Kelantan River Research Articles

Parametric Vine Copula Construction for Flood Analysis for Kelantan River Basin in Malaysia

The multivariate approach of flood characteristics such as flood peak flow (P), volume (V), and duration (D) is much beneficial in recognizing the critical behaviour of flood episodes at a river basin scale. The incorporation of 2-dimensional copulas for establishing bivariate flood dependency frequently appears, but it could be more comprehensive if we focus all the three flood characteristic simultaneously. In such circumstances, incorporation of vine or Pair-Copula Construction (PCC) could produce a better approximation of joint probability density and much practical approach in the uncertainty analysis, in comparison with conventional trivariate copula distribution. This study demonstrated the efficacy of parametric vine copula in the modelling of trivariate flood characteristics for the Kelantan River basin in Malaysia. The D-vine tree structure is selected where the Gaussian and Frank copula is recognized for bivariate flood pairs (P-V) and (P-D) pairs in the first stage, using the maximum-pseudo-likelihood (MPL) estimation procedure. Similarly, the Gumbel copula is selected in the modelling of conditioned data obtained through the conditional distribution function of bivariate copulas selected in the previous stage based on the partial differentiation, also called h-function. Finally, the full density function of the 3-dimension structure is derived and compared with the observed flood characteristics. Furthermore, tail dependence properties and behaviour of D-vine copula are also investigated, which reveals for well capturing the general behaviour of Gaussian and Frank copula fitted to flood pair (P-V) and (V-D) and reproduces the overall flood correlation structure fairely well. Both the primary ‘OR’ and ‘AND’ joint return periods for trivariate flood events are estimated which pointing that ‘AND’ joint case produces higher return value than ‘OR’ case.

Comparison of Applications to Evaluate Groundwater Recharge at Lower Kelantan River Basin, Malaysia

Groundwater has supported 70% of the water supply at the Lower Kelantan River Basin (LKRB) since the 1930s and demand for groundwater increases annually. Groundwater has been abstracted from shallow and deep aquifers. However, a comprehensive study on groundwater recharge estimation has never been reported. This study evaluated various methods to quantify recharge rate using chloride mass balance (CMB), water table fluctuation (WTF), temperature–depth profiles (TDP), and groundwater modelling coupled with water balance (GM(WB)). Recharge estimation using CMB, WTF, TDP, and GM(WB) showed high variability within 8% to 68% of annual rainfall. CMB is range from 16% to 68%, WTF 11% to 19%, TDP 8% to 11%, and GM(WB) 7% to 12% of annual rainfall, respectively. At 11%, recharge from GM(WB) was the best method for estimation because the model was constructed and calibrated using locally derived input parameters. GM(WB) is the only method involved with calibration and validation process to reduce the uncertainty. The WTF method based on long-term hydrological records gives a reasonable recharge value, in good agreement with GM(WB) and these methods can be paired to ensure the reliability of recharge value approximation in the same ranges. Applying various methods has given insight into methods selection to quantify recharge at LKRB and it is recommended that a lysimeter is installed as a direct method to estimate recharge.

SouthEast Asia HydrO-meteorological droughT (SEA-HOT) framework: A case study in the Kelantan River Basin, Malaysia

A holistic framework was introduced to project the potential hydro-meteorological droughts of the Kelantan River basin, Malaysia. The framework integrates the multi-model high-resolution climate projections of the Coordinated Regional Climate Downscaling Experiment – Southeast Asia (CORDEX-SEA) and the widely applied Soil and Water Assessment Tool (SWAT) model. The quantile mapping approach was used to reduce the biases in the CORDEX-SEA projections before applying into SWAT. The SWAT-simulated standardized streamflow index (SSI) was validated with observed data to check the capability of SWAT in drought estimation. The result indicated that SWAT was able to reproduce the historical 1982, 1987 and 1997–1998 droughts and simulate SSI from one to twelve-month scales well, with the NSE and R2 values of 0.74–0.79 and 0.76–0.80, respectively. Overall, the annual precipitation, maximum and minimum temperatures are projected to change from −8.19 to 13.11% (−13.35 to 10.10%), 0.45 to 2.41 °C (0.43 to 3.99 °C) and 0.73 to 2.98 °C (0.70 to 4.69 °C), respectively, by the end of the 21st century under the RCP4.5 (RCP8.5) scenario. This would cause the future annual streamflow to vary from −10.37 to 31.09% and − 19.87 to 13.24% under RCP4.5 and RCP8.5, respectively, with the reductions are mainly found in the north-western region of the basin. Monthly precipitation and streamflow would be likely to decrease in January and February and increase in September. Robust evidence shows that the meteorological drought duration is likely to become longer in the 2081–2100 period under RCP4.5. Meanwhile, there is not enough evidence to claim that hydrological drought will become more significant in the near future.

