Water conservation influence factor analysis method based on geographic space-time weighted regression

Abstract

The invention discloses a water conservation influence factor analysis method based on geographic space-time weighted regression. The method comprises the following steps: collecting influence factor data of a target drainage basin as a first data set; calculating a historical water source conservation value of the target area, 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 geographically weighted regression method to obtain spatial features of various influence factors; performing spatio-temporal change analysis on the first data set and the second data set by using a geographic spatio-temporal weighted regression method to obtain spatio-temporal characteristics of various influence factors; and according to the spatial features and the spatial-temporal features, obtaining influence features of various influence factors on water conservation of the target drainage basin. According to the method, the change characteristics of the influence factors on different spatial scales are captured, the change trend of the factors along with time is disclosed, the comprehensiveness of analysis is ensured, and the prediction and analysis results are more accurate by integrating the data of the spatial dimension and the time dimension.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservation, and in particular to a method for analyzing factors affecting water conservation based on geographic time-space weighted regression. Background Art

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

[0003] In recent years, Geographically Weighted Regression (GWR) has attracted widespread attention from ecologists due to its applicability to geographic data. GWR can effectively solve the problem of analyzing the driving forces of water conservation at a 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 address this problem, researchers proposed the geographic temporal weighted regression (GTWR). GTWR not only considers spatial relationships but also temporal continuity, making it a very powerful tool to reveal dynamic patterns in long time series data. However, although GTWR has been applied in other fields, its application in the field of ecohydrology, especially in the study of water conservation, is still quite challenging.

[0005] Among the various analysis methods, multiscale geographically weighted regression (MGWR) and geographic temporal weighted regression (GTWR) each have their own unique advantages. MGWR can capture the changing characteristics of influencing factors at different spatial scales, while GTWR can reveal the changing trends of these factors over time. However, combining MGWR and GTWR is not an intuitive or easy method. The reasons are:

[0006] First, MGWR and GTWR are each based on complex mathematical models, and their calculation and implementation require in-depth theoretical knowledge and practical experience. Integrating the two means that potential conflicts and mismatches between the two methods need to be resolved, 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 ecohydrology, especially in the study of water conservation. This is because water conservation is a complex process that is affected by a variety of environmental and socioeconomic factors. These factors have their own unique variation patterns in space and time, while GTWR focuses mainly on temporal continuity 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 of the invention

[0008] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a fusion of MGWR and GTWR, 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. The technical solution provided by the present invention includes:

[0009] The method for analyzing factors affecting water conservation based on geographic spatiotemporal weighted regression includes the following steps:

[0010] S1. Collecting data on influencing factors of the target watershed as a first data set; the influencing factors include historical environmental factors and historical socio-economic factors;

[0011] S2, calculate the historical water conservation value of the target area and perform spatial aggregation analysis as the second data set;

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

[0013] S4, using geographic spatiotemporal weighted regression method to perform spatiotemporal change analysis on the first data set and the second data set to obtain spatiotemporal characteristics of various influencing factors;

[0014] S5. Based on the spatial characteristics and spatiotemporal characteristics, the impact characteristics of various influencing factors on water conservation in the target watershed are obtained.

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

[0016] In some preferred embodiments, the method for calculating the water conservation value in step S2 includes:

[0017] Water conservation value WC = PRE-ET-RO; among them, PRE, ET and RO are precipitation, evaporation and runoff respectively.

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

[0019] The global spatial autocorrelation coefficient Moran's I is used for evaluation:

[0020] in, is the average water conservation value of n sub-units in the target basin; w ij Provide evidence for spatial weight; x i and x j are the water conservation values ​​of target watershed subunit i and subunit j respectively;

[0021] The global spatial autocorrelation coefficient Moran's I ranges from -1 to 1;

[0022] When Moran's I < 0, the water conservation values ​​between target watershed subunits i and j are negatively correlated, and the smaller the Moran's I, the greater the similarity of water conservation values ​​between target watershed subunits i and j;

[0023] When Moran's I = 0, the water conservation values ​​between subunit i and subunit j in the target watershed are irrelevant;

[0024] When Moran's I>0, the water conservation values ​​between target watershed subunits i and j are positively correlated, and the larger the Moran's I, the greater the similarity of water conservation values ​​between target watershed subunits i and j.

