A method for optimizing function parameters of a forest-grass community for regulating water and soil loss

Abstract

The application provides a kind of forest and grass community function parameter optimization method for regulating water and soil erosion, the method comprises: a plurality of plant community function attributes are calculated based on plant community data, and soil erosion modulus is calculated based on soil data;The mapping relationship between the random forest model and the key parameters of the model is established;Then simulate and analyze the hydrological sediment response under different parameter combinations, and determine the influence law of each function parameter on runoff and sediment yield process through sensitivity analysis;Finally, according to the law, the optimization direction is determined and the forest and grass community configuration scheme is generated. Quantitative analysis and fine optimization of forest and grass vegetation regulation of runoff and sediment yield process are realized on a regional scale, which can accurately identify the plant function parameters and their sensitive intervals that have key regulation effect on runoff and sediment yield process;And the optimization configuration of forest and grass community is directly guided by quantitative analysis results, which significantly improves the pertinence and operability of water and soil erosion regulation measures.

Description

Technical Field

[0001] This invention relates to the field of soil and water conservation optimization technology, and in particular to a method for optimizing the functional parameters of forest and grassland communities to regulate soil and water loss. Background Technology

[0002] Soil erosion is a global ecological and environmental problem that severely damages soil and water resources and land productivity, hindering the sustainable socio-economic development of regions. This challenge is particularly prominent in rapidly urbanizing areas. In recent years, with the large-scale advancement of urban greening and ecological restoration projects, forest coverage and vegetation area have been continuously increasing. However, a series of accompanying ecological problems have gradually emerged during the actual construction process. For example, newly afforested stands generally suffer from unreasonable three-dimensional structures, low understory shrub and grass cover, and soil compaction, leading to a mismatch between actual water conservation capacity and the increase in vegetation coverage, resulting in a "decoupling" phenomenon of ecological functions. The ecological problems caused by urban construction are not only reflected in the visible soil erosion and slope stability risks, but also extend to the hidden challenges of ecological restoration, such as the surge in garden waste and the shortage of high-quality soil, posing challenges to resource recycling. These visible and hidden problems overlap, not only affecting the long-term stability of ecological restoration projects, but also restricting the continuous improvement of regional ecosystem services, highlighting the urgent need for precise regulation of the soil erosion process based on the essential functions of plants.

[0003] Currently, research and practice on plant regulation of soil erosion and enhancement of ecological functions have significant limitations. At the level of mechanism research, related studies are mostly concentrated on natural slopes or small-scale experiments, lacking a systematic quantification of the synergistic regulatory mechanisms of plant community functional attributes on multiple processes such as runoff generation, sediment yield, water conservation, and slope stability. At the level of technical methods, traditional ecological restoration and greening configurations rely heavily on empirical indicators such as vegetation cover and tree species lists, failing to establish a refined design and effectiveness prediction system based on plant functional traits.

[0004] However, there is a significant technological gap between these two scales: plant function information obtained at a small scale is difficult to effectively transform into input parameters that can be identified by large-scale hydrological models, resulting in a lack of precise guidance for ecological restoration based on vegetation function mechanisms; while the vegetation-related parameters in existing hydrological models are mostly static empirical values, which cannot reflect the dynamic changes in functional characteristics under different forest and grassland community configurations, seriously restricting the scientific optimization of soil and water conservation measures.

[0005] Although existing studies have attempted to combine plant functional parameters with hydrological and mechanical models, they are mostly limited to one-way parameter input and have not yet formed a technical chain from mechanism model coupling, multi-objective sensitivity analysis to spatial optimization decision-making. This seriously restricts the application effect of ecological regulation technology in complex systems and makes it difficult to accurately configure forest and grassland communities and target the enhancement of ecological functions in response to the differentiated needs of different ecological environments. Summary of the Invention

[0006] This invention provides a method for optimizing the functional parameters of forest and grassland communities to regulate soil and water loss, thereby addressing the shortcomings of existing technologies in systematically analyzing the regulatory effects of plant community functional attributes on runoff and sediment production processes at the regional scale, and guiding the optimization of plant community configuration technology systems.

[0007] This invention provides a method for optimizing the functional parameters of forest and grassland communities to regulate soil and water loss, comprising: Acquire plant community data and soil data for the target area; Based on the plant community data, multiple plant community functional attributes were calculated, and the soil erosion modulus was calculated based on the soil data. Then, the USLE_C factor was obtained by inverting the soil erosion modulus. Based on the random forest model, the mapping relationship between the functional attributes of the plant community and the vegetation cover management factor and the Manning coefficient of surface runoff in the regional hydrological model is established by using the functional attributes of the plant community and the USLE_C factor. The functional attributes of the plant community with different value combinations are directly input into the regional hydrological model coupled with the mapping relationship to obtain the response data of hydrological and sediment parameters related to regional runoff and sediment production. Sensitivity analysis is performed on the response data to determine the influence law of each plant community functional attribute on the runoff and sediment production process. The influence law includes at least the sensitivity order and key sensitivity interval of each plant community functional attribute. Based on the aforementioned influence patterns, the optimization direction of the functional attributes of the target plant community is determined, and a forest and grassland plant community configuration scheme for soil and water conservation is generated.

