Flow hydrograph simulation method based on coupling of Xinanjiang model and depression

Abstract

The invention relates to the technical field of water resource management, in particular to a flow hydrograph simulation method based on coupling of a Xinanjiang model and a depression, and the method comprises the steps: obtaining the geographic space data and meteorological data of a target drainage basin; the geospatial data at least comprises a digital elevation model, land utilization data and soil type data; and carrying out sub-drainage-basin division on the target drainage basin based on the digital elevation model, identifying depressions in the digital elevation model by adopting an improved priority flood algorithm, and constructing a spatial topological relation of the depressions. According to the method, the hydrological characteristics of the depression are fused into the Xinanjiang model, the regulation and storage function of the depression group on the drainage basin runoff is quantified, the hydrological simulation precision of the depression dominant drainage basin is remarkably improved, and by improving the depression recognition algorithm, pre-construction of the slope confluence time matrix based on the cells and rapid judgment of the depression merging mechanism, the stability of the depression confluence time matrix is improved. The calculation speed of the distributed hydrological model is greatly improved, and the efficiency requirement of the real-time hydrological model can be met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water resources management, and in particular to a runoff hydrograph simulation method based on Xin'anjiang model and depression coupling. BACKGROUND

[0002] A hydrological model is a key tool for simulating and predicting water balance and runoff processes in a watershed, and is of great significance for water resources management and flood disaster prediction. In the plain, wetland or karst area where depressions are widely distributed, the surface microtopography (such as depression) has a significant effect on the interception, storage and redistribution of rainfall, and deeply affects the timing and magnitude of surface runoff. Therefore, how to accurately depict the hydrological effect of depression group is a core challenge faced by improving the simulation accuracy of hydrological models in such areas.

[0003] At present, the mainstream hydrological model has several limitations in dealing with depression problems. First, the traditional hydrological analysis and the model based on coarse resolution digital elevation model (DEM) often use the "filling depression" algorithm, which regards the depression as a data error and eliminates it to achieve hydrological connectivity. This method distorts the true microtopographic features when high-precision DEM is applied, and ignores the actual hydrological processes such as water storage, evaporation and seepage of the depression. Second, the lumped or semi-distributed model represented by the Xin'anjiang model can represent the spatial heterogeneity of the underlying surface through the storage capacity curve, but it cannot simulate the dynamic hydrological response caused by "filling-overflow-merging" among depression units during the rainstorm process, resulting in lag or underestimation of the flood peak simulation in the depression-dominated watershed. In addition, the SWAT model uses hydrological response unit (HRU) for aggregation calculation, which defaults to ignore the hydrological connection caused by microtopography; while the international mainstream model such as HEC-HMS can add a depression module, but its linear reservoir approximation method cannot effectively couple the depression topological network revealed by high-precision DEM spatial analysis, making it difficult to quantify the water flow acceleration effect in the depression merging stage, affecting the accuracy and timeliness of the model in the early stage of rainstorm runoff prediction. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a runoff hydrograph simulation method based on Xin'anjiang model and depression coupling to improve the accuracy and efficiency of hydrological simulation.

[0005] In a first aspect, an embodiment of the present application provides a runoff hydrograph simulation method based on Xin'anjiang model and depression coupling, which comprises: obtaining geographical spatial data and meteorological data of a target watershed; the geographical spatial data at least includes a digital elevation model, land use data and soil type data; Dividing a target basin into sub-basins based on a digital elevation model, and identifying depressions in the digital elevation model using an improved priority flood algorithm, and constructing spatial topological relations thereof; Taking a grid in the sub-basin as a basic calculation unit, calculating the runoff and evapotranspiration of each basic calculation unit based on the Xin'anjiang model structure; Dynamically updating the spatial topological relations, calculating the slope runoff of non-depression areas and the runoff of depression areas subjected to process regulation, and superimposing the runoff to calculate to the outlet of the target basin to obtain the total outflow of the outlet section of the target basin; Based on the total outflow of the outlet section of the target basin, obtaining the flow process line of the outlet section of the target basin through river channel confluence calculation.

[0006] In combination with the first aspect, the steps of dividing a target basin into sub-basins based on a digital elevation model, and identifying depressions and constructing spatial topological relations thereof using an improved priority flood algorithm, comprise: Based on the digital elevation model, using ArcGIS tools to calculate the grid flow direction and divide the basin and sub-basin boundaries; Using a priority flood algorithm combined with a hash heap to identify depressions in the digital elevation model, and obtaining the topographic parameters of each depression, the topographic parameters including the depression overflow outlet elevation, the depression water storage capacity and the depression area; Based on the grid flow direction and the topographic parameters, constructing the spatial topological relations between depressions, and dividing the area of the target basin into depression area and non-depression area.

[0007] In combination with the first aspect, the steps of calculating the runoff and evapotranspiration of each basic calculation unit based on the Xin'anjiang model structure, comprise: For each basic calculation unit, based on the land use data, determining the land use type corresponding to the basic calculation unit and the evapotranspiration calculation rule corresponding to the land use type; Based on the evapotranspiration calculation rule, calculating the target evapotranspiration of the basic calculation unit; Based on the soil type data of the basic calculation unit and the target evapotranspiration, calculating the basic calculation runoff, soil tension water capacity and free water capacity.

[0008] In combination with the first aspect, the steps of dynamically updating the spatial topological relations, comprise: Based on the spatial topological relations, establishing a topological network of hydraulic connections between depressions, and generalizing each depression as a virtual reservoir; For each depression, calculating the inflow of the depression in real time, and dynamically updating the water storage capacity of the depression according to the water balance equation; When the water storage capacity reaches or exceeds the water storage capacity corresponding to the overflow outlet elevation, determining that an overflow event occurs, and determining the flow direction of the overflow water flow based on the topological relations; Scanning all the depressions, identifying the depression combination with the same overflow outlet elevation and adjacent spatial position, performing the merging operation and updating the topographic parameters and relationship curves of the merged depression; Updating the spatial topological relationship of the depression system according to the simulation result.

