Delta change method with cyclic covariate generalized extreme value model for downscaling extreme rainfall

In meteorological data, lots of variables have annual, seasonal or diurnal cycles. These would be based on different climatic patterns in different seasons rising sea levels. The delta change approach is one of the statistical downscaling methods that used to downscale global climate model data in order to use it as a future input for hydrological models and flood risk assessment. In this work, a non-stationary GEV model with cyclic covariate structure for modelling magnitude and variation of data series with some degrees of correlation for real-world applications is proposed. All extreme events were calculated assuming that maximum annual daily precipitations follow the GEV distribution. The method makes it possible to identify and estimate the impacts of multiple time scales-such as seasonality, interdecadal variability, and secular trends-throughout the area, scale, and shape parameters of extreme sea level probability distribution. The incorporation of seasonal effects describes a huge amount of data variability, permitting the methods involved to be estimated more efficiently. Next, the technique of deltachange was implemented to the mean annual rainfall and also the regular rainfall occurrences of 5, 10, 20, 50 and 100 years of return. The capability of the proposed model will be tested to one rainfall station in Sabah. The new model suggesting improvement over the stationary model based on the p-value which is highly significant (approximate to 0). GEV model with cyclic covariate on both location and scale parameters is able to capture the seasonality factor in rainfall data. Hence, a reliable delta-change model has been developed in this study. This could produce more accurate projection of rainfall in the future

Projections of future extreme rainfall events using statistical downscaling in Malaysia

Climate change is one of the greatest challenges for water resources management. Intensity and frequency of extreme rainfalls are increasing due to enhanced greenhouse gas effect caused by climate change. A lot of research has been done in developing innovative methods for assessing the impacts of climate change on rainfall extremes. Climate change strongly depends on General Circulation Model (GCM) outputs since they play a pivotal role in the understanding of climate change. However due to their coarse resolution, statistical downscaling is widely applied to match the scale between the GCM and the station scale. This research proposed to establish statistical downscaling model that was able to generate hourly rainfall data for future projection of hourly extreme rainfall in Peninsular Malaysia. An Advanced Weather Generator (AWE-GEN) built on stochastic downscaling principles was applied for simulating hourly rainfall data. The model construction involved 40 stations over Peninsular Malaysia with observations from 1975 to 2005. To account for uncertainties, an ensemble of multi-model namely GFDL-CM3, IS-CM5A-LR, MIROC5, MRI-CGCM3 and NorESM1-M were obtained from the dataset compiled in the WCRP’s, CMIP5. The projections of extreme precipitation were based on the RCP 6.0 scenario (2081-2100). To address the problem of unavailability of rainfall data at remote areas over Peninsular Malaysia, this research also examined the spatial variability of rainfall and temperature parameters using Locally Weighted Regression. Results of the AWE-GEN showed its capability to simulate rainfall for Peninsular Malaysia. Both hourly and 24 hour extreme rainfall showed an increase for future. Extremes of dry spell was projected to decrease in future whereas extremes of wet spell was expected to remain unchanged. Simulations of present climate using interpolated parameters showed promising results for the studied regions

Variable viscosity of casson fluid flow over a stretching sheet in porous media with Newtonian heating

Casson fluid flow with variable viscosity in porous media over a heated stretching sheet is investigated. The partial differential equations representing the flow motion are first transformed to ordinary differential equations by similarity transformation before being solved numerically by the finite-difference method. The effects of the viscosity variation parameter (Ω), the permeability number (κ), Prandtl number (Pr), Biot number (Bi) and non-Newtonian fluid parameter (β) on the fluid flow and heat transfer, along with the temperature and velocity profiles, are presented graphically for some arbitrary values

Weather generator application with mixed exponential distribution representing rainfall intensity

