Method and device for identifying spatiotemporal dynamics of regional production flow

Abstract

This invention relates to the field of hydrological technology and discloses a method and apparatus for identifying the spatiotemporal dynamics of runoff in a region. The method includes: determining the thickness of the vadose zone and the thickness of the humus layer in each grid cell within the study area, as well as the soil freezing depth and thawing depth during the target period, based on meteorological and underlying surface data; performing a freeze-thaw state analysis of the soil layer based on the thickness of the vadose zone and the humus layer in each grid cell, as well as the soil freezing depth and thawing depth during the target period, to determine the runoff pattern of each grid cell; and performing a source analysis based on the runoff pattern of each grid cell and the runoff volume during the target period to obtain the spatiotemporal dynamic distribution of runoff components in the study area. This method considers the runoff mechanism under the combined effects of permafrost and climate, determines the soil runoff pattern within the study area based on the freeze-thaw state of the vadose zone and humus layer, and thus obtains the spatiotemporal dynamic distribution of runoff components in the study area, enabling more accurate simulation of hydrological processes in the study area.

Description

Technical Field

[0001] This invention relates to the field of hydrological technology, and in particular to a method and apparatus for spatiotemporal dynamic identification of regional runoff. Background Technology

[0002] Seasonal permafrost plays a crucial role in the energy and water balance of the land surface, influencing global ecosystems, hydrological processes, soil properties, and biological activity. It is widely distributed in the Northern Hemisphere.

[0003] However, despite the widespread distribution of seasonally frozen soils in cold regions and their significant hydrological and ecological importance, research progress has been relatively slow due to a lack of long-term observational data. Currently, there are no effective methods for analyzing hydrological processes in cold regions. Summary of the Invention

[0004] Therefore, it is necessary to propose a method and device for the spatiotemporal dynamic identification of regional runoff to address the above problems, so as to obtain the spatiotemporal dynamic distribution of runoff components in the study area and enable more accurate simulation of hydrological processes in the study area.

[0005] To achieve the above objectives, the first aspect of this application provides a method for spatiotemporal dynamic identification of regional runoff, the method comprising: Acquire underlying surface data and meteorological data for the study area during the target period; Based on the underlying surface data, a rasterization analysis was performed to determine the thickness of the vadose zone and the thickness of the humus layer in each raster unit within the study area. Based on the meteorological data and the underlying surface data, a raster analysis is performed to determine the soil freezing depth and thawing depth of each raster cell during the target period; Based on the thickness of the vadose zone and the thickness of the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, the soil freeze-thaw state is analyzed to determine the runoff pattern of each grid cell. Based on the runoff pattern of each grid cell and the runoff volume during the target period, a water source analysis is performed to obtain the spatiotemporal dynamic distribution of runoff components in the study area.

[0006] Furthermore, the underlying surface data includes elevation data, geomorphic feature parameters, vegetation type parameters, and soil type parameters; The step of performing rasterization analysis based on the underlying surface data to determine the vadose zone thickness and humus layer thickness of each raster cell in the study area specifically includes: The terrain index of each grid cell is calculated based on the terrain features in the elevation data; Based on the topographic index and soil type-related parameters of each grid cell in the study area, water content analysis was performed to obtain the extreme values ​​of field holding capacity and wilting water content in the study area. Based on the extreme values ​​of field water holding capacity, wilting water content, and topographic index, a first coefficient and a second coefficient are determined for calculating the thickness of the vadose zone in the study area. Soil layer thickness is calculated based on the first coefficient, the second coefficient, and the topographic index of each grid cell to obtain the vadose zone thickness of each grid cell; The soil layer thickness is calculated based on the vadose zone thickness of each grid cell, the relevant parameters of the landform features, and the relevant parameters of the vegetation type, to obtain the humus soil layer thickness of each grid cell.

[0007] Furthermore, the step of performing water content analysis based on the topographic index and soil type-related parameters of each grid cell within the study area to obtain the extreme values ​​of field capacity and wilting water content within the study area specifically includes: Based on a comparative analysis of the terrain indices of each grid cell, the maximum and minimum terrain indices within the study area are determined. Based on the soil type-related parameters of the first grid cell corresponding to the maximum topographic index and the second grid cell corresponding to the minimum topographic index, the extreme values ​​of maximum and minimum field water holding capacity, as well as the extreme values ​​of maximum and minimum wilting water content, are obtained in the study area.

[0008] Furthermore, the first coefficient and the second coefficient are calculated according to the following system of equations:

[0009] In the formula, and These are the first coefficient and the second coefficient, respectively. and These are the minimum and maximum topographic indices, respectively. and These represent the maximum and minimum values ​​of the tension water storage capacity within the pre-defined study area. and These are the minimum field capacity and the maximum field capacity, respectively. and These are the minimum wilting moisture content and the maximum wilting moisture content, respectively.

