A method for quantitatively evaluating influence of watershed ecosystem pattern change on wading function

Abstract

The application discloses a kind of quantitative evaluation methods of the influence of watershed ecosystem pattern change on wading function, comprising: constructing SWAT model;Carrying out watershed discretization and hydrological response unit HRU division, parameter setting and model operation, calibrating and verifying the model;Forest, grassland, wetland ecosystem type in land use data is replaced by bare land, and watershed hydrological model is run again;Carrying out watershed hydrological process simulation under different land use scenarios, obtaining corresponding rainfall, evapotranspiration, runoff depth, water yield and river runoff;Calculate watershed water conservation amount;Get water yield;The standardized runoff index is calculated by river runoff, and the number of flood / drought occurrence months is counted.The method of the application can quantitatively evaluate the influence of main natural ecosystem pattern change on watershed water conservation effect, water production effect, flood regulation and drought relief effect, and provide theoretical support for scientific management, protection and restoration measures of watershed ecosystem.

Description

Technical Field

[0001] This invention relates to the field of hydrology and water resources technology, and in particular to a method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions. Background Technology

[0002] Hydrological function, as a crucial component of ecological function, refers to the core capacity of an ecosystem to regulate the water cycle through physical, chemical, and biological processes based on its own structure. It is fundamental to maintaining ecological balance and ensuring the sustainable use of water resources. Ecosystem degradation directly weakens transpiration, reduces soil infiltration capacity, disrupts regional hydrological cycles, and prevents effective interception or infiltration of precipitation, leading to large amounts of surface runoff in a short period, resulting in larger and more frequent floods. Simultaneously, reduced soil infiltration capacity leads to insufficient groundwater recharge, decreased river baseflow, and a decline in regional water conservation capacity, resulting in reduced usable water resources and a significant increase in the frequency and intensity of droughts, ultimately triggering regional water shortage crises such as urban water scarcity and agricultural yield reduction. With the intensification of global climate change and the increasing impact of regional human activities, the combined effect of these two factors is accelerating ecosystem degradation and structural simplification (e.g., decreased species richness) and weakening the integrity of its ecological functions (e.g., impaired biodiversity maintenance and weakened soil and water conservation).

[0003] Therefore, quantitatively assessing the impact of changes in ecosystem patterns on watershed hydrological functions is of great practical significance for maintaining watershed ecological security, flood control and disaster reduction, drought prevention and mitigation, and achieving sustainable use of water resources.

[0004] Water conservation is a key indicator reflecting the health of an ecosystem, defined as the ecosystem's ability to maintain water reserves within a specific time and space. The main functions of water conservation include ensuring water supply, mitigating flood peaks, soil and water conservation, and maintaining the aquatic environment. To gain a deeper understanding of the water conservation effects of watershed ecosystems, water conservation volume (the amount of water stored by the ecosystem within a certain period) can be calculated for quantitative assessment of these effects.

[0005] In recent years, although significant progress has been made in research on the hydrological functions of ecosystems, most studies have focused on assessing single hydrological functions (such as water conservation) and have failed to comprehensively reflect the complex hydrological functions of ecosystems. In fact, simply calculating water conservation capacity as a single indicator cannot intuitively reflect the crucial roles of ecosystems in water production, flood control, and drought mitigation. These limitations restrict our comprehensive understanding of the multiple hydrological functions of ecosystems and also affect the scientific rigor of water resource management decisions. Therefore, there is an urgent need to establish a comprehensive assessment framework that incorporates multiple hydrological functions such as water conservation, water production, flood control, and drought mitigation into a unified analytical system. This will allow for a more comprehensive assessment of the multiple roles of ecosystems in water resource management and provide a more scientific theoretical basis for sustainable regional water resource management.

[0006] Existing studies often rely on hydrological models to assess the temporal variation characteristics of water conservation or water production capacity; or to analyze the impact of converting farmland back to forest or grassland on the overall water conservation capacity of the watershed.

[0007] However, the existing technology has the following problems and shortcomings:

[0008] (1) Most studies focus on assessing a single hydrological function (such as water conservation) and fail to fully reflect the complex hydrological functions of ecosystems. In fact, simply calculating the single indicator of water conservation capacity cannot intuitively reflect the key role played by ecosystems in water production, flood regulation, and drought mitigation;

[0009] (2) Under natural conditions, the impact of changes in watershed ecosystem cover on watershed hydrological effects is based on the combined effects of multiple ecosystem types (forests, grasslands, wetlands, etc.). Therefore, it is difficult to quantitatively analyze the impact of changes in the main ecosystem types of the watershed on watershed hydrological effects. Summary of the Invention

[0010] In view of this, the present invention provides a method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions.

[0011] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0012] A method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions includes the following steps:

[0013] Step 1: Construct a SWAT model and prepare terrain data, land use data, soil data, and meteorological data;

[0014] Perform watershed discretization and hydrological response unit (HRU) partitioning;

[0015] Configure parameters and run the model;

[0016] The model was calibrated and validated.

[0017] Step 2: Use MATLAB tools to replace the forest, grassland, and wetland ecosystem types in the land use data with bare land, and run the watershed hydrological model again;

[0018] Step 3: Conduct watershed hydrological process simulations under different land use scenarios based on the watershed hydrological model, and obtain the corresponding rainfall, evapotranspiration, runoff depth, water yield and river runoff based on the simulation results of different watershed hydrological processes;

[0019] Step 4: Based on the principle of water balance, calculate the water conservation capacity of the basin; the water yield is obtained directly from the model output; calculate the standardized runoff index through river runoff and count the number of months of flood / drought occurrence.

[0020] Preferably, in step one, the topographic data, land use data, soil data, and meteorological data are as follows:

[0021] Topographic data: Digital elevation models are used to extract watershed boundaries, river networks, sub-watershed divisions, and to calculate slope topographic features;

[0022] Data was obtained from USGS and SRTM channels and underwent projection conversion and noise removal preprocessing.

[0023] Land use data: derived from remote sensing image interpretation, and needs to be reclassified according to the classification system embedded in the SWAT model after acquisition;

[0024] Soil data: Soil type and its physicochemical properties data are required. The data should come from soil surveys or soil databases, and a soil property database is needed to build the SWAT model.

[0025] Meteorological data is crucial for driving the model's operation and includes daily precipitation, maximum / minimum temperatures, solar radiation, wind speed, and relative humidity. The data should cover a longer time series as much as possible and undergo strict quality control. SWAT models can also use weather generators to generate missing meteorological data.

