Water conservation accurate improvement method based on ecological hydrological multi-factor coupling

Abstract

The invention discloses a water conservation accurate improvement method based on ecological hydrological multi-factor coupling. The method comprises the steps that S100, multi-source data collection and standardization processing are carried out; S200, a drainage basin water conservation reference state is evaluated; S300, water conservation improvement potential is identified and quantified; s400, key repair plaques are preferably selected, and targeted technical measures are matched; and S500, generating and outputting a precise repair scheme. According to the method, extremely high precision of ecological restoration is realized, and the total amount of water conservation benefits is remarkably increased; the investment benefit and the decision-making efficiency of the repair project are greatly improved; the systematic closed loop of evaluation, planning and treatment is realized, and the scientificity and replicability of a repair scheme are enhanced.

Description

Technical Field

[0001] This invention belongs to the field of water resource management and ecological restoration technology, specifically involving a method for precise improvement of water source conservation based on multi-factor coupling of eco-hydrology. It is a systematic method that integrates precise assessment, optimized planning, targeted governance and dynamic monitoring. Background Technology

[0002] Water resources are the lifeline for maintaining socio-economic development and the health of ecosystems. However, with the dual impacts of global climate change and intensive human activities, many important river basins face severe challenges such as water shortages, degradation of aquatic ecological functions, and accelerated soil erosion. Taking the Yongding River Basin in North China as an example, its upper reaches, as an important water conservation area and ecological barrier for the Beijing-Tianjin-Hebei region, have long suffered from a fragile ecological environment and a continuous decline in water conservation capacity, becoming a key bottleneck restricting regional sustainable development. To address this, the state has vigorously promoted ecological civilization construction and major strategies such as the "Comprehensive Management and Ecological Restoration of the Yongding River," aiming to systematically restore and enhance the ecological functions of the basin.

[0003] Against this backdrop, scholars and engineers both domestically and internationally have conducted extensive research and practice on improving water conservation capacity. Existing technical solutions can be mainly categorized as follows:

[0004] Traditional engineering measures-driven technologies: These technologies primarily rely on traditional water conservancy and soil conservation projects, such as the construction of terraces, silt-retention dams, and water storage barriers. These methods alter the micro-topography to intercept runoff and retain sediment and water. Large-scale afforestation and vegetation restoration projects are also widely used. These measures played a crucial role in controlling soil erosion for a certain historical period.

[0005] Single-model assessment guidance technology: With technological advancements, researchers have begun to use various models (such as the SWAT model and the InVEST model) to simulate and assess the runoff generation and confluence processes and water conservation functions of watersheds. These models can assess the hydrological effects under different land use / cover change (LUCC) scenarios, providing a certain scientific basis for ecological restoration planning.

[0006] Decentralized technology combination application: In practice, biological measures such as vegetation restoration (e.g., reasonable configuration of trees, shrubs and grasses) and soil improvement (e.g., covering with soil and increasing the application of organic fertilizers) are usually combined with small-scale engineering measures to form local comprehensive management solutions.

[0007] However, through in-depth research and long-term practical experience, we have found that the above-mentioned existing technical solutions generally have the following urgent technical problems that need to be solved in practical applications:

[0008] ① Insufficient "precision" in restoration measures, resulting in low return on investment: Traditional ecological restoration projects often adopt a "blanket approach" and "comprehensive implementation," lacking precise identification of key restoration areas. Practice shows that the potential contribution of different plots within a watershed to water conservation varies greatly. For example, our research indicates that approximately 75% of the total benefit is contributed by a few "high-return" engineering patches. Current technology lacks an effective methodology to accurately identify these "high-return" areas, leading to a significant waste of funds and resources being invested in inefficient or even ineffective areas.

[0009] ② The assessment, planning, remediation, and monitoring stages are disconnected, lacking a systematic closed loop: In existing practices, the four key stages of "assessment-planning-technology-monitoring" are often fragmented. Model assessment results often remain at the macro-level reporting stage, failing to directly and meticulously guide the selection of specific engineering sites and the design of technical measures. After the implementation of remediation projects, there is a lack of dynamic monitoring and benefit feedback mechanisms linked to the initial assessment models, preventing the formation of a continuously optimized and iteratively improved technical closed loop. This "linear, open-loop" working model limits the continuous improvement of ecological restoration effectiveness and the scientific development of technology.

