Method for watershed ecological compensation zoning based on net ecological externality index

Abstract

The application discloses a kind of based on net ecological externality index's watershed ecological compensation zoning method, comprising: obtaining the geographical space data of target watershed, to calculate multiple preset positive, negative ecological externality index;Based on geographical space data, respectively for each spatial unit in the watershed Determine a positive ecological externality comprehensive index and a negative ecological externality comprehensive index;Based on positive, negative ecological externality comprehensive index, each spatial unit is calculated by hedge operation Net ecological externality index;According to the spatial distribution of each spatial unit net ecological externality index in target watershed, the ecological compensation zoning of target watershed is carried out.The application can more scientifically, comprehensively reflect the real ecological contribution of each region by constructing net value evaluation framework, and provides more accurate decision-making basis for formulating fair and reasonable ecological compensation strategy.

Description

Technical Field

[0001] This invention relates to the field of ecological environment assessment technology, specifically to a watershed ecological compensation zoning method based on the net ecological externality index. Background Technology

[0002] Ecological compensation, as an important environmental economic strategy tool, faces the core technical challenge of scientifically and quantitatively defining its scope and standards. This requires technical methods that can accurately identify and quantify areas benefiting from ecosystem services and areas damaging the ecological environment, and transform complex ecological processes into spatially defined zoning criteria suitable for decision-making. Developing a technical method that can comprehensively assess the ecological benefits and costs within a watershed and accordingly zonify it is of crucial technical significance for improving the scientific rigor and fairness of ecological compensation strategies.

[0003] Currently, in the technical practices related to the delineation of ecological compensation zones, methods based on ecosystem service value assessment or key ecological factor identification are mainly adopted. For example, some methods construct value equivalent factor tables to calculate the total value of ecosystem services for different land use types, thereby identifying high-value areas. Other methods utilize ecological models, such as the InVEST (Integrated Ecosystem Services and Trade-offs) model or the SWAT (Soil and Water Resources Assessment) model, to spatially quantify single or a few key ecosystem services, such as water conservation and soil retention, and use the spatial distribution patterns of these services as the main basis for zoning. These methods, to some extent, have promoted the shift from qualitative to quantitative approaches and provided technical support for identifying important ecological functional zones.

[0004] However, existing technologies still face some deep-seated technical challenges in achieving a comprehensive and accurate characterization of complex watershed ecological processes. Current methods fail to effectively distinguish the spatial mechanisms of different ecological externalities and lack a net-value assessment framework that comprehensively accounts for both positive and negative effects. Specifically, existing methods typically treat all ecological indicators as having in-situ impacts within spatial units, employing homogeneous mathematical methods for superposition or aggregation, neglecting the differences in the physical transmission mechanisms of different indicators. For example, the benefits of water conservation are transmitted downstream along hydrological pathways via water, while the benefits of habitat quality are primarily reflected locally. Simply averaging two indicators with drastically different mechanisms results in unclear physical meaning and fails to accurately depict the true flow and accumulation of ecosystem services within the watershed. Furthermore, existing technologies often focus on assessing positive ecosystem services, while insufficiently considering negative ecological externalities from human activities, such as non-point source pollution output, or treating them as independent themes. The lack of an accounting framework that offsets ecological contributions and losses within the same spatial unit leads to an incomplete and unobjective assessment of the overall ecological value of a region. How to perform differentiated modeling based on the physical mechanism of indicators, and on this basis realize net value accounting for positive and negative externalities, is a technical problem that urgently needs to be solved in the current field. Summary of the Invention

[0005] The purpose of this invention is to provide a watershed ecological compensation zoning method based on the net ecological externality index, in order to solve at least one of the aforementioned problems existing in the prior art.

[0006] Technical solution: A watershed ecological compensation zoning method based on the net ecological externality index, comprising:

[0007] Obtain geospatial data of the target watershed, which includes basic data for calculating various preset positive ecological externality indicators and various preset negative ecological externality indicators;

[0008] Based on geospatial data, a comprehensive index of positive ecological externalities is determined for each spatial unit within the target watershed.

[0009] Based on geospatial data, a comprehensive index of negative ecological externalities is determined for each spatial unit within the target watershed.

[0010] Based on the comprehensive index of positive and negative ecological externalities, the net ecological externality index of each spatial unit is calculated.

[0011] Based on the spatial distribution of the net ecological externality index of each spatial unit in the target watershed, ecological compensation zones are established for the target watershed.

[0012] Beneficial effects: By constructing a net worth assessment framework, this invention can more scientifically and comprehensively reflect the true ecological contribution of each region, providing a more accurate basis for decision-making in formulating fair and reasonable ecological compensation strategies. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the overall process of a watershed ecological compensation zoning method based on the net ecological externality index provided in this application embodiment.

[0014] Figure 2 This is a schematic diagram of the process for determining a positive ecological externality comprehensive index and a negative ecological externality comprehensive index for each spatial unit within the target watershed, provided in the embodiments of this application.

[0015] Figure 3 This is a schematic diagram illustrating the process of determining the comprehensive index of positive and negative ecological externalities based on in-situ and flow-oriented indicators, respectively, according to embodiments of this application.

[0016] Figure 4 This is a schematic diagram of the process of hierarchically clustering spatial units based on the value of the net ecological externality index using a preset threshold method, as provided in the embodiments of this application.

[0017] Figure 5 The statistical chart showing the spatiotemporal evolution trend of the net ecological externality index of a certain province from 1985 to 2023 is provided for the embodiments of this application. Detailed Implementation

[0018] Example 1: A general framework for a watershed ecological compensation zoning method based on the net ecological externality index is provided, such as... Figure 1 As shown, the ecological contribution and ecological loss of each spatial unit within the watershed can be comprehensively assessed, and scientific zoning can be carried out accordingly to guide ecological compensation practices.

[0019] To address the shortcomings of existing ecological compensation standards in considering the mobility of ecosystem services and the failure to effectively offset positive and negative externalities, this invention proposes a novel zoning method. This method quantifies not only the ecological benefits provided by a region but also the ecological pressures it generates. By calculating the net value of these two factors, a comprehensive index is formed that more fully reflects the true ecological value of the region, providing a more scientific and equitable basis for determining and spatially allocating ecological compensation amounts. In this embodiment, a lake basin can be used as an exemplary research area to apply the method disclosed in this invention.

[0020] Step 101: Obtain geospatial data of the target watershed. The geospatial data includes basic data for calculating various preset positive ecological externality indicators and various preset negative ecological externality indicators.

[0021] In some embodiments, there may be three types of preset positive ecological externalities, such as net primary productivity, habitat quality, and water conservation capacity, and in some embodiments, there may be two or four types of preset negative ecological externalities, such as nitrogen emissions, phosphorus emissions, and population pressure.

[0022] Specifically, the target watershed refers to a hydrological unit with clearly defined boundaries, such as a complete river basin or the catchment area of ​​a lake. Geospatial data mainly refers to map data with geographic coordinate information that can be stored and processed in digital form; in this embodiment, raster data format is preferred. Each raster unit, i.e., a pixel, constitutes the basic spatial analysis unit in this method. The basic data is a multi-source heterogeneous dataset designed to meet the input requirements for subsequent calculations of ecological externality indicators. For example, it may include, but is not limited to: digital elevation model data, i.e., DEM data; land use and land cover change data, i.e., LUCC data; meteorological data, such as rainfall, solar radiation, and temperature data; soil type and attribute data; and socioeconomic statistics, such as population distribution and GDP distribution data. Acquiring the basic data provides a unified and standardized data input for the operation of all subsequent ecological and environmental models and the quantitative calculation of indicators.

