Hydrological connectivity-based drainage basin phosphorus loss hot spot region identification method

Abstract

The invention relates to the technical field of drainage basin environment monitoring, and particularly discloses a drainage basin phosphorus loss hot spot area identification method based on hydrological connectivity, which comprises the following steps: firstly, determining a research area, extracting a drainage basin vector boundary, and acquiring soil total phosphorus content data by utilizing a GIS (Geographic Information System); then, data such as a digital elevation model and soil texture are obtained, the hydrological function connectivity index is obtained by calculating the relative smoothness, the sediment rainfall index and the surface runoff, and spatial distribution of the hydrological function connectivity index is defined; and finally, based on GIS (Geographic Information System) space overlay analysis, dividing regions by taking the 25th and 75th quantiles of the soil total phosphorus content and the hydrological function connectivity index as boundaries, and dividing the drainage basin phosphorus loss risk grades into high, medium and low classes, thereby identifying the drainage basin phosphorus loss hot spot regions. The method comprehensively considers hydrology and phosphorus content factors, is scientific and efficient, and provides a powerful basis for prevention and control of phosphorus pollution in the drainage basin.

Description

Technical Field

[0001] The present invention relates to the technical field of watershed environmental monitoring, and in particular to a method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity. Background Art

[0002] Phosphorus is a limiting nutrient that triggers eutrophication in water bodies, and its loss characteristics show significant spatial heterogeneity. Traditional methods for identifying watershed phosphorus loss hotspots are mostly based on static data such as land use, soil properties, and fertilizer input to characterize the spatial distribution of phosphorus, capturing areas of high phosphorus concentration as potential sources of phosphorus loss. However, due to the influence of human activities such as terraces, ditches and roads, as well as the randomness of rainfall and runoff, landscape units with high phosphorus content do not necessarily establish hydrological connectivity with streams, limiting the migration of phosphorus with runoff to receiving water bodies.

[0003] Isotope tracing technology can effectively reveal the migration process of phosphorus with runoff. In runoff generated from different sources, the "fingerprint" effect formed by the difference in the chemical composition of environmental isotopes has an identification and tracing function. Based on water balance and isotope mass conservation, combined with hydrological observations, the "transit time" of isotopes can be used to effectively identify the source of runoff, the confluence path and changes along the way, and analyze the migration path of phosphorus. However, although isotope tracing technology can effectively trace the source of phosphorus loss hotspots, its application has significant limitations: on the one hand, the test cost is high and the sample pre-treatment process is complicated (such as separation and purification, mass spectrometry calibration, etc.), requiring a lot of manpower and equipment resources; on the other hand, the analysis cycle is long (usually 2-4 weeks), making it difficult to provide real-time data support for governance decisions. Therefore, this technology has not yet become a universal solution for identifying phosphorus loss hotspots in watersheds.

[0004] Hydrological functional connectivity can dynamically reflect the changing characteristics of runoff pathways and their smoothness under varying rainfall conditions. Quantitative characterization of hydrological functional connectivity is simple and convenient, requiring no sampling. It is often based on DEM data and modifies existing structural connectivity indices using factors such as rainfall and runoff to identify runoff-producing units within a watershed, thereby clarifying the dynamic changes in phosphorus along flow pathways.

[0005] Based on this, the present invention couples the hydrological functional connectivity of the watershed with the spatial distribution characteristics of phosphorus, constructs a dynamic weight allocation algorithm, and proposes a method for identifying phosphorus loss hotspots in the watershed based on hydrological connectivity, providing a scientific basis for the precise prevention and control of non-point source pollution in the watershed and the protection of the ecological environment. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method for identifying watershed phosphorus loss hotspots based on hydrological connectivity, which can comprehensively consider multiple factors such as rainfall, topography, land use, etc., accurately identify the location and migration path of phosphorus loss in the watershed, and thus realize the identification of phosphorus loss hotspots in the watershed.

