Drainage basin measure optimal configuration method considering sediment function connectivity

Abstract

The invention discloses a drainage basin measure optimization configuration method considering sediment function connectivity. The method comprises the following steps of simulating the total sediment production amount of a drainage basin, obtaining measure data, establishing a multi-target optimization system, dynamically adjusting measure layout, generating a leading edge optimal solution set, and obtaining a final measure space distribution diagram and the achievement effect of a plurality of targets. According to the method, measures are preferentially arranged in the functional connectivity hot spot area, so that the erosion intensity is controlled from the source, and the hydrological connectivity of the upstream and the downstream is weakened. The accurate intervention strategy of the key nodes is focused, high cost of global spreading can be avoided, downstream flood and deposition risks can be relieved by interfering the peak flow of substance transportation, and therefore the purpose of obtaining high ecological benefits with the small disturbance rate is achieved.

Description

Technical Field

[0001] This invention belongs to the field of soil and water conservation technology, specifically relating to a method for optimizing the configuration of watershed measures that considers the functional connectivity of sediment. Background Technology

[0002] Currently, the total area affected by soil erosion in my country reaches 2.6019 million square kilometers, of which water erosion accounts for 40.39%, approximately 1.051 million square kilometers (China Soil and Water Conservation Bulletin, 2024). Monitoring data also shows that between 1961 and 2023, the frequency of extreme daily precipitation events nationwide increased by an average of 18 days per decade, and the erosive power of rainfall in more than 80% of areas showed an increasing trend (Gao et al., 2025). However, existing governance systems largely rely on historical average data for design, failing to fully consider the dynamic evolution mechanism of erosion and sediment yield intensity under extreme climatic conditions, leading to discrepancies between prevention and control measures and actual risk levels (Wang et al., 2024). Although the construction of ecologically clean small watersheds continues to advance, its effectiveness in preventing infrastructure flood damage caused by high-intensity rainfall remains limited.

[0003] Faced with this challenge, managers urgently hope that watershed management measures can minimize the negative impact of soil erosion on water bodies. However, in integrated watershed management, determining the timing, location, and type of best management practices (BMPs) remains a complex problem. Traditional management practices often suffer from problems such as vague objectives, broad information ranges, and policy and resource constraints. Furthermore, the neglect of dynamic changes in hydrological conditions, coupling effects between measures, and the cumulative impact of sediment load leads to insufficient spatial adaptability of management measures, making it difficult to achieve the expected results.

[0004] In watershed system management, accurately identifying key source areas and sinks of soil erosion and sediment transport is the scientific foundation for reducing the complexity of governance. However, most existing models neglect the spatiotemporal heterogeneity of sediment transport during extreme hydrological events and fail to effectively integrate functional connectivity with the erosion-sediment production response mechanism, thus limiting the transferability and applicability of model results. Furthermore, ecological engineering design often relies on empirical parameters and lacks a "blocking" mechanism based on dynamic connectivity thresholds. The theory of coordinated regulation and control based on "dredging" can easily lead to an imbalance between the local governance effect and the overall sediment transport in the basin, triggering systemic risks. Summary of the Invention

[0005] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a watershed measure optimization configuration method that considers the functional connectivity of sediment. This method solves the technical problems in the prior art, such as the lack of adaptive regulation of the dynamic response to the erosion-sediment transport process under extreme rainfall and the lack of systematic optimization methods for the scale and location of measure configuration, which leads to insufficient governance effectiveness.

[0006] To address the aforementioned technical problems, this invention provides a method for optimizing the configuration of watershed measures considering sediment functional connectivity, comprising the following steps: S1. Use a sediment yield simulation model to simulate and calculate the total sediment yield of the watershed. Total sediment yield in the basin The calculation represents the potential soil loss per unit during a single rainfall event. degree of connectivity with sediment The sum of the relevant bivariate functions is calculated using the following formula: ; In the formula, variable k represents the k-th unit, and n represents the total number of units. This represents the midpoint value of the Boltzmann curve, corresponding to DSC = 0.5. The slope parameter of the curve. Calculate the index for functional connectivity; S2. Obtain the types of measures for preventing and controlling soil erosion, their implementation costs, and the parameter changes of different types of measures in the functional connectivity and sediment yield simulation model; S3. Establish a multi-objective optimization system that maximizes sand production reduction, minimizes economic costs, and minimizes ecological disturbance. S4. Dynamically adjust the spatial location and scale combination layout of different types of measures, wherein the deployment probability of measures that set up functional connectivity hotspot areas is set to be u times that of other units, where u is greater than 1. S5. Based on the multi-objective optimization system, the simulated annealing algorithm is used to process the dynamic adjustment of the spatial location and scale combination layout. After iterative adjustment, a Pareto front optimal solution set is generated under the multi-dimensional constraints of ecological benefits and implementation feasibility. S6. Based on the optimal solution set of the Pareto front, obtain the corresponding spatial distribution map of measures and the achievement effect of multiple objectives.

