Method for analyzing influencing factors of water source conservation based on geospatially weighted regression

Abstract

The application discloses a water source conservation influence factor analysis method based on geographical space-time weighted regression, comprising the following steps: collecting influence factor data of a target basin as a first data set; calculating historical water source conservation values of a target region and performing spatial aggregation analysis as a second data set; performing spatial pattern analysis on the first data set and the second data set by using a multi-scale geographical weighted regression method to obtain spatial characteristics of various influence factors; performing space-time change analysis on the first data set and the second data set by using a geographical space-time weighted regression method to obtain space-time characteristics of various influence factors; and obtaining influence characteristics of various influence factors on water source conservation of the target basin according to the spatial characteristics and the space-time characteristics. The application captures change characteristics of the influence factors at different spatial scales, reveals change trends of the factors over time, ensures comprehensiveness of analysis, comprehensively combines data in two dimensions of space and time, and is more accurate in prediction and analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water conservation, and particularly relates to a water conservation influencing factor analysis method based on geospatial-temporal weighted regression. BACKGROUND

[0002] Water conservation is an important indicator of regional ecological environment, which is driven by multiple factors in complex mechanisms, including environmental and socio-economic factors. In order to better understand how these driving factors affect water conservation, researchers have long been exploring various analysis methods. Traditional analysis methods usually only consider a single spatial or temporal scale, making it difficult to capture the changing characteristics of influencing factors at different scales.

[0003] In recent years, geographically weighted regression (GWR) has received widespread attention from ecologists due to its applicability to geographical data. GWR can effectively solve the problem of driving force analysis of water conservation at the local scale. However, GWR mainly focuses on the spatial relationship of cross-sectional data at a single time point, and is insufficient for the study of time series data.

[0004] To solve this problem, researchers have proposed geospatial-temporal weighted regression (GTWR). GTWR not only considers spatial relationships but also considers temporal continuity, making it a very powerful tool for revealing dynamic change patterns in long time series data. However, although GTWR has been applied in other fields, its application in the field of ecological hydrology, particularly in the study of water conservation, still presents considerable challenges.

[0005] Among various analysis methods, multi-scale geographically weighted regression (MGWR) and geospatial-temporal weighted regression (GTWR) each have their unique advantages. MGWR can capture the changing characteristics of influencing factors at different spatial scales, while GTWR can reveal the trends of these factors over time. However, the combined use of MGWR and GTWR is not an intuitive or easily thought-of approach. The reasons for this are as follows:

[0006] Firstly, both MGWR and GTWR are based on complex mathematical models, and their calculation and implementation require in-depth theoretical knowledge and practical experience. The integration of the two means that potential conflicts and mismatches between the two methods need to be addressed, which undoubtedly increases the complexity of implementation.

[0007] Secondly, although GTWR has been applied in other fields, it has not been applied in the field of eco-hydrology, especially in the study of water conservation. This is because water conservation is a complex process influenced by various environmental and socio-economic factors. These factors have their unique variation patterns in space and time, while GTWR mainly focuses on the continuity of time and may ignore some important spatial changes. Therefore, directly applying GTWR to the study of water conservation may lead to the loss of some key information. SUMMARY

[0008] In order to solve the problems existing in the prior art, the present application provides a method for analyzing the influencing factors of water conservation based on GTWR, which can capture the variation characteristics of influencing factors at different spatial scales and reveal the variation trend of these factors over time. The technical scheme provided by the present application comprises:

[0009] The method for analyzing the influencing factors of water conservation based on GTWR comprises the following steps:

[0010] S1, collecting the influencing factor data of the target watershed as the first data set; the influencing factors include historical environmental factors and historical socio-economic factors;

[0011] S2, calculating the historical water conservation value of the target area and performing spatial aggregation analysis to obtain the second data set;

[0012] S3, performing spatial pattern analysis on the first data set and the second data set using multi-scale geographical weighted regression to obtain the spatial characteristics of various influencing factors;

[0013] S4, performing spatio-temporal variation analysis on the first data set and the second data set using GTWR to obtain the spatio-temporal characteristics of various influencing factors;

[0014] S5, obtaining the influence characteristics of various influencing factors on the water conservation of the target watershed according to the spatial characteristics and spatio-temporal characteristics.