[0026] Preferably, in step one, the SWAT model employs a two-level discretization method of "sub-basin—hydrological response unit" to handle the spatial heterogeneity of the watershed; wherein,

[0027] Sub-basin delineation: Based on the DEM, the entire study area is divided into several sub-basins through steps such as filling depressions, calculating water flow direction, accumulating catchment area, extracting river network and delineating watersheds;

[0028] Hydrological response unit definition: Within each sub-basin, it is further divided according to a combination of land use type, soil type, and slope grade;

[0029] HRU is the basic computational unit of the SWAT model. Each HRU is considered a homogeneous area with the same land use, soil and slope characteristics. The model first performs hydrological calculations independently on each HRU, and then summarizes them to the sub-basin and watershed scales.

[0030] Preferably, in step one, parameter settings include setting vegetation growth parameters such as canopy height and root depth for different land use types; setting hydrological parameters such as saturated hydraulic conductivity and field water holding capacity for different soil types; and setting agricultural management measures such as fertilization, irrigation, and tillage parameters.

[0031] The SWAT model comes with many default parameter databases, such as a soil database and a vegetation growth database, which can be adjusted according to the actual conditions of the study area.

[0032] Model Run: After completing all parameter settings and generating the model input file, you can run the SWAT model for simulation calculations; you can set the start and end times of the simulation and the time step, such as day, month, or year;

[0033] Preferably, in step one, parameter calibration involves first performing parameter sensitivity analysis to identify key parameters that have a significant impact on the output results, such as the number of runoff curves (CN) and the soil evaporation compensation coefficient (ESCO), and then focusing on optimizing these parameters.

[0034] Model validation: Fix the calibrated parameters and run the model using another set of independent observation data that was not involved in the calibration to evaluate the simulation effect;

[0035] Evaluation metrics: Commonly used model performance evaluation metrics include the Nash-Sutcliffe efficiency coefficient (NSE), the coefficient of determination (R²), and the Kling efficiency coefficient (KGE). The closer NSE, R², and KGE are to 1, the better the model simulation performance.

[0036] R 2 The formulas for calculating NSE and KGE are as follows:

[0037]

[0038]

[0039]

[0040] Where: r is the Pearson correlation coefficient between the measured and simulated values; α is the ratio of the variability between the simulated and measured values; β is the ratio of the mean between the simulated and measured values.

[0041] The formulas for calculating r, α, and β are as follows:

[0042]

[0043]

[0044]

[0045] in the formula , , , and k represent the measured runoff in m³. 3 / s, average measured runoff volume (m³) 3 / s, simulated runoff m 3 / s, average simulated runoff (m³) 3 / s, time series length.

[0046] Preferably, in step two, before using MATLAB to replace the forest, grassland, and wetland ecosystem types in the land use data with unused land, it is necessary to reclassify the land use data using Akrigis software. The steps are as follows:

[0047] (1) Prepare data: Ensure that your land use data is in raster format, such as .tif. If the original data is a vector polygon, such as Shapefile, you need to use a conversion tool, such as Feature to Raster, to rasterize it.

[0048] (2) Open the tool: In ArcToolbox, navigate to Spatial Analyst Tools -> Reclass -> Reclassify;

[0049] (3) Set parameters:

[0050] Input raster: Select your land use raster data;

[0051] Reclassification field: Select the VALUE field, which stores the original land use type code;

[0052] Remapping: In the Reclassification table, directly modify the new values.

[0053] (4) Run: Click “OK” to execute the operation.

[0054] Preferably, in step four,

[0055] The SWAT model's method for calculating water conservation capacity is as follows:

[0056] WC = P - E - R

[0057] Where: WC is the water conservation capacity per unit area (mm); P, E, and R are the average precipitation per unit area (mm), the actual evapotranspiration per unit area (mm), and the runoff depth per unit area (mm), respectively.

[0058] The SWAT model calculates the water production capacity (WYLD) based on the following formula:

[0059] WYLD = Q surf + Q gw + Q lat - Q loss

[0060] In the formula: Q surf Surface runoff (mm); Q gw The contribution of groundwater to runoff (in mm); Q lat Contributes mm to lateral flow; Q loss The transmission loss within the HRU is in mm;

[0061] Quantitative assessment methods for flood regulation and drought mitigation effects

[0062] Drought and flood identification in the Holingol River Basin is achieved by calculating the Standardized Runoff Index (SRI). The calculation method for the Standardized Runoff Index (SRI) is as follows:

[0063] Assuming the river's runoff *x* over a given time period, the gamma distribution probability density function is:

[0064] In the formula, For shape parameters, Scale parameters It is a Gamma function; the cumulative rate of runoff x over a certain time scale is:

[0065]

[0066] The runoff data output from the SWAT model RCH file contains zero values; therefore, a mixed distribution function is used, as shown in the following formula:

[0067]

[0068] Transform into a standard normal distribution:

[0069]

[0070] in, It is the inverse function of the standard normal distribution.

[0071] The present invention achieves the following technical effects compared to the prior art:

[0072] The method of this invention can quantitatively assess the impact of changes in the pattern of major natural ecosystems on the watershed's water conservation, water production, flood regulation, and drought mitigation effects, and provide theoretical support for scientific management, protection, and restoration measures for the watershed ecosystem. Attached Figure Description

[0073] Figure 1 For the present invention Figure 1 Distribution map of 56 sub-basins based on the SWAT model (numbers in the map are sub-basin numbers).

[0074] Figure 2 This invention describes the changes in water conservation per unit area of ​​a watershed under different ecosystem degradation scenarios. Figure 3 This is a graph showing the variation of water yield per unit area in the watershed under different ecosystem degradation scenarios according to the present invention.

[0075] Figure 4 This is a graph showing the variation in the number of months of flood occurrence under different ecosystem degradation scenarios according to the present invention;

[0076] Figure 5 This is a graph showing the variation in the number of months of drought under different ecosystem degradation scenarios according to the present invention;

[0077] Figure 6 This is a graph showing the variation of water conservation capacity per unit area of ​​the watershed under different ecosystem protection scenarios according to the present invention.

[0078] Figure 7 This is a graph showing the variation of water yield per unit area in the watershed under different ecosystem protection scenarios according to the present invention.

[0079] Figure 8 This is a graph showing the variation in the number of months of flood occurrence under different ecosystem protection scenarios according to the present invention;

[0080] Figure 9 This is a graph showing the variation in the number of months of drought under different ecosystem protection scenarios according to the present invention. Detailed Implementation

[0081] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0082] This invention discloses a method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions.