[0010] ③ The core mechanism of benefit generation is unclear, and the optimization of technical pathways is difficult: Although it is generally believed that vegetation restoration can conserve water resources, the extent to which its benefits come from the reduction of surface runoff velocity, the enhancement of soil infiltration capacity, or other factors lacks quantitative analysis through parameterization and modeling in specific projects. For example, our research clearly indicates that the benefits mainly come from a significant reduction in the surface velocity coefficient (V) (e.g., from 1500 for bare land to 5 for forests and grasslands). Existing technical solutions are often empirical "technology packages" that fail to decouple the contribution of various parameters from the perspective of eco-hydrological mechanisms, making it difficult to optimize the design of technology combinations for specific objectives (such as maximizing the increase in water conservation). Summary of the Invention

[0011] The purpose of this invention is to provide a method for precise improvement of water conservation based on multi-factor coupling of eco-hydrology. This method aims to provide a solution that: ① accurately identifies and quantifies "hotspot" areas within a watershed with the highest potential for water conservation improvement using eco-hydrological models and multi-source data; ② intelligently matches optimal combinations of technical measures (such as vegetation restoration type and soil cover thickness) to these "hotspot" areas based on core benefit mechanisms (such as regulating the surface velocity coefficient V); and ③ constructs a closed-loop data and model system from assessment, planning, to governance and monitoring, enabling dynamic optimization of restoration plans and accurate prediction and calculation of benefits.

[0012] To achieve the above objectives, the present invention provides a method for precisely enhancing water conservation based on multi-factor coupling of eco-hydrology, the method comprising the following steps:

[0013] S100, Multi-source data acquisition and standardization processing:

[0014] Acquire water resources data for the target watershed, register all water resources data in a coordinate system and resample them to a uniform spatial resolution to ensure that the data layers are fully aligned in space, forming a rasterized dataset.

[0015] S200, Assessing the baseline status of watershed water conservation:

[0016] The water conservation of each grid unit is quantified to form a spatial distribution map of the baseline water conservation.

[0017] S300 identifies and quantifies the potential for water conservation enhancement, including:

[0018] S310. Determine the current land use type of the grid unit, the type including repairable and non-repairable, and define the land use type of all repairable plots as the ideal conservation type;

[0019] S320. Calculate the ideal scenario water conservation capacity: Determine the ideal scenario water conservation capacity based on the current water conservation status of each grid cell.

[0020] S330. Calculate the benefit increment: Subtract the grid subtraction between the quantitative results of water conservation in repairable and non-repairable scenarios, calculate the water conservation benefit increment of each grid unit, and finally generate a spatial distribution map of the water conservation benefit increment.

[0021] S400, prioritizing key repair plaques and matching them with targeted technologies, including:

[0022] S410, Patch Sort and Screen:

[0023] 1. Perform connectivity analysis on the grids with benefit increment > 0 in the spatial distribution map of water conservation benefit increment to form several patches to be repaired.

[0024] 2. Sort in descending order based on the "total benefit increment" of each patch; the "total benefit increment" is the sum of the benefit increment values ​​of all grids within the patch multiplied by the grid area;

[0025] Third, based on the preset total investment or total restoration target, select the top-ranked patches from the sorting list as key restoration patches;

[0026] S420, Intelligent Matching of Targeted Techniques: For each selected repair plaque, the sources of its incremental benefits are analyzed, including:

[0027] Rule 1: If the current surface velocity coefficient of the patch is high, it indicates that the problem is exposed surface and fast runoff velocity, and then the "vegetation reconstruction project" will be automatically matched.

[0028] Rule 2: If the soil saturation hydraulic conductivity of the patch is low or the soil depth is insufficient, it indicates that the problem is poor soil infiltration capacity, and "Land Remediation Project" will be automatically matched.

[0029] Rule 3: If both Rule 1 and Rule 2 are met, then it is a "composite project";

[0030] S500 generates and outputs a precise repair plan.

[0031] The solution further includes: the water resources data of the target watershed is obtained from external data sources, including digital elevation model (DEM) data, multi-year average rainfall raster data, soil type and physicochemical property data, and current land use / cover type raster data. The soil type and physicochemical property data includes sand / clay content and soil depth.

[0032] The solution further includes the following external data sources: meteorological databases, geographic information databases, and remote sensing image databases.

