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It has been quite a long time since the Malaysian government endorsed the Urban Storm Water Management Manual (USWMM) in 2000. Until now, there is no proper and detailed database on the non-point source contamination characteristics from various land uses in tropical regions of Malaysia. As such this study was conducted to fill part of the information gap pertaining to the nature of runoff quality in the tropical regions. The combined sewer outfall of a 6.14 ha residential area in Malaysia was studied to characterise the urban runoff quality generated due to tropical rain. As the drainage outlet discharges sullage and storm runoff through the same drainage network, hourly flow pattern and contaminant concentrations were determined both for sullage and storm runoff. Basic statistical analysis was conducted to determine the mean, standard deviation and event mean concentration values for the study area, for which such data was not available. It was observed that the runoff generated from the area is polluted due to high total suspended solids (TSS), biochemical oxygen demand (BOD) and chemical oxygen demand (COD). The runoff contained more total organic carbon (TOC) than total inorganic carbon (TIC). The EMC values of BOD, COD, TSS, TOC, total Kjeldhal nitrogen, ammoniacal nitrogen and orthophosphate were 35, 168, 177, 11, 0.32, 0.54, 0.16 mg/L, respectively. The presence of heavy metals in the runoff was low. The EMC values of lead, zinc, nickel, cadmium, chromium and copper were 0.061, 0.358, 0.002, 0.002, 0.025 and 0.022 mg/L, respectively. Due to the high quantity of rainfall, a significant amount of annual contamination loading is generated from the nonpoint sources of the residential area.

In analysing the stream–aquifer interactions and riverbank filtration (RBF) systems, it is very crucial to determine the vertical riverbank and streambed hydraulic conductivity. The riverbank and streambed focused in this investigation are a riverbank of six layers with depths of 38 m and streambed with depths of 9 m connected layers of sediments at 22 test locations and 4 test wells in the Muda River, Malaysia. In the analysis, there were a few tests involved to determine riverbank and hydraulic conductivity of vertical streambed, such as analysis of grain size, pumping test and in situ falling head standpipe permeability tests. The approximate K values of 114 samples and 15 samples taken from riverbanks and streambeds, respectively, were then calculated by employing empirical equation methods [Hazen, Hazen K (cm/s) = d10 (mm), Terzaghi, Beyer, Slichter, Sauerbrei, Kruger, Kozeny–Carman, Zunker, USBR, Zamarin, Barr, Alyamani and Sen, Chapuis, and Krumbein and Monk]. The geometric mean of K for six layers, namely the sandy silt (8.30 m/day), silty sand (47.66 m/day), gravelly sand (150.24 m/day), sandy gravel layer (418.48 m/day), gravelly sand (151.09 m/day) and silty clay (9.36 m/day) as identified characteristics by using grain-size analyses, was greater than the K of pumping test (geometric mean) (31.10 m/day) and the mean obtained from K of permeability tests (7.03 m/day). In general, the K values of upper layer of sediments of streambed were recorded to be larger in comparison with their respective lower layer of sediments. The K value for the upper layer of sediments from all tests located at the left, right and middle parts of the river ranged from 7.56 to 54.77 m/day for upper layer, from 39.80 to 128.40 m/day for middle layer and from 9.11 to 49.92 m/day for lower layer, as described by the grain-size analysis. The value of K ranges based on permeability test indicated that the value of K was from 0.036 to 1.09 m/day for the upper layer and 0.16 to 0.68 m

Hydrological models are vital component and essential tools for water resources and environmental planning and manage-ment. In recent times, several studies have been conducted with a view of examining the compatibility of model results with streamflow measurements. Some modelers are of the view that even the use of complex modeling techniques does not give better assessment due to soil heterogeneity and climatic changes that plays vital roles in the behavior of streamflow. In Malaysia, several public domain hydrologic models that range from physically-based models, empirical models and conceptual models are in use. These include hydrologic modeling system (HEC-HMS), soil water assessment tool (SWAT), MIKE-SHE, artificial neural network (ANN). In view of this, a study was conducted to evaluate the hydrological models used in Malaysia, determine the coverage of the hydrological models in major river basins and to identify the methodologies used (specifically model performance and evaluation). The results of the review showed that 65% of the studies conducted used physical-based models, 37% used empirical models while 6% used conceptual models. Of the 65% of physical-based modelling studies, 60% utilized HEC-HMS an open source models, 20% used SWAT (public domain model), 9% used MIKE-SHE, MIKE 11 and MIKE 22, Infoworks RS occupied 7% while TREX and IFAS occupy 2% each. Thus, indicating preference for open access models in Malaysia. In the case of empirical models, 46% from the total of empirical researches in Malaysia used ANN, 13% used Logistic Regression (LR), while Fuzzy logic, Unit Hydrograph, Auto-regressive inte-grated moving average (ARIMA) model and support vector machine (SVM) contributed 8% each. Whereas the remaining proportion is occupied by Numerical weather prediction (NWP), land surface model (LSM), frequency ratio (FR), decision tree (DT) and weight of evidence (WoE). Majority of the hydrological modelling studies utilized one or more statistical measure o

The objective of the study is to develop a spatial prediction model of sand mass and organic matter distribution in an urban stormwater holding pond using in situ measured wet sediment bulk density profile data to spatially distinguish the most likely contaminated sediment deposit areas. The wet bulk density profiles of deposited sediment at 25 locations in the Berembang (Malaysia) stormwater holding pond were measured using a single-probe nuclear density gauge. The sand and organic matter compositions of the surface sediment sample, 5 cm thickness from the bed surface, were determined. Discriminant analysis (DA) was conducted to generate two Fisher’s linear discriminant functions for the prediction of sand mass and organic matter composition areas, respectively. The linear discriminant functions generated better area classifications of surface organic matter composition compared to the sand mass distribution using wet sediment bulk density data measured at more than 15 cm depth levels.

