Urban ecological unit scale water source conservation function evolution simulation system

Abstract

The invention discloses an urban ecological unit scale water conservation function evolution simulation system, relates to the technical field of ecological hydrological simulation, and is technically characterized in that the system combines an InVEST model, a water balance method, a geographic detector and a Moran index to construct a multi-source data fused water conservation evaluation method. According to the system, key factors are obtained through remote sensing data and geostatistical data, a mapping model between water yield and actually measured runoff depth is established based on a regression fitting relation, and a spatial heterogeneity analysis and factor interaction detection mechanism is introduced, so that time sequence dynamic simulation and spatial pattern recognition of a water conservation function are realized. The system can be widely applied to the fields of urban water resource regulation and control, ecological space planning, hydrological safety evaluation and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of ecohydrological simulation, and in particular to a water conservation function evolution simulation system at the scale of urban ecological units. Background Art

[0002] Water conservation is a crucial component of ecosystem services, directly linked to the stability of urban ecological security, the improvement of water resource regulation and storage capacity, and the coordinated development of the ecological environment during urbanization. In the context of rapid urbanization, the continuous evolution of land use patterns and the increasing intensity of human interference have significantly impacted ecosystem structure and function, thereby altering the spatiotemporal distribution of water conservation services. Therefore, it is urgent to develop an integrated system capable of dynamic monitoring, factor identification, change simulation, and functional optimization to fine-tune the simulation and dynamic regulation of water conservation functions at the scale of urban ecological units.

[0003] Currently, quantitative simulations of water conservation primarily rely on process models and empirical models. The InVEST model, based on the water balance principle, boasts excellent scalability and regional adaptability, enabling the estimation of indicators such as water yield and water conservation with limited input data. Furthermore, by integrating heterogeneous data from multiple sources, including DEM (digital elevation model), LUCC (land use data), soil factors (Ksat, PAWC, Sd), and climate factors (Pre, Pet, Temp), it effectively supports the modeling of hydrological processes at the urban scale. However, traditional models often remain at the static analysis level, making it difficult to reveal the driving mechanisms and spatial interactions that influence the evolution of water conservation.

[0004] In recent years, Geodetector has been widely used to identify and analyze the drivers of ecosystem services. It can quantify the explanatory power (q-value) of natural and social factors on spatial heterogeneity, providing effective support for mechanism diagnosis. Furthermore, methods such as the Moran index, spatial regression, and zonal mapping can further expand the system's spatial representation and trend identification capabilities.

[0005] However, existing technologies still have the following shortcomings: there is a lack of algorithmic linkage mechanism between models, making it difficult to achieve a closed loop of the entire process from data processing, function estimation to spatial evolution and mechanism attribution; there is a lack of systematic and comprehensive assessment of the sensitivity, driving mechanism, and spatial differentiation of the evolution of water conservation functions.

[0006] Therefore, it is urgent to develop a water conservation function evolution simulation system at the scale of urban ecological units to solve the above problems. Summary of the Invention

[0007] The purpose of the present invention is to solve the technical problems raised in the above background technology and provide a water conservation function evolution simulation system at the scale of urban ecological units. The above purpose of the present invention is achieved as follows: The water conservation function evolution simulation system at the urban ecological unit scale includes: Data preprocessing module: used to obtain and standardize multi-source spatial data, including precipitation , potential evapotranspiration , Percent Slope , soil depth , terrain index , soil saturated hydraulic conductivity , plant effective water content and land use types , and the watershed area derived from the digital elevation model , and unify the grid resolution to 30m 30m; Annual water production calculation module: used for calculation of annual water production based on precipitation Actual evapotranspiration The difference between Grid cells in the Annual water yield under land use type ; Water conservation calculation module: used to combine annual water production , terrain index , soil saturated hydraulic conductivity , Percent Slope , catchment area , flow rate factor , calculate the water conservation capacity of the grid unit ; Driving Factor Analysis Module: used to calculate explanatory power based on geographic detectors quantify the impact of various natural and social factors on the spatial pattern of water conservation function and evaluate the interaction between factors; Spatial Pattern Analysis Module: for bivariate global Moran's index , analyze the positive and negative correlation between each driving factor and water conservation capacity in space; Output module: used to generate water conservation spatial distribution map, geographic detector factor identification result bar chart, geographic detector two-factor interactive detection result matrix chart.

