Urban ecological unit scale water source conservation function evolution simulation system

Abstract

The application discloses a city ecological unit scale water source conservation function evolution simulation system and relates to the technical field of ecological hydrology simulation, and its technical points are as follows: the system combines an InVEST model, a water balance method, a geographic detector and a Moran index to construct a water source conservation evaluation method of multi-source data fusion.The system obtains key factors through remote sensing data and geostatistical data, establishes a mapping model between water yield and measured runoff depth based on a regression fitting relationship, and introduces spatial heterogeneity analysis and factor interaction detection mechanism to realize time sequence dynamic simulation and spatial pattern identification of water source conservation function.The system can be widely applied to the fields of city water resource regulation, ecological space planning and hydrological safety evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecological hydrological simulation, in particular to a water conservation function evolution simulation system at the scale of urban ecological units. BACKGROUND

[0002] Water conservation function is an important part of ecosystem services, directly related to the stability of urban ecological security pattern, the improvement of water resources regulation capacity and the coordinated development of ecological environment in the process of urbanization. Under the background of rapid urbanization, the continuous evolution of land use pattern and the increasing intensity of human activity disturbance have a significant impact on the structure and function of the ecological system, and thus change the spatial and temporal distribution pattern of water conservation services. Therefore, it is urgent to build an integrated system with dynamic monitoring, factor identification, change simulation and function optimization capability to finely simulate and dynamically regulate the water conservation function at the scale of urban ecological units.

[0003] At present, the quantitative simulation of water conservation function mainly relies on two types of process models and empirical models. Among them, the InVEST model based on water balance principle has good scalability and regional adaptability, and can estimate indicators such as water yield and water conservation capacity under limited input data conditions. At the same time, combined with DEM (Digital Elevation Model), LUCC (land use data), soil factors (Ksat, PAWC, Sd) and climate factors (Pre, Pet, Temp), etc. Multi-source heterogeneous data can effectively support urban scale hydrological process modeling. However, traditional models are mostly limited to static analysis, and it is difficult to reveal the driving mechanism and spatial interaction of water conservation function evolution.

[0004] In recent years, Geodetector has been widely used in ecological service driving factor identification and interaction analysis, which can quantify the explanatory power (q value) of natural and social factors to spatial heterogeneity, providing effective support for mechanism diagnosis. In addition, Moran index, spatial regression, partition mapping and other methods can further expand the spatial expression and trend identification capability of the system.

[0005] However, the existing technology still has the following shortcomings: there is a lack of algorithm linkage mechanism among models, making it difficult to realize the full-process closed loop from data processing, function estimation to spatial evolution and mechanism attribution; there is a lack of systematic comprehensive evaluation of the sensitivity, driving mechanism and spatial differentiation of water conservation function evolution.

[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

[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:

[0008] The water conservation function evolution simulation system at the urban ecological unit scale includes:

[0009] 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;

[0010] 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 ;

[0011] 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 ;

[0012] 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;

[0013] 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;

[0014] 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.

[0015] As a preferred technical scheme of the present application, the annual water yield The calculation formula is:

[0016] ;

[0017] Among them, : the annual water yield of the xth grid unit under the jth land cover type; : actual evapotranspiration; : precipitation.

[0018] As a preferred technical scheme of the present application, the actual evapotranspiration According to the Budyko function, it is calculated as follows: ;

[0019] Among them, : Budyko dry index, defined as the ratio of potential evapotranspiration to precipitation; , is a non-physical parameter representing natural climate-soil characteristics, is the volumetric plant available water content; is the annual precipitation of the xth grid unit under the jth land cover type.

[0020] As a preferred technical scheme of the present application, the volumetric plant available water content The calculation formula is:

[0021] ;

[0022] Among them, : bedrock depth; : plant root depth; : the plant available water content of the xth grid unit.

[0023] As a preferred technical scheme of the present application, the plant available water content It can be estimated by the following empirical formula:

[0024] ;

[0025] Among them, , , : soil sand, silt, clay, and organic matter content data extracted based on HWSD version 1.1 soil data, respectively.

[0026] As a preferred technical scheme of the present application, the water source conservation amount The calculation formula is:

[0027] ​ ;

[0028] wherein, is the water conservation amount; is the flow velocity coefficient; is the terrain index; is the saturated hydraulic conductivity of soil; is the water yield.

