Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin

Groundwater is an important resource for supporting domestic water use for people's livelihoods and for maintaining ecosystems. Borehole observations provide the first-hand data that characterise the fluctuation, depth, and aquifer conditions of the groundwater. Unfortunately, such observations...

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Main Authors: Hu, K.X., Awange, Joseph, Kuhn, Michael, Nanteza, J.
Format: Journal Article
Language:English
Published: 2021
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/86606
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author Hu, K.X.
Awange, Joseph
Kuhn, Michael
Nanteza, J.
author_facet Hu, K.X.
Awange, Joseph
Kuhn, Michael
Nanteza, J.
author_sort Hu, K.X.
building Curtin Institutional Repository
collection Online Access
description Groundwater is an important resource for supporting domestic water use for people's livelihoods and for maintaining ecosystems. Borehole observations provide the first-hand data that characterise the fluctuation, depth, and aquifer conditions of the groundwater. Unfortunately, such observations are not available or are insufficient for scientific use in many regions. Taking the Lake Victoria Basin (LVB) as an example of data-deficient regions, this study proposes a simple knowledge-based approach that uses the Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) for the main data, with rainfall, hydrological, topographical and geological datasets as supports, by which to infer the spatio-temporal variability and storage potential of groundwater. The method is based on analysis and inversion of impact factors on groundwater, and the feasibility of such a method is proven by showing that the groundwater results from GLDAS CLSM can correctly indicate the seasonality, as well as the link to topographical and geological features. For example, both results from the water balance equation (WBE) and GLDAS CLSM indicate that there are two groundwater recharge seasons in the basin, e.g., March to May and September to November. Compared to the eastern side of the LVB, the western side has mountains blocking surface runoff, and thus, reasonably, has larger storage potential estimates in GLDAS CLSM. Due to the low degree of weathering of the basement rocks, it is expected that there is only small storage potential and variation of groundwater in the southeastern parts of the LVB. GLDAS CLSM also correctly reflects this behaviour. Additionally, the largest groundwater storage potential over the LVB is found in regions near the Kagera River and the western shoreline, since it associates with unconsolidated rocks and behaviours of large groundwater recharge from GLDAS CSLM during the wet year of 2006. The major limitation of this knowledge-based method is that the uncertainty in terms of magnitude on GLDAS CLSM groundwater changes cannot be assessed, in addition to the fact that the reliability of the results cannot be quantified in terms of specific numbers. Therefore, the results and interpretation of groundwater behaviours using such methods can only be a guide for ‘where’ and ‘when’ to find groundwater.
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spelling curtin-20.500.11937-866062021-12-06T06:59:07Z Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin Hu, K.X. Awange, Joseph Kuhn, Michael Nanteza, J. Geology Groundwater Hydrology Lake Victoria Topograph Ecosystem Environmental Monitoring Groundwater Humans Lakes Reproducibility of Results Tanzania Groundwater is an important resource for supporting domestic water use for people's livelihoods and for maintaining ecosystems. Borehole observations provide the first-hand data that characterise the fluctuation, depth, and aquifer conditions of the groundwater. Unfortunately, such observations are not available or are insufficient for scientific use in many regions. Taking the Lake Victoria Basin (LVB) as an example of data-deficient regions, this study proposes a simple knowledge-based approach that uses the Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) for the main data, with rainfall, hydrological, topographical and geological datasets as supports, by which to infer the spatio-temporal variability and storage potential of groundwater. The method is based on analysis and inversion of impact factors on groundwater, and the feasibility of such a method is proven by showing that the groundwater results from GLDAS CLSM can correctly indicate the seasonality, as well as the link to topographical and geological features. For example, both results from the water balance equation (WBE) and GLDAS CLSM indicate that there are two groundwater recharge seasons in the basin, e.g., March to May and September to November. Compared to the eastern side of the LVB, the western side has mountains blocking surface runoff, and thus, reasonably, has larger storage potential estimates in GLDAS CLSM. Due to the low degree of weathering of the basement rocks, it is expected that there is only small storage potential and variation of groundwater in the southeastern parts of the LVB. GLDAS CLSM also correctly reflects this behaviour. Additionally, the largest groundwater storage potential over the LVB is found in regions near the Kagera River and the western shoreline, since it associates with unconsolidated rocks and behaviours of large groundwater recharge from GLDAS CSLM during the wet year of 2006. The major limitation of this knowledge-based method is that the uncertainty in terms of magnitude on GLDAS CLSM groundwater changes cannot be assessed, in addition to the fact that the reliability of the results cannot be quantified in terms of specific numbers. Therefore, the results and interpretation of groundwater behaviours using such methods can only be a guide for ‘where’ and ‘when’ to find groundwater. 2021 Journal Article http://hdl.handle.net/20.500.11937/86606 10.1016/j.scitotenv.2021.149355 eng restricted
spellingShingle Geology
Groundwater
Hydrology
Lake Victoria
Topograph
Ecosystem
Environmental Monitoring
Groundwater
Humans
Lakes
Reproducibility of Results
Tanzania
Hu, K.X.
Awange, Joseph
Kuhn, Michael
Nanteza, J.
Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin
title Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin
title_full Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin
title_fullStr Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin
title_full_unstemmed Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin
title_short Inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the Lake Victoria Basin
title_sort inference of the spatio-temporal variability and storage potential of groundwater in data-deficient regions through groundwater models and inversion of impact factors on groundwater, as exemplified by the lake victoria basin
topic Geology
Groundwater
Hydrology
Lake Victoria
Topograph
Ecosystem
Environmental Monitoring
Groundwater
Humans
Lakes
Reproducibility of Results
Tanzania
url http://hdl.handle.net/20.500.11937/86606