[0023] Step 102: Based on geospatial data, calculate the values ​​of multiple positive ecological externalities and multiple negative ecological externalities for each spatial unit within the target watershed. Then, standardize and integrate the values ​​of positive ecological externalities to determine a comprehensive positive ecological externality index for each spatial unit within the target watershed.

[0024] A spatial unit corresponds to each raster cell in the aforementioned geospatial data. The Positive Ecological Externality Composite Index is a comprehensive quantitative indicator that characterizes the total amount of ecosystem services that a spatial unit can provide, which can have a positive impact on its surrounding and downstream areas. It is a composite index, meaning it is calculated by weighting or aggregating multiple individual positive ecosystem service indicators, such as water conservation capacity, soil retention capacity, or biodiversity maintenance capacity. This step aims to quantify and unify various dispersed ecological benefits onto a single dimension to facilitate subsequent comparison and comprehensive evaluation. The specific indicator composition and calculation methods will be detailed in subsequent embodiments.

[0025] Step 103: Based on geospatial data, standardize and integrate the negative ecological externality index values ​​to determine a comprehensive negative ecological externality index for each spatial unit within the target watershed.

[0026] Corresponding to the positive ecological externality comprehensive index, the negative ecological externality comprehensive index is also a comprehensive quantitative indicator, representing the total amount of ecological pressure or environmental damage generated by a spatial unit on its surrounding and downstream areas due to human activities or land use patterns. It is a comprehensive index, typically calculated by aggregating multiple individual negative ecological pressure indicators, such as the output of non-point source pollutants, soil erosion, or habitat fragmentation. This step aims to quantify and integrate various factors that negatively impact the environment, providing a basis for subsequent net value calculations.

[0027] Step 104: Based on the positive ecological externality comprehensive index and the negative ecological externality comprehensive index, the net ecological externality index of each spatial unit is calculated through hedging operations.

[0028] The ecological value of any region should not be determined solely by its ecological contributions; the ecological losses it causes must also be accounted for. The net ecological externality index is a net value obtained by mathematically subtracting the positive ecological externality composite index from the negative ecological externality composite index, preferably through weighted subtraction. This index can more objectively and comprehensively reflect the final ecological effect of each spatial unit. Specifically, if the net ecological externality index of a spatial unit is positive, it indicates that its ecological contribution exceeds its ecological loss, exhibiting a clear positive ecological accumulation characteristic; if it is negative, it indicates that its ecological loss exceeds its ecological contribution, exhibiting a clear state of reverse ecological loss; if it is close to zero, it indicates that its ecological contribution and loss are roughly equal, and it is in a state of ecological dynamic equilibrium.

[0029] Step 105: Based on the spatial distribution of the net ecological externality index of each spatial unit in the target watershed, ecological compensation zoning is carried out in the target watershed.

[0030] After completing the aforementioned steps, each spatial unit within the target watershed obtains a net ecological externality index value, which spatially constitutes a continuous grid field. Ecological compensation zoning is the process of spatially analyzing and classifying this grid field, grouping spatial units with similar index values ​​or falling within the same range into a single category, forming distinct regions with clear management implications. For example, regions with all positive net ecological externalities can be designated as ecological compensation payment zones, while regions with negative indices can be designated as ecological compensation receiving zones.

[0031] In some more specific implementations, the index can be further subdivided into different levels based on its value, providing direct spatial decision support for formulating differentiated ecological compensation strategies. The partitioning method can be implemented using techniques familiar to those skilled in the art, such as threshold segmentation and spatial cluster analysis.

[0032] Example 2 elaborates on the concepts of multiple preset positive ecological externality indicators and multiple preset negative ecological externality indicators, constructing an indicator set that can scientifically and comprehensively reflect key ecological processes and human activity pressures within the watershed. The selection of this indicator system directly determines the scientific validity and accuracy of subsequent net ecological externality index calculations.

[0033] In a preferred embodiment of the present invention, a variety of preset positive ecological externality indicators include: net primary productivity, habitat quality, and water conservation.

[0034] Specifically, the selection and quantification process for each of the above positive indicators is explained below:

[0035] Net primary productivity (NPP), sometimes referred to as NPP in some literature, represents the total amount of organic matter fixed by green plants through photosynthesis per unit time and unit area, after deducting their own respiration consumption. In this invention, this indicator is selected as a positive ecological externality indicator, directly reflecting the basic productivity of an ecosystem. It is the foundation of energy flow and material cycling in the entire ecosystem, and also an important measure of the ecosystem's carbon sequestration capacity. The higher the NPP of a region, the better its vegetation condition, the more abundant the food source for other organisms, and the more carbon dioxide it can absorb and fix, generating positive ecological benefits. Its quantification process can be based on remote sensing data and calculated using mature ecological models. For example, the Carnegie-Ames-Stanford Approach (CASA) model can be used, which estimates the spatial distribution of NPP using geospatial data such as vegetation indices, solar radiation, temperature, and precipitation.

[0036] Habitat quality, or HQ, refers to the ability of an ecosystem to provide suitable conditions for the survival and reproduction of organisms. It is chosen as a positive ecological externality indicator because areas with high habitat quality typically represent higher biodiversity, providing shelter for rare and endangered species. Their ecological value extends beyond the local area, playing a crucial role in the ecological balance of the entire watershed and even a wider region. Quantifying habitat quality can preferably be achieved using the habitat quality module in an integrated modeling toolkit, such as the HabitatQuality module in the InVEST model. This module assesses regional habitat quality by comprehensively analyzing land use type maps, the spatial location and intensity of threat sources, and the sensitivity of different land use types to different threat factors. For example, forests and wetlands are generally considered high-quality habitats, while built-up land and farmland may be considered threat sources.

[0037] Water conservation capacity, sometimes referred to as WY in some literature, refers to the ability of an ecosystem to intercept, infiltrate, and store precipitation, ultimately forming surface and groundwater runoff to provide water resources to downstream areas after evapotranspiration. Water conservation capacity is selected as a positive ecological externality indicator, directly reflecting the important service function of the ecosystem in hydrological regulation. Good vegetation and soil conditions in upstream areas can effectively conserve water, not only providing stable water for production and domestic use in downstream areas, but also reducing flood peaks during the wet season and supplementing baseflow during the dry season. This benefit has significant spatial mobility and externalities. The calculation of water conservation capacity can preferably utilize the WaterYield module in the InVEST model. This module, based on the principle of water balance, estimates the water output of each spatial unit by inputting geospatial data such as precipitation, evapotranspiration, land use type, and soil properties.

[0038] In a preferred embodiment, a variety of preset negative ecological externality indicators include nitrogen emissions, phosphorus emissions, and population pressure.

[0039] Specifically, the selection and quantification process for each of the above negative indicators is explained below:

[0040] Nitrogen and phosphorus emissions, sometimes referred to as TN and TP respectively, refer to the total amount of nitrogen and phosphorus elements transported from terrestrial ecosystems and transported into water bodies through surface runoff and other pathways under the influence of human activities. They are chosen as negative ecological externalities because nitrogen and phosphorus are major pollutants leading to eutrophication in downstream rivers, lakes, and reservoirs. Unreasonable agricultural fertilization, livestock farming, and domestic sewage discharge in upstream areas generate large amounts of nitrogen and phosphorus pollutants, which are transported downstream with water flow, leading to a series of environmental problems such as downstream water quality deterioration and cyanobacterial blooms, generating significant negative spatial externalities. The quantification of these two indicators can preferably be achieved using the nutrient transport ratio module (NDR) in the InVEST model. This module can estimate the nitrogen and phosphorus loss and its transport efficiency to downstream water bodies for each spatial unit based on geospatial data such as land use type, fertilization intensity, topographic slope, and hydrological connectivity.

