Johor River Basin Research Articles

Comprehensive assessment of water and sediment quality to support sustainable management practices and mitigate potential risks of trace metal pollution in the Johor River Basin, South Peninsular Malaysia

Human activities have caused concerns over the contamination of trace elements in aquatic systems due to their toxicity, long-lasting nature, and ability to accumulate in living organisms. This study evaluated the spatial pattern, environmental hazard, and potential health hazard of 13 trace metals in water and sediment samples collected from the Johor River basin using multivariate statistical techniques in order to identify the impact of both natural and human-related factors on the presence of trace metals in the area. The increased levels of metals in Station S1 indicated a significant impact from municipal, domestic, and industrial waste. The Geo-accumulation Index (Igeo) values for pollution and ecological indices in the sediment of the Johor River Basin ranged from -9.44 to 6.27. The Pollution Load Index (PLI) values ranged from 0.05 to 0.18, and the Potential Ecological Risk Assessment (RI) values ranged from 2.15 to 11.18. These results indicate that the sediments in the Johor River Basin have a low risk of contamination from trace metals. The water's hazard quotient (HQ) had an average value of 0.0001, while the hazard index (HI) had an average value of 0.0003. It is important to increase stakeholders' understanding and concerns regarding the potential long-term human health and ecological implications associated with trace metals in Johor River water. The results of this study offer essential initial information for government entities to implement sustainable management strategies, reducing the likelihood of future trace metal contamination issues in the Johor River basin and similar regions.

Developing an ensembled machine learning model for predicting water quality index in Johor River Basin

Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis and prediction is conducted for Johor River Basin incorporating the upstream to downstream water quality monitoring station data of the river. In this research, the numerical method is first used to calculate the WQI and identify the classes for validating the prediction results. Then, two ensemble and optimized machine learning models including gradient boosting regression (GB) and random forest regression (RF) are employed to predict the WQI. The study area selected is the Johor River basin located in Johor, Peninsular Malaysia. The initial phase of this study involves analyzing all available data on parameters concerning the river, aiming to gain a comprehensive understanding of the overall water quality within the river basin. Through temporal analysis, it was determined that Mg, E. coli, SS, and DS emerge as critical factors affecting water quality in this river basin. Then, in terms of WQI calculation, feature importance method is used to identify the most important parameters that can be used to predict the WQI. Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. Two ensemble ML approaches are chosen to predict the WQI in the study area and achieved a R2 of 0.86 for RF-based regression and 0.85 for GB-based ML technique. Finally, this research proves that using only the biochemical oxygen demand (BOD), the chemical oxygen demand (COD) and percentage of dissolved oxygen (DO%), the WQI can be predicted accurately and almost 96 times out of 100 sample, the water class can be predicted using GB ensembled ML algorithm. Moving forward, stakeholders may opt to integrate this research into their analyses, potentially yielding economic reliability and time savings.

Enhancing flood risk assessment in the Johor River Basin through trivariate copula

ABSTRACT Copulas are a vital statistical tool, particularly in hydrology, for understanding complex relationships among flood characteristics. This study focuses on three key flood features: peak discharge, flood volume, and flood duration, using trivariate copulas to capture their interdependencies. This is crucial because bivariate and univariate analyses fall short in considering all three factors simultaneously. To handle extreme flood values, L-moment is proposed over maximum likelihood estimation and inference function margin due to its enhanced reliability and susceptibility to outliers and extreme values. Akaike information criterion was employed to identify the best-fit marginal distribution and copula. The Lognormal distribution effectively models peak discharge, while Weibull and generalized extreme value distributions fit flood volume and duration best, respectively. Various copula families, including elliptical and Archimedean, are assessed, where Clayton copula emerge as the most suitable. This analysis demonstrates that when more flood features are considered together, the return period increases, indicating the reduced likelihood of occurrence. The trivariate case of the AND-joint return period surpasses the trivariate case of the OR-joint return period where the TP, V, DAND=5, 405.93 years, while TP, V, DOR=500.46 years. This comprehensive approach enhances hydrological modeling and decision-making for water resource management and flood mitigation projects.