[0029] As a preferred technical solution of the present application, the terrain index is calculated by the following formula:

[0030] ;

[0031] wherein, is the number of accumulated grid of catchment area confluence; is the soil depth; is the percentage slope.

[0032] As a preferred technical solution of the present application, the saturated hydraulic conductivity can be estimated by the following empirical formula:

[0033] ;

[0034] wherein, , : the ratio of sand particles to clay particles in soil.

[0035] As a preferred technical solution of the present application, the calculation formula of the value in the geographic detector analysis module is as follows:

[0036] ;

[0037] wherein, q represents the explanation ability of each influencing factor to the water conservation amount, the value range is [0, 1], the greater the q value, the stronger the influence of each influencing factor on the evolution of the water conservation amount, and vice versa; h=1, 2, ……L, representing the classification number of each influencing factor, i.e. classification or partition; and N are respectively the sample numbers of the region h and the whole region; and are respectively the variances of the region h and the whole region.

[0038] As a preferred technical solution of the present application, the calculation formula of the bivariate global Moran's index value is as follows:

[0039] ;

[0040] wherein, I is the bivariate global Moran's index value, n is the number of regions, and W is all spatial weights 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.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 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.

[0043] 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.

[0044] 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

[0045] Figure 1 This is a fitting diagram of the annual average water production depth and the measured runoff depth of the present invention;

[0046] Figure 2 It is the spatial distribution map of water production from 2010 to 2023 of the present invention;

[0047] Figure 3 This is the spatial distribution map of water conservation capacity from 2010 to 2023 of the present invention;

[0048] Figure 4 is a histogram of the geographical detector factor identification results of the present invention;

[0049] Figure 5It is a double-factor interaction detection result matrix chart of the geographical detector of the application;

[0050] Figure 6 It is a spatial correlation coefficient value point line chart between water conservation and influence variables in different periods of the application. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical scheme and advantages of the application more clear and understandable, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described here are only used to explain the application and do not limit the application.

[0052] The implementation of the application is described in detail below in combination with specific examples.

[0053] Example 1: The embodiment of the application provides a water conservation function evolution simulation system at the scale of urban ecological units, which is based on the combination of remote sensing data, geographic spatial information and ecological hydrological model, and realizes the spatio-temporal evolution simulation and evaluation of water conservation function through the construction of data preprocessing, annual water yield calculation, water conservation amount calculation, driving factor analysis, spatial pattern analysis and output display modules.

[0054] The system takes a 30m x 30m resolution grid as the basic analysis unit, and is suitable for water conservation monitoring and simulation analysis of urban, suburban or composite ecological units.

[0055] In the data preprocessing module, first, the standardized spatial data is extracted, including precipitation , potential evapotranspiration , percentage slope , soil depth , terrain index , soil saturated hydraulic conductivity , effective water content of vegetation , land use type , etc. After resampling processing of remote sensing data, the grid resolution is unified to 30 meters to form a standardized input data grid.

[0056] The annual water yield module calculates the annual water yield in the grid unit based on the difference between precipitation and actual evapotranspiration , and the calculation formula is as follows: ;

[0057] Among them, According to the Budyko function modeling calculation, the expression is as follows: ;

[0058] Among them, ​: Budyko dryness index, defined as the ratio of potential evapotranspiration to precipitation; , : non-physical parameter representing natural climate-soil characteristics; : annual precipitation of the xth grid cell under the jth land cover type;

[0059] : volumetric plant available water content , which is calculated as:

[0060] ;

[0061] : bedrock layer depth; : plant root depth; : plant available water content of the jth land cover type in the xth grid cell. : plant available water content of the jth land cover type in the xth grid cell.

[0062] : plant available water content : plant available water content of the jth land cover type in the xth grid cell.

[0063] ;

[0064] : soil sand, silt, clay, and organic matter content data extracted from HWSD version 1.1 soil data, respectively. 、 、 : soil sand, silt, clay, and organic matter content data extracted from HWSD version 1.1 soil data, respectively.

[0065] In the water source conservation calculation module, the water source conservation is estimated in combination with annual water yield , topographic index , saturated hydraulic conductivity , slope percentage , flow velocity factor , catchment area , and the like, and the calculation expression is:

[0066] ;

[0067] : water source conservation; : flow velocity factor; : topographic index; : saturated hydraulic conductivity of soil; : water yield. : topographic index is calculated as follows:

[0068]

[0069] ; : topographic index is calculated as follows:

[0070] ​​Grid number for catchment accumulation; Soil depth; Slope percentage.

