Search

The rapid shifts in landuse and the frequency of flooding events brought by urbanization significantly impact the urban basins, which means that optimizing landscape patterns is crucial for improving urban hydrology. Therefore, the Liuxi River Basin (LRB), an important river in the Pearl River Delta region, was utilized as an example in the study to examine how the landscape configuration of greenspaces and water bodies influences the hydrological components. However, there are few studies on landscape hydrology in the Pearl River Delta (PRD) and the spatial linkages between landscape metrics and hydrological components are not clear. Based on this, this paper aims to explore the mathematical and spatial associations between landscape configuration and hydrological variables in the PRD region. The SWAT model was constructed to simulate hydrological components based on landuse changes from 1980 to 2018, and regressions were established for landscape pattern indices, water morphology parameters, and hydrological components using stepwise multiple linear regression and geographically weighted regression (GWR). According to the results, the LRB continued to develop, although the greenspaces spread out and changed shape considerably. The influence of water bodies on the hydrological component is mainly in evapotranspiration, and some three-dimensional parameters such as relief ratio favor surface runoff. In the case of greenspace, the runoff is less in connected and wide farmland areas. Irregular forest land will promote water yield but suppress evapotranspiration, and the effect of forest’s shape on hydrological components is more obvious in upstream areas because the regression coefficients in this range are higher, such as 90.27. Methodologically, we found that for water yield, the accuracy of GWR was significantly higher (R2 = 0.814) than that of multiple linear regression (R2 = 0.243). In addition, understanding land use changes before conducting landscape hydrology studies can help make the results more interpretable. We supplemented the relevant research gaps in the PRD region and revealed the spatial correlation between landscape metrics and the hydrological cycle. These findings can aid urban planners in improving their comprehension of hydrological processes and landscape design, leading to more sustainable management of the urban basins.

With the growing demand for cultural ecosystem services around the world, many countries and regions have recognized the importance of the scenic beauty of villages and have introduced relevant policies. Many less modernized hilly and mountain villages will be redesigned. It is important to develop a model to predict the scenic beauty of these villages. However, only limited studies have been conducted to evaluate the scenic beauty of villages. This study aimed to explore the procedures for assessing the scenic beauty of mountain and hilly villages and to investigate the relationship between scenic beauty and landscape indicators. We first summarized the characteristics of these villages. Based on these characteristics, a series of indicators such as depth values and landscape metrics were used as independent variables, while photo scores were used as dependent variables, and different prediction models were developed with multiple linear regression, principal component regression, and support vector regression. Statistical metrics and the generalization ability of different models were compared. The results indicated that the inner areas of Xitou villages had the lowest scenic beauty scores, and people preferred natural scenery. Among landscape indicators, landscape diversity metrics and shape metrics have a significant impact on scenic beauty. Landscapes with a combination of multiple elements and not overly complex shapes may be more attractive. There was a significant negative correlation between mean depth and scenic beauty, implying that people prefer open space. It is essential to consider the depth value when evaluating scenic beauty in regions with strong 3-dimensional elements. The comparison between the three models revealed that the complex nonlinear algorithm performed and generalized better than linear models in villages where these areas are smaller and more sensitive to subtle changes. The method can be applied to other hilly and mountain villages in China, helping designers and decision-makers to output more reasonable planning and management schemes to fully utilize the local landscape and enhance tourism competitiveness and villagers' well-being.