CHIRPS rainfall product application for analyzing rainfall concentration and seasonality in Johor river basin, Malaysia

It is crucial to carry out a spatiotemporal evaluation of rainfall concentration in the Johor river basin (JRB) given that erratic rainfall can lead to floods. The study utilized the CHIRPS rainfall dataset from 1983 to 2018 to determine rainfall concentration using the Concentration Index (CI). To further define the rainfall regime, the Seasonality Index (SI) and Precipitation Concentration Index (PCI) were examined. The Mann-Kendall (MK) test was utilized to examine the trend at a 95% level of significance. The results showed the better accuracy of SI than PCI in classifying the rainfall regime in the JRB, defined as “equable but with a definite wetter season” type. The CI values ranged from 0.61 to 0.72 and were typically high all over the basin, with the northwestern and western regions having the highest values. At 27 grid points on the eastern border of the upper and middle parts of the basin, CI has significantly increased as a result of an upsurge in rainfall concentration brought on by climate change. More than 67% and up to 83% of the yearly rainfall for all grid locations was accounted for by the North East monsoon (NEM), with the 25% wettest days caused by high-intensity rainfall events.

Application of relative importance metrics for CMIP6 models selection in projecting basin-scale rainfall over Johor River basin, Malaysia

The most recent set of General Circulation Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used in this work to analyse the spatiotemporal patterns of future rainfall distribution across the Johor River Basin (JRB) in Malaysia. A group of 23 GCMs were chosen for comparative assessment in simulating basin-scale rainfall based on daily rainfall from the historical period of the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS). The methodological novelty of this study lies in the application of relative importance metrics (RIM) to rank and select historical GCM simulations for reproducing rainfall at 109 CHIRPS grid points within the JRB. In order to choose the top GCMs, the rankings given by RIM were aggregated using the compromise programming index (CPI) and Jenks optimised classification (JOC). It was found that ACCESS-ESM1–5 and CMCC-ESM2 were ranked the highest in most of the grid. The final GCM was then bias-corrected using the linear scaling method before being ensemble based on the Bayesian model averaging (BMA) technique. The spatiotemporal assessment of the ensemble model for the different months over the near-future period 2021–2060 and far-future period 2061–2100 was compared with those under Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, SSP3–7.0, and SSP5-8.5. Heterogeneous changes in rainfall were projected across the JRB, with both increasing and decreasing trends. In the near-future and far-future scenarios, higher rainfall was projected for December, indicating an elevated risk of flooding during the end of the North East monsoon (NEM). Conversely, August showed a decreasing trend in rainfall, implying an increasing risk of severe drought. The findings of this study provide valuable insights for effective water resource management and climate change adaptation in the region.

Evaluating Imputation Methods for rainfall data under high variability in Johor River Basin, Malaysia

Missing values in rainfall records might result in erroneous predictions and inefficient management practices with significant economic, environmental, and social consequences. This is particularly important for rainfall datasets in Peninsular Malaysia (PM) due to the high level of missingness that can affect the inherent pattern in the highly variable time series. In this work, 21 target rainfall stations in the Johor River Basin (JRB) with daily data between 1970 and 2015 were used to examine 19 different multiple imputation methods that were carried out using the Multivariate Imputation by Chained Equations (MICE) package in R. For each station, artificial missing data were added at rates of up to 5%, 10%, 20%, and 30% for different types of missingness, namely, Missing Completely At Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR), leaving the original missing data intact. The imputation quality was evaluated based on several statistical performance metrics, namely mean absolute error (MAE), root mean square error (RMSE), normalized root mean square error (NRMSE), Nash-Sutcliffe efficiency (NSE), modified degree of agreement (MD), coefficient of determination (R2), Kling-Gupta efficiency (KGE), and volumetric efficiency (VE), which were later ranked and aggregated by using the compromise programming index (CPI) to select the best method. The results showed that linear regression predicted values (norm.predict) consistently ranked the highest under all types and levels of missingness. For example, under MAR, MNAR, and MCAR, this method showed the lowest MAE values, ranging between 0.78 and 2.25, 0.93–2.57, and 0.87–2.43, respectively. It also consistently shows higher NSE and R2 values of 0.71–0.92, 0.6–0.92, and 0.66–0.91, and 0.77–0.92, 0.71–0.93, and 0.75–0.92 under MAR, MCAR, and MNAR, respectively. The methods of mean, rf, and cart also appear to be efficient. The incorporation of the compromise programming index (CPI) as a decision-support tool has enabled an objective assessment of the output from the multiple performance metrics for the ranking and selection of the top-performing method. During validation, the Probability Density Function (PDF) demonstrated that even with up to 30% missingness, the shape of the distribution was retained after imputation compared to the actual data. The methodology proposed in this study can help in choosing suitable imputation methods for other tropical rainfall datasets, leading to improved accuracy in rainfall estimation and prediction.