[0010] Furthermore, the meteorological data includes surface temperature and snow depth data, and the underlying surface data includes soil type-related parameters; The step of performing rasterization analysis based on the meteorological data and the underlying surface data to determine the soil freezing and thawing depth of each raster unit within the target period specifically includes: The freezing index and melting index of each grid cell during the target period are obtained by classifying and summing the daily surface temperature of each grid cell during the target period. The soil freeze-thaw parameters of each grid cell are calculated based on the soil type-related parameters of each grid cell. The degree of soil freezing is calculated based on the freezing index, soil freeze-thaw parameters and snow depth data of each grid cell, and the soil freezing depth of each grid cell in the target period is obtained. The degree of soil thawing is calculated based on the thawing index and soil freeze-thaw parameters of each grid cell, and the soil thawing depth of each grid cell during the target period is obtained.

[0011] Furthermore, the soil freezing depth and soil thawing depth are calculated using the following formula:

[0012]

[0013] In the formula, and These are the soil freezing depth and the soil thawing depth, respectively. For soil freeze-thaw parameters, and These are the freezing index and the melting index, respectively. This represents the snow depth of the grid cells within a preset calculation period.

[0014] Furthermore, the step of classifying and summing the daily surface temperatures of each grid cell during the target period to obtain the freezing index and thawing index of each grid cell during the target period specifically includes: The first day number set is defined as all days in which the surface temperature of the target grid cell is less than 0°C during the target period, wherein the target grid cell is any one of all grid cells in the study area; All days in the target grid cell during the target period when the surface temperature is greater than 0°C are taken as the set of the second day numbers; The freezing index of each grid cell in the target period is obtained by summing the absolute values ​​of the surface temperatures corresponding to all dates in the first set of days. The melting index of each grid cell in the target period is obtained by summing the surface temperatures corresponding to all dates in the second day's number set.

[0015] Furthermore, the step of analyzing the freeze-thaw state of the soil layer based on the thickness of the vadose zone and the thickness of the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, to determine the runoff pattern of each grid cell specifically includes: Based on the soil freezing depth, thawing depth, vadose zone thickness, and humus layer thickness of each grid cell, determine the relative positions of the frozen soil layer and thawing layer of each grid cell within the vadose zone and humus layer. Based on the relative positions of the frozen soil layer and thawed layer of each grid unit in the vadose zone and humic soil layer, and an analysis of a preset runoff pattern comparison table, the runoff pattern of each grid unit is determined. The runoff pattern comparison table includes the freeze-thaw states corresponding to different runoff patterns.

[0016] Furthermore, the step of performing source analysis based on the runoff pattern of each grid cell and the runoff volume during the target period to obtain the spatiotemporal dynamic distribution of runoff components in the study area specifically includes: Based on the synergistic effect of different runoff patterns, the water content is calculated according to the runoff pattern, vadose zone thickness, humus layer thickness and underlying surface data of each grid unit, as well as the soil freezing depth and thawing depth during the target period, to obtain the free water storage capacity and effective free water content within the humus layer thickness of each grid unit during the target period. Based on the composition of runoff components under different runoff patterns, the spatiotemporal dynamic distribution of runoff components in the study area is obtained by analyzing the runoff pattern of each grid unit, the runoff volume during the target period, the effective free water content and free water storage capacity within the humus soil layer thickness.

[0017] To achieve the above objectives, a second aspect of this application provides a spatiotemporal dynamic identification device for regional runoff, the device comprising: The data acquisition unit is used to acquire underlying surface data and meteorological data for the study area during the target period; The data analysis unit is used to perform rasterization analysis based on the underlying surface data to determine the thickness of the vadose zone and the thickness of the humus layer in each raster unit within the study area. Based on the meteorological data and the underlying surface data, a raster analysis is performed to determine the soil freezing depth and thawing depth of each raster cell during the target period; The spatiotemporal dynamic identification unit is used to analyze the freeze-thaw state of the soil layer based on the thickness of the vadose zone and the thickness of the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, and to determine the runoff pattern of each grid cell. Based on the runoff pattern of each grid cell and the runoff volume during the target period, a water source analysis is performed to obtain the spatiotemporal dynamic distribution of runoff components in the study area.

