Climate change and road safety: a review to assess impacts in Malaysia

Climate change is very likely to cause a sharp increase in temperature, which in turn is likely to affect atmospheric water storage, and thereby the magnitudes, frequencies and intensities of rainfall. The road environment, including the weather factors is one of the major causes of accident across the world. Therefore, it is very certain that climate change induced changes in weather factors will affect road safety, if proper adaptation measures are not taken. The major objective of this article is to review the existing literatures on the influence of climatic variables on road accident in order to assess the possible impacts of climate change on road safety in Malaysia. The analysis of exiting literatures reveals that most imminent and certain impacts of climate change on road safety will be due to increase of temperature and rainfall related extremes. However, the impacts may not be very high in Malaysia due to moderate changes of those extremes over a long time. Any potential risk would be possible to mitigate by educating the people on possible impacts of climatic extremes on road environment and motivating them to change their driving behaviour during extreme weather events

Comparison of different methods in estimating potential évapotranspiration at muda irrigation scheme of Malaysia

Evapotranspiration (ET) is a complex process in the hydrological cycle that influences the quantity of runoff and thus the irrigation water requirements. Numerous methods have been developed to estimate potential evapotranspiration (PET). Unfortunately, most of the reliable PET methods are parameter rich models and therefore, not feasible for application in data scarce regions. On the other hand, accuracy and reliability of simple PET models vary widely according to regional climate conditions. The objective of the present study was to evaluate the performance of three temperature-based and three radiation-based simple ET methods in estimating historical ET and projecting future ET at Muda Irrigation Scheme at Kedah, Malaysia. The performance was measured by comparing those methods with the parameter intensive Penman-Monteith Method. It was found that radiation based methods gave better performance compared to temperature-based methods in estimation of ET in the study area. Future ET simulated from projected climate data obtained through statistical downscaling technique also showed that radiation-based methods can project closer ET values to that projected by Penman-Monteith Method. It is expected that the study will guide in selecting suitable methods for estimating and projecting ET in accordance to availability of meteorological dat

Future precipitation changes in Egypt under the 1.5 and 2.0?C global warming goals using CMIP6 multimodel ensemble

Rainfall projections for 1.5 and 2.0 °C warming can explain regional precipitation response to emission reductions under the Paris Agreements' goals. Assessment of such changes is vital for Egypt, a global climate change hotspot. The performance of 29 CMIP6 GCMs' hindcasts was evaluated according to their capability to replicate the spatial patterns of annual, winter, and summer precipitation for 1971–2014 to select a suitable GCM subset to form a robust multimodel ensemble (MME). The MME median was used to project precipitation and precipitation extremes of Egypt at the end of the century (2081–2100) for two shared socioeconomic pathways (SSP) scenarios, SSP1–1.9 and SSP1–2.6, representing 1.5 and 2.0 °C warming at the end of the present century, respectively. The results showed an increase in precipitation in the northern high precipitation region by 37% and 54% for SSP1–1.9 and SSP1–2.6, respectively, and a decrease in the southwestern low precipitation region by -35% for both scenarios. The projected increase would be mostly in winter and almost no change in summer. The projection of precipitation extremes revealed an increase in extreme precipitation amount in the northern coast between 0% and 14% and the longest dry spell over most of the country by 160%. The results indicate more heterogeneity in the spatial distribution in Egypt's precipitation, increasing extreme precipitation amount in some regions and dry spell length over the whole country. The results indicate a large increase in hydrological hazard susceptibility in Egypt, even if the global warming can be limited to 2 °C at the end of the century following the Paris Agreement

A novel simulation–optimization strategy for stochastic‐based designing of flood control dam: A case study of Jamishan dam

This study presents a novel stochastic simulation–optimization approach for optimum designing of flood control dam through incorporation of various sources of uncertainties. The optimization problem is formulated based on two objective functions, namely, annual cost of dam implementation and dam overtopping probability, as those are the two major concerns in designing flood control dams. The nondominated solutions are obtained through a multi-objective particle swarm optimization (MOPSO) approach. Results indicate that stochastic sources have a significant impact on Pareto front solutions. The distance index (DI) reveals the rainfall depth (DI = 0.41) as the most significant factor affecting the Pareto front and the hydraulic parameters (DI = 0.02) as the least. The dam overtopping probability is found to have a higher sensitivity to the variability of stochastic sources compared to annual cost of dam implementation. The values of interquartile range (IQR) indicate that the dam overtopping probability is least uncertain when all stochastic sources are considered (IQR = 0.25%). The minimum annual cost of dam implementation (2.79 M$) is also achieved when all stochastic sources are considered in optimization process. The results indicate the potential of the proposed method to be used for better designing of flood control dam through incorporation of all sources of uncertainty