[0015] In some preferred embodiments, the historical environmental factors in step S1 include climate data and surface data of the target watershed in previous years; and the historical socio-economic factors include population density data and GDP data of the target watershed in previous years.

[0016] In some preferred embodiments, the calculation method of the water conservation value in step S2 comprises:

[0017] The water conservation value WC = PRE-ET-RO; wherein PRE, ET and RO are precipitation, evaporation and runoff, respectively.

[0018] In some preferred embodiments, the method of performing spatial aggregation analysis in step S2 comprises:

[0019] The evaluation is performed using the global spatial autocorrelation coefficient Moran's I:

[0020] wherein, is the average value of water conservation of the n sub-units of the target basin; w ij is the spatial weight evidence; x i and x j are the water conservation values of the sub-unit i and the sub-unit j of the target basin, respectively;

[0021] The value range of the global spatial autocorrelation coefficient Moran's I is -1 to 1;

[0022] When Moran's I < 0, the water conservation values between the sub-unit i and the sub-unit j of the target basin are negatively correlated, and the smaller Moran's I is, the greater the similarity of the water conservation values between the sub-unit i and the sub-unit j of the target basin is;

[0023] When Moran's I = 0, the water conservation values between the sub-unit i and the sub-unit j of the target basin are irrelevant;

[0024] When Moran's I > 0, the water conservation values between the sub-unit i and the sub-unit j of the target basin are positively correlated, and the greater Moran's I is, the greater the similarity of the water conservation values between the sub-unit i and the sub-unit j of the target basin is.

[0025] In some preferred embodiments, the spatial characteristics of various influencing factors in step S3 comprise:

[0026] Classification characteristics, used to represent the development of various influencing factors at different spatial scales;

[0027] Driving characteristics, used to represent the significant degree of spatial heterogeneity of influencing factors at different spatial scales.

[0028] In some preferred embodiments, the method of performing spatio-temporal change analysis on the first data set and the second data set using the geospatial weighted regression method in step S4 comprises:

[0029]

[0030] wherein, y i is the response variable; β0(u ι ,v ι ,t i ) is the intercept value, (u ι ,v ι) is the spatial coordinate of element i, t i is the timestamp of element i; m is the total number of elements; ε i is a random error term; x ik is the kth variable of element i; β k (u ι , v ι , t i ) is the estimated local regression coefficient.

[0031] In some preferred embodiments, the spatio-temporal characteristics of the various influencing factors in step S4 include:

[0032] climatic characteristics, for characterizing the stability of climatic factors in the time series and the changes of the relationship between climatic factors and water conservation in the time series;

[0033] surface characteristics, for characterizing the changes of surface factors in the time series;

[0034] socio-economic characteristics, for characterizing the changes of socio-economic factors in the time series and the changes of the relationship between socio-economic factors and water conservation in the time series.

[0035] Advantages

[0036] The present application provides a new analysis method for the study of water conservation by fusing the multi-scale geographic weighted regression method (MGWR) and the geographic and temporal weighted regression method (GTWR), which not only captures the changing characteristics of influencing factors at different spatial scales, but also reveals the changing trends of these factors over time, ensuring the comprehensiveness of the analysis, while integrating data from both spatial and temporal dimensions, making the prediction and analysis results more accurate and better reflecting the actual situation. The present application can provide more solid decision support for policymakers and urban planners, helping them to develop more reasonable and effective strategies. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 is a flowchart of the water conservation influencing factor analysis method based on geographic and temporal weighted regression in a preferred embodiment of the present application;

[0038] Figure 2 is a schematic diagram of the data used in the experimental example of the present application and its source;

