Johor River Research Articles

River water quality assessment using APCS-MLR and statistical process control in Johor River Basin, Malaysia

River water quality assessment using APCS-MLR and statistical process control in Johor River Basin, Malaysia

Soil erosion susceptibility of Johor River basin

AbstractMalaysia is undergoing tremendous environmental changes due to population growth, economic development and urbanization. This has stimulated a rapid land use changes which in turn has increased the rate of soil erosion and river sedimentation. In this study, Universal Soil Loss Equation (USLE) and the Modified Universal Soil Loss Equation (MUSLE) are applied within a geographical information system (GIS) to assess the spatial pattern of soil erosion susceptibility of Johor River basin, Malaysia. Geomorphological, landuse and soil information along with other hydrological data such as, annual peak flow and runoff volumes are used to estimate the parameters required for the calculation of soil erosion susceptibility. The results show that annual erosion rate varies between 0.2 and 248.2 t/ha/year according to USLE, and between 19.2 and 2179.9 thousand tons per event according to MUSLE. The land cover management is found as the major factor responsible for soil erosion in the basin.

Assessment of Three Long-Term Gridded Climate Products for Hydro-Climatic Simulations in Tropical River Basins

Gridded climate products (GCPs) provide a potential source for representing weather in remote, poor quality or short-term observation regions. The accuracy of three long-term GCPs (Asian Precipitation—Highly-Resolved Observational Data Integration towards Evaluation of Water Resources: APHRODITE, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record: PERSIANN-CDR and National Centers for Environmental Prediction Climate Forecast System Reanalysis: NCEP-CFSR) was analyzed for the Kelantan River Basin (KRB) and Johor River Basin (JRB) in Malaysia from 1983 to 2007. Then, these GCPs were used as inputs into calibrated Soil and Water Assessment Tool (SWAT) models, to assess their capability in simulating streamflow. The results show that the APHRODITE data performed the best in precipitation estimation, followed by the PERSIANN-CDR and NCEP-CFSR datasets. The NCEP-CFSR daily maximum temperature data exhibited a better correlation than the minimum temperature data. For streamflow simulations, the APHRODITE data resulted in strong results for both basins, while the NCEP-CFSR data showed unsatisfactory performance. In contrast, the PERSIANN-CDR data showed acceptable representation of observed streamflow in the KRB, but failed to track the JRB observed streamflow. The combination of the APHRODITE precipitation and NCEP-CFSR temperature data resulted in accurate streamflow simulations. The APHRODITE and PERSIANN-CDR data often underestimated the extreme precipitation and streamflow, while the NCEP-CFSR data produced dramatic overestimations. Therefore, a direct application of NCEP-CFSR data should be avoided in this region. We recommend the use of APHRODITE precipitation and NCEP-CFSR temperature data in modeling of Malaysian water resources.

Non-tuned machine learning approach for hydrological time series forecasting

Stream-flow forecasting is a crucial task for hydrological science. Throughout the literature, traditional and artificial intelligence models have been applied to this task. An attempt to explore and develop better expert models is an ongoing endeavor for this hydrological application. In addition, the accuracy of modeling, confidence and practicality of the model are the other significant problems that need to be considered. Accordingly, this study investigates modern non-tuned machine learning data-driven approach, namely extreme learning machine (ELM). This data-driven approach is containing single layer feedforward neural network that selects the input variables randomly and determine the output weights systematically. To demonstrate the reliability and the effectiveness, one-step-ahead stream-flow forecasting based on three time-scale pattern (daily, mean weekly and mean monthly) for Johor river, Malaysia, were implemented. Artificial neural network (ANN) model is used for comparison and evaluation. The results indicated ELM approach superior the ANN model level accuracies and time consuming in addition to precision forecasting in tropical zone. In measureable terms, the dominance of ELM model over ANN model was indicated in accordance with coefficient determination (R2) root-mean-square error (RMSE) and mean absolute error (MAE). The results were obtained for example the daily time scale R2 = 0.94 and 0.90, RMSE = 2.78 and 11.63, and MAE = 0.10 and 0.43, for ELM and ANN models respectively.