[0041] Population pressure, or POP, is typically quantified using population density data. It was chosen as a comprehensive indicator of negative ecological externalities because population density can largely serve as a proxy for the intensity of human activities. High population density areas generally indicate higher resource consumption, increased emissions of domestic and industrial pollutants, stronger land development pressures, and encroachment on natural ecological space. These factors collectively constitute a complex pressure on the ecological environment, negatively impacting surrounding and downstream areas through media such as the atmosphere and water bodies. This indicator can be quantified directly by acquiring spatialized population density raster data.

[0042] The six indicators listed above constitute the preferred indicator system of this invention, which can comprehensively reflect the characteristics of watershed ecological externalities. In some optional embodiments, those skilled in the art can adjust, add to, or delete from this indicator system according to the specific ecological problems and management needs of the target watershed. For example, in areas with severe soil erosion, soil conservation can be used as a positive ecological externality indicator, and its corresponding soil erosion as a negative ecological externality indicator. Similarly, in the context of focusing on climate change, carbon emissions can also be included in the assessment system as a negative ecological externality indicator. The selection of indicators should follow the principles of scientific rigor, representativeness, and data availability.

[0043] Example 3 details a specific technical method for determining the comprehensive index of positive and negative ecological externalities and the net ecological externality index. This method, known as the in-situ calculation method, treats each ecological externality index of each spatial unit as an independent value that has an impact locally, without considering the spatial flow and transmission effects of the index. This method is logically clear, computationally simple, and suitable for rapid, macroscopic ecological value assessment of the entire watershed.

[0044] Multiple preset positive and negative ecological externality indicators are normalized. The normalized positive ecological externality indicators are then weighted and aggregated or mean-aggregated to obtain a comprehensive positive ecological externality index. Similarly, the normalized negative ecological externality indicators are weighted and aggregated or mean-aggregated to obtain a comprehensive negative ecological externality index. Figure 2 As shown.

[0045] Specifically, this step includes two stages: normalization and aggregation.

[0046] Normalization is performed. As in Example 2, the various indicators, such as net primary productivity (NPP) and nitrogen emissions (TN), have different physical units, numerical dimensions, and ranges, making direct mathematical comparison and calculation difficult. Normalization aims to eliminate these differences by mapping the values ​​of all indicators to a uniform, dimensionless interval. In this example, range standardization is preferably used to scale the values ​​of all indicators to a closed interval between 0 and 1. The calculation formula for this method is as follows:

[0047] X norm =(XX min ) / (X max -X min );

[0048] Among them, X norm Here, X represents the normalized index value, and X is the original calculated value of a predetermined index on a certain spatial unit. maxX represents the maximum value of this index across all spatial units in the entire target watershed. min This is the minimum value of this indicator across all spatial units in the entire target watershed.

[0049] For example, assuming that within the target watershed, the highest NPP value is 1200 g carbon per square meter per year and the lowest is 50 g carbon per square meter per year, for a spatial unit with an NPP value of 600 g carbon per square meter per year, its normalized NPP value is (600-50) / (1200-50), which is approximately 0.478. The normalization process will be performed on each of the six indicators listed in Example 2, namely net primary productivity, habitat quality, water conservation, nitrogen emissions, phosphorus emissions, and population pressure, one by one, for each spatial unit.

[0050] Aggregation processing is performed. After normalization, all indicators are on a comparable starting line. Aggregation processing combines indicators with the same properties, i.e., those that are all positive or all negative, into a single composite index. In this embodiment, the arithmetic mean method is preferably used for aggregation. Specifically, the composite index of positive ecological externalities is obtained by summing all normalized positive ecological externality indicator values ​​and dividing by the number of positive indicators. Similarly, the composite index of negative ecological externalities is obtained by summing all normalized negative ecological externality indicator values ​​and dividing by the number of negative indicators.

[0051] After completing the above steps, the final net ecological externality index (EEI) can be calculated. In this embodiment, this index is EEI. net The calculation formula is as follows:

[0052] EEI net =EEI pos -EEI neg ;

[0053] Among them, EEI net The Net Ecological Externality Index (EEI) pos The EEI is a positive ecological externality comprehensive index. neg The EEI is a comprehensive index of negative ecological externalities. pos and EEI neg It can be calculated in the following ways:

[0054] EEI pos =(∑(P norm_i )) / N p ;

[0055] EEI neg =(∑(N norm_j )) / N n ;

[0056] Among them, Pnorm_i Let N be the normalized value of the i-th positive ecological externality indicator. p N represents the total number of positive ecological externality indicators, for example, in the system of Example 2. p It is 3. N norm_j Let N be the normalized value of the j-th negative ecological externality indicator. n N represents the total number of negative ecological externality indicators, for example, in the system of Example 2. n It is also 3. ∑ represents the summation of all indices of the same type.

[0057] In some alternative implementations, the above calculation process can be adjusted. For example, in the normalization stage, in addition to range standardization, other statistical methods such as Z-score standardization can be used. In the aggregation stage, if the importance of different indicators is considered to differ, a weighted average method can be used instead of the arithmetic average method. For example, domain experts can assign a weight coefficient to each indicator using methods such as the Analytic Hierarchy Process (AHP), and then perform a weighted summation, making the composition of the composite index more targeted. Correspondingly, when calculating the final net ecological externality index, different weight coefficients can be assigned to the positive and negative composite indices to reflect the different strategic tendencies of decision-makers in ecological construction and pollution control.

[0058] Example 4 provides a preferred and more refined implementation of the method in Example 1. Unlike the in-situ calculation method in Example 3, the flow-weighted calculation method proposed in this example fully considers the spatial mobility of ecosystem services and ecological pressures, especially indicators with significant upstream-downstream transmission effects mediated by water. This method is based on a set of explicit physical modeling principles, making its assessment results more consistent with the objective laws of watershed ecological processes.

[0059] In this embodiment, the theoretical framework constructed by this method is based on the following three modeling principles: the physical mechanism classification principle, which states that different ecological externality indicators have different spatial action mechanisms and can be classified into in-situ types that take effect locally and flow-directed types that are transmitted along hydrological paths; the distance attenuation principle, which states that the effect intensity of flow-directed indicators will decrease as the transmission distance along the hydrological path increases; and the cost-benefit composition principle, which states that the net ecological externality of any spatial unit is the sum of the benefits or pressures generated locally and the benefits or pressures accumulated from all upstream areas.

[0060] Step 401: Based on the physical transmission mechanism of each indicator, the various preset positive ecological externality indicators and the various preset negative ecological externality indicators are divided into in-situ indicators and flow-direction indicators; among them, the contribution of in-situ indicators is calculated in local spatial units, and the contribution of flow-direction indicators is spatially accumulated along the hydrological path.

[0061] The indicators are divided into in-situ indicators and flow-direction indicators, specifically:

[0062] For each positive and negative ecological externality indicator, a determination is made:

[0063] If the ecological benefits or ecological pressure represented by a certain indicator are spatially transmitted through water as a medium and along hydrological pathways, then the indicator is classified as a flow-oriented indicator.