[0039] Figure 3 is a schematic diagram of the spatial distribution of WC of YRS in five years in the experimental example of the present application;

[0040] Figure 4 is a Moran scatter plot based on sub-basin units in each period in the experimental example of the present application;

[0041] Figure 5 This is a schematic diagram of the parameters selected for spatial pattern analysis in the experimental examples of this invention;

[0042] Figure 6 This is a visualization of the spatial heterogeneity of the parameter estimation for each factor in the MGWR in the experimental example of this invention;

[0043] Figure 7 This is a schematic diagram illustrating the relationship between different factors and water conservation in different branch basins within the same region, as shown in the experimental examples of this invention.

[0044] Figure 8 This is a schematic diagram illustrating the driving force analysis of different water source conservation priority areas in the experimental examples of this invention;

[0045] Figure 9 This is a schematic diagram of spatiotemporal variation analysis in the experimental examples of this invention; Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described below with reference to the accompanying drawings. In the description of this invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention.

[0047] Example

[0048] like Figure 1 As shown in the figure, this embodiment presents a method for analyzing the influencing factors of water conservation based on geographic spatiotemporal weighted regression, including the following steps:

[0049] S1, collect the influencing factor data of the target basin as the first data set; the influencing factors include historical environmental factors and historical social and economic factors. It should be understood that water conservation is a complex process involving the interaction of multiple natural and human factors. Traditional research methods often only focus on the influence of a single factor on water conservation, ignoring the mutual relationship and comprehensive effect between these factors. For example, climate change may lead to changes in precipitation patterns, but it may also affect vegetation coverage and soil moisture, thereby affecting water conservation. In addition, human activities such as agriculture, industry and urbanization may also have direct or indirect effects on water conservation. Although the existing technology has recognized the existence of these influencing factors, there is still much controversy about their relative importance and interaction mechanisms. In addition, since water conservation is a dynamic process, the role of influencing factors may vary at different time and spatial scales.

[0050] Therefore, the present application does not limit the specific selection of influencing factors, which can be selected by the person skilled in the art according to experience and knowledge reserves. However, in some preferred embodiments, a preferred definition of influencing factors is given: the historical environmental factors include the climate data and surface data of the target basin in previous years; the historical social and economic factors include the population density data and GDP data of the target basin in previous years.

[0051] S2, calculate the historical water conservation value of the target area and perform spatial aggregation analysis to obtain the second data set. Those skilled in the art should know that there are many methods for calculating water conservation, the most commonly used method is to use the water balance equation to quantify water conservation, the specific equation is:

[0052] WC = PR - ET - RO; where PR, ET and RO are precipitation, evapotranspiration and runoff, respectively.

[0053] The spatial aggregation analysis refers to the process of aggregating or merging geographical data in space, which is commonly used to simplify geographical information, making it easier to interpret or providing a more general perspective for subsequent analysis. Common methods include gridding aggregation, aggregation based on administrative boundaries and buffer aggregation. However, for the water conservation of the basin involved in the present application, the types of space involved are diverse and the mutual relationship between various types of space is complex, so simple spatial aggregation analysis based on physical entities does not work well. Based on this, in some preferred embodiments, a method of spatial aggregation analysis based on spatial autocorrelation theory is provided, which specifically includes:

[0054] Use global spatial autocorrelation coefficient Moran's I for evaluation:

[0055] wherein, is the average water conservation value of the target basin n sub-units; w ij is the spatial weight evidence; x i and x j are the water conservation values of the target basin sub-unit i and sub-unit j, respectively;

[0056] The global spatial autocorrelation coefficient Moran's I ranges from -1 to 1.

[0057] When Moran's I < 0, the water conservation values between the target basin sub-unit i and sub-unit j are negatively correlated, and the smaller Moran's I is, the greater the similarity of the water conservation values between the target basin sub-unit i and sub-unit j is.

[0058] When Moran's I = 0, the water conservation values between the target basin sub-unit i and sub-unit j are irrelevant.

