual target rainfall and the annual target runoff based on a preset formula, which specifically includes:

Obtain the first spatial raster data of the annual target rainfall;

Obtain the second spatial raster data of the annual heavy rain runoff;

The annual vegetation flood storage amount is calculated based on the first spatial grid data and the second spatial grid data using the preset formula. The preset formula is as follows:

Among them, C _vc is the annual vegetation flood storage amount, _Ph is the annual target rainfall, R _fi is the annual target runoff, and S _iv is the spatial resolution.

In the second aspect, this application provides a spatially refined accounting system adapted to vegetation flood storage volume. The system includes:

An acquisition module, used to acquire basic data of the target area, where the basic data includes monitoring station data and soil data;

A division module, used to perform numerical division based on the basic data to obtain the annual target rainfall;

A combination module used to divide the ecological type regions based on the annual target rainfall combined with the early soil moisture level and the soil hydrology grouping level to obtain the annual target runoff under different ecological types;

The calculation module is used to calculate the annual vegetation flood storage amount of the target area based on the preset formula based on the annual target rainfall and the annual target runoff.

In a third aspect, the present application provides the above-mentioned computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the spatially refined accounting method adapted to the vegetation flood storage amount is implemented.

In a fourth aspect, the present application provides the above-mentioned electronic device. The electronic device includes: a processor and a memory; wherein the memory is used to store a computer program, and the processor is used to load and execute the computer program, so as to The electronic device is caused to execute the spatialized fine accounting method adapted to the vegetation flood storage amount.

As mentioned above, the present invention combines open source remote sensing image data with hydrological models to provide a more refined calculation method for vegetation flood storage volume compared to the rainstorm runoff coefficient method. The hypothesis method is used to calculate the data. The calculation method of preprocessing and then spatializing data simplifies the complex spatialization process of intermediate links. Compared with the existing technology, the present invention can efficiently and conveniently calculate the next year of different ecosystems with less reliance on data support. The amount of heavy rainstorm runoff and the annual vegetation flood storage volume realize accurate and spatial accounting of the flood storage volume of different ecosystem types.

Description of the drawings

Figure 1 shows a method step diagram in one embodiment of the spatially refined accounting method adapted to vegetation flood storage amount according to the present invention;

Figure 2 shows a schematic diagram of the method steps in one embodiment of the spatially refined accounting method adapted to vegetation flood storage amount according to the present invention;

Figure 3 shows a schematic diagram of the method steps in one embodiment of the spatially refined accounting method adapted to vegetation flood storage amount according to the present invention;

Figure 4 shows a schematic diagram of the raster data of the target rainfall in one embodiment of the spatially refined accounting method adapted to vegetation flood storage amount according to the present invention;

Figure 5 shows a schematic diagram of the method steps in one embodiment of the spatially refined accounting method adapted to vegetation flood storage amount according to the present invention;

Figure 6 shows a schematic diagram of the method steps in one embodiment of the spatially refined accounting method adapted to vegetation flood storage amount according to the present invention;

Figure 7 shows a schematic diagram of the target stormwater runoff raster data in one embodiment of the spatially refined accounting method adapted to vegetation flood storage amount according to the present invention;

Figure 8 shows a raster data diagram of the vegetation flood storage amount in one embodiment of the spatially refined accounting method adapted to the vegetation flood storage amount of the present invention;

Figure 9 shows a schematic structural diagram of the spatially refined accounting system adapted to vegetation flood storage volume in one embodiment of the present invention;

FIG. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Component label description

Steps S102～S108

Steps S202～S204

Steps S302～S308

Steps S502～S510

Steps S602～S604

80 A spatially refined accounting system adapted to vegetation flood storage volume

81 Get module

82 Divide modules

83 Combined Modules

84 computing modules

Detailed ways

The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, as long as there is no conflict, the following embodiments and the features in the embodiments can be combined with each other.

It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the drawings only show the components related to the present invention and do not follow the number, shape and number of components during actual implementation. Dimension drawing, in actual implementation, the type, quantity and proportion of each component can be arbitrarily changed, and the component layout type may also be more complex.

Please refer to Figure 1. In one embodiment of the present invention, the spatially refined accounting method adapted to vegetation flood storage amount includes the following steps:

Step S102, obtain basic data of the target area, where the basic data includes monitoring station data and soil data;

Step S104: Perform numerical division based on the basic data to obtain the annual target rainfall;

Step S106, divide the ecological type regions based on the annual target rainfall combined with the early soil moisture level and the soil hydrology grouping level to obtain the annual target runoff under different ecological types;

Step S108: Calculate the annual vegetation flood storage amount of the target area based on a preset formula based on the annual target rainfall and the annual target runoff.

