GroMoPo Metadata for Al-Fara regional groundwater flow model

Authors:

GroMoPo Kyle Compare

Owners:

gromopo_admin

Type:

Resource

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The size of this resource is 1.8 KB

Created:

Feb 08, 2023 at 2:45 p.m.

Last updated:

Feb 08, 2023 at 2:45 p.m.

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Abstract

Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol' method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system. (C) 2018 Elsevier B.V. All rights reserved.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:

WGS 84 EPSG:4326

Coordinate Units:

Decimal degrees

Place/Area Name:

Oman

North Latitude

23.8480°

East Longitude

57.3185°

South Latitude

23.2916°

West Longitude

57.1549°

Streets Terrain Satelite

Spatial Extent

Content

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Additional Metadata

Name	Value
DOI	10.1016/j.jhydrol.2017.12.071
Depth
Scale	1 001 - 10 000 kmÂ²
Layers	6
Purpose	Groundwater resources;Scientific investigation (not related to applied problem)
GroMoPo_ID	281
IsVerified	True
Model Code	MODFLOW;Bayesian Multivariate Adaptive Regression Spline
Model Link	https://doi.org/10.1016/j.jhydrol.2017.12.071
Model Time	1993-2013
Model Year	2018
Model Authors	Chen, MJ; Izady, A; Abdalla, OA; Amerjeed, M
Model Country	Oman
Data Available	Report/paper only
Developer Email	cmj1014@gmail.com
Dominant Geology	Model focuses on multiple geologic materials
Developer Country	Oman
Publication Title	A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model
Original Developer	No
Additional Information	Creating a Bayesian surrogate groundwater flow model and comparing it to a MODFLOW model of the wadi Al Fara in Oman.
Integration or Coupling	None of the above
Evaluation or Calibration	Dynamic water levels
Geologic Data Availability	No

How to Cite

GroMoPo, K. Compare (2023). GroMoPo Metadata for Al-Fara regional groundwater flow model, HydroShare, http://www.hydroshare.org/resource/521c3dfdc9b1456c8fba9f779cf136fd

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

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

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