[0064] Otherwise, if the ecological benefits or ecological pressures are spatially localized, the indicator should be classified as an in-situ indicator.

[0065] This step performs a secondary classification of the indicator system constructed in Example 2. The physical transmission mechanism is further clarified as follows: to determine whether the main pathway by which the ecological benefits or ecological pressure represented by an indicator are spatially transmitted through water as a medium along a defined hydrological path.

[0066] Specifically, if the benefits or pressures of an indicator are primarily felt in its place of origin or its immediate vicinity, are spatially localized, and do not rely on long-distance transport via surface runoff networks, then it is classified as an in-situ indicator. Based on this criterion, the following indicators are classified as in-situ indicators:

[0067] Net primary productivity (NPP): Its benefits in carbon sequestration and oxygen release primarily affect the local atmospheric environment.

[0068] Habitat quality (HQ): Its benefits in providing habitat for organisms are directly reflected in the quality of local ecological patches.

[0069] Population pressure (POP): The resource consumption and some pollution pressure it generates are mainly concentrated in local spatial units where the population is concentrated.

[0070] If the benefits or pressures of an indicator enter the river system via surface runoff and are continuously transported downstream along the river network, significantly impacting the areas it flows through and ultimately flows into, then it is classified as a flow-direction indicator. Based on this criterion, the following indicators are classified as flow-direction indicators:

[0071] Water conservation capacity WY: The water resources replenishment benefits it provides are directly transported downstream through surface and groundwater runoff.

[0072] Nitrogen emissions (TN): Nitrogen pollutants from agriculture and daily life flow into rivers through surface runoff, negatively impacting downstream water quality.

[0073] Phosphorus emissions (TP): Similar to nitrogen emissions, phosphorus pollutants also migrate downstream through water systems and are a key factor leading to eutrophication in downstream water bodies.

[0074] Step 402: Based on in-situ and flow-direction indicators, calculate the in-situ contribution value and flow-direction contribution value for each spatial unit. Based on the in-situ and flow-direction contribution values, determine the positive ecological externality comprehensive index and the negative ecological externality comprehensive index by weighted summation.

[0075] This step defines the overall framework for differentiating the two types of indicators mentioned above, such as... Figure 3 As shown. The specific implementation process is as follows:

[0076] For in-situ type indicators, the in-situ contribution value is calculated based on their local values ​​in each spatial cell. This calculation applies to all indicators classified as in-situ type.

[0077] The calculation process is as follows: For each in-situ index, such as NPP, HQ, and POP, normalization is performed using the range standardization method. Normalized in-situ indices belonging to the same externality direction are then aggregated, preferably using an arithmetic mean. For example, averaging the normalized NPP and HQ values ​​yields a positive in-situ contribution value; the normalized POP value itself constitutes a negative in-situ contribution value.

[0078] For flow-direction indicators, the flow-direction contribution value is calculated based on hydrological connectivity information in geospatial data. This calculation applies to all indicators classified as flow-direction, and its detailed implementation process will be discussed in the next step.

[0079] Step 403: Based on the digital elevation model in the geospatial data, determine the hydrological flow direction between spatial units within the target watershed; for any spatial unit, along the hydrological flow direction, accumulate the values ​​of the flow direction index of all its upstream spatial units to obtain the flow direction contribution value.

[0080] This step details the calculation method for the flow direction contribution value. Its implementation relies on a series of standard hydrological analysis procedures. Based on a digital elevation model and hydrological flow direction, it calculates the hydrological flow length between each spatial unit. When accumulating the values ​​of flow direction indicators from all upstream spatial units, a distance attenuation function based on the hydrological flow length is introduced to weight the values ​​of flow direction indicators from different upstream spatial units to obtain the flow direction contribution value. The distance attenuation function can be an exponential attenuation function or a piecewise linear attenuation function.

[0081] Specifically, the first stage involves hydrological analysis to determine spatial connectivity. The input data for this stage is a Digital Elevation Model (DEM). The following operations are performed sequentially through a Geographic Information System (GIS) platform:

[0082] Depression filling involves preprocessing the original DEM data to fill in pseudo-depressions caused by data errors, ensuring the continuity of the water flow path.

[0083] Flow direction analysis, based on the processed DEM, uses the D8 algorithm or a similar algorithm to determine the flow direction of each grid spatial cell, i.e., which of its eight adjacent grid cells the water will flow to.

[0084] Hydrological path length calculation: Based on the flow direction grid, calculate the surface distance along the hydrological path between any spatial cell and all its upstream spatial cells. This is a crucial step, and its result forms the basis for subsequent distance decay function calculations.

[0085] The second stage involves weighted cumulative calculation with attenuation. For any flow-direction indicator, such as water conservation capacity WY, the flow-direction contribution value for any target spatial unit i within the watershed is not simply the sum of upstream values, but rather the sum of the contributions of all upstream spatial units j after distance attenuation weighting. The calculation formula can be expressed as:

[0086] FWCI i =∑ j (S j ×exp(-k×D ij ));

[0087] Among them, FWCI i S is the flow direction contribution value received by target space cell i. j This represents the source intensity of the flow-oriented index on a certain upstream spatial cell j. This value is typically the normalized value of the index. j D represents the summation of all spatial units j located upstream of the target spatial unit i. ij is the hydrological flow length from upstream unit j to target unit i, exp(·) is an exponential function, and k is a preset distance attenuation coefficient, reflecting the rate attenuation of the indicator effect with distance. Its dimensions are the same as D. ij The dimensions of the two variables are reciprocals of each other to ensure that the independent variable k×D of the exponential function is a reciprocal. ij As a dimensionless quantity, a larger k value indicates a faster decay. In this embodiment, k is preferably set to 0.05. This value can be determined by those skilled in the art through calibration analysis of measured hydrological data based on the average river network density and the length characteristics of the main rivers in the target watershed. The exponential decay function is preferably in the form of a distance decay function.

[0088] Taking water conservation capacity WY as an example, a complete example of flow-direction weighted calculation is presented. Assume there are three upstream spatial units j1, j2, and j3, with normalized WY values ​​of 0.8, 0.6, and 0.4 respectively, and hydrological distances from them to target unit i of 5 km, 15 km, and 30 km respectively. The distance attenuation coefficient k is taken as 0.05. Then, the flow-direction contribution of each upstream unit to target unit i is as follows:

[0089] 0.8×exp(-0.05×5)≈0.623;

[0090] 0.6×exp(-0.05×15)≈0.283;

[0091] 0.4×exp(-0.05×30)≈0.089.

[0092] The WY flow direction contribution value FWCI of target unit i i :

[0093] FWCI i =0.623+0.283+0.089=0.995.

[0094] It can be seen that the upstream unit j1, which is closer in distance, contributes the most, while the contribution of the upstream unit j3, which is farther away, decreases significantly due to attenuation, which is consistent with the physical law that the water conservation benefits decrease with the transmission distance.

[0095] In some alternative implementations, the distance decay function can also be a piecewise linear decay function. For example, a distance threshold can be set, within which the contribution does not decay; beyond the threshold, the contribution decreases linearly with increasing distance.

[0096] After completing all the above calculations, each spatial unit obtained its positive in-situ contribution and flow-direction contribution values, as well as its negative in-situ contribution and flow-direction contribution values. Based on the in-situ contribution and flow-direction contribution values, the comprehensive index of positive ecological externalities and the comprehensive index of negative ecological externalities were determined, respectively.

