10.4122/1.1000001599 Sutani, D. Kawaguchi, T. Shirahama, M. Distributed hydrologic model with Bayesian Monte Carlo technique to estimate surface loading in urban area DTU Library, Technical University of Denmark (DTU) 2005 Non-point loadings Surface loading Probability density function Suspended Solid (SS) Distributed hydrologic model Bayesian Monte Carlo (BMC) technique Nihon Suidou Consultant Co.,Ltd, Environment Engineering Department en Conference presentation 10.4122/1.1000001600 text/xml 1 There are a lot of uncertain factors involved in runoff analysis for urban area. Among others, accumulated non-point loadings on urban surface and in ditch or drains are eminent parameters, which are generally introduced as definite values into runoff model. However, it is desirable that these parameters are treated as continuous probability variables so as to discuss and make decision for effective counter measures based on the degree-of-belief of the model parameters. In order to obtain probability density function (PDF) of the parameters, distributed hydrologic model with Bayesian Monte Carlo (BMC) technique is applicable. BMC technique is a combination method of Bayes\342\200\231 inference and Monte Carlo technique. Although BMC technique has a significant benefit of the ability to reduce total model output uncertainty, it includes limitations of computational requirement. Especially, when it is necessary to treat a lot of parameters like distributed hydrologic model, the application of BMC becomes difficult. In this paper, new methodology for handling BMC procedure to remain the above benefit and to overcome its disadvantage is developed and proposed. And application results of distributed hydrologic model with BMC technique are presented.