Application of first-order and Monte Carlo analysis in watershed water quality models

Summary, in English

Functional analysis and Monte Carlo simulation were used to quantify uncertainties in model simulation of pollution behaviour and effects. The first-order part of the functional analysis method provides a measure of uncertainties in dependent variables in terms of uncertainties in independent variables. The procedure is based on first-order terms in the Taylor series expansion of the dependent variable about its mean value with respect to one or more independent variables. The major assumption is that all independent and dependent variables are the second moment variables (SMV), which means that the behaviour of any SMV is completely described by its mean and standard deviation. The mathematical simplicity of the procedure allows application by simple input-output models. Consequently, it has been applied to many environmental simulators, e.g. hydrological models, stream water quality models, lake water quality models and ground water pollution models. The Monte Carlo simulation method uses a large number of repeated trials or simulations with the values for stochastic inputs or uncertain variables selected at random from their assumed parent probability distributions to establish an expected range of model uncertainty.

• Author: A. G. Bobba; V. P. Singh; Lars Bengtsson
• Department/s: Division of Water Resources Engineering
• Publishing year: 1996
• Language: English
• Pages: 219-240
• Publication/Series: Water Resources Management
• Volume: 10
• Issue: 3
• Links: http://dx.doi.org/10.1007/BF00424204
• Document type: Journal article
• Publisher: Springer
• Topic: Water Engineering
• Status: Published
• ISBN/ISSN/Other: ISSN: 0920-4741