The theory of critical slowing down (CSD) suggests an increasing pattern in the time series of CSD indicators near catastrophic events. This theory has been successfully used as a generic indicator of early warning signals in various fields, including climate research. In this paper, we present an application of CSD on water level data with the aim of producing an early warning signal for floods. To achieve this, we inspect the trend of CSD indicators using quantile estimation instead of using the standard method of Kendall's tau rank correlation, which we found is inconsistent for our data set. For our flood early warning system (FLEWS), quantile estimation is used to provide thresholds to extract the dates associated with significant increases on the time series of the CSD indicators. We apply CSD theory on water level data of Kelantan River and found that it is a reliable technique to produce a FLEWS as it demonstrates an increasing pattern near the flood events. We then apply quantile estimation on the time series of CSD indicators and we manage to establish an early warning signal for ten of the twelve flood events. The other two events are detected on the first day of the flood.
A major programme of dam building is underway in many of the world's tropical countries. This raises the question of whether existing research is sufficient to fully understand the impacts of dams on tropical river systems. This paper provides a systematic review of what is known about the impacts of dams on river flows, sediment dynamics and geomorphic processes in tropical rivers. The review was conducted using the SCOPUS® and Web of Science® databases, with papers analysed to look for temporal and geographic patterns in published work, assess the approaches used to help understand dam impacts, and assess the nature and magnitude of impacts on the flow regimes and geomorphology ('hydromorphology') of tropical rivers. As part of the review, a meta-analysis was used to compare key impacts across different climate regions. Although research on tropical rivers remains scarce, existing work is sufficient to allow us to draw some very broad, general conclusions about the nature of hydromorphic change: tropical dams have resulted in reductions in flow variability, lower flood peaks, reductions in sediment supply and loads, and complex geomorphic adjustments that include both channel incision and aggradation at different times and downstream distances. At this general level, impacts are consistent with those observed in other climate regions. However, studies are too few and variable in their focus to determine whether some of the more specific aspects of change observed in tropical rivers (e.g. time to reach a new, adjusted state, and downstream recovery distance) differ consistently from those in other regions. The review helps stress the need for research that incorporates before-after comparisons of flow and geomorphic conditions, and for the wider application of tools available now for assessing hydromorphic change. Very few studies have considered hydromorphic processes when designing flow operational policies for tropical dams.
Water level forecasting is an essential topic in water management affecting reservoir operations and decision making. Recently, modern methods utilizing artificial intelligence, fuzzy logic, and combinations of these techniques have been used in hydrological applications because of their considerable ability to map an input-output pattern without requiring prior knowledge of the criteria influencing the forecasting procedure. The artificial neurofuzzy interface system (ANFIS) is one of the most accurate models used in water resource management. Because the membership functions (MFs) possess the characteristics of smoothness and mathematical components, each set of input data is able to yield the best result using a certain type of MF in the ANFIS models. The objective of this study is to define the different ANFIS model by applying different types of MFs for each type of input to forecast the water level in two case studies, the Klang Gates Dam and Rantau Panjang station on the Johor river in Malaysia, to compare the traditional ANFIS model with the new introduced one in two different situations, reservoir and stream, showing the new approach outweigh rather than the traditional one in both case studies. This objective is accomplished by evaluating the model fitness and performance in daily forecasting.
The Soil and Water Assessment Tool (SWAT) ecohydrological model was utilized to simulate fecal contamination in the 1937 km2 Selangor River Watershed in Malaysia. The watershed conditions posed considerable challenges owing to data scarcity and tropical climate conditions, which are very different from the original conditions that SWAT was developed and tested for. Insufficient data were compensated by publicly available data (e.g., land cover, soil, and weather) to run SWAT. In addition, field monitoring and interviews clarified representative situations of pollution sources and loads, which were used as input for the model. Model parameters determined by empirical analyses in the USA (e.g., surface runoff, evapotranspiration, and temperature adjustment for bacteria die-off) are thoroughly discussed. In particular, due consideration was given to tropical climate characteristics such as intense rainfall, high potential evapotranspiration, and high temperatures throughout the year. As a result, the developed SWAT successfully simulated fecal contamination ranging several orders of magnitude along with its spatial distribution (i.e., Nash-Sutcliffe Efficiency (NSE) = 0.64, Root Mean Square Error-Observations Standard Deviation Ratio (RSR) = 0.64 at six mainstem sites, and NSE = 0.67 and RSR = 0.57 at 12 major tributaries). Moreover, mitigation countermeasures for future worsening of fecal contamination (i.e., E.coli concentration > 20,000 CFU/100 mL for 690 days during nine years at a raw water intake point for Kuala Lumpur [KL] residents) were analyzed through scenario simulations, thereby contributing to discussing effective watershed management. The results propose improving decentralized sewage treatment systems and treating chicken manure with effective microorganisms in order to guarantee water safety for KL residents (i.e., E.coli concentrations <20,000 CFU/100 mL throughout the period, considering Malaysian standards). Accordingly, this study verified the applicability of SWAT to simulate fecal contamination in areas that are difficult to model and suggests solutions for watershed management based on quantitative evidence.