[0033] The solution further includes: the formula for quantitatively calculating water conservation is as follows:

[0034]

[0035]

[0036] in:

[0037] WC baseline Water conservation capacity under baseline conditions (mm);

[0038] Y: Annual average water yield (mm), calculated by the InVEST model based on rainfall, evapotranspiration, land use type and soil data, is a known calculation quantity;

[0039] V baseline Surface velocity coefficient (cm / hr) under baseline conditions, extracted from the parameter database based on the current land use type, is a known quantity;

[0040] K sat Soil saturated hydraulic conductivity (cm / d) is calculated based on the content of soil sand and clay particles using transfer functions such as Cosby, and is a known quantity for calculation.

[0041] D: Topographic index (dimensionless), calculated from DEM data, reflects the impact of topography and soil depth on water storage capacity, and is a known calculation quantity;

[0042] S d Soil depth (mm), extracted from the parameter library based on the current soil type, is a known quantity;

[0043] S p : Percentage slope (%), calculated from DEM data, is a known calculation quantity.

[0044] The scheme is further defined as follows: In step S420, rule 1 states that the current surface velocity coefficient is high, meaning the velocity is > 1000 cm / hr, and the "vegetation reconstruction project" includes planting trees or grass.

[0045] Rule 2: The "land consolidation project" mentioned above refers to the application of topsoil and organic fertilizer.

[0046] Rule 3: The "composite project" refers to land reclamation plus vegetation reconstruction.

[0047] The scheme further includes: in step S500, the precise repair scheme includes: a patch distribution map and a detailed engineering task list. The patch distribution map includes a geographical location map of the patches and an attribute table for each patch. The attribute table includes: number, area, expected benefit increment, and recommended technical measures. The output is visualized and formatted.

[0048] The solution further involves establishing a precise water conservation enhancement system based on a computer server. This system includes: a data acquisition module, a watershed benchmark assessment module, a restoration potential identification module, a precise restoration planning module, and a solution output module, which receives data from external data sources.

[0049] The data acquisition module is configured to execute step S100;

[0050] The watershed benchmark assessment module is configured to execute step S200;

[0051] The repair potential identification module is configured to execute step S300;

[0052] The precise repair planning module is configured to execute step S400;

[0053] The solution output module is configured to execute step S500.

[0054] The technical effects and advantages of this invention are:

[0055] 1. It has achieved extremely high precision in ecological restoration, significantly increasing the total amount of water conservation benefits.

[0056] •Detailed advantages:

[0057] This invention can accurately identify "hotspot" areas (i.e., key remediation patches) that contribute the most to improving water conservation capacity and have the highest potential from the complex underlying surface conditions of the entire watershed. Compared with traditional "comprehensive" or macro-zoning-based remediation models, this invention can concentrate limited remediation resources on the most effective locations, thereby achieving a total ecological benefit far exceeding that of traditional methods with the same remediation area or investment. As the demonstration study report reveals, approximately 75% of the total benefit is contributed by a few "high-return" projects, and this invention aims to precisely identify and prioritize the remediation of these areas.

[0058] • Technology source:

[0059] This advantage mainly comes from method step S300: identifying and quantifying the potential for water conservation enhancement.

[0060] • Explanation of the reason:

[0061] Existing technologies typically rely on qualitative analysis or macro-level models for planning, failing to quantify the restoration potential of each individual plot (grid cell). This invention uniquely constructs an "ideal restoration scenario" and compares it grid-by-grid with the "baseline status quo" to calculate the incremental water conservation benefits (ΔWC). This ΔWC value is essentially a quantified "potential score," integrating all influencing factors such as the plot's current land use (determining the current V-value), topography, and soil conditions. It scientifically assesses "the specific incremental benefits that could be achieved if this plot were restored to its optimal state." This quantitative potential assessment transforms the selection of restoration targets from vague qualitative judgments to precise data-driven decisions, fundamentally ensuring the accuracy of restoration site selection and thus maximizing ecological benefits.

[0062] 2. Significantly improved the investment efficiency and decision-making efficiency of the restoration project.

[0063] •Detailed advantages:

[0064] This invention not only identifies "where to repair," but also intelligently matches "how to repair," and automatically prioritizes investments based on their benefits, providing decision-makers with a clear, objective, and efficient list of investment priorities. This significantly shortens the planning cycle, avoids wasting time and money due to insufficient comparison and justification of solutions, and ensures that every investment generates predictable and maximized ecological returns.

