Method, device and equipment for dynamic analysis of spatial distribution of soil moisture content at hourly level

Abstract

The present invention provides a method, device and equipment for dynamic analysis of spatial distribution of hourly soil moisture content, including: analyzing grid soil moisture content decay coefficient based on meteorological station evaporation data, soil texture and land use type distribution data in the study area; performing daily recursion of soil moisture content in combination with daily rainfall data in the study area to obtain the initial soil moisture content corresponding to the target date in the study area; performing n-hourly rolling simulation of soil moisture content based on hourly dynamic measured meteorological grid surface precipitation data, grid soil moisture content decay coefficient and initial soil moisture content corresponding to the target date in the study area through a pre-constructed n-hourly recursion model of soil moisture content, and obtaining soil moisture content simulation results corresponding to multiple moments in the study area within the target date. The present invention can overcome the defect that the daily recursion formula method affecting rainfall in the early stage is difficult to meet the short-term forecast and warning needs of flash flood disasters.

Description

Technical Field

[0001] The present invention relates to the field of hydrological technology, and in particular to a method, device and equipment for dynamically analyzing the spatial distribution of hourly soil moisture content. Background Art

[0002] The soil moisture content in a watershed has an important impact on the runoff generation in the watershed. It can be used for net rain analysis and the determination of critical rainfall thresholds, and is an important basic information for rainfall warning. Develop an hourly dynamic analysis module for soil moisture content to conduct a dynamic rolling analysis of the soil moisture content in the study watershed, and provide technical support for research and practice related to short-term forecasting and dynamic warning of flash flood disasters.

[0003] At present, the soil moisture simulation based on the daily recursive formula method of the previous rainfall influencing amount can only realize the calculation on a daily scale, simulating the soil moisture content at 8 o'clock every day, and cannot reflect the soil moisture content at a finer time within a day. In addition, since the maximum water storage capacity of the basin is relatively complicated to obtain, the study area is generally generalized as an average value, which is difficult to reflect the impact of the spatial differences of the basin's underlying surface on the soil moisture content. Therefore, the existing methods are insufficient in describing the spatiotemporal variability of soil moisture content, and it is difficult to meet the needs of short-term forecasting and warning of flash flood disasters. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device and equipment for dynamic analysis of spatial distribution of soil moisture content at the hourly level, so as to overcome the defect that the daily recursive formula method of the previous rainfall influencing factor is difficult to meet the short-term forecast and warning needs of flash flood disasters.

[0005] In a first aspect, an embodiment of the present invention provides a method for dynamically analyzing the spatial distribution of soil moisture content at the hourly level, comprising:

[0006] Based on the evaporation data of meteorological stations, soil texture and land use type distribution data in the study area, the grid soil moisture decay coefficient corresponding to the study area is analyzed;

[0007] Based on the daily rainfall data in the study area and the grid soil moisture decay coefficient, the soil moisture content is recursively calculated daily to obtain the initial soil moisture content corresponding to the target date in the study area;

[0008] Through the pre-constructed n-hourly recursive model of soil moisture, based on the hourly dynamic measured meteorological grid surface precipitation data in the study area, the grid soil moisture decay coefficient and the initial soil moisture content of the study area on the target date, a n-hourly rolling simulation of soil moisture is performed to obtain the simulated soil moisture results corresponding to multiple times in the study area within the target date.

[0009] In one embodiment, based on the evaporation data of the meteorological station, the soil texture and the land use type distribution data in the study area, the grid soil moisture decay coefficient corresponding to the study area is analyzed, including:

[0010] Based on the evaporation data of the meteorological stations in the study area, the daily evaporation capacity grid data corresponding to the study area is calculated;

[0011] Also, soil texture and land use type distribution data in the study area are analyzed to calculate the maximum water storage capacity of the basin corresponding to the study area;

[0012] According to the daily evaporation capacity grid data and the maximum water storage capacity of the basin, the grid soil moisture content decline coefficient corresponding to the study area is determined.

[0013] In one embodiment, based on the daily rainfall data in the study area and the grid soil moisture decay coefficient, the soil moisture content is recursively calculated daily to obtain the initial soil moisture content corresponding to the target date in the study area, including:

[0014] Based on the maximum water storage capacity of the watershed, determine the initial soil moisture content of the study area corresponding to the specified historical date;

[0015] Through the daily recursive model of soil moisture, based on the daily rainfall data of the study area from the specified historical date to the target date, the grid soil moisture decay coefficient and the initial soil moisture content of the study area on the specified historical date, the soil moisture content is recursively calculated on a daily basis to obtain the initial soil moisture content of the study area on the target date.