It should be noted that obtaining the basic data of the target area, as shown in Figure 2, specifically includes the following steps:

Step S202: Obtain the monitoring station data based on the monitoring station installed in the target area. The monitoring station data includes the longitude and latitude coordinates of the monitoring station and the daily rainfall data of the monitoring station;

Step S204, obtain the soil data based on the world soil database and the target area. The soil data includes remote sensing raster data of land use types, remote sensing raster data of soil clay, soil sand and soil organic matter percentage content.

Specifically, there are multiple hydrological monitoring stations in different target areas. Therefore, it is necessary to obtain corresponding monitoring data based on the monitoring stations set up in the target area. Correspondingly, the monitoring data includes the longitude and latitude coordinates of the monitoring stations. As well as the daily rainfall data of the monitoring station, the soil data is obtained based on the Harmonized World Soil Database version (HWSD) data table and the corresponding target area. Correspondingly, the soil data includes soil clay particles, Remote sensing raster data of soil sand and soil organic matter percentage content. Remote sensing raster data of land use type is obtained through remote sensing data. Among them, soil clay, soil sand and soil organic matter percentage content are rasterized and processed according to the target area. The vector boundary is processed by the mask tool in ArcGIS to obtain the raster data of soil clay, soil sand, and soil organic matter percentage content in the target area. In one embodiment, the resampling tool in ArcGIS can be used to map the soil clay, soil sand, and soil organic matter percentage content. The raster data corresponding to the soil sand grains and soil organic matter percentage content are resampled according to the spatial resolution of "30×30m" to obtain processed data consistent with the resolution of the land use type remote sensing raster data.

Further, in an embodiment of the invention, as shown in Figure 3, numerical division is performed based on the basic data to obtain the annual target rainfall, which specifically includes the following steps:

Step S302, perform numerical division based on the daily rainfall data of the monitoring station;

Step S304: If the day's rainfall data is greater than or equal to the preset threshold, then extract the corresponding rainfall data as the day's target rainfall;

Step S306: If the rainfall data for the day is less than the preset threshold, reset the rainfall data to zero as the target rainfall for the day;

Step S308: Screen the target rainfall daily and perform summation to obtain the annual target rainfall corresponding to each monitoring station.

It should be noted that the numerical division is carried out based on the daily rainfall data of the monitoring station. Specifically, according to the daily rainfall data of each hydrological monitoring station in the target area, P-day heavy rains are divided according to the "24" hourly precipitation exceeding "50mm". Standard, where the preset boundary value is "50mm". If the rainfall data on that day is greater than or equal to the preset boundary value, the corresponding rainfall data is extracted as the target rainfall on that day. Correspondingly, that is, on that day The corresponding P is the specific rainfall data; if the rainfall data of the day is less than the preset threshold value, the rainfall data is reset to zero and used as the target rainfall of the day, that is, the corresponding P of the day is zero.

Then, the daily heavy rain amounts are screened and summed to obtain the annual heavy rain rainfall P _h at each hydrological monitoring station, and the spatial raster data of the annual heavy rain rainfall in the target area is obtained through spatial interpolation. Specifically, the annual heavy rain rainfall spatial raster data will be obtained with the annual heavy rain rainfall The hydrological monitoring stations with quantitative data were imported into ArcGIS and the Kriging interpolation method was used to interpolate to obtain spatial raster data of annual heavy rain rainfall in the target area. The spatial resolution was selected as "30×30m". According to the vector boundary of the target area, the spatial raster data of the annual heavy rain rainfall in the target area is obtained through the mask tool in ArcGIS, as shown in Figure 4, which is a schematic diagram of the raster data of the target rainfall.

Further, in one embodiment of the invention, as shown in Figure 5, the annual target path under different ecological types is obtained by dividing the ecological type regions based on the annual target rainfall combined with the early soil moisture level and the soil hydrology grouping level. traffic, specifically including the following steps:

Step S502, determine the early soil moisture level and soil hydrology grouping level;

Step S504, based on the early soil moisture level and the soil hydrology grouping level, using the hypothesis method in combination with different ecological types to determine the runoff curve values under different ecosystem types;

Step S606: Calculate the potential maximum water storage load corresponding to the soil based on the runoff curve value;

Step S508: Calculate the target stormwater runoff volume based on the potential maximum water storage load and the target rainfall volume;

Step S510: Calculate the annual target runoff volume based on the target stormwater runoff volume.