[0059] When Moran's I > 0, the water conservation values between the target basin sub-unit i and sub-unit j are positively correlated, and the greater Moran's I is, the greater the similarity of the water conservation values between the target basin sub-unit i and sub-unit j is.

[0060] S3, using a multiscale geographically weighted regression method to analyze the spatial patterns of the first and second data sets, obtaining the spatial characteristics of various influencing factors. Multiscale geographically weighted regression (MGWR) is developed on the basis of existing geographically weighted regression (GWR), which can provide more accurate spatial variation range and intensity details, allow different bandwidths for explanatory variables, and calculate the bandwidth of each variable to make the regression results more accurate. The equation is as follows:

[0061]

[0062] wherein, y i is the response variable; (u ι , v ι ) is the spatial coordinates of element i; m is the total number of elements; ε i is a random error term; x ik is the kth variable of element i; β bwkFor the explanatory variable of the regression coefficient, bwk is the bandwidth. In some preferred embodiments, the optimal value of the bandwidth can be determined by using a quadratic kernel function estimation with adaptive bandwidth for the spatial weights, and using the corrected Akaike information criterion (AICc) to determine the optimal bandwidth.

[0063] The spatial characteristics of various influencing factors obtained through this step can include:

[0064] Classification characteristics, used to represent the development of various influencing factors at different spatial scales, such as climate, land cover, and soil type, etc.

[0065] Driving characteristics: used to represent the significant degree of spatial heterogeneity of influencing factors at different spatial scales, to identify that the influencing factors show significant spatial heterogeneity at different spatial scales, which is particularly important for understanding regional differences and local effects of water conservation;

[0066] Spatial distribution characteristics: used to represent how various influencing factors are distributed in space and their relationship with water conservation.

[0067] S4, performing spatio-temporal variation analysis on the first data set and the second data set by using a geospatial-temporal weighted regression method to obtain spatio-temporal characteristics of various influencing factors. Those skilled in the art can know that although the driving mechanism of each factor has been preliminarily agreed upon in previous studies, the understanding of the spatio-temporal characteristics of the main driving factors is still quite insufficient, especially the comprehensive perspective of multiple spatial scales and multiple time dimensions in remote sensing data. GWR can effectively solve the problem of driving force analysis of water conservation at the local scale. However, GWR mainly focuses on the spatial relationship of cross-sectional data at a single time point, and it is insufficient for the study of time series data. Although MGWR solves the problem that the actual action scales of different explanatory variables are inconsistent when there are multiple explanatory variables, it also does not consider the influence of time series data. Currently, researchers have provided a geographically and temporally weighted regression method (Geographically and Temporally Weighted Regression, GTWR) for studying ecosystem services and their driving factors, which can not only capture the changing characteristics of influencing factors at different spatial scales, but also reveal the changing trends of these factors over time. Although GTWR has been applied in other fields, its application in the field of eco-hydrology, especially in the study of water conservation, is still non-obvious. This is because water conservation is a complex process that is affected by multiple environmental and socio-economic factors. These factors have their unique variation patterns in space and time, and GTWR mainly focuses on the continuity of time, which may ignore some important changes in space. Therefore, directly applying GTWR to the study of water conservation may result in the loss of some key information. The solution of the present application is to successfully overcome these challenges by fusing MGWR and GTWR, providing a new analysis framework for the study of water conservation. This fusion method can not only capture the changing characteristics of influencing factors at different spatial scales, but also reveal the changing trends of these factors over time, thereby bringing major theoretical and practical breakthroughs to the field. Specifically, the method for performing spatio-temporal variation analysis on the first data set and the second data set by using a geospatial-temporal weighted regression method comprises the following steps:

[0068]

[0069] wherein y i is a response variable; β0(u ι ,v ι ,t i ) is an intercept value, (u ι ,v ι ) is a spatial coordinate of element i, t i is a time stamp of element i; m is the total number of elements; ε i is a random error term; xik is the kth variable for element i; β k (u ι , v ι , t i ) are the estimated local regression coefficients.

