Assessing impact of urban impervious surface on watershed hydrology using distributed object-oriented simulation and spatial regression
Date of Original Version
In this study, we investigated the relationship between watershed characteristics and hydrology using high spatial resolution impervious surface area (ISA), hydrologic simulations and spatial regression. We selected 20 watersheds at HUC 12 level with different degrees of urbanization and performed hydrologic simulation using a distributed object-oriented rainfall and runoff simulation model. We extracted the discharge per area and ratio of runoff to base flow from simulation results and used them as indicators of hydrology pattern. We derived percentage of ISA, distance from ISA to streams, and stream density as the watershed characteristics to evaluate the relationship with hydrology pattern in watersheds using ordinary least square, spatial error and spatial lag regression models. The comparison indicates that spatial lag regression model can achieve better performance for the evaluation of relationship between ratio of runoff to base flow and watershed characteristics, and that three models provide similar performance for the evaluation of relationship between discharge per area and watershed characteristics. The results from regression analyses demonstrate that ISA plays an important role in watershed hydrology. Ignorance of spatial dependence in analyses will likely cause inaccurate evaluation for relationship between ISA and watershed hydrology. The hydrologic model, regression methods and relationships between watershed characteristics and hydrology pattern provide important tools and information for decision makers to evaluate the effect of different scenarios in land management. © 2013 Springer Science+Business Media Dordrecht.
Publication Title, e.g., Journal
Zhou, Yuyu, Yeqiao Wang, Arthur J. Gold, Peter V. August, and Thomas B. Boving. "Assessing impact of urban impervious surface on watershed hydrology using distributed object-oriented simulation and spatial regression." GeoJournal 79, 2 (2014): 155-166. doi: 10.1007/s10708-013-9506-x.