[0018] The present invention has the following beneficial effects: This invention proposes a method for identifying the spatiotemporal dynamics of runoff in a region. The method includes: acquiring underlying surface data and meteorological data for the study area during a target period; performing rasterization analysis based on the underlying surface data to determine the vadose zone thickness and humus layer thickness of each raster unit within the study area; performing rasterization analysis based on the meteorological and underlying surface data to determine the soil freezing depth and thawing depth of each raster unit during the target period; performing soil freeze-thaw state analysis based on the vadose zone thickness, humus layer thickness, and soil freezing and thawing depths during the target period to determine the runoff pattern of each raster unit; and performing source analysis based on the runoff pattern of each raster unit and the runoff volume during the target period to obtain the spatiotemporal dynamic distribution of runoff components in the study area. This invention considers the runoff mechanism under the synergistic effect of permafrost and climate, determines the soil runoff pattern within the study area based on the freeze-thaw state of the vadose zone and humus layer, and thus obtains the spatiotemporal dynamic distribution of runoff components in the study area, enabling more accurate simulation of hydrological processes in the study area. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] in: Figure 1 This is a flowchart illustrating the spatiotemporal dynamic identification method for regional runoff in an embodiment of the present invention. Figure 2 This is a schematic diagram of grid cell flow generation mode 1 in an embodiment of the present invention; Figure 3 This is a schematic diagram of grid cell flow generation mode 2 in an embodiment of the present invention; Figure 4 This is a schematic diagram of grid cell flow generation mode 3 in an embodiment of the present invention; Figure 5 This is a schematic diagram of grid cell flow generation mode 4 in an embodiment of the present invention; Figure 6 This is a schematic diagram of grid cell flow generation mode 5 in an embodiment of the present invention; Figure 7This is a structural block diagram of the spatiotemporal dynamic identification device for regional flow generation in an embodiment of the present invention. Figure 8 This is an internal structural diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The formation of seasonally frozen soil is closely related to geothermal conditions. When soil freezes or thaws, its thermal and hydraulic properties change significantly, thus affecting regional hydrological cycles and ecosystem functions. Frozen soil is crucial to hydrological processes, especially in its frozen state, where ice blocks previously water-rich soil pores, hindering water infiltration and consequently affecting seasonal permeability of the vadose zone and groundwater recharge. The impact of the soil freeze-thaw cycle on watershed hydrological processes varies seasonally; spring runoff mainly consists of surface runoff and intermediate flow, while summer permafrost thawing enhances groundwater recharge. Furthermore, ground freezing conditions are highly dependent on snow cover. Due to snow's low thermal conductivity, its insulating effect significantly influences ground freezing depth; generally, thicker snow cover results in shallower freezing depth. Therefore, even when temperatures are below freezing, thick early winter snow can significantly weaken or even completely prevent surface freezing.

[0023] Based on this, one embodiment of the present invention proposes a spatiotemporal dynamic identification method for regional runoff, in order to identify the spatiotemporal dynamic characteristics of runoff patterns in high-altitude cold regions under the combined action of snow cover and permafrost, with reference to... Figure 1 , Figure 1 This is a flowchart illustrating the spatiotemporal dynamic identification method for regional runoff in an embodiment of the present invention. The method includes: Step 110: Obtain underlying surface data and meteorological data for the study area during the target period.

[0024] In this embodiment, underlying surface data and meteorological data for the target period within the study area are acquired based on satellite remote sensing. The underlying surface data includes at least relevant parameters such as elevation data, geomorphic features, vegetation type, and soil type, while the meteorological data includes at least surface temperature and snow depth data. The underlying surface data can be annual values, and the meteorological data can be daily values.

[0025] Step 120: Perform rasterization analysis based on the underlying surface data to determine the thickness of the vadose zone and the thickness of the humus layer in each raster unit within the study area.

[0026] In this embodiment, the study area is regularly divided and adjusted to a uniform resolution, resulting in several grid cells. Based on the underlying surface data of each grid cell's region, soil thickness is analyzed and calculated to obtain the vadose zone thickness and humus layer thickness of each grid cell within the study area. This process not only improves calculation accuracy but also allows for the intuitive identification and analysis of the spatiotemporal dynamics of runoff patterns in high-altitude and cold regions, providing crucial data support for subsequent hydrological and ecological research.

[0027] Step 130: Perform rasterization analysis based on meteorological data and underlying surface data to determine the soil freezing depth and thawing depth of each raster cell during the target period.

[0028] In this embodiment, based on step 120, the study area is rasterized to obtain several raster cells. The dynamic process of soil freeze-thaw state is analyzed based on the underlying surface data and meteorological data of each raster cell, determining the soil freezing depth and thawing depth of each raster cell within the study area during the target period. This process not only improves computational accuracy but also allows for intuitive identification and analysis of the spatiotemporal dynamic characteristics of runoff patterns in high-altitude and cold regions, providing crucial data support for subsequent hydrological and ecological research.

[0029] This embodiment can accurately capture the dynamic changes of soil freezing by using meteorological data and underlying surface data, providing basic data for the study of hydrological processes.

[0030] Step 140: Analyze the freeze-thaw state of the soil layer based on the thickness of the vadose zone and the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, to determine the runoff pattern of each grid cell.

[0031] In this embodiment, multiple runoff patterns exist, and different grid cells may correspond to different runoff patterns. This is determined by factors such as the underlying surface data and climate data of each grid cell. Specifically, the thickness of the vadose zone, the thickness of the humus layer, and the soil freezing and thawing depths of each grid cell are calculated based on the underlying surface data and climate data. The freeze-thaw state of the soil layers is then analyzed, and the runoff pattern of each grid cell is determined based on the freeze-thaw state of each grid cell.

