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Kamruzzaman, M.; Chowdhury, A. |
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Title |
Flash flooding considerations aside: Knowledge brokering by the extension and advisory services to adapt a farming system to flash flooding |
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Journal Article |
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Year |
2023 |
Publication |
Heliyon |
Abbreviated Journal |
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9 |
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9 |
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19662 |
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Keywords |
Flash flooding, Knowledge brokering, Extension and advisory services, Farming system, Climate change |
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Abstract  |
The development of agriculture sector and livelihood in Bangladesh are threatened by various climatic stressors, including flash flooding. Therefore, Extension and advisory services (EAS) need to navigate the knowledge landscape effectively to connect various farm actors and help secure the optimum benefits of knowledge and information for making rational decisions. However, little is known how EAS can perform this task to combat various effects of climate change. This study investigates the means of brokering knowledge by the EAS to help the farming sector adapt to flash flooding. The research was conducted in the north-eastern part of Bangladesh with 73 staff of the Department of Agricultural Extension (DAE), the largest public EAS in Bangladesh. The results showed that DAE primarily dealt with crop production-related information. However, EAS did not navigate knowledge and information about flash flooding, such as weather forecasting and crop-saving-embankments updates, among the farming actors. Moreover, they missed the broad utilization of internet-based-communication channels to rapidly navigate information and knowledge about possible flash flooding and its adaptation strategies. This article provides some policy implications to effectively support the adaptation of farming system to flash flooding through EAS. |
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2405-8440 |
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THL @ christoph.kuells @ KAMRUZZAMAN2023e19662 |
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235 |
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Tamagnone, P.; Comino, E.; Rosso, M. |
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Title |
Rainwater harvesting techniques as an adaptation strategy for flood mitigation |
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Journal Article |
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Year |
2020 |
Publication |
Journal of Hydrology |
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586 |
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124880 |
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Rainwater harvesting techniques, Extreme rainfall, Runoff, Hydraulic modelling, Flood mitigation, Arid and semi-arid climate |
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The development of adaptation and mitigation strategies to tackle anthropic and climate changes impacts is becoming a priority in drought-prone areas. This study examines the capabilities of indigenous rainwater harvesting techniques (RWHT) to be used as a viable solution for flood mitigation. The study analyses the hydraulic performance of the most used micro-catchment RWHT in sub-Saharan regions, in terms of flow peak reduction (FPR) and volume reduction (VR) at the field and basin scale. Parametrized hyetographs were built to replicate the extreme precipitations that strike Sahelian countries during rainy seasons. 2D hydrodynamic simulations showed that half-moons placed with a staggered configuration (S-HM) have the best performances in reducing runoff. At the field scale, S-HM showed a remarkable FPR of 77% and a VR of 70% in case of extreme rainfall. Instead at the basin scale, in which only 5% of the surface was treated, 13% and 8% respectively for FPR and VR were obtained. In addition, the reduction of the runoff coefficient (Rc) between the different configuration was analyzed. The study critically evaluates hydraulic performances of the different techniques and shows how pitting practices cannot guarantee high performance in case of extreme precipitations. These results will enrich the knowledge of the hydraulic behavior of RWHT; aspect marginally investigated in the scientific literature. Moreover, this study presents the first scientific application of HEC-RAS as a rainfall-runoff model. Despite some limitations, this model has the effective feature of using very high-resolution topography as input for hydraulic simulations. The results presented in this study should encourage stakeholders to upscale the use of RWHT in order to lessen the flood hazard and land degradation that oppresses arid and semi-arid areas. |
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0022-1694 |
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THL @ christoph.kuells @ Tamagnone2020124880 |
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240 |
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Aderemi, B.A.; Olwal, T.O.; Ndambuki, J.M.; Rwanga, S.S. |
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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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Smedley, P.L.; Kinniburgh, D.G. |
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Title |
Uranium in natural waters and the environment: Distribution, speciation and impact |
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Journal Article |
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2023 |
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Applied Geochemistry |
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148 |
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105534 |
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Drinking water, Mine water, NORM, Radionuclide, Redox, U isotopes, Uranium, Uranyl |