Specifically, as shown in Figure 6, determining the early soil moisture level and soil hydrological grouping level includes the following steps:

Step S602, if the rainfall data of the monitoring station on that day is greater than or equal to the preset threshold value, filter and sum up to obtain the cumulative rainfall of the preset days before that day to determine the early soil moisture level;

Step S604: Calculate the soil hydrology grouping level corresponding to the monitoring station based on the saturated hydraulic conductivity formula.

It should be noted that the soil early moisture level (AMC, Antecedent MoistureCondition) and soil hydrology grouping level must first be determined. Among them, the preset number of days is taken as "5" days, and the daily heavy rain rainfall at each hydrological monitoring station in the target area is determined. Distribution date (where the corresponding heavy rain rainfall is the target rainfall with a non-zero value), filter and sum to obtain the cumulative rainfall of the site "5" days before the day of the heavy rain, combined with the period of the heavy rain day, it is the growing period or The dormant period (if the heavy rain day occurs in the "April to October" months of the year, it is generally treated as the growing period, otherwise it is considered to be in the dormant period) to determine the soil moisture level in the early stage of each heavy rain day at each hydrological monitoring station. Table 1 shows The preliminary soil moisture level determination table is provided.

Table 1. Early soil moisture level determination table

Further, it is necessary to determine the soil hydrological grouping level, wherein the soil hydrological grouping level corresponding to the monitoring station is calculated based on the saturated hydraulic conductivity formula. The saturated hydraulic conductivity formula is as follows:

K′=(0.056×Clay+0.016×Sand+0.231×Orga-0.693)×60;

Among them, K′ is the saturated hydraulic conductivity, the unit is mm/h; Clay is the proportion of soil clay particles on the unit grid, the unit is %; Sand is the proportion of soil sand particles on the unit grid, the unit is %; Orga is the proportion of soil organic matter on the unit grid, the unit is %, and then different soil hydrological groupings K correspond to different values, where,

Among them, A, B, C, and D correspond to different soil hydrological groupings, K is the soil hydrological grouping, which is a dimensionless quantity, and then the K′ saturation is calculated based on the remote sensing grid data of soil clay, soil sand, and soil organic matter percentage content. The hydraulic conductivity remote sensing raster data is used to determine the soil hydrological grouping level corresponding to each hydrological monitoring station in the target area according to the data interval where K′ is located.

Furthermore, the hypothesis method is used to determine the CN values under different ecosystem types such as farmland, forest, shrub, wetland and hardened surface in the target area. CN is the runoff curve number, that is, it is assumed that the target area is all farmland. , or all forests, or all shrubs, or all wetlands, or all hardened surfaces, determine the CN values of different hydrological monitoring stations under different rainstorm day conditions, and calculate the soil CN values from the obtained CN values. The potential maximum water storage load S, combined with the relationship between the daily heavy rain rainfall P and the potential maximum water storage load S of the soil, determines the Q day heavy rain runoff and further accumulates it to obtain the annual heavy rain runoff R _fi under different ecosystem types. Through spatial interpolation, the spatial raster data of annual heavy rainfall runoff in the target area under different ecosystem types is obtained, and then combined with the land use type data, a raster calculator is used in ArcGIS to calculate the annual heavy rainfall runoff consistent with the land use type of the target area. Spatial raster data.

Specifically, based on the early soil moisture level and the soil hydrology grouping level, the hypothesis method is used to determine the runoff curve values under different ecosystem types in combination with different ecological types, wherein, according to the soil hydrology grouping level where the hydrological monitoring site is located , combined with the soil moisture level in the early days of heavy rain at each site, assuming that the ecosystem types are all farmland, forest, shrub, wetland and hardened surface, determine the CN values of different ecosystem types at each site under different rainstorm days. Correspondingly, the table 2 shows the CN value table.