[0016] In one embodiment, a soil moisture content daily recursion model is used to perform soil moisture content daily recursion based on daily rainfall data of the study area from a specified historical date to a target date, a grid soil moisture content decay coefficient, and a starting soil moisture content of the study area corresponding to a specified historical date, to obtain the starting soil moisture content of the study area corresponding to the target date, including:

[0017] For any date between the specified historical date and the target date, if the daily rainfall data of the study area on the previous date corresponding to the date is 0, the initial soil moisture content of the study area on the previous date and the grid soil moisture decay coefficient are used to determine the initial soil moisture content of the study area on the date.

[0018] Furthermore, for any date between the specified historical date and the target date, if the daily rainfall data of the study area on the previous date corresponding to the date is not zero, the initial soil moisture content of the study area on the date is determined based on the initial soil moisture content of the study area on the previous date, the daily rainfall data, and the grid soil moisture decay coefficient.

[0019] In one embodiment, a soil moisture content n-hourly recursive model is pre-constructed, based on the hourly dynamic measured meteorological grid surface precipitation data in the study area, the grid soil moisture content decay coefficient and the initial soil moisture content of the study area on the target date, and a soil moisture content n-hourly rolling simulation is performed to obtain soil moisture content simulation results corresponding to multiple moments in the study area within the target date, including:

[0020] According to the grid soil moisture decay coefficient and the initial soil moisture content corresponding to the target date in the study area, the average n-hour decay water content of the previous soil moisture content is determined;

[0021] For any moment within the target date, the soil moisture simulation result corresponding to the current moment of the study area is determined based on the soil moisture simulation result of the study area at the previous moment corresponding to the moment, the hourly dynamic measured meteorological grid surface precipitation data of the study area from the previous moment corresponding to the moment to the moment, the average n-hour soil moisture retreat of the previous period and the grid soil moisture retreat coefficient.

[0022] In one embodiment, the expression of the n-hour recursive model of soil moisture content is as follows:

[0023] ;

[0024] in, For the study area on the target date Neidi The simulation results of soil moisture content at time For the study area on the target date Neidi The simulation results of soil moisture content at time Represents the number of hours, is the grid soil moisture decay coefficient, Study area on target date The corresponding initial soil moisture content is is the average amount of water that disappears in the previous n hours of soil moisture, Study area on target date Neidi Time to Hourly precipitation data at time.

[0025] In one embodiment, the method further comprises:

[0026] Obtain the measured soil moisture data from the soil moisture station in the study area;

[0027] Convert the measured soil moisture data into the vertical average soil weight moisture content, and convert the soil moisture content simulation results at the soil moisture station location into the average weight moisture content simulation results;

[0028] The vertical average soil weight moisture content and the average weight moisture content simulation results were compared to evaluate the accuracy of the n-hourly recursive model of soil moisture content based on the comparison results.

[0029] In a second aspect, an embodiment of the present invention further provides a device for dynamically analyzing the spatial distribution of soil moisture content at the hourly level, comprising:

[0030] The grid decay coefficient analysis module is used to analyze the grid soil moisture decay coefficient corresponding to the study area based on the evaporation data of the meteorological station, soil texture and land use type distribution data in the study area;

[0031] The moisture content daily recursion module is used to recurse the soil moisture content daily based on the daily rainfall data in the study area and the grid soil moisture content decay coefficient to obtain the initial soil moisture content corresponding to the target date in the study area;

[0032] The soil moisture content n-hourly recursive module is used to perform n-hourly rolling simulation of soil moisture content through a pre-built n-hourly recursive model, based on the hourly dynamic measured meteorological grid surface precipitation data in the study area, the grid soil moisture content decay coefficient and the initial soil moisture content of the study area corresponding to the target date, to obtain the soil moisture content simulation results corresponding to multiple times in the study area within the target date.