[0009] In combination with the first aspect, the step of combining the slope surface runoff of the non-depression area and the runoff of the depression area subjected to the process regulation and updating the runoff to the outlet of the target basin includes: Based on the preset rule, the runoff path and the runoff time of each basic calculation unit of the non-depression area to the basin outlet are determined; at the same time, the runoff time of each basic calculation unit in the depression area after being subjected to regulation and storage is dynamically calculated based on the spatial topological relationship of the depression; Based on the runoff time of each basic calculation unit, the surface runoff of all basic calculation units that can flow to the basin outlet within a specified period is superimposed and calculated to obtain the surface runoff process of the outlet section of the basin; The linear reservoir method is used to calculate the runoff process of the slope surface interflow and the groundwater runoff; and the surface runoff process of the outlet section is superimposed to obtain the total outflow of the outlet section of the target basin. Based on the runoff time of each basic calculation unit, the surface runoff of all basic calculation units that can flow to the basin outlet within a specified period is superimposed and calculated to obtain the surface runoff process of the outlet section of the basin;

[0010] In combination with the first aspect, based on the total outflow of the outlet section of the target basin, the step of obtaining the flow process line of the outlet section of the target basin through river runoff calculation includes: The total outflow of the outlet section of the basin is taken as the input, and the Muskingum method is used for river runoff calculation to obtain the complete flow process line of the outlet section of the target basin.

[0011] In combination with the first aspect, after the step of obtaining the flow process line of the outlet section of the target basin through river runoff calculation based on the total outflow of the outlet section of the target basin, the step further includes: The simulated flow process line is compared with the measured flow process line, and the simulation accuracy of the model is evaluated by calculating the certainty coefficient, the relative error of runoff depth, and the relative error of peak flow.

[0012] In the second aspect, the embodiments of the present application also provide a flow process line simulation device based on the Xin'anjiang model and depression coupling, which comprises: An acquisition module is configured to acquire geographical spatial data and meteorological data of a target basin; the geographical spatial data at least includes a digital elevation model, land use data and soil type data; The division construction module is used for sub-basin division of the target basin based on a digital elevation model, and a modified priority flood algorithm is used to identify a depression in the digital elevation model and construct a spatial topological relationship thereof; The first calculation module is used for taking a grid in the sub-basin as a basic calculation unit, and calculating the yield and evaporation of each basic calculation unit based on the Xin'anjiang model structure; The slope confluence module is used for dynamically updating the spatial topological relationship, calculating the slope confluence of the non-depression area and the confluence of the depression area subjected to the process regulation and storage, and superimposing and calculating the yield to the outlet of the target basin to obtain the total outflow of the outlet section of the target basin. The river confluence module is used for obtaining the flow process line of the outlet section of the target basin through river confluence calculation based on the total outflow of the outlet section of the target basin.

[0013] In a third aspect, the present application provides an electronic device, which comprises a memory and a processor, the memory is used for storing a computer program, and the processor runs the computer program to make the electronic device execute the above method.

[0014] In a fourth aspect, the present application provides a storage medium, which stores computer program instructions, and the computer program instructions are read and run by a processor to execute the above method.

[0015] The method provided by the present application has the following beneficial effects: the method for simulating a flow process line based on a Xin'anjiang model and a depression coupling comprises the following steps: obtaining geographical space data and meteorological data of a target basin; the geographical space data at least comprises a digital elevation model, land use data and soil type data; sub-basin division of the target basin is performed based on the digital elevation model, and a modified priority flood algorithm is used to identify a depression in the digital elevation model and construct a spatial topological relationship thereof; a grid in the sub-basin is taken as a basic calculation unit, and the yield and evaporation of each basic calculation unit are calculated based on the Xin'anjiang model structure; the spatial topological relationship is dynamically updated, the slope confluence of the non-depression area and the confluence of the depression area subjected to the process regulation and storage are calculated, and the yield is superimposed and calculated to the outlet of the target basin to obtain the total outflow of the outlet section of the target basin; and the flow process line of the outlet section of the target basin is calculated based on the total outflow of the outlet section of the target basin.

[0016] The present application quantifies the regulation function of the depression group on the runoff of the basin by fusing the hydrological characteristics of the depression into the Xin'anjiang model, significantly improves the hydrological simulation accuracy of the depression dominant basin, greatly improves the calculation speed of the distributed hydrological model by improving the depression identification algorithm, the pre-construction of the slope confluence time matrix based on the cell, and the rapid determination of the depression merging mechanism, and can meet the efficiency requirements of the real-time hydrological model.

[0017] Other features and advantages of the present application will be set forth in the descriptions that follow, and in part will be apparent from the description, or can be learned by practice of the application. The purposes and other advantages of the present application will be realized and attained by the structures particularly pointed out in the description, claims and drawings.

[0018] In order to make the above objectives, features and advantages of the present application more apparent, the following preferred embodiments are specifically described below, and the accompanying drawings are referred to, and the detailed description is as follows. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 A structure schematic diagram of a flow process line simulation method based on the Xin'anjiang model and depression coupling provided by the embodiment of the present application; Figure 2 A spatial topological relationship example diagram provided by the embodiment of the present application; Figure 3 A surface runoff convergence path example diagram provided by the embodiment of the present application; Figure 4 A grid direction encoding example diagram provided by the embodiment of the present application; Figure 5 A structure schematic diagram of a flow process line simulation device based on the Xin'anjiang model and depression coupling provided by the embodiment of the present application; Figure 6 An electronic device structure schematic diagram provided by the embodiment of the present application.

[0021] Reference signs: 10-acquisition module, 20-division and construction module, 30-first calculation module, 40-slope convergence module, 50-river convergence module; 130-processor, 131-memory, 132-bus, 133-communication interface. DETAILED DESCRIPTION

[0022] In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the present application will be clearly and completely described below in combination with the drawings. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0023] To facilitate the understanding of the present embodiment, the technical terms designed by the present application are briefly introduced below.

[0024] The Xin'anjiang model is one of the most representative hydrological models in China's hydrological community, and is also a conceptual hydrological model with world influence, which uses a series of physical concepts (such as storage capacity curve, free water reservoir, etc.) to simulate the real runoff process.

[0025] After introducing the technical terms involved in the present application, the application scenarios and design ideas of the embodiments of the present application are briefly introduced.

[0026] At present, the mainstream hydrological model has several limitations in dealing with the problem of depression, which makes it difficult to quantify the hydrological characteristics of depression and affects the simulation accuracy and efficiency. Based on this, the embodiments of the present application provide a flow process line simulation method based on the coupling of the Xin'anjiang model and the depression, to integrate the hydrological effect of the depression and the Xin'anjiang model, so as to improve the simulation accuracy of the flow process line.

[0027] Embodiment 1 The present application provides a flow process line simulation method based on the coupling of the Xin'anjiang model and the depression, which combines Figure 1 As shown in the figure, the method comprises: S110, obtaining geographical space data and meteorological data of a target basin; the geographical space data at least includes digital elevation model, land use data and soil type data.