[0083] The SWAT (Soil and Water Assessment Tool) model is a physical-mechanism-based distributed watershed hydrological model developed by the USDA Agricultural Research Service. It simulates the migration and transformation processes of water, sediment, nutrients, and pesticides within a watershed, and is particularly suitable for assessing the impacts of land use and management changes on water resources.

[0084] The MATLAB code for replacing forest, grassland, wetland, and other ecosystem types in land use data with unused land is as follows:

[0085] % Set the file path input_file = 'D:\1980 Land Use Type Processing\hlhLUCC1980TYZBcfl.tif';

[0086] % Input file path output_file = 'D:\1980 Land Use Type Processing\1980SD\hlhLUCC1980TYZBcflSC0.25.tif'; % Output file path

[0087] % Read raw land use data [landuse, R] = geotiffread(input_file); landuse = double(landuse);

[0088] % Convert to double type for easier handling original_size = size(landuse);

[0089] % Get georeferenced information info = geotiffinfo(input_file);

[0090] % Define land type codes; for example, wetland code is 5, unused land code is 7. WETLAND = 5; BARELAND = 7; % Create output matrix output_landuse = landuse; % Get the mask of wetland pixels wetland_mask = (landuse == WETLAND); % Find the linear indices of all wetland pixels wetland_indices = find(wetland_mask); % Calculate the number of wetland pixels to be converted (25%). If the calculated number of wetland pixels to be converted is 100%, change 0.75 to 0 in the following formula. num_to_convert = round(0.75 * numel(wetland_indices)); % Randomly select wetland pixels to be converted rng('default');

[0091] % Set a random seed to ensure repeatability selected_indices = wetland_indices(randperm(numel(wetland_indices), num_to_convert)); % Perform the conversion: convert the selected wetlands to bare land output_landuse(selected_indices) = BARELAND; % Save the results geotiffwrite(output_file, output_landuse, R, ... 'GeoKeyDirectoryTag', info.GeoTIFFTags.GeoKeyDirectoryTag); disp('Processing complete, results saved.');

[0092] The thresholds for floods and droughts are defined based on the SRI (Self-Reliance Index): a SRI greater than 0.5 corresponds to a flood event, and a SRI less than -0.5 corresponds to a drought event. The specific flood and drought classifications based on the SRI are shown in Table 1. This study counted the number of months with a 1-month SRI (SRI-1) greater than 0.5 and less than -0.5 under different ecosystem change scenarios to quantitatively assess the flood regulation and drought mitigation effects under these scenarios.

[0093] Table 1: Standardized Runoff Index Drought and Flood Levels

[0094] Flood types SRI value drought type SRI value Mild flooding 0.5<SRI<1.0 mild drought -1.0<SRI<-0.5 moderate flooding 1.0<SRI<1.5 drought -1.5<SRI<-1.0 Severe flooding 1.5<SRI<2.0 Severe drought -2.0<SRI<-1.5 extreme flooding 2.0<SRI severe drought SRI<-2.0

[0095] To quantitatively reveal the impact of changes in the cover of major ecosystem types in the Holingol River Basin on the basin's hydrological effects, individual degradation and protection scenarios for forests, grasslands, and wetlands (slight, moderate, severe, and all) and a combined degradation and protection scenario for forests, grasslands, and wetlands were designed (based on 30-meter resolution land use data from the Chinese Academy of Sciences in 1980), as shown in Table 2. In the S0 scenario (baseline scenario), the proportion of ecosystem area per grid scale remains unchanged from the 1980 average. The difference between S0 and other scenarios quantitatively reflects the impact of ecosystem cover changes on the basin's hydrological effects. When the protection levels of SA+, SB+, SC+, and SD+ are the same, the impact of forest, grassland, and wetland protection, as well as the combined protection of forests, grasslands, and wetlands, on the basin's hydrological effects can be quantitatively assessed and compared, providing a certain reference value for spatial planning of the basin's land use patterns.

[0096] Table 2: Ecosystem degradation and conservation scenario design.

[0097] .

[0098] To improve computational efficiency, the thresholds for land use, soil type, and slope were all set at 10%. The Holingol River Basin was divided into 56 sub-basins. Figure 1 ) and 324 hydrological response units (HRUs).