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

[0026] Classification features are used to characterize the development of various influencing factors at different spatial scales;

[0027] Driving characteristics are used to characterize the significance of spatial heterogeneity of influencing factors at different spatial scales.

[0028] In some preferred embodiments, the method of performing spatiotemporal change analysis on the first data set and the second data set using geographic spatiotemporal weighted regression in step S4 includes:

[0029]

[0030] Among them, 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, ti is the timestamp of element i; m is the total number of elements; ε i is the 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 spatiotemporal characteristics of various influencing factors in step S4 include:

[0032] Climate characteristics, used to characterize the stability of climate factors in time series, and the changes in the relationship between climate factors and water conservation in time series;

[0033] Surface characteristics, used to characterize the changes of surface factors in time series;

[0034] Socioeconomic characteristics are used to characterize the changes in socioeconomic factors in the time series, and the changes in the relationship between socioeconomic factors and water conservation in the time series.

[0035] Beneficial Effects

[0036] The present invention provides a new analysis method for the study of water conservation by integrating the multi-scale geographical weighted regression method (MGWR) and the geographical spatiotemporal weighted regression method (GTWR). It 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, ensuring the comprehensiveness of the analysis. At the same time, it integrates the data of the two dimensions of space and time, and its prediction and analysis results are more accurate and can better reflect the actual situation. The present invention can provide more solid decision-making support for policy makers and urban planners, helping them to formulate more reasonable and effective strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic flow chart of a method for analyzing factors affecting water conservation based on geographic spatiotemporal weighted regression in a preferred embodiment of the present invention;

[0038] Figure 2 A schematic diagram of the data and their sources used in the experimental examples of the present invention;

[0039] Figure 3 Schematic diagram of spatial distribution of WC of YRS in five years in the experimental example of the present invention;

[0040] Figure 4 It is the Moran scatter plot based on the sub-basin unit at each period in the experimental example of the present invention;

[0041] Figure 5A schematic diagram of parameters selected for spatial pattern analysis in an experimental example of the present invention;

[0042] Figure 6 It is a schematic diagram for visualizing the spatial heterogeneity of parameter estimation of each factor in MGWR in the experimental example of the present invention;

[0043] Figure 7 It is a schematic diagram showing the changes of the association between different factors and water conservation in different branch basins in the same region in the experimental example of the present invention;

[0044] Figure 8 It is a schematic diagram of the factor driving force analysis of different water conservation priority focus areas in the experimental example of the present invention;

[0045] Fig. 9 It is a schematic diagram of the analysis of spatiotemporal changes in the experimental example of the present invention; DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described below in conjunction with the accompanying drawings. In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "left", "right", "top", "bottom", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention.

[0047] Example

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

[0049] S1. Collect 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. 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 impact of a single factor on water conservation, while ignoring the interrelationships and combined effects between these factors. For example, climate change may lead to changes in precipitation patterns, but it may also affect vegetation cover 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 a lot of controversy about their relative importance and interaction mechanisms. In addition, since water conservation is a dynamic process, the role of influencing factors may be different at different time and spatial scales.

[0050] Therefore, the present invention does not limit the specific selection of influencing factors, which can be selected by those skilled in the art based on their 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 over the years; the historical socio-economic factors include the population density data and GDP data of the target basin over the years.

[0051] S2. Calculate the historical water conservation value of the target area and perform spatial aggregation analysis as the second data set. Those skilled in the art should know that there are many methods for calculating water conservation (WC). The most commonly used method is to quantify water conservation using the water balance equation, and 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 geographic data in space, which is usually used to simplify geographic information to make it easier to interpret or provide a more general perspective for subsequent analysis. Common methods include grid aggregation, aggregation based on administrative boundaries, and buffer zone aggregation. However, for the watershed water conservation involved in the present invention, the space involved is of various types and the relationships between the various spaces are complex. Therefore, the effect of simple spatial aggregation analysis based on physical entities is not very good. Based on this, in some preferred embodiments, a method of spatial aggregation analysis based on spatial autocorrelation theory is provided, which specifically includes:

[0054] The global spatial autocorrelation coefficient Moran's I is used for evaluation:

[0055] in, is the average water conservation value of n sub-units in the target basin; w ij Provide evidence for spatial weight; x i and x j are the water conservation values ​​of target watershed subunit i and subunit 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 target watershed subunits i and j are negatively correlated, and the smaller the Moran's I, the greater the similarity of water conservation values ​​between target watershed subunits i and j;

[0058] When Moran's I = 0, the water conservation values ​​between subunit i and subunit j in the target watershed are irrelevant;

[0059] When Moran's I>0, the water conservation values ​​between target watershed subunits i and j are positively correlated, and the larger the Moran's I, the greater the similarity of water conservation values ​​between target watershed subunits i and j.