[0071] Saturated hydraulic conductivity It can be estimated by the following empirical formula:

[0072]

[0073] Where, , : the ratio of sand particles to clay particles in the soil.

[0074] In the driving factor analysis module, the system calculates the explanatory power of each factor to the spatial difference of water conservation function through the geographic detector, and the detection result is expressed by q statistics, and the calculation formula is:

[0075]

[0076] Where, q value represents the explanatory ability of each influencing factor to water conservation, the value range is [0, 1], the greater 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, that is, classification or partition; And N are the sample numbers of region h and the whole region respectively; And are the variances of region h and the whole region respectively.

[0077] The spatial pattern analysis module uses Moran's index to analyze the spatial aggregation and spatial correlation of water conservation, and its calculation formula is:

[0078]

[0079] Where, I is the bivariate global Moran's index value, n is the number of regions, W is the sum of all spatial weights , And are the variable values of region i and region j respectively, And are the average values of variables x and y respectively, is the spatial weight between region i and region j.

[0080] Finally, the output module generates water conservation spatial distribution map, factor value map, Moran's index map and sensitivity analysis map from the simulation results, and displays the evolution pattern of water conservation at urban ecological unit scale and its driving mechanism in the form of image and numerical value, which provides scientific support for urban ecological safety pattern evaluation and planning optimization. ​​​

[0081] Example two: The system takes a typical urban ecological unit as the research object, and builds a water conservation function evolution simulation system suitable for 30m spatial precision and carries out empirical application in Chengdu City.

[0082] The system is composed of data processing module, water yield calculation module, water conservation calculation module, driving factor analysis module, spatial pattern analysis module and result output module.

[0083] The system takes 30m x 30m resolution grid as the basic analysis unit, which is suitable for water conservation monitoring and simulation analysis of urban, suburban or complex ecological unit. In the data preprocessing module, first, the standardized spatial data is extracted, including precipitation , potential evapotranspiration , percentage slope , soil depth , terrain index , soil saturated hydraulic conductivity , effective water content of vegetation . After resampling of remote sensing data, the grid resolution is unified to 30 meters to form the standardized input data grid. The research area covers 19 administrative districts of Chengdu City, and the terrain gradually decreases from west to east (as shown in Figure 1 ).

[0084] Water yield is defined as the difference between precipitation and actual evapotranspiration of each grid unit, and the formula is: ;

[0085] Among them, the actual evapotranspiration is approximately calculated by Budyko function: ;

[0086] Among them, : Budyko dry index, defined as the ratio of potential evapotranspiration to precipitation; : is a non-physical parameter representing natural climate-soil characteristics; is the annual precipitation of the xth grid unit under the jth land cover type;

[0087] Among them ;

[0088] The empirical formula of PAWC is as follows:

[0089] ;

[0090] The simulation results are compared with the measured runoff depth (as shown in Figure 1 ), showing a high fitting relationship, with the determination coefficient , verifying the credibility of the model.

[0091] The system further calculates the water conservation capacity of each grid cell based on the following formula:

[0092] ;

[0093] The terrain index is: ; The empirical expression of saturated hydraulic conductivity is:

[0094] ;

[0095] 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 8 m³. 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.

[0096] 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.

[0097] 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:

[0098] ;

[0099] 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.

[0100] Finally, the system identifies the spatial correlation between each factor and the conservation pattern through the bivariate Moran index, which is calculated as follows:

[0101] ;

[0102] The results showed that factors such as Ksat, TI, and Pre had positive spatial clustering with conservation values ​​(I>0.4).

[0103] Two-factor interaction detection results ( Figure 5 )show:

[0104] (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.

[0105] (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.

[0106] (3) Among the climate factors, the interaction between Pre and PAWC was stronger, with q values ranging from 0.107 to 0.264. Among the soil factors, the interaction between SD and Pet and Temp was the weakest, with q values of 0.081-0.164 and 0.08-0.164, respectively.

[0107] This indicates that the urban water conservation function is highly dependent on the synergistic effect of precipitation input and soil precipitation retention capacity, and the deep soil conservation function is relatively stable and less affected by short-term atmospheric evaporation and temperature fluctuations.

