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Röttcher, K. |
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2018 |
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Risikomanagement und Nachhaltigkeit in der Wasserwirtschaft: Erfolgreiche Navigation durch die Komplexität und Dynamik des Risikos |
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165-174 |
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Im vorliegenden Beitrag werden beispielhaft unterschiedliche Ansätze des Risikomanagements und das Verständnis von Nachhaltigkeit in der Wasserwirtschaft dargelegt. Die Darstellung richtet sich insbesondere an Leser aus anderen Fachdisziplinen, wie das Rechts- und Finanzwesen, den Fahrzeug- und Maschinenbau oder auch die sozialen Berufe. Die Zusammenhänge werden überblicksartig mit einzelnen konkreten Beispielen dargestellt mit dem Fokus auf die grundsätzlichen Denk- und Vorgehensweisen. |
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Springer Fachmedien Wiesbaden |
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Wiesbaden |
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Michalke, A.; Rambke, M.; Zeranski, S. |
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978-3-658-19684-4 |
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THL @ christoph.kuells @ Röttcher2018 |
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90 |
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Author |
Stone, A.E.C.; Edmunds, W.M. |
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Title |
Naturally-high nitrate in unsaturated zone sand dunes above the Stampriet Basin, Namibia |
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Journal Article |
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Year |
2014 |
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Journal of Arid Environments |
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105 |
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41-51 |
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Kalahari, Namibia, Nitrate in the unsaturated zone, Stampriet Basin, Transboundary basin, Unsaturated zone recharge |
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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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THL @ christoph.kuells @ Stone201441 |
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218 |
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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
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Title |
Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa |
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Journal Article |
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2023 |
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Systems and Soft Computing |
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5 |
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200049 |
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Artificial intelligence, Forecasting model, Groundwater levels, Machine learning, Neural networks, Rainfall, Regression, Temperature, Time series |
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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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Author |
Wolfe, P. |
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Title |
The Simplex Method For Quadratic Programming |
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Journal Article |
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1959 |
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Econometrica |
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27 |
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170 |
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THL @ christoph.kuells @ Wolfe1959 |
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285 |
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Author |
United Nations |
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Title |
Stampriet Transboundary Aquifer System Assessment: governance of Groundwater resources in Transboundary Aquifers (GGRETA), phase 1: technical report |
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Miscellaneous |
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1998 |
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Incl. bibl. |
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THL @ christoph.kuells @ |
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286 |
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Brutsaert, W. |
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Global land surface evaporation trend during the past half century: Corroboration by Clausius-Clapeyron scaling |
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Journal Article |
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2017 |
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Advances in Water Resources |
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106 |
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3-5 |
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Evaporation, Climate change, Evaporation trend |
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Analyses of satellite data mainly over the world’s ocean surfaces have shown that during 1986–2006 global average values of atmospheric water vapor, precipitation and evaporation have increased at a relative rate of 0.0013a−1; this is roughly in accordance with the Clausius-Clapeyron equation for the average temperature trend during this period, and amounts to 0.065K−1 at the average temperature of T=14∘C. Application of this concept over the world’s land surfaces yields an average global evaporation trend during the past half century of around 0.4 to 0.5 mma−2; this confirms the values obtained in previous studies with totally different methods. |
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0309-1708 |
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Tribute to Professor Garrison Sposito: An Exceptional Hydrologist and Geochemist |
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THL @ christoph.kuells @ Brutsaert20173 |
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287 |
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Ollivier, C.C.; Carrière, S.D.; Heath, T.; Olioso, A.; Rabefitia, Z.; Rakoto, H.; Oudin, L.; Satgé, F. |
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Ensemble precipitation estimates based on an assessment of 21 gridded precipitation datasets to improve precipitation estimations across Madagascar |
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Journal Article |
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2023 |
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Journal of Hydrology: Regional Studies |
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47 |
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101400 |
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Precipitation products, Remote sensing, Ensemble approach, Hydrology, Madagascar |
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Study region this study focuses on Madagascar. This island is characterized by a great diversity of climate, due to trade winds and the varying topography. This country is also undergoing extreme rainfall events such as droughts and cyclones. Study focus the rain gauge network of Madagascar is limited (about 30 stations). Consequently, we consider relevant satellite-based precipitation datasets to fill gaps in ground-based datasets. We assessed the reliability of 21 satellite-based and reanalysis precipitation products (P-datasets) through a direct comparison with 24 rain gauge station measurements at the monthly time step, using four statistical indicators: Kling-Gupta Efficiency (KGE), Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Bias. Based on this first analysis, we produced a merged dataset based on a weighted average of the 21 products. New hydrological insights for the region based on the KGE and the CC scores, WFDEI (WATCH Forcing Data methodology applied to ERA-Interim), CMORPH-BLD (Climate Prediction Center MORPHing satellite-gauge merged) and MSWEP (Multi-Source Weighted Ensemble Precipitation) are the most accurate for estimating rainfall at the national scale. Additionally, the results reveal a high discrepancy between bio-climatic regions. The merged dataset reveals higher performance than the other products in all situations. These results demonstrate the usefulness of a merging approach in an area with a deficit of rainfall data and a climatic and topographic diversity. |
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2214-5818 |
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THL @ christoph.kuells @ Ollivier2023101400 |
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288 |
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Zwartendijk, B.W.; Ghimire C. P.; Ravelona M.; Lahitiana J.; van Meerveld H. J. |
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Hydrometric data and stable isotope data for streamflow and rainfall in the Marolaona catchment, Madagascar, 2015-2016 |
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Miscellaneous |
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2023 |
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NERC EDS Environmental Information Data Centre |
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THL @ christoph.kuells @ ref10.5285/f93d87ed-7bc4-4d03-9690-3856e6cbbd11 |
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289 |
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Author |
Severi, A.; Masoudian, M.; Kordi, E.; Roettcher, K. |
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Title |
Discharge coefficient of combined-free over-under flow on a cylindrical weir-gate |
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2015 |
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ISH Journal of Hydraulic Engineering |
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21 |
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1 |
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42-52 |
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Taylor & Francis |
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THL @ christoph.kuells @ doi:10.1080/09715010.2014.939503 |
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88 |
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Heaton, T.H.E.; Talma, A.S.; Vogel, J.C. |
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Origin and history of nitrate in confined groundwater in the western Kalahari |
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1983 |
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Journal of Hydrology |
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62 |
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1 |
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243-262 |
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Data are presented for nitrate, dinitrogen and argon concentrations and 15N14N ratios in groundwater, with radiocarbon ages up to 40,000 yr. for three confined sandstone aquifers in the western Kalahari of South West Africa/Namibia. The nitrate is probably generated within the soil of the recharge areas, and its production rate during the period 3000-40,000 B.P. has remained between 0.5 and 1.6 meq NO−3l−1 of recharge water, with ° 15N between + 4 and + 8‰. Variations in the amount of nitrate and of “excess air” in groundwater recharge are found, and can only reflect changes in the environmental conditions during recharge. They must therefore be caused by the climatic changes that have taken place during the past 25,000 yr. |
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0022-1694 |
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THL @ christoph.kuells @ heaton_origin_1983 |
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95 |
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