[0008] According to the present invention, a method for optimizing forest and grassland community functional parameters is provided, wherein the plant community functional attributes include plant functional characteristic parameters and community functional diversity parameters. The plant functional characteristic parameters include: weighted average leaf area, weighted average vegetation height, and weighted average leaf thickness; The community functional diversity parameters include: community functional richness, community functional differentiation, community functional evenness, and community functional dispersion.

[0009] According to a method for optimizing forest and grassland community functional parameters provided by the present invention, sensitivity analysis is performed on the response data, specifically using a single-factor control variable method, including: Other plant community functional attributes were fixed, and the target parameters were adjusted for simulation at change levels of ±10%, ±20%, and ±30%.

[0010] According to the present invention, a method for optimizing forest and grassland community functional parameters calculates soil erosion modulus based on the soil data, specifically employing a radionuclide tracing method, including: Determination of soil samples 137 Specific activity of Cs is used to calculate soil surface activity; The target year is obtained using the decay correction formula. 137 Cs theoretical background value; Calculate the annual soil erosion thickness based on the soil area activity and the theoretical background value; By combining soil bulk density, the soil erosion modulus is calculated, which then serves as the basic data for constructing the USLE_C factor in the random forest model.

[0011] According to a method for optimizing forest and grassland community functional parameters provided by the present invention, sensitivity analysis is performed on the response data, specifically using a single-factor control variable method, including: While fixing the functional attributes of other plant communities, simulations are performed by setting multiple levels of variation for the target parameter only; Calculate the sensitivity coefficients of runoff and sediment yield parameters at various levels of change; The sensitivity order is determined based on the comparison of the sensitivity coefficients, and the parameter variation range where the sensitivity coefficients show a peak is determined as the critical sensitivity range.

[0012] According to the present invention, a method for optimizing forest and grassland community functional parameters further includes the following steps: Based on the simulation results and measured hydrological data of the aforementioned regional hydrological model, the coefficient of determination R is used. 2 The Nash efficiency coefficient (NSE) is used to quantitatively evaluate the simulation accuracy.

[0013] According to a method for optimizing the functional parameters of forest and grassland communities provided by the present invention, the method generates a configuration scheme for the forest and grassland plant community, including: selecting species and determining the configuration ratio according to the optimization direction so that the overall functional parameters of the community reach the target range; and the configuration scheme is designed differently for different slope areas within the region, focusing on improving the weighted average leaf area and functional uniformity for gentle slope areas, and focusing on improving the weighted average vegetation height and functional dispersion for steep slope areas.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the forest and grassland community functional parameter optimization method described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the forest and grassland community functional parameter optimization method as described above.

[0016] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for optimizing forest and grassland community functional parameters.

[0017] This invention provides a method for optimizing the functional parameters of forest and grassland communities in regulating soil and water loss. By systematically coupling the functional attributes of plant communities with regional hydrological models, it achieves quantitative analysis and fine optimization of the processes by which forest and grassland vegetation regulate runoff and sediment production at the regional scale. It can accurately identify the plant functional parameters and their sensitive ranges that play a key regulatory role in hydrological processes. Furthermore, the quantitative analysis results directly guide the optimal configuration of forest and grassland communities, significantly improving the pertinence and operability of soil and water loss control measures. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method for optimizing the functional parameters of forest and grassland communities in regulating soil and water loss in the region, provided by the present invention. Figure 2 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0021] The following is combined Figure 1 This invention describes a method for optimizing forest and grassland community functional parameters for regulating regional soil and water loss, comprising: Acquire plant community data and soil data for the target area; Based on the plant community data, multiple plant community functional attributes were calculated, and the soil erosion modulus was calculated based on the soil data. Then, the USLE_C factor was obtained by inverting the soil erosion modulus. Based on the random forest model, the mapping relationship between the functional attributes of the plant community and the vegetation cover management factor and the Manning coefficient of surface runoff in the regional hydrological model is established by using the functional attributes of the plant community and the USLE_C factor. The functional attributes of the plant community with different value combinations are directly input into the regional hydrological model coupled with the mapping relationship to obtain the response data of hydrological and sediment parameters related to regional runoff and sediment production. Sensitivity analysis is performed on the response data to determine the influence law of each plant community functional attribute on the runoff and sediment production process. The influence law includes at least the sensitivity order and key sensitivity interval of each plant community functional attribute. Based on the aforementioned influence patterns, the optimization direction of the functional attributes of the target plant community is determined, and a forest and grassland plant community configuration scheme for soil and water conservation is generated.