Water access and sanitation facilities in the Johor River Basin, Malaysia: A comparison between indigenous and more modernised communities

Abstract Water access, sanitation and hygiene (WASH) data for Malaysia suggest that almost 100% of the population have access to basic sanitation and basic drinking water, but this data may not include marginalised communities and stateless individuals, indigenous or otherwise. The aim of this study was to evaluate WASH provision for residents of the Johor River Basin and evaluate the disparity in facilities between indigenous Orang Asli and more modernised communities. Questionnaires were distributed to eight communities. In total, 899 questionnaires were completed including 100 from indigenous communities and 799 from more modernised communities. Significant differences existed between communities with regard to education levels (p < 0.001), occupation (p < 0.001), household income (p < 0.001), type of toilet, sewage and sanitation facilities (p < 0.001) and perception of health risks and concerns (p < 0.001). There is a significant difference in access to water and sanitation facilities between indigenous and modernised communities. Further study is now required to understand behaviours and educate river-based communities on relevant potential health risks from water contamination in the Johor River Basin.

Analysis of Water Yield Changes in the Johor River Basin, Peninsular Malaysia Using Remote Sensing Satellite Imagery

Changes in land-use–land-cover (LULC) affect the water balance of a region by influencing the water yield (WY) along with variations in rainfall and evapotranspiration (ET). Remote sensing satellite imagery offers a comprehensive spatiotemporal distribution of LULC to analyse changes in WY over a large area. Hence, this study mapped and analyse successive changes in LULC and WY between 2000 and 2015 in the Johor River Basin (JRB) by specifically comparing satellite-based and in-situ-derived WY and characterising changes in WY in relation to LULC change magnitudes within watersheds. The WY was calculated using the water balance equation, which determines the WY from the equilibrium of precipitation minus ET. The precipitation and ET information were derived from the Tropical Rainfall Measuring Mission (TRMM) and moderate-resolution imaging spectroradiometer (MODIS) satellite data, respectively. The LULC maps were extracted from Landsat-Enhanced Thematic Mapper Plus (ETM+) and Landsat Operational Land Imager (OLI). The results demonstrate a good agreement between satellite-based derived quantities and in situ measurements, with an average bias of ±20.04 mm and ±43 mm for precipitation and ET, respectively. LULC changes between 2000 and 2015 indicated an increase in agriculture land other than oil palm to 11.07%, reduction in forest to 32.15%, increase in oil palm to 11.88%, and increase in urban land to 9.82%, resulting in an increase of 15.76% WY. The finding can serve as a critical initiative for satellite-based WY and LULC changes to achieve targets 6.1 and 6.2 of the United Nations Sustainable Development Goal (UNSDG) 6.

Application of CHIRPS dataset in the selection of rain-based indices for drought assessments in Johor River Basin, Malaysia

This paper aims to select the most appropriate rain-based meteorological drought index for detecting drought characteristics and identifying tropical drought events in the Johor River Basin (JRB). Based on a multi-step approach, the study evaluated seven drought indices, including the Rainfall Anomaly Index (RAI), Standardized Precipitation Index (SPI), China-Z Index (CZI), Modified China-Z Index (MCZI), Percent of Normal (PN), Deciles Index (DI), and Z-Score Index (ZSI), based on the CHIRPS rainfall gridded-based datasets from 1981 to 2020. Results showed that CZI, MCZI, SPI, and ZSI outperformed the other indices based on their correlation and linearity (R2 = 0.96–0.99) along with their ranking based on the Compromise Programming Index (CPI). The historical drought evaluation revealed that MCZI, SPI, and ZSI performed similarly in detecting drought events, but SPI was more effective in detecting spatial coverage and the occurrence of ‘very dry’ and ‘extremely dry’ drought events. Based on SPI, the study found that the downstream area, north-easternmost area, and eastern boundary of the basin were more prone to higher frequency and longer duration droughts. Furthermore, the study found that prolonged droughts are characterized by episodic drought events, which occur with one to three months of ‘relief period’ before another drought event occurs. The study revealed that most drought events that coincide with El Niño, positive Indian Ocean Dipole (IOD), and negative Madden-Julian Oscillation (MJO) events, or a combination of these events, may worsen drought conditions. The application of CHIRPS datasets enables better spatiotemporal mapping and prediction of drought for JRB, and the output is pertinent for improving water management strategies and adaptation measures. Understanding spatiotemporal drought conditions is crucial to ensuring sustainable development and policies through better regulation of human activities. The framework of this research can be applied to other river basins in Malaysia and other parts of Southeast Asia.

