Predicting Rainfall Infiltration Losses: A Rainfall Simulation Study of Land Cover, Slope and Soil Type

Abstract

Rainfall infiltration is a key hydrological process influencing agriculture, pollutant transport, and flood modeling. Accurate prediction of rainfall losses, defined as rainfall that does not contribute to surface runoff is critical in rainfall-runoff models. Rainfall–runoff models are typically calibrated using historical data to estimate loss parameters, which often deviate from physically realistic infiltration behavior as they compensate for other sources of error and uncertainty in the model. This study addresses this gap by investigating infiltration losses based on physical attributes under controlled rainfall conditions. Seventy-five sites in southeastern Queensland, Australia, were subjected to rainfall of approximately 60 mm/hr for 1-hr, allowing detailed analysis of infiltration responses. Key predictors of infiltration included grass cover, leaf litter, soil organic carbon, and bulk density, while slope had minimal predictive power. Findings indicate that, during short, high-intensity rainfall events, initial losses were relatively low, with runoff beginning within 10–30 min, while continuing loss rates exceeded expectations within the first hour. Multiple Linear Regression (MLR) techniques were used to develop prediction equations for several loss models, including lumped loss, initial loss–continuing loss, and Horton infiltration. These equations explained approximately 60% of the variance between observed and predicted losses. The equations provide a practical tool for estimating infiltration losses in ungauged catchments. The prediction equations are suitable for 1-hr, 60 mm/hr intensity rainfall events, with limited applicability to longer, low-intensity rainfall. The results offer insights for improving flash flood predictions, particularly in ungauged catchments experiencing intense, short-duration storms.