[0022] By systematically coupling the functional attributes of plant communities with regional hydrological models, quantitative analysis and fine optimization of the processes by which forest and grassland vegetation regulate runoff and sediment production at the regional scale have been achieved. This enables accurate identification of plant functional parameters and their sensitive ranges that play a key regulatory role in hydrological processes. Furthermore, the quantitative analysis results directly guide the optimal configuration of forest and grassland communities, significantly improving the pertinence and operability of soil and water conservation measures.

[0023] Specifically, establishing the mapping relationship between the functional attributes of plant communities and the key parameters of the regional hydrological model includes: using a random forest regression model, with the functional attributes of the plant communities as input variables, and the C factor and Manning coefficient obtained by inverting the soil erosion modulus through the nuclide tracing method as output variables, training to obtain the mapping relationship; and embedding the mapping relationship into the SWAT model to realize the dynamic assignment of corresponding parameters in the model.

[0024] Specifically, the core of establishing the mapping relationship between the functional attributes of plant communities and the key parameters of regional hydrological models lies in quantifying the functional characteristics of plant communities into key parameters that can be identified and dynamically updated by the hydrological model. This method mainly focuses on two hydrological model parameters that have a direct regulatory effect on runoff and sediment production processes: the vegetation cover and management factor USLE_C and the surface runoff Manning roughness coefficient OV_N.

[0025] The vegetation cover and management factor USLE_C is a key parameter in the modified Universal Soil Loss Equation (MUSLE). Its physical definition is the ratio of soil loss on vegetated or managed land to soil loss on clear-cultivated, fallow bare land, under the same soil, slope, and rainfall conditions. This value ranges from 0 to 1; a smaller value indicates a better inhibitory effect of vegetation or management measures on soil erosion. Traditional methods often use empirical formulas for vegetation cover to estimate this value. However, to obtain a more accurate C-factor value that better reflects the functional nature of plants, this invention uses soil erosion modulus measured using a radionuclide tracer method for calculation. The calculation formula is as follows: In the formula A cover The amount of soil erosion caused by forest and grassland cover. A barren The value represents soil erosion on bare land, expressed in g / m². The USLE_C factor obtained through this method can more accurately reflect the soil and water conservation effectiveness of a specific plant community in the actual environment.

[0026] The Manning roughness coefficient (OV_N) for surface runoff is a key parameter in hydrological models such as SWAT, characterizing the resistance to surface runoff. It describes the magnitude of resistance encountered during surface runoff and directly affects runoff velocity and confluence time. A higher value indicates greater surface resistance and slower runoff velocity, which is beneficial for increasing infiltration and delaying flood peaks. This parameter is closely related to surface cover conditions, particularly showing a positive correlation with "vegetation resistance," which is composed of the structure and density of vegetation communities and surface roughness.

[0027] Based on the above definitions, this invention utilizes a random forest regression model, taking the set of plant community functional attributes obtained from both measured and calculated data as input variables, such as the weighted average leaf area and functional diversity index, and using the USLE_C factor obtained through the aforementioned method and the OV_N value correlated with community function through model calibration as output variables for training. This establishes a nonlinear mapping relationship from plant community functional characteristics to key parameters of the regional hydrological model. Embedding this mapping relationship into the SWAT model enables dynamic and precise simulation of hydrological responses based on plant community configurations, overcoming the limitations of traditional models using static empirical parameters.

[0028] In one specific embodiment, the plant community functional attributes include plant functional characteristic parameters and community functional diversity parameters; The plant functional characteristic parameters include: weighted average leaf area, weighted average vegetation height, and weighted average leaf thickness; The community functional diversity parameters include: community functional richness, community functional differentiation, community functional evenness, and community functional dispersion.

[0029] Within a specific area, 96 target forest and grassland vegetation plots were established, distributed along the main stream and its tributaries according to the hydrological pathway system. At each plot, 10m×10m tree plots, 5m×5m shrub plots, and 1m×1m herb plots were set up. Community structure data, including total plot cover, species coverage, number of plants, clump diameter, crown width, diameter at breast height (DBH) (for trees), and plant height, were recorded. For each plant species in the plots, 5–10 healthy leaves were collected from each of the four quadrants. Leaf length, width, and thickness were measured using calipers, and single leaf area was obtained using a leaf area scanner (CI-202) as individual functional trait data.