[0032] Step 150: Based on the runoff pattern of each grid cell and the runoff volume during the target period, perform source analysis to obtain the spatiotemporal dynamic distribution of runoff components in the study area.

[0033] In this embodiment, the components of soil runoff differ under each runoff pattern. Therefore, the components of runoff in each grid cell can be determined based on the runoff pattern of each grid cell. The spatiotemporal dynamic distribution of runoff components in each grid cell during the target period is calculated based on the runoff of each grid cell during the target period, thereby simulating the hydrological process in the study area.

[0034] This invention takes into account the runoff generation mechanism under the synergistic effect of permafrost and climate, determines the runoff generation pattern of the soil in the study area based on the freeze-thaw state of the vadose zone and humus layer, and then obtains the spatiotemporal dynamic distribution of runoff components in the study area, so as to more accurately simulate the hydrological process in the study area.

[0035] In one embodiment of the present invention, the underlying surface data includes at least elevation data, geomorphic feature-related parameters, vegetation type-related parameters, and soil type-related parameters. Based on this, step 120, performing rasterization analysis based on the underlying surface data to determine the vadose zone thickness and humus layer thickness of each raster unit in the study area, specifically includes: Step 210: Calculate the terrain index for each raster cell based on the terrain features in the elevation data. In this embodiment, elevation data is mainly used to describe landform morphology. The landform features of the study area can be observed based on the elevation data. The specific topographic index of each grid cell can be calculated based on the following formula:

[0036] In the formula, Here, A represents the topographic index of the raster cell, and A represents the upstream cumulative catchment area of ​​the raster cell. The gradient between the k-th downslope grid and the grid cell. Let n be the length of the contour line perpendicular to the k-th downhill direction, and n be the total number of contour lines in the downhill direction.

[0037] Step 220: Based on the topographic index and soil type-related parameters of each grid cell in the study area, perform water content analysis to obtain the extreme values ​​of field holding capacity and wilting water content in the study area.

[0038] Specifically, the extreme values ​​of field water holding capacity and wilting water content in the study area were obtained through the following steps: Step 221: Based on the comparative analysis of the terrain index of each grid cell, determine the maximum and minimum terrain index within the study area.

[0039] In this embodiment, the terrain index of each grid cell in the study area is calculated, and the maximum and minimum values ​​of the terrain indices of all grid cells are taken as the maximum and minimum terrain indices in the study area, respectively.

[0040] Step 222: Based on the soil type-related parameters of the first grid cell corresponding to the maximum topographic index and the second grid cell corresponding to the minimum topographic index, the extreme values ​​of maximum and minimum field water holding capacity, as well as the extreme values ​​of maximum and minimum wilting water content, are obtained in the study area.

[0041] In this embodiment, after determining the maximum and minimum topographic indices of the study area, the first grid cell corresponding to the maximum topographic index and the second grid cell corresponding to the minimum topographic index are found. Based on the underlying surface data of the first and second grid cells, the maximum field capacity and maximum wilting water content corresponding to the first grid cell, and the minimum field capacity and minimum wilting water content corresponding to the second grid cell are determined. It is understood that different underlying surface data correspond to different field capacity and wilting water contents, which can be determined based on literature searches, and will not be elaborated upon here.

[0042] Step 230: Based on the extreme values ​​of field water holding capacity, wilting water content, and topographic index, determine the first and second coefficients used to calculate the thickness of the vadose zone in the study area.

[0043] In this embodiment, the first coefficient and the second coefficient are calculated based on the maximum field capacity extreme value, the minimum field capacity extreme value, the maximum wilting water content extreme value, the minimum wilting water content extreme value, the maximum topographic index, and the minimum topographic index within the study area. The first coefficient and the second coefficient are used to calculate the correlation coefficients after the vadose zone of the study area.

[0044] Specifically, the first and second coefficients are calculated based on the following system of equations:

[0045] In the formula, and These are the first coefficient and the second coefficient, respectively. and These are the minimum and maximum topographic indices, respectively. and These represent the maximum and minimum values ​​of the tension water storage capacity within the pre-defined study area. The maximum value can be obtained through model parameter analysis and empirical methods, while the minimum value can be set to 0. and These are the minimum field capacity and the maximum field capacity, respectively. and These are the minimum wilting moisture content and the maximum wilting moisture content, respectively.

[0046] Step 240: Calculate the soil layer thickness based on the first coefficient, the second coefficient, and the topographic index of each grid cell to obtain the vadose zone thickness of each grid cell.

[0047] In this embodiment, the thickness of the vadose zone is calculated using the following formula:

[0048] In the formula, This represents the thickness of the vapor band in the i-th grid cell. The terrain index of the i-th grid cell. and These are the first coefficient and the second coefficient, respectively.