[0008] As the preferred technical solution of the present invention, the annual water production The calculation formula is: ; in, : annual water yield of the xth grid cell under the jth land cover type; : actual evapotranspiration; : precipitation.

[0009] As a preferred technical solution of the present invention, the actual evaporation rate The calculation according to the Budyko function is as follows: ; in, : Budyko aridity index, defined as the ratio of potential evapotranspiration to precipitation; , Non-physical parameters that characterize natural climate-soil characteristics. is the volumetric plant available water content; is the annual precipitation of the xth grid cell under the jth land cover type.

[0010] As a preferred technical solution of the present invention, the volumetric plant effective water content The calculation formula is: ; in, : depth of bedrock layer; : plant root depth; ;No. The plant available water content of the unit.

[0011] As a preferred technical solution of the present invention, the effective water content of the plant It can be estimated by the following empirical formula: ; in, 、 、 : These are the soil sand ratio, silt ratio, clay ratio, and organic matter content ratio data extracted based on HWSD version 1.1 soil data.

[0012] As the preferred technical solution of the present invention, the water conservation capacity The calculation formula is: ; in, is the water conservation capacity; is the flow rate coefficient; is the topographic index; is the saturated hydraulic conductivity of soil; The water production.

[0013] As a preferred technical solution of the present invention, the terrain index The calculation formula is: ; in, Cumulative grid number for catchment flow; is the soil depth; is the percentage slope.

[0014] As a preferred technical solution of the present invention, the saturated hydraulic conductivity It can be estimated by the following empirical formula: ; in, 、 : The ratio of sand to clay in soil.

[0015] As a preferred technical solution of the present invention, the geographic detector analysis module The value is calculated as: ; The q value represents the explanatory power of each influencing factor on water conservation, and its value range is [0, 1]. The larger the q value, the stronger the influence of each influencing factor on the evolution of water conservation, and vice versa. h = 1, 2, ... L, represents the classification number of each influencing factor, i.e., classification or partition. and N are the number of samples in region h and the whole region, respectively; and are the variances of region h and the entire region, respectively.

[0016] As a preferred technical solution of the present invention, the two-variable global Moran index value The calculation formula is: ; Where I is the bivariate global Moran index value, n is the number of regions, and W is the total spatial weight The sum of and are the variable values ​​of region i and region j respectively, and are the means of variables x and y, respectively. is the spatial weight between region i and region j.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention integrates multi-source spatial data processing modules to standardize multiple ecological and topographic data types, including precipitation, evapotranspiration, slope, soil depth, soil type, vegetation cover, and land use, achieving spatial accuracy down to a 30m x 30m grid scale. Compared to traditional methods, this system establishes a more realistic dynamic water conservation assessment system, accurately calculating regional annual water volume changes and conservation capacity distribution, significantly improving model accuracy and spatial adaptability.

[0018] 2. This system incorporates a factor detection and interaction analysis module, comprehensively considering variables such as topography, climate, vegetation, land use, population density, and economic indicators to systematically analyze their impact on the spatial pattern of water conservation and their interactive effects. Through factor sensitivity assessment and temporal response analysis, it effectively reveals the dominant factors and trends in water conservation changes across different years and regions, providing a scientific basis for ecological space optimization.