[0060] S3. Use the multiscale geographically weighted regression method to perform spatial pattern analysis on the first and second data sets to obtain the spatial characteristics of various influencing factors. The multiscale geographically weighted regression method (Multiscale Geographically Weighted Regression, MGWR) is developed on the basis of the existing geographically weighted regression (Geographically Weighted Regression, GWR). Its characteristics are that it can provide more accurate spatial variation range and intensity details, allow explanatory variables to have different bandwidths, and calculate the bandwidth of each variable to make the regression results more accurate. Its equation is as follows:

[0061]

[0062] Among them, y i is the response variable; (u ι ,v ι ) is the spatial coordinate of element i; m is the total number of elements; ε i is the random error term; x ik is the kth variable of element i; β bwk is the explanatory variable of the regression coefficient, and bwk is the bandwidth. In some preferred embodiments, the optimal value of the bandwidth can be determined by: the spatial weight is estimated using a quadratic kernel function under an adaptive bandwidth, and the modified Akaike Information Criterion (AICc) is used to determine the optimal bandwidth.

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

[0064] Classification features, which characterize the development of various influencing factors at different spatial scales, such as climate, land cover, and soil type;

[0065] Driving characteristics: used to characterize the significance of spatial heterogeneity of influencing factors at different spatial scales, so as to identify influencing factors that show significant spatial heterogeneity at different spatial scales, which is particularly important for understanding regional differences and local impacts on water conservation;

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

[0067] S4. Use the geographic spatiotemporal weighted regression method to analyze the spatiotemporal changes of the first and second data sets to obtain the spatiotemporal characteristics of various influencing factors. Those skilled in the art will know that although a preliminary consensus has been reached on the driving mechanism of each factor in previous studies, the understanding of the spatiotemporal 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 a local scale. However, GWR mainly focuses on the spatial relationship of cross-sectional data at a single time point, and the research on time series data is insufficient. Although MGWR solves the problem of inconsistent actual action scales of different explanatory variables when there are multiple explanatory variables, it also does not consider the impact of time series data. At present, 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 ecohydrology, especially in the study of water conservation, is still not obvious. This is because water conservation is a complex process that is affected by a variety of environmental and socioeconomic factors. These factors have their own unique change patterns in space and time, while GTWR mainly focuses on temporal continuity and may ignore certain important changes in space. Therefore, directly applying GTWR to the study of water conservation may lead to the loss of certain key information. The solution of the present invention is to successfully overcome these challenges by fusing MGWR and GTWR, providing a new analytical 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, thus bringing major theoretical and practical breakthroughs to this field. Specifically, the method of using geographic spatiotemporal weighted regression to perform spatiotemporal change analysis on the first data set and the second data set includes:

[0068]

[0069] Among them, 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; εi is the 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.

[0070] The spatial characteristics of various influencing factors obtained through this step may include:

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

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

[0073] Socioeconomic characteristics, used to characterize the changes in socioeconomic factors in the time series, and the changes in the relationship between socioeconomic factors and water conservation in the time series;

[0074] Temporal variation characteristics are used to characterize seasonal and annual changes in time series, because water conservation may be affected by seasonal and annual changes.

[0075] S5. Based on the spatial characteristics and spatiotemporal characteristics, the impact characteristics of various influencing factors on water conservation in the target watershed are obtained.

[0076] Experimental example

[0077] This experimental example is an example of the method of the above-mentioned embodiment 1. The research target basin of this experimental example is the Yellow River Basin in Sichuan Province (YRS), which specifically includes 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] 1. Data Collection

[0079] The data used in this experiment and their sources are as follows Figure 2 shown.