[0049] Step 250: Calculate the soil layer thickness based on the vadose zone thickness, geomorphological feature parameters, and vegetation type parameters of each grid cell to obtain the humus soil layer thickness of each grid cell.

[0050] In this embodiment, the thickness of the humus layer in the grid cell is related to the geomorphological features and vegetation type of the grid cell. Specifically, the thickness of the humus layer in the grid cell can be calculated using the following formula:

[0051] In the formula, This represents the thickness of the humus layer in the i-th grid cell. This represents the soil thickness conversion factor for the i-th grid cell, which is mainly determined based on the vegetation type of the grid cell. This represents the thickness of the vapor band in the i-th grid cell.

[0052] In one embodiment of the present invention, the meteorological data includes at least surface temperature and snow depth data, and the underlying surface data includes at least soil type-related parameters. Based on this, step 130, performing rasterization analysis based on the meteorological data and underlying surface data to determine the soil freezing and thawing depth of each raster cell within the target period, specifically includes: Step 131: Classify and sum the daily surface temperature of each grid cell during the target period to obtain the freezing index and thawing index of each grid cell during the target period.

[0053] In one embodiment, the specific steps for calculating the freeze index and the melt index include: A1. Take all days in the target raster unit where the surface temperature is less than 0℃ during the target period as the first day set, where the target raster unit is any one of all raster units in the study area; take all days in the target raster unit where the surface temperature is greater than 0℃ during the target period as the second day set.

[0054] In this embodiment, when calculating the freezing index and thawing index of the target grid cell, the target grid cell is any one of all grid cells in the study area. It is necessary to classify the surface temperature of the grid cell in each year during the target period into two categories: greater than 0°C and less than 0°C, to obtain the first day set and the second day set. The first day set is the set of all days with a surface temperature less than 0°C, and the second day set is the set of all days with a surface temperature greater than 0°C.

[0055] A2. Calculate the freezing index for each raster cell in the target period by summing the absolute values ​​of the surface temperatures corresponding to all dates in the first day's data set. Calculate the melting index for each raster cell in the target period by summing the surface temperatures corresponding to all dates in the second day's data set.

[0056] In this embodiment, the surface temperatures corresponding to the dates in the two sets are summed to obtain the freezing index and melting index of each grid cell in the target period.

[0057] Specifically, the formulas for calculating the freeze index and melt index are as follows:

[0058]

[0059] In the formula, This represents the freeze index of the raster cell during the target period. This represents the melting index of the grid cells during the target period. Let be the daily average surface temperature on day i and day j. and These represent the total number of days in the first day's set and the total number of days in the second day's set, respectively.

[0060] Step 132: Calculate the soil freeze-thaw parameters for each grid cell based on the soil type-related parameters of each grid cell.

[0061] In this embodiment, the soil freeze-thaw parameters are affected by soil type and moisture content, and the specific calculation formula is as follows:

[0062] In the formula, For the soil freeze-thaw parameters of the grid cells, The thermal conductivity coefficient of the grid cell under frozen soil conditions. This is the ratio of the ground freezing index to the air freezing index for each grid cell. The soil bulk density of the grid cell. Soil moisture content of the grid cell. The latent heat of ice melting in the grid cells.

[0063] Step 133: Calculate the degree of soil freezing based on the freezing index, soil freeze-thaw parameters and snow depth data of each grid cell to obtain the soil freezing depth of each grid cell in the target period.

[0064] In this embodiment, the degree of soil freezing can be calculated based on the freezing index of the grid cell, soil freeze-thaw parameters, and snow depth data to obtain the soil freezing depth of each grid cell in the target period. For example, the degree of soil freezing can be calculated based on the freezing index of the grid cell, soil freeze-thaw parameters, and daily snow depth data to obtain the daily soil freezing degree of each grid cell in the target period.

[0065] Specifically, the formula for calculating the daily soil freezing depth of a raster cell is as follows:

[0066] In the formula, The daily soil freezing depth of the grid cell. For the soil freeze-thaw parameters of the grid cells, For the frozen index, This refers to the snow depth of the grid cells within a preset calculation period (daily).

[0067] Step 134: Calculate the degree of soil thawing based on the thawing index and soil freeze-thaw parameters of each grid cell to obtain the soil thawing depth of each grid cell in the target period.

[0068] In this embodiment, the degree of soil thawing can be calculated based on the thawing index of the grid cell and the soil freeze-thaw parameters to obtain the soil thawing depth of each grid cell in the target period.

[0069] Specifically, the formula for calculating the soil melting depth of the grid cells in the target period is as follows:

[0070] In the formula, This represents the soil melting depth of the grid cell during the target period. For the soil freeze-thaw parameters of the grid cells, This refers to the melting index. For seasonally cold regions, the threshold value within a single freeze-thaw cycle is [value missing]. .