[0099] Example 1: Changes in water conservation effect

[0100] Under the baseline (S0) scenario, the water conservation capacity of the upper reaches of the Holingol River is higher than that of the lower reaches. Figure 2 The average annual water conservation volume of the basin from 1980 to 2019 was 898 million m³, with a water conservation volume per unit area of ​​29.61 mm. The reduction in water conservation volume varied under different forest, grassland, and wetland degradation scenarios, as well as integrated forest, grassland, and wetland degradation scenarios. Forest and grassland degradation scenarios (SA-, SB-, ...) Figure 2 The water conservation capacity in the upper reaches of the lower reaches of the basin is significantly reduced compared to the baseline (S0). Under the scenarios of total forest degradation (SA100%-) and total grassland degradation (SB100%-), the total water conservation capacity is 834 million m³ and 719 million m³, respectively, representing reductions of 7.15% and 19.99% compared to the baseline (S0). Due to the small wetland area in the basin, the wetland degradation scenario (SC-) is also significant. Figure 2 The total water conservation capacity under the following scenarios is relatively small compared to the baseline (S0); under the scenario of total wetland degradation (SC100%-), the total water conservation capacity is 861 million m³, a decrease of 4.24% compared to the baseline (S0) scenario. Under the scenario of total forest, grassland, and wetland degradation (SD100%-), the total water conservation capacity is 622 million m³, showing the largest decrease. Figure 2 Compared to the baseline (S0) scenario, this represents a reduction of 30.82%. Under four scenarios—25% forest degradation (SA25%-), 50% forest degradation (SA 50%-), 75% forest degradation (SA 75%-), and 100% forest degradation (SA 100%-)—the watershed forest area decreased by 531.46%. 1062.92 1594.38 and 2125.84 Compared to the baseline (S0) scenario, the water conservation capacity per unit area decreased by 0.80 mm, 1.65 mm, 2.18 mm, and 2.12 mm, respectively. Figure 2Further analysis showed that for every 1 km² decrease in forest area, the water conservation capacity per unit area of ​​the watershed decreased by 0.0015 mm, 0.0016 mm, 0.0014 mm, and 0.0010 mm, respectively. Considering all four scenarios, forest degradation led to an average decrease of 0.0014 mm / km² in watershed water conservation capacity per unit area. Under the four grassland and wetland degradation scenarios, the results showed that for every 1 km² decrease in grassland area, the water conservation capacity per unit area of ​​the watershed decreased by an average of 0.0005 mm; while for every 1 km² decrease in wetland area, the water conservation capacity decreased by an average of 0.0014 mm. This indicates that wetlands have a more significant impact on maintaining the water conservation function of the watershed compared to grasslands. Considering the integrated degradation scenario of forests, grasslands, and wetlands (SD-), Figure 2 Under the scenario of total forest degradation (SA100%-), the analysis shows that for every 1 km² decrease in comprehensive vegetation area, the average water conservation capacity per unit area of ​​the watershed decreases by 0.0006 mm. Under the scenario of total forest degradation, the upper and middle reaches of the Holingol River Basin exhibit significant water conservation capacity degradation, with its water conservation capacity per unit area decreasing significantly compared to the baseline (S0) scenario. Figure 2 In the entire basin, approximately 31% of the area experienced a decrease in water conservation capacity per unit area exceeding 10% compared to the baseline (S0) scenario. This was particularly pronounced in sub-basin 15, located on the right bank of the middle reaches of the basin. This area has a forest cover of 301.94 km², representing 60.14% of the total forest area. Under the baseline (S0) scenario, the water conservation capacity per unit area in this sub-basin was 37.76 mm, but under the scenario of total forest degradation (SA100%-), it decreased to 30.93 mm, a reduction of 18.08% compared to the baseline (S0) scenario. Under the scenario of total grassland degradation (SB100%-), approximately 73% of the entire basin experienced a decrease in water conservation capacity per unit area exceeding 10% compared to the baseline (S0) scenario. Sub-basin 8, located in the upper reaches of the basin, has a grassland area of ​​2520.45 km². 2 The forest, grassland, and wetland coverage reached 99.79%. Under the baseline (S0) scenario and the scenario of total grassland degradation (SB100%-), the water conservation per unit area in this sub-basin was 27.85 mm and 16.44 mm, respectively, representing a relative decrease of 40.96%. Under the scenario of total wetland degradation (SC100%-), the water conservation per unit area in the Holingol River Basin did not decrease significantly compared to the baseline (S0) scenario, but about 22% of the area (mainly located in the downstream region) experienced a decrease of more than 10% in water conservation per unit area compared to the baseline (S0) scenario. Under the scenario of total forest, grassland, and wetland degradation (SD100%-), the water conservation per unit area in the Holingol River Basin decreased most significantly compared to the baseline (S0) scenario, with about 87% of the area experiencing a decrease of more than 10% in water conservation per unit area compared to the baseline (S0) scenario.

[0101] Figure 2 This represents the water conservation capacity per unit area of ​​different sub-basins within the watershed under the baseline (S0) scenario and different levels of ecosystem degradation scenarios (SA-, SB-, SC-, SD-). The bar chart below shows the average water conservation capacity per unit area of ​​the watershed under the baseline (S0) scenario and different levels of ecosystem degradation scenarios; SA, SB, SC, and SD represent forest, grassland, wetland, and integrated forest, grassland, and wetland ecosystems, respectively; 25%-, 50%-, 75%-, and 100%- represent ecosystem area reductions of 25%, 50%, 75%, and 100%, respectively. See Table 2 for abbreviations of the different scenarios (e.g., SA25%- represents 25% forest degradation).

[0102] Example 2: Changes in water production effect

[0103] The average total water yield of the Holingol River Basin from 1980 to 2019 was 2.179 billion m³. 3 The water yield per unit area was 71.81 mm. Under individual and combined degradation scenarios of forests, grasslands, and wetlands, the watershed water yield significantly increased compared to the baseline (S0) scenario. Figure 3 Under scenarios of total forest degradation (SA 100%-), total grassland degradation (SB 100%-), and total wetland degradation (SC 100%-), the water yield is 2.47 billion m³. 3 3.341 billion m 3 2.336 billion m 3 Compared to the baseline (S0) scenario, these figures represent increases of 13.35%, 53.34%, and 7.21%, respectively. Under the scenario of total degradation of forests, grasslands, and wetlands (SD100%-), the watershed yield is 3.716 billion m³. 3 The largest increase ( Figure 3 This represents an increase of 70.55% compared to the baseline (S0) scenario.

[0104] Based on the four individual degradation scenarios of forests, grasslands, and wetlands, for every 1 km² decrease in forest, grassland, and wetland area, the water yield per unit area of ​​the watershed increases by an average of 0.0047 mm, 0.0031 mm, and 0.0061 mm, respectively. For every 1 km² decrease in the combined vegetation area of ​​forests, grasslands, and wetlands, the water yield per unit area of ​​the watershed increases by an average of 0.0034 mm under the four scenarios (SD25%-, SD50%-, SD75%-, and SD100%-).

[0105] Under the scenario of total forest degradation (SA 100%-), the water conservation capacity per unit area is significantly increased compared to the baseline scenario (S0). Figure 3In the entire basin, approximately 37% of the area saw an increase in water yield per unit area exceeding 10% compared to the baseline (S0) scenario. Under the baseline (S0) scenario and the scenario of total forest degradation (SA 100%-), the water yield per unit area in sub-basin 15 was 42.72 mm and 100.69 mm, respectively, showing the most significant relative increase of 135.67%. Under the scenario of total grassland degradation (SB 100%-), approximately 90% of the entire basin saw an increase in water yield per unit area exceeding 10% compared to the baseline (S0) scenario. Under the baseline (S0) scenario and the scenario of total grassland degradation (SB 100%-), the water yield per unit area in sub-basin 8 was 46.19 mm and 136.66 mm, respectively, showing a relative increase as high as 195.88%. Under the scenario of total wetland degradation (SC 100%-), the water yield per unit area in the lower reaches of the Holingol River Basin increased significantly compared to the baseline (S0) scenario. In this scenario, approximately 26% of the basin experienced an increase in water yield per unit area exceeding 10% compared to the baseline (S0) scenario. Under the scenario of total degradation of forests, grasslands, and wetlands (SD 100%-), the increase in water yield per unit area in the Holingol River Basin was most significant compared to the baseline (S0) scenario. In this scenario, approximately 99% of the basin experienced an increase in water yield per unit area exceeding 10% compared to the baseline (S0) scenario.