[0019] 3. The system's output module supports the generation of multi-scale visualization images, including spatial distribution maps, evolution trend charts, sensitivity factor histograms, and interaction heat maps, enabling governments and planning agencies to intuitively understand regional eco-hydrological changes. The system can also generate water conservation optimization recommendations and regulatory priority zone demarcation reports, applicable to practical needs such as urban water resource management, ecological protection redline demarcation, and national land space planning, and has promising prospects for widespread application. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a fitting diagram of the annual average water production depth and the measured runoff depth of the present invention; Figure 2 It is the spatial distribution map of water production from 2010 to 2023 of the present invention; Figure 3 This is the spatial distribution map of water conservation capacity from 2010 to 2023 of the present invention; Figure 4 is a histogram of the geographical detector factor identification results of the present invention; Figure 5 It is a matrix diagram of the double-factor interactive detection results of the geographic detector of the present invention; Figure 6 It is a point-line diagram of the spatial correlation coefficient values ​​between water conservation and influencing variables in different periods of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0022] The implementation of the present invention is described in detail below with reference to specific embodiments.

[0023] Example 1: The implementation plan of the present invention provides a water conservation function evolution simulation system at the scale of urban ecological units. Relying on the combination of remote sensing data, geographic spatial information and eco-hydrological models, it realizes the simulation and evaluation of the spatiotemporal evolution of water conservation functions by constructing modules such as data preprocessing, annual water production calculation, water conservation calculation, driving factor analysis, spatial pattern analysis and output display.

[0024] The system uses a 30m×30m resolution grid as the basic analysis unit and is suitable for water conservation monitoring and simulation analysis in cities, suburbs or complex ecological units.

[0025] In the data preprocessing module, the standardized spatial data including precipitation is first extracted. , potential evapotranspiration , Percent Slope , soil depth , terrain index , soil saturated hydraulic conductivity , effective water content of vegetation , land use type After resampling, the remote sensing data was unified to a grid resolution of 30 meters to form a standardized input data grid.

[0026] Annual water production module is based on precipitation Actual evapotranspiration Difference calculation of annual water production within the grid cell , the calculation formula is as follows: ; in, According to the Budyko function modeling calculation, the expression is: ; in, : Budyko aridity index, defined as the ratio of potential evapotranspiration to precipitation; , Non-physical parameters to characterize natural climate-soil properties; is the annual precipitation of the xth grid cell under the jth land cover type; Volumetric plant available water content , the specific calculation is: ; in, : depth of bedrock layer; : plant root depth; : No. The plant available water content of the unit.

[0027] Plant available water content It can be estimated by the following empirical formula: ; in, 、 、 : These are the soil sand ratio, silt ratio, clay ratio, and organic matter content ratio data extracted based on HWSD version 1.1 soil data.

[0028] In the water conservation calculation module, water conservation Combined with annual water production , terrain index , hydraulic conductivity , slope percentage , flow rate factor , catchment area The calculation expression is: ; in: is the water conservation capacity; is the flow rate coefficient; is the topographic index; is the saturated hydraulic conductivity of soil; The water production.

[0029] The terrain index is calculated as follows: ; in, Cumulative grid number for catchment flow; is the soil depth; is the percentage slope.

[0030] Saturated hydraulic conductivity It can be estimated by the following empirical formula: ; in, 、 : The ratio of sand to clay in soil.

[0031] In the driving factor analysis module, the system calculates the explanatory power of each factor on the spatial differences in water conservation function through geographic detectors. The test results are expressed as q statistics, and the calculation formula is: ; The q value represents the explanatory power of each influencing factor on water conservation, and its range is [0, 1]. The larger the q value, the stronger the influence of each influencing factor on the evolution of water conservation, and vice versa. h = 1, 2, ... L represents the classification number of each influencing factor, i.e., classification or partition. and N are the number of samples in region h and the whole region, respectively; and are the variances of region h and the entire region, respectively.

[0032] Moran's index The spatial aggregation and spatial correlation of water conservation are analyzed, and the calculation formula is: ; Where I is the bivariate global Moran index value, n is the number of regions, and W is the total spatial weight The sum of and are the variable values ​​of region i and region j respectively, and are the means of variables x and y, respectively. is the spatial weight between region i and region j.