[0108] In this study, the water conservation capacity and influencing factor data of 19 county-level administrative regions in Chengdu were used as samples, and the bivariate global Moran's I index in Geoda software was used to analyze the spatial correlation of water conservation function in Chengdu from 2010 to 2023. The main results (shown in the table) are as follows: Figure 6 The results showed that: (1) Except for the negative correlation between precipitation and water conservation capacity in 2013, the global Moran's I index was greater than zero in the remaining years. This indicates that in 2013, precipitation had an inhibitory effect on the spatial distribution pattern of water conservation. (2) The global spatial autocorrelation between driving factors and water conservation capacity showed similar trends in different years. Among them, the average global Moran's I index 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 capacity in space, among which the driving effects of soil change factors (Ksat, SD) and climate change factors (Pre, Pet) were the most significant. On the contrary, the average global Moran's I index 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 had a strong negative correlation with water conservation capacity in space, among which the inhibitory effect of social and economic factors was the most significant.

[0109] In summary, the influence mechanism of various influencing factors on the spatial distribution pattern of water conservation in Chengdu during different periods is complex, mainly driven and inhibited by soil factors, climate factors, and social and economic factors, accompanied by the significant influence of topographic factors.

[0110] As a key indicator of urban ecosystem service assessment and a core component of water quantity regulation service, water source conservation plays a vital role in regulating hydrological cycle, alleviating water shortage, enhancing urban resilience, and improving water environmental quality. However, this function is easily affected by natural environmental factors (such as climate change, topography, soil properties, and vegetation cover) and human activities (such as social and economic development, land use change, and local policies), and its capacity often shows a declining trend. Previous studies have confirmed that the formation of water source conservation spatial pattern is the result of the synergistic effect of multiple driving factors, mainly including climate, soil, topography, and socio-economic factors, which is consistent with the conclusions of this study.

[0111] According to the factor detection results ( Figure 4 ), it was found that elevation in topographic factors and saturated hydraulic conductivity in soil factors were the dominant factors driving the spatial differentiation of water source conservation. This finding is highly consistent with the geographical environmental characteristics of Chengdu City. High-altitude and steep-slope areas are mainly distributed in the western hilly and mountainous areas, where the vegetation coverage is high. The vegetation roots effectively hold the soil, and the abundant rainfall (especially in the rainy season) can promote soil water transmission and speed up infiltration through the connected root network, significantly improving the soil water holding capacity and permeability, and thus enhancing the water source conservation function of the region. On the contrary, low-altitude areas are mainly the urban central areas with high-density human activities. Although Chengdu as a whole belongs to a humid climate with abundant annual rainfall, in recent years, the continuous expansion of the built-up area due to intensive urban development has significantly increased the proportion of impervious surfaces, severely hindering rainwater infiltration and causing a large amount of rainwater to be converted into surface runoff. In addition, the vegetation coverage in the central city is relatively low, and the soil structure is significantly damaged due to compaction and hardening, combined with the continuous reduction of natural water systems and wetland areas, which collectively weakens the water source conservation capacity of the region, resulting in a low water conservation capacity.

[0112] Based on the double-factor interaction detection results ( Figure 5 ), it was found that the coupling effect of human activities and natural environmental factors had a profound impact on the spatial pattern of water source conservation during the process of urban economic development. Specifically, in the socio-economic factors, the interaction effects of GDP and slope and Ksat were strong, while the interaction effect of population density POP and PAWC was significant. This interaction pattern is mainly due to the spatial selectivity of urban development: the location and development intensity of high-GDP and high-population-density areas are significantly constrained by topography (slope) and soil infiltration capacity (Ksat). For example, flat areas are often the preferred choice for large-scale urban construction due to their ease of development. This development process significantly weakens the regional water source conservation function by changing the land use type (such as increasing the impervious surface) and damaging the soil structure (such as compaction). In contrast, steep-slope areas retain more natural habitats due to the high cost and difficulty of development, thus maintaining relatively superior water source conservation conditions.

[0113] The results of bivariate spatial autocorrelation analysis Figure 6 Further confirmed that the soil change factor (such as Ksat), the climate change factor (such as Pre, Pet) and the terrain factor (such as Slope) have significant positive spatial correlation on the spatial distribution of water conservation. The social and economic factors (especially GDP) show significant negative spatial correlation, indicating that the expansion of the high value area of water conservation is in spatial repulsion.