[0030] Based on the above survey and measurement results, weighted averages of functional traits at the community scale were calculated using the relative cover of each species as weights. These included weighted averages of leaf area, vegetation height, and leaf thickness. Simultaneously, a three-dimensional trait space composed of leaf area, leaf thickness, and plant height was constructed. Functional ecology methods were used to calculate community functional diversity indices, specifically including: community functional richness represented by the minimum convex hull volume; community functional differentiation reflecting the degree of species deviation from the functional center of gravity; community functional evenness measuring the evenness of trait distribution; and community functional dispersion representing the functional similarity within the community. Ultimately, a set of plant community functional attributes comprising eight indicators was formed: vegetation cover, weighted average of leaf area, weighted average of vegetation height, weighted average of leaf thickness, community functional richness, community functional differentiation, community functional evenness, and community functional dispersion.

[0031] Optionally, the soil erosion modulus is calculated based on the soil data, specifically using a nuclide tracing method, including: Determination of soil samples 137 Specific activity of Cs is used to calculate soil surface activity; The target year is obtained using the decay correction formula. 137 Cs theoretical background value; Calculate the annual soil erosion thickness based on the soil area activity and the theoretical background value; By combining soil bulk density, the soil erosion modulus is calculated, which then serves as the basic data for constructing the USLE_C factor in the random forest model.

[0032] The measured erosion data provided by this method have advantages such as strong spatial representativeness and good time integration effect, which provides a high-quality calibration and verification basis for establishing a reliable mapping relationship between plant community functional parameters and key parameters of hydrological models (such as C factor and Manning coefficient), thereby significantly improving the simulation accuracy and decision reliability of the entire optimization model.

[0033] Target forest and grassland vegetation sample plots were selected in the study area, and the following methods were adopted: 137Soil erosion modulus was determined using the Cs radionuclide tracer method. Sampling points were located in the upper and middle parts of the slope, avoiding areas prone to human interference, and a total of 12 representative sampling points were collected. Soil samples were drilled from a depth of 0-15 cm at each sampling point, and measurements were performed using a high-purity germanium gamma spectrometer. 137 Cs specific activity, expressed in Bq / kg.

[0034] In the formula A area Soil surface activity, unit Bq / m³ 2 A is 137 Cs specific activity, in Bq / kg; M is the mass of soil sample <2mm, in g; S1 is the cross-sectional area of ​​the soil drill, in m³. 2 .

[0035] Annual soil erosion thickness h at sampling points: In the formula, h is the annual soil erosion thickness at the sampling point, in cm / a; t is the sampling year, in a; and λ is... 137 The profile index of the Cs depth distribution is 0.3, which is dimensionless.

[0036] Soil erosion modulus E R : In the formula E R Soil erosion modulus at the sampling points, in t / km² 2 / a,ρ b Soil bulk density, unit: g / cm³ 3 .

[0037] 137 Cs decay correction formula: In the formula A ref Soil for the target year 137 Cs correction value, in Bq / m 2 N is the target year, n is the year the background value was collected, and A n Year of background value collection 137 Cs surface activity, in units of Bq / m² 2 T is 137 The half-life of Cs is 30.17 years.

[0038] First, soil samples were collected from the target forest and grassland vegetation plots. Taking a sampling point in the upper part of a slope as an example, three soil cores, each 20 cm deep, were drilled along the direction of water flow using a soil auger with a diameter of 2.8 cm. After mixing, the fraction with a particle size <2 mm was air-dried and sieved for further processing. 137 Cs specific activity determination. The sample was analyzed using a high-purity germanium gamma spectrometer.137 The specific activity of Cs, A, is 38.6 Bq / kg. The cross-sectional area S of the soil drill is 6.16 × 10⁻⁶. -4 m 2 (Corresponding to a diameter of 2.8cm), the mass M of the soil sample <2mm is 980g.

[0039] Current sampling point 137 The area activity of Cs is: Secondly, the background value is determined and decay correction is performed. (Refer to the paper "Based on...") 137 A Study on Soil Erosion in the Agro-Pastoral Ecotone of Northern China Using Cs Technology: A Case Study of Lanzhou and Datong, Shanxi Province; Flat Undisturbed Grassland in Datong, Shanxi Province 137 Cs background value A n 1836.3 Bq / m 2 (Data collected in 2008). Target year N is 2023. 137 The half-life of Cs is 30.17 years.

[0040] The theoretically uneroded area in 2023 137 Cs area activity correction value: Annual soil erosion thickness h at sampling points: In the formula, h is the annual soil erosion thickness at the sampling point, in cm / a; t is the sampling year, in a; and λ is... 137 The profile index of the Cs depth distribution is 0.3, which is dimensionless.