[0070] The spatial characteristics of various influencing factors obtained through the step can include:

[0071] Climate characteristics, used to represent the stability of climate factors in the time series, and the changes in the relationship between climate factors and water conservation in the time series;

[0072] Surface characteristics, used to represent the changes in surface factors in the time series;

[0073] Socio-economic characteristics, used to represent the changes in socio-economic factors in the time series, and the changes in the relationship between socio-economic factors and water conservation in the time series;

[0074] Temporal variation characteristics, used to represent the changes in seasons and years in the time series, as water conservation may be affected by seasonal and annual changes.

[0075] S5, according to the spatial characteristics and spatiotemporal characteristics, obtaining the influence characteristics of various influencing factors on water conservation of the target basin.

[0076] Experimental Example

[0077] This experimental example is an example of the method of Embodiment 1 described above. The research target basin of this experimental example is The Yellow River Basin in Sichuan Province (YRS), specifically including the entire area of Shiqu, Aba, Ruoergai, Hongyuan and Songpan counties. In the target basin, the main stream of the Yellow River is 174 kilometers long, and the basin area is 18,700 square kilometers.

[0078] I. Data Collection

[0079] The data used in this experimental example and its sources are shown in Table 1. Figure 2

[0080] II. Spatial Distribution Characteristics of Water Conservation

[0081] The spatial distribution of WC in YRS over five years was obtained by using the water balance equation. As shown in Table 2. Figure 3 ​The WC distribution pattern in the basin was high in the south and low in the north over the 20 years. At the sub-basin scale, the sub-basin units with high WC were mainly concentrated in the lower reaches of Aba and Hongyuan, where the terrain was relatively low and the average temperature was higher. In the upper reaches of Shiqu, far from the built-up area, there was a relatively high terrain and lower WC.

[0082] To study the spatial and temporal distribution characteristics of WC in YRS based on sub-basin units, spatial autocorrelation analysis was applied to assess spatial heterogeneity. Moran scatterplot explained the instability of local space by representing the correlation between the standardized value of WC and its spatial lag value. Figure 4 It was shown that the Moran's I of WC values in the four periods were 0.897, 0.889, 0.896 and 0.931, respectively. These always high values indicated the presence of significant positive spatial correlation (i.e. strong spatial clustering). Moran's I quantified the strong spatial clustering characteristics of WC. The higher the value of Moran's I, the stronger the spatial clustering characteristics of WC over time.

[0083] After 20 years of land surface processes, the high water conservation (WC) area in the downstream of the study area expanded significantly. The spatial distribution characteristics of WC showed obvious clustering, i.e. the similarity of water conservation between sub-basin units and adjacent units was increasingly high. The centers of H-H clustering or L-L clustering gradually became significant. In particular, Shiqu has become a low-value center, while Aba and the southern Hongyuan have become high-value centers, where water conservation has increased most significantly.

[0084] III. Comparison of different analysis models

[0085] Four models were selected:

[0086] 1. Multiscale Geographically Weighted Regression (MGWR);

[0087] 2. Geographically Weighted Regression (GWR);

[0088] 3. Geographically and Temporally Weighted Regression (GTWR);

[0089] 4. Ordinary Least Squares (OLS).

[0090] III. Spatial pattern analysis

[0091] AsFigure 5 and Figure 6 As shown in Figure 8, the results of MGWR analysis indicate that the specified bandwidths for each parameter help to more accurately and completely analyze the spatial patterns of the influencing factors of WC. The spatial scale differences of this effect can be revealed by the optimal bandwidths detected by MGWR for each individual predictor variable. Compared with the same bandwidth of 68 nearest neighbors of GWR, the bandwidths of MGWR can be classified as global, regional, and local indicator variables. LAI and land cover are global variables with the largest scale of influence, population density and soil available water capacity are regional variables with larger scale of influence, and temperature, wind speed, terrain, soil depth, and GDP are local variables with smaller scale of influence. This means that global variables indicate that almost all units participate in parameter estimation and are affected by LAI and land cover. Similarly, regional and local variables imply the influence on county-level and sub-basin scale water conservation, respectively.

