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Akter, A., Tanim, A. H., & Islam, M. K. (2020). Possibilities of urban flood reduction through distributed-scale rainwater harvesting. Water Science and Engineering, 13(2), 95–105.
Abstract: Urban flooding in Chittagong City usually occurs during the monsoon season and a rainwater harvesting (RWH) system can be used as a remedial measure. This study examines the feasibility of rain barrel RWH system at a distributed scale within an urbanized area located in the northwestern part of Chittagong City that experiences flash flooding on a regular basis. For flood modeling, the storm water management model (SWMM) was employed with rain barrel low-impact development (LID) as a flood reduction measure. The Hydrologic Engineering Center’s River Analysis System (HEC-RAS) inundation model was coupled with SWMM to observe the detailed and spatial extent of flood reduction. Compared to SWMM simulated floods, the simulated inundation depth using remote sensing data and the HEC-RAS showed a reasonable match, i.e., the correlation coefficients were found to be 0.70 and 0.98, respectively. Finally, using LID, i.e., RWH, a reduction of 28.66% could be achieved for reducing flood extent. Moreover, the study showed that 10%–60% imperviousness of the subcatchment area can yield a monthly RWH potential of 0.04–0.45 m3 from a square meter of rooftop area. The model can be used for necessary decision making for flood reduction and to establish a distributed RWH system in the study area.
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Aderemi, B. A., Olwal, T. O., Ndambuki, J. M., & Rwanga, S. S. (2023). Groundwater levels forecasting using machine learning models: A case study of the groundwater region 10 at Karst Belt, South Africa. Systems and Soft Computing, 5, 200049.
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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Abiye, T. (2016). Synthesis on groundwater recharge in Southern Africa: A supporting tool for groundwater users. Groundwater for Sustainable Development, 2-3, 182–189.
Abstract: This synthesis on groundwater recharge targets the Southern African region as a result of the dependence of the community and economic sector on the groundwater resource. Several literature based recharge studies were collected and assessed in order to find out the main controls to the occurrence of recharge. The Water Table Fluctuation and Base flow separation methods have been tested in the catchment that drains crystalline basement rocks and dolostones close to the city of Johannesburg, South Africa. Based on the assessed data the Chloride Mass Balance method resulted in groundwater recharge of less than 4% of the rainfall, while it reaches 20%, when rainfall exceeds 600mm. For the classical water balance method, recharge proportion is less than 3% of rainfall as a result of very high ambient temperature in the region. Based on the Saturated Volume Fluctuation and Water Table Fluctuation methods, recharge could be less than 6% for annual rainfall of less than 600mm. Observational results further suggest that sporadic recharge from high intensity rainfall has important contribution to the groundwater recharge in the region, owing to the presence of permeable geological cover, which could not be fully captured by most of the recharge estimation methods. This study further documents an evaluation of the most reliable recharge estimation methods in the area such as the chloride mass balance, saturated volume fluctuation and water table fluctuation methods in order to successfully manage the groundwater resource.
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Abadi, B., Sadeghfam, S., Ehsanitabar, A., & Nadiri, A. A. (2023). Investigating socio-economic and hydrological sustainability of ancient Qanat water systems in arid regions of central Iran. Groundwater for Sustainable Development, 23, 100988.
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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