Bivariate copula for flood frequency analysis in Johor river basin

Flooding is a multi-attribute event that is described by many factors such as peak flow and flood volume. It is extremely vital to consider both the flood volume and the flood peak while studying the flood frequency analysis as the univariate analysis cannot accurately portray the flood issue and suffers from an underestimation and an overestimation problem. Traditional univariate and multivariate modeling techniques have several mathematical shortcomings including the inability to distinguish between the marginal and joint behavior of the variables under study. Therefore, the copula function was introduced to tackle the above restriction. Six copula models will be applied in this study to find the best bivariate distribution between the flood variables in Johor River Basin, Malaysia, including Gaussian, Student-t, Clayton, Gumbel, Frank, and Joe. Before that, several marginal distributions were fitted to select the most appropriate distribution for flood variables. It was found that the Pearson Type-III fits both the flood peak flow and the flood volume best. The evaluation of the best univariate marginal distribution and the copula model will be based on Akaike Information Criterion (AIC). Our findings suggested that Frank Copula is more suited to represent the relationship between peak flow and flood volume as it portrays the lowest AIC values of -69.41 and highest log-likelihood values of 35.7, where both values outperform the other proposed copula models. However, future research which considers all three flood variables which are peak flow, volume, and duration should be conducted to attain a more reliable result.

Long-term homogeneity and trend analysis of seasonality and extreme rainfall under the influence of climate change in Johor River basin, Malaysia

Long-term homogeneity and trend analysis of seasonality and extreme rainfall under the influence of climate change in Johor River basin, Malaysia

A FRAMEWORK FOR CRITERIA WEIGHTING IN A SUSTAINABLE RIVER BASIN

River basin systems involve complex, diverse and dynamic nature. The Malaysia River Basin Sustainability Index (MyRIBASIN) monitors the sustainability status in river basin ecosystems by addressing multi-element involved in river basin systems. The study highlights a link between the multi-criteria in MyRIBASIN as a sound basis for measures design. The multi-criteria judgment aims to define the river basin performance from criteria indicators which Johor River Basin is a case study. The analytic hierarchy process (AHP) weighs four criteria for elements in the river basin: Environment, Social Awareness and Health, Governance and Economic. The group decision-making process is used to assess priorities based on the inputs from Malaysia’s authorities and academicians. Results show that the weighting values for Environment criteria have the highest value, followed by Social and Health, Governance and Economic. The findings show the perception of respondents on the importance of basin conservation and community awareness.

Analyzation of water demand projection for Johor River Basin

This paper presents the projecting or forecasting of water demand for the Johor River Basin (JRB) from the year 2020 up to 2050. The aim for this research is to quantify and analyse the demand for water in the future from each sector which is most crucial around JRB such as domestic/residential, industrial, agriculture, livestock and non-revenue water; it is a key element for water demand quantification for JRB. Recent work for this quantification is more generalizing in terms of water demand as a whole in Johor state. Meanwhile, this research are using composite approaches which involve secondary data from various local authorities and involved four different districts only around JRB. This research also covers the scope of water consumption baseline data and information of JRB with the recent report. It appears that quality of data in terms of availability and reliability is very important and must be dealt with carefully, with good judgement and good engagement with local stakeholders or authorities when it comes to decision making, therefore must be able to get a rapid and simple understanding of water demand for JRB. It is significant to this research which to accommodate with United Nations Development Program and also promote greater awareness of the Sustainable Development Goal (SDG-6) which to ensure availability and sustainable management of water and sanitation for all.