A comparison between index of entropy and catastrophe theory methods for mapping groundwater potential in an arid region

In this study, index of entropy and catastrophe theory methods were used for demarcating groundwater potential in an arid region using weighted linear combination techniques in geographical information system (GIS) environment. A case study from Badra area in the eastern part of central of Iraq was analyzed and discussed. Six factors believed to have influence on groundwater occurrence namely elevation, slope, aquifer transmissivity and storativity, soil, and distance to fault were prepared as raster thematic layers to facility integration into GIS environment. The factors were chosen based on the availability of data and local conditions of the study area. Both techniques were used for computing weights and assigning ranks vital for applying weighted linear combination approach. The results of application of both modes indicated that the most influential groundwater occurrence factors were slope and elevation. The other factors have relatively smaller values of weights implying that these factors have a minor role in groundwater occurrence conditions. The groundwater potential index (GPI) values for both models were classified using natural break classification scheme into five categories: very low, low, moderate, high, and very high. For validation of generated GPI, the relative operating characteristic (ROC) curves were used. According to the obtained area under the curve, the catastrophe model with 78 % prediction accuracy was found to perform better than entropy model with 77 % prediction accuracy. The overall results indicated that both models have good capability for predicting groundwater potential zones

Prediction in ungauged river basin in the west coast of peninsular Malaysia using linear regression model

A linear multiple regression based regionalization method has been proposed in this study to simulate streamflow in ungauged catchment in the east coast of peninsular Malaysia. Identification of unit Hydrographs And Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES) rainfall-runoff model was used to develop the relationship between model parameters and physical catchment descriptors of eight gauged catchments distributed over the west coast of peninsular Malaysia. The IHACRES model was calibrated and validated individually for each catchment with the available data for the time periods varying between three to sixteen years. The Nash-Sutcliffe efficiency index was used as criteria to evaluate the model performance. As the relationships between the physical catchment descriptors and hydrologic response characteristics are not necessarily linear, different forms of transformations were performed in order to find the most appropriate relationship. Finally, the obtained regression equations were used for simulating stream discharge in Sg Layang catchment located in the south of peninsular Malaysia. The result of the regional model was compared with observed monthly stream flow data of the catchment to assess the ability of regional model. The obtained results revealed that the regional model was able to replicate the historical monthly average flow. However, the relationship between the catchment area and hydrologic response characteristics were not fully understood by regional model which emphasize the need of consideration of other dominant catchment factors for prediction in ungauged basin

Performance assessment of different bias correction methods in statistical downscaling of precipitation

Global circulation models (GCMs) are widely used for the modeling and assessing the impacts of climate change. These models do not always accurately simulate climate variables due to the risk of considerable biases. Several bias correction methods have been proposed and applied so far. The selection and application of appropriate bias correction can improve accuracy and reduce uncertainty in downscaled precipitation in arid regions. In this study, initially multilayer perceptron (MLP) neural network was applied to downscale the mean monthly precipitation. The MLP model was calibrated by using National Center for environmental prediction (NCEP) reanalysis dataset and monthly precipitation observations located in selected hyper-arid, arid and semi-arid regions. Later, the performance of four bias correction methods namely, power transformation, simple multiplicative, variance inflation and quantile mapping were evaluated by comparing the mean and standard deviation of observed and downscaled precipitation. It has been found that the power transformation method is the most reliable and suitable method for downscaling precipitation in the arid region

An exploratory study to examine abundance of PM2.5 and associated disease burden in Bangladesh

This study examined selected disease burdens in Bangladesh associated with particulate matter exposure using gridded population and PM2.5 data between 2001 and 2019. The Global Exposure Mortality Model (GEMM) was used to determine hazard ratio (HR) and disease specific mortality. Besides, trend of PM2.5 and selected diseases were evaluated. Results revealed that strong seasonality existed in PM2.5 with winter exhibited maximum concentration. The trend assessment showed PM2.5 was increasing over time. Among five diseases assessed, LRI was most sensitive to an increase of PM2.5, followed by IHD, LC, CEV and COPD. Excess mortality was found to be elevating because of PM2.5, particularly in major cities. This study could be useful in advancing research in the disease burden attributable to ambient air pollution in Bangladesh