[0114] Overall, the spatial pattern of water conservation is not dominated by a single factor, but is formed by the complex synergistic effect of social and economic development factors and key natural environmental factors (terrain, soil, climate).

[0115] The system can output water conservation function distribution map, geographical probe factor identification result column chart and geographical probe double factor interaction detection result matrix chart, and is widely applicable to urban ecosystem hydrological service function evolution identification, spatial planning optimization and resilience construction evaluation scene.

[0116] Through the collaborative calculation of the above modules, the system realizes the evolution simulation and quantitative evaluation of the urban ecological unit scale water conservation function under different driving factors, has good spatial adaptability and algorithm portability, and is suitable for multiple scenes such as urban hydrological regulation, ecological infrastructure site selection, green city planning and the like.

[0117] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A simulation system for evolution of water conservation function at the scale of urban ecological unit, characterized in that, The system comprises: Data preprocessing module: for obtaining and standardizing processing of multi-source spatial data, including precipitation , potential evapotranspiration , percent slope , soil depth , terrain index , soil saturated hydraulic conductivity , plant available water content and land use type , and catchment area derived based on digital elevation model , and unified grid resolution to 30m×30m; Annual water yield calculation module: for calculating annual water yield of each grid cell based on precipitation and actual evapotranspiration of each grid cell in each land use type ;​​ water source conservation amount calculation module: for combining annual water production , terrain index , soil saturated hydraulic conductivity , percentage slope , catchment area , flow rate factor , calculate the water source conservation amount of the grid cell ; Driving factor analysis module: for calculating explanatory power based on geographic probes Values, quantify the impact of each natural and social factor 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; An output module: for generating a water conservation space distribution map, a geographical detector factor identification result column chart, and a geographical detector double-factor interaction detection result matrix chart; The geographic probe analysis module The calculation formula of the value is: ; wherein, The value represents the explanatory ability of each impact factor to water conservation, and the value range is [0, 1], The greater the value, the stronger the influence of each impact factor on the evolution of water conservation, and vice versa; h = 1, 2, … L, represents the classification number of each impact factor, that is, classification or partition; and N h and N are the sample sizes of region h and the whole region, respectively; and and S h and S are the variances of region h and the whole region, respectively. The bivariate global Morlet index value The formula for calculating is: ; where I is the bivariate global Moran's I value, n is the number of regions, W is the sum of all spatial weights, xi and xj are the variable values of region i and region j, respectively, and and is the spatial weight between region i and region j. 2.The urban eco-unit scale water source conservation function evolution simulation system according to claim 1, characterized in that, The annual water production The calculation formula is: ; wherein, : annual water yield of the xth grid cell under the jth land cover type; : actual evapotranspiration; : precipitation. 3.The urban eco-unit scale water source conservation function evolution simulation system according to claim 2, characterized in that, the actual evapotranspiration According to the Budyko function, calculated as follows: ; wherein, : is the Budyko dryness index, defined as the ratio of potential evapotranspiration to precipitation; : is a non-physical parameter representing 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 urban eco-unit scale water source conservation function evolution simulation system according to claim 3, characterized in that, The volumetric plant water content The formula for the calculation is: ; in, : depth of bedrock layer; : plant root depth; ;No. The plant available water content of the unit. 5.The urban eco-unit scale water source conservation function evolution simulation system according to claim 4, characterized in that, The plant effective water content Estimation by the following empirical formula: ; wherein, , , , : are the soil sand ratio, silt ratio, clay ratio, organic matter content ratio data extracted based on the HWSD version 1.1 soil data, respectively. 6.The urban eco-unit scale water source conservation function evolution simulation system according to claim 1, characterized in that, The water conservation amount of the water source The calculation formula is: ; wherein, is the water retention amount; is the flow rate coefficient; is the terrain index; is the soil saturated hydraulic conductivity; is the water production amount. 7.The urban eco-unit scale water source conservation function evolution simulation system according to claim 6, characterized in that, The terrain index The formula for calculating the terrain index is: ; wherein, is the number of catchment accumulation grids; is the soil depth; is the percent slope. 8.The urban eco-unit scale water source conservation function evolution simulation system according to claim 6, characterized in that, the saturated hydraulic conductivity It is estimated by the following empirical equation: ; wherein, , : ratio of sand particles to clay particles in the soil.

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