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.
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.
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.
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.
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
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.
An integrated source apportionment methodology is developed by amalgamating the receptor-oriented model (ROM) and source-oriented numerical simulations (SOM) together to eliminate the weaknesses of individual SA methods. This approach attempts to apportion and dissect the PM2.5 sources in the Yangtze River Delta region during winter. First, three ROM models (CMB, PMF, ME2) are applied and compared for the preliminary SA results, with information from PM2.5 sampling and lab analysis during the winter seasons. The detailed source category contribution of SOM to PM2.5 is further simulated using the WRF-CAMx model. The two pieces of information from both ROM and SOM are then stitched together to give a comprehensive information on the PM2.5 sources over the region. With the integrated approach, the detailed contributing sources of the ambient PM2.5 at different receptors including rural and urban, coastal and in-land, northern and southern receptors are analyzed. The results are compared with previous data and shows good agreement. This integrative approach is more comprehensive and is able to produce a more profound and detailed understanding between the sources and receptors, compared with single models.
Ridleyandra merohmerea, a new species of Gesneriaceae, is described and illustrated. It is endemic in Peninsular Malaysia and known from a few populations along the Tuang River in the lowland dipterocarp forest of the Ulu Galas Forest Reserve in Kelantan, Peninsular Malaysia. Its conservation status is assessed as Critically Endangered.
Fishes from five streams in Gunung Machinchang and six streams in Gunung Raya areas of Pulau Langkawi were surveyed with the aim to investigate their diversity and distribution. Fish samples were collected from 23rd to 29th November 2007. Samplings took place along the 50 m reach of each of the site using an electrofisher and scoop nets. A total of 619 individuals of fish comprising 27 species and 14 families were recorded. Sixty-six percent from the taxa listed were of the cyprinids and Puntius binotatus was the most abundant species. Carassius auratus auratus was recorded for the first time in Pulau Langkawi. Streams of the Gunung Machinchang area were dominated by secondary freshwater fish species, but in the Gunung Raya area the streams were dominated by primary freshwater fish species. The highest diversity of fish was recorded for Sg. Kubang Badak with Simpson Index Ds = 0.838 and the lowest was for Sg. Perangin with Ds = 0.450. The highest evenness index of fish species was detected for Sg. Temurun with Es = 0.684 and the lowest was for Sg. Perangin with Es = 0.299. Species overlapping between streams of the two areas was 9.6%.
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.
In Southeast Asia, biodiversity-rich forests are being extensively logged and converted to oil palm monocultures. Although the impacts of these changes on biodiversity are largely well documented, we know addition to samples we collected in 201 little about how these large-scale impacts affect freshwater trophic ecology. We used stable isotope analyses (SIA) to determine the impacts of land-use changes on the relative contribution of allochthonous and autochthonous basal resources in 19 stream food webs. We also applied compound-specific SIA and bulk-SIA to determine the trophic position of fish apex predators and meso-predators (invertivores and omnivores). There was no difference in the contribution of autochthonous resources in either consumer group (70-82%) among streams with different land-use type. There was no change in trophic position for meso-predators, but trophic position decreased significantly for apex predators in oil palm plantation streams compared to forest streams. This change in maximum food chain length was due to turnover in identity of the apex predator among land-use types. Disruption of aquatic trophic ecology, through reduction in food chain length and shift in basal resources, may cause significant changes in biodiversity as well as ecosystem functions and services. Understanding this change can help develop more focused priorities for mediating the negative impacts of human activities on freshwater ecosystems.
We studied the water quality of the riparian firefly sanctuary of Sungai Rembau, or Rembau River, in Negeri Sembilan, Malaysia, from January 2018 to November 2018 to determine the possible influence of the physico-chemical characteristics of the water on the firefly populations living within the sanctuary. We set up a total of five water quality sampling stations and 10 firefly sampling stations along the river. Dissolved oxygen (DO), temperature, pH and electrical conductivity (EC) were measured in situ, while chemical oxygen demand (COD), total suspended solids (TSS), biochemical oxygen demand (BOD) and ammonia-nitrogen (NH3-N) were analysed in the laboratory. Firefly samples were collected using a sweep net at both day and night for 1 min. Sungai Rembau was categorized as Class II on the Malaysian water quality index (WQI), which indicates slight pollution. Except for EC and DO, the water quality parameter values were not significantly different (p > 0.05) between the sampling stations. A total of 529 firefly individuals consisting of Pteroptyx tener (n = 525, 99.24%), P. malaccae (n = 3, 0.57%) and P. asymmetria (n = 1, 0.19%) were collected. There was significant correlation between firefly abundance and BOD (r = - 0.198, p