Knowledge related to distributions of rainfall amounts are of great importance for the design of water related structures. The greater problem facing hydrologists and engineers identifying the best distribution form for regional data. The main goal of the study is to perform regional frequency analysis of maximum daily rainfalls selected each year among daily rainfalls measured over stations in Selangor and Kuala Lumpur by using the L-moment method. Several distributions were taken into account in this study which include two-parameter normal (NOM), lognormal (LN2), three-parameter lognormal (LN3), logistic (LOG), generalized logistic (GLO), extreme value type I (EV1), generalized extreme value (GEV) and generalized Pareto (GPA) distribution. The most suitable distribution was determined according to the mean absolute deviation index (MADI), mean square deviation index (MSDI) and the L-moment ratio diagram. The result of this study showed that the GLO distribution is the most suitable distribution to fit the data of maximum daily rainfalls for stations in Selangor and Kuala Lumpur.
Proper integrated management of a dam reservoir requires that all components of the water resource system be known. One of these components is the daily reservoir inflow which is the subject matter of this study, i.e. to establish predictions of what is coming in the next rainfall-runoff process over a catchment. The transformation of rainfall into runoff is an extremely complex, dynamic, and more of a non-linear process. The available six-year average daily rainfall data across the Sembrong dam catchment were computed using the well-known Theissen’s polygon method. Daily reservoir inflow data were extracted by applying the water balance model to the Sembrong dam reservoir. Modelling of relationship between rainfall and reservoir inflow data was done using feed-forward back-propagation neural networks. The final selected model has one hidden layer with 11 neurons in the hidden layer. The selected model was applied for an independent data series testing. Results in relation to specific climatic and hydrologic properties of a small tropical catchment suggested that the model is suitable to be used in forecasting the next day’s reservoir inflow. The efficiencies of the model Abtained indicated the validity of using the neural network for modelling reservoir inflow series.
Identification of the true statistical distributions for various hydrologic data sets is a major problem facing engineers. The four-parameter kappa distribution is a combination of the established distribution including the Generalised Extreme Value (GEV), Generalised Logistic (GL), Generalised Pareto (GP) and the Gumbel distribution were considered in this study. The main objective of this study was to develop the method of LQ-moments for the kappa distribution. The performance of the LQ-moments was compared with L-moments through eight problems using published data sets. The results showed that the performance of both methods, the LQ-moments and L-moments worked equally well.
This paper reviews the advances made on studies related to bank erosion. Bank erosion has been an area of interest by researchers in geological, geotechnical, hydraulic, hydrology and river engineering disciplines. With anticipated global challenges from climate change impacts, bank erosion studies could support challenges faced in ensuring sustainable environmental management. The evolution in the theoretical and laboratory findings have led to the advances in bank erosion and contributed to new knowledge in the said field. This review summarises the findings of previous investigators including measurements approach and prediction of rates of bank erosion through the use of physical models and numerical approach.
As a crucial demand in urban areas, flood risk management has been considered by researchers and decision makers around the world. In this case, hydrological modelling that simulates rainfall-runoff process plays a significant role. This paper quantified the roles of three main parameters in river basin hydrological response, namely, rainfall event duration, rainfall event ARI (magnitude) and land-use development condition. The case study area of this research was Sungai Kayu Ara basin which is located in the western part of Kuala Lumpur, Malaysia. A total of twenty seven scenario were defined for this research, including three different rainfall event durations (60, 120 and 360 minutes), three different ARIs (20, 50 and 100 years) and in three different land-use conditions (existing, intermediate and ultimate). The results of this research indicate that rainfall event duration, rainfall event ARI (magnitude) and land-use development condition have considerable effects of the surface runoff hydrographs in terms of peak discharge and volume.
Rainfalls data have been broadly used in researches including in hydrological and meteorological areas. Two common ways in extracting observations from hourly rainfalls data are the window-based analysis (WBA) and storm-event analysis (SEA) approach. However, the differences in the qualitative and quantitative properties of both methods are still vaguely discussed. The aim of studying these dissimilarities is to understand the effects of each approach in modelling and analysis. The qualitative difference is due to the way the two analyses define the accumulated rainfalls for observations which are referred to as rainfall and storm depths, respectively. The repetitiveness of rainfall depths provide nested structure while the storm depths are considered independent. The quantitative comparisons include their statistical and scaling properties that are linked by the self-similarity concept from simple scaling characteristics. If self-similarity concept
holds, then the rainfall or storm depths follow simple scaling and the analysis would be simplified. The rainfall depths showed clearer simple scaling characteristics compared to the storm depths. Though the storm depths do not yield self-similarity for a large range of storm duration but the characteristics of simple scaling can be observed for a reduced range of the considered duration. In general, the context of the research and the region of the time interval and duration will be an important aspects to consider in choosing which method is best to use for analyzing the data.
