Environmental Modelling & Software
An effective screening design for sensitivity analysis of large models
Environmental Modelling & Software
Environmental Modelling & Software
Dynamic modelling of metals - Time scales and target loads
Environmental Modelling & Software
A GIS-based tool for modelling large-scale crop-water relations
Environmental Modelling & Software
Bioclogging in porous media: Model development and sensitivity to initial conditions
Environmental Modelling & Software
GUI-HDMR - A software tool for global sensitivity analysis of complex models
Environmental Modelling & Software
Management Option Rank Equivalence (MORE) - A new method of sensitivity analysis for decision-making
Environmental Modelling & Software
How to avoid a perfunctory sensitivity analysis
Environmental Modelling & Software
Global sensitivity analysis in the development of first principle-based eutrophication models
Environmental Modelling & Software
Environmental Modelling & Software
A methodology for the design and development of integrated models for policy support
Environmental Modelling & Software
Component-based development and sensitivity analyses of an air pollutant dry deposition model
Environmental Modelling & Software
Environmental Modelling & Software
Environmental Modelling & Software
Position Paper: A general framework for Dynamic Emulation Modelling in environmental problems
Environmental Modelling & Software
Environmental Modelling & Software
Review: Three complementary methods for sensitivity analysis of a water quality model
Environmental Modelling & Software
Sampling strategies in density-based sensitivity analysis
Environmental Modelling & Software
Position paper: Characterising performance of environmental models
Environmental Modelling & Software
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The paper aims to demonstrate the relative ease of algebraic sensitivity analysis in many cases and to discuss its advantages and limitations in situations typical of environmental models. Sensitivity results for the operations in an equation can be combined to find algebraically the sensitivities of its output to variations in contributing factors. Algebraic sensitivity analysis has the advantage that it yields insight not readily available from numerical experiments alone. It exploits the fact that a simulation model is fully known and not a black box. By producing relations valid for changes of any size, it can save much computational experiment. The paper gives sensitivity results for common operations on one or more arguments (parameters and/or input variables). A second-order approximation is also given for each. An illustrative example of algebraic sensitivity analysis in a model of pathogen generation and transport in a catchment is presented. Two formulae useful in first- or second-order approximations to normalized sensitivity relations are presented: a Taylor expansion relating the proportional change in effect on a scalar variable to the proportional changes in a number of causal factors and a chain rule for propagating normalized sensitivities through a series of submodels.