## 4 Discussions  
The KAPZs is an important component of the ECZ. Exploring its water cycle characteristics is the basis for optimizing the use of water resources. In this study, we used a variety of theories and methods such as spatial and statistical analysis under geography to construct a groundwater response unit for typical areas in KAPZs. The response unit could be used to study the trend and law of the collection and divergence of underground runoff in space, and analyze its distribution law in two dimensions of space and time. Furthermore, the intrinsic driving force of land cover could be determined through the construction of a geographic regression model combining spatial information. To study the characteristics of groundwater burial depth in the KAPZs, the data of groundwater burial depth needs to be displayed in space first. Compared with ordinary kriging interpolation results, the use of cokriging interpolation methods combined with precipitation, vegetation coverage, and hydrogeological conditions interpolation results can effectively avoid errors caused by the spatial distribution of observation points (Figures 5 and 6), to make the spatial distribution more regular, changes more gentle and more in line with the actual differentiation of groundwater depth. [PERSON] et al. used the Dalabu Plain as the study area and compared the accuracy of kriging interpolation and synergistic kriging interpolation in groundwater depth simulation in arid and semi-arid areas, indicating that kriging is more accurate [71]. The performance of the collaborative kriging interpolation method is superior to other geostatistical methods. [PERSON] applied ordinary synergy kriging to the calculation of groundwater results in two watersheds in Australia [6]. In the age \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **AICc** & **R2** & **R2 Adjusted** \\ \hline
2005 & \(-143.7\) & \(0.8153\) & \(0.7766\) \\
2009 & \(-44.38\) & \(0.8318\) & \(0.7823\) \\
2013 & \(-282.2\) & \(0.9398\) & \(0.9170\) \\
2017 & \(-54.83\) & \(0.7267\) & \(0.6805\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Normalized difference drought index (NDDI) geographic weighted regression fitting parameters. Figure 12: Normalized difference drought index (NDDI) geographic weighted regression standardized residuals. structure of groundwater, modern groundwater is the uppermost and most active part of groundwater, mainly distributed within 3 m from the surface. Inverse distance interpolation has poor interpolation results for regional studies, and ordinary collaborative kriging is determined as the best interpolation method for spatial distribution. In this paper, the ordinary interpolation results are extremely irregular in space, the spatial and temporal distribution differences are too large and scattered, and there are no obvious rules to follow. The results of the groundwater depth of the coordinated kriging interpolation have certain interannual and spatial changes over the years, but there are rules to follow. The overall distribution characteristics show a relatively stable trend, although it fluctuates slightly. The groundwater depth is between two and five, and the overall distribution shows a trend of high in the east and low in the west; therefore, it is more effective to use the kriging interpolation to analyze the results of groundwater depth affected by various factors. Compared with previous studies on groundwater, we focused on the research of spatial analysis and vector patch division. After studying 605 catchment areas, [PERSON] et al. concluded that the forest ratio and soil type have a significant effect on runoff and groundwater characteristics [72]. In this paper, under different land types and soil environments, the study area was divided into many response units of different sizes according to the topographic fluctuation and the direction of groundwater confluence.