[0106] Figure 3 This represents the water yield per unit area in different sub-basins within the watershed under the baseline (S0) scenario and different levels of ecosystem degradation (SA-, SB-, SC-, SD-). The bar chart below shows the average water yield per unit area of ​​the watershed under the baseline (S0) scenario and different levels of ecosystem degradation scenarios; SA, SB, SC, and SD represent forest, grassland, wetland, and integrated forest, grassland, and wetland ecosystems, respectively; 25%-, 50%-, 75%-, and 100%- represent ecosystem area reductions of 25%, 50%, 75%, and 100%, respectively. See Table 2 for abbreviations of the different scenarios (e.g., SA25%- represents 25% forest degradation).

[0107] Example 3: Changes in flood storage effect

[0108] In the baseline (S0) scenario, the number of months with flooding in the lower reaches of the Holingol River basin is much greater than that in the upper reaches. Figure 4 The average number of months with flooding in the 56 sub-basins from 1980 to 2019 was 114.34. Compared with the baseline (S0) scenario, the number of months with flooding increased significantly under the forest (SA-), grassland (SB-), wetland (SC-) and integrated forest-grassland-wetland degradation (SD-) scenarios. The increase in the number of months with flooding was particularly significant under the grassland degradation (SB-) and integrated forest-grassland-wetland degradation (SD-) scenarios. Figure 4Under the scenarios of complete forest degradation (SA 100%-), complete grassland degradation (SB 100%-), and complete wetland degradation (SC 100%-), the number of months with flooding is 118.79, 132.37, and 115.83, respectively, representing increases of 3.89%, 15.77%, and 1.31% compared to the baseline (S0) scenario. The scenario of complete degradation of forests, grasslands, and wetlands (SD 100%-) shows the largest increase, with 134.41 months with flooding. Figure 4 The increase is 17.55% compared to the baseline (S0) scenario. Under the scenario of total forest degradation (SA 100%-), the number of months with flooding in the upper and middle reaches of the watershed is significantly higher than that under the baseline (S0) scenario. Figure 4 In the entire basin, approximately 14% of the area experienced an increase of over 20% in the number of months with flooding compared to the baseline (S0) scenario. Specifically, under the baseline (S0) scenario, sub-basin 15 experienced 97 months with flooding; under the scenarios of 75% forest degradation (SA75%-) and complete forest degradation (SA100%-), the number of months with flooding in this sub-basin was 121, an increase of 24.74% compared to the baseline (S0) scenario. Under the scenario of complete grassland degradation (SB100%-), approximately 32% of the entire basin experienced an increase of over 20% in the number of months with flooding compared to the baseline (S0) scenario. Under the baseline (S0) scenario and the scenario of complete grassland degradation (SB100%-), sub-basin 8 experienced 81 and 129 months with flooding, respectively, representing a relative increase of 59.26%. Under the scenario of total wetland degradation (SC 100%-), only a portion of the lower Holingol River basin shows an increase in the number of months with flooding compared to the baseline (S0) scenario. In this scenario, approximately 3% of the basin experiences an increase of more than 20% in the number of months with flooding compared to the baseline (S0) scenario. Under the scenario of total degradation of forests, grasslands, and wetlands (SD 100%-), the increase in the number of months with flooding in the Holingol River basin is the most significant compared to the baseline (S0) scenario. In this scenario, approximately 34% of the basin experiences an increase of more than 20% in the number of months with flooding compared to the baseline (S0) scenario.

[0109] Figure 4 This represents the number of months of flooding in different sub-basins within the watershed under the baseline (S0) scenario and different levels of ecosystem degradation (SA-, SB-, SC-, SD-). The bar chart below shows the average number of months of flooding in the watershed under the baseline (S0) scenario and different levels of ecosystem degradation scenarios; SA, SB, SC, and SD represent forest, grassland, wetland, and integrated forest, grassland, and wetland ecosystems, respectively; 25%-, 50%-, 75%-, and 100%- represent ecosystem area reductions of 25%, 50%, 75%, and 100%, respectively. See Table 2 for abbreviations of the different scenarios (e.g., SA25%- represents 25% forest degradation).

[0110] Example 4: Changes in drought mitigation effect

[0111] In the baseline (S0) scenario, the number of drought-affected months in the lower reaches of the Holingol River basin is greater than that in the upper reaches. Figure 5 The average number of drought-affected months in the 56 sub-basins from 1980 to 2019 was 152.83. Compared with the baseline (S0) scenario, the number of drought-affected months increased significantly under the forest (SA-), grassland (SB-), wetland (SC-), and integrated forest-grassland-wetland degradation (SD-) scenarios. The increase in the number of drought-affected months was particularly significant under the grassland degradation (SB-) and integrated forest-grassland-wetland degradation (SD-) scenarios. Figure 5 Under the scenarios of complete forest degradation (SA 100%-), complete grassland degradation (SB 100%-), and complete wetland degradation (SC 100%-), the number of drought-affected months are 160.09, 168.93, and 155.25, respectively, representing increases of 4.75%, 10.53%, and 1.58% compared to the baseline (S0) scenario. The scenario of complete forest, grassland, and wetland degradation (SD 100%-) shows the largest increase, with 170.42 drought-affected months in the watershed. Figure 5 Compared to the baseline (S0), the increase was 11.51%. Under the scenario of total forest degradation (SA100%-), approximately 11% of the entire basin experienced an increase of over 20% in the number of drought-affected months compared to the baseline (S0) scenario. Specifically, under the baseline (S0) scenario, sub-basin 15 experienced 158 drought-affected months, while under the scenarios of 75% forest degradation (SA75%-) and total forest degradation (SA100%-), both sub-basins experienced 167 drought-affected months, representing an increase of 5.70% compared to the baseline (S0) scenario. Under the scenario of total grassland degradation (SB100%-), the number of drought-affected months in the Holingol River Basin increased significantly, with approximately 21% of the entire basin experiencing an increase of over 20% compared to the baseline (S0) scenario. Under the baseline (S0) scenario and the scenario of total grassland degradation (SB100%-), sub-basin 8 experienced 112 and 172 drought-affected months, respectively, representing a relative increase of 53.57%. Under the scenario of total wetland degradation (SC100%-), approximately 37% of the basin experienced an increase in the number of drought-affected months compared to the baseline (S0) scenario, but the increase was small, not exceeding 20%. Under the scenario of total forest, grassland, and wetland degradation (SD100%-), the Holingol River Basin saw the most significant increase in the number of drought-affected months compared to the baseline (S0) scenario, with approximately 24% of the basin experiencing an increase of more than 20% in the number of drought-affected months compared to the baseline (S0) scenario.

