Modelling Rainfall-Runoff From Large Catchment to Basin Scale: The Goulburn Valley, Victoria

Abstract

A lumped parameter dynamic rainfall-runoff model, IHACRES, is applied to the large upland area (more than 4500 km2) of the Goulburn Valley Basin, Victoria, Australia to predict streamflow under different climatic conditions. This paper presents the first evaluation of a rainfall-runoff model at large catchment scale, which is comprehensive in terms of the number of catchments investigated and the number of calibration and simulation periods used. The basin is subdivided into 12 catchments (from 100 to 700 km2), each of which is calibrated separately. High values of model efficiency and low bias are consistently obtained for different calibration sub-periods for all catchments in the basin. Simulation or so-called validation tests are used to select the best models for each catchment. This allows simulation of the water regime during long historical (approximately 90 year) periods when only climatological (rainfall and temperature) data were available. This procedure is extremely important for the estimation of the effect of climate variability and of the possible impact of climate change on the hydrological regime in the region and, in particular, for supporting irrigation management of the basin.Analysis of a composite catchment (241 7 km2) and its five separate subcatchments indicates that the information content in the rainfall-streamflow data is independent of catchment size. Dynamic modelling of the daily water balance at the macroscale is limited principally by the adequacy of the precipitation gauging network. When a good estimate of areal precipitation is available for a catchment, it is not necessary to consider subcatchment-scale variability for modelling if the only interest is the daily discharge and evaporation losses from the catchment. KEY WORDS rainfall-runoff; models; model calibration; streamflow; climate variability; droughts; basins. of the Murray-Darling Irrigation District for Australian rural industry and the importance of the Goulburn Basin for irrigation supply within this district.

Coupling Hydrologic and Infectious Disease Models To Explain Regional Differences in Schistosomiasis Transmission in Southwestern China

“…A conceptual rainfall−runoff model (IHACRES; , ) was used to estimate daily channel flow in representative channels in the two sites. The IHACRES model, having been applied to diverse catchments worldwide including small and ephemeral catchments – , is particularly attractive in this application because it is parametrically efficient. It consists of a nonlinear rainfall loss module, which converts observed rainfall (mm) into effective or excess rainfall, and a linear module, which converts excess rainfall into observed streamflow (m 3 s −1 ; Supporting Information Figure S1).…”

Using GIS Tools and Data for Hydrological Modelling in Arid Areas: A Literature Review

“…Using GIS Tools and Data for Hydrological Modelling in Arid Areas: A Literature Review Dr. Hassan M. Khormi model was intended for applications to temperate catchments (Jakeman et al, 1990). The model was later modified to include applications to ephemeral streams (Ye et al, 1995(Ye et al, , 1997Schreider et al, 1996). According to Littlewood (2003), the main goal of using the IHACRES model is to describe catchment-scale hydrological behavior using a limited number of parameters.…”

Statistical downscaling of climate data to estimate streamflow in a semi-arid catchment

“…This version of the model is more general than the version used within the Ihacres PC model (Post and Jakeman, 1996) which can be recovered by setting parameters l to zero and p to one (with the soil moisture index) in the original model given by S k = cΦ k This version of the non-linear module is described in detail in Jakeman et al (1990), Jakeman and Hornberger (1993) and Jakeman and Hornberger (1993) Examples of studies that have used this version of Ihacres (with minor modifications to the Eq. 3) can be found in Post and Jakeman (1996), Schreider et al (1996) and Ye et al (1997). The success of these models is often influenced by the calibration skills of the user.…”