[0092] Figure 6 The spatial heterogeneity of parameter estimation for each factor in MGWR is further visualized. Colored areas represent units with significant local parameter estimation, dark color indicates positive estimation, and light color indicates negative estimation. The estimation within the diagonally filled area is not significantly different from zero. Compared with OLS, MGWR finds different individual variable parameter estimations.

[0093] In terms of climate, both variables show spatial variations. The effect of temperature is significantly positive throughout the study area and much higher than other positive variables. From Shiqu in the upstream to Songpan in the downstream, the estimated value gradually decreases ( Figure 6 a). However, the effect of wind is significantly negative only in the downstream area, especially in Hongyuan and Songpan, much higher than other negative variables ( Figure 6 b). Higher estimated values indicate that water conservation is susceptible to changes in climate factors. In units with low water conservation, their water conservation has a more significant positive correlation with temperature and increases with temperature. The impact pattern for wind is the opposite. It has a single negative cluster in Hongyuan and Songpan, where water conservation values are higher and decrease with increasing wind speed. In terms of surface features, terrain variables show clusters: one negative cluster in Ruo'ergai and another positive cluster in the southeast corner of Shiqu and Songpan ( Figure 6 c). Their effect directions are inconsistent within the study area. Further analysis is needed to determine the possible reasons behind this result. Leaf area index (LAI) and land cover variables show different spatial patterns. The former is significantly positive in Shiqu in the upstream, but the latter is more significant in the downstream area. Both have lower estimated values, indicating that water conservation has a low response to them ( Figure 6d and 6e). For the soil depth variable, the estimated surfaces show a single negative cluster in the southwest corner of Hongyuan, indicating that the negative effect is more pronounced where the soil depth is smaller Figure 6 f).

[0094] For the socio-economic factors, only the GDP variable's estimate surface is significant. It appears as two different negative clusters: one in the central region of Aba and Hongyuan, and another covering almost all of Songpan Figure 6 g). The estimated surfaces show an opposite effect pattern, i.e., the negative effect is more pronounced where the GDP is higher. Unlike OLS, the intercept estimates are found to be significant in the downstream counties of Zoige, Aba, and Hongyuan Figure 6 h). However, little spatial variation is detected in these surfaces. The intercept here can represent the effect of the un-detected variables explaining the remaining spatial variance after controlling for the existing 9 indicator variables in the MGWR. It also shows that the regression model has a better fit in Songpan and the upstream areas.

[0095] The results of MGWR also provide important practical implications for the identification of dominant factors at specific locations and multiscale policy making, which help to improve strategies for water retention and adaptation of land surface soil erosion processes.

[0096] Water retention is spatially heterogeneous, with different factors at global, regional, and local scales, thus requiring scalable interventions. At the global scale, areas with more vegetation cover have higher water retention capacity. This finding helps to protect extensive low-water retention sub-basins. Some large cities (e.g., Los Angeles and Philadelphia in the United States, and Athens in Greece) demonstrate the effectiveness of large-scale enhancements in vegetation cover and surface albedo in reducing runoff and evaporation. At the regional scale, different socio-economic factors have different impacts on water retention. This result is important as it indicates the necessity of targeted regional development strategies. As shown, at the local sub-basin level, the correlation between wind, topography, soil factors, and water retention varies between different sub-basins within the same region. Figure 7

[0097] Based on the estimated coefficient surfaces, we find that the Yangtze River Source Region can be divided into 10 areas with different priorities for water retention focus, as shown in Figure 6. These 10 areas are labeled 1 to 10 in the center map, and each area corresponds to a small map (labeled A1 to A10) showing the specific location indicator variables ranked by priority. Figure 8

[0098] ​​Among all the regions from A1 to A10, temperature plays a dominant role, where climate management strategies aimed at adjusting the intensity of regional runoff are expected to yield greater water conservation benefits than other strategies, as higher temperatures in winter can be the main intervention for more glacier melt water in these hotspots. Analysis based on more accurate and reliable results obtained using MGWR helps to delineate the driving "hotspots" of different factors. In branch watershed units of A2, A3, A4, A8, and A10, areas with faster wind speed can be more susceptible to the reduction of WC than other areas.