[0097] Specifically, the positive ecological externality index is obtained by weighted summing the (positive) in-situ contribution value corresponding to the positive ecological externality index and the (positive) flow direction contribution value corresponding to the positive ecological externality index; similarly, the negative ecological externality index is obtained by weighted summing the (negative) in-situ contribution value corresponding to the negative ecological externality index and the (negative) flow direction contribution value corresponding to the negative ecological externality index. By subtracting the positive and negative comprehensive indices, the preferred flow direction-weighted net ecological externality index, FW-EEI, can be obtained in this embodiment. net .

[0098] For example, the formula for calculating the comprehensive index of positive ecological externalities is as follows:

[0099] EEI pos =α×Cin pos +(1-α)×Fflow pos ;

[0100] Among them, EEI pos Cin is a positive ecological externality comprehensive index.pos For positive in-place contribution values, Fflow pos The positive flow contribution value is represented by α, which is the in-situ contribution weighting coefficient and is a real number between 0 and 1.

[0101] The formula for calculating the comprehensive index of negative ecological externalities is as follows:

[0102] EEI neg =β×Cin neg +(1-β)×Fflow neg ;

[0103] Among them, EEI neg Cin is a comprehensive index of negative ecological externalities. neg For negative in-place contribution values, Fflow neg β represents the negative flow contribution value, and β is the in-situ contribution weighting coefficient, which is a real number between 0 and 1.

[0104] In this embodiment, both α and β are set to 0.5, meaning that the in-situ contribution value and the flow direction contribution value are equally weighted. In some optional implementations, those skilled in the art can adjust the weighting coefficients according to the specific management needs of the target watershed using conventional methods such as the analytic hierarchy process, expert scoring, or sensitivity analysis based on historical data.

[0105] Example 5 provides a detailed description of the basic data required for index calculation in the foregoing examples, the preferred model tools, and their key parameter configurations to ensure the reproducibility and operability of the method disclosed in this invention. It also provides specific implementation details for step 101 in Example 1.

[0106] In one specific implementation of this invention, the first step is to perform comprehensive data preparation, including determining the data list and preprocessing the data.

[0107] An example data list includes the following:

[0108] Digital elevation model (DEM) data is in raster format, such as GeoTIFF, and can be obtained from public data platforms such as the Space Shuttle Radar Topographic Mapping Mission (SRTM). Its main purpose is to perform hydrological analysis and extract topographic factors such as slope, slope length, flow direction, and runoff accumulation.

[0109] Land use and land cover data, or LUCC data, is in raster format and can be obtained from institutions such as the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences. It is used to identify different ecosystem types and areas of human activity and is the basic input for calculating almost all ecological externality indicators.

[0110] Meteorological data, mainly including annual average precipitation, annual average temperature and annual average total solar radiation over many years, are in raster format and can be obtained from institutions such as the National Meteorological Information Center. They serve as driving factors for ecosystem productivity and hydrological process simulation.

[0111] Soil data, which mainly includes soil type, soil texture, soil organic matter content and soil available water content, is in raster or vector format and can be obtained from sources such as the World Soil Database. Its main purpose is to provide soil physicochemical parameters for hydrological and nutrient cycle simulation.

[0112] Socioeconomic data, mainly including spatial distribution data of population density and GDP, are in raster format and can be obtained from sources such as world population datasets. They are used to quantify the pressure of human activities.

[0113] After obtaining the aforementioned basic data, rigorous preprocessing is required to ensure the spatial consistency and validity of all data. The preprocessing process mainly includes:

[0114] Project all geospatial data to the same coordinate system and projection system, such as WGS 1984 UTM Zone 50N (WGS 1984 Universal Transverse Mercator Projection Zone 50 (Northern Hemisphere)), to eliminate spatial location deviations caused by different coordinate systems.

[0115] Using the vector boundary of the target watershed as a mask, all raster data is cropped to ensure that the analysis scope of all data is strictly limited to within the watershed.

[0116] Furthermore, resampling techniques, such as bilinear interpolation or nearest neighbor methods, are employed to unify the spatial resolution of all raster data to a standard, such as 30 meters × 30 meters, to ensure that subsequent raster calculations and model operations can correspond one-to-one at the pixel level.

[0117] To check and handle invalid or missing values ​​in the data, methods such as interpolation or masking can be used to avoid them interfering with the calculation results.

[0118] Step 501: At least one of habitat quality, water conservation capacity, nitrogen emissions, and phosphorus emissions is obtained through calculation using the InVEST model.

[0119] In a preferred embodiment of the present invention, the InVEST model, a comprehensive assessment model of ecosystem services and trade-offs jointly developed by Stanford University, The Nature Conservancy, and the World Wildlife Fund, is selected as the tool for calculating the aforementioned key ecological externality indicators. It is based on well-defined ecological processes, highly modular, open-source and free, and has been widely used and validated globally.

[0120] Specifically, the correspondence between each indicator and the InVEST model module in this embodiment is as follows:

[0121] The calculation of habitat quality (HQ) is achieved by calling the HabitatQuality module in the InVEST model.

[0122] The calculation of water conservation capacity WY is achieved by calling the WaterYield module in the InVEST model.

[0123] The calculation of nitrogen (TN) and phosphorus (TP) emissions is achieved by uniformly calling the nutrient transport ratio (NDR) module in the InVEST model, which can calculate the output of nitrogen and phosphorus pollutants separately.

[0124] Step 502: The parameters of the InVEST model are set as follows: the half-saturation constant of the habitat quality module is a preset value; the precipitation seasonality coefficient of the water conservation module is a preset value; and the Borselli K parameter (Borselli weight coefficient) of the nutrient transport ratio module is a preset value.

[0125] To ensure the accuracy and comparability of the model's calculation results, it is necessary to configure the key parameters of the selected model modules. Some parameter settings are derived from the model's recommended default values, while others require localized calibration based on the specific conditions of the target watershed and relevant research literature.

[0126] Specifically, one of the key parameters of the habitat quality module, the half-saturation constant, also known as the k-parameter, determines the response relationship between the degree of habitat quality degradation and the threat level. In this embodiment, this parameter is set to a preset value, preferably the default value of 0.5 recommended by the model in this module.

[0127] Furthermore, one of the key parameters of the water conservation module, the precipitation seasonality coefficient, also known as the Zhang coefficient or Z parameter, is an empirical constant reflecting the seasonal variation characteristics of regional precipitation. The value of this parameter affects the model's estimation of the actual annual evapotranspiration, thus affecting the calculation results of water output. In this embodiment, this parameter is calculated and calibrated based on meteorological data of the target watershed over many years, and it is set to a preset value. For example, for a lake watershed in a subtropical monsoon climate zone, this value can be set to 15.

[0128] Furthermore, one of the key parameters of the nutrient transport ratio module, the Borselli K parameter, also known as the calibration parameter of the connectivity index, is a crucial factor determining the efficiency of nutrient transport by surface runoff. The setting of this parameter directly affects the model's estimation of the transport ratio of pollutants such as nitrogen and phosphorus from the source to the water body. In this embodiment, this parameter is set to a preset value, preferably the default value of 2 recommended by the model in this module.

[0129] It should be noted that, in addition to the parameters explicitly defined above, running the InVEST model requires configuring a series of other biophysical parameter tables. For example, running the HabitatQuality module requires providing tables of threat factors, their impact distances and weights, as well as tables of the sensitivities of different land use types to threat factors. Those skilled in the art can reasonably determine the settings of these parameters by referring to the model's user manual and relevant regional research literature.

