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Kelantan is a flood-prone area where in the past years flood had occurred quite frequently. Determining a hydrological model that can represent Kelantan River basin by giving plausible simulated runoff according to the observed runoff is essential as this will allow appropriate prediction of future flood by using forecasted rainfall and other data. In this study, the IHACRES model was used to simulate runoff and the calibrated simulated runoff by daily scale and seasonal flood events were compared with observed runoff. In general, the IHACRES model performed better in seasonal scale as compared with annual scale in terms of calibration. However, performance of IHACRES degraded during validation stage, whereby the model tends to underestimate the high peak flows but estimate rather more accurate when no peak flows were present. In terms of annual scale, the best model was obtained by calibrating the streamflow in 2012 – 2013 (2 years), the validation results were not satisfactory with NSE = 0.473 and PBIAS = 27.7%. On the other hand, for seasonal analysis, the best model was obtained by calibrating the data of NEM 6 (November 2017 – March 2018). 3 out of 5 of the validation periods show unsatisfactory results (NSE ≤ 0.50). NEM 1 (November 2012 – March 2013) show the best validation results with NSE = 0.853. Further calibration is required in order to enhance the accuracy of the model.

Concentrated suspensions are very complex in nature and exhibit non-Newtonian flow properties although the suspending fluid might behave as a Newtonian fluid. Among the interesting properties, wall slip will be the main focus of this study. The formation of wall slip layer adjacent to the solid boundary may lead to inaccurate measurement of rheological properties. So, the measured viscosity can be lower than the actual viscosity and thus a basic understanding on wall slip is critical. Concentration, particle size, and temperature are the factors affecting the wall slip mechanism. Therefore, this research study tends to study the relationship between the parameters (concentration, particle size, and temperature) and wall slip. The result shows that the slip velocity increases with shear stress under the conditions where (i) concentration decreases, (ii) particle size increases, and (iii) temperature increases. Two regression models considering the three parameters are proposed and can be used respectively as an alternative to predict slip velocity and true shear rate.

Prediction of the groundwater dynamics via models can help better manage the groundwater resources and guarantee their sustainable use. Three types of data-driven models are built for groundwater depth prediction in the plain of Shijiazhuang, the capital of Hebei Province in North China. The data-driven models include the Power Function Model (PFM), Back-Propagation Artificial Neural Network (BPANN) and Support Vector Machines (SVM) with two kernel functions of linear kernel function (LKF) and radial basis function (RBF). Five classes of factors (including 12 indices) are considered as potential model input variables. The Gamma Test (GT) is adopted in this study to help identify the relative importance of the input indices and tackle the tricky issue of the optimal input combinations for the data-driven models. The established models are evaluated in both fitting and testing procedures based on the root mean squared error (RMSE) and Nash-Sutcliffe efficiency (E) for different input combination schemes. The results show that SVM (RBF) performs the best. It is interesting to find that the natural factors (i.e., precipitation and evaporation) are less relevant to the groundwater depth variations. The methods used in this study have much significance for groundwater depth prediction in areas lacking hydrogeological data. [ABSTRACT FROM AUTHOR]

Regional frequency analysis is an important tool in estimating design flood for ungauged catchments. Index flood is an important component in regionalized flood formulas. In the past, many formulas have been developed based on various numbers of calibration catchments (e.g. from less than 20 to several hundred). However, there is a lack of systematic research on the model uncertainties caused by the number of calibration catchments (i.e. what is the minimum number of calibration catchment? and how should we choose the calibration catchments?). This study uses the statistical resampling technique to explore the impact of calibration catchment numbers on the index flood estimation. The study is based on 182 catchments in England and an index flood formula has been developed using the input variable selection technique in the data mining field. The formula has been used to explore the model uncertainty due to a range of calibration catchment numbers (from 15 to 130). It is found that (1) as expected, the more catchments are used in the calibration, the more reliable of the models developed are (i.e. with a narrower band of uncertainty); (2) however, poor models are still possible with a large number of calibration catchments (e.g. 130). In contrast, good models with a small number of calibration catchments are also achievable (with as low as 15 calibration catchments). This indicates that the number of calibration catchments is only one of the factors influencing the model performance. The hydrological community should explore why a smaller calibration data set could produce a better model than a large calibration data set. It is clear from this study that the information content in the calibration data set is equally if not more important than the number of calibration data. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]