Adequate and accurate rainfall information is vital in hydrological forecasting, however historical data are sometimes inadequate or nonexistence at location of interest. Stochastic weather generator which is developed based on historical metrological data, is often employed to generate synthetic rainfall series. In this study, the Advanced Weather Generator or AWE-GEN is employed to generate hourly rainfall series in the state of Johor, Malaysia. Within the AWE-GEN, is the Neyman Scott model to assess rainfall series. This study proposed the use of Mixed Exponential distribution in representing rainfall intensity of the Neyman Scott model. AWE-GEN is developed based on meteorological data from period 1975-2015. The model is then used to generate rainfall series separately at two sites within Johor. Generated results were found to be comparable to the historical rainfall series at both sites. Although rainfall distribution at the two sites are influenced by different monsoon winds, the model is able to capture significant statistical characteristics of rainfall behavior at each site. The successful development of this model could be beneficial in addressing issues such as insufficiency of rainfall data at rainfall stations. In addition the model could be employed to generate data as input to various hydrological models

Comparison of Gamma and Weibull distributions in simulating hourly rainfall in peninsular Malaysia

Prior knowledge of rainfall is essential in the planning and lessening of the risks associated with extreme events such as floods, landslides and erosions. The development of a reliable model is therefore, crucial in predicting rainfall series that is parallel to the local climate. In this study, a weather generator is chosen to model hourly rainfall time series in Peninsular Malaysia. Short duration rainfall such as hourly scale is an inherent aspect of tropical rainfall, and instigates many flooding events, especially in the western part of the peninsular. Two distributions, Gamma and Weibull are incorporated into the rainfall generator and their performances in generating rainfall series are then compared. Simulations using both Gamma and Weibull distributions are individually conducted at forty stations across the peninsular. Results reveal that both Gamma and Weibull distributions are able to capture rainfall characteristics at the study sites. However, Gamma is found best to represent rainfall at sites located in eastern and southern parts of the peninsular whilst Weibull is more suitable for western and northern parts. Hence, Gamma is more suitable for representing monsoon rainfall while Weibull is more appropriate for inter-monsoon rainfall

Mathematical modelling of dengue pattern in Penang, Malaysia

Dengue fever is an endemic disease in many tropical and subtropical regions. In Malaysia, it is the leading public health challenge despite the extensive intervention programs by the related authorities. Distribution of dengue cases in Malaysia varies according to states and districts where cases are more distinct in urban and suburban areas. Preparedness strategies of dengue cases could be more successful with some comprehensive and technical analysis on disease incidences. Hence, the present study analyses dengue cases using mathematical modelling in the state of Penang, one of the more urbanised state. In particular, two time series models are fitted to the dengue data from the region in order to identify the mathematical model that best describe the data. Results show that both proposed models are able to represent the cases rather well; however numerical inspection revealed that Double Exponential Smoothing method is the better choice. Subsequently, the identified model is used to make forecasting on the number of expected cases. Results show that dengue cases in Penang are expected to increase gradually

Climate projections of future extreme events in Malaysia

In Malaysia, extreme rainfall events are often linked to a number of environmental disasters such as landslides, monsoonal and flash floods. In response to the negative impacts of such disaster, studies assessing the changes and projections of extreme rainfall are vital in order to gather climate change information for better management of hydrological processes. This study investigates the changes and projections of extreme rainfall over Peninsular Malaysia for the period 2081-2100 based on the RCP 6.0 scenario. In particular, this study adopted the statistical downscaling method which enables high resolution, such as hourly data, to be used for the input. Short duration and high intensity convective rainfall is a normal feature of tropical rainfall especially in the western part of the peninsular. The proposed method, the Advanced Weather Generator model is constructed based on thirty years of hourly rainfall data from forty stations. To account for uncertainties, an ensemble multi-model of five General Circulation Model realizations is chosen to generate projections of extreme rainfall for the period 2081-2100. Results of the study indicate a possible increase in future extreme events for both the hourly and 24 h extreme rainfall with the latter showing a wider spatial distribution of increase

Probability distributions comparative analysis in assessing rainfall process in time and space