[0033] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0034] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0035] The embodiment of the present invention provides a method, device and equipment for dynamic analysis of spatial distribution of hourly soil moisture content. First, based on the evaporation data of meteorological stations in the study area, soil texture and land use type distribution data, the grid soil moisture decay coefficient corresponding to the study area is analyzed; then, based on the daily rainfall data in the study area and the grid soil moisture decay coefficient, the soil moisture content is recursively deduced daily to obtain the starting soil moisture content of the study area corresponding to the target date; finally, through a pre-constructed n-hourly recursive model of soil moisture content, based on the hourly dynamic measured meteorological grid surface precipitation data in the study area, the grid soil moisture decay coefficient and the starting soil moisture content of the study area corresponding to the target date, the soil moisture content is rolled over n hours to obtain the soil moisture simulation results corresponding to multiple moments in the study area within the target date. The above method uses the soil moisture content to recursively obtain the starting soil moisture content corresponding to the target date in the study area on a daily basis, and uses the starting soil moisture content as the starting amount for n-hourly recursion. Combined with the hourly dynamic measured meteorological grid surface precipitation data and the grid soil moisture decay coefficient in the study area, the n-hourly recursive model of soil moisture is used to quickly realize the hourly rolling simulation of the spatial distribution of soil moisture, thereby overcoming the defect that the daily recursive formula method affecting rainfall in the early stage is difficult to meet the short-term forecast and warning needs of flash flood disasters.

[0036] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic diagram of a flow chart of a method for dynamic analysis of spatial distribution of soil moisture content at hourly level provided by an embodiment of the present invention;

[0040] Figure 2 A schematic diagram of the overall process of a method for dynamic analysis of spatial distribution of soil moisture content at hourly level provided by an embodiment of the present invention;

[0041] Figure 3 A schematic diagram of the structure of a device for dynamic analysis of spatial distribution of soil moisture content at hourly level provided by an embodiment of the present invention;

[0042] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] At present, the existing methods do not adequately characterize the spatiotemporal variability of soil moisture content, and are unable to meet the needs of short-term forecasting and warning of flash flood disasters. Based on this, the present invention provides a method, device and equipment for dynamic analysis of the spatial distribution of soil moisture content at the hourly level to overcome the defect that the daily recursive formula method for the previous rainfall influencing factor is unable to meet the needs of short-term forecasting and warning of flash flood disasters.

[0045] To facilitate understanding of this embodiment, firstly, a method for dynamic analysis of spatial distribution of soil moisture content at hourly level disclosed in an embodiment of the present invention is described in detail. Figure 1 The flowchart of a method for dynamic analysis of spatial distribution of hourly soil moisture content is shown in FIG. 1 , and the method mainly includes the following steps S102 to S106:

[0046] Step S102, based on the evaporation data of the meteorological station in the study area, the soil texture and the land use type distribution data, the grid soil moisture decay coefficient corresponding to the study area is analyzed.

[0047] In one example, the 5km grid daily evaporation capacity value of the study area can be analyzed based on the evaporation data of the meteorological station, and the maximum water storage capacity of the study area can be analyzed based on the soil texture and land use type distribution data. The grid soil moisture decay coefficient corresponding to the study area is determined by combining the 5km grid daily evaporation capacity value and the maximum water storage capacity. The decay coefficient is specifically the 5km grid decay coefficient for each month.

[0048] Step S104, based on the daily rainfall data in the study area and the grid soil moisture decay coefficient, the soil moisture content is recursively extrapolated daily to obtain the initial soil moisture content of the study area corresponding to the target date.

[0049] In one example, the starting soil moisture content of the study area on the target date can be determined through the daily recursive model of soil moisture, combined with the daily rainfall data in the study area and the grid soil moisture decay coefficient. The starting soil moisture content is the starting value of the n-hourly recursive model of soil moisture.

[0050] Step S106, through the pre-constructed n-hourly recursive model of soil moisture, based on the hourly dynamic measured meteorological grid surface precipitation data in the study area, the grid soil moisture decay coefficient and the initial soil moisture content of the study area corresponding to the target date, a rolling simulation of the soil moisture content is performed every n hours to obtain the soil moisture simulation results corresponding to multiple times in the study area within the target date.

[0051] In one example, the average n-hour soil moisture decay of the previous period can be determined based on the grid soil moisture decay coefficient and the initial soil moisture content of the study area corresponding to the target date; for any moment within the target date, the soil moisture simulation result of the previous moment, the hourly dynamic measured meteorological grid surface precipitation data of the previous moment, the average n-hour soil moisture decay of the previous period and the grid soil moisture decay coefficient can be combined to determine the soil moisture simulation result corresponding to the study area at the current moment, and the soil moisture simulation results corresponding to multiple moments of the study area within the target date can be obtained through rolling simulation.

