Estimation of the long term dryness pattern for Pahang state using integrated SDSM-SPI model

Drought or long dry season is a global issue that adversely gives huge impact on the world. Plenty of calamities have been reported by Malaysian Meteorological Department (MET) since 1900s. Malaysia faced at least 12 times of extreme drought from 1951 to 1998, especially during southwest season changes. Although the formation of this calamity was influenced by the season changes, however frequent occurrence of this event majorly affected by the uncertainty of global climate changes and drastic emission of greenhouse gases (GHGs). In this study, the integrated model of Statistical Downscaling Model and Standardised Precipitation Index (SDSM-SPI) was applied to estimate the probability of extreme dry events in Pahang. SDSM is a statistical climate model that been used to understand the change on present or long-term future climate condition in response to the long-term dispersion of greenhouse gases and aerosols emission into the atmospheric system. The chosen of the best suitable atmospheric variables is vital in obtaining a good long-term future climate condition prediction. The projected rainfall and temperature pattern for the interval year of Δ2020, Δ2050, and Δ2080 were used as important data input to estimate the event pattern in the study region. Therefore, the identification of potential dryness event in the long-term become significant to monitor how frequent the event and how huge the impact on water resources efficiency. By AR5, all RCPs agreed the annual rainfall was predicted to decrease until end of century. RCP4.5 produced a larger decrement (−3.1 %) from the historical record compared to RCP2.6 (−2.7 %), and RCP8.5 (−2.9 %). The heaviest rainfall was predicted to occur at most regions in Kuantan, Pekan, and southern of Bentong. Due to non-uniform rainfall pattern, almost 42 % of Pahang was predicted to receive lower rainfall intensity. Meanwhile, the temperature was predicted to have small increment in April to June, reaches over 33°C which might be influenced by the season interchanges. The highest and lowest temperature was estimated to be in May (34°C) and January (22°C), respectively. Even the changes were not too high, however it will still contribute to the water scarcity problem when it frequently happens in a longer period. The SDSM-SPI result shows the probability percentage of a normal level pattern is about 84 – 85 %. However, about 2 – 3 % of probability extreme pattern were detected at this state. Several areas such as Janda Baik and Kg Manchis are exposed to the dryness in the certain year with the SPI value detected to drop to −2. The calibration and validation processes were conducted to identify the fundamental rules and the predictand-predictors relationships are suited to original data. The calibration results obtained a good agreement for temperature and rainfall stations with low RMSE ranges 0.01 - 0.04°C and 5.0 - 15.0mm, respectively. However, the error slightly increased in the validation part for temperature and rainfall intensity with ranges 0.3 - 0.4°C and 25.0 - 72.0mm, respectively. Thus, it is reliable to be significant information to the respective agencies for the long term planning and management of water resources

Assessment of the Potential Occurrence of Dry Period in the Long Term for Pahang State, Malaysia

The interference of climate circulation and continuous rising of surface temperature every year has caused the atmosphere composition change which gives serious impact to water resource management. Pahang is among of the affected states by El Nino that hit Malaysia in recent years which led to water depletion at several water plants. Based on the current situation, this study focuses on 1) simulate the average rain pattern using statistical downscaling; 2) identify the severity index and dry duration occurrence in the catchment area. Predicting potential changes in the climate events is important to evaluate the level of climate change in the critical region. Therefore, the integration of Statistical Downscaling Model (SDSM) and Standard Precipitation Index (SPI) have been conducted to study the potential occurrence of the dry period due to climate change for year 2020s and year 2050s. The results reveal that the dry condition is high during the mid-year. The lowest SPI value is estimated to reach -2.2 which can be classified as extreme. The potential dry period is expected to increase 2.5% and 3.3% in 2020 and 2050, respectively

Evaluation of climate variability performances using statistical climate models

Uncertainty of the climates nowadays bring the crucial calamities problems especially at unexpected areas and in anytime. Thus, the projection of climate variability becomes significant information especially in the designing, planning and managing of water resources and hydrological systems. Numerous climate models with varies methods and purposes have been developed to generate the local weather scenarios with considered the greenhouse gasses (GHGs) effect provided by General Circulation Models (GCMs). However, the accuracy and suitability of each climate models are depending on the atmospheric characters’ selection and the variables consideration to form the statistical equation of local-global weather relationship. In this study, there are two well-known statistical climate models were considered; Lars-WG and SDSM models represent for the regression and weather typing methods, respectively. The main aim was to evaluate the performances among these climate models suit for the Pahang climate variability for the upcoming year Δ2050. The findings proved the Lars-WG as a reliable climate modelling with undemanding data sources and use simpler analysis method compared to the SDSM. It is able to produce better rainfall simulated results with lesser %MAE and higher R value close to 1.0. However, the SDSM lead in the temperature simulation with considered the most influenced meteorological parameters in the analysis. In year Δ2050, the temperature is expected to rise achieving 35°C. The rainfall projection results provided by these models are not consistent whereby it is expecting to increase 2.6% by SDSM and reduce 1.0% by Lars-WG from the historical trend and concentrated on Nov