[0028] S120, sub-basin division is performed on the target basin based on the digital elevation model, and a priority flood algorithm is used to identify the depression and construct the spatial topological relationship thereof.

[0029] S130, taking the sub-basin as a basic calculation unit, the runoff and evapotranspiration of each basic calculation unit are calculated based on the structure of the Xin'anjiang model.

[0030] S140, dynamically updating the spatial topological relationship, calculating the slope flow concentration of the non-depression area and the depression area affected by the process storage, and superimposing the runoff to the target basin outlet to obtain the total outflow of the target basin outlet section.

[0031] S150, based on the total outflow of the target basin outlet section, the flow process line of the target basin outlet section is obtained through river channel concentration calculation.

[0032] The application realizes high-precision and high-efficiency simulation of the hydrological process of a main watershed from a mechanism level by coupling the hydrological feature depth of a depression to a distributed Xin'anjiang model framework, and realizes accurate extraction of topographic parameters (such as overflow outlet elevation and water storage capacity) of each depression in a watershed by using an improved priority flood algorithm to identify the depression and construct the spatial topological relationship of the depression; and then by dynamically simulating the hydrological process of the depression, the dynamic chain reaction mechanism of "filling-overflowing-merging" of the depression, which is ignored in the traditional model, is introduced into the confluence calculation, which is combined with the physical runoff scheme based on the Xin'anjiang model structure to calculate the runoff, and the interception, storage and delay effects of the depression group on the surface runoff are quantified systematically, so that the simulation accuracy of the peak flow, peak time and the whole flow process line is improved significantly, and the problem of response lag in the early stage of rainstorm runoff prediction of the traditional model is solved.

[0033] In step S110, the "target watershed" refers to a geographical area with a clear topographic watershed boundary as the research object of the hydrological simulation of the application, which is a complete catchment area, the range of which is determined by a digital elevation model DEM, and has a specific watershed outlet section as the final output position of the hydrological simulation; the value can be a natural catchment area with hydrological connectivity extracted automatically based on the digital elevation model (DEM) described above by a GIS hydrological analysis tool (such as the "watershed division" tool in ArcGIS), or can be an official management unit boundary delimited by government departments such as water conservancy management and natural resources, which is only an example and is not limited.

[0034] Geospatial data describes the relatively static and spatial physical properties of the underlying surface of the watershed, and is the basis for building the model structure framework, including at least a digital elevation model, land use data and soil type data.

[0035] "Digital Elevation Model" (DEM): refers to a digital data model that discretely represents the spatial distribution of surface elevation in a regular grid form, wherein each grid cell (grid) contains a value representing the elevation of the location, which is the most basic topographic data input for watershed division, depression identification, flow direction analysis and confluence calculation in the application. The data is usually global or regional high-precision elevation data obtained by satellite radar or optical mapping methods, and can also be local higher-resolution topographic data obtained by sensors carried by an airplane, which is only an example and is not limited.

[0036] "Land use data" refers to geographic information data describing the types of land cover in the target watershed and their spatial distribution, which divides each basic calculation unit (grid) in the watershed into different types such as farmland, forest land, grassland, water body, residential area, etc., for determining differentiated evapotranspiration calculation schemes and runoff calculation rules in the present application. This data can be obtained from official land cover data, and can also be a land use or land cover map produced based on multi-spectral satellite images (such as Landsat, Sentinel-2) through machine learning, etc.

[0037] "soil type data" refers to geographic information data describing the spatial distribution of different soil types and their physical properties in the target watershed, which provides soil category information for each basic calculation unit (grid) for calculating key runoff model parameters, including soil tension water capacity (WM) and free water capacity (SM) in the present application. This data is a spatial distribution of soil properties generated based on field investigation, sampling and spatial interpolation techniques, and the main sources include national or global soil databases such as the World Soil Database, the China Soil Database, etc., which contain various attribute information such as soil type, texture, organic matter content, etc.; it can also be a regional soil survey result, a regional high-precision soil survey data organized by the agriculture, land and other departments.

[0038] Meteorological data is a dynamic input for driving the operation of the hydrological model, representing the external forcing conditions affecting the hydrological cycle. Mainly includes: rainfall data, evaporation data, other meteorological data (such as air temperature, sunshine, wind speed, humidity, etc.). Among them, "rainfall data" is the most core driving variable in the model, which is the direct source of runoff calculation, usually requiring period rainfall (such as hourly rainfall, daily rainfall); "evaporation data" usually refers to potential evapotranspiration or evaporation pan evaporation, which is a key input for calculating actual evapotranspiration loss in the watershed, used for water balance calculation, see step S130 for details.

[0039] In step S110, first, the above-mentioned data is obtained by public access or by standard surveying, remote sensing, and investigation methods to build a complete geographic spatial database. Among them, the digital elevation model provides terrain elevation information, which is the basis for subsequent hydrological analysis; land use data reflects the characteristics of land cover, which directly affects the runoff process; soil type data determines the soil hydrological parameters, which provides the basis for runoff calculation.

[0040] Subsequently, in step S120, sub-basin division is performed based on the digital elevation model DEM using GIS hydrological analysis tools to establish a hierarchical structure of the watershed; at the same time, the improved priority flood algorithm is used to quickly identify depressions, not only obtaining the basic topographic parameters of the depressions, but also constructing a spatial topological network reflecting the hydraulic connection between the depressions, realizing the fine analysis of the watershed space.

[0041] In combination with the first aspect, step S120 comprises: S121, using ArcGIS tools to calculate the grid flow direction and divide the watershed and sub-watershed boundaries based on the digital elevation model.

[0042] In this embodiment, the D8 single flow direction algorithm is used, which assumes that the water flow of each grid cell can only flow to the steepest direction of its eight neighborhood grids. The water flow direction of each grid cell is summarized as eight possibilities, which correspond to Figure 4 the direction encoding shown. Figure 4 The flow direction relationship between the grid cell and its eight neighborhoods is shown in FIG. 2, and the encoding rule is generally: the positive east direction is 1, the southeast direction is 2, the positive south direction is 4, the southwest direction is 8, the positive west direction is 16, the northwest direction is 32, the positive north direction is 64, and the northeast direction is 128. This encoding system is used to uniquely identify the outflow direction of each grid in the flow direction matrix, which is the basis for subsequent flow path analysis, depression topology construction, and flow time calculation. It is realized by the FlowDirection tool of ArcGIS to generate a matrix in which each grid cell stores the water flow direction encoding.

