Spatial patterns in baseflow mean response time across a watershed in the loess plateau: Linkage with land-use types

Abstract

Understanding the relation between land-use types and baseflow mean response time (BMRT) is important to explore the response mechanism of baseflow processes in watersheds. BMRT was determined using an instantaneous unit hydrograph. The instantaneous unit hydrograph parameters were estimated by autocorrelation functions. The relative importance of land-use types in determining BMRT dynamics was assessed by hydrological model and partial least-squares regression. Our study suggests greater effects of urban area on BMRT than the effects of forest and agricultural land. This may be because the urban interception impervious area may impede baseflow generation over a short timescale. The effects of agricultural land are greater than those of forest in areas with steeper hillslopes, but lower than those of the forest in areas with more plains, reflecting the varied ability of forest and agricultural lands with different topography to hinder overland flow. Variations of BMRT are strongly linked to land use in the watershed. Overall, our study provides insight into the BMRT and dominant factors of land-use types in watersheds, planning of sustainable water resource use, and ecological protection in watersheds. Study Implications: For those who are considering the use of a mathematical model to represent the development of forest ecohydrological process, the comprehensive application of a hydrological model and multiple regression methods may seem a reasonable strategy to generate a mathematical model to represent the ecohydrological process. However, our results showed that the use of conventional regression to investigate baseflow mean response time responses to land-use change has limited power because of insufficient data and the lack of independence of land-use types. Therefore, coupling of a soil and water assessment tools model and partial least-squares regression to handle multicollinear and noisy data may be a more useful strategy to determine the contributions of variables to hydrological changes and to provide quantitative information for decisionmakers to seek an optimal land-use pattern in watersheds.