[0052] The hourly soil moisture spatial distribution dynamic analysis method provided by the embodiment of the present invention utilizes the daily recursion of soil moisture content to obtain the starting soil moisture content corresponding to the target date in the study area, and uses the soil moisture content as the starting amount for n-hourly recursion. Combined with the hourly dynamic measured meteorological grid surface precipitation data and the grid soil moisture decay coefficient in the study area, the n-hourly recursion model of soil moisture content is utilized to quickly realize the hourly rolling simulation of the spatial distribution of soil moisture content, thereby overcoming the defect that the daily recursion formula method affecting the rainfall in the early stage is difficult to meet the short-term forecast and warning needs of mountain torrent disasters.

[0053] For ease of understanding, an embodiment of the present invention provides a specific implementation of a method for dynamically analyzing the spatial distribution of soil moisture content at the hourly level.

[0054] For the aforementioned step S102, the embodiment of the present invention provides a specific implementation method for analyzing the grid soil moisture decay coefficient corresponding to the study area based on the evaporation data of the meteorological station, the soil texture and the land use type distribution data in the study area, including the following steps 1A to 1C:

[0055] Step 1A, based on the evaporation data of the meteorological stations in the study area, the daily evaporation capacity grid data corresponding to the study area is analyzed and calculated. In specific implementation, the evaporation data of the meteorological stations in the study area are collected, the average daily evaporation capacity of each month of the station is analyzed and calculated, and the inverse distance spatial interpolation is performed based on the daily evaporation capacity of the station to obtain the daily evaporation capacity of the 5 km grid in the entire area for 12 months of the year.

[0056] Step 1B, analyze and calculate the maximum water storage capacity of the basin corresponding to the study area based on the soil texture and land use type distribution data in the study area. In specific implementation, collect the soil texture and land use type distribution data of the study area, analyze the field water holding capacity and wilting coefficient of different land types, and calculate the maximum water storage capacity of the basin according to the following formula:

[0057] The maximum water storage capacity of the basin WM (mm) = ∑ field water holding capacity (%) × soil thickness (mm) - ∑ wilting coefficient (%) × soil thickness (mm).

[0058] Step 1C: Determine the grid soil moisture decay coefficient corresponding to the study area based on the daily evaporation capacity grid data and the maximum water storage capacity of the basin. In specific implementation, based on the spatial distribution map of the 5 km grid of the average daily evaporation capacity of each month and the maximum water storage capacity of the basin, the 5 km grid decay coefficient of each month is calculated by spatial superposition. The basic formula is as follows:

[0059] ;

[0060] Among them, EM is the daily evaporation capacity of the basin, and WM is the maximum water storage capacity of the basin.

[0061] For the aforementioned step S104, the embodiment of the present invention provides a specific implementation method for daily recursion of soil moisture content. The embodiment of the present invention collects the daily rainfall 15 days before the calculation date, and uses the daily recursion formula to calculate the soil moisture content at 8:00 a.m. on the starting day. For details, please refer to the following steps 2A to 2B:

[0062] Step 2A: Based on the maximum water storage capacity of the watershed, determine the initial soil moisture content of the study area corresponding to the specified historical date. The specified historical date can be 15 days before the target date. If the target date is recorded as , then the specified historical date is recorded as In one example, the 15th day before the calculation date is taken as the starting date, and the starting soil moisture content of the study area corresponding to the specified historical date is taken. =50%WM, as the starting value of the daily recursive formula, combined with the daily rainfall 15 days before the calculation day, the soil moisture content at 8:00 on the calculation day is deduced.

[0063] Step 2B, through the soil moisture daily recursive model, based on the daily rainfall data of the study area from the specified historical date to the target date, the grid soil moisture decay coefficient and the initial soil moisture content of the study area corresponding to the specified historical date, the soil moisture content is recursively calculated daily to obtain the initial soil moisture content of the study area corresponding to the target date.

[0064] In one example, a historical date is specified. Until target date Any date within , mainly divided into the following situations:

[0065] Case 1: For any date between the specified historical date and the target date, if the daily rainfall data of the study area on the previous date corresponding to the date is 0, then based on the initial soil moisture content of the study area on the previous date and the grid soil moisture decay coefficient, determine the initial soil moisture content of the study area on the date. Specifically, the product of the initial soil moisture content of the study area on the previous date and the grid soil moisture decay coefficient can be used as the initial soil moisture content of the study area on the date.

[0066] Specifically, assume the date Previous Date If there is no rain, the following formula is used to determine the study area on that date. Corresponding initial soil moisture content:

[0067] ;

[0068] in, For the study area on date The corresponding previous impact rainfall, mm, is the grid soil moisture decay coefficient, For the study area on the previous date The corresponding previous influencing rainfall, mm. The previous influencing rainfall is also the initial soil moisture content.