[0007] To solve the above technical problems, the present invention provides a technical solution: a method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity, comprising the following steps:

[0008] S1. Determine the study area and extract the basin vector boundary;

[0009] S2. Use the mask extraction function of GIS software to obtain the total soil phosphorus content data of the watershed and clarify its spatial distribution;

[0010] S3. Obtain the digital elevation model, soil texture, land use, and rainfall data of the watershed, calculate the hydrological functional connectivity index, and clarify the spatial distribution of the watershed's hydrological functional connectivity;

[0011] S4. Through GIS spatial overlay analysis, the spatial coincidence between areas with high hydrological functional connectivity and areas with high phosphorus concentrations was revealed, and the phosphorus loss hotspots in the basin were identified.

[0012] Furthermore, the specific steps for calculating the hydrological functional connectivity index in step S3 are as follows:

[0013] (1) Calculate relative smoothness RS:

[0014]

[0015] Where: n min The value of is 0.01; n is the Manning coefficient, and the value is determined by the land use type: urban land is 0.02, forest land is 0.40, grassland is 0.25, cultivated land is 0.20, and bare land or wasteland is 0.05;

[0016] (2) Calculation of sediment rainfall index Ips:

[0017]

[0018] Where: m represents the current rainfall event, j represents the number of rainfall events between the current rainfall event and the previous rainfall event (j = 3); mj of Imax is the maximum rainfall intensity of the previous rainfall event (mm / d); V m-i is the cumulative rainfall of the previous rainfall event mi (mm); Δt m-i is the duration of rainfall event mi (d);

[0019] (3) Calculate the surface runoff Q based on the runoff curve modelrunoff :

[0020]

[0021] Ia=0.2×Sa;

[0022] Where: P is the total rainfall of the rainfall event; Sa is the storage parameter; Ia is the initial amount; CN is the runoff curve number;

[0023] (4) Based on steps (1)-(3), calculate the hydrological functional connectivity index IHC:

[0024]

[0025] Where: S is the slope; d i is the length of the path from grid i along the downstream to the river channel.

[0026] Furthermore, in step S4, the specific method for identifying the phosphorus loss hotspots in the watershed is as follows:

[0027] Based on the total phosphorus content data of the watershed soil, the watershed was divided into low, medium and high total phosphorus content zones with the 25th and 75th percentiles as boundaries, respectively;

[0028] Based on the hydrological functional connectivity index, the basin was divided into low, medium, and high hydrological connectivity zones with the 25th and 75th percentiles as the boundaries, respectively;

[0029] According to the spatial distribution characteristics of total phosphorus content and hydrological connectivity, the phosphorus loss risk level in the basin is divided into three categories: high-risk area for phosphorus loss, medium-risk area for phosphorus loss, and low-risk area for phosphorus loss.

[0030] Furthermore, the specific basis for the classification of watershed phosphorus loss risk levels is as follows:

[0031] (1) High-risk areas for phosphorus loss: areas with high total phosphorus content ∩ areas with high hydrological connectivity;

[0032] (2) Medium-risk areas for phosphorus loss: high total phosphorus content area ∩ medium hydrological connectivity area, medium total phosphorus content area ∩ high hydrological connectivity area, medium total phosphorus content area ∩ medium hydrological connectivity area;

[0033] (3) Low risk areas for phosphorus loss: high total phosphorus content area ∩ low hydrological connectivity area, low total phosphorus content area ∩ high hydrological connectivity area, medium total phosphorus content area ∩ low hydrological connectivity area, low total phosphorus content area ∩ medium hydrological connectivity area, low total phosphorus content area ∩ low hydrological connectivity area.

[0034] The advantages of the present invention compared with the prior art are:

[0035] (1) Based on the clarification of the spatial distribution of soil phosphorus content in the watershed, the present invention couples the dynamic changes of hydrological functional connectivity and clarifies the dynamic changes of phosphorus migration paths in the watershed.

[0036] (2) The present invention realizes dynamic identification of phosphorus loss hotspots based on the rainfall event scale. Compared with traditional seasonal or annual scale methods, the temporal resolution is significantly improved, which is more in line with the real-time decision-making needs of watershed management.

