| Records |
| Author |
Stone, A.E.C.; Edmunds, W.M. |
| Title |
Naturally-high nitrate in unsaturated zone sand dunes above the Stampriet Basin, Namibia |
Type |
Journal Article |
| Year |
2014 |
Publication |
Journal of Arid Environments |
Abbreviated Journal |
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| Volume |
105 |
Issue |
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Pages |
41-51 |
| Keywords |
Kalahari, Namibia, Nitrate in the unsaturated zone, Stampriet Basin, Transboundary basin, Unsaturated zone recharge |
| Abstract |
Elevated groundwater nitrate levels are common in drylands, often in excess of WHO guidelines, with concern for human and animal health. In light of recent attempts to identify nitrate sources in the Kalahari this paper presents the first unsaturated zone (USZ) nitrate profiles and recharge rate estimates for the important transboundary Stampriet Basin, alongside the first rainfall chemistry records. Elevated subsurface nitrate reaches 100–250 and 250–525 mg/L NO3–N, with NO3–N/Cl of 4–12, indicating input above evapotranspiration. Chloride mass balance recharge rates range from 4 to 27 mm/y, indicating a vertical movement of these nitrate pulses toward the water table over multi-decadal timescales. These profiles are sampled from dune crests, away from high concentrations of animals and without termite mounds. Given low-density animal grazing is unlikely to contribute consistent spot-scale nitrate over decades, these profiles give an initial estimate of naturally-produced concentrations. This insight is important for the management of the Stampriet Basin and wider Kalahari groundwater. This study expands our knowledge about elevated nitrate in dryland USZs, demonstrating that it can occur as pulses, probably in response to transient vegetation cover and that it is not limited to long-residence time USZs with very limited downward moisture flux (recharge). |
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0140-1963 |
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| Call Number |
THL @ christoph.kuells @ Stone201441 |
Serial |
218 |
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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
| Title |
Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
Type |
Journal Article |
| Year |
2023 |
Publication |
Systems and Soft Computing |
Abbreviated Journal |
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| Volume |
5 |
Issue |
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Pages |
200049 |
| Keywords |
Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
| Abstract |
The crucial role which groundwater resource plays in our environment and the overall well-being of all living things can not be underestimated. Nonetheless, mismanagement of resources, over-exploitation, inadequate supply of surface water and pollution have led to severe drought and an overall drop in groundwater resources’ levels over the past decades. To address this, a groundwater flow model and several mathematical data-driven models have been developed for forecasting groundwater levels. However, there is a problem of unavailability and scarcity of the on-site input data needed by the data-driven models to forecast the groundwater level. Furthermore, as a result of the dynamics and stochastic characteristics of groundwater, there is a need for an appropriate, accurate and reliable forecasting model to solve these challenges. Over the years, the broad application of Machine Learning (ML) and Artificial Intelligence (AI) models are gaining attraction as an alternative solution for forecasting groundwater levels. Against this background, this article provides an overview of forecasting methods for predicting groundwater levels. Also, this article uses ML models such as Regressions Models, Deep Auto-Regressive models, and Nonlinear Autoregressive Neural Networks with External Input (NARX) to forecast groundwater levels using the groundwater region 10 at Karst belt in South Africa as a case study. This was done using Python Mx. Version 1.9.1., and MATLAB R2022a machine learning toolboxes. Moreover, the Coefficient of Determination (R2);, Root Mean Square Error (RMSE), Mutual Information gain, Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Mean Absolute Error (MAE), and the Mean Absolute Scaled Error (MASE)) models were the forecasting statistical performance metrics used to assess the predictive performance of these models. The results obtained showed that NARX and Support Vector Machine (SVM) have higher performance metrics and accuracy compared to other models used in this study. |
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2772-9419 |
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THL @ christoph.kuells @ Aderemi2023200049 |
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219 |
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Rajfur, M.; Kłos, A.; Wacławek, M. |
| Title |
Sorption properties of algae Spirogyra sp. and their use for determination of heavy metal ions concentrations in surface water |
Type |
Journal Article |
| Year |
2010 |
Publication |
Bioelectrochemistry |
Abbreviated Journal |
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| Volume |
80 |
Issue |
1 |
Pages |
81-86 |
| Keywords |
Biomonitoring, Heavy metal ions, Algae sp., Sorption kinetics, Langmuir isotherm |
| Abstract |
Kinetics of heavy-metal ions sorption by alga Spirogyra sp. was evaluated experimentally in the laboratory, using both the static and the dynamic approach. The metal ions – Mn2+, Cu2+, Zn2+ and Cd2+ – were sorbed from aqueous solutions of their salts. The static experiments showed that the sorption equilibria were attained in 30min, with 90-95% of metal ions sorbed in first 10min of each process. The sorption equilibria were approximated with the Langmuir isotherm model. The algae sorbed each heavy metal ions proportionally to the amount of this metal ions in solution. The experiments confirmed that after 30min of exposition to contaminated water, the concentration of heavy metal ions in the algae, which initially contained small amounts of these metal ions, increased proportionally to the concentration of metal ions in solution. The presented results can be used for elaboration of a method for classification of surface waters that complies with the legal regulations. |
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1567-5394 |
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| Notes |
A Selection of Papers presented at the 4th International Workshop on Surface Modification for Chemical and Biochemical Sensing (SMCBS 2009) |
Approved |
no |
| Call Number |
THL @ christoph.kuells @ Rajfur201081 |
Serial |
283 |
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| Author |
Konapala, G.; Mishra, A.K.; Wada, Y.; Mann, M.E. |
| Title |
Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation |
Type |
Journal Article |
| Year |
2020 |
Publication |
Nature Communications |
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| Volume |
11 |
Issue |
1 |
Pages |
3044 |
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| Abstract |
Both seasonal and annual mean precipitation and evaporation influence patterns of water availability impacting society and ecosystems. Existing global climate studies rarely consider such patterns from non-parametric statistical standpoint. Here, we employ a non-parametric analysis framework to analyze seasonal hydroclimatic regimes by classifying global land regions into nine regimes using late 20th century precipitation means and seasonality. These regimes are used to assess implications for water availability due to concomitant changes in mean and seasonal precipitation and evaporation changes using CMIP5 model future climate projections. Out of 9 regimes, 4 show increased precipitation variation, while 5 show decreased evaporation variation coupled with increasing mean precipitation and evaporation. Increases in projected seasonal precipitation variation in already highly variable precipitation regimes gives rise to a pattern of “seasonally variable regimes becoming more variable”. Regimes with low seasonality in precipitation, instead, experience increased wet season precipitation. |
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English |
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Edition |
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2041-1723 |
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no |
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THL @ christoph.kuells @ Konapala2020 |
Serial |
284 |
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| Author |
Wolfe, P. |
| Title |
The Simplex Method For Quadratic Programming |
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Journal Article |
| Year |
1959 |
Publication |
Econometrica |
Abbreviated Journal |
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| Volume |
27 |
Issue |
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Pages |
170 |
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no |
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THL @ christoph.kuells @ Wolfe1959 |
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285 |
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