A comprehensive sensitivity analysis for an ecological simulation model

Recknagel, Friedrich

doi:10.1016/0304-3800(84)90095-4

Cited by 15 publications

(3 citation statements)

References 8 publications

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“…Error and uncertainty analysis constitute a fundamental aspect of simulation studies of models (e.g. Gertner et al 1981;Recknagel;Dale et al 1984;Gertner 1987;Hannes et al 1991;Summers et al 1993, McRoberts 1996. Important objectives of this type of analysis include identification and quantification of different sources of input uncertainty, which propagate through the model to produce error in model predictions and projections.…”

Section: Uncertainly Analysis In Modeling and Monitoring For Sfmmentioning

confidence: 99%

Potential contributions of statistics and modelling to sustainable forest management: review and synthesis.

Rennolls

Tomé

McRoberts³

et al. 2007

Sustainable Forestry: From Monitoring and Modelling to Knowledge Management and Policy Science

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This chapter provides a review of the statistical and modelling disciplines, their techniques and potential contribution to sustainable forest management (SFM). The main topics covered are: Mensuration and models for sustainable forest management (SFM) Inventory and monitoring for forest sustainability: criteria and indicators Models of tropical forests for the conservation of biodiversity Integrating information and models across spatial and temporal scales for SFM Climate and carbon models in relation to sustainability New techniques for the statistical analysis of sustainability data Uncertainly analysis in modeling and monitoring for SFM Forest data, information and model archives There are major contributions to be made, in particular in the areas of information and model integration where a synthesis of information and models across both spatial and temporal scales is required. There is a great need for international collaboration on the development of open and shared forest data and model repositories/archives, as well as continued development of forest information systems.

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Section: Uncertainly Analysis In Modeling and Monitoring For Sfmmentioning

confidence: 99%

Potential contributions of statistics and modelling to sustainable forest management: review and synthesis.

Rennolls

Tomé

McRoberts³

et al. 2007

Sustainable Forestry: From Monitoring and Modelling to Knowledge Management and Policy Science

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“…It is important to note that this study is not a classical sensitivity analysis for assessing systematically the responses of models to changes in input data and model parameters (e.g. [ 18 - 21 ]), but a model variability analysis.…”

Section: Introductionmentioning

confidence: 99%

How sensitive are estimates of carbon fixation in agricultural models to input data?

Tum

Strauss

McCallum

et al. 2012

Carbon Balance Manage

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BackgroundProcess based vegetation models are central to understand the hydrological and carbon cycle. To achieve useful results at regional to global scales, such models require various input data from a wide range of earth observations. Since the geographical extent of these datasets varies from local to global scale, data quality and validity is of major interest when they are chosen for use. It is important to assess the effect of different input datasets in terms of quality to model outputs. In this article, we reflect on both: the uncertainty in input data and the reliability of model results. For our case study analysis we selected the Marchfeld region in Austria. We used independent meteorological datasets from the Central Institute for Meteorology and Geodynamics and the European Centre for Medium-Range Weather Forecasts (ECMWF). Land cover / land use information was taken from the GLC2000 and the CORINE 2000 products.ResultsFor our case study analysis we selected two different process based models: the Environmental Policy Integrated Climate (EPIC) and the Biosphere Energy Transfer Hydrology (BETHY/DLR) model. Both process models show a congruent pattern to changes in input data. The annual variability of NPP reaches 36% for BETHY/DLR and 39% for EPIC when changing major input datasets. However, EPIC is less sensitive to meteorological input data than BETHY/DLR. The ECMWF maximum temperatures show a systematic pattern. Temperatures above 20°C are overestimated, whereas temperatures below 20°C are underestimated, resulting in an overall underestimation of NPP in both models. Besides, BETHY/DLR is sensitive to the choice and accuracy of the land cover product.DiscussionThis study shows that the impact of input data uncertainty on modelling results need to be assessed: whenever the models are applied under new conditions, local data should be used for both input and result comparison.

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“…It is important to note that this study is not a classical sensitivity analysis for assessing systematically the responses of models to changes in input data and model parameters (e.g. Lamboni et al (2009), Larocque et al (2008), White et al (2000), Recknagel (1984)), but a model variability analysis.…”

Section: Introductionmentioning

confidence: 99%

Modelling and validation of agricultural and forest biomass potentials for Germany and Austria

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A comprehensive sensitivity analysis for an ecological simulation model

Cited by 15 publications

References 8 publications

Potential contributions of statistics and modelling to sustainable forest management: review and synthesis.

Potential contributions of statistics and modelling to sustainable forest management: review and synthesis.

How sensitive are estimates of carbon fixation in agricultural models to input data?

Modelling and validation of agricultural and forest biomass potentials for Germany and Austria

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