[0065] • Technology source:

[0066] This advantage mainly comes from method step S400: selecting key repair plaques and matching them with targeted technical measures, specifically including plaque sorting and screening in S410 and intelligent matching of targeted technical measures in S420.

[0067] • Explanation of the reason:

[0068] Regarding investment efficiency (derived from S410): Traditional solutions often struggle to compare the urgency and benefits of remediation across different plots. This invention simplifies complex decision-making by automatically sorting the "total benefit increment" of all potential patches in descending order, transforming it into a clear priority list. Decision-makers can directly select projects at the top of the list based on their budget, ensuring funds are invested in areas with the highest "cost-effectiveness" and achieving optimal investment efficiency.

[0069] Regarding decision-making efficiency (derived from S420): Traditional technology selection often relies on expert experience, which is subjective and uncertain. This invention establishes a "problem-technology" intelligent matching rule base based on the core mechanism of benefit generation. The system analyzes whether the benefit potential of a patch mainly comes from changing the surface velocity coefficient (V) or increasing the soil saturated hydraulic conductivity (Ksat), and automatically matches it with the most direct and effective technical measures (such as vegetation restoration or land reclamation). This targeted approach avoids inefficient or ineffective remediation caused by technology mismatch, making the formulation of technical solutions more scientific, efficient, and standardized.

[0070] 3. It has achieved a systematic closed loop of assessment, planning and governance, enhancing the scientific nature and replicability of the remediation plan.

[0071] •Detailed advantages:

[0072] This invention integrates previously fragmented processes such as ecological assessment, restoration planning, and technical design into a seamless and logically rigorous automated process. The entire methodology is based on clear eco-hydrological mechanisms and quantitative models, and its process is transparent and the results are verifiable, enabling successful restoration experiences (such as those from the Yongding River demonstration area) to be quickly and accurately replicated in other similar watersheds.

[0073] • Technology source:

[0074] This advantage stems from the systematic design of the entire method (seamless integration of S100-S500) and the water conservation calculation model on which its core relies, which clearly defines the contribution of key parameters (V and Ksat).

[0075] • Explanation of the reason:

[0076] Regarding the systematic closed loop: In existing working models, a significant "translation gap" exists between model evaluation reports and engineering design schemes. This invention, through a programmed system, constructs a complete data and logical flow, encompassing data input, model computation, potential identification, patch selection, technology matching, and ultimately, solution output. The output of the evaluation module (benefit increment map) directly becomes the input of the planning module, and the output of the planning module (list of key patches and targeted measures) can be directly used to guide engineering design. This end-to-end systematic solution eliminates distortion and efficiency losses during information transmission.

[0077] Regarding scientific validity and replicability: The core scientific aspect of this invention lies in the fact that it is not merely a "black box" tool, but rather clearly defines the fundamental mechanism by which water conservation benefits primarily stem from a significant reduction in the surface velocity coefficient (V). All technical measures are tailored to the most effective regulation of V and Ksat values. This methodology, based on a clear physical mechanism, possesses strong scientific validity and universality. As long as the relevant basic data for the target watershed can be obtained, any technician can utilize this system and method to reproduce objective and consistent restoration plans, thereby efficiently promoting successful paradigms in specific areas and providing standardized technical support for broader ecological restoration efforts.

[0078] The invention will be further explained in detail below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0079] Figure 1 This is a flowchart of the method of the present invention;

[0080] Figure 2 This is a structural block diagram of the system of the present invention;

[0081] Figure 3 This is a schematic diagram of the matching scheme between the key repair areas and the targeting technology measures of the present invention. Detailed Implementation

[0082] A method for precise enhancement of water conservation based on multi-factor coupling of eco-hydrology, such as Figure 1 , Figure 2 and Figure 3 As shown, the method steps include:

[0083] S100, Multi-source data acquisition and standardization processing:

[0084] Acquire water resources data for the target watershed, register all water resources data in a coordinate system and resample them to a uniform spatial resolution (e.g., 30m x 30m) to ensure that the data layers are fully aligned in space, forming a standardized input dataset in raster units;

[0085] Wherein: the water resources data of the target watershed are obtained from external data sources. The water resources data include digital elevation model (DEM) data, multi-year average rainfall raster data, soil type and physicochemical property data, and current land use / cover type (LULC) raster data. The soil type and physicochemical property data include sand / clay content and soil depth.