[0071] In one embodiment of the present invention, step 140, analyzing the freeze-thaw state of the soil layer based on the thickness of the vadose zone and the thickness of the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, to determine the runoff pattern of each grid cell, specifically includes: Step 141: Based on the soil freezing depth, thawing depth, vadose zone thickness, and humus layer thickness of each grid cell, determine the relative positions of the frozen soil layer and thawing layer in the vadose zone and humus layer of each grid cell.

[0072] In this embodiment, the relative positions of the frozen soil layer and the thawed layer in the vadose zone and the humus soil layer can be determined based on the freeze-thaw state of the soil, that is, the freezing depth and the thawing depth.

[0073] Step 142: Analyze the relative positions of the frozen soil layer and thawed layer of each grid cell in the vadose zone and humus soil layer, and the preset runoff pattern comparison table to determine the runoff pattern of each grid cell. The runoff pattern comparison table includes the freeze-thaw states corresponding to different runoff patterns.

[0074] For reference Figure 2-6 The diagrams shown are schematic diagrams of grid cell flow generation mode 1, 2, 3, 4, and 5 in this embodiment of the invention. In this embodiment, the relative positions of the frozen soil layer and the thawed layer in the vadose zone and the humic soil layer are different for each flow generation mode. Therefore, the flow generation mode of the grid cell can be determined based on the relative positions of the frozen soil layer and the thawed layer in the vadose zone and the humic soil layer.

[0075] Specifically, the generation modes of grid cells are divided into the following five types: Mode 1: Topsoil is frozen and there is snow cover: Only surface runoff is generated. ; Mode 2: Topsoil freezes, but surface snow melts completely: only surface runoff is generated. ; Mode 3: Topsoil melts, and the melting depth is less than the humus layer depth: This generates surface runoff. He Rang Zhong Liu ; Mode 4: The humus layer is completely thawed, but parts of the deep vadose zone remain frozen: surface runoff is generated. He Rang Zhong Liu ; Mode 5: The permafrost layer completely thaws, and the vadose zone returns to an unfrozen state: surface runoff is generated. , soil in the stream and underground runoff .

[0076] In one embodiment of the present invention, step 150, performing source analysis based on the runoff pattern of each grid cell and the runoff volume during the target period, to obtain the spatiotemporal dynamic distribution of runoff components in the study area, specifically includes: Step 151: Based on the synergistic effect of different runoff patterns, calculate the water content of each grid cell according to the runoff pattern, vadose zone thickness, humus layer thickness, and underlying surface data, as well as the soil freezing and thawing depths during the target period. This will yield the free water storage capacity and effective free water content within the humus layer thickness of each grid cell during the target period.

[0077] In this embodiment, due to the synergistic effect of snow accumulation and permafrost, the effective tension water storage capacity corresponding to each runoff generation mode is... Free water storage capacity and soil effective water content for: Mode 1: , , ; Mode 2: , , ; Mode 3: , , ; Mode 4: , , ; Mode 5: , , ; In the formula, , and These represent the field holding capacity, wilting water content, and saturation water content of the grid unit, respectively. This refers to the soil moisture content before the soil freezes. This represents the difference in melting depth between the current period (today) and the previous period (the day before). This represents the actual tension water content at the beginning of the current period (day). This represents the soil melting depth of the grid cell during the target period. Indicates the thickness of the vadose band of the grid cell. This indicates the thickness of the humus layer in the grid cell.

[0078] The effective free water content S within the humus layer thickness corresponding to each runoff pattern is: Mode 3:

[0079] Mode 4 and Mode 5:

[0080] In the formula, This represents the free water content of the soil in the humus layer of the grid cell before the soil freezes. This represents the actual free water content at the beginning of the current time period for the grid cell. and The free water content of the humus layer in the grid cell is the outflow coefficient of the midstream and groundwater, which is related to the soil type of the grid cell.

[0081] Step 152: Based on the composition of runoff components under different runoff patterns, the spatiotemporal dynamic distribution of runoff components in the study area is obtained by analyzing the runoff pattern of each grid cell, the runoff volume during the target period, the effective free water content and free water storage capacity within the humus soil layer.

[0082] In this embodiment, the runoff is the sum of the flows of all runoff components, meaning the runoff consists of any one or more of surface runoff, interflow, and groundwater runoff. The runoff components differ under different runoff patterns. The hydrological processes of the study area during the target period can be obtained by analyzing the runoff pattern of each grid cell, the runoff during the target period, the effective free water content within the humic soil layer, and the free water storage capacity. This analysis reveals the spatiotemporal dynamic distribution of the runoff components in the study area during the target period.

