Infrastructure Engineering - Research Publications

Permanent URI for this collection

http://hdl.handle.net/11343/347

Filters

2005 1
Reset filters

Settings
Statistics
Citations
Author: Western, Andrew × Author: KANDEL, DURGA × Type: Journal Article × Start date: 2000 × End date: 2009 ×

Search Results

Now showing 1 - 1 of 1
  • Item
    Thumbnail Image
    Scaling from process timescales to daily time steps: A distribution function approach
    Kandel, DD ; Western, AW ; Grayson, RB (American Geophysical Union, 2005-02)
    A new temporal scaling method applicable to many rainfall-runoff-erosion models is presented. The method is based on the probability distribution approach used in a number of spatial hydrological models, and it uses statistical distributions of rainfall intensity to represent subdaily intensity variations in a daily time step model. This allows the effect of short timescale nonlinear processes to be captured while modeling at a daily time step, which is often attractive due to the wide availability of total daily rainfall data. The approach relies on characterizing the rainfall intensity variation within a day using a probability distribution function (pdf). This pdf is then modified by various linear and nonlinear processes typically represented in hydrological and erosion models. The statistical description of subdaily variability is thus propagated through the model, allowing the effects of variability to be captured in the simulations. This results in pdfs of various fluxes, the integration of which over a day gives respective daily totals. The method is tested using 42 plot years of daily runoff and erosion plot data from field studies in different environments from Australia and Nepal. Significant improvements in the simulation of surface runoff and erosion are achieved, compared with a similar model using average daily rainfall intensities. The probability-based model compares well with a subhourly (2 and 6 min) model using similar process descriptions. This suggests that the probability-based approach captures the important effects of sub–time step variability while utilizing commonly available information. It is also found that the model parameters are more robustly defined using the probability-based approach compared with the daily effective parameter model. This suggests that the probability-based approach may offer improved model transferability spatially (to other areas) and temporally (to other periods).