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Typical Model Studies01:30

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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Design Example: Creating a Hydraulic Model of a Dam Spillway

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Modeling and Similitude01:12

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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Related Experiment Video

Updated: May 10, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Complexity vs. simplicity: groundwater model ranking using information criteria.

I Engelhardt1, J G De Aguinaga, H Mikat

  • 1Bauhaus-Universität Weimar, Research Training Group 1462, Weimar, Germany, and Technische Universität Dresden, Institute for Groundwater Management, Dresden, Germany; agui001-bauhaus@yahoo.com.

Ground Water
|June 12, 2013
PubMed
Summary

Model uncertainty in groundwater studies is reduced by carefully selecting parameters. Information criteria like BIC and KIC help choose simpler, more reliable groundwater models, avoiding overparameterization.

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Area of Science:

  • Hydrogeology
  • Environmental Modeling
  • Geosciences

Background:

  • Groundwater model calibration often faces challenges due to limited field data for hydraulic parameters and boundary conditions.
  • Abundant observational data are available for calibration, necessitating robust methods to assess model uncertainty.

Purpose of the Study:

  • To investigate model uncertainty in a groundwater model with limited parameter data but extensive calibration datasets.
  • To compare different conceptual models with varying numbers of adjustable parameters using various information criteria.

Main Methods:

  • Seven conceptual models with 0 to 30 adjustable parameters were calibrated using PEST (Parameter Estimation).
  • Model performance was evaluated using residuals, sensitivities, Akaike Information Criterion (AIC, AICc), Bayesian Information Criterion (BIC), and Kashyap's Information Criterion (KIC).
  • Model likelihood was computed for inverse-calibrated models of increasing complexity.

Main Results:

  • Relying solely on residuals can lead to overparameterization and reduced certainty.
  • Models using uncalibrated parameters derived from sedimentology performed poorly across all criteria.
  • For datasets with many calibration points, BIC and KIC favored simpler models over AIC.
  • The model with 15 parameters, favored by AIC, had a 98% likelihood, but AIC overlooked potential model structure errors.

Conclusions:

  • Kashyap's Information Criterion (KIC) is more appropriate than AIC for selecting groundwater models due to its consideration of model structure error.
  • Overparameterization and reliance on sedimentological estimates for hydraulic parameters should be avoided to ensure model reliability.
  • Model complexity and parameterization significantly influence uncertainty and the selection of optimal groundwater models.