This paper aims to estimate the Generalized Pareto Distribution (GPD) parameters and predicts the T-year return levels of extreme rainfall events using the Partial Duration Series (PDS) method based on the hourly rainfall data of five stations in Peninsular Malaysia. In particular, the GPD parameters are estimated using five methods namely the method of Moments (MOM), the probability weighted moments (PWM), the L-moments (LMOM), the Trimmed L-moments (TLMOM) and the Maximum Likelihood (ML) and the performance of the T-year return level of each estimation method is analyzed based on the RMSE measure obtained from Monte Carlo simulation. In addition, we suggest the weighted average model, a model which assigns the inverse variance of several methods as weights, to estimate the T-year return level. This paper contributes to the hydrological literatures in terms of three main elements. Firstly, we suggest the use
of hourly rainfall data as an alternative to provide a more detailed and valuable information for the analysis of extreme rainfall events. Secondly, this study applies five methods of parametric approach for estimating the GPD parameters and predicting the T-year return level. Finally, in this study we propose the weighted average model, a model that assigns the inverse variance of several methods as weights, for the estimation of the T-year return level.
This paper highlights a preliminary study on the potential of a tipping flush gate to be used in an open storm drain to remove sediment. The investigation was carried out by using a plasboard model of the tipping flush gate installed in a rectangular flume. A steady flow experiment was carried out to determine the discharge coefficients and also the outflow relationship of the tipping flush gate. The velocity produced by the gate at various distances downstream of the gate during flushing operation was measured using a flowmeter and the velocity at all the points was higher than the recommended self-cleansing design available in the literature. A preliminary experiment on the efficiency of flushing was conducted using uniform sediment with d50 sizes of 0.81, 1.53 and 4.78 mm. Results generally showed that the number of flushes required to totally remove the sediment from the initial position by a distance of 1 m increased by an average of 1.50 times as the sediment deposit bed thickness doubled. An equation relating the number of flushes required to totally remove the sediment bed for 1 m with the sediment bed deposit thickness was also developed for the current study.
The expansion of oil palm plantations at the expense of tropical forests is causing declines in many species and altering ecosystem functions. Maintaining forest-dependent species and processes in these landscapes may therefore limit the negative impacts of this economically important industry. Protecting riparian vegetation may be one such opportunity; forest buffer strips are commonly protected for hydrological reasons, but can also conserve functionally important taxa and the processes they support.We surveyed leaf litter ant communities within oil palm-dominated landscapes in Sabah, Malaysia, using protein baits. As the scavenging activity of ants influences important ecological characteristics such as nutrient cycling and soil structure, we quantified species-specific rates of bait removal to examine how this process may change across land uses and establish which changes in community structure underlie observed shifts in activity.Riparian reserves had similar ant species richness, community composition and scavenging rates to nearby continuous logged forest. Reserve width and vegetation structure did not affect ant species richness significantly. However, the number of foraging individuals decreased with increasing reserve width, and scavenging rate increased with vegetation complexity.Oil palm ant communities were characterized by significantly lower species richness than logged forest and riparian reserves and also by altered community composition and reduced scavenging rates.Reduced scavenging activity in oil palm was not explained by a reduction in ant species richness, nor by replacement of forest ant species by those with lower per species scavenging rates. There was also no significant effect of land use on the scavenging activity of the forest species that persisted in oil palm. Rather, changes in scavenging activity were best explained by a reduction in the mean rate of bait removal per individual ant across all species in the community.Synthesis and applications. Our results suggest that riparian reserves are comparable to areas of logged forest in terms of ant community composition and ant-mediated scavenging. Hence, in addition to protecting large continuous areas of primary and logged forest, maintaining riparian reserves is a successful strategy for conserving leaf litter ants and their scavenging activities in tropical agricultural landscapes.