Prediction of future climate trend using stochastic weather generator

The issue of climate change and its effects on various aspects of the environment has become more challenges for society. It is desirable to analyse and predict the changes of critical climatic variables, such as rainfall, temperature and potential evapotranspiration affect in the content of global climate change. This change is also affected by the increment of gas carbon dioxide (CO2) and other gases GHGs emissions. This study is focus of analyse the prediction patterns of rainfall, temperature and potential evapotranspiration of Pahang state. The rainfall pattern can be estimate the future climate change, general circulation models (GCMs) are applied. Therefore, Long Ashton Research Station Weather Generator (LARS-WG), which utilized the Stochastic Weather Generators approach, is applied in order to convert the coarse spatial resolution of the GCMs output into a fine resolution. The result show that the changes in rainfall, temperature and potential evapotranspiration can be consider is state to the trend of change in the respective by years. Therefore, the quantity of annual rainfall decreases had reached above 64%, while the distribution of temperature can increases had reached above 10% and potential evapotranspiration raise had reached above 44% increases of the end of century. In this study, have seen different results from the PRECIS and LARS-WG models though we have used the same GCMs (HadCM3) and emission scenario, which reveals the uncertainties due to the downscaling method. The LARS-WG result shows difference with PRECIS for rainfall and temperature. However the monthly rainfall prediction by LARS-WG is performed well closer to the history compare to the PRECIS. The annually LARS-WG is performance well closer with 1.19% to the history compare to the PRECIS with 32.86%

Assessment of the Potential Occurrence of Dry Period in the Long Term for Pahang State, Malaysia

The interference of climate circulation and continuous rising of surface temperature every year has caused the atmosphere composition change which gives serious impact to water resource management. Pahang is among of the affected states by El Nino that hit Malaysia in recent years which led to water depletion at several water plants. Based on the current situation, this study focuses on 1) simulate the average rain pattern using statistical downscaling; 2) identify the severity index and dry duration occurrence in the catchment area. Predicting potential changes in the climate events is important to evaluate the level of climate change in the critical region. Therefore, the integration of Statistical Downscaling Model (SDSM) and Standard Precipitation Index (SPI) have been conducted to study the potential occurrence of the dry period due to climate change for year 2020s and year 2050s. The results reveal that the dry condition is high during the mid-year. The lowest SPI value is estimated to reach -2.2 which can be classified as extreme. The potential dry period is expected to increase 2.5% and 3.3% in 2020 and 2050, respectively

Evaluation of Climate Variability Performances using Statistical Climate Models

Uncertainty of the climates nowadays bring the crucial calamities problems especially at unexpected areas and in anytime. Thus, the projection of climate variability becomes significant information especially in the designing, planning and managing of water resources and hydrological systems. Numerous climate models with varies methods and purposes have been developed to generate the local weather scenarios with considered the greenhouse gasses (GHGs) effect provided by General Circulation Models (GCMs). However, the accuracy and suitability of each climate models are depending on the atmospheric characters’ selection and the variables consideration to form the statistical equation of local-global weather relationship. In this study, there are two well-known statistical climate models were considered; Lars- WG and SDSM models represent for the regression and weather typing methods, respectively. The main aim was to evaluate the performances among these climate models suit for the Pahang climate variability for the upcoming year ∆2050. The findings proved the Lars-WG as a reliable climate modelling with undemanding data sources and use simpler analysis method compared to the SDSM. It is able to produce better rainfall simulated results with lesser % MAE and higher R value close to 1.0. However, the SDSM lead in the temperature simulation with considered the most influenced meteorological parameters in the analysis. In year ∆2050, the temperature is expected to rise achieving 35°C. The rainfall projection results provided by these models are not consistent whereby it is expecting to increase 2.6% by SDSM and reduce 1.0% by Lars-WG from the historical trend and concentrated on Nov

Heavy metal removal from wastewater using various adsorbents: a review

Heavy metals are discharged into water from various industries. They can be toxic or carcinogenic in nature and can cause severe problems for humans and aquatic ecosystems. Thus, the removal of heavy metals from wastewater is a serious problem. The adsorption process is widely used for the removal of heavy metals from wastewater because of its low cost, availability and eco-friendly nature. Both commercial adsorbents and bioadsorbents are used for the removal of heavy metals from wastewater, with high removal capacity. This review article aims to compile scattered information on the different adsorbents that are used for heavy metal removal and to provide information on the commercially available and natural bioadsorbents used for removal of chromium, cadmium and copper, in particular