[0041] Finally, the soil erosion modulus was calculated. The soil bulk density ρ at this sampling point was also calculated. b =1.35g / cm 3 , That is, the average annual soil erosion modulus of the forest and grassland sample plot is obtained. .

[0042] Furthermore, sensitivity analysis of the response data includes: using the single-factor control variable method, changing the value of each individual parameter in the selected plant community functional attributes one by one and rerunning the regional hydrological model to analyze the independent impact of the parameter change on runoff and sediment production processes.

[0043] In one specific embodiment, sensitivity analysis is performed on the response data, specifically using the single-factor controlled variable method, including: While keeping other plant community functional attributes fixed, the simulation is performed by setting multiple levels of variation for the target parameter, including ±10%, ±20%, and ±30%. Calculate the sensitivity coefficients of runoff and sediment yield parameters at various levels of change; The sensitivity coefficients are sorted in order, and the parameter variation range in which the sensitivity coefficients show a peak value is determined as the key sensitivity range.

[0044] Where: S is the sensitivity coefficient; Y is the parameter value of the variable factor under the gradient ΔX; Y0 is the baseline value, variable factor = 0, gradient = 0; ΔX is the gradient value.

[0045] Based on the sensitivity analysis method described in this invention, in a specific embodiment, a typical forest and grassland community in a certain region was used as the object to systematically analyze the seasonal regulation characteristics of plant community functional attributes on hydrological processes.

[0046] Taking the weighted average vegetation height as an example, its baseline value is 3.15m. Under the ±20% disturbance scenario (i.e., 2.52m and 3.78m), the coupled SWAT model is run and the monthly scale output results are extracted.

[0047] In a further specific embodiment, to demonstrate the application of this method in the spatial differentiation regulation of complex regions, the target area can be divided into several functional zones, such as upstream, midstream, and downstream zones, based on its hydrological characteristics and geomorphological units. By classifying the entire region into upstream, midstream, and downstream zones, for example, dividing it into 33 sub-units, the differential regulatory effects of plant community functional attributes on different regions can be studied separately.

[0048] This embodiment employs the single-factor controlled variable method, systematically modifying the numerical values ​​of a certain plant community functional attribute, such as the weighted average leaf thickness, while keeping other conditions constant. By running a SWAT model coupled with the updated parameters, the impact of this attribute change on key hydrological processes such as runoff and sediment production in the upstream, midstream, and downstream regions is analyzed, while its global effect on the entire region is also assessed. This analysis can reveal the differences in the intensity and direction of local regulation of the same functional attribute at different spatial locations.

[0049] For example, analysis revealed that increasing the weighted average leaf thickness was more sensitive to reducing sediment output in upstream areas dominated by slope erosion, while having a more significant impact on regulating peak flood flow in downstream areas with significant runoff. Based on this spatial differentiation pattern, different target intervals for optimizing the functional attributes of plant communities can be determined to address core ecological issues in different zones, such as upstream soil conservation, midstream water storage, and downstream flood control. This will guide the development of "one policy per zone" vegetation configuration schemes, achieving systematic and precise prevention and control of soil erosion across the entire region.

[0050] Furthermore, the method of the present invention can also be used to simulate and optimize the configuration of specific vegetation types.

[0051] Sensitivity analysis was performed on the response data, specifically using the single-factor control variable method, including: Other plant community functional attributes were fixed, and the target parameters were adjusted by variation levels of ±10%, ±20%, and ±30% for simulation. Among them, at a change level of ±10%, the sensitivity coefficients of the plant community functional attributes to runoff and sediment production parameters generally reached their peak, indicating that this change level is the optimal change level for identifying key sensitive intervals.

[0052] The table below, for example, shows the baseline values ​​of the weighted average leaf surface thickness of various typical forest and grassland communities, and the simulated values ​​of their corresponding key hydrological parameters (OV_N, USLE_C) under baseline and changing conditions, such as +10%. These specific data provide key samples for constructing and validating random forest models, and also directly reflect the quantitative correlation between the functional attributes of different vegetation types and hydrological responses.

[0053] Table 1. Weighted average leaf surface thickness of typical forest and grassland communities and their corresponding hydrological parameters. Calculations of the weighted average of vegetation height show that the sensitivity of vegetation height to surface runoff exhibits a significant seasonal reversal: before the flood season, the sensitivity coefficient is -0.03, indicating that increased vegetation height slightly inhibits runoff generation; after the flood season begins, the sensitivity coefficient reverses to +0.02, indicating that taller vegetation, by promoting root zone infiltration and deep water replenishment, actually contributes to runoff formation; after the flood season ends, the sensitivity coefficient drops back to -0.005, and the regulatory effect weakens significantly.