[0099] Although past studies have demonstrated the importance of vegetation cover on water conservation (WC), this study shows that vegetation-based protection strategies can only work in specific locations (e.g., A1 and A7, almost all located in Shiqu County), as the impact of vegetation on WC is controlled by multiple factors, including rainfall, slope, soil, etc. For regions affected by GDP (e.g., A2, A5, A8, A9, and A10), more stringent sustainability policy restrictions are needed. To address the combined effects of water consumption for economic production and damage to the ecological environment in these areas, increasing local water conservation through increasing vegetation cover and albedo was found in past studies to have the most protective effect on the downstream.

[0100] The results derived by identifying the dominant factors in specific locations show that there are large spatial differences in driving water conservation between local environmental factors and socio-economic factors. Overall, temperature is the most important determinant of water conservation in the source region of the Yangtze River, followed by wind speed, slope, and leaf area index (LAI). The spatial heterogeneity of driving forces suggests that it is unlikely that there is a one-size-fits-all solution strategy for water conservation.

[0101] IV. Spatiotemporal change analysis

[0102] The results of GTWR are shown in Figure 9 It can be seen that the use of grid data for multi-time series analysis provides a new spatiotemporal perspective on temporal changes. In terms of climate factors, the temperature results show that initially, its positive impact on water conservation has been important throughout the study area. Over time, the hotspots of temperature impact on WC show a trend of shifting from downstream to upstream. Recently, Shiqu and Aba have become the main positive correlation areas. There is no significant pattern change in the surface estimates of wind over time. In Shiqu, upstream of the basin, the impact of wind shows a slight weakening trend.

[0103] In terms of surface factors, the LAI results showed that initially only the LAI in the downstream area had a positive impact on water conservation. Over time, the range and area of the negative impact gradually expanded from 2001 to 2010, especially in the Shiqu River, while the range and area of the positive impact increased after 2010, especially in the downstream area. Overall, the impact of LAI presented a shift from negative to positive and dramatically expanded in recent years with the increase of LAI.

[0104] In terms of socio-economic factors, the GDP results showed that initially it exhibited extremely high spatial heterogeneity, with a positive impact on water conservation in the upstream but a negative impact in the downstream. Over time, from 2001 to 2015, the range and area of the positive impact gradually expanded in the upstream. As GDP continued to increase, the estimated values of all branch watershed units there became insignificant in recent years. However, as GDP was higher in the downstream than in the upstream, the range and area of the negative impact gradually decreased.

[0105] V. Conclusion

[0106] Water conservation (WC) is an important indicator of regional ecological environment, influenced by various environmental and socio-economic factors, with complex mechanisms. This experiment used MGWR and GTWR to analyze the spatiotemporal characteristics of water conservation changes in the Yangtze River source region. These results show which measures should be taken to effectively increase water conservation and protect the ecological environment.

[0107] Overall, the results showed that water conservation presented a slight upward trend, and the distribution pattern of WC within the basin was significantly higher in the south than in the north.

[0108] In terms of spatial characteristics, some factors such as vegetation and land cover may change at a more global scale, while others may change at a non-local scale. Improving agricultural productivity and improving the industrial structure are crucial for the trade-off between ecological protection and economic development.

[0109] In terms of spatiotemporal characteristics, due to the relative stability of the climate during the study period, the influence of temperature and wind speed was almost dominant and consistent over time. In contrast, the impact of vegetation on WC shifted from negative to positive in the 2000s, which was attributed to the shift from forest-dominated to grassland-dominated local vegetation restoration projects. The study concluded that water conservation in the Yangtze River source region benefited more from lower vegetation. Therefore, vegetation restoration projects based on specific locations of local climate, slope, and soil are necessary.