Performance Enhancement of Rainfall Pattern – Water Level Prediction Model Utilizing Self-Organizing-Map Clustering Method

During recent two decades, Artificial Neural Network (ANN) has become one of the most widely used methods in hydrology. One solution for better capturing the existing non-linear and complex nature of data is to develop new hybrid approaches. These hybrid models can be developed in a way that two or more techniques are combined in order to benefit from the advantages of these available approaches and eliminate their limitations. The main scope of this paper is to improve the performance of rainfall-water level modeling by combining ANN with Self Organizing Map (SOM) as an unsupervised clustering method. The proposed method in this study consists of two phases. In the first phase, with the aim of reducing the complexity and dimensionality of input data, a two-step clustering using SOM technique is carried out. Then, in the second phase, separate ANN models are used to model each cluster of data, and final results are obtained by combining the outputs of all models. The proposed new hybrid approach is evaluated using real hydrological data of Johor River. The results of the study indicate that the new proposed SOM-ANN hybrid model has a better performance in daily rainfall-water level forecasting compared to ANN model alone.

Lead isotope exchange between dissolved and fluvial particulate matter: a laboratory study from the Johor River estuary.

Atmospheric aerosols are the dominant source of Pb to the modern marine environment, and as a result, in most regions of the ocean the Pb isotopic composition of dissolved Pb in the surface ocean (and in corals) matches that of the regional aerosols. In the Singapore Strait, however, there is a large offset between seawater dissolved and coral Pb isotopes and that of the regional aerosols. We propose that this difference results from isotope exchange between dissolved Pb supplied by anthropogenic aerosol deposition and adsorbed natural crustal Pb on weathered particles delivered to the ocean by coastal rivers. To investigate this issue, Pb isotope exchange was assessed through a closed-system exchange experiment using estuarine waters collected at the Johor River mouth (which discharges to the Singapore Strait). During the experiment, a known amount of dissolved Pb with the isotopic composition of NBS-981 (206Pb/207Pb = 1.093) was spiked into the unfiltered Johor water (dissolved and particulate 206Pb/207Pb = 1.199) and the changing isotopic composition of the dissolved Pb was monitored. The mixing ratio of the estuarine and spike Pb should have produced a dissolved 206Pb/207Pb isotopic composition of 1.161, but within a week, the 206Pb/207Pb in the water increased to 1.190 and continued to increase to 1.197 during the next two months without significant changes of the dissolved Pb concentration. The kinetics of isotope exchange was assessed using a simple Kd model, which assumes multiple sub-reservoirs within the particulate matter with different exchange rate constants. The Kd model reproduced 56% of the observed Pb isotope variance. Both the closed-system experiment and field measurements imply that isotope exchange can be an important mechanism for controlling Pb and Pb isotopes in coastal waters. A similar process may occur for other trace elements.This article is part of the themed issue 'Biological and climatic impacts of ocean trace element chemistry'.

A numerical study on land-based pollutant transport in Singapore coastal waters with a coupled hydrologic-hydrodynamic model

Frequent flooding events and intensive agricultural, economic and industrial activities in Singapore-Malaysia Catchment have made Singapore coastal waters come under high risk of land-based pollution. A coupled hydrologic-hydrodynamic model is employed to perform three-dimensional land-based pollutant transport simulations in Singapore coastal waters. The hydrologic model component (2012 version of Soil and Water Assessment Tool) is found to be able to predict streamflow accurately, with correlation coefficients larger than 0.9 and a high Nash-Sutcliffe efficiency of more than 0.8. The hydrologic model (SWAT) is coupled with the hydrodynamic model (SUNTANS) by transferring streamflow and pollutant concentrations at nine rivers along common boundaries. The coupled hydrologic-hydrodynamic model is validated with observed sea surface elevations and velocities. A low Root-Mean-Square-Error (RMSE) of 0.10m and a high correlation coefficient of 0.98 are observed for sea surface elevations. The coupled model predicts depth-averaged U and V velocities accurately, with low RMSEs of 0.06m/s and 0.07m/s respectively and high correlation coefficients exceed 0.95. During the Northeast monsoon, pollutants from Source 1 (Johor River), Source 2 (Tiram River), Source 3 (Layang River) and Source 4 (Layau River) are dispersed into the Singapore Strait after around 2days of release, with Johor Estuary and Tekong Island highly affected. During the Southwest monsoon, it takes around 9days for pollutants from Sources 1–4 to affect the whole Johor Estuary and Tekong Island, which almost four times of dispersion time during the Northeast monsoon. The overall mean dispersion coefficient K¯ of 47.99m2/s during the Northeast monsoon is roughly four times that of 11.33m2/s during the Southwest monsoon, due to larger amount of land-based pollutants are introduced by streamflow in December and differences in the large-scale monsoon effects. It is found that the dispersion coefficient K obeys a “4/3-law”, with the length scale L defined as the distance of the center of concentration travelled.

