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Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

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Related Experiment Video

Updated: Mar 17, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Assessing uncertainties in land cover projections.

Peter Alexander^1,2, Reinhard Prestele³, Peter H Verburg³

¹School of GeoSciences, University of Edinburgh, Drummond Street, Edinburgh, EH8 9XP, UK.

Global Change Biology

|July 31, 2016

Summary

Land cover projections show high uncertainty, especially for future cropland areas. Model characteristics contribute significantly to this uncertainty, necessitating diverse modeling approaches for climate impact assessments.

Keywords:

cropland land cover land use model inter-comparison uncertainty

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Measuring and Mapping Patterns of Soil Erosion and Deposition Related to Soil Carbonate Concentrations Under Agricultural Management

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Published on: September 12, 2017

Area of Science:

Earth System Science
Climate Change Research
Land Use Modeling

Background:

Accurate land cover projections are vital for climate mitigation and adaptation strategies.
Previous studies primarily focused on agro-economic models, limiting a comprehensive understanding of uncertainties.

Purpose of the Study:

To quantify uncertainties in global and European land cover projections across diverse model types and scenarios.
To compare uncertainties arising from different modeling approaches versus scenario variations.

Main Methods:

Analysis of 75 simulations from 18 different land use models.
Inclusion of a wider range of model types beyond traditional agro-economic models.

Main Results:

Significant variability observed in projected land cover areas, particularly for future croplands.
Modeling approach characteristics explain substantial differences in land cover projections, comparable to scenario variations.
Land use projection uncertainty exceeds that typically included in climate and Earth system models.

Conclusions:

A higher degree of uncertainty exists in land use projections than previously recognized.
Utilizing diverse models and approaches is crucial for assessing land cover change impacts on climate.
Further research is needed to understand land use model assumptions and reduce projection uncertainty.