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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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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. |
format | Online Article Text |
id | pubmed-7235926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT rwangasophia datasetofdevelopmentofstochasticgroundwaterflowunderuncertainrecharge AT ndambukijulius datasetofdevelopmentofstochasticgroundwaterflowunderuncertainrecharge |