English

Large-scale reclamation at Tekong Island, Singapore and the construction of a tidal barrage across Johor River in Kota Tinggi is expected to alter the local hydrodynamics in Johor River Estuary (JRE) and East Tebrau Strait (ETS). Using fl ow fi eld generated from a set of hyperbolic shallow water equations solved on hierarchical quadtree grid system, Lagrangian particle tracking is performed to examine the individual and combined effects of the above developments on particle fate. Results show that particle escape is highly dependent on the tidal cycle and the initial positions. Stretching of particles is observed in all cases, whereas dispersion occurs only in ETS. While the fl ushing effect in JRE is enhanced after the completion of the above projects, the resident time of particles released in the ETS increases, suggesting potential environmental threat to the already polluted water body.

Multiple Regression Model Using Performance Indices for Storage Capacity of a Reservoir System in Johor Catchment

Storage–yield–reliability (S–Y–R) relationship is useful in many areas of hydrology and water resources. With the availability of such a relationship, reservoir planning analysis can be implemented much more rapidly and at a much shorter time needed for sequential analysis applying time series data. Existing relationships have been developed mostly for over-year capacity without considering both reliability and vulnerability performance indices. Hence, in this study, streamflow data from Johor river was used to develop a predictive relationship for total (i.e. within-year plus over-year) storage capacity involving both reliability and vulnerability performance indices as for use during reservoir planning. The reservoir was analyzed using 1000 sequences of synthetic data having the same length as historical data involving both time-based reliability and vulnerability performance indices by modified SPA. The model was then calibrated based on the mean of 1000 simulation results. Subsequently, the performance of the model was observed by comparing the model’s results with simulation outcomes for study systems. It was found that the performance of the model was very good in reproducing, the total storage capacity.

Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow

The sensitivity of streamflow simulated with the Soil and Water Assessment Tool (SWAT) model to Digital Elevation Model (DEM) resolution, DEM source and DEM resampling technique is still poorly understood. The objective of this study is to compare SWAT model streamflow estimates in the Johor River Basin (JRB), Malaysia for DEMs differing in resolution (from 20 to 1500 m), sources (Shuttle Radar Topography Mission: SRTM v4.1, Advanced Space-borne Thermal Emission and Reflection Radiometer: ASTER GDEM2, EarthEnv-DEM90 and Global Multi-resolution Terrain Elevation Data 2010: GMTED2010) and resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority). The key findings were as follows: (1) SRTM v4.1 (Root Mean Square Error (RMSE) = 11.16 m) and EarthEnv-DEM90 (RMSE = 12.4 m) had better vertical accuracy over the JRB compared to the ASTER GDEM2 (RMSE = 16.95 m); (2) Accurate annual streamflow simulations were obtained by using nearly all of the DEM resolutions, as pointed out by a relative error (RE) lower than 7% from 20 to 50 m and from 100 to 800 m DEMs; (3) Prediction errors were the lowest for ASTER GDEM2 (RE = 3.9%), followed by SRTM v4.1 (RE = 5.4%), EarthEnv-DEM90 (RE = 6.3%), and GMTED2010 (RE = 7.3%); (4) the majority and nearest neighbour resampling techniques performed the best (RE of 6.0%), followed by bilinear interpolation (RE of 7.2%) and cubic convolution (7.5%). The study indicates that DEM resolution is the most sensitive SWAT model DEM parameter compared to DEM source and DEM resampling technique for streamflow simulation within SWAT.

RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia

Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999---2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.