[0043] Subsequently, the watershed and sub-watershed boundaries are divided. First, based on the flow direction matrix, the flow accumulation is calculated, which is the total number of upstream grids flowing into each grid cell, which identifies the catchment capacity, and the path with high value is usually the river network; then, a flow accumulation threshold is set to automatically extract the river network; then, the total outlet of the watershed and the confluence point of each branch are specified as the sub-watershed outlet; finally, taking these outlet points as the starting point, all upstream grids flowing to the point are traced in reverse to determine the boundaries of the entire target watershed and each sub-watershed.

[0044] S122, a priority flood algorithm with hash heap fusion is used to identify the depressions in the digital elevation model, and the topographic parameters of each depression are obtained, including the depression overflow outlet elevation, the depression water storage capacity, and the depression area.

[0045] The priority flood algorithm with hash heap fusion simulates the physical process of rainwater accumulation and filling from the lowest point in nature. It starts from an initial, untreated lowest elevation grid, which is marked as the starting point of the current depression, and then processes the lower grids in its neighborhood like a flood spreading, gradually "filling" and delineating the boundaries of a complete depression.

[0046] The priority flood algorithm using the above fusion hash heap simulates the natural water accumulation process. Based on the minimum heap data structure, the grid cell with the lowest elevation is always processed first, ensuring that the "water flows downhill" rule is strictly followed for depression identification. By using a hash table to quickly map the grid coordinates to the position in the heap, the time complexity of updating the grid state is optimized from O(n) to O(log n), enabling high-speed processing of massive DEM data and meeting the efficiency requirements of real-time hydrological forecasting.

[0047] While identifying the spatial extent of the depression, three key topographic parameters are simultaneously extracted: a. Depression overflow outlet elevation: the lowest point on the depression boundary, serving as a threshold for determining overflow occurrence; b. Depression water storage capacity: the total volume below the overflow outlet elevation, representing the maximum water storage capacity of the depression; c. Depression area: the horizontal projection area covered by the depression, used for calculating water evaporation and visualization.

[0048] Based on the extracted topographic parameters, two key functional relationships are further established: the depression water depth-storage capacity relationship curve and the depression water depth-area relationship curve. The former describes the dynamic relationship between water level and storage capacity, while the latter describes the expansion relationship between water level and water surface area.

[0049] These curves convert static topographic parameters into functional relationships required for dynamic simulation, providing a complete data foundation for the subsequent accurate simulation of the "filling-overflow-merging" process of the depression, achieving seamless connection from topographic identification to hydrological simulation.

[0050] S123, based on the grid flow direction and topographic parameters, constructs the spatial topological relationship between depressions and divides the target watershed area into depression area and non-depression area.

[0051] Based on the grid flow direction generated in step S121, the connectivity network between depressions is established. The specific process includes: analyzing the water flow path of each depression overflow outlet to determine the upstream and downstream relationship between depressions; abstracting the depression system as a directed graph structure, where nodes represent depressions and edges represent possible water flow paths; establishing a topological adjacency matrix to record the overflow direction and connection relationship between depressions, thereby constructing the spatial topological relationship between depressions.

[0052] Subsequently, based on the depression identification results of step S122, the target watershed is functionally partitioned: the total area of all identified depressions is taken as the depression area (DPA), and the area of other regions in the watershed except for the depressions is taken as the non-depression area (NDPA). Further, the area mapping relationship is established: A = DPA + NDPA; where A is the area of the target watershed.

[0053] After step S120, in step S130, actual evapotranspiration is calculated based on a three-layer evapotranspiration model with sub-basin as a calculation unit; and a differentiated runoff calculation scheme is adopted according to land use types, surface runoff, interflow and groundwater runoff are calculated for natural land surface, and the calculation process is simplified for impervious surface and water body, which embodies the adaptability of the model to different underlying surfaces.

[0054] In combination with the first aspect, step S130 comprises: S131, for each basic calculation unit, land use type corresponding to the basic calculation unit and evapotranspiration calculation rule corresponding to the land use type are determined based on land use data.

[0055] Land use data corresponding to each DEM grid (basic calculation unit) is read, and the data is classified into predefined categories according to the data, and a specific and preset evapotranspiration calculation rule is bound for each category. This essentially establishes an "IF-THEN" logical rule: IF the land use data is farmland, forest land or grassland, THEN the "three-layer evapotranspiration estimation scheme" is adopted; IF the land use type is water body or residential area, THEN the "water body evaporation calculation scheme" is adopted.

[0056] S132, the target evapotranspiration of the basic calculation unit is calculated based on the evapotranspiration calculation rule.

[0057] For the basic calculation unit classified as "three-layer evapotranspiration estimation scheme" (farmland, forest land, grassland): the soil is divided into upper layer, lower layer and deeper layer, and the actual evapotranspiration is the sum of the evapotranspiration of the three layers, which is related to the soil moisture and potential evapotranspiration capacity of each layer, which finely simulates the physical process of water absorption by plant roots from different depth soil layers.

[0058] And for the basic calculation unit classified as "water body evaporation calculation scheme" (water body, residential area), the calculation is greatly simplified, and the evapotranspiration is directly equal to the observed evaporation pan evaporation. For water body unit evapotranspiration, the following formula is used for calculation: E w,t = E p,t ; Wherein, E w,t is the evapotranspiration of a basic calculation unit with water body land use data at time t; E p,t is the observed evaporation pan evaporation at time t.

[0059] S133, based on the soil type data and the target evapotranspiration of the basic calculation unit, the runoff, soil tension water capacity and free water capacity of the basic calculation unit are calculated.

[0060] Subsequently, according to the soil type data of each basic calculation unit, the key soil hydrological parameters of the basic calculation unit are queried or calculated, that is, the soil tension water capacity (WM) and the free water capacity (SM). The soil tension water capacity represents the maximum amount of water that the soil can hold and is not removed by gravity, and the free water capacity determines the speed of runoff formation. This step realizes the spatial heterogeneity of the model parameters.

[0061] The process of calculating the runoff is as follows: The precipitation (P t ) and the target evapotranspiration (E t ) calculated by S132 are substituted into the runoff formula of the Xin'anjiang model.

[0062] Based on the land use data classification of S131, a differentiated runoff scheme is adopted: For natural land surfaces (farmland, forest land, and cultivated land), the complete Xin'anjiang runoff model is used to calculate the runoff of each basic calculation unit, where the runoff includes three components: surface runoff (RS), interflow (RI), and groundwater runoff (RG).