The need for a reliable rainfall model to produce accurate simulation of rainfall series is imperative in water resources planning. Simulated series are used when there are shortages of observed series at location of interest. This study focuses on modelling of rainfall series with a range of probability distributions representing rainfall intensity of the Space-Time Neyman Scott (ST-NS) model. Theoretically, the ST-NS model is constructed by having parameters to represent the physical attributes of rainfall process. Therefore having appropriate distributions to describe the parameters are critical so that credible rainfall series could be generated. In this study, the performance of four probability distributions namely Mixed-Exponential, Gamma, Weibull and Generalized Pareto in representing rainfall intensity are assessed and compared. Model construction of the ST-NS model involved the merging of rainfall data from sixteen stations located all over Peninsular Malaysia. Simulations of hourly rainfall series for each distribution are carried at out of sample site. Performance assessments between the distributions are conducted using Root Mean Square Error, Akaike Information Criterion, Bayesian Information Criterion, Kolmogrov-Smirnov Test and Anderson-Darling Test. Results revealed that mixture type distributions tend to perform better. The performance of both Mixed-Exponential and Generalized Pareto are very similar and both are equally good at representing rain intensity in Peninsular Malaysia. The adopted method and the results could also be extended to other tropical regions

Analysis of rainfall indices at JPS Ampang Station

Understanding characteristics of rainfall of a region could provide vital information for management of water resources. This study aims to explore the spatial pattern and trends of the daily rainfall data based on seasonal rainfall indices. Rainfall indices are adopted to explain main characteristics of rainfall such as the total amount of rainfall, frequency of wet days, rainfall intensity, extreme frequency, and extreme intensity. In particular, these five rainfall indices are used to capture the changes in a variety of aspects of rainfall distribution at a rainfall station, namely JPS Ampang station in Malaysia. The correlation between total amount of rainfall (TAR), frequency of wet days of at least 1 mm of rain (FREQ), mean rainfall amount on wet days or rainfall intensity (RI), frequency of wet days exceeding the 95th percentile (XFREQ) and rainfall intensity exceeding the 95th percentile (XI) indices is estimated. Results show that there is a strong positive correlation between TAR and extreme indices (XFREQ and XI). There is also high positive correlation between TAR and RI. However, a moderate positive correlation is shown between TAR and FREQ as well as between extreme indices (XFREQ and XI) and FREQ.This indicates that higher XFREQ, XI and RI will result in higher value of TAR at JPS Ampang station. Annual rainfall indices show that higher indices including extremes (XFREQ and XRI) are more prominent during La Nina while lower indices are more prominent during El Nino

Climate change impact on coastal communities in Malaysia

Climate change is undeniably the greatest issue facing our society. Around the globe, increasingly unpredictable weather patterns and extreme weather events are observed, causing considerable risks to human lives, properties and health safety and also on the natural ecosystem. The magnitude and impacts of climate change are growing, and particularly in Malaysia, studies show increases in temperature and changes in rainfall regimes. Such changes have profound implications, especially for coastal communities. Since knowledge and perceptions of the public on climate change could affect the success of implemented adaptation and mitigation options, it is essential to conduct assessments to gather such information. A public awareness and perception study was conducted at Sabak and Tanjung Karang, two coastal communities which were affected by changes in sea level and flooding incidences. The knowledge level and perceptions of climate change among respondents were assessed covering areas such as level of awareness of the respondents, their perceptions of climate change issues, their sentiments on climate change and adaptation measures, their socio-economic activity and the effect on their lives. Results show that majority of respondents were aware of climate change issues and challenges. High levels of concern about climate change were expressed with the majority were worried and uncertain about the climate change impact and hoped for government measures. Almost half of respondents cited significant damage to their properties and reduction in income generation. Overall, the results of the present study gave insights of the affected parties on perceptions and awareness pertaining to climate change, which could potentially be used to promote greater awareness of climate change matters and to gauge the public response to related policies and strategies