COMPARISON OF FLOOD DISTRIBUTION MODELS FOR JOHOR RIVER BASIN

One of the most useful and commonly used parameters to describe a flood event is peak flow or annual maximum flood. In many localities, storm water control facilities are required and their sizes are determined based on certain peak flow magnitude. This study aimed at estimating the average recurrent interval (ARI) of flood event for Johor River basin based on the distributions of annual peak flow. The analysis used annual maximum flow data from July 1965 to June 2010 recorded at the Rantau Panjang gauging station. Five distribution models, namely Generalized Extreme Value (GEV), Lognormal, Pearson 5, Weibull and Gamma were tested. The goodness fit test (GOF) of Kolmogorov-Smirnov (K-S) was used to evaluate and estimate the best-fitted distribution. The results reaffirm the current practice that GEV is still the best-fitted distribution model for fitting the annual peak flow data. On the other hand, gamma distribution showed the poorest result.

Flood Risk Pattern Recognition Using Integrated Chemometric Method and Artificial Neural Network: A Case Study in the Johor River Basin

Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.

Impacts of land-use and climate variability on hydrological components in the Johor River basin, Malaysia

This study aims to investigate separate and combined impacts of land-use and climate variability on hydrological components in the Johor River Basin (JRB), Malaysia. The Mann-Kendall and Sen’s slope tests were applied to detect the trends in precipitation, temperature and streamflow of the JRB. The Soil and Water Assessment Tool (SWAT) was calibrated and validated using measured monthly streamflow data. The validation results showed that SWAT was reliable in the tropical JRB. The trend analysis showed that there was an insignificant increasing trend for streamflow, whereas significant increasing trends for precipitation and temperature were found. The combined (climate + land-use change) impact caused the annual streamflow and evaporation to increase by 4.4% and 1.2%, respectively. Climate (land-use) raised annual streamflow by 4.4% (0.06%) and evaporation by 2.2% (−0.2%). Climate change imposed a stronger impact than land-use change on the streamflow and evaporation. These findings are useful for decision makers to develop better water and land-use policies.Editor Z. W. Kundzewicz; Guest editor V. Krysanova

Assessing the impact of different sources of topographic data on 1-D hydraulic modelling of floods

Abstract. Topographic data, such as digital elevation models (DEMs), are essential input in flood inundation modelling. DEMs can be derived from several sources either through remote sensing techniques (spaceborne or airborne imagery) or from traditional methods (ground survey). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Shuttle Radar Topography Mission (SRTM), the light detection and ranging (lidar), and topographic contour maps are some of the most commonly used sources of data for DEMs. These DEMs are characterized by different precision and accuracy. On the one hand, the spatial resolution of low-cost DEMs from satellite imagery, such as ASTER and SRTM, is rather coarse (around 30 to 90 m). On the other hand, the lidar technique is able to produce high-resolution DEMs (at around 1 m), but at a much higher cost. Lastly, contour mapping based on ground survey is time consuming, particularly for higher scales, and may not be possible for some remote areas. The use of these different sources of DEM obviously affects the results of flood inundation models. This paper shows and compares a number of 1-D hydraulic models developed using HEC-RAS as model code and the aforementioned sources of DEM as geometric input. To test model selection, the outcomes of the 1-D models were also compared, in terms of flood water levels, to the results of 2-D models (LISFLOOD-FP). The study was carried out on a reach of the Johor River, in Malaysia. The effect of the different sources of DEMs (and different resolutions) was investigated by considering the performance of the hydraulic models in simulating flood water levels as well as inundation maps. The outcomes of our study show that the use of different DEMs has serious implications to the results of hydraulic models. The outcomes also indicate that the loss of model accuracy due to re-sampling the highest resolution DEM (i.e. lidar 1 m) to lower resolution is much less than the loss of model accuracy due to the use of low-cost DEM that have not only a lower resolution, but also a lower quality. Lastly, to better explore the sensitivity of the 1-D hydraulic models to different DEMs, we performed an uncertainty analysis based on the GLUE methodology.