In this study, the implementation of the Regional Climate Model into the hydrodynamic model has been applied for streamflow projection on a river located at the south of Peninsular Malaysia within the years 2070 till 2099. The data has been obtained from a Regional Climate Model (RCM), named Précis, on a daily basis. It begins by comparing historical rainfall data generated from Précis versus the actual gauged recorded rainfall data from Department of Irrigation and Drainage Malaysia (DID). The bias of the generated rainfall data has been reduced by statistical techniques. The same has been applied to the future generated rainfall data from 2070 to 2099. Using the generated precipitation data as input to the hydrological model, results in the daily output of river discharge identified as the main contributor of flood occurrences. Based on the results of the hydrological model utilised, e.g. HEC-HMS, comparison was made between the future and historical generated discharge data using Précis between the years 1960 till 1998. Dividing a year into three segments, e.g. January-April, May-August, SeptemberDecember, the results show that there would be a significant drop of peak discharge in the third segment and an increase in discharge during the second segment. The first part remains almost with no changes. As an addition, the drop of the peak shows reduction in the probability of flood occurrences. It also indicates the reduction in water storage capacity which coherently affects the water supply scheme
Analytical study of the influence of both the pumping well discharge rate and pumping time on contaminant transport and attenuation is significant for hydrological and environmental science applications. This article provides an analytical solution for investigating the influence of both pumping time and travelling time together for one-dimensional contaminant transport in riverbank filtration systems by using the Green's function approach. The basic aim of the model is to understand how the pumping time and pumping rate, which control the travelling time, can affect the contaminant concentration in riverbank filtration systems. Results of analytical solutions are compared with the results obtained using a MODFLOW numerical model. Graphically, it is found that both analytical and numerical solutions have almost the same behaviour. Additionally, the graphs indicate that any increase in the pumping rate or simulation pumping time should increase the contamination in groundwater. The results from the proposed analytical model are well matched with the data collected from a riverbank filtration site in France. After this validation, the model is then applied to the first pilot project of a riverbank filtration system conducted in Malaysia. Sensitivity analysis results highlight the importance of degradation rates of contaminants on groundwater quality, for which higher utilization rates lead to the faster consumption of pollutants.
Rainfall intensity is the main input variable in various hydrological analysis and modeling. Unfortunately, the quality of rainfall data is often poor and reliable data records are available at coarse intervals such as yearly, monthly and daily. Short interval rainfall records are scarce because of high cost and low reliability of the measurement and the monitoring systems. One way to solve this problem is by disaggregating the coarse intervals to generate the short one using the stochastic method. This paper describes the use of the Bartlett Lewis Rectangular Pulse (BLRP) model. The method was used to disaggregate 10 years of daily data for generating hourly data from 5 rainfall stations in Kelantan as representative area affected by monsoon period and 5 rainfall stations in Damansara affected by inter-monsoon period. The models were evaluated on their ability to reproduce standard and extreme rainfall model statistics derived from the historical record over disaggregation simulation results. The disaggregation of daily to hourly rainfall produced monthly and daily means and variances that closely match the historical records. However, for the disaggregation of daily to hourly rainfall, the standard deviation values are lower than the historical ones. Despite the marked differences in the standard deviation, both data series exhibit similar patterns and the model adequately preserve the trends of all the properties used in evaluating its performances.
Litter decomposition is vital for carbon and nutrient turnover in terrestrial ecosystems, and this process has now
been thoroughly demonstrated to be regulated by various mechanisms. The total environment has been continuously
changing in recent decades, especially in high-latitude regions; these alterations, however, profoundly contribute to the
decomposition process, but a comprehensive recognition has not available. Here we reviewed the empirical observations
and current knowledge regarding how hydrological leaching and freeze-thaw events modulate early decomposition of
plant litter. Leaching contributes a considerable percentage of mass loss and carbon and nutrient release in early stage of
decomposition, but the magnitudes are different between species levels depending on the chemical traits. Frequent freezing
and thawing events could positively influence decomposition rate in cold biomes but also hamper soil decomposer and
there is no general and predictable pattern has been emerged. Further experiments should be manipulated to estimate
how the altered freezing and thawing effect on carbon and nutrient release from plant litter to better understanding the
changing environment on litter decomposition.