With the advance of GIS technology, hydrology model can simulated at catchment wide scale. The objective is to integrate National Resource Conservation Service (NRCS) Curve Number (CN) with kinematic wave and manning’s equation using GIS to develop a simple GIS-based distributed model to simulate rainfall runoff in Bentong catchment. Model was built using Spatial Distributed Direct Hydrograph (SDDH) concept and applying the time area (TA) approach in presenting the predicted discharge hydrograph. The effective precipitation estimation was first calculated using the NRCS CN method. Then, the core maps that consists of digital elevation model (DEM), soil and land use map in grid. DEM was used to derive slope, flow direction and flow accumulation while soil and land use map used to derive roughness coefficient and CN. The overland velocity and channel velocity estimation derived from combination of kinematic wave theory with Manning’s equation. To capture the time frame, the travel time map was divided into isochrones in order to generate the TA histogram and finally. The creation of SDDH using the TA histogram which will lead to the estimation of travel time for the catchment. Simulated hydrograph was plotted together with the observed discharge for comparison. Six storm events used for model performance evaluation using statistical measure such as Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS) and coefficient of determination (R²). SDDH model performed quite well as NSE gave result ranging from 0.55 to 0.68 with mean of 0.6. PBIAS indicate that the model slightly over predicted compared to observed hydrograph with result ranges from -46.71 (the most over predicted) to +4.83 (the most under predicted) with average of -20.73%. R² ranges between 0.55 to 0.82 with mean of 0.67. When comparing the time to peak, (tp), min, and peak discharge, (pd), m3 /s, results gave NSEtp 0.82, PBIAStp 0.65, R2 tp 0.32, NSEpd 0.95, PBIASpd 14.49 and R2 pd 0.98, respectively. Resul

The different factors (seasonal changes) and variables (physicochemical) controlling the groundwater hydrochemistry of Kapas Island were identified using multivariate techniques principal component analysis (PCA), discriminant analysis (DA) and hierarchy cluster analysis (HCA). In the present study, the hydrochemistry of 216 groundwater samples, consisting of information concerning the in situ parameters and major ions in six monitoring boreholes, was studied and compared in two different monsoon seasons. The dominant variables derived from four components by PCA in the pre-monsoon indicated the influence of the salinity process, while the dominant variables derived from three components in the post-monsoon mostly indicated on the mineralization process. The DA gave the final variables after discriminating the insignificant variables based on the pre- and post-monsoon classifications. This provided important data reduction in terms of the mineralization process, as it only discriminated physical variables (TDS, EC, salinity, DO and temperature). Based on the HCA result, samples belonging to stations KW 3 and KW 4 were under Ca-rich water, while the remaining boreholes were grouped in Na-rich water.

Landslide has become a common problem especially in tropical countries such as in Malaysia. This study was carried out in Fraser Hill Catchment using a GIS based deterministic slope stability analysis model, that combine infinite slope stability and steady state hydrology assumptions to quantify the stability called SINMAP. The model requires some inputs. Historical landslide inventory for the catchment were obtained from interpretation of multispectral SPOT 5 image and Global Positioning Survey (GPS) survey. Topographic maps at scale of 1:50,000 were used to construct Digital Elevation Model (DEM). Soil strength parameters and hydrologic parameters were gathered from in situ test as well as previous records. The purposes of this study were to map the landslide susceptibility of Fraser Hill Catchment and to test the usage of different DEM spatial resolution towards the accuracyof the model. Landslide susceptibility map for the study area was produced as the output of this model. The result will be compared with the actual location of slope failure that occur within the catchment to assess the model performance. Results showed that, for this catchment, SINMAP gives good results in predicting the landslide with 68% of the current landslide inventory fall within unstable class as their calculation of Stability Index (SI) are less than 1. Results from the spatial resolution analysis showed that 20 and 30 meter resolution gave optimum result compared to others.

Groundwater tables forecasting during implemented river bank infiltration (RBI) method is important to identify adequate storage of groundwater aquifer for water supply purposes. This study illustrates the development and application of artificial neural networks (ANNs) to predict groundwater tables in two vertical wells located in confined aquifer adjacent to the Langat River. ANN model was used in this study is based on the long period forecasting of daily groundwater tables. ANN models were carried out to predict groundwater tables for 1 day ahead at two different geological materials. The input to the ANN models consider of daily rainfall, river stage, water level, stream flow rate, temperature and groundwater level. Two different type of ANNs structure were used to predict the fluctuation of groundwater tables and compared the best forecasting values. The performance of different models structure of the ANN is used to identify the fluctuation of the groundwater table and provide acceptable predictions. Dynamics prediction and time series of the system can be implemented in two possible ways of modelling. The coefficient correlation (R), Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient determination (R2) were chosen as the selection criteria of the best model. The statistical values for DW1 are 0.8649, 0.0356, 0.01, and 0.748 respectively. While for DW2 the statistical values are 0.7392, 0.0781, 0.0139, and 0.546 respectively. Based on these results, it clearly shows that accurate predictions can be achieved with time series 1-day ahead of forecasting groundwater table and the interaction between river and aquifer can be examine. The findings of the study can be used to assist policy marker to manage groundwater resources by using RBI method.