Hydrology method for environmental flows assessment of Johor River Basin under regulated and non-regulated conditions

This paper evaluates the capability of hydrology method for environmental flow (Eflow) assessment of Johor river basin under non-regulated and regulated conditions due to dam construction. Daily river flow data from 1971 to 2017 was used for the assessment. Generalized extreme value distribution was applied in order to estimate exceedance probabilities associated with historical observation data at different return periods. Environmental water allocations of the river at different ecological conditions were estimated using the intra-annual and inter-annual flows. This study has estimated the hydrological yearly conditions (very dry, dry, average and wet) for the river basin. Moreover, the method has also successfully proposed the environmental water allocations to achieve the desired ecological conditions, which are very good ecological (Eflow class A), good ecological (Eflow class B), medium ecological (Eflow class C), and deficient ecological conditions (Eflow class D). In general, the proposed method has been capable and acceptable for the assessment of Eflow of Johor river basin in line with sustainable water management goal.

Distributed Hydrological Model Based on Machine Learning Algorithm: Assessment of Climate Change Impact on Floods

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events.

Land use and land cover change and its impact on river morphology in Johor River Basin, Malaysia

Study Region Johor River Basin (JRB), Malaysia. Study Focus The study generates long time-series land use and land cover (LULC) change at 5- years interval using Google Earth Engine and investigate the LULC changes and river morphology changes of JRB. Intensity analysis on the LULC changes was conducted to highlight the transition between LULC. Targeted transition from natural to disturbed lands were detected, which impact the river morphology of the basin. River morphology changes were identified along Johor River, based on the centerline migration derived from Channel Migration Toolbox. New Hydrological Insights for the Region JRB has undergone high intensity LULC change over the past decades due to significant economic and population growth which impacted the river morphology. Channel migration analysis reported high river centerline migration (498609 m2 to 1853886 m2) of Johor River. Higher migration rate was identified at the estuary of the Johor River and several locations along the river. Specific locations with high channel migration were highlighted where human-induced changes are the factors to affect the morphology of the river. The proposed method is first applied in the region and the results enhance local and regional policies and decision making on food and water security, prevent extreme events such as floods, and further degradation of natural land.

Inter-comparison on the Suitability of Rain-Based Meteorological Drought in Johor River Basin, Malaysia

Drought is identified and quantified using a variety of methods. This study aims to evaluate the performance of several rain-based meteorological droughts, namely, Rainfall Anomaly Index (RAI), Standard Precipitation Index (SPI), China-Z Index (CZI), Percent of Normal (PN), Deciles Index (DI), and Z-Score Index (ZSI) on 1-, 3-, 6-, 9-, 12-, 18-, 24-, 48-, and 72-month time scales based on monthly precipitation totals for 24 Johor River Basin (JRB) stations from 1970 to 2015. After imputation of missing data with Multiple Imputation by Chained Equations (MICE), homogeneity test for the annual time series found that 21 stations were ‘useful’, 1 station ‘doubtful’ and 2 stations ‘suspect’, meanwhile monthly time series found 17 stations ‘useful’, 1 station ‘doubtful’ and 6 stations ‘suspect’. The performance of CZI, RAI, and SPI were found to be higher compared to others based on the correlation between drought indices, average correlation of a combination of drought indices for all stations (correlation plot and heatmap), scatter plot linearity between drought indices in capturing the wet/dry conditions, and simulating the historical drought events. However, based on the ability to simulate the historical event, spatial assessment, and frequency analysis, it was found that RAI is more responsive in identifying the ‘drought prone’ area to enable a better spatial mapping of the 3- and 6-months drought condition in JRB. ‘Moderately dry’ condition is expected to be temporally and spatially widespread as North East (NE) monsoon becomes drier. Meanwhile, a condition of ‘very dry’ and ‘extremely dry’ is expected to be more prevalent in the South East area of the basin, particularly due to the drier South West (SW) monsoon period. Study results indicate that RAI can perform comparatively better than other studied indices, and because the calculations of RAI are relatively simple, they can be used as better tools to monitor the conditions of drought in JRB.