[0033] Finally, the output module generates water conservation spatial distribution map, factor The water conservation evolution pattern and its driving mechanism at the scale of urban ecological units are displayed in images and numerical forms, providing scientific support for the assessment of urban ecological security pattern and planning optimization.

[0034] Example 2: This system takes typical urban ecological units as research objects, constructs a water conservation function evolution simulation system suitable for 30m spatial accuracy, and conducts empirical application in Chengdu.

[0035] The system consists of a data processing module, a water production calculation module, a water conservation calculation module, a driving factor analysis module, a spatial pattern analysis module and a result output module.

[0036] The system uses a 30m×30m resolution grid as the basic analysis unit and is suitable for water conservation monitoring and simulation analysis in cities, suburbs or complex ecological units. In the data preprocessing module, the standardized spatial data is first extracted, including precipitation. , potential evapotranspiration , Percent Slope , soil depth , terrain index , soil saturated hydraulic conductivity , effective water content of vegetation After resampling, the remote sensing data was unified to a grid resolution of 30 meters to form a standardized input data grid. The study area covers 19 administrative districts in Chengdu, and the terrain gradually decreases from west to east (e.g. Figure 1 shown).

[0037] Water yield is defined as the difference between precipitation and actual evapotranspiration in each grid cell, and the formula is: ; The actual evapotranspiration Approximate calculation through Budyko function: ; in, : Budyko aridity index, defined as the ratio of potential evapotranspiration to precipitation; : Non-physical parameters to characterize natural climate-soil properties; is the annual precipitation of the xth grid cell under the jth land cover type; in ; The empirical formula for PAWC is as follows: ; The simulation results were compared with the measured runoff depth (e.g. Figure 1 As shown), it shows a high fitting relationship, and the coefficient of determination , verifying the credibility of the model.

[0038] The system further calculates the water conservation capacity of each grid cell based on the following formula: ; The terrain index is: ; The empirical expression of saturated hydraulic conductivity is: ; Depend on Figure 3 Monitoring data show that the total water conservation capacity of Chengdu in 2010, 2013, 2015, 2018, 2020 and 2023 is 3.89×10 8 m³、4.44×10 8 m³、2.75×10 8 m³、5.36×10 8 m³、4.97×10 8 m³ and 2.38×10 8m³. Correspondingly, the regional average water conservation depths were 32.09 mm, 36.64 mm, 22.72 mm, 44.20 mm, 41.05 mm, and 19.63 mm, respectively. Time series analysis shows that the changing trend of total water conservation is highly consistent with water production. Between 2010 and 2023, total water conservation decreased by 38.82%, and the regional average depth decreased by 12.46 mm.

[0039] from Figure 3 As can be seen from the data, water conservation capacity exhibits strong spatial heterogeneity within the study area, with significant differences between low-value areas and high-value areas, and the spatial pattern evolves dynamically over time. From 2010 to 2020, the spatial distribution pattern remained largely consistent, exhibiting a stratified structure dominated by an elevation gradient. High-value areas were concentrated in administrative units surrounding the Longquan and Longmen Mountains (average depth ≥ 23.79 mm). This region is primarily mountainous and hilly, with high vegetation cover and a well-developed root system enhancing water retention. Low-value areas are located in the central urban plains. Due to the dominant land use of cultivated land and construction land, soil permeability is weak and water-holding capacity is low. By 2023, the spatial pattern shifted significantly, exhibiting a decreasing gradient from southwest to northeast. The low-value area expanded dramatically, covering approximately three-quarters of the city's administrative area. This sudden change is closely related to climate fluctuations, vegetation dynamics, and evolving soil properties. Overall, Chengdu's water conservation function showed a systematic decline between 2010 and 2023, and its spatiotemporal evolution was driven by a combination of topography, land use, vegetation cover, and climate factors.