[0054] Similarly, analysis of the weighted average leaf area showed a baseline value of 12.74 cm. 2 Before the flood season, the sensitivity coefficient of this parameter to evapotranspiration was -0.02, reflecting the enhanced interception and transpiration of the canopy; during the flood season, its sensitivity coefficient to surface runoff reached -0.2, showing a strong runoff inhibition effect; and after the flood season, its sensitivity to groundwater runoff turned to +0.47, indicating that the litter layer and residual canopy structure can still promote water infiltration.

[0055] By visualizing the sensitivity of parameters in each month, such as monthly radar charts, the dominant periods of different functional parameters can be clearly identified: the weighted average of leaf area and the weighted average of vegetation height play a core regulatory role mainly during the flood season; functional evenness maintains a stable influence throughout the year; and functional differentiation becomes prominent after the flood season.

[0056] Community functional parameters exhibit a nonlinear regulatory relationship on sediment transport and nutrient transport processes. The sensitivity curves of various sediment and nutrient parameters show two distinct trends: organic phosphorus and organic nitrogen output show a negative correlation with the sensitivity of other sediment parameters. Runoff parameters show higher sensitivity at ±10% and ±20% levels for most functional parameters, exhibiting a different trend from runoff sensitivity. Parameters such as total sediment load are most sensitive to functional parameters including vegetation cover, weighted average vegetation height, weighted average leaf thickness, weighted average leaf area, and functional evenness. Plant communities enhance their regulatory effect on soil erosion and sediment transport by improving community functional parameters. The weighted average leaf area has a weakening effect on runoff and is also negatively correlated with sediment transport and generation.

[0057] By applying the sensitivity analysis method described in this invention, not only were the differentiated impacts of key plant community functional attributes on runoff and sediment production processes in different seasons quantified, but the seasonal reversal pattern of their sensitivity direction and intensity was also identified. This overcomes the limitations of traditional static assessment methods in reflecting the dynamic regulatory role of vegetation functions, and provides an important time-series management basis for regional ecological restoration practices.

[0058] For example, in spring, the focus is on increasing vegetation cover and early canopy development; in summer, the focus is on strengthening canopy structure and leaf area function; and in autumn, the focus is on maintaining functional diversity to enhance water conservation capacity in the later stages, thereby achieving targeted water and soil erosion control throughout the season.

[0059] By calculating the sensitivity coefficients of key parameters such as surface runoff and sediment output at various levels of change, this embodiment accurately quantifies the independent impact of the leaf area weighted average. The analysis results show that when surface runoff is used as the response parameter, the sensitivity coefficient reaches its peak at a parameter change of ±10%, exhibiting a significant threshold effect; when the leaf area weighted average increases by 10% from the baseline value to 14.01 cm³, the sensitivity coefficient reaches its peak. 2 When the simulated annual average surface runoff decreased from 0.95 billion cubic meters under the baseline scenario to 0.82 billion cubic meters, it indicated a significant runoff suppression effect. Conversely, when this parameter decreased by 30% to 8.92 cm... 2 At that time, surface runoff increased to 115 million cubic meters. By repeating the above process for all eight community functional parameters, the sensitivity order of each parameter to runoff and sediment production processes and the key sensitive range in which they play a core regulatory role were finally systematically determined.

[0060] The hydrological model is the SWAT model, and its soil erosion is calculated based on the modified general soil loss equation, MUSLE.

[0061] Optionally, when running the SWAT model with the updated parameters for simulation, the soil erosion is calculated according to the modified general soil loss equation (MUSLE), the formula for which is: Where, m sed Q represents soil erosion. surf q represents surface runoff. peak For the peak flow, A hru K represents the area of ​​the hydrological response unit. USLE C is a soil erodibility factor. USLE P is a vegetation cover and management factor. USLE For soil and water conservation measures factors, LS USLE is the slope length factor, and CFRG is the coarse debris factor.

[0062] Taking a typical forest and grassland restoration area as an example, the simulation period was from 2015 to 2020, and the total area of ​​the study region was 38.6 km². 2 It contains multiple sub-units. During model calibration, one sub-unit with an area of ​​1.2 km² was selected. 2 The sub-unit is used as the analysis unit, with an average topographic slope of 12° and land use mainly consisting of shrubland and mixed forest. According to the SWAT model output, the surface runoff Q generated by this sub-unit during a heavy rainfall event... surf It is 1.8m 3 / s, peak flow rate q peak It is 2.4m 3 / s, area of ​​hydrological response unit A hru 1.2×10 6 m 2 .