[0063] For water bodies or residential areas, the runoff calculation is simplified as: = P t - , or, = P t - And all of them are surface runoff. This conforms to the physical reality that impermeable surface runoff is rapid and almost no infiltration. Wherein, represents the runoff of a unit of water body at time t; represents the runoff of a unit of residential area at time t; P t represents the precipitation of the grid unit at time t; is the evapotranspiration of a basic calculation unit of water body at time t; is the evapotranspiration of a basic calculation unit of residential area at time t.

[0064] In this way, through the progressive logic of classification, calculation, and integration in step S130, the traditional Xin'anjiang model is transformed into an intelligent and distributed runoff calculation engine. It not only retains the physical mechanism advantages of the original model in permeable areas, but also improves the adaptability, calculation accuracy, and efficiency of the model under complex underlying surface conditions through rule-based simplification and parameter spatialization based on soil type.

[0065] In combination with the first aspect, the dynamic updating of the spatial topological relationship in step S140 specifically includes: S141, based on the spatial topological relationship, a topological network of hydraulic connection between each depression is established, and each depression is generalized as a virtual reservoir.

[0066] Based on the spatial topological relationship established in the early stage, the relationship is concretized as a directed graph network as shown in FIG. 2, in which each depression is a node (as shown in the circular pattern in FIG. 2), and the hydraulic connection between depressions based on the flow direction determined constitutes a directed edge (as shown by the edge with an arrow in FIG. 2, and the arrow direction represents the flow direction of water flow). Generalizing each depression as a virtual reservoir is the key to giving it dynamic hydrological behavior, which means that each depression will have its own "account" (water balance equation) to independently account for changes in its income (inflow), expenditure (outflow, evaporation), and savings (storage capacity). Figure 2 Figure 2 Figure 2

[0067] S142, for each depression, the inflow of the depression is calculated in real time, and the storage capacity of the depression is dynamically updated according to the water balance equation.

[0068] The inflow of the depression includes the local runoff (runoff generated by the catchment area of the depression itself) and the upstream inflow (water overflowing from the upstream depression in the topological network and flowing into the depression). The storage capacity is dynamically updated by solving the water balance equation.

[0069]

[0070] wherein, is the inflow of the depression at time t; is the outflow capacity of the depression at time t, if the depression does not overflow, the outflow is 0; is the water surface evaporation at time t; is the water surface area in the depression at time t, which can be obtained by querying the depression storage capacity-water surface area relationship curve.

[0071] S143, when the storage capacity reaches or exceeds the storage capacity corresponding to the overflow outlet elevation, it is determined that an overflow event occurs, and the flow direction of the overflow water flow is determined based on the topological relationship.

[0072] The real-time storage capacity of each depression is continuously monitored and compared with the storage capacity corresponding to the overflow outlet elevation of the depression (obtained in S122), and once the storage capacity is reached or exceeded, it is immediately determined that an overflow event occurs. At this time, the outflow capacity of the depression is no longer 0, but is calculated according to the overflow formula. After the overflow event occurs, the Xin'anjiang model will immediately determine the accurate flow direction of the water flow according to the topological network established in S141. For example, is it flowing to the directly adjacent downstream depression B, or is it flowing into the main river C, which ensures that the water flow in the depression system is physically realistic and traceable.​​​

[0073] S144, scan all the depressions, identify the combination of depressions with the same overflow outlet elevation and adjacent spatial position, perform the merging operation and update the topographic parameters and relationship curves of the merged depression.

[0074] After step S143, all depressions are scanned to quickly identify the combination of depressions that meet the merging conditions. The core judgment condition is "the overflow outlets of two depressions are in the same position", which means that these depressions share the same drainage point and are physically connected when the water level reaches this elevation, forming a common water surface. At this time, the multiple depressions identified with the same overflow outlet and adjacent spatial position are merged into a new, larger depression unit, which is not only a geometric merging, but also a fusion of hydrological properties. After fusion, the total storage capacity, total water surface area and other key topographic parameters of the new depression are recalculated, and a new water level-area-storage relationship curve is constructed based on the new geometric shape.

[0075] S145, update the spatial topological relationship of the depression system according to the simulation results.

[0076] When the depression merging occurs in step S144, the number, spatial range and connection relationship of the depressions in the watershed have changed, for example, three originally independent small depressions A, B and C are merged into a large depression D. At this time, the spatial topological relationship network of the entire depression system must be dynamically updated. Specifically, the nodes of the original depressions A, B and C that are merged are deleted and the node of the new depression D after merging is added in the topological graph; the hydraulic connection is reconnected, i.e. all upstream links originally pointing to A, B and C now need to point to D; the links pointing to the downstream of D also need to be determined according to the new overflow outlet position.

[0077] It can be understood that the updated topological relationship will serve as the input basis for the next calculation period S142 and S143 steps, thus forming a closed-loop and self-adaptive simulation system that can reflect the dynamic changes of the landscape.

[0078] In combination with the first aspect, the step S140 of calculating the slope flow convergence of the non-depression area and the convergence of the depression area subjected to the process regulation and storage, and superimposing the flow to the target watershed outlet specifically includes: S146, determine the flow convergence path and convergence time to the watershed outlet of each basic calculation unit in the non-depression area based on the preset rules; at the same time, dynamically calculate the flow convergence time of each basic calculation unit in the depression area after being subjected to regulation and storage based on the spatial topological relationship of the depression.

[0079] For each grid cell in the non-depression area, based on the calculation order matrix (based on the D8 method to analyze the confluence path between grid cells), the runoff realized in step S130 has each component (slope surface RS, interflow RI, groundwater runoff RG), which is calculated by the hillside unit and enters the channel unit as lateral inflow.

[0080] Therefore, the surface runoff at the outlet section of the basin is the surface runoff of all units that can reach the section during that period.

[0081] The time it takes for a water particle to reach the outlet section within the watershed is equal to the quotient of its confluence path L and its confluence velocity V. Considering that the velocity of the water particle changes continuously during its movement due to differences in topography and geomorphological conditions, that is... For a given water quality point, along the path At that point, through a tiny movement path The required time is This allows us to obtain the time it takes for a water particle to travel through the confluence path L. .Right now:

[0082] Using ArcGIS tools and the D8 method, surface runoff confluence paths can be estimated, such as... Figure 3 As shown, a calculation order matrix is ​​formed. The formula for calculating the confluence path L of this unit is:

[0083] In the formula, n is the number of line segments within the polyline, which is also the number of grids through which the water flows (L), and the path (L) of each grid through which the water flows. i It is obtained by summing up the values ​​flowing through each grid cell. The grid width and the outflow direction of the grid are both determined by the grid width and the outflow direction of the grid. The grid width is determined by the DEM resolution, and the outflow direction can be determined by the flow direction.