Table 2.CN value table

Further, the potential maximum water storage load corresponding to the soil is calculated based on the first formula. The first formula is as follows:

Among them, S is the potential maximum water storage load, in mm, and CN is the value of the runoff curve, a dimensionless value. According to the CN value of each hydrological monitoring station under different heavy rain days, the target area is calculated according to the above formula. Potential maximum water storage load of S soil when all are farmland, forest, shrubland, wetland and hard surface conditions;

The target stormwater runoff is calculated based on the second formula, which is as follows:

Among them, Q is the target amount of rainstorm runoff (i.e., the amount of runoff), in mm, and P is the target rainfall amount, in mm. According to the maximum potential of S soil at each hydrological monitoring station on different rainstorm days The water storage load is calculated according to the above formula to obtain the target heavy rain runoff of each hydrological monitoring station in the target area, all of which are farmland, forest, shrub, wetland and hardened surface conditions on different rainy days. Among them, the target heavy rain runoff of the wetland ecosystem is directly All values are "0";

The annual target runoff is calculated based on the third formula, which is as follows:

Among them, R _fi is the annual target runoff, in mm, and n is the number of natural days. According to the daily heavy rain runoff on different rainy days at each hydrological monitoring station throughout the year, it is accumulated that all hydrological monitoring stations in the target area are farmland, The R _fi annual heavy rain runoff in forests, shrubs, wetlands and hardened surface conditions is assumed to be all farmland ecosystems, all forest ecosystems, or all shrub ecosystems for each hydrological monitoring station in the target area. Or perform spatial interpolation on the annual heavy rain runoff data of various hydrological monitoring stations that are all wetland ecosystems or all hardened surface ecosystems to obtain spatial raster data of annual heavy rain runoff in different ecosystems. The spatial resolution is "30×30m". , according to the vector boundary of the target area, the mask tool in ArcGIS is used to obtain the annual heavy rain runoff spatial raster data in the target area. Correspondingly, as shown in Figure 7, a schematic diagram of the target heavy rain runoff raster data is displayed. In addition , according to the vector boundary of the target area, the remote sensing raster data of land use types within the target area is obtained through processing with the mask tool in ArcGIS. The land use type remote sensing raster data is assigned a value according to different ecosystem types. The farmland ecosystem is assigned a value of "1", the forest ecosystem is assigned a value of "2", the shrub ecosystem is assigned a value of "3", and the hardened surface ecosystem is assigned a value of "3". is "4", and the wetland ecosystem is assigned a value of "5". Use the raster calculator to perform the following operations Con("Ecosystem Classification"<2,"Total Annual Heavy Rainfall Volume of Farmland Ecosystem", Con("Ecosystem Classification"<3,"Total Annual Heavy Rainfall Volume of Forest Ecosystem", Con("Ecosystem Classification"<4,"Total Annual Heavy Rainfall Volume of Shrub Ecosystem",Con("Ecosystem Classification"<5,"Total Annual Heavy Rainfall Volume of Hardened Surface",0)))), output The raster file is the spatial raster data of annual heavy rainfall runoff that is consistent with the land use type in the target area.

Further, based on the annual target rainfall and the annual target runoff, the annual vegetation flood storage amount of the target area is calculated based on a preset formula, which specifically includes: obtaining the first spatial grid of the annual target rainfall. data; obtain the second spatial grid data of the annual heavy rain runoff; calculate the annual vegetation flood adjustment based on the first spatial grid data and the second spatial grid data using the preset formula Storage capacity, the default formula is as follows:

Among them, C _vc is the annual vegetation flood storage amount, P _h is the annual target rainfall, R _fi is the annual target runoff, and S _iv is the spatial resolution. Correspondingly, due to this The spatial resolution used in the application embodiment is "30×30m". Therefore, based on the obtained spatial raster data of annual heavy rain rainfall and annual heavy rain runoff in the target area, in the grid calculator according to the formula C _vc = (P _h -R _fi ) × 10 ^-3 × 900 to obtain the spatial raster data of annual vegetation flood storage volume in the target area at the "30 × 30m" resolution. Correspondingly, as shown in Figure 8, it is displayed as vegetation Raster data map of flood storage volume.

Embodiments of the present application also provide a spatially fine accounting system adapted to vegetation flood regulation and storage. The spatial fine accounting system adapted to vegetation flood regulation and storage can realize the method described in this application and adapted to vegetation flood regulation. However, the implementation device of the spatially refined accounting method adapted to the vegetation flood storage amount described in this application includes but is not limited to the spatially refined spatial accounting method adapted to the vegetation flood storage amount listed in this embodiment. As for the structure of the accounting system, all structural modifications and replacements of the prior art based on the principles of this application are included in the protection scope of this application.