Bivariate joint distribution analysis of the flood characteristics under semiparametric copula distribution framework for the Kelantan River basin in Malaysia

Flood is becoming the severe hydrologic issue at the Kelantan River basin in Malaysia. The joint distribution analysis amongst multiple interacting flood characteristics, i.e., flood peak discharge flow, volume, and duration series usually provide a comprehensive understanding of the hydrologic risk assessments through visualizing the multivariate exceedance probability or return periods. The traditional copulas-based methodology is frequently employed under parametric settings where parametric family functions are often employed to model univariate marginal distribution before capturing their dependence structure. Actually, no universal rules and literature are imposed to model any flood vectors through any fixed or predefined density function, which would follow the different distribution and needs to model by fitting most parsimonious function. Also, the copula function already relaxes the restriction of selecting marginal distributions from the same distribution families. Therefore, incorporation of non-parametric kernel density estimations or KDE would be much stable and less biased smoothing alternatives than the parametric approach. In this literature, the semi-parametric copula-based methodology is incorporated, where the flood marginals are modelled under the kernel functions and applied as a case study for 50 years annual maximum (AM) flood samples of the Kelantan River basin at the Gulliemard Bridge gauge station in Malaysia. The Archimedean families copulas (i.e., Frank, Gumbel and Clayton) and Elliptical copula (i.e., Gaussian copula) are tested, and thus best-fitted copulas are employed to model the bivariate joint distribution amongst flood characteristics, and which further employed to derive joint and conditional return periods.

Tropical rainfall trend and stationarity analysis

Abstract Climate change is most likely to cause changes to the temporal and spatial variability of rainfall. A trend analysis to investigate the rainfall pattern can detect changes over temporal and spatial scales for a rainfall series. In this study, trend analysis using the Mann–Kendall test and Sen's slope estimator was conducted in the Kelantan River Basin, Malaysia. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test was applied to evaluate the stationarity of the rainfall series. This basin annually faces onslaughts of varying year-end flooding conditions. The trend analysis was applied for monthly, seasonal and yearly rainfall series between 1989 and 2018. The temporal analysis results showed that both increasing and decreasing trends were detected for all rainfall series. The spatial analysis results indicated that the northern region of the Kelantan River Basin showed an increasing trend, whilst the southwest region showed a decreasing trend. It was found that almost all the rainfall series were stationary except at two rainfall stations during the Inter Monsoon 1, Inter Monsoon 2 and yearly rainfall series. Results obtained from this study can be used as reference for water resources planning and climate change assessment.

Bivariate flood distribution analysis under parametric copula framework: a case study for Kelantan River basin in Malaysia

Flood is becoming an intensive hydro-climatic issue at the Kelantan River basin in Malaysia. Univariate frequency analysis would be unreliable due to multidimensional behaviour of flood, which often demands multivariate flow exceedance probabilities. The joint distribution analysis of multiple interacting flood characteristics, i.e. flood peak, volume and duration, is very useful for understanding critical hydrologic behaviour at a river basin scale. In this paper, a copula-based methodology is incorporated for multivariate flood frequency analysis for the 50-year annual basis flood characteristics of Kelantan River basin at Guillemard bridge station in Malaysia. Investigation reveals that the Lognormal (2P), Johnson SB-4P and Gamma-3P are selected as marginal distributions for the flood peak flow, volume and duration series. Several bivariate families such as mono-parametric, bi-parametric (i.e. mixed version) and rotated version of Archimedean copulas and also the elliptical copula are introduced to cover a large dependence pattern of flood characteristics. The dependence parameter of bivariate copulas is estimated by the method of moments (MOM) based on the inversion of Kendall’s tau and maximum pseudo-likelihood estimator. To analytically validate and recognize most parsimonious copulas, GOF test and Cramer–von Mises distance statistics (Sn) with the parametric bootstrap method are employed. The Gaussian copula is identified as the most justifiable model for joint modelling of the flood peak–volume and peak–duration combination for MOM-based parameter estimation procedure. Similarly, the Frank copula is selected as the best-fitted structure for modelling peak–duration combination based on MPL estimators, but the MOM estimator recognized Gaussian copula as most suitable for peak–volume pair. Furthermore, the best-fitted copulas are used for obtaining the joint and conditional return periods of the flood characteristics.