[0069] Case 2: For any date between the specified historical date and the target date, if the daily rainfall data of the study area on the previous date corresponding to the date is not 0, then based on the initial soil moisture content, daily rainfall data, and grid soil moisture decay coefficient of the study area on the previous date, determine the initial soil moisture content of the study area on the date. Specifically, determine the sum of the initial soil moisture content and daily rainfall data of the study area on the previous date, and take the product of the sum and the grid soil moisture decay coefficient as the initial soil moisture content of the study area on the date.

[0070] Specifically, assume the date If there is rain, the following formula is used to determine the study area on the date Corresponding initial soil moisture content:

[0071] ;

[0072] in, For the study area on date Corresponding previous impact rainfall, mm, is the grid soil moisture decay coefficient, For the study area on the previous date Corresponding previous impact rainfall, mm, For date Daily rainfall data, mm. The previous rainfall is an indicator of soil moisture content. Here, the previous rainfall can be directly used as the initial soil moisture content.

[0073] Furthermore, The value should never be greater than the maximum water storage capacity WM of the basin. When the value is greater than WM, WM is taken as the day's value.

[0074] For the aforementioned step S106, the embodiment of the present invention provides a specific implementation method for rolling simulation of soil moisture content every n hours: accessing hourly dynamic measured meteorological grid surface rainfall data, and using an n-hourly (1≤n≤24) recursive formula to perform rolling simulation of soil moisture content.

[0075] In one embodiment, the average n-hour soil moisture decay of the previous period can be determined based on the grid soil moisture decay coefficient and the initial soil moisture content of the study area corresponding to the target date; for any moment within the target date, based on the soil moisture simulation result of the study area at the previous moment corresponding to the moment, the hourly dynamic measured meteorological grid surface precipitation data of the study area at the previous moment corresponding to the moment, the average n-hour soil moisture decay of the previous period and the grid soil moisture decay coefficient, the soil moisture simulation result corresponding to the current moment of the study area is determined.

[0076] In one example, based on the above-mentioned calculation formula for the previous rainfall impact, the daily previous rainfall impact recursion formula is improved, and the daily retreat amount is averaged to 24 / n periods to obtain the n-hour recursion model of soil moisture content. The model derivation process is as follows:

[0077] (1) Daily water loss of soil moisture in the early stage Δ1d:

[0078] (2) The average amount of water that disappears after n hours of soil moisture content in the previous period Δnh:

[0079] (3) The n-hourly rainfall value can be obtained by accumulating the hourly dynamic measured meteorological grid surface rainfall data. It is not affected by the previous soil moisture content and does not need to be deducted according to the average retreat amount. The rainfall reduction term in the original formula can be retained. The expression of the n-hourly recursive model of soil moisture content is as follows:

[0080] ;

[0081] in, For the study area on the target date Neidi The time of the preceding day affects the amount of rainfall. For the study area on the target date Neidi The time of the preceding day affects the amount of rainfall. Represents the number of hours, is the grid soil moisture decay coefficient, Study area on target date The corresponding initial soil moisture content is is the average amount of water that disappears in the previous n hours of soil moisture, Study area on target date Neidi The hourly dynamic measured meteorological grid surface precipitation data at the moment. The previous rainfall is an indicator of soil moisture content. Here, the previous rainfall can be directly used as the soil moisture simulation result.

[0082] Furthermore, the accuracy of the n-hourly recursive model of soil moisture content can be evaluated. Specifically, the measured soil moisture data is collected, the simulated soil moisture value at the soil moisture station location is extracted, and the simulated soil moisture result is verified. Please refer to the following steps 3A to 3C:

[0083] Step 3A, obtain the measured soil moisture data from the soil moisture station in the study area.

[0084] Step 3B, converting the measured soil moisture data into the vertical average soil weight moisture content, and converting the soil moisture content simulation result at the soil moisture station location into the average weight moisture content simulation result.

[0085] In one example, the vertical average soil weight moisture content at the monitoring point is calculated according to the soil moisture content related factor calculation formula in the "Soil Moisture Monitoring Specification" (SL 364-2015). The calculation formula is as follows:

[0086] ;

[0087] In the formula, is the vertical average soil moisture content; is the average soil moisture content of the i-th soil layer; is the average soil thickness of the i-th soil layer; H is the sampling depth.

