Estimating Uncertainty in the Revised Universal Soil Loss Equation via Bayesian Melding M. G. FALK, R. J. DENHAM, and K. L. MENGERSEN Deterministic simulation models are used to understand environmental processes and guide policy development by decision makers. In order to make informed deci- sions, uncertainty about input and output of these models needs to be incorporated into the modeling. We use a method known as Bayesian melding to quantify the uncer- tainty in the Revised Universal Soil Loss Equation (RUSLE), an important component of water quality models. This technique allows for this uncertainty through prior dis- tributions on both the input parameters and the outcomes of interest. There have been relatively few applications of this methodology to complex problems and none to date in soil loss modelling. Moreover, land based spatial data, which are now commonly available in environmental research as well as many other disciplines, have not previ- ously been used to inform Bayesian melding. The results demonstrate that the slope steepness factor of the RUSLE is the main contributor to total uncertainty. We conclude that Bayesian melding provides a good method for exploring the sources of uncertainty in a deterministic model. Key Words: Geographic information system; GIS; Prior specification; RUSLE. 1. INTRODUCTION Landscape monitoring systems provide water quality estimates for catchment areas and measure the potential significance and impact of these changes on the environment. This requires the synthesis of a large number of data sources including field work and remotely sensed imagery and model output, each with their own distinct spatial and temporal res- olution, and each of varying quality. Such systems are generally deterministic and do not include estimates of uncertainty in the input to the monitoring system, and consequently cannot determine uncertainties in output. Landscape monitoring systems are also used as M. G. Falk is Ph.D. Student in Environmental Statistics, School of Mathematical Sciences, Queensland Univer- sity of Technology, Brisbane, Australia (E-mail: m.falk@qut.edu.au). R. J. Denham is Environmental Scientist, Remote Sensing Centre, Queensland Department of Environment and Resources Management, Indooroopilly, Queensland, Australia. K. L. Mengersen is Research Professor of Statistics, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia. © 2009 American Statistical Association and the International Biometric Society Journal of Agricultural, Biological, and Environmental Statistics, Volume 15, Number 1, Pages 20–37 DOI: 10.1007/s13253-009-0005-y 20