Testing different cross-section spacing in 1D hydraulic modelling: a case study on Johor River, Malaysia

Cross-section data are essential to describe the topography of rivers and floodplain areas and support flood propagation and inundation modelling. To date, there are only a few guidelines available to assist hydraulic modellers in determining the required quantity and location of cross-sections. The aim of this paper is to investigate the impact of cross-section spacing on the results of hydraulic models. To this end, four hydraulic models were built using the HEC-RAS model code and four different numbers of cross-sections. The geometric data were extracted from a high-resolution digital elevation model (LiDAR DEM) of a 30-km reach of the Johor River in Malaysia. These hydraulic models were then compared using flood water levels recorded at two stream gauging stations during two recent flood events. The independent calibration and validation of the models allowed the evaluation of a set of equations proposed in the literature for determining the distance between cross-sections for hydraulic modelling.Editor D. Koutsoyiannis; Associate editor I. Nalbantis

English

The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and Root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable.

ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

Modeling sediment load is a significant factor in water resources engineering as it affects directly the design and management of water resources. In this study, artificial neural networks (ANNs) are employed to estimate the daily sediment load. Two different ANN algorithms, the feed forward neural network (FFNN) and radial basis function (RBF) have been used for this purpose. The neural networks are trained and tested using daily sediment and flow data from Rantau Panjang station on Johor River. The results show that combining flow data with sediment load data gives an accurate model to predict sediment load. The comparison of the results indicate that the FFNN model has superior performance than the RB model in estimating daily sediment load.

Impacts and uncertainties of climate change on streamflow of the Johor River Basin, Malaysia using a CMIP5 General Circulation Model ensemble

The impact of climate change and uncertainty of climate projections from general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) on streamflow in the Johor River Basin, Malaysia was assessed. Eighteen GCMs were evaluated, and the six that adequately simulated historical climate were selected for an ensemble of GCMs under three Representative Concentration Pathways (RCPs; 2.6 (low emissions), 4.5 (moderate emissions) and 8.5 (high emissions)) for three future time periods (2020s, 2050s and 2080s) as inputs into the Soil and Water Assessment Tool (SWAT) hydrological model. We also quantified the uncertainties associated with GCM structure, greenhouse gas concentration pathways (RCP 2.6, 4.5 and 8.5), and prescribed increases of global temperature (1–6 °C) through streamflow changes. The SWAT model simulated historical monthly streamflow well, with a Nash–Sutcliffe efficiency coefficient of 0.66 for calibration and 0.62 for validation. Under RCPs 2.6, 4.5, and 8.5, the results indicate that annual precipitation changes of 1.01 to 8.88% and annual temperature of 0.60–3.21 °C will lead to a projected annual streamflow ranging from 0.91 to 12.95% compared to the historical period. The study indicates multiple climate change scenarios are important for a robust hydrological impact assessment.

Free internet datasets for streamflow modelling using SWAT in the Johor river basin, Malaysia

Streamflow modelling is a mathematical computational approach that represents terrestrial hydrology cycle digitally and is used for water resources assessment. However, such modelling endeavours require a large amount of data. Generally, governmental departments produce and maintain these data sets which make it difficult to obtain this data due to bureaucratic constraints. In some countries, the availability and quality of geospatial and climate datasets remain a critical issue due to many factors such as lacking of ground station, expertise, technology, financial support and war time. To overcome this problem, this research used public domain datasets from the Internet as "input" to a streamflow model. The intention is simulate daily and monthly streamflow of the Johor River Basin in Malaysia. The model used is the Soil and Water Assessment Tool (SWAT). As input free data including a digital elevation model (DEM), land use information, soil and climate data were used. The model was validated by in-situ streamflow information obtained from Rantau Panjang station for the year 2006. The coefficient of determination and Nash-Sutcliffe efficiency were 0.35/0.02 for daily simulated streamflow and 0.92/0.21 for monthly simulated streamflow, respectively. The results show that free data can provide a better simulation at a monthly scale compared to a daily basis in a tropical region. A sensitivity analysis and calibration procedure should be conducted in order to maximize the "goodness-of-fit" between simulated and observed streamflow. The application of Internet datasets promises an acceptable performance of streamflow modelling. This research demonstrates that public domain data is suitable for streamflow modelling in a tropical river basin within acceptable accuracy.