[0110] These findings not only enhance the understanding of the spatiotemporal characteristics of water conservation driving factors but also provide theoretical support for how ecosystem managers can achieve a trade-off between water conservation related to economic development and ecological protection.

[0111] Six, the advantages of the present application in analyzing water source conservation driving force

[0112] The present application explores the driving mechanism of WC by GTWR and MGWR with multiple factors, and provides a case study for spatiotemporal integrated regression analysis using GTWR and MGWR models. The dynamics of WC is a long-term time process, and its change is not isolated, which will be affected by the previous dynamics, and the current change will also affect the future results. Therefore, using GTWR to analyze the relevant long-term sequence data reveals the pattern of dynamic change, thereby providing prediction and suggestion for future change. The simultaneous analysis of the two models will lead to new conclusions and a more comprehensive understanding of the driving factors of the overall dynamics of WC.

[0113] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing influencing factors of water source conservation based on geospatial-temporal weighted regression, characterized in that, The method comprises the steps of: S1, collecting influence factor data of a target basin as a first data set; the influence factors include historical environmental factors and historical social and economic factors; S2, calculating the historical water source conservation value of the target region and performing spatial aggregation analysis to obtain a second data set; S3, performing spatial pattern analysis on the first data set and the second data set by using a multi-scale geographical weighted regression method to obtain spatial characteristics of various influence factors; S4, performing spatio-temporal change analysis on the first data set and the second data set by using a geographical spatio-temporal weighted regression method to obtain spatio-temporal characteristics of various influence factors; S5, obtaining influence characteristics of various influence factors on water source conservation of the target basin according to the spatial characteristics and the spatio-temporal characteristics. The method for performing spatial aggregation analysis in step S2 comprises: Evaluations were performed using the global spatial autocorrelation coefficient Moran's I: ; wherein, is the average water source conservation value of the n sub-units of the target watershed; is the spatial weight; and are the water source conservation values of the sub-unit i and the sub-unit j of the target watershed, respectively. The global spatial autocorrelation coefficient Moran's I has a value range of -1 to 1. When the water source conservation value between the target basin sub-unit i and the sub-unit j is negatively correlated, and the smaller, the greater the similarity of the water source conservation value between the target basin sub-unit i and the sub-unit j; When the water conservation value between the target basin sub-unit i and sub-unit j is irrelevant; When the water source conservation value between the target basin sub-unit i and the sub-unit j is positively correlated, and the greater the water source conservation value between the target basin sub-unit i and the sub-unit j is, the greater the similarity is.

2. The method for analyzing factors affecting water conservation according to claim 1, wherein: The historical environmental factors in step S1 include climate data and surface data of the target basin in different years; and the historical social and economic factors include population density data and GDP data of the target basin in different years. 3.The method of claim 1, wherein, The spatial characteristics of various influence factors in step S3 comprise: Classification characteristics for representing development of various influence factors at different spatial scales; Driving characteristics for representing significant degree of spatial heterogeneity of various influence factors at different spatial scales. 4.The method of claim 1, wherein, The method for performing spatio-temporal change analysis on the first data set and the second data set by using a geographical spatio-temporal weighted regression method in step S4 comprises: ； where, is the response variable; is the intercept value, is the spatial coordinate of element i, is the timestamp of element i; is the total number of elements; is the random error term; is the kth variable of element i; is the estimated local regression coefficient.

5. The method for analyzing factors affecting water conservation according to claim 2, wherein, The spatio-temporal characteristics of various influence factors in step S4 comprise: Climate characteristics for representing stability of climate factors in a time sequence and change of a relationship between the climate factors and water source conservation in the time sequence; Surface characteristics for representing change of surface factors in a time sequence; Social and economic characteristics for representing change of social and economic factors in a time sequence and change of a relationship between the social and economic factors and water source conservation in the time sequence.

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