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The concentrations of U in natural waters are usually low, being typically less than 4 μg/L in river water, around 3.3 μg/L in open seawater, and usually less than 5 μg/L in groundwater. Higher concentrations can occur in both surface water and groundwater and the range spans some six orders of magnitude, with extremes in the mg/L range. However, such extremes in surface water are rare and linked to localized mineralization or evaporation in alkaline lakes. High concentrations in groundwater, substantially above the WHO provisional guideline value for U in drinking water of 30 μg/L, are associated most strongly with (i) granitic and felsic volcanic aquifers, (ii) continental sandstone aquifers especially in alluvial plains and (iii) areas of U mineralization. High-U groundwater provinces are more common in arid and semi-arid terrains where evaporation is an additional factor involved in concentrating U and other solutes. Examples of granitic and felsic volcanic terrains with documented high U concentrations include several parts of peninsular India, eastern USA, Canada, South Korea, southern Finland, Norway, Switzerland and Burundi. Examples of continental sandstone aquifers include the alluvial plains of the Indo-Gangetic Basin of India and Pakistan, the Central Valley, High Plains, Carson Desert, Española Basin and Edwards-Trinity aquifers of the USA, Datong Basin, China, parts of Iraq and the loess of the Chaco-Pampean Plain, Argentina. Many of these plains host eroded deposits of granitic and felsic volcanic precursors which likely act as primary sources of U. Numerous examples exist of groundwater impacted by U mineralization, often accompanied by mining, including locations in USA, Australia, Brazil, Canada, Portugal, China, Egypt and Germany. These may host high to extreme concentrations of U but are typically of localized extent. The overarching mechanisms of U mobilization in water are now well-established and depend broadly on redox conditions, pH and solute chemistry, which are shaped by the geological conditions outlined above. Uranium is recognized to be mobile in its oxic, U(VI) state, at neutral to alkaline pH (7–9) and is aided by the formation of stable U–CO3(±Ca, Mg) complexes. In such oxic and alkaline conditions, U commonly covaries with other similarly controlled anions and oxyanions such as F, As, V and Mo. Uranium is also mobile at acidic pH (2–4), principally as the uranyl cation UO22+. Mobility in U mineralized areas may therefore occur in neutral to alkaline conditions or in conditions with acid drainage, depending on the local occurrence and capacity for pH buffering by carbonate minerals. In groundwater, mobilization has also been observed in mildly (Mn-) reducing conditions. Uranium is immobile in more strongly (Fe-, SO4-) reducing conditions as it is reduced to U(IV) and is either precipitated as a crystalline or ‘non-crystalline’ form of UO2 or is sorbed to mineral surfaces. A more detailed understanding of U chemistry in the natural environment is challenging because of the large number of complexes formed, the strong binding to oxides and humic substances and their interactions, including ternary oxide-humic-U interactions. Improved quantification of these interactions will require updating of the commonly-used speciation software and databases to include the most recent developments in surface complexation models. Also, given their important role in maintaining low U concentrations in many natural waters, the nature and solubility of the amorphous or non-crystalline forms of UO2 that result from microbial reduction of U(VI) need improved quantification. Even where high-U groundwater exists, percentage exceedances of the WHO guideline value are variable and often small. More rigorous testing programmes to establish usable sources are therefore warranted in such vulnerable aquifers. As drinking-water regulation for U is a relatively recent introduction in many countries (e.g. the European Union), testing is not yet routine or established and data are still relatively limited. Acquisition of more data will establish whether analogous aquifers elsewhere in the world have similar patterns of aqueous U distribution. In the high-U groundwater regions that have been recognized so far, the general absence of evidence for clinical health symptoms is a positive finding and tempers the scale of public health concern, though it also highlights a need for continued investigation. |
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0883-2927 |
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THL @ christoph.kuells @ smedley_uranium_2023 |
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118 |
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Author |
Lartigue, J.E.; Charrasse, B.; Reile, B.; Descostes, M. |
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Title |
Aqueous inorganic uranium speciation in European stream waters from the FOREGS dataset using geochemical modelling and determination of a U bioavailability baseline |
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Journal Article |
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2020 |
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Chemosphere |
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251 |
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126302 |
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Keywords |
Bioavailable fraction, Geochemical mapping / baseline, Modelling, Speciation, Stream water, Uranium |
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The concentration of the bioavailable uranium fraction (Ubio) at the European scale was deduced by geochemical modelling considering several definitions found in the literature and the FOREGS European stream waters geochemical atlas dataset to produce a Ubio baseline. A sensitivity analysis was performed using three thermodynamic databases. We also investigated the link between total dissolved uranium (Uaq) concentrations, speciation and global stream water chemistry on the one hand, and the lithology and ages of the surrounding rocks on the other. The more U-enriched the stream sediments or rock type contexts are, which tends to be the case with rocks containing silicates (4.1 mg/kg), the less U-concentrated the stream waters are (0.15 μg/L). Sedimentary rocks lead to slightly higher Uaq concentrations (0.34 μg/L) even if the concentration in sediment (Used) is relatively low (1.6 mg/kg). This trend is reversed for Ubio, with higher concentrations in a crystalline context. The mean estimated Ubio value ranges from 1.5.10−3 to 65.3 ng/L and can fluctuate by 3 orders of magnitude depending on the considered definition as opposed to by 2 orders of magnitude accountable to differences between thermodynamic databases. The classification of the water in relation to the two surrounding rock lithologies makes it possible to reduce the mean variability for the Ubio concentrations. Irrespective of the definition of Ubio considered, in 59% of cases the Ubio fraction represents less than 1% of Uaq. Several threshold values relating to Ubio were proposed, assuming knowledge only of the aqueous concentrations of the major elements and Uaq. |
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0045-6535 |
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THL @ christoph.kuells @ lartigue_aqueous_2020 |
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141 |
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