[0130] For example, the settings and basis for all key parameters of the InVEST model are as follows:

[0131] The habitat quality module has a half-saturation constant of 0.5. Threat factors and their influence distances and weights are shown in Table 1.

[0132] Table 1

[0133]

[0134] The sensitivities of different land use types to threat factors are shown in Table 2.

[0135] Table 2

[0136]

[0137] Water conservation module, precipitation seasonality coefficient (Z value): 15, input parameters of water conservation module are shown in Table 3.

[0138] Table 3

[0139]

[0140] Nutrient transport ratio module, Borselli K parameter: 2, input parameters for nutrient transport ratio module are shown in Table 4.

[0141] Table 4

[0142]

[0143] In some alternative implementations, if the InVEST model is not used, other biophysical process models with similar functions can be used as alternatives, such as the SWAT model, the ARIES model (Artificial Intelligence Environmental Services Assessment System), etc., as long as the model can generate a spatial distribution raster map of the required ecological externality indicators, it can be compatible with the subsequent calculation steps of this invention.

[0144] Example 6 provides a detailed technical implementation path and specific numerical application cases for the process of ecological compensation zoning. The spatially continuous net ecological externality index calculated in the previous steps is transformed into a discrete zoning map with clear strategic and management implications, providing an intuitive and scientific basis for ecological compensation decisions.

[0145] Step 601 involves dividing the target watershed into ecological compensation zones based on the spatial distribution of the net ecological externality index of each spatial unit within the target watershed. This can be achieved by using a spatial clustering algorithm or a preset threshold method for hierarchical clustering. In other words, this step could also involve dividing the target watershed into ecological compensation zones, specifically by using the natural discontinuity method or a preset threshold method to hierarchically cluster spatial units based on the values ​​of the net ecological externality index.

[0146] Specifically, the input to this step is the net ecological externality index raster map calculated in the aforementioned embodiments, where the value of each raster cell represents the net ecological effect at that location. This step classifies these consecutive values, merging raster cells with similar values ​​into the same region. This invention preferably provides two technical approaches to implement the hierarchical clustering process.

[0147] The first approach uses a pre-set threshold method, requiring domain experts or decision-makers to pre-define one or more explicit numerical thresholds based on the actual conditions of the study area, management objectives, or historical data. By performing conditional judgments on the raster map, the index value of each raster cell is compared with these thresholds, classifying them into different levels. This method has clear classification criteria, is easy to understand and interpret, and facilitates strategy formulation and implementation.

[0148] The second approach employs spatial clustering algorithms, a data-driven classification method that automatically analyzes the statistical distribution characteristics of the net ecological externality index to identify natural breakpoints or cluster centers in the data, thereby achieving optimal grouping. A preferred spatial clustering algorithm is the natural breakpoint method, also known as Jenks' algorithm. This algorithm iteratively calculates and seeks a grouping scheme that minimizes the sum of variances within each group while maximizing the variances between different groups. This method is highly objective, preserving the spatial differentiation characteristics of the original data to the greatest extent possible and avoiding the subjectivity that may arise from manually setting thresholds.

[0149] Step 602: Perform hierarchical clustering using a preset threshold method, such as... Figure 4 As shown, the specific steps include: setting a first preset threshold and a second preset threshold, wherein the first preset threshold is greater than the second preset threshold; classifying spatial units with a net ecological externality index greater than the first preset threshold as high net externality zones; classifying spatial units with a net ecological externality index less than the second preset threshold as low net externality zones; and classifying spatial units with a net ecological externality index between the first preset threshold and the second preset threshold as medium net externality zones.

[0150] In a specific numerical case of this embodiment, a certain province is used as the target watershed or research area, and the process of three-level zoning using the preset threshold method is described in detail.

[0151] Set the thresholds required for the classification. Based on a comprehensive analysis of the region's ecological environment background and economic development level, the first preset threshold can be set to 0.45, and the second preset threshold can be set to 0.2. These two thresholds divide the numerical range of the net ecological externality index into three intervals.

[0152] Spatial classification is performed based on a set threshold. Each spatial unit on the net ecological externality index layer is evaluated by performing raster reclassification operations in the Geographic Information System (GIS) software.

[0153] If the net ecological externality index of a spatial unit is greater than 0.45, the unit is classified as a high net externality area. This typically corresponds to the upstream of a watershed, high vegetation cover, and a strong ecosystem service function, making it a major provider of positive ecological externalities. In ecological compensation mechanisms, it should be a primary beneficiary of such compensation. For example, this method can be used to identify mountainous and forest ecosystems belonging to a specific region.

[0154] If the net ecological externality index of a spatial unit is less than 0.2, the unit is classified as a low net externality area. This typically corresponds to densely populated, highly industrialized and urbanized central urban areas or heavily agricultural development zones, which are major sources of negative ecological externalities. In ecological compensation mechanisms, these areas, due to their development occupying ecological capacity and generating ecological pressure, should bear the internalized obligation of ecological responsibility. For example, a central city and its surrounding areas are often identified as such areas.

[0155] If the net ecological externality index of a spatial unit is between 0.2 and 0.45, including the values ​​equal to the two boundary values, then the unit is classified as a medium net externality zone. This typically represents a transitional zone where positive and negative ecological externalities are roughly balanced, or an area where ecological functions and human activities intersect. Strategies for these areas can focus on maintaining the existing ecological balance and preventing them from degrading into low net externality zones.

[0156] In some alternative implementations, the number of zoning levels is not limited to three. Decision-makers can set more thresholds to divide the target watershed into five or more levels, such as very high, relatively high, moderate, relatively low, and very low, to implement more differentiated ecological compensation and spatial control strategies, depending on the need for more refined management.

[0157] It should be noted that the above-mentioned regional examples of specific values ​​for the province, such as a net value of 0.14 for region 1 and a net value of 0.52 for region 2, are large-scale benchmark calculation results obtained based on the in-situ calculation method described in Example 3, used to demonstrate the basic ecological pattern at a macro scale. In actual, more refined watershed management scenarios, if the preferred spatial flow direction model described in Example 4 is further introduced into the target watershed for secondary calculation, the above benchmark values ​​will undergo logical dynamic corrections according to the actual hydrophysical transfer mechanism.

[0158] On the one hand, for upstream high-net-externality areas, such as a city located at the headwaters of a river, the positive benefits generated, such as water conservation, will be transported downstream along a certain river system. Under the flow-direction weighted model, the role of this area in the supply of ecosystem services across the entire basin will be further quantified and highlighted. Under the flow-direction weighted model, since this area hardly accumulates negative flow-direction contributions from further upstream, its flow-direction weighted net ecological externality index is expected to receive a positive adjustment compared to the baseline value of 0.52. The specific magnitude depends on the index weighting configuration and the hydrological characteristics of the basin.

[0159] On the other hand, for downstream areas with low net externalities, such as a region serving as the catchment center of the downstream basin, they not only bear the in-situ pressure from the dense local population, but also passively accumulate flow-oriented pollution loads such as large amounts of nitrogen and phosphorus emissions from upstream agriculture and urban areas through surface runoff. After spatial flow accumulation weighting, the actual negative ecological externalities of this region will surge significantly, causing its final net ecological externality index to undergo a further negative correction from the baseline value of 0.14, exhibiting a deeper ecological deficit.