[0007] Preferably, in the watershed measure optimization configuration method considering sediment functional connectivity of the present invention, the soil loss amount The following formula is used to calculate: ; In the formula, It is the runoff depth; This is the peak traffic. It is the area of ​​each unit; It is a soil erodibility factor; It is a vegetation cover and management factor; These are factors related to soil and water conservation measures; It is a topographic factor; runoff depth and peak traffic Calculated using the SCS-CN curve equation: ; ; ; in, It is the total rainfall of a single rainfall event; It is the potential maximum retention or infiltration rate; It is the number of runoff curves; 3600 is the maximum rainfall intensity; 3600 is the time unit conversion factor.

[0008] In the watershed measure optimization configuration method considering sediment functional connectivity of the present invention It is calculated using the following formula: ; In the formula, superscript This represents the mean. It is the runoff depth; This is the peak traffic. It is a soil erodibility factor; It is a vegetation cover and management factor; These are factors related to soil and water conservation measures; It's the slope; It is the area of ​​the upstream catchment area; This represents the length of the downstream confluence path of the unit, where i represents the unit downstream of unit k to the outlet, and n is the total number of units. As a weighting factor characterizing the resistance to sediment transport, .

[0009] In the watershed measure optimization configuration method considering sediment functional connectivity of the present invention, the functional connectivity hotspot area refers to the degree of sediment connectivity. The region where the value is greater than the preset value, The calculation formula is as follows: .

[0010] In the watershed measure optimization configuration method considering sediment functional connectivity of the present invention, the preset value is 0.5.

[0011] In the watershed measure optimization configuration method considering sediment functional connectivity of the present invention, u=5.

[0012] In the watershed measure optimization configuration method considering sediment functional connectivity of the present invention, the parameter changes of different measure types in the functional connectivity and sediment yield simulation model in step S2 specifically refer to the parameter changes of the following factors: runoff depth Q, These are peak flow, vegetation cover and management factor C, and soil and water conservation measures factor P.

[0013] In the watershed measure optimization configuration method considering sediment functional connectivity of the present invention, ecological disturbance in step S3 refers to the total area changed by the implementation of the measures.

[0014] In summary, the above technical solutions constructed by this invention can achieve the following beneficial effects: This invention prioritizes deploying measures in functional connectivity hotspots, which helps control erosion intensity at its source and weaken hydrological connectivity between upstream and downstream areas. This precise intervention strategy, focusing on key nodes, avoids the high costs of comprehensive deployment while mitigating downstream flooding and siltation risks by disrupting peak material transport flows, thus achieving the goal of obtaining higher ecological benefits with a smaller disturbance rate. Attached Figure Description

[0015] Figure 1 This is a flowchart of the present invention; Figure 2 Spatial distribution of functional connectivity hotspots for a 20-year flood event in the basin.

[0016] Figure 3 Real-world images showing the expected effects of the four types of measures before and after implementation in the Xiangshui River Basin; Figure 4 The optimal management measures for spatial distribution are achieved by prioritizing the comprehensive configuration of the three scenario functions connected to the hotspot area. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] refer to Figure 1 A method for optimizing the allocation of watershed measures considering sediment functional connectivity includes the following steps: S1. Use a sediment yield simulation model to simulate and calculate the total sediment yield of the watershed. Total sediment yield in the basin The calculation represents the potential soil loss per unit during a single rainfall event. degree of connectivity with sediment The sum of the relevant bivariate functions is calculated using the following formula: ; In the formula, variable k represents the k-th unit, and n represents the total number of units. The value at the midpoint of the Boltzmann curve is [value missing]. The slope parameter of the curve. Calculate the index for functional connectivity.

[0019] The amount of soil loss The following formula is used to calculate: ; In the formula, This represents the potential soil loss per unit during a single rainfall event (unit: t). It is the runoff depth (unit: mm); Peak flow rate (unit: m³) 3 / s); It is the area of ​​each unit (unit: m). 2 (The grid resolution in this study is 30 m). Soil erodibility factor (unit: t·m) 2 ·h / m 3 ·t·cm), using parameters such as soil mechanical composition and organic matter content in the basic attribute dataset of the China High-Resolution National Soil Information Grid, the EPIC model algorithm and Zhang Keli's correction factor were used to calculate (Zhang Keli et al. 2007). It is a vegetation cover and management factor (dimensionless), calculated using NDVI data corresponding to the time of the corresponding rainfall and flood events, referring to the method and correction layer of Yan et al. (Yan et al. 2020). It is a soil and water conservation measure factor (dimensionless), which is obtained by looking up a table based on the slope and the type of measure; The topographic factor (dimensionless) was calculated using the topographic factor tool of the soil erosion model developed by Beijing Normal University based on the DEM; runoff depth. and peak traffic The following was calculated using the SCS-CN curve equation (SCS, 2004): ; ; ; in, It is the total rainfall of a single rainfall event (unit: mm); It is the potential maximum retention or infiltration rate (unit: mm). It is the number of runoff curves (dimensionless), which is related to soil type, land use and previous soil moisture conditions, and is assigned by looking up a table (Lian et al 2020). 3600 is the maximum rainfall intensity (unit: mm / h); 3600 is the time unit conversion factor.