A nonparametric statistical framework using a kernel density estimator to approximate flood marginal distributions – a case study for the Kelantan River Basin in Malaysia

Abstract Floods are becoming the most challenging hydrologic issue in the Kelantan River basin in Malaysia. All three flood characteristics, i.e. peak flow, flood volume and flood duration, are important when formulating actions and measures to manage flood risk. Therefore, estimating the multivariate designs and their associated return periods is an essential element of making informed risk-based decisions in this river basin. In this paper, the efficacy of a kernel density estimator is tested by assessing the adequacy of kernel functions for capturing flood marginal density of 50 years (from 1961 to 2016) of daily streamflow data collected at Gulliemard Bridge gauge station in the Kelantan River basin. Tests for stationarity or the existence of serial correlation within the flood series is often a pre-requisite before introducing the random samples into a univariate or a multivariate framework. It was found that homogeneity existed within the flood vector series. It was concluded therefore that time series of the flood vectors do not exhibit any significant trend. Based on analytically based fitness measures, it was concluded that it is likely that Triweight kernel function is the best-fitted distribution for defining the marginal distribution of peak flows, flood volumes and flood durations in the Kelantan River basin.

Investigation of the best fit probability distribution for annual maximum rainfall in Kelantan River Basin

Flooding is one of the natural disasters that happens annually in Malaysia. Flooding is induced by the extreme rainfall event and this can cause severe impacts in terms of environment, society, health and safety. Consequently, investigating the best fit probability distribution for annual maximum rainfall can provide the fundamental ideas for government departments and relevant authorities to mitigate the flooding problems. The aim of this study is to investigate the best fit probability distribution in describing the characteristics of annual maximum rainfall for the period of 1994 to 2013 at the Kelantan River Basin. The Gamma, Gumbel, Generalized Extreme Value and Log Pearson Type-III distributions were fitted to the historical rainfall series. Three goodness of fit tests, namely the Kolmogorov-Smirnov, the Anderson-Darling and the Chi-Square tests were used to evaluate the probability distributions. The performances of each probability distribution generated by the goodness of fit tests were compared. Overall, the Generalized Extreme Value distribution seems to be the best fit probability distribution for the annual maximum rainfall at most of the rainfall stations except for the RPS Kuala Betis station that had the Log Pearson Type-III distribution as its best fit.

Occurrence and distribution of estrogenic chemicals in river waters of Malaysia

This study investigated the occurrence of eight endocrine-disrupting compounds (EDCs) in river water across five major regions in Malaysia, which are recognized as emerging micropollutants in the environment. The water samples were collected from surface water of Klang River, Pahang River, Kuantan River, Kelantan River and Terengganu River from Malaysia. The samples were extracted by liquid–liquid extraction using hexane and ethyl acetate and loaded on silica column for column chromatography. Gas chromatography–flame ionization detector was used to determine the concentration of the target compounds. The maximum concentration of E1, E2, E3, EE2, 1,2,4-triazole, MP, 3,4,4-trichlorocarbanilide and BPA detected is 26.5 ng/L, 15.5 ng/L, 3.6 μg/L, 68.8 ng/L, 6.5 ng/L, 5.75 ng/L, 392.5 ng/L and 37 μg/L, respectively. The high discharge of various emerging organic pollutants in the Klang River estuary ecosystem was caused by heavy industrial, plantation, residential and commercial activities from urbanized and populated cities. Overall, the occurrences show a higher concentration of EDCs when compared to developed countries. Other rivers such as Kuantan, Terengganu, Pahang and Kelantan are known as the major sources of water supply for domestic, industrial and agricultural purposes. Eight endocrine-disrupting chemicals were detected and estimated in five rivers of Malaysia. These target compounds are widely present in various rivers in the range of ND to 37 μg/L. The difference of occurrence of EDCs in these selected sampling sites reflects the distinct usage patterns of various chemicals, pesticide, cosmetic products, drugs all over Malaysia. The presence of BPA, E1 and E3 was observed to be a major contributor of endocrine disruptors.

