Open AccessPrediction of surface and subsurface flow in catchments using the GIUH
Author(s) -
Sabzevari T.,
Fattahi M.H.,
Mohammadpour R.,
Noroozpour S.
Publication year - 2013
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/j.1753-318x.2012.01165.x
Subject(s) - hydrograph , surface runoff , subsurface flow , flood myth , hydrology (agriculture) , groundwater recharge , saturation (graph theory) , flow (mathematics) , environmental science , richards equation , base flow , geology , mathematics , soil science , drainage basin , soil water , geotechnical engineering , geometry , geography , groundwater , aquifer , archaeology , ecology , cartography , combinatorics , biology
Flood estimation in ungauged catchments is one of the important points in designing hydraulic structures. In this paper, the geomorphological instantaneous unit hydrograph ( GIUH ) model previously suggested by L ee and C hang to estimate the surface and subsurface hydrographs of catchments was extended. To improve the model, a new equation was presented based on the D unne– B lack mechanism for separating the overland into saturated and unsaturated zones. Two new factors, soil thickness and rainfall recharge rate, were taken into account in calculations of the saturation zone length, and the travel times of surface and subsurface flows. The effect of saturation upon subsurface flow travel time was regarded in the GIUH model. The convolution equation for the estimation of the direct run‐off hydrograph ( DRH ) was improved according to the response of subsurface flow to rainfall recharge, and the response of surface flow to excess rainfall. The final model was employed to predict the surface and subsurface flows in the K asilian catchment. The performance of the GIUH was found to be more effective, as reflected in lower root mean square error (1.24 m 3 / s ) and relative mean absolute error (0.07). The average error was 8.78% to estimate the flood peak. In general, the efficiency of the model in the estimation of DRH was assessed as proper.