[0037] (3) The present invention does not require sample collection and testing and analysis. It only needs to integrate multi-source geographic spatial data and rainfall observation data of the basin to accurately identify the phosphorus loss hotspots in the basin. It has the outstanding advantages of simplicity and strong universality. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a flow chart of a method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity.

[0039] Figure 2 This is a flow chart for calculating the functional connectivity index HIC of the present invention. DETAILED DESCRIPTION

[0040] Various exemplary embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be noted that unless otherwise specifically stated, the relative arrangements of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention.

[0041] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0042] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0043] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0044] The following is a detailed description of a method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity according to the present invention, with reference to the accompanying drawings.

[0045] Combined with attachment Figure 1-2 The specific implementation process of the method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity of the present invention is as follows:

[0046] A method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity includes the following steps:

[0047] S1. Determine the study area and extract the basin vector boundary.

[0048] S2. Use the mask extraction function of GIS software to obtain the total soil phosphorus content (g / kg) data of the basin and clarify its spatial distribution.

[0049] S3. Obtain the digital elevation model (DEM), soil texture, land use, and rainfall data of the watershed, calculate the hydrological functional connectivity index (IHC), and clarify the spatial distribution of the watershed's hydrological functional connectivity. The specific steps are as follows:

[0050] (1) Calculate the relative smoothness (RS). RS is a dimensionless impedance factor based on the Manning coefficient, which reflects the flow smoothness or friction resistance of water in the basin.

[0051]

[0052] Where: n min The value of is 0.01; n is the Manning coefficient, and the value is determined according to the land use type: urban land is 0.02, forest land is 0.40, grassland is 0.25, cultivated land is 0.20, and bare land or wasteland is 0.05.

[0053] (2) Calculate the sediment precipitation index (Ips), which reflects the impact of the intensity of the previous rainfall event on sediment detachment or migration.

[0054]

[0055] Where: m represents the current rainfall event, j represents the number of rainfall events between the current rainfall event and the previous rainfall event (j = 3); mj of Imax is the maximum rainfall intensity of the previous rainfall event (mm / d); V m-i is the cumulative rainfall of the previous rainfall event mi (mm); Δt m-i is the duration (d) of the rainfall event mi.

[0056] (3) Calculate the surface runoff Q based on the runoff curve model runoff .

[0057]

[0058] Ia=0.2×Sa;

[0059] Where P is the total rainfall of the rainfall event (mm); Sa is the storage parameter (mm); Ia is the initial amount (mm); and CN is the runoff curve number, which indicates the strength of the surface's ability to retain rainwater. The value is determined by land use type and soil texture (see Table 1 for details).

[0060] Table 1 CN value reference table

[0061]

[0062] (4) Based on steps (1)-(3), the hydrological functional connectivity index (IHC) is calculated to clarify the spatial distribution of the hydrological functional connectivity of the basin.

[0063]

[0064] Where: S is the slope (m / m); d i is the path length (m) from grid i along the downstream to the river channel.

[0065] S4. Through GIS spatial overlay analysis, the spatial coincidence between areas with high hydrological functional connectivity and areas with high phosphorus concentrations was revealed, and the hot spots of phosphorus loss in the watershed were identified. Based on the total phosphorus content data of the watershed soil, the watershed was divided into low, medium, and high total phosphorus content areas with the 25th and 75th percentiles as boundaries respectively; based on the hydrological functional connectivity index, the watershed was divided into low, medium, and high hydrological connectivity areas with the 25th and 75th percentiles as boundaries respectively. According to the spatial distribution characteristics of total phosphorus content (low, medium, and high total phosphorus content areas) and hydrological connectivity (low, medium, and high hydrological connectivity areas), the watershed phosphorus loss risk level was divided into three categories, as follows:

[0066] (1) High-risk areas for phosphorus loss: areas with high total phosphorus content ∩ areas with high hydrological connectivity;

[0067] (2) Medium-risk areas for phosphorus loss: high total phosphorus content area ∩ medium hydrological connectivity area, medium total phosphorus content area ∩ high hydrological connectivity area, medium total phosphorus content area ∩ medium hydrological connectivity area;

[0068] (3) Low risk areas for phosphorus loss: high total phosphorus content area ∩ low hydrological connectivity area, low total phosphorus content area ∩ high hydrological connectivity area, medium total phosphorus content area ∩ low hydrological connectivity area, low total phosphorus content area ∩ medium hydrological connectivity area, low total phosphorus content area ∩ low hydrological connectivity area.