[0088] In one example, the soil moisture simulation results are converted into soil volumetric moisture content by combining the soil layer thickness, and then converted into average weight moisture content by soil dry bulk density.

[0089] Step 3C, comparing the vertical average soil weight moisture content with the average weight moisture content simulation results, so as to evaluate the accuracy of the n-hourly recursive model of soil moisture content based on the comparison results.

[0090] In summary, the purpose of the present invention is to provide a method for dynamic simulation of soil moisture content in a watershed with simple calculation and high spatiotemporal resolution, so as to overcome the defect that the daily recursive formula method for the previous rainfall influencing the previous period is difficult to meet the short-term forecast and warning needs of flash flood disasters. The key technical points are: 1. The daily recursive formula for the previous rainfall influencing the previous period is improved to the recursive formula for the soil moisture content every n hours, so as to realize the formula calculation of the soil moisture content at the hourly scale. 2. Considering the spatial variation characteristics of soil moisture content, the formula method is used for the distributed simulation of soil moisture content in a 5 km grid in the watershed.

[0091] The embodiment of the present invention mainly includes 6 parts, such as Figure 2 The overall process diagram of a dynamic analysis method for the spatial distribution of hourly soil moisture content is shown, including: collecting multi-year average monthly evaporation data from meteorological stations in the study area, calculating the monthly average daily evaporation capacity, and using the inverse distance weighted method for spatial interpolation to obtain the daily evaporation capacity value of the 5km grid; collecting soil texture and land use type distribution data in the study area, analyzing the field water holding capacity and wilting coefficient of different land types, and calculating the maximum water storage capacity of the 5km grid basin; calculating the monthly evaporation coefficient through the average daily evaporation capacity of each month and the maximum water storage capacity of the basin; collecting and calculating the daily rainfall 15 days before the day before, and using the daily recursive formula to calculate the soil moisture content at 8:00 on the starting day; accessing the hourly dynamic measured meteorological grid surface rainfall data, and using the n-hourly recursive formula to perform rolling simulation of soil moisture content; collecting measured soil moisture data, extracting the simulated values ​​of soil moisture content at the soil moisture station to verify the soil moisture simulation results. The basic idea of ​​the embodiment of the present invention is to convert the daily-scale soil moisture calculation formula into an hourly-scale simulation formula by improving the daily recursive formula of the previous rainfall impact, and to realize the hourly rolling analysis of soil moisture by accessing the hourly dynamic measured meteorological grid surface rainfall data. Based on this, the embodiment of the present invention can quickly realize the hourly rolling simulation of the spatial distribution of soil moisture content by automatically accessing the hourly dynamic measured meteorological grid surface rainfall data, combining the results of the 5 km grid spatial decay coefficient, and using the n-hourly previous rainfall impact recursive formula.

[0092] Based on the above-mentioned embodiments, the present invention provides a device for dynamically analyzing the spatial distribution of soil moisture content at the hourly level. Figure 3 The schematic diagram of the structure of a device for dynamic analysis of spatial distribution of soil moisture content at hourly level is shown in FIG. The device mainly includes the following parts:

[0093] A grid recession coefficient analysis module 302 is used to analyze the grid soil moisture recession coefficient corresponding to the study area based on the evaporation data of the meteorological station at the study area, the soil texture and the land use type distribution data;

[0094] The moisture content daily recursion module 304 is used to perform daily recursion of soil moisture content based on the daily rainfall data in the study area and the grid soil moisture content decay coefficient to obtain the initial soil moisture content corresponding to the target date in the study area;

[0095] The soil moisture content n-hourly recursive module 306 is used to perform a rolling simulation of the soil moisture content n-hourly through a pre-constructed soil moisture content n-hourly recursive model based on the hourly dynamic measured meteorological grid surface precipitation data in the study area, the grid soil moisture content decay coefficient and the initial soil moisture content of the study area corresponding to the target date, so as to obtain the soil moisture content simulation results corresponding to multiple moments in the study area within the target date.

[0096] The hourly soil moisture spatial distribution dynamic analysis device provided by the embodiment of the present invention uses the soil moisture content to recursively obtain the starting soil moisture content corresponding to the target date in the study area, and uses the starting soil moisture content as the starting amount for n-hourly recursion. Combined with the hourly dynamic measured meteorological grid surface precipitation data and the grid soil moisture decay coefficient in the study area, the n-hourly recursive model of the soil moisture content is used to quickly realize the hourly rolling simulation of the spatial distribution of soil moisture, thereby overcoming the defect that the daily recursive formula method affecting the rainfall in the early stage is difficult to meet the short-term forecast and warning needs of mountain torrent disasters.