[0112] Figure 5This indicates the number of drought months in different sub-basins within the watershed under the baseline (S0) scenario and different levels of ecosystem degradation (SA-, SB-, SC-, SD-). The bar chart below shows the average number of drought months in the watershed under the baseline (S0) scenario and different levels of ecosystem degradation scenarios; SA, SB, SC, and SD represent forest, grassland, wetland, and integrated forest, grassland, and wetland ecosystems, respectively; 25%-, 50%-, 75%-, and 100%- represent ecosystem area reductions of 25%, 50%, 75%, and 100%, respectively. See Table 2 for abbreviations of the different scenarios (e.g., SA25%- represents 25% forest degradation).

[0113] Example 5: Changes in Water Conservation Effect

[0114] Unused land in the Holingol River Basin is mainly distributed in the downstream area. Therefore, under the scenario of integrated protection of forests, grasslands, and wetlands, and forests, grasslands, and wetlands, the water conservation capacity in the downstream area of ​​the basin is significantly increased compared to the baseline (S0) scenario. Figure 6 Under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), and maximum wetland protection (SC100%+), the water conservation capacity is 971 million m3, 931 million m3, and 958 million m3, respectively, representing increases of 8.09%, 3.56%, and 6.59% compared to the baseline (S0). Under the same level of protection scenarios, forest water conservation capacity shows the largest increase, followed by wetlands, and grassland the smallest. Under the scenario of comprehensive maximum protection of forests, grasslands, and wetlands (SD100%+), the water conservation capacity is 977 million m3, showing the largest increase. Figure 6 Compared to the baseline (S0), this represents an increase of 8.73%. The unused land area in the Holingol River Basin is 4261.01 km². Under the scenarios of 25% forest protection (SA25%+), 50% forest protection (SA 50%+), 75% forest protection (SA 75%+), and 100% forest protection (SA 100%+), the forest area increases by 1065.25 km², 2130.50 km², 3195.75 km², and 4261.01 km², respectively. The water conservation capacity per unit area increases by 1.11 mm, 2.32 mm, 2.38 mm, and 2.39 mm (SA+) compared to the baseline (S0) scenario, respectively. Figure 6The four scenarios show that for every 1 km² increase in forest area, the water conservation capacity per unit area of ​​the watershed increases by 0.0010 mm, 0.0011 mm, 0.0007 mm, and 0.0006 mm, respectively. Overall, the increase in forest area leads to an average increase of 0.0009 mm / km² in watershed water conservation capacity. Under the four grassland and wetland protection scenarios: for every 1 km² increase in grassland area, the water conservation capacity per unit area of ​​the watershed increases by an average of 0.0002 mm; while for every 1 km² increase in wetland area, the water conservation capacity increases by an average of 0.0005 mm. Under the four integrated protection scenarios (SD25%+, SD50%+, SD75%+, and SD100%+): for every 1 km² increase in integrated vegetation area, the water conservation capacity per unit area of ​​the watershed increases by an average of 0.0005 mm. This value is the same as in the wetland protection scenario alone. Compared with the baseline (S0) scenario, under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum integrated protection of forest, grassland and wetland (SD100%+), the water conservation per unit area of ​​the entire watershed increased by more than 10% in approximately 35%, 23%, 35%, and 28% of the regions, respectively. Among them, the unused land area of ​​sub-basin No. 33, located on the right bank of the downstream section, is 65.44 km2, accounting for as much as 86.55% of the sub-basin area. Under the baseline (S0) scenario, the water conservation per unit area of ​​sub-basin No. 33 is 6.86 mm. Under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum combined protection of forest, grassland and wetland (SD100%+), the water conservation per unit area of ​​the sub-basin is 18.75 mm, 14.80 mm, 15.26 mm, and 16.79 mm, respectively, which are 173.22%, 115.61%, 122.41%, and 144.68% higher than the baseline (S0) scenario.

[0115] Figure 6 This represents the water conservation per unit area in different sub-basins within the watershed under the baseline (S0) scenario and different levels of ecosystem protection scenarios (SA+, SB+, SC+, SD+). The bar chart below represents the average water conservation per unit area of ​​the watershed under the baseline (S0) scenario and different levels of ecosystem protection scenarios. SA, SB, SC, and SD represent forest, grassland, wetland, and integrated forest, grassland, and wetland ecosystems, respectively. 25%+, 50%+, 75%+, and 100%+ represent 25%, 50%, 75%, and 100% conversion of unused land, respectively. Please see Table 2 for abbreviations of different scenarios (e.g., SA25%+ represents 25% conversion of unused land into forest).

[0116] Example 6: Changes in water production effect

[0117] Under the scenario of integrated protection of forests, grasslands, and wetlands, the water yield per unit area in the lower reaches of the Holingol River Basin is significantly lower than that under the baseline (S0) scenario. Figure 7 Under the scenarios of maximum grassland protection (SB100%+) and maximum wetland protection (SC100%+), water yield is 1.773 billion m³ and 1.779 billion m³, respectively, representing decreases of 18.65% and 18.36% compared to the baseline (S0) scenario. With the same level of protection, the decrease in grassland water yield is slightly greater than that of wetlands. Under the scenario of maximum combined protection of forests, grasslands, and wetlands (SD100%+), water yield is 1.741 billion m³, a decrease of 20.11% compared to the baseline (S0) scenario. Under the scenario of maximum forest protection (SA100%+), watershed water yield is 1.682 billion m³, showing the largest decrease compared to the baseline (S0) scenario. Figure 7 (), which is 22.80%.

[0118] Under the four integrated protection scenarios for forests, grasslands, and wetlands, for every 1 km² increase in forest, grassland, and wetland area, the average water yield per unit area of ​​the watershed decreases by 0.0043 mm, 0.0037 mm, and 0.0037 mm, respectively. For every 1 km² increase in the combined vegetation area of ​​forests, grasslands, and wetlands, the average water yield per unit area of ​​the watershed decreases by 0.0038 mm under the four integrated protection scenarios (SD25%-, SD50%-, SD75%-, and SD100%-).

[0119] Compared with the baseline (S0) scenario, under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum integrated protection of forest, grassland and wetland (SD100%+), the water yield per unit area in the entire watershed decreased by more than 10% in approximately 50%, 50%, 48%, and 50% of the regions, respectively. Specifically, under the baseline (S0) scenario, the water yield per unit area of ​​sub-basin 33 is 152.62 mm. Under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum integrated protection of forest, grassland, and wetland (SD100%+), the water yield per unit area of ​​the sub-basin is 34.92 mm, 54.87 mm, 54.95 mm, and 48.84 mm, respectively, which are 77.12%, 64.05%, 64.00%, and 68.00% lower than the baseline (S0) scenario.