[0040] To identify the key factors affecting water conservation, the system integrates the geographic detector algorithm to quantitatively analyze the explanatory power of all factors. The q value is calculated as follows: ; The analysis results show that the q values ​​of natural factors such as DEM, Ksat, and Slope are significantly higher than those of social factors. The maximum value of DEM can reach 0.131. The results of geographic detectors show that ( Figure 4 ), natural environmental factors significantly outweigh socioeconomic factors in explaining the spatial pattern of water conservation in Chengdu. Among them, DEM is the strongest driving factor, with a multi-year average q-value of 0.131, revealing the central control role of elevation gradients on water redistribution. Ksat comes in second, with a multi-year average q-value of 0.120, reflecting the crucial influence of soil infiltration on water conservation. The multi-year average q-values ​​of other factors are as follows: Pre (0.115) > Temp (0.113) > Pet (0.102) > GDP=Slope (0.099) > PAWC (0.092) > SD (0.079) > TI (0.075) > POP (0.057), indicating that socioeconomic factors generally have weak explanatory power and that human activities have a limited direct impact.

[0041] Finally, the system identifies the spatial correlation between each factor and the conservation pattern through the bivariate Moran index, which is calculated as follows: ; The results showed that factors such as Ksat, TI, and Pre had positive spatial clustering with conservation values ​​(I>0.4). Two-factor interaction detection results ( Figure 5 )show: (1) The interaction test results between the influencing factors in different years all showed a double factor or nonlinear enhanced interaction type, and the interaction effect of any double factor on the change of water conservation capacity was stronger than that of a single factor.

[0042] (2) Among socioeconomic factors, GDP had the strongest interaction with Slope and Ksat, with q values ​​ranging from 0.113 to 0.263 and 0.109 to 0.263, respectively. POP had the strongest interaction with PAWC, with q values ​​ranging from 0.09 to 0.264. This indicates that the spatial pattern of water conservation is mainly shaped by the synergistic effects of socioeconomic factors and topographic and soil factors.

[0043] (3) Among the climate factors, Pre had a strong interaction with PAWC, with q values ​​ranging from 0.107 to 0.264. Among the soil factors, SD had the weakest interactions with Pet and Temp, with q values ​​ranging from 0.081 to 0.164 and 0.08 to 0.164, respectively.

[0044] This shows that the urban water conservation function is highly dependent on the synergistic effect of precipitation input and the soil's ability to retain precipitation, and the conservation function of deep soil is relatively stable and is less affected by short-term atmospheric evaporation and temperature fluctuations.

[0045] This study used the water conservation capacity and influencing factor independent variable data of 19 municipal and county-level administrative districts in Chengdu as samples, and used the bivariate global Moran index in Geoda software to conduct a spatial correlation analysis on the water conservation function of Chengdu from 2010 to 2023. Figure 6(as shown in Figure 2) show that: (1) except for 2013, when precipitation showed a negative correlation with water conservation, the global Moran index was greater than zero in all other years. This indicates that precipitation had an inhibitory effect on the spatial distribution pattern of water conservation in 2013. (2) The global spatial autocorrelations between the driving factors and water conservation showed similar trends in different years. Among them, the global Moran index averages of Ksat (I-mean = 0.521), Pre (I-mean = 0.449), Pet (I-mean = 0.431), SD (I-mean = 0.398), Slope (I-mean = 0.223) and DEM (I-mean = 0.185) were all greater than zero, indicating that these factors had a strong positive correlation with water conservation in space, among which the driving effects of soil change factors (Ksat, SD) and climate change factors (Pre, Pet) were the most significant. In contrast, the global Moran's index averages of GDP (I-mean = -0.542), POP (I-mean = -0.386), PAWC (I-mean = -0.288), TI (I-mean = -0.251), and Temp (I-mean = -0.176) were all less than zero, indicating that these factors were spatially strongly negatively correlated with water conservation, among which the socioeconomic factor had the most significant inhibitory effect.