Resolution Dependence of Regional Hydro-Climatic Projection: A Case-Study for the Johor River Basin, Malaysia

High resolution models from the High-Resolution Model Intercomparison Project (HighResMIP), part of CMIP6, have the capacity to allow a better representation of the climate system in tropical regions, but how different model resolutions affect hydrological outputs remains unclear. This research aims to evaluate projections of hydro-climatic change of the Johor River Basin (JRB) in southern Peninsular Malaysia between 1985 to 2015 and 2021 to 2050, focusing on uncertainty quantification of hydrological outputs from low (>1°), medium (0.5° to 1°) and high (≤0.5°) horizontal resolution models. These projections show future increases in annual precipitation of 0.4 to 3.1%, minimum and maximum temperature increases of 0.8 to 0.9 °C and 0.9 to 1.1 °C, respectively. These projected climate changes lead to increases in annual mean streamflow of 0.9% to 7.0% and surface runoff of 7.0% to 20.6% in the JRB. These annual mean changes are consistent with those during the wet period (November to December), e.g., streamflow increases of 4.9% to 10.8% and surface runoff of 28.8 to 39.9% in December. Disagreement in the direction of change is found during the dry seasons, (February to March and May to September), where high resolution models project a decrease in future monthly precipitation and streamflow, whilst increases are projected by the medium- and low-resolution models.

Comparison of Landsat 8, Sentinel-2 and spectral indices combinations for Google Earth Engine-based land use mapping in the Johor River Basin, Malaysia

Accurate land use information is the basis for scientific research related to carbon cycle analysis, hydro-climatic modelling, soil degradation assessment, etc. It is also an indispensable basic information for local land management departments in land use planning and management. With development in big data and internet network, Google Earth Engine (GEE), a cloud-based computing platform allows users to perform satellite images processing more efficiently. This study aims to improve the land use mapping in a tropical region based on the GEE platform. Seven satellite images and indices combinations include Landsat 8 (C1), Sentinel-2 (C2), Landsat 8+Sentinel-2 (C3), Landsat 8+Indices (C4), Sentinel-2+Indices (C5), Landsat 8+Sentinel-2+Indices (C6), Normalized Difference Vegetation Index (NDVI)+Normalized Difference Water Index (NDWI)+Enhanced Vegetation Index (EVI)+Elevation (C7) were developed to evaluate the best combination for land use mapping in the Johor River Basin (JRB), Malaysia. The Random Forest (RF) algorithm was used to classify the land use land cover (LULC) with 222 training samples and 78 verification samples obtained through the Google Earth Pro higher resolution satellite images and field samplings. The results show that the overall accuracies of all the seven combinations are mostly more than 75%, ranging from 72% (C1) to 86% (C6). The findings show that adding of additional indices information before the land use classification helps to increase the overall accuracy significantly. For instance, the overall accuracy of the C6 combination is 14% higher than that of the Landsat 8 image solely. This study can act as a reference to effectively improve the land use mapping in cloud-prone tropical regions. Keywords: Google Earth Engine, Image combination, Johor River Basin, Land Use, Malaysia, Random Forest

Effects of land use configuration, seasonality and point source on water quality in a tropical watershed: A case study of the Johor River Basin.

Land use plays a significant role in determining the spatial patterns of water quality in the Johor River Basin (JRB), Malaysia. In the recent years, there have been several occurrences of pollution in these rivers, which has generated concerns over the long-term sustainability of the water resources in the JRB. Specifically, this water resource is a shared commodity between two states, namely, Johor state of Malaysia and Singapore, a neighbouring country adjacent to Malaysia. Prior to this study, few research on the influence of land use configuration on water quality have been conducted in Johor. In addition, it is also unclear how water quality varies under different seasonality in the presence of point sources. In this study, we investigated the influence of land use and point sources from wastewater treatment plants (WWTPs) on the water quality in the JRB. Two statistical techniques - Multivariate Linear Regression (MLR) and Redundancy Analysis (RA) were undertaken to analyse the relationships between river water quality and land use configuration, as well as point sources from WWTPs under different seasonality. Water samples were collected from 49 sites within the JRB from March to December in 2019. Results showed that influence from WWTPs on water quality was greater during the dry season and less significant during the wet season. In particular, point source was highly positively correlated with ammoniacal‑nitrogen (NH3-N). On the other hand, land use influence was greater than point source influence during the wet season. Residential and urban land use were important predictors for nutrients and organic matter (chemical oxygen demand); and forest land use were important sinks for heavy metals but a significant source of manganese.