We sampled the Klang estuary during the inter-monsoon and northeast monsoon period (July-Nov 2011, Oct-Nov 2012), which coincided with higher rainfall and elevated Klang River flow. The increased freshwater inflow into the estuary resulted in water column stratification that was observed during both sampling periods. Dissolved oxygen (DO) dropped below 63 μM, and hypoxia was observed. Elevated river flow also transported dissolved inorganic nutrients, chlorophyll a and bacteria to the estuary. However, bacterial production did not correlate with DO concentration in this study. As hypoxia was probably not due to in situ heterotrophic processes, deoxygenated waters were probably from upstream. We surmised this as DO correlated with salinity (R2 = 0.664, df = 86, p 6.7 h), hypoxia could occur at the Klang estuary. Here, we presented a model that related riverine flow rate to the post-heavy rainfall hypoxia that explicated the episodic hypoxia at Klang estuary. As Klang estuary supports aquaculture and cockle culture, our results could help protect the aquaculture and cockle culture industry here.
Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.
The determinant factors for macroinvertebrate assemblages in river ecosystems are varied and are unique and specific to the type of macroinvertebrate family. This study aims to assess the determinant factors for macroinvertebrate assemblages in a recreational river. The study was conducted on the Ulu Bendul River, Negeri Sembilan, Malaysia. A total of ten sampling stations were selected. The research methodology included (1) water quality measurement, (2) habitat characterization, and (3) macroinvertebrate identification and distribution analysis. The statistical analysis used in this study was canonical correspondence analysis (CCA) to represent the relationship between the environmental factors and macroinvertebrate assemblages in the recreational river. This study found that most of the families of macroinvertebrates were very dependent on the temperature, DO, NH3-N, type of riverbed, etc. All of these factors are important for the survival of the particular type of macroinvertebrate, plus they are also important for selecting egg-laying areas and providing suitable conditions for the larvae to grow. This study advises that improved landscape design for watershed management be implemented in order to enhance water quality and physical habitats, and hence the protection and recovery of the macroinvertebrate biodiversity.
Anthropogenic pressures are causing substantial degradation to the freshwater ecosystems globally and Malaysia has not escaped such a bleak scenario. Prompted by the predicament, this study's objective was to pioneer a river assessment system that can be readily adopted to monitor, manage and drive improvement in a wholesome manner. Three sets of a priori metrics were selected to form the Ichthyofaunal Quality Index (IQI: biological), Water Quality Index (WQI: chemical) and River Physical Quality Index (RPQI: physical). These indices were further integrated on equal weighting to construct a novel Malaysian River Integrity Index (MyRII). To test its robustness, the MyRII protocol was field tested in four eco-hydrological zones located in the Kampar River water basin for 18 months to reveal its strengths, weaknesses, and establish the "excellent", "good", "average", "poor" and "impaired" thresholds based on the "best performer" reference site in an empirical manner. The resultant MyRII showed a clear trend that corresponded with different levels of river impairment. Test site zone A which was a reference site with minimal disturbance achieved the highest MyRII (88.95 ± 4.29), followed by partially disturbed zone B (61.95 ± 5.90) and heavily disturbed zone C (50.00 ± 4.29). However, the MyRII in zone D (59.9 ± 6.39), which was a heavily disturbed wetland that was disjointed from the river, did not conform to such trend. Also unveiled and recognized, however, are some unexpected nuances, limitations and challenges that emerged from this study. These are critically discussed as precautions when interpreting and implementing the MyRII protocol. This study adds to the mounting body of evidence that water resource stakeholders and policymakers must look at the big picture and adopt the "balanced ecosystem" mind-set when assessing, restoring and managing the rivers as a freshwater resource.
