Post, D. A.; Jones, J. A.; Grant, G. E. 1998. An improved methodology for predicting the daily hydrologic response of ungauged catchments. Environmental Modelling and Software. 13: 395-403.
In order to model fluxes of water from the land surface to the atmosphere, and from one grid cell to another in climate models,predictions of hydrologic response are required for catchments where hydrologic data are not available. A methodology has beenpresented previously that has the capability of producing estimates of catchment scale hydrologic response for ungauged catchmentson a daily timestep (Post and Jakeman, 1998, Ecol. Mod. submitted). In the present paper, it is demonstrated that these dailypredictions of hydrologic response can be improved by incorporating information about the hydrologic response of the catchmenton a longer timestep. This is because the influence of large scale phenomena such as climate and vegetation may produce a similarwater yield in nearby catchments, even though their daily hydrologic response may be different, due for example, to differencesin drainage density. Thus, the water yield of an ungauged catchment is inferred on an inter-annual timestep, and this informationis used to balance the water budget of a daily timestep rainfall-runoff model. It was found that using tree stocking densities topredict water yields for small experimental catchments in the Maroondah region of Victoria produced better results than thoseobtained by inferring the water balance parameter of a daily timestep rainfall-runoff model from channel gradient and catchmentelongation. Good predictions of inter-annual water yield were also obtained for small experimental catchments in the H. J. Andrews,Hubbard Brook, and Coweeta long term ecological research (LTER) sites in the United States, indicating that it may be possibleto produce high quality predictions of daily hydrologic response for ungauged catchments in these regions also. © 1998 ElsevierScience Ltd. All rights reserved.
Keywords: Hydrologic regionalisation; Rainfall-runoff model; LTER network