Nurul Nadrah Aqilah Tukimat’s research while affiliated with Universiti Malaysia Pahang Al-Sultan Abdullah and other places

The intensity-duration-frequency (IDF) curves are the most common form of design rainfall data used for peak discharge estimation. Thus, the IDF curve needs to be improved with the expectation that rainfall intensity and frequency have increased as a result of climate change. The main purpose of this study was to investigate the changes of IDF curves considering the climate change impacts on Hulu Terengganu. The climate projection from MRI-ESM2-0, CMCC-CM2-SR5, and GFDL-ESM4 under 3 different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) were used to provide the climate changes pattern in the future year (∆2050). In order to downscale the climate projection, the statistical downscaling method (SD-LS) was employed to correct the biases of these three GCMs. The IDF curves for the return periods of 2, 5, 10, 20, 50, 100, and 200 year were then developed based on the maximum rainfall intensity that were projected by the SD-LS model. The results clearly indicated that there are possibilities for increasing patterns in the projected annual and monthly rainfall for both time periods compared to historical data. Thus, the future extreme rainfall events for various durations with different return periods are all likely to increase over time. The largest potential increase is predicted at Sg. Gawi (+2.0% to +86.0%) based on the different return periods and rainfall durations. It could change the pattern of IDF curve that been developed based on projected rainfall by various SSPs. The developed IDF curves shows higher rainfall intensities in a shorter duration under the same return periods. Therefore, comprehensive action must be taken immediately to regulate and manage the effects of climate change.

This study investigates the thermal performance of green roof systems in a tropical climate, focusing on the small-scale building prototypes. Batu Pahat, Malaysia is experiencing the increasing temperatures due to climate change. Green roofs are considered as a potential solution, but their effectiveness depends on various factors such as building orientation, solar shading, and thermal resistance (R-value). Therefore, modeling and simulation are crucial for understanding green roof thermal behaviour. This study employs the Integrated Environmental Simulation Virtual Environment (IES-VE) software for analysis. Three identical small-scale buildings were constructed, one with Portulaca Grandiflora (PGR) plants, another with Alternanthera Paronychioides (ATN) plants, and a control roof with no vegetation. The R-values from the on-site green roofs were measured at 0.8899 m²K/W for PGR and 1.1477 m²K/W for ATN, while the control roof had an R-value of 0.1 m²K/W. Green roofs with higher R-values demonstrated a substantial reduction in indoor temperatures, making them a valuable solution for improving thermal comfort in tropical climates. This study underscores the importance of green roofs in mitigating rising temperatures in tropical climates. Simulation using IES-VE approved that green roofs can potentially reduce indoor temperatures, demonstrating their suitability for tropical regions. These findings have significant implications for sustainable building design and urban planning in hot and humid climates.

This paper explores the impact of climate change on rainfall patterns, particularly extreme intensity, in Johor, Malaysia. The study focuses on addressing uncertainties in climate change projections by selecting suitable Global Climate Models (GCMs) based on location and topography. Four CMIP6 models (GFDL-ESM4, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0) were chosen for analysis. The research employs statistical downscaling, using historical observed data (1988-2020) and GCM output data, with a bias correction through linear scaling. The performance of the GCMs is assessed using various metrics including Root Mean Square Error (RMSE), Coefficient of Determination (R2), Percentage of Bias (Pbias), and Nash-Sutcliffe Efficiency (NSE). The IPSL-CM6A model is identified as the most suitable for rainfall projection in Johor. Under the severe climate scenario (SSP5-8.5), the analysis indicates increasing rainfall intensity in January from 2025 to 2054, notably at the Pusat Pertanian Endau station with a significant 50% increment. However, for the projected period 2055 to 2084, most stations experience a decrease in rainfall from January to June, with the Ladang Sg. Plentong station showing the largest reduction of about 40% in January. Conversely, the latter half of the year shows increased rainfall for all stations. The Mann-Kendall Test method highlights a significant decreasing trend in rainfall across all stations from 2025 to 2084 under the SSP5-8.5 scenario. This suggests that without mitigation efforts, the area will likely experience decreasing rainfall intensity due to the effects of climate change.

This study evaluated the role of RCPs as a significant development in the climate research. RCP presents different scenarios based on specific radiation forcing that would lead to the climate formulations known as RCP2.6 (2.6W/m ² ), RCP4.5 (4.5W/m ² ) and RCP8.5 (8.5W/m ² ). In recent years, Malaysia has conducted climate research using Fourth Assessment Report (AR4) under the A1B scenario. To address the AR4's flaws, these situations had been updated, and the Fifth Assessment Report (AR5) had been launched since 2014. Therefore, the main objective of this study was to investigate the impact of RCPs on the climate generation and to identify the suitable radiation level for Malaysia. The study used statistical climate model (SDSM) as a climate driver to downscale the long-term local climates with regard to the possible emission changes in the local weather. According to the findings, 4 out of 10 states agreed that the RCP2.6 had a good association to the regional climate with higher R (> 0.8) and lower %MAE (< 23%). This result demonstrated that the RCPs plays a main role in the long-term climate assessment.

