B. Fentie

PLOT-SCALE RAINFALL-RUNOFF CHARACTERISTICS AND MODELING AT SIX SITES IN AUSTRALIA AND SOUTHEAST ASIA

During major runoff events when most soil loss occurs, runoff is likely to dominate the rainfall-driven erosion processes. Thus accurate estimation of the runoff rate is critical to soil loss predictions. At plot scale, the Green-Ampt infiltration model is commonly assumed to be able to describe the temporal variation of the infiltration rate over a storm event. Field measurements of both rainfall intensity and runoff rate at 1-min intervals at six sites in the tropical and subtropical regions of Australia and Southeast Asia, however, strongly suggest that the apparent infiltration rate is closely related to the rainfall intensity and it is essentially independent of the cumulative infiltration amount, features not accord with the Green-Ampt infiltration equation. Furthermore, the storage effect and runoff rate attenuation are not negligible at the plot scale. With an initial infiltration amount to determine when runoff begins, an exponential distribution to describe the spatial variation in the maximum infiltration rate and a linear storage formulation to model the lag between runoff and rainfall, we were able to develop a satisfactory three-parameter model for the runoff rate at 1-min intervals within a storm event.

Toward a Framework for Runoff and Soil Loss Prediction using GUEST Technology

In recent years, a number of physically based models have been developed for soil loss predictions. GUEST is one such model based on fundamental physical principles and the current understanding of water erosion processes. GUEST is mainly used to determine a soil erodibility parameter. To apply the model in a predictive mode, the model is simplified in a physically meaningful manner for flow-driven erosion processes, and 2 essential hydrologic variables are identified, namely total runoff amount and an effective runoff rate. These variables are required to determine soil loss for individual runoff events. A simple water balance model was developed and used to predict runoff amount from rainfall amount. The efficiency of this runoff amount model in prediction was over 90% using field data. A 1-parameter regression model (r2 ~ 0·9) for the effective runoff rate was also established which uses peak rainfall intensity in addition to rainfall and runoff amounts. The prediction of peak rainfall intensity for a given rainfall amount and storm type was also sought. The field data were from Goomboorian, near Gympie, in south-east Queensland and these data were used to test and validate both models. Results overall are satisfactory and the approach adopted is promising. A framework for soil loss prediction is established within which individual parts can be further refined and improved.

Evaluation of eight different methods to predict hillslope runoff rates for a grazing catchment in Australia

Abstract Unlike the USLE/RUSLE models, which require only rainfall intensity data to quantify climatic effects on soil erosion, physically based erosion models require data on runoff rates as their input. However, runoff rate data are rarely measured in the field. This study evaluates eight models in terms of their performance in predicting peak ( Q p ) and effective ( Q e ) runoff rates required by erosion models. The eight models are: (1) a multiple regression model (MR), (2) a power function model (PF), (3) a scaling technique (ST), (4) a constant infiltration model (CI), (5) a constant runoff coefficient model (RC), (6) a spatially variable infiltration model (VI), (7) the CREAMS peak runoff rate equation ( Q p_ CREAMS), and (8) an empirical peak runoff rate equation ( Q P _SAL). Rainfall and runoff data from experimental plots in a grazing catchment in central Queensland (Australia) were used. A commonly used model efficiency statistic ( E ) was used to compare the performance of these models. Models resulting in high E values are said to perform better than models resulting in low values of E . Hence, with E values of 0.85 and 0.81 in predicting Q p and Q e , respectively, the PF model ranked first. On the other hand, with an E value of −12.7, the Q p_ CREAMS performed the worst in predicting peak runoff rates. On the basis of input data requirements and number of free parameters involved in each model, however, the VI model, with E values of 0.82 and 0.79 for Q p and Q e , respectively, is found to be the best choice when breakpoint rainfall is available for an event. If only peak rainfall intensity is available, the ST with E values of 0.80 and 0.63 for Q p and Q e , respectively, would be the best model to use to predict these two runoff rate characteristics for the site.

Griffith University Erosion System Template (GUEST)

Excluding gully processes and mass movement, the rate of erosion of bare soil depends on the rate of overland flow and rainfall, on the erodibility and depositability of surface soil, and on the features of filling if this occurs. The program GUEST is designed to analyse data collected from runoff plots of simple form’, and to yield an approximate non-dimensional erodibility parameter denoted by β. The parameter β has a theoretical basis, and is more physically meaningful if flow-driven erosion processes dominate those due to rainfall impact.