Developing a quantitative landscape regionalization framework integrating driving factors and response attributes of landscapes

Regionalization plays an important role in delineating landscape heterogeneity and providing spatial frameworks for environmental management. In this study, we developed a comprehensive regionalization approach that integrated multiple quantitative techniques and differentiated two distinct types of landscape variables, i.e., response attributes and driving factors. This approach was applied to the regionalization of surface-water resource in the Huai River Basin (HRB), China. In the regionalization scheme, the 25 subwatersheds of the HRB were adopted as the basic spatial unit; surface-water capacity, runoff depth, and drainage system density were used to characterize surface-water distribution (i.e., response attributes). The HRB subwatersheds were classified into a certain number of groups using the k-means cluster analysis based on the three response attributes. A goodness-of-fit index, calculated as the ratio of between-group/within-group variation, was employed as a quantitative criterion to assess the statistical performance of the classification results. Ultimately, the 25 subwatersheds were partitioned into five distinct regions. Results from redundancy analysis suggested that such regional pattern of surface-water resource was primarily correlated with driving factors representing climate conditions; soil and geological properties also had significant influences. Overall, our approach presents two advantages over previous regionalization frameworks: (1) It improves objectivity of landscape regionalization and reveals underlying mechanisms, generating landscape patterns by integrating response attributes and driving factors; (2) Goodness-of-fit evaluation can substantially reduce subjectivity in determining regionalization results.