[0063] Soil erodibility factor K in the model USLE The value was 0.32 t·h / (MJ·mm·ha), determined based on soil texture and organic matter content; Vegetation cover and management factor C USLE The value is dynamically output by the random forest model built in the previous steps, and is 0.18. Soil and water conservation measures factor P USLE The value of 0.75 was taken into account the impact of artificial measures such as terraces and vegetation buffer zones; Slope length factor LS USLE It is 1.45; The coarse debris factor (CFRG) was 0.92, reflecting the inhibitory effect of forest and grassland litter on sediment transport.

[0064] Calculated m sed ≈161.5t.

[0065] Used to verify the consistency between model output and measured sediment data, and as a basic indicator to evaluate the soil conservation effect of different forest and grassland configuration schemes, the MUSLE calculation results can more realistically reflect the impact of vegetation function changes on the erosion process, and improve the simulation accuracy of the SWAT model in ecological restoration scenarios.

[0066] Optionally, the method further includes the step of: Based on the simulation results and measured hydrological data of the aforementioned regional hydrological model, the coefficient of determination R is used. 2 The Nash efficiency coefficient (NSE) is used to quantitatively evaluate the simulation accuracy, where R... 2 The formulas for calculating NSE are as follows: Coefficient of determination R 2 : Nash efficiency coefficient (NSE): In the formula Q mi Let Q be the measured value of sediment or runoff in the i-th time period. si Let be the simulated value for sediment or runoff, and i be the i-th measured or observed value. It is the mean of sediment or runoff within this time series. This is the mean of the simulated values.

[0067] Taking the monthly runoff data of the outlet section of a certain region from 2015 to 2020 as an example, the coefficient of determination R is used. 2 The Nash efficiency coefficient (NSE) was used as the main evaluation index. A total of 72 time periods were measured in the runoff series, with 2015-2018 as the calibration period and 2019-2020 as the validation period. The simulated runoff output by the model corresponded to the measured values.

[0068] For example, the measured runoff Q in the first month. m,1 =1.2m 3 / s, simulated value Q s,1 =1.15m 3 / s; The measured value Q in the second month m,2 =0.8m 3 / s, simulated value Q s,2 =0.82m 3 / s, and so on.

[0069] In the calculation process, the mean of the measured values ​​is first obtained. =0.98m 3 / s, average of simulated values =0.96m 3 / s.

[0070] Substituting the data, we can calculate R. 2 =0.84, indicating a strong linear correlation between the simulation results and the measured values.

[0071] The NSE was calculated to be 0.78 according to the Nash efficiency coefficient formula, indicating that the model can capture the trend of runoff change well and the error is small.

[0072] Optionally, generating the forest and grassland plant community configuration scheme includes: selecting species and determining the configuration ratio according to the optimization direction so that the overall functional parameters of the community reach the target range; and the configuration scheme is designed differently for different slope areas in the region, focusing on improving the weighted average leaf area and functional uniformity for gentle slope areas, and focusing on improving the weighted average vegetation height and functional dispersion for steep slope areas.

[0073] In one specific embodiment, with the goal of comprehensive water and soil erosion control in a certain area, the optimization directions determined in the aforementioned steps, such as increasing leaf area and enhancing functional uniformity, are applied to generate differentiated configuration schemes for areas with different slopes.

[0074] In gentle slope areas with an average slope of <15°, a broad-leaved mixed forest configuration was constructed, primarily composed of *Quercus liaotungensis* and *Populus tomentosa*. Specifically, *Quercus liaotungensis* comprised 60% of the forest, *Populus tomentosa* 30%, and 10% was supplemented with associated shrubs such as *Hippophae rhamnoides*, increasing the overall weighted average leaf area of ​​the community to 13-15 cm². 2 Within this range, the thick leaves of *Quercus liaodongensis* and the large leaf area of ​​*Populus tomentosa* complement each other's traits, maintaining a functional richness above 0.75. Compared to monoculture forests, this configuration reduces average annual surface runoff in gentle slope areas by approximately 18% and sediment output by approximately 22%.

[0075] In steep slope areas with an average slope >25°, a multi-layered tree and shrub configuration was designed, with deep-rooted species *Pinus tabuliformis* and *Platycladus orientalis* as the core, mixed with various shrubs such as *Prunus persica* and *Rhamnus spp.*. *Pinus tabuliformis* and *Platycladus orientalis* comprised 50% of the total vegetation, while the shrub layer comprised the remaining 50%. The aim was to achieve a weighted average vegetation height of over 3.5m, and to increase the functional dispersion to over 0.045 by leveraging significant differences in traits among trees and shrubs, such as plant height and root system architecture. This effectively enhanced the shear strength of the steep slope soil, reducing the area of ​​potential gravity erosion risk zone by approximately 15%.

