Abstract: Study region Wadi Tayyibah is located in south Sinai, Egypt, in a region called Abou Zenima, and it is used to develop this study. Study focus Flash floods tremendously impact many facets of human life due to their destructive consequences and the costs associated with mitigating efforts. This study aims to evaluate the harvesting of Runoff by delineating the watersheds using the Hydrologic Engineering Center-1 (HEC-1) model and ArcGIS software in trying to benefit from it in different ways. All morphometric parameters of the basin were considered, and the risk degree of the different sub-basins was determined. The suitable locations of dams were identified using a Geographical Information System (GIS) using the basin’s morphometric characteristics. New hydrological insights for the region The study proposed a total number of eight dams, including five dams that were recommended for sub-basin (1) and three dams in sub-basin (4), while sub-basins (2) and (3) are not suitable locations to build dams according to the contour map of Wadi Tayyibah. Results indicate that, based on the constructed flash flood hazard maps and the basin’s detailed morphometric characteristics, the best locations of dams are Dam (3) in sub-basin (1) and Dam (7) in sub-basin (4), where the runoff volume reached 3.13 million cubic meters (Mm3) and 5.56 Mm3 for return period 100, respectively. This study is useful for decision-makers and designers for using morphometric parameters and flash flood hazard degree maps to select dam locations. Also, the cost-benefit analysis for using the morphometric parameters is required to be investigated.

Abstract: Numerous groundwater analyses from the crystalline bedrock in the Forsmark area have been performed between 2002 and 2019, together with thorough geological, geophysical, and hydrogeological studies, within the site investigations carried out by the Swedish Nuclear Fuel and Waste Management Company. The groundwater samples have been taken from boreholes down to ≈ 1000 m and the analysis include major- and trace-elements, stable and radiogenic isotopes, gases and microbes. The chemical and isotopic composition of these groundwaters evidences the presence of non-marine brackish to saline groundwaters with very long residence times (many hundreds of thousands of years) and a series of complex mixing events resulting from the recharge of different waters over time: glacial meltwaters, probably from different glaciations of which the latest culminated some 20,000 years ago, and marine waters from the Baltic starting some 7000 years ago. Later, meteoric water and present Baltic Sea water have recharged in different parts of the upper 100 m. These mixing events have also triggered chemical and microbial reactions that have conditioned some of the important groundwater parameters and, together with the structural complexity of the area, they have promoted a heterogeneous distribution of groundwater compositions in the bedrock. Due to these evident differences in chemistry, residence time and origin of the groundwater, several groundwater types were defined in order to facilitate the visualisation and communication. The differentiation (linked to the paleohydrological history of the area) was based on Cl concentration, Cl/Mg ratio (marine component), and δ18O value (glacial component). The work presented in this paper increases the understanding of the groundwater evolution in fractured and compartmentalised aquifers where mixing processes are the most important mechanisms. The model proposed to characterise the present groundwater system of the Forsmark area will also help to predict the future hydrogeochemical behaviour of the groundwater system after the construction of the repositories for the nuclear wastes.

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.

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.

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.