[0020] This study considers the impact of spatial variations in rainfall characteristics and land surface properties on connectivity. The spatial distribution of runoff generation for different events is closely related to the spatiotemporal variations of rainfall. Watershed runoff is controlled by total rainfall and rainfall duration. Therefore, runoff depth and peak runoff factors are introduced to reflect the role of runoff as a carrier for sediment transport. Soil erodibility, vegetation cover, and soil and water conservation measures are used to reflect the impact of spatial heterogeneity of the watershed's underlying surface properties on transport resistance, enriching the weighting factors characterizing the resistance to sediment transport. With the basin outlet as the target "sink," a functional connectivity index (FCI) is formed, determined by the intensity of actual rainfall events and specific basin attributes. It is calculated using the following formula: ; In the formula, superscript This represents the mean. It is the runoff depth (unit: mm); Peak flow rate (unit: m³) 3 / s); Soil erodibility factor (unit: t·m) 2 ·h / m 3 ·t·cm); It is a vegetation cover and management factor (dimensionless). It is a soil and water conservation measure factor (dimensionless). It is the slope (unit: m / m); It is the area of ​​the upstream catchment area (unit: m). 2 ); This is the length of the downstream confluence path of the unit (in meters), where i represents the unit downstream of unit k to the outlet, and n is the total number of units. As a weighting factor characterizing the resistance to sediment transport, .

[0021] To facilitate comparisons across watersheds or between different events, the FCI was normalized using the Boltzmann curve (Sigmoid function) to obtain the Degree of Sediment Connectivity (DSC), which ranges from 0 (complete blockage) to 1 (complete connectivity). ; In the formula, This is the midpoint value of the Boltzmann curve, corresponding to DSC = 0.5; The slope parameter is the curve slope. A larger absolute value indicates a more drastic change in connectivity. A DSC greater than 0.5 indicates a functional connectivity hotspot; otherwise, it indicates a non-functional connectivity hotspot. Obtaining these two parameter values ​​requires calibration based on watershed surface characteristics and historical rainfall and flood event data. (Reference) Figure 2 This is a spatial distribution map of functional connectivity hotspots for a 20-year flood event in the basin.

[0022] S2. Obtain the types of measures for preventing soil erosion of different categories, implementation costs, and parameter changes of different types of measures in the functional connectivity and sediment yield simulation model. The measures and parameter changes adopted in this embodiment are as follows, and you can refer to Table 1 and Table 2 below for details.

[0023] Table 1. Types, Functions, Specific Contents, and Implementation Costs of Watershed Measures (BMPs) ; Table 2. Parameter variations of different types of measures in functional connectivity and sediment yield models. ; refer to Figure 3 The images show the actual effects of the four types of measures before and after their expected implementation in the Xiangshui River Basin.

[0024] S3. Establish a multi-objective optimization system that maximizes sand production reduction, minimizes economic costs, and minimizes ecological disturbance.

[0025] Maximizing sand yield can be calculated before implementing the measures in step S2, yielding the sand yield before and after the parameter changes; the difference between the two is the sand yield reduction. Economic costs can be calculated based on the costs in step S2. Ecological disturbance is the total area altered by the implementation of the measures.

[0026] S4. The spatial location and scale combination layout of four different types of measures, namely, returning farmland to forest, ditches and ponds, terraced field transformation and vegetation buffer zone, are dynamically adjusted. Among them, the probability of setting up functional connectivity hotspot areas is set to 5 times that of other units. S5. Based on the multi-objective optimization system, the simulated annealing algorithm is used to process the situation of dynamically adjusting the spatial location and scale combination layout of the four types of measures. After iterative adjustment, a Pareto front optimal solution set is generated under the multi-dimensional constraints of ecological benefits and implementation feasibility. S6. Based on the optimal solution set of the Pareto front, obtain the corresponding spatial distribution map of measures and the achievement effects of multiple objectives. (Reference) Figure 4 The spatial distribution of the optimal management measures after prioritizing the comprehensive configuration of the three scenario functions and the hotspot area is shown in the figure.