[0120] Figure 7This represents the water yield per unit area in different sub-basins within the watershed under the baseline (S0) scenario and different levels of ecosystem protection (SA+, SB+, SC+, SD+). The bar chart below represents the average water yield per unit area of ​​the watershed under the baseline (S0) scenario and different levels of ecosystem protection scenarios. SA, SB, SC, and SD represent forest, grassland, wetland, and integrated forest, grassland, and wetland ecosystems, respectively. 25%+, 50%+, 75%+, and 100%+ represent 25%, 50%, 75%, and 100% conversion of unused land, respectively. Please see Table 2 for abbreviations of different scenarios (e.g., SA25%+ represents 25% conversion of unused land into forest).

[0121] Example 7: Changes in flood storage effect

[0122] Under the scenario of integrated protection of forests, grasslands, and wetlands, the number of months with flooding in the lower reaches of the Holingol River Basin is slightly reduced compared to the baseline (S0) scenario. Figure 8 Under the scenarios of maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum integrated protection of forest, grassland, and wetlands (SD100%+), the number of months with flood occurrence in the watershed are 111.27, 111.95, and 112.10, respectively, representing reductions of 2.69%, 2.09%, and 1.96% compared to the baseline (S0). The reduction in the number of months with flood occurrence under the scenario of maximum integrated protection of forest, grassland, and wetlands (SD100%+) is greater than that under the scenarios of maximum grassland protection (SB100%+) and maximum wetland protection (SC100%+). Under the scenario of maximum forest protection (SA100%+), the number of months with flood occurrence is 111.09, showing the largest reduction. Figure 8 (), which is 2.84%.

[0123] Compared with the baseline (S0) scenario, the number of months of regional flooding in the entire watershed decreased by more than 20% under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum combined protection of forest, grassland and wetland (SD100%+), respectively, by approximately 4%, 1%, 1%, and 1%. Under the baseline (S0) scenario, the number of months with flooding in sub-basin 33 is 123. Under the scenarios of 50% forest protection (SA50%+), 50% grassland protection (SB50%+), 50% wetland protection (SC50%+), and 50% integrated protection of forests, grasslands, and wetlands (SD50%+), the number of months with flooding in this sub-basin is 117, 116, 118, and 115, respectively, representing reductions of 4.88%, 5.69%, 4.07%, and 6.50% compared to the baseline (S0) scenario. The maximum protection of forests (SA100%+), maximum protection of grasslands (SB100%+), and maximum protection of wetlands (SD50%+) also represent significant reductions. Under the scenarios of maximum protection (SC100%+) and maximum integrated protection of forests, grasslands and wetlands (SD100%+), the number of months with flooding in this sub-basin is 123, 121, 121 and 121, respectively. Under the scenario of maximum forest protection (SA100%+), the number of months with flooding in this sub-basin is unchanged compared with the baseline (S0) scenario. Under the scenarios of maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum integrated protection of forests, grasslands and wetlands (SD100%+), the number of months with flooding in this sub-basin is reduced by 1.63% compared with the baseline (S0) scenario.

[0124] Figure 8 This represents the number of months of flooding in different sub-basins within the watershed under the baseline (S0) scenario and different levels of ecosystem protection (SA+, SB+, SC+, SD+). The bar chart below represents the average number of months of flooding in the watershed under the baseline (S0) scenario and different levels of ecosystem protection scenarios. SA, SB, SC, and SD represent forest, grassland, wetland, and integrated forest, grassland, and wetland ecosystems, respectively. 25%+, 50%+, 75%+, and 100%+ represent 25%, 50%, 75%, and 100% conversion of unused land, respectively. Please see Table 2 for abbreviations of different scenarios (e.g., SA25%+ represents 25% conversion of unused land to forest).

[0125] Example 8: Changes in drought mitigation effect

[0126] Under the scenario of integrated protection of forests, grasslands, and wetlands, the number of drought-affected months in the lower reaches of the Holingol River Basin is significantly reduced compared to the baseline (S0) scenario. Figure 9Under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), and maximum wetland protection (SC100%+), the number of drought-affected months were 143.78, 143.53, and 143.69, respectively, representing reductions of 5.92%, 6.09%, and 5.98% compared to the baseline (S0). Under the same level of protection, the reduction in drought-affected months for grassland was slightly greater than that for forest and wetland. Under the scenario of combined maximum protection of forests, grasslands, and wetlands (SD100%+), the number of drought-affected months was 142.97, showing the largest reduction. Figure 9 This represents a 6.45% reduction compared to the baseline (S0) scenario.

[0127] Compared with the baseline (S0) scenario, the number of drought-affected months in the entire watershed decreased by more than 20% in approximately 4%, 3%, 5%, and 7% under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum integrated protection of forest, grassland, and wetlands (SD100%+), respectively. Under the baseline (S0) scenario, the number of drought-affected months in sub-watershed 33 was 155. Under the scenarios of maximum forest protection (SA100%+), maximum grassland protection (SB100%+), maximum wetland protection (SC100%+), and maximum integrated protection of forest, grassland, and wetlands (SD100%+), the number of drought-affected months in this sub-watershed were 141, 141, 142, and 140, respectively, representing reductions of 9.03%, 9.0%, and 9.0% compared to the baseline (S0) scenario. The figures are 3%, 8.39%, and 9.68%, respectively. However, under the scenarios of forest protection 25% (SA25%+), grassland protection 25% (SB25%+), wetland protection 25% (SC25%+), and integrated protection of forest, grassland and wetland 25% (SD25%+), the number of drought-affected months in this sub-basin are 161, 161, 159, and 161, respectively, which are 3.87%, 3.87%, 2.58%, and 3.87% higher than the baseline (S0) scenario.

[0128] Figure 9 This represents the number of drought months in different sub-basins within the watershed under the baseline (S0) scenario and different levels of ecosystem protection (SA+, SB+, SC+, SD+). The bar chart below represents the average number of drought months in the watershed under the baseline (S0) scenario and different levels of ecosystem protection scenarios. SA, SB, SC, and SD represent forest, grassland, wetland, and integrated forest, grassland, and wetland ecosystems, respectively. 25%+, 50%+, 75%+, and 100%+ represent 25%, 50%, 75%, and 100% conversion of unused land, respectively. Please see Table 2 for abbreviations of different scenarios (e.g., SA25%+ represents 25% conversion of unused land to forest).

