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| Author |
Arya, S.; Kumar, A. |
Title  |
Evaluation of stormwater management approaches and challenges in urban flood control |
Type |
Journal Article |
| Year |
2023 |
Publication |
Urban Climate |
Abbreviated Journal |
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| Volume |
51 |
Issue |
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Pages |
101643 |
| Keywords |
Flood risk, Green infrastructure (GI), Stormwater management, Stormwater modelling, Vulnerability assessment, Urban floods |
| Abstract |
Across the globe, the damage caused by urban floods has increased manifold. The unchecked development has encroached the natural drainage, and the conventional drainage systems are inadequate in handling the augmented hydrological response. To counter this, a variety of approaches with the ability to adjust within the constraints of complex environments by managing surface runoff are being widely investigated and applied worldwide. These can put the flood water to better use, and the ecological balance may get restored. This review discusses recent progress made in the area of Green Infrastructure (GI), modelling tools that help in stormwater management, vulnerability analysis and flood risk assessment. Different ways of handling the problem are summarized through an extensive literature survey. The gaps and barriers that impede the implementation of stormwater management solutions and strategies for further improvement have also been presented. A case study of Gurugram city, India depicting the challenges being faced by urban flooding and the possible solutions through an expert survey is also presented. |
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2212-0955 |
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THL @ christoph.kuells @ Arya2023101643 |
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224 |
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Jroundi, F.; Povedano-Priego, C.; Pinel-Cabello, M.; Descostes, M.; Grizard, P.; Purevsan, B.; Merroun, M.L. |
| Title |
Evidence of microbial activity in a uranium roll-front deposit: Unlocking their potential role as bioenhancers of the ore genesis |
Type |
Journal Article |
| Year |
2023 |
Publication |
Science of The Total Environment |
Abbreviated Journal |
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| Volume |
861 |
Issue |
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Pages |
160636 |
| Keywords |
ISR, Metatranscriptomes, Microbial metabolisms, Ore genesis, Roll-front deposit, Uranium |
| Abstract |
Uranium (U) roll-front deposits constitute a valuable source for an economical extraction by in situ recovery (ISR) mining. Such technology may induce changes in the subsurface microbiota, raising questions about the way their activities could build a functional ecosystem in such extreme environments (i.e.: oligotrophy and high SO4 concentration and salinity). Additionally, more information is needed to dissipate the doubts about the microbial role in the genesis of such U orebodies. A U roll-front deposit hosted in an aquifer driven system (in Zoovch Ovoo, Mongolia), intended for mining by acid ISR, was previously explored and showed to be governed by a complex bacterial diversity, linked to the redox zonation and the geochemical conditions. Here for the first time, transcriptional activities of microorganisms living in such U ore deposits are determined and their metabolic capabilities allocated in the three redox-inherited compartments, naturally defined by the roll-front system. Several genes encoding for crucial metabolic pathways demonstrated a strong biological role controlling the subsurface cycling of many elements including nitrate, sulfate, metals and radionuclides (e.g.: uranium), through oxidation-reduction reactions. Interestingly, the discovered transcriptional behaviour gives important insights into the good microbial adaptation to the geochemical conditions and their active contribution to the stabilization of the U ore deposits. Overall, evidences on the importance of these microbial metabolic activities in the aquifer system are discussed that may clarify the doubts on the microbial role in the genesis of low-temperature U roll-front deposits, along the Zoovch Ovoo mine. |
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0048-9697 |
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THL @ christoph.kuells @ jroundi_evidence_2023 |
Serial |
138 |
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| Author |
Kamruzzaman, M.; Chowdhury, A. |
| Title |
Flash flooding considerations aside: Knowledge brokering by the extension and advisory services to adapt a farming system to flash flooding |
Type |
Journal Article |
| Year |
2023 |
Publication |
Heliyon |
Abbreviated Journal |
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| Volume |
9 |
Issue |
9 |
Pages |
19662 |
| Keywords |
Flash flooding, Knowledge brokering, Extension and advisory services, Farming system, Climate change |
| 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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Liu, Z.; Tan, K.; Li, C.; Li, Y.; Zhang, C.; Song, J.; Liu, L. |
| Title |
Geochemical and S isotopic studies of pollutant evolution in groundwater after acid in situ leaching in a uranium mine area in Xinjiang |
Type |
Journal Article |
| Year |
2023 |
Publication |
Nuclear Engineering and Technology |
Abbreviated Journal |
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| Volume |
55 |
Issue |
4 |
Pages |
1476-1484 |
| Keywords |
Acid in situ leaching of uranium, Pollution evolution, Sulfate elimination, Sulfur isotopes analysis |
| Abstract |
Laboratory experiments and point monitoring of reservoir sediments have proven that stable sulfate reduction (SSR) can lower the concentrations of toxic metals and sulfate in acidic groundwater for a long time. Here, we hypothesize that SSR occurred during in situ leaching after uranium mining, which can impact the fate of acid groundwater in an entire region. To test this, we applied a sulfur isotope fractionation method to analyze the mechanism for natural attenuation of contaminated groundwater produced by acid in situ leaching of uranium (Xinjiang, China). The results showed that δ34S increased over time after the cessation of uranium mining, and natural attenuation caused considerable, area-scale immobilization of sulfur corresponding to retention levels of 5.3%–48.3% while simultaneously decreasing the concentration of uranium. Isotopic evidence for SSR in the area, together with evidence for changes of pollutant concentrations, suggest that area-scale SSR is most likely also important at other acid mining sites for uranium, where retention of acid groundwater may be strengthened through natural attenuation. To recapitulate, the sulfur isotope fractionation method constitutes a relatively accurate tool for quantification of spatiotemporal trends for groundwater during migration and transformation resulting from acid in situ leaching of uranium in northern China. |
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1738-5733 |
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THL @ christoph.kuells @ liu_geochemical_2023 |
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192 |
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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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