[0083] Specifically, the flow rate calculation method for each flow generation mode and its components is as follows: Mode 1: , ; Mode 2: , ; Mode 3: , , ; Mode 4: , , ; Mode 5: , , ; In the formula, , , These represent surface runoff, interflow, and groundwater runoff, respectively, with S representing the available free water content. R represents the free water storage capacity, and R represents the production flow rate. and The free water content of the humus layer in the grid cell is the outflow coefficient of the soil runoff and groundwater.

[0084] This invention, from a physical perspective, fully considers the impact of snow cover, permafrost, and their freeze-thaw cycles on the hydrological runoff process, thereby determining the spatiotemporal dynamic distribution of seasonal cold-region runoff patterns and providing scientific support for the understanding and prediction of distributed hydrological models in the process of cold-region hydrological simulation.

[0085] One embodiment of the present invention proposes a spatiotemporal dynamic identification device for regional runoff, which can be referred to as [reference needed]. Figure 7 , Figure 7 This is a structural block diagram of the spatiotemporal dynamic identification device for regional runoff in an embodiment of the present invention. The device includes: The data acquisition unit 701 is used to acquire underlying surface data and meteorological data of the study area during the target period; Data analysis unit 702 is used to perform rasterization analysis based on underlying surface data to determine the thickness of the vadose zone and the thickness of the humus layer in each raster unit within the study area. Raster analysis was performed based on meteorological and underlying surface data to determine the soil freezing and thawing depth of each grid cell during the target period. The spatiotemporal dynamic identification unit 703 is used to analyze the freeze-thaw state of the soil layer based on the thickness of the vadose zone and the thickness of the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, and to determine the runoff pattern of each grid cell. Based on the runoff pattern and runoff volume of each grid cell during the target period, a source-specific analysis was performed to obtain the spatiotemporal dynamic distribution of runoff components in the study area.

[0086] The spatiotemporal dynamic identification device for regional runoff proposed in this invention takes into account the runoff generation mechanism under the synergistic effect of permafrost and climate. It determines the runoff generation pattern of the soil in the study area based on the freeze-thaw state of the vadose zone and humus layer, and then obtains the spatiotemporal dynamic distribution of runoff components in the study area, so as to more accurately simulate the hydrological process in the study area.

[0087] Figure 8 An internal structural diagram of a computer device according to one embodiment of the present invention is shown. This computer device can specifically be a terminal or a system. Figure 8 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program causes the processor to perform the steps in the above-described method embodiments. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform the steps in the above-described method embodiments. Those skilled in the art will understand that... Figure 8The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0088] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps in the above method embodiments.

[0089] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps in the above method embodiments.

[0090] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0091] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for spatiotemporal dynamic identification of regional runoff, characterized in that, The method includes: Acquire underlying surface data and meteorological data for the study area during the target period; Based on the underlying surface data, a rasterization analysis was performed to determine the thickness of the vadose zone and the thickness of the humus layer in each raster unit within the study area. Based on the meteorological data and the underlying surface data, a raster analysis is performed to determine the soil freezing depth and thawing depth of each raster cell during the target period; Based on the thickness of the vadose zone and the thickness of the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, the soil freeze-thaw state is analyzed to determine the runoff pattern of each grid cell. Based on the runoff pattern of each grid cell and the runoff volume during the target period, a water source analysis is performed to obtain the spatiotemporal dynamic distribution of runoff components in the study area.

2. The method as described in claim 1, characterized in that, The underlying surface data includes elevation data, geomorphic feature parameters, vegetation type parameters, and soil type parameters; The step of performing rasterization analysis based on the underlying surface data to determine the vadose zone thickness and humus layer thickness of each raster cell in the study area specifically includes: The terrain index of each grid cell is calculated based on the terrain features in the elevation data; Based on the topographic index and soil type-related parameters of each grid cell in the study area, water content analysis was performed to obtain the extreme values ​​of field holding capacity and wilting water content in the study area. Based on the extreme values ​​of field water holding capacity, wilting water content, and topographic index, a first coefficient and a second coefficient are determined for calculating the thickness of the vadose zone in the study area. Soil layer thickness is calculated based on the first coefficient, the second coefficient, and the topographic index of each grid cell to obtain the vadose zone thickness of each grid cell; The soil layer thickness is calculated based on the vadose zone thickness of each grid cell, the relevant parameters of the landform features, and the relevant parameters of the vegetation type, to obtain the humus soil layer thickness of each grid cell.

3. The method as described in claim 2, characterized in that, The method involves performing water content analysis based on the topographic index and soil type-related parameters of each grid cell within the study area to obtain the extreme values ​​of field capacity and wilting water content within the study area. Specifically, this includes: Based on a comparative analysis of the terrain indices of each grid cell, the maximum and minimum terrain indices within the study area are determined. Based on the soil type-related parameters of the first grid cell corresponding to the maximum topographic index and the second grid cell corresponding to the minimum topographic index, the extreme values ​​of maximum and minimum field water holding capacity, as well as the extreme values ​​of maximum and minimum wilting water content, are obtained in the study area.

