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Abadi, B.; Sadeghfam, S.; Ehsanitabar, A.; Nadiri, A.A. |
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Title |
Investigating socio-economic and hydrological sustainability of ancient Qanat water systems in arid regions of central Iran |
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Journal Article |
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Year |
2023 |
Publication |
Groundwater for Sustainable Development |
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23 |
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100988 |
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Keywords |
Ancient irrigation, QWSs, GIS, Indigenous knowledge, Maintenance, Distribution |
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Abstract |
The Qanat water systems (QWSs), the ancient water engineering systems in Iran belonging to the very distant past, have harvested groundwater from drainages to convey it toward the surface with no use of energy. The present article highlights the socio-economic aspects of the sustainability of the QWSs and gives a satisfactory explanation of why the QWSs should be restored. In doing so, we subscribe to the view that indigenous and scientific knowledge should be incorporated. The former serves to tackle the restoration of the QWSs, the latter contributes to the distribution of water into the farmlands as efficiently as possible. Measured by (a) resilience, (b) reliability, (c) vulnerability, and (d) sustainability, the GIS technique made clear the performance of the QWSs has, therefore, the worst condition observed in terms of resiliency; the best condition observed concerning the vulnerability. Moreover, the QWSs have intermediate performance in terms of reliability. Finally, the sustainability index (SI) classifies the QWSs into different bands, which provide explicit support to take priority of the selection of the QWSs for restoration. In conclusion, a theoretical framework has been drawn to keep the QWSs sustainable. |
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2352-801x |
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THL @ christoph.kuells @ Abadi2023100988 |
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268 |
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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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Alexander, A.C.; Ndambuki, J.M. |
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Title |
Impact of mine closure on groundwater resource: Experience from Westrand Basin-South Africa |
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Journal Article |
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2023 |
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Physics and Chemistry of the Earth, Parts A/B/C |
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131 |
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103432 |
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Acid mine drainage, Groundwater quality, Mine closure, Spatio-temporal variation, Westrand Basin |
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The mining sector is at the edge of expanding to cater for natural resources that are much needed for technological development and manufacturing. Mushrooming of mines will consequently increase the number of mines closure. Moreover, mines closure have adverse impact on the environment at large and specifically on water resources. This study analyses historical groundwater quality parameters in mine intensive basin of Westrand Basin (WRB) to understand the status of groundwater quality in relation to mining activities and mine closure. Geographic information system (GIS) was used to map the spatio-temporal variation of groundwater quality in the basin and groundwater quality index (GQI) to evaluate its status. The coefficient of variation (CV) was applied to understand the stability of groundwater quality after the mine closure. Results indicated unstable and altered trend with increasing levels of acidity and salts concentration around the mines vicinity following the mine closure. The resultant maps indicated a significant deterioration of groundwater quality around the WRB with concentrations decreasing downstream. Obtained average GQI for the study period of 1996–2015 suggested a moderate groundwater quality at a range of GQI = 64–73. The CV indicated varying water quality at CV \textgreater 30% suggesting presence of source of contamination. Observed groundwater quality trends in Westrand basin suggested that mines closure present potential threat on groundwater quality and thus, a need for a robust mine closure plan and implementation. |
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1474-7065 |
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THL @ christoph.kuells @ alexander_impact_2023 |
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134 |
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Arya, S.; Kumar, A. |
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Title |
Evaluation of stormwater management approaches and challenges in urban flood control |
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2023 |
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Urban Climate |
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51 |
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101643 |
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Flood risk, Green infrastructure (GI), Stormwater management, Stormwater modelling, Vulnerability assessment, Urban floods |
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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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Author |
Asare, P.; Atun, F.; Pfeffer, K. |
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Title |
Nature-Based Solutions (NBS) in spatial planning for urban flood mitigation: The perspective of flood management experts in Accra |
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Journal Article |
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Year |
2023 |
Publication |
Land Use Policy |
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133 |
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106865 |
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Naure-Based Solutions, Urban expansion, Urban floods, Urban flood management, Spatial planning, NBS integration |
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The rapid rate of urban expansion with its associated physical development in recent years conflicts with the urban ecosystem and the services it provides. In most Sub-Saharan African cities, rapid urban expansion often does not conform to existing spatial plans. Physical developments are sometimes carried out in unauthorized areas, contributing to urban floods. The Sub-Saharan African regions’ flood management strategies mainly focus on engineering solutions but have not been fully functional in mitigating urban floods. There is a scarcity of knowledge on how urban flood-related NBS measures can be part of the spatial development in Sub-Saharan African cities for effective flood management. In order to address this gap, this study employed content and text analysis of policy documents and interviews to understand how current spatial and flood mitigation schemes in Accra, Ghana reflect possible NBS applicability and identify possible approaches to integrating NBS into existing planning schemes to prevent urban floods. The study found that Accra’s spatial plans and flood mitigation schemes reflect a possibility of NBS integration. Additionally, the study unveiled techniques for integrating NBS measures and possible implementation barriers and facilitation in the Ghanaian context, which can be linked to combating the challenges that the Ghanaian spatial planning and flood management authorities face. The research, therefore, contributes to knowledge of how NBS can be integrated into spatial planning systems and flood mitigation schemes in Sub-Saharan African regions. |
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0264-8377 |
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THL @ christoph.kuells @ Asare2023106865 |
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236 |
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