Functional classification of phytoplankton could be a valuable tool in water quality monitoring in the eutrophic riverine ecosystems. This study is novel from the Bangladeshi perspective. In this study, phytoplankton cell density and diversity were studied with particular reference to the functional groups (FGs) approach during pre-monsoon, monsoon, and post-monsoon at four sampling stations in Karatoya River, Bangladesh. A total of 54 phytoplankton species were recorded under four classes, viz. Chlorophyceae (21 species) Cyanophyceae (16 species), Bacillariophyceae (15 species), and Euglenophyceae (2 species). A significantly higher total cell density of phytoplankton was detected during the pre-monsoon season (24.20 × 103 cells/l), while the lowest in monsoon (9.43 × 103 cells/l). The Shannon-Wiener diversity index varied significantly (F = 16.109, P = 000), with the highest value recorded during the post-monsoon season. Analysis of similarity (ANOSIM) identified significant variations among the three seasons (P
Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach - the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.
Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.
The investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA, where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport, many sediment transport equations were recommended in the literature. However, the accuracy of the prediction results remains low, particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method, machine learning algorithms have advanced and can produce a useful prediction model. In this research, three machine learning models, namely evolutionary polynomial regression (EPR), multi-gene genetic programming (MGGP) and M5 tree model (M5P), were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models, in terms of different statistical measurement criteria, EPR gives the best prediction model, followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.
Radon (222Rn), an inert gas, is considered a silent killer due to its carcinogenic characteristics. Dhaka city is situated on the banks of the Buriganga River, which is regarded as the lifeline of Dhaka city because it serves as a significant source of the city's water supply for domestic and industrial purposes. Thirty water samples (10 tap water from Dhaka city and 20 surface samples from the Buriganga River) were collected and analyzed using a RAD H2O accessory for 222Rn concentration. The average 222Rn concentration in tap and river water was 1.54 ± 0.38 Bq/L and 0.68 ± 0.29 Bq/L, respectively. All the values were found below the maximum contamination limit (MCL) of 11.1 Bq/L set by the USEPA, the WHO-recommended safe limit of 100 Bq/L, and the UNSCEAR suggested range of 4-40 Bq/L. The mean values of the total annual effective doses due to inhalation and ingestion were calculated to be 9.77 μSv/y and 4.29 μSv/y for tap water and river water, respectively. Although all these values were well below the permissible limit of 100 μSv/y proposed by WHO, they cannot be neglected because of the hazardous nature of 222Rn, especially considering their entry to the human body via inhalation and ingestion pathways. The obtained data may serve as a reference for future 222Rn-related works.
The concentration, distribution, and risk assessment of parabens were determined in the surface water of the Terengganu River, Malaysia. Target chemicals were extracted via solid-phase extraction, followed by high-performance liquid chromatography analysis. Method optimization produced a high percentage recovery for methylparaben (MeP, 84.69 %), ethylparaben (EtP, 76.60 %), and propylparaben (PrP, 76.33 %). Results showed that higher concentrations were observed for MeP (3.60 μg/L) as compared with EtP (1.21 μg/L) and PrP (1.00 μg/L). Parabens are ubiquitously present in all sampling stations, with >99 % of detection. Salinity and conductivity were the major factors influencing the level of parabens in the surface water. Overall, we found no potential risk of parabens in the Terengganu River ecosystem due to low calculated risk assessment values (risk quotient
Island biogeography is one of the most powerful subdisciplines of ecology: its mathematical predictions that island size and distance to mainland determine diversity have withstood the test of time. A key question is whether these predictions follow at a population-genomic level. Using rigorous ancient-DNA protocols, we retrieved approximately 1,000 genomic markers from approximately 100 historic specimens of two Southeast Asian songbird complexes from across the Sunda Shelf archipelago collected 1893-1957. We show that the genetic affinities of populations on small shelf islands defy the predictions of geographic distance and appear governed by Earth-historic factors including the position of terrestrial barriers (paleo-rivers) and persistence of corridors (Quaternary land bridges). Our analyses suggest that classic island-biogeographic predictors may not hold well for population-genomic dynamics on the thousands of shelf islands across the globe, which are exposed to dynamic changes in land distribution during Quaternary climate change.