Impacts of climate change on the hydrologic processes under future climate change conditions were assessed over Muda and Dungun watersheds of Peninsular Malaysia by means of a coupled regional climate and physically-based hydrology model utilizing an ensemble of future climate change projections. An ensemble of 15 different future climate realizations from coarse resolution global climate models' (GCMs) projections for the 21st century was dynamically downscaled to 6km resolution over Peninsular Malaysia by a regional climate model, which was then coupled with the watershed hydrology model WEHY through the atmospheric boundary layer over Muda and Dungun watersheds. Hydrologic simulations were carried out at hourly increments and at hillslope-scale in order to assess the impacts of climate change on the water balances and flooding conditions in the 21st century. The coupled regional climate and hydrology model was simulated for a duration of 90years for each of the 15 realizations. It is demonstrated that the increase in mean monthly flows due to the impact of expected climate change during 2040-2100 is statistically significant from April to May and from July to October at Muda watershed. Also, the increase in mean monthly flows is shown to be significant in November during 2030-2070 and from November to December during 2070-2100 at Dungun watershed. In other words, the impact of the expected climate change will be significant during the northeast and southwest monsoon seasons at Muda watershed and during the northeast monsoon season at Dungun watershed. Furthermore, the flood frequency analyses for both watersheds indicated an overall increasing trend in the second half of the 21st century.
This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of groundwater resources in the area. The overall results proved the efficacy of the data mining model and the geospatial technology in groundwater potential mapping.
The conversions of forests and grass land to urban and farmland has exerted significant changes on terrestrial ecosystems. However, quantifying how these changes can affect the quality of water resources is still a challenge for hydrologists. Nitrate concentrations can be applied as an indicator to trace the link between land use changes and groundwater quality due to their solubility and easy transport from their source to the groundwater. In this study, 25year records (from 1989 to 2014) of nitrate concentrations are applied to show the impact of land use changes on the quality of groundwater in Northern Kelantan, Malaysia, where large scale deforestation in recent decades has occurred. The results from the integration of time series analysis and geospatial modelling revealed that nitrate (NO3-N) concentrations significantly increased with approximately 8.1% and 3.89% annually in agricultural and residential wells, respectively, over 25years. In 1989 only 1% of the total area had a nitrate value greater than 10mg/L; and this value increased sharply to 48% by 2014. The significant increase in nitrate was only observed in a shallow aquifer with a 3.74% annual nitrate increase. Based on the result of the Autoregressive Integrated Moving Average (ARIMA) model the nitrate contamination is expected to continue to rise by about 2.64% and 3.9% annually until 2030 in agricultural and residential areas. The present study develops techniques for detecting and predicting the impact of land use changes on environmental parameters as an essential step in land and water resource management strategy development.
The selection of curve number to represent watersheds with similar land use and land cover is often subjective and ambiguous. Watershed with several soil groups further complicates curve number selection process while wrong curve number selection often produces unrealistic runoff estimates. The 1954 simplified Soil Conservation Services (SCS) runoff model over-predicted runoff with significant amount and further magnified runoff prediction error toward higher rainfall depths in this study. The model was statistically insignificant with the rejection of two null hypotheses and paved the way for regional model calibration study. This paper proposes a new direct curve number derivation technique from the given rainfall-runoff conditions under the guide of inferential statistics. The technique offers a swift and economical solution to improve the runoff prediction ability of the SCS runoff model with statistically significant results. A new rainfall-runoff model was developed with calibration according to the regional hydrological conditions. It out-performed the runoff prediction of the simplified SCS runoff model and the asymptotic runoff model. The derived curve number = 89 at alpha = 0.01 level. The technique can be adopted to predict flash flood and forecast urban runoff.
The hydrological effects of climate variation and land use conversion can occur at various spatial scales, but the most important sources of these changes are at the regional or watershed scale. In addition, the managerial and technical measures are primarily implemented at local and watershed scales in order to mitigate adverse impacts of human activities on the renewable resources of the watershed. Therefore, quantitative estimation of the possible hydrological consequences of potential land use and climate changes on hydrological regime at watershed scale is of tremendous importance. This paper focuses on the impacts of climate change as well as land use change on the hydrological processes of river basin based on pertinent published literature which were precisely scrutinized. The various causes, forms, and consequences of such impacts were discussed to synthesize the key findings of literature in reputable sources and to identify gaps in the knowledge where further research is required. Results indicate that the watershed-scale studies were found as a gap in tropical regions. Also, these studies are important to facilitate the application of results to real environment. Watershed scale studies are essential to measure the extent of influences made to the hydrological conditions and understanding of causes and effects of climate variation and land use conversion on hydrological cycle and water resources.
Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.
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.