[0097] In one implementation, the fading coefficient analysis module 302 is specifically used to:

[0098] Based on the evaporation data of the meteorological stations in the study area, the daily evaporation capacity grid data corresponding to the study area is calculated;

[0099] Also, soil texture and land use type distribution data in the study area are analyzed to calculate the maximum water storage capacity of the basin corresponding to the study area;

[0100] According to the daily evaporation capacity grid data and the maximum water storage capacity of the basin, the grid soil moisture content decline coefficient corresponding to the study area is determined.

[0101] In one embodiment, the water content daily recursive module 304 is specifically used to:

[0102] Based on the maximum water storage capacity of the watershed, determine the initial soil moisture content of the study area corresponding to the specified historical date;

[0103] Through the daily recursive model of soil moisture, based on the daily rainfall data of the study area from the specified historical date to the target date, the grid soil moisture decay coefficient and the initial soil moisture content of the study area on the specified historical date, the soil moisture content is recursively calculated on a daily basis to obtain the initial soil moisture content of the study area on the target date.

[0104] In one embodiment, the water content daily recursive module 304 is specifically used to:

[0105] For any date between the specified historical date and the target date, if the daily rainfall data of the study area on the previous date corresponding to the date is 0, the initial soil moisture content of the study area on the previous date and the grid soil moisture decay coefficient are used to determine the initial soil moisture content of the study area on the date.

[0106] Furthermore, for any date between the specified historical date and the target date, if the daily rainfall data of the study area on the previous date corresponding to the date is not zero, the initial soil moisture content of the study area on the date is determined based on the initial soil moisture content of the study area on the previous date, the daily rainfall data, and the grid soil moisture decay coefficient.

[0107] In one embodiment, the water content n-hour recursive module 306 is specifically used to:

[0108] According to the grid soil moisture decay coefficient and the initial soil moisture content corresponding to the target date in the study area, the average n-hour decay water content of the previous soil moisture content is determined;

[0109] For any moment within the target date, the soil moisture simulation result corresponding to the current moment of the study area is determined based on the soil moisture simulation result of the study area at the previous moment corresponding to the moment, the hourly dynamic measured meteorological grid surface precipitation data of the study area at the previous moment corresponding to the moment, the average n-hour soil moisture retreat water volume of the previous period and the grid soil moisture retreat coefficient.

[0110] In one embodiment, the expression of the n-hour recursive model of soil moisture content is as follows:

[0111] ;

[0112] in, For the study area on the target date Neidi The simulation results of soil moisture content at time For the study area on the target date Neidi The simulation results of soil moisture content at time Represents the number of hours, is the grid soil moisture decay coefficient, Study area on target date The corresponding initial soil moisture content is is the average amount of water that disappears in the previous n hours of soil moisture, Study area on target date Neidi Dynamic hourly measured meteorological grid surface precipitation data at all times.

[0113] In one embodiment, the system further includes an accuracy assessment module for:

[0114] Obtain the measured soil moisture data from the soil moisture station in the study area;

[0115] Convert the measured soil moisture data into the vertical average soil weight moisture content, and convert the soil moisture content simulation results at the soil moisture station location into the average weight moisture content simulation results;

[0116] The vertical average soil weight moisture content and the average weight moisture content simulation results were compared to evaluate the accuracy of the n-hourly recursive model of soil moisture content based on the comparison results.

[0117] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0118] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0119] Figure 4 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected via the bus 42; the processor 40 is used to execute an executable module stored in the memory 41, such as a computer program.

[0120] The memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0121] The bus 42 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0122] Among them, the memory 41 is used to store programs, and the processor 40 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0123] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 40. The above processor 40 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiment of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the above method in combination with its hardware.