[0076] By establishing a quantitative coupling relationship between the functional attributes of plant communities and regional hydrological processes, the limitations of relying on a single cover index are overcome. This approach can accurately identify key regulatory parameters such as leaf area and functional evenness, as well as their effective range of action. It also reveals the seasonal patterns and "temporal division of labor" mechanism by which parameters affect hydrological processes, thereby enhancing the pertinence and predictability of soil and water conservation measures.

[0077] Figure 2 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 2 As shown, the electronic device may include a processor 810, a communication interface 820, a memory 830, and a communication bus 840. The processor 810, communication interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions from the memory 830 to execute a method for optimizing the functional parameters of forest and grassland communities in the control area for soil and water conservation.

[0078] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0079] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the optimization method for forest and grassland community functional parameters for regulating soil and water loss in the above-mentioned areas.

[0080] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for optimizing forest and grassland community functional parameters for regulating soil and water loss in the above-described areas.

[0081] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0082] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the functional parameters of forest and grassland communities in regulating soil and water loss, characterized in that, include: Acquire plant community data and soil data for the target area; Based on the plant community data, multiple plant community functional attributes were calculated, and the soil erosion modulus was calculated based on the soil data. Then, the USLE_C factor was obtained by inverting the soil erosion modulus. Based on the random forest model, the mapping relationship between the functional attributes of the plant community and the vegetation cover management factor and the Manning coefficient of surface runoff in the regional hydrological model is established by using the functional attributes of the plant community and the USLE_C factor. The functional attributes of the plant community with different value combinations are directly input into the regional hydrological model coupled with the mapping relationship to obtain the response data of hydrological and sediment parameters related to regional runoff and sediment production. Sensitivity analysis is performed on the response data to determine the influence law of each plant community functional attribute on the runoff and sediment production process. The influence law includes at least the sensitivity order and key sensitivity interval of each plant community functional attribute. Based on the aforementioned influence patterns, the optimization direction of the functional attributes of the target plant community is determined, and a forest and grassland plant community configuration scheme for soil and water conservation is generated.

2. The method for optimizing forest and grassland community functional parameters according to claim 1, characterized in that, The functional attributes of the plant community include plant functional characteristic parameters and community functional diversity parameters; The plant functional characteristic parameters include: weighted average leaf area, weighted average vegetation height, and weighted average leaf thickness; The community functional diversity parameters include: community functional richness, community functional differentiation, community functional evenness, and community functional dispersion.

3. The method for optimizing forest and grassland community functional parameters according to claim 1 or 2, characterized in that, Sensitivity analysis was performed on the response data, specifically using the single-factor control variable method, including: The functional attributes of other plant communities were fixed, and the target parameters were adjusted by ±10% variation level for simulation.

4. The method for optimizing forest and grassland community functional parameters according to claim 1, characterized in that, The soil erosion modulus is calculated based on the aforementioned soil data, specifically using a radionuclide tracing method, including: Determination of soil samples 137 Specific activity of Cs is used to calculate soil surface activity; The target year is obtained using the decay correction formula. 137 Cs theoretical background value; Calculate the annual soil erosion thickness based on the soil area activity and the theoretical background value; By combining soil bulk density, the soil erosion modulus is calculated, which then serves as the basic data for constructing the USLE_C factor in the random forest model.

5. The method for optimizing forest and grassland community functional parameters according to claim 1, characterized in that, Sensitivity analysis was performed on the response data, specifically using the single-factor control variable method, including: While fixing the functional attributes of other plant communities, simulations are performed by setting multiple levels of variation for the target parameter only; Calculate the sensitivity coefficients of runoff and sediment yield parameters at various levels of change; The sensitivity order is determined based on the comparison of the sensitivity coefficients, and the parameter variation range where the sensitivity coefficients show a peak is determined as the critical sensitivity range.

6. The method for optimizing forest and grassland community functional parameters according to claim 1, characterized in that, The method further includes the following steps: Based on the simulation results and measured hydrological data of the aforementioned regional hydrological model, the coefficient of determination R is used. 2 The Nash efficiency coefficient (NSE) is used to quantitatively evaluate the simulation accuracy.

7. The method for optimizing forest and grassland community functional parameters according to claim 1, characterized in that, Generating the forest and grassland plant community configuration scheme includes: selecting species and determining the configuration ratio according to the optimization direction so that the overall functional parameters of the community reach the target range; and the configuration scheme is designed differently for different slope areas in the region, focusing on improving the weighted average leaf area and functional uniformity for gentle slope areas, and focusing on improving the weighted average vegetation height and functional dispersion for steep slope areas.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the forest and grassland community functional parameter optimization method as described in any one of claims 1 to 7.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the forest and grassland community functional parameter optimization method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the forest and grassland community functional parameter optimization method as described in any one of claims 1 to 7.

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