[0046] In summary, the impact mechanism of various influencing factors on the spatial distribution pattern of water conservation in Chengdu during different periods is relatively complex, mainly driven and inhibited by soil factors, climate factors, and socioeconomic factors, and accompanied by a significant influence of topographic factors.

[0047] Water conservation, a key indicator for assessing urban ecosystem services and a core component of water regulation, plays a vital role in regulating the hydrological cycle, alleviating water scarcity, enhancing urban resilience, and improving water quality. However, this function is susceptible to both natural environmental factors (such as climate change, topography, soil properties, and vegetation cover) and anthropogenic activities (such as socioeconomic development, land use change, and local policies), and its capacity often declines. Previous studies have confirmed that the spatial pattern of water conservation is shaped by the synergistic effects of multiple driving factors, primarily climate, soils, topography, and socioeconomic factors, consistent with the findings of this study.

[0048] According to the factor detection results ( Figure 4), the study found that elevation in the topographic factor and saturated hydraulic conductivity in the soil factor are the dominant factors driving the spatial differentiation of water conservation. This finding is highly consistent with the geographical environment characteristics of Chengdu. High-altitude and steep slope areas are mainly distributed in the western mountainous and hilly areas, where vegetation coverage is high. Vegetation roots effectively hold the soil, and abundant precipitation (especially in the rainy season) can promote soil water transfer and accelerate infiltration through a connected root network, significantly improving soil water holding capacity and permeability, thereby enhancing the water conservation function of the region. In contrast, low-altitude areas are mostly urban centers with highly intensive human activities. Although Chengdu as a whole has a humid climate and abundant annual precipitation, intensive urban development and construction in recent years have led to the continuous expansion of built-up areas and a significant increase in the proportion of impervious surfaces, which has seriously hindered the infiltration of rainwater and caused a large amount of it to be converted into surface runoff. In addition, the vegetation coverage in the central urban area is relatively low, and the soil structure has been significantly damaged due to compaction and hardening. In addition, the area of ​​natural water systems and wetlands has continued to shrink, which has jointly weakened the region's water conservation capacity, resulting in low water conservation capacity.

[0049] Based on the two-factor interaction detection results ( Figure 5 ) found that the coupled effects of human activities and natural environmental factors during urban economic development have profoundly impacted the spatial pattern of water conservation. Specifically, among socioeconomic factors, GDP has strong interactive effects with both slope and Ksat, while population density (POP) has a significant interactive effect with PAWC. This interactive pattern is primarily due to the spatial selectivity of urban development: the location and development intensity of areas with high GDP and high population density are significantly constrained by topography (slope) and soil infiltration capacity (Ksat). For example, flat areas, due to their ease of development, are often the preferred choice for large-scale urban construction. This development process significantly weakens regional water conservation by altering land use (such as increasing impervious surfaces) and disrupting soil structure (such as compaction). In contrast, areas with steep slopes, where development is more difficult and costly, retain more natural habitats, thus maintaining relatively superior water conservation conditions.

[0050] The results of bivariate spatial autocorrelation analysis ( Figure 6 ) further confirmed that soil change factors (such as Ksat), climate change factors (such as Pre and Pet), and topographic factors (such as Slope) have significant positive spatial correlations with the spatial distribution of water conservation. Socioeconomic factors (especially GDP) show significant negative spatial correlations, indicating that their expansion is spatially exclusive of areas with high water conservation values.

[0051] Overall, the spatial pattern of water conservation is not dominated by a single factor, but is formed through the complex synergy of socio-economic development factors and key natural environmental factors (topography, soil, and climate).

[0052] This system can output a water conservation function distribution map, a bar chart of geographic detector factor identification results, and a matrix chart of geographic detector dual-factor interactive detection results. It is widely applicable to the evolution identification of hydrological service functions in urban ecosystems, spatial planning optimization, and resilience construction assessment scenarios.