[0160] Qualitative comparisons between baseline values ​​and flow direction calibration trends reveal that the spatial flow direction model proposed in this invention can expose the hidden spatial transmission of negative externalities downstream, accurately widening the net ecological externality gap between upstream and downstream areas. This provides effective technical support for formulating differentiated and highly sensitive ecological compensation standards, requiring downstream heavily benefited areas or pollution accumulation areas to bear higher amounts of horizontal transfer payments to upstream ecological source areas.

[0161] Example 7 describes the application of the present invention in spatiotemporal dynamic evolution analysis, demonstrating the advanced application value of the method beyond static, single-point-of-time assessment. By applying the method of the present invention to long-term geospatial data, the historical evolution patterns and future development trends of net ecological externalities in target watersheds can be revealed, providing decision support for assessing the long-term effectiveness of ecological engineering and diagnosing the dynamic root causes of regional ecological and environmental problems.

[0162] In this embodiment, the application process of the method is extended to a spatiotemporal dynamic monitoring framework, which specifically includes the following steps:

[0163] Preparing multi-temporal data differs from preparing data for a single point in time. This embodiment requires collecting geospatial datasets covering a long historical period, such as from 1985 to 2023, encompassing multiple key years. The datasets should maintain as much consistency as possible in type. For example, it requires acquiring land use and land cover (LUCC) data for multiple years, including 1985, 1995, 2005, 2015, and 2023, along with matching historical meteorological and socioeconomic data.

[0164] Iterative index calculations are performed, using the dataset for each year as an independent input, repeatedly executing the core calculation process disclosed in this invention. Preferably, a flow-direction weighted calculation method is used to generate a spatial distribution map of the net ecological externality index covering the entire target watershed for each year. This results in a time-series raster dataset composed of multiple annual or interdecadal index maps.

[0165] Spatiotemporal evolution trend analysis can be conducted based on the aforementioned generated time-series raster dataset, which can be analyzed from two dimensions. First, from a temporal dimension, by calculating the average net ecological externality index of all spatial units within the entire target watershed for each year, a trend curve reflecting the overall ecological net value of the region over time can be plotted. Second, from a spatial dimension, by performing pixel-by-pixel trend analysis on the time-series raster data, such as calculating the slope of the index value change for each pixel during the study period, a spatial evolution pattern map can be generated to identify which areas are experiencing continuous improvement in ecological net value and which are experiencing continuous degradation.

[0166] In a specific application case, the method of this invention was applied to the long-term series data analysis of a certain province from 1985 to 2023. By calculating the average net ecological externality index of the province over the years, it was found that the overall trend exhibited a clear M-shaped fluctuation characteristic, such as... Figure 5 As shown.

[0167] Specifically, the first decline occurred roughly from 1985 to the late 1990s, with the index showing a continuous downward trend. This was followed by the first upward phase, lasting from the late 1990s to around 2010, during which the index rebounded significantly. Afterward, the index entered a second period of fluctuation, characterized by an initial leveling off or slight decline, followed by another rebound.

[0168] This embodiment demonstrates that the present invention can construct a time-continuous ecological index sequence, effectively identify its multi-stage change characteristics, including dynamic patterns such as decline, recovery and fluctuation, and support quantitative monitoring and analysis of the long-term evolution of regional ecological status.

[0169] Example 8, in conjunction with the flow direction weighted calculation method described in Example 4, i.e., the spatial flow direction model, provides a specific micro-level numerical extrapolation case. Due to the computational cost of massive long-term spatial data in macro-basins and the difficulty of localizing and calibrating model parameters (such as attenuation coefficients), a simplified virtual hydrological connectivity grid is constructed to intuitively demonstrate the operational logic of the new algorithm compared to the in-situ calculation method, thus fully verifying the feasibility and beneficial effects of this invention.

[0170] Step 801: Set up a virtual hydrological grid scene. Assume that the target watershed contains three adjacent spatial unit grids along a hydrological path: unit A, unit B, and unit C, corresponding to the upstream, middle, and downstream sections, respectively. The water flow direction is from unit A to unit B, and from unit B to unit C. Set the hydrological flow length D between adjacent units to 1 standard unit, i.e., D0... AB =1,D BC =1,D AC =2.

[0171] The distance decay function for flow-oriented indicators is set as an exponential decay function. The preset distance decay coefficient k is approximately 0.693, which means that the indicator effect decays by 50% for every standard unit distance traveled along the hydrological path. The formula for calculating the distance decay factor is as follows:

[0172] F decay =exp(-k×D ij );

[0173] Among them, F decay Let exp be the distance attenuation factor, k be the natural exponential function, and D be the preset distance attenuation coefficient. ij Let be the hydrological flow length from spatial unit j to target spatial unit i.

[0174] Step 802: Calculate the transmission effect of positive flow-oriented indicators. Taking water conservation capacity as an example, assuming that after normalization, the local source intensity (i.e., in-situ contribution) of unit A, unit B, and unit C are respectively: S A=0.8, S B =0.4, S C =0.1.

[0175] If the in-situ calculation method of Example 3 is used, the score of each unit is only its local source intensity. The score of downstream unit C is 0.1, which does not reflect the spatial transmission effect of upstream water source on downstream.

[0176] If the flow-weighted calculation method of Example 4 is adopted, the total flow contribution value finally received by each unit includes local output and upstream accumulation. The specific calculation process is as follows:

[0177] The upstream unit A has no upstream inflow, so the total contribution value of the flow direction is the local value of 0.8.

[0178] Midstream unit B receives the inflow from unit A at a distance of 1. The formula for calculating its total inflow contribution is as follows:

[0179] FWCI B =S B +S A ×exp(-k×D AB );

[0180] Among them, FWCI B S contributes to the total value of the flow direction in unit B. B S represents the local source intensity of element B. A Let be the local source intensity of element A, exp be the natural exponential function, k be the preset distance attenuation coefficient, and D be the local source intensity of element A. AB The hydrological flow length from unit A to unit B.

[0181] After substituting the specific values, the total contribution of unit B to the flow direction is: 0.4 + 0.8 * 0.5 = 0.8.

[0182] Downstream unit C receives input from unit A at a distance of 2, and unit B at a distance of 1. The formula for calculating the total flow contribution is as follows:

[0183] FWCI C =S C +S B ×exp(-k×D BC )+S A ×exp(-k×D AC );

[0184] Among them, FWCI C S contributes to the total value of the flow direction in unit C. C S represents the local source intensity of element C. B Let be the local source intensity of element B, exp be the natural exponential function, k be the preset distance attenuation coefficient, and D be the local source intensity of element B. BCS represents the hydrological flow length from unit B to unit C. A For the local source strength of cell A, D AC The hydrological flow length from unit A to unit C.

[0185] After substituting the specific values, the total flow contribution of unit C is: 0.1 + 0.4 * 0.5 + 0.8 * 0.25 = 0.5.

[0186] Step 803: Calculate the cumulative effect of negative flow-type indicators. Taking nitrogen emissions as an example, assuming that after normalization, the local pollution source intensity of each unit is as follows: N A =0.2, N B =0.6, N C =0.8.

[0187] Similarly, using the flow-weighted calculation method, downstream unit C not only bears the brunt of high local emissions but also inevitably accumulates pollution from upstream discharges. The formula for calculating the total negative flow contribution of downstream unit C is as follows:

[0188] FWCI neg_C =N C +N B ×exp(-k×D BC )+N A ×exp(-k×D AC );

[0189] Among them, FWCI neg_C N represents the total contribution of the negative flow direction to unit C. C N represents the local sewage source intensity of unit C. B Let be the local sewage source intensity of unit B, exp be the natural exponential function, k be the preset distance attenuation coefficient, and D be the local sewage source intensity of unit B. BC N represents the hydrological flow length from unit B to unit C. A Let D be the intensity of local sewage sources in Unit A. AC The hydrological flow length from unit A to unit C.