[0080] 2. Spatial distribution characteristics of water conservation

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

[0082] In order to investigate the spatiotemporal distribution characteristics of WC in YRS based on sub-catchment units, spatial autocorrelation analysis was applied to assess spatial heterogeneity. Moran scatter plots were used to represent the correlation between the standardized values ​​of WC and their spatially lagged values ​​through a two-dimensional plot, thus explaining the local spatial instability. Figure 4 The Moran's I for WC values ​​in the four periods is shown to be 0.897, 0.889, 0.896, and 0.931, respectively. These consistently high values ​​indicate significant positive spatial correlation (i.e., strong spatial aggregation). Moran's I quantifies the strong spatial aggregation properties of WC. The higher the value of Moran's I over time, the stronger the spatial aggregation properties of WC.

[0083] After 20 years of land surface processes, the high water conservation (WC) area downstream of the study area has expanded significantly. The spatial distribution characteristics of WC show obvious aggregation, that is, the water conservation similarity between the branch basin units and the adjacent units is getting higher and higher. The centers of HH aggregation or LL aggregation are gradually becoming prominent. In particular, Shiqu has become a low-value center, while Aba and southern Hongyuan have become high-value centers, where the water conservation increase is most significant.

[0084] 3. 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) regression model.

[0090] 3. Spatial pattern analysis

[0091] like Figure 5 and Figure 6 As shown in Figure 2, the analysis results of MGWR indicate that the specified bandwidth for each parameter helps to analyze the spatial pattern of the influencing factors of WC more accurately and completely. The spatial scale differences of this effect can be revealed by the optimal bandwidth detected by MGWR for each individual predictor variable. Compared with the same bandwidth of the 68 nearest neighbors of GWR, the bandwidth of MGWR can be classified into global, regional, and local indicator variables. LAI and land cover are global variables with the largest influence scale, population density and soil available water capacity are regional variables with large influence scales, while temperature, wind speed, topography, soil depth, and GDP are local variables with small influence scales. This means that the global variables indicate that almost all units participate in parameter estimation and are affected by LAI and land cover. Similarly, regional variables and local variables imply the influence on water conservation at the county and branch watershed scales, respectively.

[0092] Figure 6 The spatial heterogeneity of the parameter estimates for each factor in MGWR is further visualized. The colored areas represent cells with significant local parameter estimates, with darker colors indicating positive estimates and lighter colors indicating negative estimates. The estimates within the areas filled with slashes are not significantly different from zero. Compared to OLS, MGWR finds different parameter estimates for individual variables.

[0093] In terms of climate, both variables show spatial variation. The effect of temperature is significantly positive across the study area and is much higher than other positive variables. The estimated values ​​decrease gradually from Shiqu in the upper reaches to Songpan in the lower reaches ( Figure 6 a). However, the effect of wind is significantly negative only in the downstream areas, especially in Hongyuan and Songpan, which is much higher than other negative variables ( Figure 6 b). Higher estimates indicate that water conservation is susceptible to changes in climatic factors. In units with low water conservation, their positive correlation with temperature is more significant and increases with increasing temperature. The effect pattern for wind is the opposite. It has a single negative cluster in Hongyuan and Songpan, where water conservation values ​​are high and decreases with increasing wind speed. In terms of surface characteristics, topographic variables appear as clusters: one negative cluster in Zoige and another positive cluster in the southeast corner of Songpan and Shiqu ( Figure 6 c). The direction of their effects is 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 the upstream Shiqu, but the latter is more significant in the downstream area. Both estimates are low, indicating that water conservation is not highly responsive to them ( Figure 6d and 6e). For the soil depth variable, the estimated surface has a single negative cluster in the southwest corner of Hongyuan, indicating that the negative impact is more significant where the soil depth is smaller ( Figure 6 f).

[0094] Among the socioeconomic factors, only the GDP variable is estimated to be significant. It shows two different negative clusters: one in the central areas of Aba and Hongyuan, and the other covering almost all of Songpan ( Figure 6 g). The estimated surface shows the opposite pattern of effects, with the negative effect being more pronounced in places with higher GDP. Unlike OLS, in the downstream regions of Zoige, Aba, and Hongyuan, the intercept estimate is found to be significant ( Figure 6 h). However, little spatial variation was detected in these surfaces. The intercept here may represent the effect of the remaining spatial variance explained by the undetected variables after controlling for the nine indicator variables present in MGWR. It also shows that the regression model has a better fit in Songpan and upstream areas.