[0084]

[0085]

[0086] In the formula, Here, k is the direction function, and k is the direction code (combined with...). Figure 4 (as shown) This represents the grid width.

[0087] The water flow velocity in each grid is influenced by a combination of topographical and geomorphological conditions, resulting in spatial and temporal variations. These variations can be expressed using a velocity formula:

[0088] In the formula, is the unit cell flow velocity; S is the unit cell slope, which can be obtained by DEM; n is the land surface roughness coefficient, which can be obtained by land use parameters; R is the hydraulic radius, which is replaced by the runoff depth H. A function relationship between the water depth and the catchment area is established:

[0089] wherein, the network constant, which is related to the flood frequency; is a geometric parameter, which is related to the flood frequency; is the catchment area of the upstream of the unit cell.

[0090] According to the above formula, the discrete can be obtained:

[0091] According to the definition, the average flow of the outlet section T at the time period is formed by the rainfall runoff of the grid whose confluence time is between [T, T+T] in the watershed, that is, the surface runoff of the period can be obtained.

[0092] The above formula is the confluence time of the grid in the non-depression area. Considering the time of the unit cell in the depression area to the overflow outlet of the depression area, the time of the overflow outlet of the depression area to the outlet section of the watershed, and whether the overflow event occurs through the depression filling-merging-overflow mechanism, the confluence time of a certain grid in the depression area is

[0093] Therefore, the formula can be updated as: , wherein is the time of the depression from the initial state to overflow, which depends on the distribution of rainfall in the depression area and the amount of rainfall; is the time of the confluence of a certain grid in the depression area to the outlet section of the watershed; is the time of the unit cell in the depression area to the overflow outlet of the depression area; is the time of the overflow outlet of the depression area to the outlet section of the watershed.

[0094] Therefore, the above formula can be updated as:

[0095] wherein, x represents the number of depressions that a certain basic calculation unit flows through.

[0096] S147, based on the confluence time of each basic calculation unit, the surface runoff of all basic calculation units that can flow to the outlet of the watershed within a specified period is superimposed and calculated to obtain the surface confluence process of the outlet section of the watershed.

[0097] ​For a specified target period (e.g. from T to T+At), the Xin'anjiang model scans the entire basin to find all the elementary calculation units whose "concentration time" falls within this time window, which means that the runoff produced by these elementary calculation units at the beginning of the period reaches the outlet at the end of the period. That is, if a grid has a Tc = 2.5 hours, then it contributes to the outlet flow at 2.5 hours after the start of the rainfall. For the period T = 2 hours, At = 1 hour (i.e. the 2nd to 3rd hour), the grid with Tc = 2.5 hours will contribute to the outlet flow during this period. The total of the runoff produced by all these "effective units" in the period is the average surface runoff at the outlet section of the basin during the specified period (T ~ T+At). A grid that produces runoff at t = 0 will contribute to the outlet flow at t = 2.5 hours if its Tc = 2.5 hours. Therefore, the runoff that is superimposed here corresponds to the runoff produced by each grid at the time (current period T - Tc of the grid).

[0098] By repeating this operation for all successive periods (T = 0, At, 2At, 3At...), we obtain a complete surface runoff hydrograph over time.

[0099] S148, using the linear reservoir routing method to calculate the concentration process of the slope soil and groundwater runoff, and superimposing the surface concentration process at the outlet section to obtain the total outflow at the outlet section of the target basin.

[0100] Surface runoff refers to the movement of water on the slope surface in the form of open channel flow or sheet flow, and its speed is dominated by the surface slope and roughness, which can be described by the Manning formula, etc. The flow path is clear and the concentration speed is fast.

[0101] Soil runoff (RI) and groundwater runoff (RG) move in the complex pores of soil matrix and rock fissures, which belong to seepage. Its movement speed does not depend on the surface slope, but on the soil or rock permeability and hydraulic gradient. This process is extremely slow and the path is complex, and it is impossible to trace a clear "concentration path" as in surface runoff, so its concentration process is not suitable for the above formula based on surface flow speed.

[0102] The linear reservoir routing method regards the aquifer as a linear reservoir, and the outlet flow is proportional to the reservoir storage:

[0103] wherein, is the outlet flow, is the storage, is the recession coefficient.

[0104] The interflow is calculated by the following formula:

[0105] The groundwater runoff is calculated by the following formula:

[0106] In the formula, is a linear reservoir recession coefficient of interflow, is a linear reservoir recession coefficient of groundwater runoff, is a conversion coefficient; is the interflow runoff at the end of the period; is the interflow runoff at the beginning of the period; is the groundwater runoff at the end of the period; is the groundwater runoff at the beginning of the period; is the interflow yield in the current period; is the groundwater runoff yield in the current period.

[0107] In the present embodiment, the recession law of water flow is simulated by recession coefficients (K1, K2) , which can well depict the characteristics of long confluence time and smooth process of interflow and groundwater runoff.

[0108] The continuous simulation of hydrological processes is realized by recursive calculation. The recession coefficients K1 and K2 characterize the speed of hydrological response, tend to 1 when a slow recession process is characterized; tend to 0 when a slow recession process is characterized.

[0109] Finally, the total outflow at the outlet of the basin is obtained by superimposing the flow:

[0110] In the formula, is the total water quantity that flows into the river system at a specific time t and needs to be calculated to the outlet of the basin via river network, which represents the total input of all water sources (fast, medium, and slow) to the river system at time t; is the surface runoff that directly and quickly enters the river through surface slope confluence at a specific time t , which is directly from the calculation result of the previous step S147; is the water quantity that enters the river through interflow at time t, which is the real-time output result of the linear reservoir calculation formula in step S148, and it is based on the newly generated interflow yield ( ) at the current time t and the interflow state at the previous time ( ) the calculated outflow at the end of the period; At time t, the amount of water entering the river channel through the groundwater runoff path (i.e., the base flow) is also the real-time output result of the linear reservoir calculation formula in step S148, which is calculated based on the newly generated groundwater runoff yield ( ) at the current time t and the groundwater runoff state ( ) at the previous time.

[0111] The present application integrates the contributions of the three different motion mechanisms of water flow (surface rapid flow, lateral flow in soil, and groundwater base flow) to the river channel system at the same time t, thereby forming a complete water amount sequence as the input of the river channel confluence module . Through the linear reservoir model with clear physical concept, the hydrological characteristics of slow and long duration of confluence of lateral flow and groundwater runoff are accurately described under the premise of ensuring calculation efficiency, and the completeness of the expression of the hydrological process of the basin is improved.