4. The method as described in claim 2, characterized in that, The first coefficient and the second coefficient are calculated according to the following system of equations: In the formula, and These are the first coefficient and the second coefficient, respectively. and These are the minimum and maximum topographic indices, respectively. and These represent the maximum and minimum values ​​of the tension water storage capacity within the pre-defined study area. and These are the minimum field capacity and the maximum field capacity, respectively. and These are the minimum wilting moisture content and the maximum wilting moisture content, respectively.

5. The method as described in claim 1, characterized in that, The meteorological data includes: surface temperature and snow depth data, and the underlying surface data includes soil type-related parameters; The step of performing rasterization analysis based on the meteorological data and the underlying surface data to determine the soil freezing and thawing depth of each raster unit within the target period specifically includes: The freezing index and melting index of each grid cell during the target period are obtained by classifying and summing the daily surface temperature of each grid cell during the target period. The soil freeze-thaw parameters of each grid cell are calculated based on the soil type-related parameters of each grid cell. The degree of soil freezing is calculated based on the freezing index, soil freeze-thaw parameters and snow depth data of each grid cell, and the soil freezing depth of each grid cell in the target period is obtained. The degree of soil thawing is calculated based on the thawing index and soil freeze-thaw parameters of each grid cell, and the soil thawing depth of each grid cell during the target period is obtained.

6. The method as described in claim 5, characterized in that, The soil freezing depth and soil thawing depth are calculated using the following formula: In the formula, and These are the soil freezing depth and the soil thawing depth, respectively. For soil freeze-thaw parameters, and These are the freezing index and the melting index, respectively. This represents the snow depth of the grid cells within a preset calculation period.

7. The method as described in claim 5, characterized in that, The step of classifying and summing the daily surface temperature of each grid cell during the target period to obtain the freezing index and thawing index of each grid cell during the target period specifically includes: The first day number set is defined as all days in which the surface temperature of the target grid cell is less than 0°C during the target period, wherein the target grid cell is any one of all grid cells in the study area; All days in the target grid cell during the target period when the surface temperature is greater than 0°C are taken as the set of the second day numbers; The freezing index of each grid cell in the target period is obtained by summing the absolute values ​​of the surface temperatures corresponding to all dates in the first set of days. The melting index of each grid cell in the target period is obtained by summing the surface temperatures corresponding to all dates in the second day's number set.

8. The method as described in claim 1, characterized in that, The step of analyzing the freeze-thaw state of the soil layer based on the thickness of the vadose zone and the thickness of the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, to determine the runoff pattern of each grid cell, specifically includes: Based on the soil freezing depth, thawing depth, vadose zone thickness, and humus layer thickness of each grid cell, determine the relative positions of the frozen soil layer and thawing layer of each grid cell within the vadose zone and humus layer. Based on the relative positions of the frozen soil layer and thawed layer of each grid unit in the vadose zone and humic soil layer, and an analysis of a preset runoff pattern comparison table, the runoff pattern of each grid unit is determined. The runoff pattern comparison table includes the freeze-thaw states corresponding to different runoff patterns.

9. The method as described in claim 1, characterized in that, The step of performing source analysis based on the runoff pattern of each grid cell and the runoff volume during the target period to obtain the spatiotemporal dynamic distribution of runoff components in the study area specifically includes: Based on the synergistic effect of different runoff patterns, the water content is calculated according to the runoff pattern, vadose zone thickness, humus layer thickness and underlying surface data of each grid unit, as well as the soil freezing depth and thawing depth during the target period, to obtain the free water storage capacity and effective free water content within the humus layer thickness of each grid unit during the target period. Based on the composition of runoff components under different runoff patterns, the spatiotemporal dynamic distribution of runoff components in the study area is obtained by analyzing the runoff pattern of each grid unit, the runoff volume during the target period, the effective free water content and free water storage capacity within the humus soil layer thickness.

10. A spatiotemporal dynamic identification device for regional runoff, characterized in that, The device includes: The data acquisition unit is used to acquire underlying surface data and meteorological data for the study area during the target period; The data analysis unit is used to perform rasterization analysis based on the underlying surface data to determine the thickness of the vadose zone and the thickness of the humus layer in each raster unit within the study area. Based on the meteorological data and the underlying surface data, a raster analysis is performed to determine the soil freezing depth and thawing depth of each raster cell during the target period; The spatiotemporal dynamic identification unit is used to analyze the freeze-thaw state of the soil layer based on the thickness of the vadose zone and the thickness of the humus layer of each grid cell, as well as the soil freezing depth and thawing depth during the target period, and to determine the runoff pattern of each grid cell. Based on the runoff pattern of each grid cell and the runoff volume during the target period, a water source analysis is performed to obtain the spatiotemporal dynamic distribution of runoff components in the study area.

Images