The seasonal variation of petroleum pollution including n-alkanes in surface sediments of the Selangor River in Malaysia during all four climatic seasons was investigated using GC-MS. The concentrations of n-alkanes in the sediment samples did not significantly correlate with TOC (r = 0.34, p > 0.05). The concentrations of the 29 n-alkanes in the Selangor River ranged from 967 to 3711 µg g-1 dw, with higher concentrations detected during the dry season. The overall mean per cent of grain-sized particles in the Selangor River was 85.9 ± 2.85% sand, 13.5 ± 2.8% clay, and 0.59 ± 0.34% gravel, respectively. n-alkanes are derived from a variety of sources, including fresh oil, terrestrial plants, and heavy/degraded oil in estuaries. The results of this study highlight concerns and serve as a warning that hydrocarbon contamination is affecting human health. As a result, constant monitoring and assessment of aliphatic hydrocarbons in coastal and riverine environments are needed.
A field study was performed to describe the functional feeding groups (FFGs) of Ephemeroptera, Plecoptera and Trichoptera (EPT) in the Tupah, Batu Hampar and Teroi Rivers in the Gunung Jerai Forest Reserve (GJFR), Kedah, Malaysia. Twenty-nine genera belonging to 19 families were identified. The EPTs were classified into five FFGs: collector-gatherers (CG), collector-filterers (CF), shredders (SH), scrapers (SC) and predators (P). In this study, CG and CF were the dominant groups inhabiting all three rivers. Ephemeroptera dominated these rivers due to their high abundance, and they were also the CG (90.6%). SC were the lowest in abundance among all groups. Based on the FFGs, the Teroi River was suitable for CG, whereas the Tupah and Batu Hampar Rivers were suitable for CG and CF. The distribution of FFGs differed among the rivers (CG, χ(2) = 23.6, p = 0.00; SH, χ(2) = 10.02, p = 0.007; P, χ(2) = 25.54, p = 0.00; CF, χ(2) = 21.95, p = 0.00; SC, χ(2) = 9.31, p = 0.01). These findings indicated that the FFGs found in rivers of the GJFR represent high river quality.
We present an assessment of the diversity of Malaysian bats at two contrasting habitat types (secondary forest and oil palm plantation) along the Kerian River surveyed between February 2009 and February 2010. Three hundred and twenty nine individual bats from 13 species representing 4 families were recorded using 10 mist nets. The most commonly caught bat in the secondary forest was Cynopterus brachyotis (n=75), followed by Macroglossus minimus (n=10). Meanwhile, in the oil palm plantation, the most commonly caught bat was Cynopterus brachyotis (n=109), followed by Cynopterus horsfieldi (n=76). The netting efforts were equal for both habitat types. The total sampling nights for each habitat type was 5460. The oil palm plantation had a greater bat abundance that was significantly different from that of the secondary forest, with 209 and 120 individuals, respectively (Mann-Whitney U-test = 31.5, p<0.05). Our results suggest that there is no significant difference in species richness between the two sites. However, the invasion by disturbance-associated species of the secondary forest is indicative of negative effects on the forest and animal diversity in this area. Forest managers should consider multiple measures of forest fragmentation sensitivity before making any forest management decisions.
The management of suspended solids and associated contaminants in rivers requires knowledge of sediment sources. In-situ sampling can only describe the integrated impact of the upstream sources. Empirical models that use surface reflectance from satellite images to estimate total suspended solid (TSS) concentrations can be used to supplement measurements and provide spatially continuous maps. However, there are few examples, especially in narrow, shallow and hydrologically dynamic rivers found in mountainous areas. A case study of the Didipio catchment in Philippines was used to address these issues. Four 5-m resolution RapidEye images, from between the years 2014 and 2016, and near-simultaneous ground measurements of TSS concentrations were used to develop a power law model that approximates the relationship between TSS and reflectance for each of four spectral bands. A second dataset using two 2-m resolution Pleiades-1A and a third using a 6-m resolution SPOT-6 image along with ground-based measurements, were consistent with the model when using the red band data. Using that model, encompassing data from all three datasets, gave an R2 value of 65% and a root mean square error of 519mgL-1. A linear relationship between reflectance and TSS exists from 1mgL-1 to approximately 500mgL-1. In contrast, for TSS measurements between 500mgL-1 and 3580mgL-1 reflectance increases at a generally lower and more variable rate. The results were not sensitive to changing the pixel location within the vicinity of the ground sampling location. The model was used to generate a continuous map of TSS concentration within the catchment. Further ground-based measurements including TSS concentrations that are higher than 3580mgL-1 would allow the model to be developed and applied more confidently over the full relevant range of TSS.