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Dataset of development of stochastic groundwater flow under uncertain recharge

Groundwater is a valuable resource of limited extent both in quantity and in space. In order to ensure its careful use, proper evaluation of its availability is required. The majority of groundwater flow numerical models still provide only deterministic predictions with no supplementary information...

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Autores principales: Rwanga, Sophia, Ndambuki, Julius
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235926/
https://www.ncbi.nlm.nih.gov/pubmed/32455123
http://dx.doi.org/10.1016/j.mex.2020.100907
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author Rwanga, Sophia
Ndambuki, Julius
author_facet Rwanga, Sophia
Ndambuki, Julius
author_sort Rwanga, Sophia
collection PubMed
description Groundwater is a valuable resource of limited extent both in quantity and in space. In order to ensure its careful use, proper evaluation of its availability is required. The majority of groundwater flow numerical models still provide only deterministic predictions with no supplementary information on uncertainty predictions. Stochastic groundwater management aims at treating uncertainty within decision oriented models in a logical and systematic manner which cannot be experienced in deterministic modelling approach. This paper presents a detailed methodology followed in the development of a stochastic groundwater model under uncertain recharge. The methodology is comprised of two steps (1) The development of a groundwater flow model and (2) the development of a stochastic solution- stochastic groundwater flow model considering recharge as uncertain parameter. • The groundwater flow model was developed and simulated using MODFLOW 2000 software and calibrated using Parameter Estimation Program (PEST). Validated model compared well with calibrated model. • The uncertainty in recharge was propagated to the flow model through Monte Carlo (MC) sampling technique. • The methodology demonstrated the importance of developing groundwater flow models that acknowledge the existence of recharge uncertainty and hence consider it in the development of groundwater management solutions.
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spelling pubmed-72359262020-05-22 Dataset of development of stochastic groundwater flow under uncertain recharge Rwanga, Sophia Ndambuki, Julius MethodsX Engineering Groundwater is a valuable resource of limited extent both in quantity and in space. In order to ensure its careful use, proper evaluation of its availability is required. The majority of groundwater flow numerical models still provide only deterministic predictions with no supplementary information on uncertainty predictions. Stochastic groundwater management aims at treating uncertainty within decision oriented models in a logical and systematic manner which cannot be experienced in deterministic modelling approach. This paper presents a detailed methodology followed in the development of a stochastic groundwater model under uncertain recharge. The methodology is comprised of two steps (1) The development of a groundwater flow model and (2) the development of a stochastic solution- stochastic groundwater flow model considering recharge as uncertain parameter. • The groundwater flow model was developed and simulated using MODFLOW 2000 software and calibrated using Parameter Estimation Program (PEST). Validated model compared well with calibrated model. • The uncertainty in recharge was propagated to the flow model through Monte Carlo (MC) sampling technique. • The methodology demonstrated the importance of developing groundwater flow models that acknowledge the existence of recharge uncertainty and hence consider it in the development of groundwater management solutions. Elsevier 2020-04-30 /pmc/articles/PMC7235926/ /pubmed/32455123 http://dx.doi.org/10.1016/j.mex.2020.100907 Text en © 2020 The Author(s). Published by Elsevier B.V. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Engineering
Rwanga, Sophia
Ndambuki, Julius
Dataset of development of stochastic groundwater flow under uncertain recharge
title Dataset of development of stochastic groundwater flow under uncertain recharge
title_full Dataset of development of stochastic groundwater flow under uncertain recharge
title_fullStr Dataset of development of stochastic groundwater flow under uncertain recharge
title_full_unstemmed Dataset of development of stochastic groundwater flow under uncertain recharge
title_short Dataset of development of stochastic groundwater flow under uncertain recharge
title_sort dataset of development of stochastic groundwater flow under uncertain recharge
topic Engineering
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7235926/
https://www.ncbi.nlm.nih.gov/pubmed/32455123
http://dx.doi.org/10.1016/j.mex.2020.100907
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