[0086] The external data sources include: meteorological databases, geographic information databases, and remote sensing image databases, wherein: the meteorological databases, geographic information databases, and remote sensing image databases each contain data parameter databases;

[0087] S200, Assessing the baseline status of watershed water conservation:

[0088] The water conservation of each grid unit is quantified to form a spatial distribution map of the baseline water conservation.

[0089] S300 identifies and quantifies the potential for water conservation and improvement, addressing issues such as unclear restoration goals and imprecise spatial planning, including:

[0090] S310. Construct an ideal restoration scenario: Determine the current land use type of the grid unit, which includes restoreable and non-restoreable types, and define the land use type of all restoreable plots as the ideal conservation type (such as "forest" or "high-coverage grassland").

[0091] S320. Calculate the ideal scenario water conservation capacity: Determine the ideal scenario water conservation capacity based on the current water conservation status of each grid cell, and calculate using the water conservation quantification formula. In this case, the surface velocity coefficient (V) adopts the value under the ideal restoration scenario (V0). ideal For example, if the density is uniformly set to 5 cm / hr (high-coverage grassland), the water conservation capacity WC under the ideal scenario can be calculated. ideal ;

[0092] S330: Calculate the benefit increment: Subtract the quantification results of water conservation under the two scenarios of repairability and non-repairability by raster subtraction, and calculate the water conservation benefit increment (ΔWC) for each raster unit. The final result is a spatial distribution map of the incremental water conservation benefits, which visually shows which areas in the watershed have the highest potential returns from ecological restoration.

[0093] S400, which prioritizes key repair plaques and matches them with targeted technologies, aims to address the mismatch between repair measures and benefit mechanisms, as well as low return on investment. These measures include:

[0094] S410, Patch Sort and Screen:

[0095] 1. Perform connectivity analysis on the grids with benefit increment > 0 in the spatial distribution map of water conservation benefit increment to form several patches to be repaired.

[0096] 2. Sort the patches in descending order based on their "total benefit increment" (i.e., the sum of the ΔWC values ​​of all grids within the patch multiplied by the grid area); the "total benefit increment" is the sum of the benefit increment values ​​of all grids within the patch multiplied by the grid area.

[0097] Third, based on the preset total investment or total restoration target (e.g., achieving 80% of the total potential benefit), select the top-ranked patches from the sorting list as key restoration patches;

[0098] S420, Targeted Technology Measures Intelligent Matching: For each selected repair plaque, analyze the main sources of its benefit increment, including:

[0099] Rule 1: If the current surface velocity coefficient (V) of the patch is... baseline If the surface velocity coefficient is high, it indicates that the main problem is exposed surface and fast runoff velocity, then the "vegetation reconstruction project" will be automatically matched; if the current surface velocity coefficient is high, it means the flow velocity is > 1000 cm / hr, and the "vegetation reconstruction project" is such as planting trees or grass.

[0100] Rule 2: If the soil saturated hydraulic conductivity (KsatKsat) of a patch is low or the soil depth is insufficient, indicating that its main problem is poor soil infiltration capacity, then the "Land Remediation Project" will be automatically matched; the "Land Remediation Project" is to add topsoil and cover the soil with new soil and apply more organic fertilizer.

[0101] Rule 3: If the conditions of Rule 1 and Rule 2 are met simultaneously, then it is classified as a "composite project"; the "composite project" is land reclamation + vegetation reconstruction.

[0102] S500: Generate and output a precise repair solution; the precise repair solution includes: such as Figure 3 The output includes a patch distribution map and a detailed list of engineering tasks. The patch distribution map includes a geographic location map of the patches and an attribute table for each patch, which includes: number, area, expected benefit increment, and recommended technical measures. The output is visualized and formatted.

[0103] A visualized map of key restoration patches: On the watershed base map, the location of each key restoration patch and the recommended project type are clearly marked using different line types or fill styles (e.g., 301 - diagonal fill represents vegetation restoration projects, 302 - cross fill represents composite projects).