[0112] Steps S146 to S148 collectively constitute a set of distributed confluence solution with clear physical mechanism and clear calculation path, which successfully solves the problem that the traditional distributed hydrological model cannot accurately simulate the delay and storage effect of runoff in the depression-dominated basin by introducing a dynamic depression confluence time, ensuring that the model can accurately describe the spatial movement of water flow and efficiently synthesize the total response of the basin outlet. It is the key technical guarantee for the realization of high-precision simulation of the present application.

[0113] In combination with the first aspect, step S150 includes: S151, taking the total outflow of the basin outlet section as input, using the Muskingum method to calculate the river channel confluence to obtain the complete flow process line of the target basin outlet section.

[0114] The calculation of the Muskingum method is realized by the following formula: Q2=C0I2+C1I1+C2Q1; wherein Q1 is the outflow at the beginning of the period; Q2 is the outflow at the end of the period; I1 is the inflow at the beginning of the period; I2 is the inflow at the end of the period, and C0, C1, C2 are the calculation coefficients of the Muskingum method.

[0115] It can be understood that for the river channel in the plain area, a one-dimensional hydrodynamic model is used for calculation.

[0116] In combination with the first aspect, after step S150, it further includes: S160, comparing the simulated flow process line with the measured flow process line, and evaluating the simulation accuracy of the model by calculating the certainty coefficient, the relative error of runoff depth, and the relative error of peak flow.

[0117] It can be understood that by comparing with the actually observed flow process line, the accuracy of the model is verified by a series of recognized mathematical indexes for quantitative evaluation, so as to optimize these indexes by adjusting the parameters in the model construction stage, and thus find the parameter group which can best represent the characteristics of the basin.

[0118] It can be understood that after step S150, the flood characteristic parameters can also be extracted from the flow process line, and the flood characteristic parameters at least include one of the flood peak flow, the flood occurrence time, the total flood volume and the flood duration. The complex curve is converted into a numerical value with clear physical meaning and decision mechanism, which provides direct input for subsequent disaster prevention and resource utilization.

[0119] It can be understood that after step S150, the model calibration and verification step is also included: S160, obtaining hydrological observation data of an outlet section of the target basin in the same simulation period, and the hydrological observation data at least includes a measured flow process line.

[0120] S170, comparing the simulated flow process line with the measured flow process line.

[0121] S180, minimizing the error between the simulation result and the observation data by adjusting the model parameters, so as to optimize the simulation accuracy of the model.

[0122] Firstly, in step S160, the hydrological observation data completely synchronized with the simulation period is obtained from the hydrological station of the target basin outlet section, wherein the most core is the "measured flow process line", which is used as an objective benchmark and is an absolute reference for evaluating the performance of the model. Subsequently, in step S170, the "flow process line" simulated by the model is compared with the "measured flow process line" in a quantitative and qualitative manner; the qualitative comparison is mainly through visual analysis of the coincidence degree of the shape and peak arrival time (peak occurrence time) of the two process lines, and the quantitative comparison needs to use a series of predefined mathematical indicators such as the determination coefficient (Nash-Sutcliffe Efficiency, NSE), the relative error of runoff depth, and the relative error of flood peak flow, so as to objectively quantify the deviation between the current simulation result and the actual situation. Based on this, in step S180, the iterative optimization process is started: the key parameters in the model which cannot be directly measured but have a significant impact on the simulation results (for example, the tension water capacity WM and the free water capacity SM representing the soil water storage capacity, the soil infiltration recession coefficient CI and the groundwater runoff recession coefficient CG controlling the convergence speed, and the storage capacity representing the depression storage capacity) are adjusted systematically, and then the model is re-run and the results are compared again; through repeated execution of the cycle of "parameter adjustment-model running-result comparison", usually assisted by automatic optimization algorithms (such as the Rosenbrock method, the SCE-UA algorithm, etc.), a set of optimal parameter combination is finally found, which can minimize the error indicators between the simulation and the measured data, so as to complete the calibration (or calibration) of the model; in order to test the generalization ability of the model, the calibrated model needs to be verified by using another piece of independent hydrological observation data which does not participate in the calibration, and if the verification period also performs well, it proves that the model has reliable prediction accuracy and completes the transformation from a theoretical model to a usable tool.

[0123] In a second aspect, the embodiments of the present application also provide a flow process line simulation device based on the Xinanjiang model and the coupling of depressions, which combines Figure 5 As shown in the figure, the device comprises an acquisition module 10, a division and construction module 20, a first calculation module 30, a slope convergence module 40 and a river convergence module 50.

[0124] The acquisition module 10 is used to acquire the geographical space data and meteorological data of the target basin; the geographical space data at least includes a digital elevation model, land use data and soil type data.

[0125] The division and construction module 20 is used to divide the sub-basins of the target basin based on the digital elevation model, and identify the depressions in the digital elevation model by using an improved priority flood algorithm, and construct the spatial topological relationship thereof.

[0126] The first calculation module 30 is used to calculate the flow rate and evapotranspiration of each basic calculation unit based on the Xin'anjiang model structure, using the grid within the sub-basin as the basic calculation unit.

[0127] The slope confluence module 40 is used to dynamically update the spatial topology relationship, calculate the slope confluence in non-depression areas and the confluence in depression areas subject to process regulation, and superimpose the production flow to the target watershed outlet to obtain the total outflow at the target watershed outlet section.

[0128] The river confluence module 50 is used to obtain the flow process line of the target watershed outlet section based on the total outflow of the target watershed outlet section through river confluence calculation.

[0129] Thirdly, embodiments of this application provide an electronic device, combined with Figure 6 As shown, the electronic device includes a memory 131 and a processor 130. The memory 131 stores a computer program, and the processor 130 runs the computer program to make the electronic device perform the above-described method.

[0130] Furthermore, combined Figure 6 The electronic device shown also includes a bus 132 and a communication interface 133, with the processor 130, the communication interface 133 and the memory 131 connected via the bus 132.

[0131] The memory 131 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 133 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 132 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0132] The processor 130 can be an integrated circuit chip having a processing capability of signals. In the implementation process, each step of the above method can be completed by the integrated logic circuit of hardware in the processor 130 or the instruction in the form of software. The processor 130 described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. Each method, step and logic block disclosed in the embodiment of the present application can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiment of the present application can be directly embodied as a hardware decoding processor for execution, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the storage 131, and the processor 130 reads the information in the storage 131 and combines the hardware to complete the steps of the method of the above embodiment.