[0095] The MGWR results also provide important practical implications for site-specific identification of dominant factors and multi-scale policy formulation, which can help improve strategies for water conservation and adaptation to soil erosion processes on the land surface.

[0096] Water conservation is spatially heterogeneous, with factors varying at global, regional, and local scales, necessitating scalable interventions. At the global scale, areas with greater vegetation cover have higher water conservation capacity. This finding could help protect widespread sub-catchment areas with low water conservation. Several large cities (e.g., Los Angeles and Philadelphia in the United States, and Athens in Greece) have demonstrated the effectiveness of large-scale enhancement of vegetation cover and surface reflectivity in reducing runoff and evaporation. At the regional scale, key socioeconomic factors differ in their impact on water conservation. This result is important because it suggests the need for targeted regional development strategies. Figure 7 As shown, at the local sub-basin level, the associations between wind, topography, soil factors and water conservation clearly vary between different sub-basins within the same region.

[0097] Based on the result coefficient estimation surface, we found that the Yangtze River source area can be divided into 10 regions with different priorities for water conservation, such as Figure 8 As shown, the 10 regions are labeled 1 to 10 in the central map, and each region corresponds to a small map (labeled A1 to A10) showing the location-specific indicator variables ranked by priority.

[0098] Temperature plays a dominant role in all regions from A1 to A10, where climate management strategies aimed at adjusting regional runoff intensity are expected to produce greater water conservation benefits than other strategies, as higher winter temperatures may be the main intervention for more glacial meltwater in these hotspots. Analyses based on more accurate and reliable results obtained using MGWR help delineate the driving “hotspots” of different factors. In the sub-basin units of A2, A3, A4, A8, and A10, areas with faster wind speeds may be more sensitive to reductions in WC than other areas.

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

[0100] Results obtained by identifying site-specific dominant factors indicate that there is large spatial variation in the local environmental and socioeconomic factors driving water conservation. Overall, temperature is the most important determinant of water conservation in the Yangtze River headwaters, followed by wind speed, slope, and leaf area index (LAI). The spatial heterogeneity of driving forces suggests that there is unlikely to be a one-size-fits-all solution strategy for water conservation.

[0101] 4. Analysis of spatiotemporal changes

[0102] The results of GTWR are as follows Fig. 9 As shown. It can be seen that the use of raster 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 was always important throughout the study area. Over time, the hotspots of temperature impact on WC showed a trend of shifting from downstream to upstream. Recently, Shiqu and Aba have become the main positively correlated areas. For the estimated surface of wind, there is no significant pattern change over time. In Shiqu, upstream of the basin, the impact of wind shows a slightly weakening trend.

[0103] In terms of surface factors, the LAI results show that initially only the LAI in the downstream area had a positive impact on water conservation. Over time, the scope and area of ​​the negatively affected area gradually expanded from 2001 to 2010, especially in Shiqu, while the scope and area of ​​the positive impact increased after 2010, especially in the downstream area. In general, the impact of LAI showed a change from negative to positive, and it has expanded dramatically in recent years with the increase of LAI.

[0104] In terms of socioeconomic factors, GDP results show that initially it exhibits extremely high spatial heterogeneity, with a positive impact on water conservation in the upstream, but a negative impact in the downstream. Over time, the extent and area of ​​the positive impact area gradually expanded in the upstream from 2001 to 2015. With the continuous increase in GDP, the estimated values ​​for all branch basin units there have recently become insignificant. However, as the GDP in the downstream is higher than that in the upstream, the extent and area of ​​the negative impact area gradually decreases.

[0105] V. Conclusion

[0106] Water conservation (WC) is an important indicator of regional ecological environment, which is affected by multiple environmental and socioeconomic factors and has complex mechanisms. This experiment uses MGWR and GTWR to analyze the spatiotemporal characteristics of water conservation changes in the source region of the Yangtze River. These results show what measures should be taken to effectively increase water conservation and protect the ecological environment.

[0107] Overall, the results showed that water conservation showed a slight upward trend, and the distribution pattern of WC in 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 vary at a more global scale, while others may vary at a non-local scale. Improving agricultural production efficiency and improving industrial structure are crucial to the trade-off between ecological protection and economic development.