Prevalence and characterization of Escherichia coli in the Kelantan River and its adjacent coastal waters

AbstractThe presence of Escherichia coli in river and sea water may cause different levels of infections and constitutes a risk to public health. In this study, water samples were collected from 15 sites along the Kelantan River, estuaries and its adjacent coastal waters to investigate the prevalence and diversity of E. coli. A membrane filtration technique was used to enumerate E. coli and phylogenetic grouping was performed using triplex polymerase chain reaction. E. coli abundance ranged from 3.1 × 10 to 1.6 × 105 colony forming units 100 mL−1, and total suspended solids correlated significantly with E. coli abundance (r2 = 0.165, p < 0.001) and rainfall (r2 = 0.342, p < 0.001). Phylogenetic group B1 and A (59.4%) were the most prevalent, whereas groups B2 and D were least abundant. The higher abundance of phylogenetic group D at upstream sites of the Kelantan River suggested fecal contamination mainly of animal origin. Canonical-correlation analysis showed phylogenetic group B2, and phylogenetic groups A and D were greater in waters with higher inorganic nutrients (e.g. NH4, NO2 and NO3), whereas phylogenetic group B1 appeared to have better salinity tolerance between phylogenetic groups.

Effect of rainfall station density, distribution and missing values on SWAT outputs in tropical region

Rainfall is one of the main inputs to the Soil and Water Assessment Tool (SWAT) model. Any error in the rainfall data could transfer to the model outputs and lead to inaccurate judgements and decisions. This study aims to evaluate the effect of spatial (station number and location effect) and temporal (randomly and selective missing values) rainfall scarcities on SWAT streamflow simulations. A total of 58 rainfall data scarcity scenarios was incorporated into a calibrated SWAT model to test the effect on monthly streamflow simulations of the Kelantan River Basin (KRB), Malaysia, during the period of 1980 to 1999. This study provides new insights of rainfall station distance, missing values in specific periods and different streamflow intensities elements on SWAT-based rainfall data scarcity (SWAT-Rainfall) assessment framework, with new evaluation indicators of extreme flows, trend and magnitude changes analysis. The results show that (1) a minimum of four stations is needed to achieve accurate monthly streamflow in the KRB; (2) distance of rainfall stations to the streamflow station has a considerable impact on SWAT modelling; (3) temporal rainfall scarcity has a large impact on general and extreme streamflow simulations compared to spatial rainfall scarcity; (4) missing rainfall values of more than 20% in rainfall data would significantly affect tropical streamflow simulation; (5) the impact of missing rainfall values during the high flow period is lower than those in the moderate and low flow periods; and (6) excessive missing value in rainfall data would lead to errors in trend analysis. A special caution need to be taken in the selection and pre-processing of rainfall data before their incorporation into SWAT. Future works include integration of satellite precipitation products in SWAT, improvement of SWAT weather generator and development of a SWAT-based rainfall data pre-processing scheme to reduce rainfall data uncertainty.

Deep Learning Data-Intelligence Model Based on Adjusted Forecasting Window Scale: Application in Daily Streamflow Simulation

Streamflow forecasting is essential for hydrological engineering. In accordance with the advancement of computer aids in this field, various machine learning (ML) models have been explored to solve this highly non-stationary, stochastic, and nonlinear problem. In the current research, a newly explored version of an ML model called the long short-term memory (LSTM) was investigated for streamflow prediction using historical data for forecasting for a particular period. For a case study located in a tropical environment, the Kelantan river in the northeast region of the Malaysia Peninsula was selected. The modelling was performed according to several perspectives: (i) The feasibility of applying the developed LSTM model to streamflow prediction was verified, and the performance of the developed LSTM model was compared with the classic backpropagation neural network model; (ii) In the experimental process of applying the LSTM model to the prediction of streamflow, the influence of the training set size on the performance of the developed LSTM model was tested; (iii) The effect of the time interval between the training set and the testing set on the performance of the developed LSTM model was tested; (iv) The effect of the time span of the prediction data on the performance of the developed LSTM model was tested. The experimental data show that not only does the developed LSTM model have obvious advantages in processing steady streamflow data in the dry season but it also shows good ability to capture data features in the rapidly fluctuant streamflow data in the rainy season.