[0133] In a fourth aspect, the embodiment of the present application provides a readable storage medium, and the readable storage medium stores computer program instructions. When the computer program instructions are read and run by a processor, the above method is executed.

[0134] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system and device can refer to the corresponding process in the foregoing method embodiment, which will not be described here.

[0135] In addition, in the description of the embodiment of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood in a broad sense, for example, can be fixedly connected, can also be detachably connected, or integrally connected; can be mechanically connected, can also be electrically connected; can be directly connected, can also be indirectly connected through an intermediate medium, can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0136] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0137] In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second", "third" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0138] Finally, it should be noted that: the above embodiments are only specific embodiments of the present application, used to illustrate the technical solutions of the present application, and are not limited thereto, the protection scope of the present application is not limited thereto, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art within the technical scope disclosed by the present application can still modify or easily think of changes to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to part of the technical features; and these modifications, changes or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for simulating flow process lines based on the Xin'anjiang model coupled with depressions, characterized in that, The method includes: Acquire geospatial and meteorological data for the target watershed; the geospatial data includes at least a digital elevation model, land use data, and soil type data. The target watershed is divided into sub-watersheds based on the digital elevation model, and an improved priority flood algorithm is used to identify depressions in the digital elevation model and construct their spatial topological relationships. Using the grid within the sub-basin as the basic calculation unit, the runoff and evapotranspiration of each basic calculation unit are calculated based on the Xin'anjiang model structure. The spatial topology is dynamically updated to calculate the slope runoff in non-depression areas and the runoff in depression areas subject to process regulation. The runoff is then superimposed and calculated to the target watershed outlet to obtain the total outflow at the target watershed outlet section. Based on the total outflow at the outlet section of the target basin, the flow process curve at the outlet section of the target basin is obtained through river confluence calculation.

2. The method according to claim 1, characterized in that, The steps of dividing the target watershed into sub-watersheds based on the digital elevation model and identifying depressions and constructing their spatial topological relationships using an improved priority flood algorithm include: Based on the digital elevation model, ArcGIS tools were used to calculate the raster flow direction and delineate the boundaries of watersheds and sub-watersheds. The priority flood algorithm with fused hash heap is used to identify depressions in the digital elevation model and obtain the topographic parameters of each depression, including the elevation of the depression overflow outlet, the water storage capacity of the depression, and the area of ​​the depression. Based on the grid flow direction and the terrain parameters, a spatial topological relationship between depressions is constructed, and the target watershed area is divided into depression area and non-depression area.

3. The method according to claim 1, characterized in that, The steps for calculating the flow rate and evapotranspiration of each basic calculation unit based on the Xin'anjiang model structure include: For each basic calculation unit, the land use type corresponding to the basic calculation unit and the evapotranspiration calculation rules corresponding to the land use are determined based on the land use data; Based on the evaporation calculation rules, the target evaporation of the basic calculation unit is calculated; Based on the soil type data of the basic calculation unit and the target evapotranspiration, the basic calculated runoff, soil tension water capacity, and free water capacity are calculated.

4. The method according to claim 1, characterized in that, The steps for dynamically updating the spatial topology include: Based on the aforementioned spatial topological relationships, a topological network of hydraulic connections between the depressions is established, and each depression is generalized as a virtual reservoir. For each depression, the inflow rate is calculated in real time, and the water storage capacity of the depression is dynamically updated according to the water balance equation. When the water storage volume reaches or exceeds the water storage capacity corresponding to the elevation of its overflow outlet, an overflow event is determined, and the flow direction of the overflow water is determined based on the topological relationship. Scan all the depressions, identify combinations of depressions with the same overflow elevation and adjacent spatial locations, perform a merging operation, and update the terrain parameters and relationship curves of the merged depressions; The spatial topology of the depression system is updated based on the simulation results.

5. The method according to claim 1, characterized in that, The steps for calculating the slope runoff in non-depression areas and the runoff in depression areas subject to process regulation, and then calculating the runoff to the target watershed outlet, include: The confluence paths of each basic computing unit in the non-depression area and the confluence time to the watershed outlet are determined based on preset rules; at the same time, the confluence time of each basic computing unit in the depression area after regulation is dynamically calculated based on the spatial topology of the depression. Based on the confluence time of each basic computing unit, the surface runoff of all basic computing units that can reach the watershed outlet within a specified time period is superimposed and calculated to obtain the surface confluence process of the watershed outlet section. The linear reservoir algorithm is used to calculate the confluence process of surface runoff and groundwater runoff; and the surface runoff process of the outlet section is superimposed to obtain the total outflow of the target watershed outlet section.

6. The method according to claim 1, characterized in that, The steps for obtaining the flow process curve of the target watershed outlet section based on the total outflow of the target watershed outlet section through river confluence calculation include: Using the total outflow at the outlet section of the basin as input, the Muskingen method is used to calculate the river confluence, resulting in the complete flow process curve of the outlet section of the target basin.

7. The method according to claim 1, characterized in that, After obtaining the flow process curve of the target watershed outlet section based on the total outflow of the target watershed outlet section through river confluence calculation, the method further includes: The simulated flow process line is compared with the measured flow process line, and the simulation accuracy of the model is evaluated by calculating the coefficient of determination, relative error of runoff depth, and relative error of peak flow.

8. A flow process line simulation device based on the Xin'anjiang model coupled with a depression, characterized in that, The device includes: The acquisition module is used to acquire geospatial data and meteorological data of the target watershed; the geospatial data includes at least a digital elevation model, land use data, and soil type data. The subdivision module is used to divide the target watershed into sub-watersheds based on the digital elevation model, and to identify depressions in the digital elevation model using an improved priority flood algorithm, as well as to construct their spatial topological relationships. The first calculation module is used to calculate the flow rate and evapotranspiration of each basic calculation unit based on the Xin'anjiang model structure, using the grid within the sub-basin as the basic calculation unit. The slope confluence module is used to dynamically update the spatial topology relationship, calculate the slope confluence in non-depression areas and the confluence in depression areas subject to process regulation, and superimpose the production flow to the target watershed outlet to obtain the total outflow at the target watershed outlet section. The river confluence module is used to obtain the flow process line of the outlet section of the target watershed based on the total outflow of the outlet section of the target watershed through river confluence calculation.

9. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program and the processor running the computer program to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A storage medium storing computer program instructions, characterized in that, When the computer program instructions are read and executed by a processor, they perform the method as described in any one of claims 1-7.

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