The current study aims to verify the existing equations for incipient motion for a rigid rectangular channel. Data from experimental work on incipient motion from a rectangular flume with two different widths, namely 0.3 and 0.6 m, were compared with the critical velocity value predicted by the equations of Novak & Nalluri and El-Zaemey. The equation by El-Zaemey performed better with an average discrepancy ratio value of 1.06 compared with the equation by Novak & Nalluri with an average discrepancy ratio value of 0.87. However, as the sediment deposit thickness increased, the equation by El-Zaemey became less accurate. A plot on the Shields Diagram using the experimental data had shown the significant effect of the sediment deposit thickness where, as the deposit becomes thicker, the dimensionless shear stress θ value also increased. A new equation had been proposed by incorporating the sediment deposit thickness. The new equation gave improved prediction with an average discrepancy ratio value of 1.02.

In this paper the flow characteristics of stormwater are analyzed as it travels from a roof gutter down-pipe and the turbulent flow generated on entering an individual lot on-site stormwater detention (OSD) unit beneath a residential carport. Comparison was made between a full-scale model and computational fluid dynamic (CFD) simulations to determine the flow characteristics. These modular tanks with multi-unit chambers can capture the roof run-off from a 15-minute, 10-year return period storm. The results from the physical and CFD models matched well, suggesting that turbulent flow occurs when stormwater is directed to an individual lot stormwater detention tank. However, turbulence in the OSD was concentrated around the inlet, after which the pattern changed from turbulent to laminar flow. This work implies that the use of modular underground storage tanks is practical for managing stormwater from a roof.

Severe droughts in the year 1998 and 2014 in Sarawak due to the strong El Nino has impacted the water supply and irrigated agriculture. In this study, the Standardized Precipitation Index (SPI) was used for drought identification and monitoring in Sarawak River Basin. Using monthly precipitation data between the year 1975 and 2016 for 15 rainfall stations in the basin, the drought index values were obtained for the time scale of three, six and nine months. Rainfall trend for the years in study was also assessed using the Mann–Kendall test and Sen's slope estimator and compared with the drought index. Findings showed that generally there was a decreasing trend for the SPI values for the three time scales, indicating a higher tendency of increased drought event throughout the basin. Furthermore, it was observed that there was an increase in the numbers of dry months in the recent decade for most of the rainfall stations as compared to the previous 30 to 40 years, which could be due to climate change. Findings from this study are valuable for the planning and formulating of drought strategies to reduce and mitigate the adverse effects of drought.

The understanding of how the sediment deposit thickness influences the incipient motion characteristic is still lacking in the literature. Hence, the current study aims to determine the effect of sediment deposition thickness on the critical velocity for incipient motion. An incipient motion experiment was conducted in a rigid boundary rectangular flume of 0.6 m width with varying sediment deposition thickness. Findings from the experiment revealed that the densimetric Froude number has a logarithmic relationship with both the thickness ratios t/d and t/y (t: sediment deposit thickness; d: grain size; y: normal flow depth). Multiple linear regression analysis was performed using the data from the current study to develop a new critical velocity equation by incorporating thickness ratios into the equation. The new equation can be used to predict critical velocity for incipient motion for both loose and rigid boundary conditions. The new critical velocity equation is an attempt toward unifying the equations for both rigid and loose boundary conditions.

This study investigates the performance of artificial neural networks in predicting the incipient sediment motion in sewers. Two neural network algorithms, i.e. feed forward neural network (FFNN) and radial basis function (RBF), were employed to estimate the critical velocity over varying sediment thickness, median grain size and water depth. Empirical data from five studies were fed into the models and the performance of each model was scrutinized based on three performance criteria. Prediction from FFNN was found to give higher accuracy than values obtained from RBF. Analysis was also extended to observe the correlation between the predicted critical velocity V cp with calculated critical velocity V cm using five empirical equations developed using non-linear regression analysis. Prediction by FFNN proved to have the highest accuracy compared to the RBF and the values obtained through empirical equations described in this study.