A nonparametric copula distribution framework for bivariate joint distribution analysis of flood characteristics for the Kelantan River basin in Malaysia

The joint distribution analysis of multidimensional flood characteristics i.e., flood peak flow, volume and duration, often facilitates a comprehensive understanding in the hydrologic risk assessments. Copula-based methodology are frequently incorporated via parametric approach to model dependence structure of parametric based univariate marginal distributions. But, if the targeted copulas and univariate marginal distributions belongs to some specific parametric families, it might be problematic, if the underlying assumption are violated. Also, no universal rules and literatures are imposed to model any hydrologic vectors and their joint dependence structure through any fixed or pre-defined distributions. In this literature, a nonparametric copula simulation are incorporated and applied as a case study for 50 years annual maximum flood samples of the Kelantan River basin at the Gulliemard bridge station in Malaysia. In this study, a combination of both parametric and nonparametric marginal distribution separately conjoined by a nonparametric copulas framework, which is based on the Beta kernel function. The Beta kernel copula function are incorporated to estimate bivariate copula density which further used to derived joint cumulative density of flood peak-volume, volume-duration and peak-duration pairs and their associated joint as well as conditional return periods.

Trivariate distribution modelling of flood characteristics using copula function—A case study for Kelantan River basin in Malaysia

Water resources operational planning, managements or either flood defence infrastructure designs often demand the estimations of flow exceedance probability for visualizing the risk of flood episodes. Numerous literature often incorporated copulas for the development of bivariate joint dependence structure among the flood characteristics, flood peak flow, volume and duration, but it could be more realistic and comprehensive if we focus all the three mutually correlated flood characteristics simultaneously. Actually, the inclusion of more flood characteristics could provide better and much justifiable information in correlation and dependency modelling. In this study, trivariate copulas are incorporated and applied to a case study to analyse flood episodes in the Kelantan River basin at Gulliemard bridge gauge station in Malaysia. Firstly, for describing best-fitted bivariate copulas for establishing the joint dependence structure of each flood attribute pairs, the Gaussian copula is recognized most justifiable model for peak-volume pair and the Frank copula for peak-duration and volume-duration pairs. After that, the trivariate joint distribution is modelled using one Archimedean copula, the Frank copula and one Elliptical copula, the Gaussian copula. Based on Cramer-von-Mises-type statistics, Sn and p-value, the Gaussian copula best representing the trivariate dependence structure of flood and which further employed in the deriving of trivariate joint and conditional return periods.

Impact of Potential Evapotranspiration Method on Sensitivity and Uncertainty in Streamflow Analysis for Kelantan River Basin

Impact of Potential Evapotranspiration Method on Sensitivity and Uncertainty in Streamflow Analysis for Kelantan River Basin

Application of Satellite Rainfall Products for Flood Inundation Modelling in Kelantan River Basin, Malaysia

The advent of satellite rainfall products can provide a solution to the scarcity of observed rainfall data. The present study aims to evaluate the performance of high spatial-temporal resolution satellite rainfall products (SRPs) and rain gauge data in hydrological modelling and flood inundation mapping. Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) - Early, - Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. GSMaP showed the best performance in simulating hourly runoff with the lowest relative bias (RB) and the highest Nash-Sutcliffe efficiency (NSE) of 4.9% and 0.79, respectively. Meanwhile, the rain gauge data was able to produce runoff with −12.2% and 0.71 for RB and NSE, respectively. The other three SRPs showed acceptable results in daily discharge simulation (NSE value between 0.42 and 0.49, and RB value between −23.3% and −31.2%). The generated flood map also agreed with the published information. In general, the SRPs, particularly the GSMaP, showed their ability to support rapid flood forecasting required for early warning of floods.