[0129] The above description is merely a preferred embodiment of the present invention and does not constitute any limitation on the technical scope of the present invention. Therefore, any minor modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions, characterized in that, Includes the following steps: Step 1: Construct a SWAT model and prepare terrain data, land use data, soil data, and meteorological data; Perform watershed discretization and hydrological response unit (HRU) partitioning; Configure parameters and run the model; The model was calibrated and validated. Step 2: Use MATLAB to replace the forest, grassland, and wetland ecosystem types in the land use data with bare land, and run the SWAT model again; Step 3: Conduct SWAT process simulations under forest, grassland, and wetland ecological utilization scenarios based on the SWAT model, and obtain the corresponding rainfall, evapotranspiration, runoff depth, water yield, and river runoff based on the simulation results of different SWAT processes. Step 4: Based on the principle of water balance, calculate the water conservation capacity of the basin; the water yield is obtained directly from the model output; calculate the standardized runoff index through river runoff and count the number of months of flood / drought occurrence; In step four, The SWAT model's method for calculating water conservation capacity is as follows: WC = P - E - R Where: WC is the water conservation capacity per unit area (mm); P, E, and R are the average precipitation per unit area (mm), the actual evapotranspiration per unit area (mm), and the runoff depth per unit area (mm), respectively. The SWAT model calculates the water production capacity (WYLD) based on the following formula: WYLD = Q surf + Q gw + Q lat - Q loss In the formula: Q surf Surface runoff (mm); Q gw The contribution of groundwater to runoff (in mm); Q lat Contributes mm to lateral flow; Q loss The transmission loss within the HRU is in mm; Quantitative assessment methods for flood regulation and drought mitigation effects: Drought and flood identification in the Holingol River Basin is achieved by calculating the Standardized Runoff Index (SRI). The calculation method for the Standardized Runoff Index (SRI) is as follows: Assuming the river's runoff *x* over a given time period, the gamma distribution probability density function is: In the formula, For shape parameters, Scale parameters, It is a Gamma function; the cumulative rate of runoff x over a certain time scale is: The runoff data output from the SWAT model RCH file contains zero values; therefore, a mixed distribution function is used, as shown in the following formula: Transform into a standard normal distribution: in, It is the inverse function of the standard normal distribution.

2. The method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions according to claim 1, characterized in that, In step one, the topographic data, land use data, soil data, and meteorological data are as follows: Topographic data: Digital elevation models are used to extract watershed boundaries, river networks, sub-watershed divisions, and to calculate slope topographic features; Data was obtained from USGS and SRTM channels and underwent projection conversion and noise removal preprocessing. Land use data: derived from remote sensing image interpretation, and needs to be reclassified according to the classification system embedded in the SWAT model after acquisition; Soil data: Soil type and its physicochemical properties data are required. The data should come from soil surveys or soil databases, and a soil property database is needed to build the SWAT model. Meteorological data is crucial for driving model operation, including daily precipitation, maximum / minimum temperature, solar radiation, wind speed, and relative humidity. The SWAT model uses a weather generator to generate missing meteorological data.

3. The method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions according to claim 1, characterized in that, In step one, the SWAT model employs a two-level discretization method—"sub-basin—hydrological response unit"—to handle the spatial heterogeneity of the watershed; wherein... Sub-basin delineation: Based on the DEM, the entire study area was divided into several sub-basins by filling depressions, calculating water flow direction, accumulating catchment area, extracting river network, and delineating watersheds. Hydrological Response Unit (HRU) definition: Within each sub-basin, HRUs are further subdivided based on a combination of land use type, soil type, and slope grade; HRU is the basic computational unit of the SWAT model. Each HRU is considered a homogeneous area with the same land use, soil and slope characteristics. The model first performs hydrological calculations independently on each HRU, and then summarizes them to the sub-basin and watershed scales.

4. The method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions according to claim 1, characterized in that, In step one, parameter settings include setting vegetation growth parameters for different land use types, including canopy height and root depth; setting hydrological parameters for different soil types, including saturated hydraulic conductivity and field capacity; and setting agricultural management measures, including fertilization, irrigation, and tillage parameters. The SWAT model comes with many default parameter databases, including a soil database and a vegetation growth database, which can be adjusted according to the actual conditions of the study area. Model Run: After completing all parameter settings and generating the model input file, you can run the SWAT model for simulation calculations; you can set the start and end times and time steps of the simulation.

5. The method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions according to claim 1, characterized in that, In step one, parameter calibration involves first performing parameter sensitivity analysis to identify the runoff curve number CN and the soil evaporation compensation coefficient ESCO, and then optimizing these parameters. Model validation: Fix the calibrated parameters and run the model using another set of independent observation data that was not involved in the calibration to evaluate the simulation effect; Evaluation metrics: Commonly used model performance evaluation metrics include the Nash-Sutcliffe efficiency coefficient (NSE), the coefficient of determination (R²), and the Kling efficiency coefficient (KGE). The closer NSE, R², and KGE are to 1, the better the model simulation performance. R 2 The formulas for calculating NSE and KGE are as follows: Where: r is the Pearson correlation coefficient between the measured and simulated values; α is the ratio of the variability between the simulated and measured values; β is the ratio of the mean between the simulated and measured values. The formulas for calculating r, α, and β are as follows: in the formula , , , and k represent the measured runoff in m³. 3 / s, average measured runoff volume (m³) 3 / s, simulated runoff m 3 / s, average simulated runoff (m³) 3 / s, time series length.

6. The method for quantitatively assessing the impact of changes in watershed ecosystem patterns on water-related functions according to claim 1, characterized in that, In step two, before using MATLAB to replace the forest, grassland, and wetland ecosystem types in the land use data with unused land, it is necessary to reclassify the land use data using Akrigis software. The steps are as follows: (1) Prepare data: Ensure that the land use data is in raster format. If the original data is a vector surface, it needs to be rasterized using a conversion tool first. (2) Open the tool: In ArcToolbox, navigate to Spatial Analyst Tools -> Reclass -> Reclassify; (3) Set parameters: Input raster: Select land use raster data; Reclassification field: Select the VALUE field, which stores the original land use type code; Remapping: In the Reclassification table, directly modify the new values; (4) Run: Click "OK" to execute the operation.

Images