## 5 Discussion  
HPAM define slope units as regions between ridge and valley lines based on geomorphic features. While operationally straightforward and widely applied in engineering projects, this approach exhibits significant limitations in terms of refinement and automation. For instance, HPAM-derived slope units often fail to accurately represent complex terrain, as they may merge multiple natural slopes into single units or encompass entire watersheds in mountainous areas. The method's strong reliance on subjective parameter settings leads to inconsistent results across different regions. Moreover, the segmentation process frequently generates elongated polygons that require extensive manual adjustments, reducing efficiency and accuracy for large-scale, high-resolution analyses. These inherent drawbacks restrict the HPAM's applicability in geologically heterogeneous regions where precise slope unit delineation is critical for landslide risk assessments, soil erosion modeling, and sustainable land management. Compared to HPAM, the SUD-CGHP integrates existing slope unit definitions with computer graphics techniques and hydrological principles to enable automated, high-precision slope unit delineation, significantly enhancing operational efficiency. This method demonstrates superior consistency in flow direction, aspect, and gradient parameters across derived units, overcoming the limitations of traditional approaches. Its strong adaptability extends to complex terrain and diverse geographic conditions, making it suitable for generating fundamental units in geological hazard prediction and assessments. Consequently, the proposed SUD-CGHP method offers notable advantages in terms of segmentation fineness, intelligent automation capabilities, and broader applicability compared to traditional hydrological techniques. In this study, the SUD-CGHP and HPAM were implemented on an NVIDIA RTX 3060 laptop GPU platform, with processing times of 30 min and 15 min, respectively. Notably, the HPMA required an additional 48 h of manual correction, while SUD-CGHP achieved fully automated post-processing with zero human intervention. Although SUD-CGHP demonstrates significant advantages in automation and efficiency, its resource-intensive nature imposes strict computational demands, potentially limiting its application in environments with constrained computing resources. Specifically, handling large-scale datasets or real-time tasks may encounter performance bottlenecks due to its high hardware requirements. Therefore, while the new method offers theoretical superiority in segmentation precision and operational, practical deployment must carefully balance computational efficiency against available resources to optimize its applicability across diverse scenarios. Compared to the HPAM that relies on empirical threshold parameters (e.g., flow thresholds), the SUD-CGHP method employs a dual-constraint fusion mechanism to achieve threshold-free parameterization. By integrating flow direction and aspect consistency as merging constraints during slope unit delineation, this method aligns with the fundamental characteristics of natural slope units. Specifically, it categorizes the continuous 0-360\({}^{\circ}\) directional spectrum into eight compass azimuth classes and uses overall directional coherence as a merging criterion. However, this discrete classification approach may introduce inherent biases when applied to real-world scenarios where both flow direction and aspect are inherently continuous variables. To address this limitation, future research could explore dynamic merging strategies under continuous numerical conditions, such as incorporating fuzzy logic methodology to reconcile slope unit micro-variations with continuous directional gradients, thereby enhancing the precision of merging processes through better integration of topographic features. Such advancements would bridge the gap between discrete categorization and continuous terrain variability, advancing the method's applicability in complex geomorphic environments.