Rainfall Runoff Model Evaluation for Lebir River, Kelantan, Malaysia

On January 5, 2015, the National Security Council (NSC) confirmed the massive flood that hit Kelantan was the worst in the history of the state. According to the council’s report, the water level of Kelantan River at Tambatan Diraja which has a danger level of 25 meters, reached 34.17 meters. The Kelantan River basin is located in the north eastern part of Peninsular Malaysia between latitudes 4° 40’ and 6° 12’ North, and longitudes 10° 20’ and 102° 20’ East. The river is about 248 km long and drains an area of 13,100 km2. It divides into the Galas and Lebir Rivers near Kuala Krai, about 100 km from the river mouth. The Lebir River itself has catchments area of 2430 km2. This current paper investigates a calibration and confirmation method of hydrologic model using HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) applied in Lebir River. General speaking, the degree of confidence of hydrologic models prediction will normally depend on how well the model can replicate and imitate the observations recorded data. The stream flow data used in this analysis is obtained from station 5222452 at Kg. Tualang with a duration period from years 2004–2014. The results of investigation revealed that the observed and simulated discharge hydrographs in the calibration and confirmation exercises were reasonably close.

Soil erosion in disturbed forests and agricultural plantations in tropical undulating terrain: in situ measurement using a laser erosion bridge method

Abstract The rapid growth of agricultural plantations and climatic extremes has raised concerns pertaining to enhanced soil erosion. Soil erosion studies are still relatively limited in Malaysia. In this study, soil erosion in four sites such as high conservation value forests (HCVFs), logged forest (LF), mature oil palm (MOP), and mature rubber (MR) within the Kelantan River Basin was measured. A total of 3,207 measurements were conducted via the Modified Laser Erosion Bridge in all sites over 1 year. Results of soil erosion are 87.63, 25.45, 8.44, and 5.90 t ha−1 yr−1 for the HCVF, LF, MOP and MR, respectively – classified as very severe (HCVF), very high (LF), moderate (MP) and slight (MR) according to the Indian condition classification. Steep slope gradient (significant positive correlation to erosion) and logging are the main factors attributed to the high erosion rates. This is to be further explored in the future and more detailed studies should be conducted on the HCVF and LF areas, respectively. Mitigation measures and sustainable agricultural practices should be planned to control and reduce soil erosion.

Contamination status and ecological risk of heavy metals in surface sediment of Kelantan River and its nearshore area, Malaysia

Abstract A study on contamination status and ecological risk of heavy metals in surface sediment at selected sites on Kelantan River and its nearshore area was carried out. Ten samples along Kelantan River and 25 samples from the nearshore were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) to determine heavy metal concentrations. Sediment samples were also analyzed for particle size compositions, total organic matter and pH content. The average concentrations for As, Cd, Cr, Cu, Mn, Ni, Pb, Zn were 7.38, 1.31, 17.71, 11.40, 507.15, 5.97, 22.61, 32.95 mg/kg for riverine and 14.14, 4.59, 29.79, 14.07, 389.96, 9.65, 62.21, 41.04 mg/kg for nearshore samples respectively. The potential ecological risk index showed stations Bekok, Manek Urai, and RH under considerable risk followed by station Pasir Mas under moderate risk. The pollution load index classified four nearshore sites (KW10, KW17, KW18, KW37) as polluted. The geo-accumulation index (Igeo) categorized moderate contamination for Cd and Pb. The enrichment factor (EF) along the river categorized extremely high enrichment for Cd, and significant enrichment for As, Pb and Mn while Pb and As were under very high and significant enrichment in nearshore areas. Pb, Cu, Zn, As, Ni and Cr showed significant correlations with each other.

Distribution of Nutrients in Kelantan River Basin, Malaysia

This study aims to assess the influences of land use on nutrient concentrations in Kelantan river basin (southern South China Sea), in addition to the effects of monsoon seasons on nutrient concentrations. The dissolved inorganic and organic nutrients in this river were demonstrated to be related to land use during the study period. The middle and lower reaches of the river, which are surrounded by urban areas with high population densities, have enhanced levels of both dissolved inorganic and organic nutrients. Increased rainfall decreased most of the nutrient concentrations in Kelantan river basin, probably due to the dilution effect. According to The Malaysia National Water Quality Standards (NWQS) classification, the mean concentrations of dissolved inorganic phosphorus, nitrite and nitrate in Kelantan river basin fell into Class 1, which is considered at natural levels in the water column.