Please refer to Figure 9. In one embodiment, this embodiment provides a spatially refined accounting system 90 adapted to vegetation flood storage volume. The system includes:

The acquisition module 91 is used to acquire basic data of the target area, where the basic data includes monitoring station data and soil data;

The division module 92 is used to perform numerical division based on the basic data to obtain the annual target rainfall;

The combination module 93 is used to divide the ecological type regions based on the annual target rainfall combined with the early soil moisture level and the soil hydrology grouping level to obtain the annual target runoff under different ecological types;

The calculation module 94 is configured to calculate the annual vegetation flood storage amount of the target area based on a preset formula based on the annual target rainfall and the annual target runoff.

Since the specific implementation manner of this embodiment corresponds to the foregoing method embodiment, the same details will not be repeated here. Those skilled in the art should also understand that the division of each module in the embodiment of Figure 9 is just a The division of logical functions can be fully or partially integrated into one or more physical entities during actual implementation, and these modules can all be implemented in the form of software calls through processing elements, or they can all be implemented in the form of hardware, or some modules It is implemented by calling software through processing elements, and some modules are implemented by hardware.

Referring to Figure 10, this embodiment provides an electronic device. Specifically, the electronic device at least includes: a memory and a processor connected through a bus, wherein the memory is used to store computer programs, and the processor is used to execute the computer program stored in the memory, so as to Perform all or part of the steps in the foregoing method embodiments.

In summary, by combining open source remote sensing image data with hydrological models, a more refined method for calculating vegetation flood storage volume is provided compared to the heavy rain runoff coefficient method. The hypothesis method is used to predict the data. The calculation method of processing and then spatializing the data simplifies the complex spatialization process of the intermediate links. Compared with the existing technology, the present invention can efficiently and conveniently calculate next year's heavy rainstorms in different ecosystems with less reliance on data support. The runoff volume and annual vegetation flood storage volume realize accurate and spatial accounting of flood storage volume of different ecosystem types.

In the several embodiments provided in this application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of modules/units is only a logical function division. In actual implementation, there may be other division methods, for example, multiple modules or units may be combined or can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be indirect coupling or communication connection through some interfaces, devices or modules or units, which may be in electrical, mechanical or other forms.

Modules/units described as separate components may or may not be physically separate. Components shown as modules/units may or may not be physical modules, that is, they may be located in one place, or they may be distributed to multiple network units. superior. Some or all of the modules/units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, each functional module/unit in various embodiments of the present application can be integrated into a processing module, or each module/unit can exist physically alone, or two or more modules/units can be integrated into one module/unit. in the unit.

Those of ordinary skill in the art should further realize that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of both. In order to clearly illustrate the hardware and software interchangeability. In the above description, the composition and steps of each example have been generally described according to functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each specific application, but such implementations should not be considered beyond the scope of this application.

An embodiment of the present application also provides a computer-readable storage medium. Those of ordinary skill in the art can understand that all or part of the steps in the methods for implementing the above embodiments can be completed by instructing the processor through a program. The program can be stored in a computer-readable storage medium, and the storage medium is non-transitory. (non-transitory) media, such as random access memory, read-only memory, flash memory, hard disk, solid state drive, magnetic tape, floppy disk, optical disc and any combination thereof. The above-mentioned storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrated with one or more available media. The available media may be magnetic media (eg, floppy disk, hard disk, tape), optical media (eg, digital video disc (DVD)), or semiconductor media (eg, solid state disk (SSD)), etc.

Embodiments of the present application may also provide a computer program product, where the computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computing device, the processes or functions described in accordance with the embodiments of the present application are generated in whole or in part. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, e.g., the computer instructions may be transmitted over a wired connection from a website, computer, or data center. (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) to another website, computer or data center.

When the computer program product is executed by a computer, the computer executes the method described in the foregoing method embodiment. The computer program product can be a software installation package. If the foregoing method needs to be used, the computer program product can be downloaded and executed on the computer.

The descriptions of the processes or structures corresponding to each of the above drawings have different emphasis. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

The above embodiments only illustrate the principles and effects of the present application, but are not used to limit the present application. Anyone familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical ideas disclosed in this application shall still be covered by the claims of this application.

Claims (10)

1. A spatialization fine accounting method adapted to vegetation flood regulation, characterized by comprising:

basic data of a target area is obtained, wherein the basic data comprise monitoring station data and soil data;

performing numerical division based on the basic data to obtain annual target rainfall;

carrying out ecological type area division based on the annual target rainfall and the soil early-stage wettability level and the soil hydrologic grouping level to obtain annual target runoff under different ecological types;

and calculating annual vegetation flood regulation quantity of the target area based on the annual target rainfall and annual target runoff and a preset formula.