[0124] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0125] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0126] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for dynamic analysis of spatial distribution of soil moisture content at hourly level, characterized in that: include: Based on the evaporation data of the meteorological station at the study area, the soil texture and the land use type distribution data, the grid soil moisture decay coefficient corresponding to the study area is analyzed, including: based on the evaporation data of the meteorological station at the study area, the daily evaporation capacity grid data corresponding to the study area is analyzed and calculated; and based on the soil texture and the land use type distribution data at the study area, the maximum water storage capacity of the watershed corresponding to the study area is analyzed and calculated; according to the daily evaporation capacity grid data and the maximum water storage capacity of the watershed, the grid soil moisture decay coefficient corresponding to the study area is determined; Based on the daily rainfall data in the study area and the grid soil moisture decay coefficient, the soil moisture content is recursively deduced daily to obtain the initial soil moisture content of the study area corresponding to the target date, including: based on the maximum water storage capacity of the watershed, determining the initial soil moisture content of the study area corresponding to the specified historical date; through a daily recursive model of soil moisture content, based on the daily rainfall data of the study area during the period from the specified historical date to the target date, the grid soil moisture decay coefficient and the initial soil moisture content of the study area corresponding to the specified historical date, the soil moisture content is recursively deduced daily to obtain the initial soil moisture content of the study area corresponding to the target date; Through the pre-constructed n-hourly recursive model of soil moisture, based on the hourly dynamic measured meteorological grid surface precipitation data in the study area, the grid soil moisture decay coefficient and the initial soil moisture content of the study area corresponding to the target date, a rolling simulation of soil moisture content is performed every n hours to obtain the soil moisture simulation results corresponding to multiple moments in the study area within the target date, including: determining the average n-hourly decay of soil moisture in the previous period according to the grid soil moisture decay coefficient and the initial soil moisture content of the study area on the target date; for any moment within the target date, determining the soil moisture simulation result corresponding to the current moment of the study area based on the soil moisture simulation result of the study area at the previous moment corresponding to the moment, the hourly dynamic measured meteorological grid surface precipitation data of the study area at the previous moment corresponding to the moment, the average n-hourly decay of soil moisture in the previous period and the grid soil moisture decay coefficient; The expression of the soil moisture content n-hour recursive model is as follows: ； in, For the study area on the target date Neidi The simulation results of soil moisture content at time For the study area on the target date Neidi The simulation results of soil moisture content at time Represents the number of hours, is the grid soil moisture decay coefficient, The study area is on the target date The corresponding initial soil moisture content, is the average amount of water that disappears in the previous n hours of soil moisture, The study area is on the target date Neidi Time to Hourly precipitation data at time.

2. The method for dynamic analysis of spatial distribution of hourly soil moisture content according to claim 1, characterized in that: The soil moisture content is recursively deduced daily based on the daily rainfall data of the study area from the specified historical date to the target date, the grid soil moisture content decay coefficient and the initial soil moisture content of the study area corresponding to the specified historical date through the soil moisture content daily recursion model to obtain the initial soil moisture content of the study area corresponding to the target date, including: For any date between the specified historical date and the target date, if the daily rainfall data of the study area on the previous date corresponding to the date is 0, then based on the initial soil moisture content of the study area on the previous date and the grid soil moisture decay coefficient, determine the initial soil moisture content of the study area on the date; Furthermore, for any date between the specified historical date and the target date, if the daily rainfall data of the study area on the previous date corresponding to the date is not 0, then the starting soil moisture content of the study area on the date is determined based on the starting soil moisture content of the study area on the previous date, the daily rainfall data, and the grid soil moisture decay coefficient.

3. The method for dynamic analysis of spatial distribution of hourly soil moisture content according to claim 1, characterized in that: The method further comprises: Obtaining soil moisture data measured at soil moisture stations in the study area; Converting the measured soil moisture data into vertical average soil weight moisture content, and converting the soil moisture content simulation result at the soil moisture station location into an average weight moisture content simulation result; The vertical average soil weight moisture content is compared with the average weight moisture content simulation result to evaluate the accuracy of the n-hourly recursive model of soil moisture content based on the comparison result.

4. A device for dynamic analysis of spatial distribution of soil moisture content at hourly level, characterized in that: The device is used to implement the hourly soil moisture spatial distribution dynamic analysis method according to claim 1, and the device comprises: A grid recession coefficient analysis module is used to analyze the grid soil moisture recession coefficient corresponding to the study area based on the evaporation data of the meteorological station, the soil texture and the land use type distribution data at the study area; A moisture content daily recursion module is used to perform daily recursion of soil moisture content based on daily rainfall data in the study area and the grid soil moisture content decay coefficient to obtain the initial soil moisture content corresponding to the target date in the study area; The soil moisture content n-hourly recursive module is used to perform a rolling simulation of the soil moisture content n-hourly through a pre-constructed soil moisture content n-hourly recursive model based on the hourly dynamic measured meteorological grid surface precipitation data in the study area, the grid soil moisture content decay coefficient and the initial soil moisture content of the study area corresponding to the target date, so as to obtain the soil moisture content simulation results corresponding to multiple moments in the study area within the target date.

5. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 3.

Images