[0190] After substituting the specific values, the total negative flow contribution of unit C is: 0.8 + 0.6 * 0.5 + 0.2 * 0.25 = 1.15.

[0191] Taking Unit C as an example, when using the traditional algorithm, its evaluation results only reflect the extremely low local water conservation capacity of 0.1 and the extremely high sewage discharge of 0.8 in isolation. After adopting the spatial flow model of this invention, Unit C is accurately quantified as passively receiving the water conservation service flux and environmental pollution accumulation of upstream Unit A and Unit B. Its true water conservation capacity score is corrected to 0.5, and its sewage discharge score is corrected to 1.15.

[0192] This numerical simulation example demonstrates that the distance attenuation and flow direction accumulation model introduced in this invention overcomes the shortcomings of homogeneous superposition in existing technologies from the underlying algorithm level, making the spatial transmission process of negative externalities of upstream pollution passively borne by downstream areas explicit, and providing an accurate quantitative basis for the identification of compensation subjects and the calculation of compensation amounts in cross-regional ecological compensation.

[0193] This application proposes a differentiated modeling method based on physical transfer mechanisms, explicitly classifying ecological externality indicators into two main categories: in-situ and flow-oriented. For flow-oriented indicators with significant spatial mobility, such as water conservation and nitrogen and phosphorus emissions, a digital elevation model (DEM) is used for hydrological analysis to accurately characterize their transfer paths along surface runoff, and a distance decay function is introduced to simulate the weakening of their effects with transport distance. This makes the calculation of the spatial cumulative effects of ecosystem services and pollutants no longer a simple numerical superposition, but a simulation that more closely resembles real physical processes, solving the problem of unclear physical meaning caused by homogenizing indicators with different mechanisms. It also addresses the problem that existing technologies have failed to effectively distinguish the spatial action mechanisms of different indicators.

[0194] This application constructs a complete net ecological externality assessment system, quantifying not only the ecological benefits provided by the region but also the ecological pressures it generates. By standardizing and integrating both positive and negative externality indicators, a cost-benefit offsetting calculation is performed. The negative externality composite index is subtracted from the positive externality composite index to obtain a net value. This net value index comprehensively and objectively reflects the final ecological contribution or depletion of each spatial unit, overcoming the one-sidedness caused by existing methods' focus on positive service assessment. It also addresses the problem of existing technologies lacking a comprehensive accounting framework for both positive and negative effects.

[0195] Through differentiated modeling and net value calculation, this application can generate a comprehensive index with clear physical meaning and comprehensive evaluation results, providing solid technical support for achieving scientific and fair ecological compensation zoning.

[0196] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all fall within the protection scope of the present invention.

Claims

1. A method for watershed ecological compensation zoning based on a net ecological externality index, characterized in that, include: Acquire geospatial data of the target watershed, including basic data for calculating various preset positive ecological externality indicators and various preset negative ecological externality indicators; Based on geospatial data, a comprehensive index of positive ecological externalities is determined for each spatial unit within the target watershed. Based on geospatial data, a comprehensive index of negative ecological externalities is determined for each spatial unit within the target watershed. Based on the comprehensive index of positive and negative ecological externalities, the net ecological externality index of each spatial unit is calculated. Based on the spatial distribution of the net ecological externality index of each spatial unit in the target watershed, ecological compensation zones are established for the target watershed. For each spatial unit within the target watershed, determine a comprehensive index for positive ecological externalities and a comprehensive index for negative ecological externalities, including: Based on the physical transmission mechanism of each indicator, various pre-set positive ecological externality indicators and various pre-set negative ecological externality indicators are divided into in-situ indicators and flow-direction indicators, specifically: For each positive and negative ecological externality indicator, a determination is made: If the ecological benefits or ecological pressure represented by a certain indicator are spatially transmitted through water as a medium and along hydrological pathways, then the indicator is classified as a flow-oriented indicator. Otherwise, if the ecological benefits or ecological pressures are spatially localized, the indicator should be classified as an in-situ indicator. For in-situ indicators, the in-situ contribution value is calculated based on their local values ​​in each spatial unit; For flow direction indicators, the flow direction contribution value is calculated based on hydrological connectivity information in geospatial data; Based on in-situ contribution and flow-direction contribution, a comprehensive index of positive ecological externalities and a comprehensive index of negative ecological externalities are determined, including: The positive ecological externality index is obtained by weighting and summing the in-situ contribution value corresponding to the positive ecological externality index and the flow direction contribution value corresponding to the positive ecological externality index. The negative ecological externality index is obtained by weighting and summing the in-situ contribution value corresponding to the negative ecological externality index and the flow direction contribution value corresponding to the negative ecological externality index. Calculate the flow direction contribution value, including: Based on the digital elevation model in geospatial data, the hydrological flow direction between spatial units within the target watershed is determined, specifically: The original DEM data is filled with depressions, the flow direction of each grid spatial cell is determined, and the surface distance along the hydrological path between any spatial cell and all its upstream spatial cells is calculated. For any spatial unit, along the hydrological flow direction, the values ​​of the flow direction index of all its upstream spatial units are accumulated to obtain the flow direction contribution value. When accumulating the values ​​of flow direction indicators from all upstream spatial units, a distance attenuation function based on the hydrological flow length is introduced to weight the values ​​of flow direction indicators from different upstream spatial units to obtain the flow direction contribution value (FWCI). FWCI i =∑ j (S j ×exp(-k×D ij )) where i is the target spatial unit number, S j is the source intensity of the flow-oriented indicator on the upstream spatial unit j, D ij is the hydrological flow length from the upstream unit j to the target unit i, and k is a preset distance attenuation coefficient.

2. The method according to claim 1, characterized in that, Several pre-defined positive ecological externality indicators include: net primary productivity, habitat quality, and water conservation. Several pre-defined negative ecological externalities include: nitrogen emissions, phosphorus emissions, and population pressure; At least one of habitat quality, water conservation capacity, nitrogen emissions, and phosphorus emissions was calculated using the InVEST model.

3. The method of claim 2, wherein, For each spatial unit within the target watershed, determine a comprehensive index for positive ecological externalities and a comprehensive index for negative ecological externalities, including: The various preset positive ecological externality indicators and the various preset negative ecological externality indicators were normalized respectively. The normalized positive ecological externality indicators are aggregated to obtain the comprehensive positive ecological externality index. The normalized negative ecological externality indicators are aggregated to obtain the comprehensive negative ecological externality index.

4. The method of claim 1, wherein, Ecological compensation zoning of the target watershed is carried out by using the natural breakpoint method or the preset threshold method, and the spatial units are hierarchically clustered according to the value of the net ecological externality index. Hierarchical clustering is performed using a preset threshold method, including: Set a first preset threshold and a second preset threshold, wherein the first preset threshold is greater than the second preset threshold; Spatial units with a net ecological externality index greater than a first preset threshold are classified as high net externality zones; Spatial units with a net ecological externality index less than a second preset threshold are classified as low net externality zones; Spatial units with net ecological externality indices between a first preset threshold and a second preset threshold are classified as medium net externality zones.

5. The method of claim 4, wherein, Calculate the in-situ contribution value, including: Normalize each in-situ index; In-situ indicators that have been normalized and belong to the same externality direction are aggregated to obtain the in-situ contribution value.

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