[0027] Example Referring to Table 3, case studies of a 20-year return period rainstorm and flood event in the Xiangshui River Basin show that when governance measures are prioritized in the connected hotspot area, which accounts for only 3.06% of the basin area, the sediment yield reduction rates for the three scenarios are 30.13%, 36.17%, and 17.92%, respectively. This spatial optimization configuration can achieve sediment yield reductions of 26.56% to 56.25% when dealing with floods of other scales. Among these, the ecological priority scenario, by reducing 17.78 km² of cultivated land, achieves an average sediment yield reduction rate of 50.36% with an ecological disturbance rate of 1.86% and an economic cost of 14.8565 million yuan, demonstrating the best overall benefit. This strategy avoids the resource dispersion drawbacks of traditional comprehensive governance while also meeting the requirements of low-disturbance ecological management.

[0028] Table 3. Sediment yield and comprehensive benefits after different watershed measures are configured under different scenarios. ; Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the allocation of watershed measures considering the functional connectivity of sediment, characterized in that, It includes the following steps: S1. Use a sediment yield simulation model to simulate and calculate the total sediment yield of the watershed. Total sediment yield in the basin The calculation represents the potential soil loss per unit during a single rainfall event. degree of connectivity with sediment The sum of the relevant bivariate functions is calculated using the following formula: ； In the formula, variable k represents the k-th unit, and n represents the total number of units. The value at the midpoint of the Boltzmann curve is [value missing]. The slope parameter of the curve. Calculate the index for functional connectivity; S2. Obtain the types of measures for preventing and controlling soil erosion, their implementation costs, and the parameter changes of different types of measures in the functional connectivity and sediment yield simulation model; S3. Establish a multi-objective optimization system that maximizes sand production reduction, minimizes economic costs, and minimizes ecological disturbance. S4. Dynamically adjust the spatial location and scale combination layout of different types of measures, wherein the deployment probability of measures that set up functional connectivity hotspot areas is set to be u times that of other units, where u is greater than 1. S5. Based on the multi-objective optimization system, the simulated annealing algorithm is used to process the dynamic adjustment of the spatial location and scale combination layout. After iterative adjustment, a Pareto front optimal solution set is generated under the multi-dimensional constraints of ecological benefits and implementation feasibility. S6. Based on the optimal solution set of the Pareto front, obtain the corresponding spatial distribution map of measures and the achievement effect of multiple objectives.

2. The method for optimizing the allocation of watershed measures considering sediment functional connectivity according to claim 1, characterized in that, The amount of soil loss The following formula is used to calculate: ； In the formula, It is the runoff depth; This is the peak traffic. It is the area of ​​each unit; It is a soil erosion factor; It is a vegetation cover and management factor; These are factors related to soil and water conservation measures; It is a topographic factor; runoff depth and peak traffic Calculated using the SCS-CN curve equation: ； ； ； in, It is the total rainfall of a single rainfall event; It is the potential maximum retention or infiltration rate; It is the number of runoff curves; 3600 is the maximum rainfall intensity; 3600 is the time unit conversion factor.

3. The method for optimizing the allocation of watershed measures considering sediment functional connectivity according to claim 1, characterized in that, It is calculated using the following formula: ； In the formula, superscript This represents the mean. It is the runoff depth; This is the peak traffic. It is a soil erosion factor; It is a vegetation cover and management factor; These are factors related to soil and water conservation measures; It's the slope; It is the area of ​​the upstream catchment area; This represents the length of the downstream confluence path of the unit, where i represents the unit downstream of unit k to the outlet, and n is the total number of units. As a weighting factor characterizing the resistance to sediment transport, .

4. The method for optimizing the allocation of watershed measures considering sediment functional connectivity according to claim 1, characterized in that, The functional connectivity hotspots refer to the degree of sediment connectivity. The region where the value is greater than the preset value, The calculation formula is as follows: 。 5. The method for optimizing the allocation of watershed measures considering sediment functional connectivity according to claim 1, characterized in that, The preset value is 0.

5.

6. The method for optimizing the allocation of watershed measures considering sediment functional connectivity according to claim 1, characterized in that, u=5。 7. The method for optimizing the allocation of watershed measures considering sediment functional connectivity according to claim 1, characterized in that, In step S2, the parameter changes for different measure types in the functional connectivity and sediment yield simulation model specifically refer to the parameter changes of the following factors: runoff depth Q, These are peak flow, vegetation cover and management factor C, and soil and water conservation measures factor P.

8. The method for optimizing the allocation of watershed measures considering sediment functional connectivity according to claim 1, characterized in that, In step S3, ecological disturbance refers to the total area changed by the implementation of the measures.

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