[0104] A detailed engineering task list (table) lists the unique number, geographical coordinates, area, current land use type, expected increase in water conservation benefits (m³) for each key remediation patch, as well as specific matching targeted technical measures and recommended engineering quantities (such as soil cover thickness, planting density, etc.).

[0105] Wherein: the quantitative calculation formula for water source conservation is:

[0106]

[0107]

[0108] in:

[0109] WC baseline Water conservation capacity under baseline conditions (mm);

[0110] Y: Annual average water yield (mm), calculated by the InVEST model based on rainfall, evapotranspiration, land use type and soil data, is a known calculation quantity;

[0111] V baseline : The surface flow velocity coefficient (cm / hr) under the baseline condition is extracted from the parameter database of water resources data based on the current land use type and is a known quantity;

[0112] K sat Soil saturated hydraulic conductivity (cm / d) is calculated based on the content of soil sand and clay particles using transfer functions such as Cosby, and is a known quantity for calculation.

[0113] D: Topographic index (dimensionless), calculated from DEM data, reflects the impact of topography and soil depth on water storage capacity, and is a known calculation quantity;

[0114] S d Soil depth (mm), extracted from the parameter database of water resources data based on the current soil type, is a known quantity;

[0115] S p : Percentage slope (%), calculated from digital elevation model (DEM) data, is a known calculation amount.

[0116] The method establishes a water conservation precision enhancement system based on a computer server. The system includes: a data acquisition module 10, a watershed benchmark assessment module 20, a restoration potential identification module 30, a precision restoration planning module 40, and a scheme output module 50. The computer server hardware structure includes conventional computer components such as processors, memory, and input / output interfaces. The system receives data from external data sources.

[0117] The data acquisition module 10 is configured to execute step S100; to acquire and preprocess multi-source data required for model construction from external data sources (such as meteorological databases, geographic information databases, and remote sensing image databases), including but not limited to: digital elevation model (DEM) data, multi-year average rainfall raster data, soil type and physicochemical property data (such as sand / clay content and soil depth), and current land use / cover type (LULC) raster data.

[0118] The watershed benchmark assessment module 20 is configured to execute step S200, which is used to assess the water conservation capacity of the target watershed under current conditions. This module receives data processed by the data acquisition module 10, and calculates and generates a benchmark state map characterizing the spatial distribution of water conservation capacity across the entire watershed based on a preset eco-hydrological model.

[0119] The restoration potential identification module 30 is configured to execute step S300; it is one of the core modules of this embodiment, and its function is to identify and quantify the water conservation and enhancement potential of each plot (grid unit) within the watershed. This module constructs an ideal restoration scenario and compares it with the baseline state to calculate the spatial distribution map of the incremental benefits, thereby accurately identifying the "hotspot" areas with the highest restoration value.

[0120] The precise repair planning module 40 is configured to execute step S400; another core module in this embodiment transforms repair potential into specific, executable engineering solutions. This module sorts the benefit increments generated by the repair potential identification module 30, automatically selects key repair patches based on preset investment thresholds or repair targets, and intelligently matches the optimal targeted technical measures for each patch based on a pre-set "problem-technology" matching rule base.

[0121] The scheme output module 50 is configured to execute step S500; it is used to visualize and format the final scheme generated by the precise repair planning module 40. The output content includes, but is not limited to: the geographical location map of key repair patches, and a detailed attribute table of each patch (including number, area, expected benefit increment, recommended technical measures, etc.), providing direct basis for engineering design and construction.

[0122] The above-described implementation of the precise enhancement method for water conservation based on multi-factor coupling of eco-hydrology achieves extremely high precision in ecological restoration, significantly increases the total amount of water conservation benefits, greatly improves the investment efficiency and decision-making efficiency of restoration projects, realizes a systematic closed loop of assessment, planning and governance, and enhances the scientific nature and replicability of restoration schemes.