2. The method for spatially fine accounting adapted to vegetation flood regulation according to claim 1, wherein the acquiring basic data of the target area specifically comprises:

acquiring monitoring station data based on monitoring stations arranged in the target area, wherein the monitoring station data comprise longitude and latitude coordinates of the monitoring stations and daily rainfall data of the monitoring stations;

and combining the target area based on a world soil database to obtain the soil data, wherein the soil data comprises land utilization type remote sensing grid data, and soil clay, soil sand and soil organic matter percentage content remote sensing grid data.

3. The spatialization fine accounting method adapted to vegetation flood regulation according to claim 2, wherein the numerical division based on the basic data to obtain annual target rainfall specifically comprises:

numerical division is performed based on the daily rainfall data of the monitoring station, wherein,

if the rainfall data of the current day is larger than or equal to a preset limit value, extracting the corresponding rainfall data as the target rainfall of the current day;

if the rainfall data of the current day is smaller than the preset limit value, zeroing the rainfall data to be used as the target rainfall of the current day;

and screening the target rainfall every day and summing to obtain the annual target rainfall corresponding to each monitoring station.

4. The spatial fine accounting method suitable for vegetation flood regulation according to claim 3, wherein the annual target runoff under different ecological types is obtained by carrying out ecological type region division based on the annual target rainfall and the soil early-stage wettability level and the soil hydrologic grouping level, and the method specifically comprises the following steps:

determining a soil early-stage wettability grade and a soil hydrologic grouping grade;

based on the soil early-stage wettability level and the soil hydrologic grouping level, adopting a hypothesis method to determine runoff curve values under different ecological system types by combining different ecological types;

calculating a potential maximum water storage load corresponding to the soil based on the runoff curve value;

calculating a target storm track flow based on the potential maximum water storage load in combination with the target rainfall;

and calculating the annual target runoff based on the target storm runoff.

5. The spatialization fine accounting method adapted to vegetation flood regulation according to claim 4, wherein the determining soil pre-wetting grade and soil hydrologic grouping grade specifically comprises:

if the rainfall data of the monitoring station in the current day is larger than or equal to a preset limit value, screening and summing to obtain the accumulated rainfall of the preset days before the current day, and determining the soil early-stage wettability grade;

and calculating the soil hydrologic grouping grade corresponding to the monitoring station based on the saturated water conductivity formula.

6. The method of spatialization fine accounting for vegetation flood regulation according to claim 5, wherein the potential maximum impounded load for the soil is calculated based on a first formula, the first formula being as follows:

wherein S is the potential maximum water storage load, and CN is the runoff curve value; calculating the target storm runoff based on a second formula, wherein the second formula is as follows:

wherein Q is the target storm runoff, and P is the target rainfall; calculating the annual target runoff based on a third formula, wherein the third formula is as follows:

wherein R is _fi And n is the natural days for the annual target runoff.

7. The spatialization fine accounting method adapted to vegetation flood regulation according to claim 6, wherein the annual vegetation flood regulation of the target area is calculated based on a preset formula based on the annual target rainfall and annual target runoff, specifically comprising:

acquiring first space grid data of the annual target rainfall;

acquiring second space grid data of the annual storm runoff;

calculating the annual vegetation flood regulation quantity by using the preset formula based on the first space raster data and the second space raster data, wherein the preset formula is as follows:

wherein C is _vc For the annual vegetation flood regulation, P _h For the annual target rainfall, R _fi For the annual target runoff, S _iv For the spatial resolution.

8. A spatialization fine accounting system adapted to vegetation flood regulation, comprising:

the acquisition module is used for acquiring basic data of a target area, wherein the basic data comprise monitoring station data and soil data;

the dividing module is used for carrying out numerical division based on the basic data to obtain annual target rainfall;

the combination module is used for carrying out ecological type area division based on the annual target rainfall and the soil early-stage wettability grade and the soil hydrologic grouping grade to obtain annual target runoff under different ecological types;

the calculation module is used for calculating annual vegetation flood regulation quantity of the target area based on the annual target rainfall and annual target runoff and a preset formula.

9. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when executed by a processor, implements the spatialization fine accounting method adapted to vegetation flood regulation according to any one of claims 1 to 7.

10. An electronic device, the electronic device comprising: a processor and a memory; wherein the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, to cause the electronic device to execute the spatialization fine accounting method adapted to vegetation flood regulation according to any one of claims 1 to 7.