[0053] Through the collaborative calculation of the above modules, the system realizes the simulation and quantitative evaluation of the evolution of water conservation function at the scale of urban ecological units under different driving factors. It has good spatial adaptability and algorithm portability, and is suitable for multiple scenarios such as urban hydrological regulation, ecological infrastructure site selection, and green city planning.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The water conservation function evolution simulation system at the urban ecological unit scale is characterized by: The system includes: Data preprocessing module: used to obtain and standardize multi-source spatial data, including precipitation , potential evapotranspiration , Percent Slope , soil depth , terrain index , soil saturated hydraulic conductivity , plant effective water content and land use types , and the watershed area derived from the digital elevation model , and unify the grid resolution to 30m×30m; Annual water production calculation module: used for calculation of annual water production based on precipitation Actual evapotranspiration The difference between Grid cells in the Annual water yield under land use type ; Water conservation calculation module: used to combine annual water production , terrain index , soil saturated hydraulic conductivity , Percent Slope , catchment area , flow rate factor , calculate the water conservation capacity of the grid unit ; Driving Factor Analysis Module: used to calculate explanatory power based on geographic detectors quantify the impact of various natural and social factors on the spatial pattern of water conservation function and evaluate the interaction between factors; Spatial Pattern Analysis Module: for bivariate global Moran's index , analyze the positive and negative correlation between each driving factor and water conservation capacity in space; Output module: used to generate water conservation spatial distribution map, geographic detector factor identification result bar chart, geographic detector two-factor interactive detection result matrix chart.

2. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 1 is characterized in that: The annual water production The calculation formula is: ； in, : annual water yield of the xth grid cell under the jth land cover type; : actual evapotranspiration; : precipitation.

3. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 2 is characterized in that: The actual evapotranspiration The calculation according to the Budyko function is as follows: ； in, : Budyko aridity index, defined as the ratio of potential evapotranspiration to precipitation; , Non-physical parameters that characterize natural climate-soil characteristics. is the volumetric plant available water content; is the annual precipitation of the xth grid cell under the jth land cover type.

4. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 3 is characterized in that: The volume of plant effective water content The calculation formula is: ； in, : depth of bedrock layer; : plant root depth; ;No. The plant available water content of the unit.

5. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 4 is characterized in that: The effective water content of the plant It can be estimated by the following empirical formula: ； in, 、 、 : These are the soil sand ratio, silt ratio, clay ratio, and organic matter content ratio data extracted based on HWSD version 1.1 soil data.

6. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 1 is characterized in that: The water conservation capacity The calculation formula is: ； in, is the water conservation capacity; is the flow rate coefficient; is the topographic index; is the saturated hydraulic conductivity of soil; The water production.

7. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 6 is characterized in that: The terrain index The calculation formula is: ； in, Cumulative grid number for catchment flow; is the soil depth; is the percentage slope.

8. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 6 is characterized in that: The saturated hydraulic conductivity It can be estimated by the following empirical formula: ； in, 、 : The ratio of sand to clay in soil.

9. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 1 is characterized in that: The geographic detector analysis module The value is calculated as: ； The q value represents the explanatory power of each influencing factor on water conservation, and its value range is [0, 1]. The larger the q value, the stronger the influence of each influencing factor on the evolution of water conservation, and vice versa. h = 1, 2, ... L, represents the classification number of each influencing factor, i.e., classification or partition. and N are the number of samples in region h and the whole region, respectively; and are the variances of region h and the entire region, respectively.

10. The water conservation function evolution simulation system at the urban ecological unit scale according to claim 1 is characterized in that: The bivariate global Moran's index value The calculation formula is: ； Where I is the bivariate global Moran index value, n is the number of regions, and W is the total spatial weight The sum of and are the variable values ​​of region i and region j respectively, and are the means of variables x and y, respectively. is the spatial weight between region i and region j.

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