Groynes are popular hydraulic structures often used to control the erosion of banks by altering flow and sediment transport. In this paper, the effects of altering groyne orientation and spatial setup (from large to small and vice versa) on flow patterns, bed erosion, and sedimentation are numerically investigated. Studied groynes were parallel to each other, non-submerged, and impermeable. Numerical simulations were conducted in FLOW-3D. A nested mesh configuration combined with Van-Rijn formula on sediment transport yielded more accurate results when comparing numerical results to experiments. Groynes arranged from large to small at an angle of 45° decreased the scour depth by up to 55%, and an arrangement from small to large at an angle of 135° reduced the scour depth by up to 72%. Additionally, it was observed that simulations with an orientation closer to 90 degrees needed more equilibrium time when compared to other simulations.

A new type of collar, the hooked-collar, was studied through experiments and numerical methods. Tests were conducted using a hooked collar of a width of 1.25b and a height of 0.25b, where b is the bridge-pier width. The hooked-collar efficiency was evaluated by testing different hooked-collar placements within the bridge-pier, which were compared to the bridge-pier without any collar. A double hooked-collar configuration, one placed at the bed level and the other buried 0.25b, was the most efficient at reducing the scour hole. In other cases, a hooked-collar positioned 0.25b above the bed slightly reduced the scour hole and had similar scour patterns when compared to the pier without the hooked-collar. The flow fields along the vertical symmetrical plane in the experiments are also presented. Laboratory experiments and numerical tests show that maximal downflow is highly reduced along with a corresponding decrease in horseshoe vortex strength for the experiments with the hooked-collar, compared to cases without the hooked-collar. The flow fields reveal that the maximum turbulent kinetic energy decreases with the installation of the hooked-collar.

This study analyzed the influence of climate change on sediment yield variation, sediment transport and erosion deposition distribution at the watershed scale. The study was based on Gaoping River basin, which is among the largest basins in southern Taiwan. To carry out this analysis, the Physiographic Soil Erosion Deposition (PSED) model was utilized. Model results showed a general increase in soil erosion and deposition volume under the A1B-S climate change scenario. The situation is even worsened with increasing return periods. Total erosion volume and total sediment yield in the watershed were increased by 4–25% and 8–65%, respectively, and deposition volumes increased by 2–23%. The study showed how climate change variability would influence the watershed through increased sediment yields, which might even worsen the impacts of natural disasters. It has further illustrated the importance of incorporating climate change into river management projects.

Sediment in river is usually transported during extreme events related to intense rainfall and high river flows. The conventional means of collecting data in such events are risky and costly compared to water discharge measurements. Hence, the lack of sediment data has prompted the use of sediment rating curves (SRC). The aim of this study is to explore the abilities of artificial neural networks (ANNs) in advancing the precision of stream flow-suspended discharge relationships during storm events in the Shiwen River, located in southern Taiwan. The ANNs used were multilayer perceptrons (MLP), the coactive neurofuzzy inference system model (CANFISM), time lagged recurrent networks (TLRN), fully recurrent neural networks (FRNN) and the radial basis function (RBF). A comparison is made between SRC and the ANNs. Hourly based water and sediment discharge during 8 storms were manually collected and used as inputs for the SRC and the ANNs. Results have shown that the ANN models were superior in reproducing hourly sediment discharge compared to SRC. The findings further suggest that MLP can provide the most accurate estimates of sediment discharge, (R² of 0.903) compared to CANFISM, TLRN, FRNN and RBF. SRC had the lowest R² (0.765), and resulted in underestimations of peak sediment discharge (−47%).

Hydrological data are often missing due to natural disasters, improper operation, limited equipment life, and other factors, which limit hydrological analysis. Therefore, missing data recovery is an essential process in hydrology. This paper investigates the accuracy of artificial neural networks (ANN) in estimating missing flow records. The purpose is to develop and apply neural networks models to estimate missing flow records in a station when data from adjacent stations is available. Multilayer perceptron neural networks model (MLP) and coactive neurofuzzy inference system model (CANFISM) are used to estimate daily flow records for Li‐Lin station using daily flow data for the period 1997 to 2009 from three adjacent stations (Nan‐Feng, Lao‐Nung and San‐Lin) in southern Taiwan. The performance of MLP is slightly better than CANFISM, having R² of 0.98 and 0.97, respectively. We conclude that accurate estimations of missing flow records under the complex hydrological conditions of Taiwan could be attained by intelligent methods such as MLP and CANFISM.

This article explores the escalating impact of natural disasters, particularly droughts, in the Southern African Development Community (SADC), with a specific focus on Eswatini. Over the last century, approximately 63 million people in SADC countries have been affected by droughts, leading to challenges in agriculture, livestock losses, and severe food and water shortages. Despite being the smallest SADC nation, the Kingdom of Eswatini faces disproportionate consequences due to its susceptibility to climate variability, particularly drought. The inadequacy and unreliability of rainfall have resulted in a drastic reduction in food production, with maize, a staple crop, experiencing a 70% decline. This adverse trend, spanning three decades, has heightened the vulnerability of farmers to climatic shocks, hindering sustainable agricultural development and impeding poverty alleviation efforts. To address the growing threat of drought in the kingdom, a comprehensive approach is imperative, involving coordinated plans and the development of swift humanitarian relief strategies. This study utilized remote sensing technologies to monitor drought and assess its repercussions, evaluating the impact on agricultural production. Additionally, geospatial tools, including Open Route Service (ORS) and Near Neighbor Analysis algorithms, were employed to optimize humanitarian supply chain logistics. Results from the analysis, including Vegetation Health Index (VHI) fluctuations and drought severity mapping, reveal that 1990 was the year the kingdom was most severely hit by drought. This study also found that smallholder farmers practicing rainfed agriculture in vulnerable regions, such as the lower Middleveld and western Lowveld, suffered the severe socioeconomic consequences of agricultural drought, including income loss, food insecurity, and migration patterns. Through this integrated approach, decision makers can engage in targeted interventions, focusing on farming areas needing irrigation infrastructures or populated areas requiring a coordinated humanitarian response amidst climate variability.