Claims

1. A method for precisely enhancing water conservation based on multi-factor coupling of eco-hydrology, characterized in that, The method steps include: S100, Multi-source data acquisition and standardization processing: Acquire water resources data for the target watershed, register all water resources data in a coordinate system and resample them to a uniform spatial resolution to ensure that the data layers are fully aligned in space, forming a rasterized dataset. S200, Assessing the baseline status of watershed water conservation: The water conservation of each grid unit is quantified to form a spatial distribution map of the baseline water conservation. S300 identifies and quantifies the potential for water conservation enhancement, including: S310. Determine the current land use type of the grid unit, the type including repairable and non-repairable, and define the land use type of all repairable plots as the ideal conservation type; S320. Calculate the ideal scenario water conservation capacity: Determine the ideal scenario water conservation capacity based on the current water conservation status of each grid cell. S330. Calculate the benefit increment: Subtract the grid subtraction between the quantitative results of water conservation in repairable and non-repairable scenarios, calculate the water conservation benefit increment of each grid unit, and finally generate a spatial distribution map of the water conservation benefit increment. S400, prioritizing key repair plaques and matching them with targeted technologies, including: S410, Patch Sort and Screen:

1. Perform connectivity analysis on the grids with benefit increment > 0 in the spatial distribution map of water conservation benefit increment to form several patches to be repaired.

2. Sort in descending order based on the "total benefit increment" of each patch; the "total benefit increment" is the sum of the benefit increment values ​​of all grids within the patch multiplied by the grid area; Third, based on the preset total investment or total restoration target, select the top-ranked patches from the sorting list as key restoration patches; S420, Intelligent Matching of Targeted Techniques: For each selected repair plaque, the sources of its incremental benefits are analyzed, including: Rule 1: If the current surface velocity coefficient of the patch is high, it indicates that the problem is exposed surface and fast runoff velocity, and then the "vegetation reconstruction project" will be automatically matched. Rule 2: If the soil saturation hydraulic conductivity of the patch is low or the soil depth is insufficient, it indicates that the problem is poor soil infiltration capacity, and "Land Remediation Project" will be automatically matched. Rule 3: If both Rule 1 and Rule 2 are met, then "composite project" is matched. S500 generates and outputs a precise repair plan.

2. The precise lifting method according to claim 1, characterized in that, The water resources data for the target watershed are obtained from external data sources. The water resources data includes digital elevation model (DEM) data, multi-year average rainfall raster data, soil type and physicochemical property data, and current land use / cover type raster data. The soil type and physicochemical property data includes sand / clay content and soil depth.

3. The precise lifting method according to claim 2, characterized in that, The external data sources include: meteorological databases, geographic information databases, and remote sensing image databases.

4. The precise lifting method according to claim 1, characterized in that, The formula for quantitatively calculating water source conservation is as follows: ； ； in: WC baseline Water conservation capacity under baseline conditions (mm); Y: Annual average water yield (mm), calculated by the InVEST model based on rainfall, evapotranspiration, land use type and soil data, is a known calculation quantity; V baseline Surface velocity coefficient (cm / hr) under baseline conditions, extracted from the parameter database based on the current land use type, is a known quantity; K sat Soil saturated hydraulic conductivity (cm / d) is calculated based on the content of soil sand and clay particles using transfer functions such as Cosby, and is a known quantity for calculation. D: Topographic index (dimensionless), calculated from DEM data, reflects the impact of topography and soil depth on water storage capacity, and is a known calculation quantity; S d Soil depth (mm), extracted from the parameter library based on the current soil type, is a known quantity; S p : Percentage slope (%), calculated from DEM data, is a known calculation quantity.

5. The precise lifting method according to claim 1, characterized in that, In step S420, rule 1: the current surface velocity coefficient is high, meaning the velocity is > 1000 cm / hr, and the "vegetation reconstruction project" is such as planting trees or grass; Rule 2: The "land consolidation project" mentioned above refers to the application of topsoil and organic fertilizer. Rule 3: The "composite project" refers to land reclamation plus vegetation reconstruction.

6. The precise lifting method according to claim 1, characterized in that, In step S500, the precise remediation plan includes: a patch distribution map and a detailed engineering task list. The patch distribution map includes a geographical location map of the patches and an attribute table for each patch. The attribute table includes: number, area, expected benefit increment, and recommended technical measures. The output is visualized and formatted.

7. The precise lifting method according to claim 1, characterized in that, A water conservation precision improvement system is established based on a computer server. The system includes: a data acquisition module, a watershed benchmark assessment module, a restoration potential identification module, a precision restoration planning module, and a scheme output module, which receives data from external data sources. The data acquisition module is configured to execute step S100; The watershed benchmark assessment module is configured to execute step S200; The repair potential identification module is configured to execute step S300; The precise repair planning module is configured to execute step S400; The solution output module is configured to execute step S500.

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