Climate modeling data are typically available in the daily climate time series for a particular year of observation. However, studies for urban drainage and stormwater management require rainfall data on sub-daily time scales for design such as the development of IDF curves. Most hydrological studies dealing with the impacts of climate change are particularly challenging due to this explicit requirement. Therefore, this study aims to establish more accurate disaggregation methods for constructing hourly rainfall under the projected climate scenarios. Three disaggregation methods with different theoretical underpinnings have been evaluated: Scaling Properties (SP), Indian Reduction Formula (IRF), and Stochastic Method (SM). The results show that the SP method generally outperforms the other methods based on statistical analyses and comparisons of statistical properties with historical data. The SP method performs well by having the lowest RMSE and percentage difference values across all rainfall stations. Moreover, the hourly mean and standard deviation of disaggregated rainfall from the SP method correspond well to the historical data. The projected rainfall data from 2025 to 2100 were obtained from the MRI-ESM2-0 model and disaggregated from daily-time to hourly-time series using the SP method. In general, the SSP5-8.5 scenario showed the highest projected rainfall compared with the other scenarios.

The impact of global warming is resulting in flood, land slide, soil erosion and drought that are anticipated to become more intense and frequent. The history record shows that the flood has been occur repeatedly in this study area. The study of future climate needs to be done to a greater extent as a planning for the infrastructure and as mitigation actions on flood based on the future climate. The aims of this study, to ascertain the climate projection by CMIP6 are compatible to be subjected for the future climate and to identify the trend of future climate projection in Kemaman, Terengganu. The comprehensive study of future climate that using CMIP6 that consist of SSPs help in providing a clear explanation of future society’s socio-economic appraisal in assessment modelling. The Sen’s Slope Test were used to analyze the trend of future climate projection in this study area. In this study, we found out that the trend of future rainfall is increasing with the positive values of Sen’s Slope Test. Study shows that the Sen’s Slope Test values for eight (8) stations in Kemaman, which is Ban Ho, Hulu Jabor, Rumah Pam Paya Kempian, JPS Kemaman, Klinik Bidan Kg Ibok, SK Kemasek, Jambatan Tebak, and Jambatan Air Putih are 2.381, 1.158, 1.333, 1.252, 2.293, 1.06, 3.113, and 1.961 respectively. The frequency and intensity of forecasting rainfall also increasing compared to the historical data. In conclusion, the proper planning and mitigation action need to be done to minimize the losses that happen due to climate changes impacts.

The uncertainties of climate change in the future year cause the contribution factors and greenhouse gasses (GHGs) effects on the local climates need to be revised. The development of new climate scenarios in the 6 th Coupled Model Intercomparison Project (CMIP6) is consistent with the technological exploration and increment of GHGs dispersion compared to the consideration factors in CMIP5. The purpose of this study was to compare the performance of CMIP5 (based on Representative Concentration Pathways, RCPs) and CMIP6 (based on Shared Socioeconomic Pathways, SSPs) in simulating seasonal rainfall and estimating trends in Hulu Terengganu, Malaysia. The linear scaling (LS) method was used in this study to treat the gaps between observed and simulated results, and the climate trend was examined using the Mann-Kendall (MK) and Sen’s Slope tests. The results show that the SSPs scenario outperforms the RCPs in simulating historical rainfall (2015-2020) by producing a higher r value and a smaller percentage difference. According to the MK test, there was no significant trend in projected rainfall across all scenarios (2020-2099). Based on Sen’s Slope test, RCP 4.5 and RCP 8.5 show an increasing trend for all rainfall stations. However, all SSP scenarios show a declining trend in projected rainfall, with SSP1-2.6 producing the largest declining trend magnitude. In contrast, when compared to observed rainfall from the baseline period (1988-2017), the SSPs scenario indicates the potential for a greater increase in future annual rainfall projections than the RCPs scenario. All SSP scenarios show an increasing annual rainfall magnitude in 2040-2069 (Δ2050). However, the annual rainfall for SSP2-4.5 and SSP5-8.5 began to decrease in 2070-2099 (Δ2080). Meanwhile, RCP 2.6 has the greatest reduction in annual rainfall projections for both projected time periods when compared to other scenarios. It can be concluded that although all SSPs scenarios show a declining trend in projected rainfall from 2020 to 2099, the total annual rainfall projected for SSPs remains higher than RCPs in Δ2050 and Δ2080 periods.

POME, a residual liquid waste obtained after oil extraction from the fruits of oil palm, is considered one of Malaysia’s primary sources of contamination of watercourses. POME contains total suspended particles, organic materials, and vital nutrients. Nutrient recovery from POME is advantageous for agricultural uses. Several studies have been carried out to make use of the nutrient availability in POME. POME is used directly, mixed with other palms solid waste to form compost or extracted via sludge extraction using coagulants such as chitosan and alum. It has been demonstrated that the struvite precipitation process offers a feasible means of handling nutrient rich wastewater while producing high-quality fertilizer. A previous study showed that struvite precipitate or struvite recovered from wastewater is a viable method for the pre-treatment of wastewater.

Uncertainty of climate extreme nowadays causes an alteration in the local climate trend and variability. Terengganu, Malaysia recorded series of extreme drought and flood events throughout a year affected by North-East monsoonwhich will change the next climate pattern. Thus, itwill be affectingthe long-term planning and sustainability that related to the water resources in the long-term. The objective of this study was to analyse the trend changes of rainfall and temperature at Kuala Terengganu, Malaysia due to climate changes impact. The trends changes were analysed usingMan-Kendall and Sen’s Slope. The climate projection result shows the annual mean temperature is expected to have decreasing trend until end of century. However,Mar to June are expected to bit higher than historical reach to 29oCby RCP8.5. Then it will be dropped to 24oC (-5% from historical) during Northeast monsoon. Consistent to the annual rainfall, it was expected to have increasingtrendover time. The highest increasing trendwas expected to occur on Nov toDec more than40% by RCP8.5.