[0109] In terms of spatiotemporal characteristics, due to the relatively stable climate during the study period, the effects of temperature and wind speed were almost dominant and consistent in time. In contrast, the impact of vegetation on WC changed from negative to positive in the 2000s, which was due to the local vegetation restoration project shifting from mainly woodland to mainly grassland. The study concluded that water conservation in the source area of ​​the Yangtze River benefits more from understory vegetation. Therefore, site-specific vegetation restoration projects based on local climate, slope, and soil are necessary.

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

[0111] VI. Advantages of the present invention in analyzing the driving force of water conservation

[0112] This paper explores the driving mechanism of WC by multiple factors through GTWR and MGWR, and provides a case study for using GTWR and MGWR models for spatiotemporal integrated regression analysis. The dynamics of WC is a long-term time process, and its changes are not isolated and will be affected by previous dynamics, and current changes will also affect future results. Therefore, using GTWR to analyze relevant long-term series data reveals the pattern of dynamic changes, thereby providing predictions and suggestions for future changes. The simultaneous analysis of these 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 invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. The water conservation influencing factor analysis method based on geographic spatiotemporal weighted regression is characterized by: Includes steps: S1. Collecting data on influencing factors of the target watershed as a first data set; the influencing factors include historical environmental factors and historical socio-economic factors; S2, calculate the historical water conservation value of the target area and perform spatial aggregation analysis as the second data set; S3, using a multi-scale geographically weighted regression method to perform spatial pattern analysis on the first data set and the second data set to obtain spatial characteristics of various influencing factors; S4, using the geographic spatiotemporal weighted regression method to perform spatiotemporal change analysis on the first data set and the second data set to obtain the spatiotemporal characteristics of various influencing factors; S5. Based on the spatial characteristics and spatiotemporal characteristics, the impact characteristics of various influencing factors on water conservation in the target watershed are obtained.

2. The method for analyzing factors affecting water conservation based on geographic spatiotemporal weighted regression according to claim 1, characterized in that: The historical environmental factors in step S1 include the climate data and surface data of the target watershed over the years; the historical socio-economic factors include the population density data and GDP data of the target watershed over the years.

3. The method for analyzing factors affecting water conservation based on geographic spatiotemporal weighted regression according to claim 1, characterized in that: The method for performing spatial aggregation analysis in step S2 includes: The global spatial autocorrelation coefficient Moran's I is used for evaluation: in, is the average water conservation value of n sub-units in the target basin; w ij Provide evidence for spatial weight; x i and x j are the water conservation values ​​of target watershed subunit i and subunit j respectively; The global spatial autocorrelation coefficient Moran's I ranges from -1 to 1; When Moran's I < 0, the water conservation values ​​between the target watershed subunit i and subunit j are negatively correlated, and the smaller the Moran's I, the greater the similarity of the water conservation values ​​between the target watershed subunit i and subunit j; When Moran's I = 0, the water conservation values ​​between subunit i and subunit j in the target watershed are irrelevant; When Moran's I>0, the water conservation values ​​between target watershed subunits i and j are positively correlated, and the larger the Moran's I, the greater the similarity of water conservation values ​​between target watershed subunits i and j.

4. The method for analyzing factors affecting water conservation based on geographic spatiotemporal weighted regression according to claim 1, characterized in that: The spatial characteristics of various influencing factors in step S3 include: Classification features are used to characterize the development of various influencing factors at different spatial scales; Driving characteristics are used to characterize the significance of spatial heterogeneity of various influencing factors at different spatial scales.

5. The method for analyzing factors affecting water conservation based on geographic spatiotemporal weighted regression according to claim 1, characterized in that: The method of performing spatiotemporal change analysis on the first data set and the second data set using the geographic spatiotemporal weighted regression method in step S4 includes: Among them, y i is the response variable; β0(u ι ,v ι ,y 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 the 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.

6. The method for analyzing factors affecting water conservation based on geographic spatiotemporal weighted regression according to claim 2, characterized in that: The spatiotemporal characteristics of various influencing factors described in step S4 include: Climate characteristics, used to characterize the stability of climate factors in time series, and the changes in the relationship between climate factors and water conservation in time series; Surface characteristics, used to characterize the changes of surface factors in time series; Socioeconomic characteristics are used to characterize the changes in socioeconomic factors in the time series, and the changes in the relationship between socioeconomic factors and water conservation in the time series.

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