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Advances in Meteorology
Volume 2016 (2016), Article ID 5173984, 15 pages
Research Article

Spatial Combination Modeling Framework of Saturation-Excess and Infiltration-Excess Runoff for Semihumid Watersheds

1College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
2National Cooperative Innovation Center for Water Safety & Hydro-Science, Hohai University, Nanjing 210098, China
3School of Earth Science and Engineering, Hohai University, Nanjing 210098, China

Received 5 October 2015; Revised 22 December 2015; Accepted 30 December 2015

Academic Editor: Jingfeng Wang

Copyright © 2016 Pengnian Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There exist two types of direct runoff generation mechanisms in semihumid watersheds: saturation-excess mechanism and infiltration-excess mechanism. It has always been a difficult problem for event hydrological simulation to distinguish the two types of runoff processes. Based on the concept of dominant runoff processes, combined with GIS and RS techniques, this paper proposed an event-based spatial combination modeling framework and built two spatial combination models (SCMs) accordingly. The CN parameter and topographic index, both of which are widely used in hydrological researches, are adopted by the SCM to divide the entire watershed into infiltration-excess dominated (IED) areas and saturation-excess dominated (SED) areas. Dongwan watershed was taken as an example to test the performances of infiltration-excess model, saturation-excess model, and SCM, respectively. The results of parameter optimization showed that the parameter values and state variables of SCM are much more realistic than those of infiltration-excess model and saturation-excess model. The more accurate the divisions of infiltration-excess and saturation-excess dominated areas, the more realistic the SCM parameter values. The simulation results showed that the performance of SCM was improved in both calibration and validation periods. The framework is useful for flood forecasting in semihumid watersheds.