Scale Effects on Spatially Varying Relationships Between Urban Landscape Patterns and Water Quality
- 749 Downloads
Scientific interpretation of the relationships between urban landscape patterns and water quality is important for sustainable urban planning and watershed environmental protection. This study applied the ordinary least squares regression model and the geographically weighted regression model to examine the spatially varying relationships between 12 explanatory variables (including three topographical factors, four land use parameters, and five landscape metrics) and 15 water quality indicators in watersheds of Yundang Lake, Maluan Bay, and Xinglin Bay with varying levels of urbanization in Xiamen City, China. A local and global investigation was carried out at the watershed-level, with 50 and 200 m riparian buffer scales. This study found that topographical features and landscape metrics are the dominant factors of water quality, while land uses are too weak to be considered as a strong influential factor on water quality. Such statistical results may be related with the characteristics of land use compositions in our study area. Water quality variations in the 50 m buffer were dominated by topographical variables. The impact of landscape metrics on water quality gradually strengthen with expanding buffer zones. The strongest relationships are obtained in entire watersheds, rather than in 50 and 200 m buffer zones. Spatially varying relationships and effective buffer zones were verified in this study. Spatially varying relationships between explanatory variables and water quality parameters are more diversified and complex in less urbanized areas than in highly urbanized areas. This study hypothesizes that all these varying relationships may be attributed to the heterogeneity of landscape patterns in different urban regions. Adjustment of landscape patterns in an entire watershed should be the key measure to successfully improving urban lake water quality.
KeywordsUrban landscape pattern Water quality Spatially varying relationship Ordinary least squares regression Geographically weighted regression Scale effects
This research was financially supported by the project of Science and Technology of Xiamen (3502Z20122001) and the National Natural Science Foundation of China (NSFC) (30800148).
- Azous AL, Horner RR (2001) Wetlands and urbanization: implications for the future. CRC Press LLC, Boca RatonGoogle Scholar
- Brunsdon C, Fotheringham S, Charlton M (1998) Geographically weighted regression: modeling spatial non-stationarity. J Royal Stat Soc 47:431–443Google Scholar
- Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, ChichesterGoogle Scholar
- Lin JH (2009) Assessment of sustainable utilization of water resources in Xiamen. Environ Sci Manag 34:155–164Google Scholar
- McGarigal K, Marks BJ (1995) Fragstats: spatial pattern analysis program for quantifying landscape structure. General Technical Report PNW-GTR-351. Pacific Northwest Research Station, USDA-Forest Service, PortlandGoogle Scholar
- Winchell M, Srinivasan R, Di Luzio M, Arnold JG (2007) ArcSWAT interface for SWAT user’s guide. Blackland Research Center, Texas Agricultural Experiment station and USDA Agricultural Research Service, TexasGoogle Scholar