[0069] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. A method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity, characterized by: The following steps are involved: S1. Determine the study area and extract the basin vector boundary; S2. Use the mask extraction function of GIS software to obtain the total soil phosphorus content data of the watershed and clarify its spatial distribution; S3. Obtain the digital elevation model, soil texture, land use, and rainfall data of the watershed, calculate the hydrological functional connectivity index, and clarify the spatial distribution of the watershed's hydrological functional connectivity; S4. Through GIS spatial overlay analysis, the spatial coincidence between areas with high hydrological functional connectivity and areas with high phosphorus concentrations was revealed, and the phosphorus loss hotspots in the watershed were identified.

2. The method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity according to claim 1, characterized in that: The specific steps for calculating the hydrological functional connectivity index in step S3 are as follows: (1) Calculate relative smoothness RS: Where: n min The value of is 0.01; n is the Manning coefficient, and the value is determined by the land use type: urban land is 0.02, forest land is 0.40, grassland is 0.25, cultivated land is 0.20, and bare land or wasteland is 0.05; (2) Calculation of sediment rainfall index Ips: Where: m represents the current rainfall event, j represents the number of rainfall events between the current rainfall event and the previous rainfall event (j = 3); mj of Imax is the maximum rainfall intensity of the previous rainfall event (mm / d); V m-i is the cumulative rainfall of the previous rainfall event mi (mm); Δt m-i is the duration of rainfall event mi (d); (3) Calculate the surface runoff Q based on the runoff curve model runoff : Ia=0.2×Sa; Where: P is the total rainfall of the rainfall event; Sa is the storage parameter; Ia is the initial amount; CN is the runoff curve number; (4) Based on steps (1)-(3), calculate the hydrological functional connectivity index IHC: Where: S is the slope; d i is the length of the path from grid i along the downstream to the river channel.

3. The method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity according to claim 2, characterized in that: In step S4, the specific method for identifying the phosphorus loss hotspots in the watershed is as follows: Based on the total phosphorus content data of the watershed soil, the watershed was divided into low, medium and high total phosphorus content zones with the 25th and 75th percentiles as boundaries, respectively; Based on the hydrological functional connectivity index, the basin was divided into low, medium, and high hydrological connectivity zones with the 25th and 75th percentiles as the boundaries, respectively; According to the spatial distribution characteristics of total phosphorus content and hydrological connectivity, the phosphorus loss risk level in the basin is divided into three categories: high-risk area for phosphorus loss, medium-risk area for phosphorus loss, and low-risk area for phosphorus loss.

4. The method for identifying phosphorus loss hotspots in a watershed based on hydrological connectivity according to claim 3, characterized in that: The specific basis for the classification of watershed phosphorus loss risk levels is as follows: (1) High-risk areas for phosphorus loss: areas with high total phosphorus content ∩ areas with high hydrological connectivity; (2) Medium-risk areas for phosphorus loss: high total phosphorus content area ∩ medium hydrological connectivity area, medium total phosphorus content area ∩ high hydrological connectivity area, medium total phosphorus content area ∩ medium hydrological connectivity area; (3) Low risk areas for phosphorus loss: high total phosphorus content area ∩ low hydrological connectivity area, low total phosphorus content area ∩ high hydrological connectivity area, medium total phosphorus content area ∩ low hydrological connectivity area, low total phosphorus content area ∩ medium hydrological connectivity area, low total phosphorus content area ∩ low hydrological connectivity area.

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