GroMoPo Metadata for Kleine Nete catchment Bayesian model

Authors:

GroMoPo Daniel Kretschmer

Owners:

gromopo_admin

Type:

Resource

Storage:

The size of this resource is 1.6 KB

Created:

Feb 08, 2023 at 7:35 p.m.

Last updated:

Feb 08, 2023 at 7:36 p.m.

Citation:

See how to cite this resource

Sharing Status:	Public
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Abstract

This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches, e.g., Marzouk and Xiu (2009) and Marzouk and Najm (2009), and uses generalized polynomial chaos (gPC) theory and dimensionality reduction to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a two to five times speed-up in sampling efficiency.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:

WGS 84 EPSG:4326

Coordinate Units:

Decimal degrees

Place/Area Name:

Belgium

North Latitude

51.2804°

East Longitude

5.1761°

South Latitude

51.1618°

West Longitude

4.9118°

Streets Terrain Satelite

Spatial Extent

Content

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Additional Metadata

Name	Value
DOI	10.1002/wrcr.20226
Depth	200
Scale	11 - 101 kmÂ²
Layers	7
Purpose	Scientific investigation (not related to applied problem)
GroMoPo_ID	376
IsVerified	True
Model Code	MODFLOW
Model Link	https://doi.org/10.1002/wrcr.20226
Model Time
Model Year	2013
Model Authors	Laloy, E; Rogiers, B; Vrugt, JA; Mallants, D; Jacques, D
Model Country	Belgium
Data Available	Report/paper only
Developer Email	elaloy@sckcen.be
Dominant Geology	Model focuses on multiple geologic materials
Developer Country	Belgium; CA USA; Netherlands; Australia
Publication Title	Efficient posterior exploration of a high- dimensional groundwater model from two- stage Markov chain Monte Carlo simulation and polynomial chaos expansion
Original Developer	No
Additional Information
Integration or Coupling
Evaluation or Calibration	Unsure
Geologic Data Availability	No

How to Cite

GroMoPo, D. Kretschmer (2023). GroMoPo Metadata for Kleine Nete catchment Bayesian model, HydroShare, http://www.hydroshare.org/resource/2383d35b0df14e1e977695906817f577

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/

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