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Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the ERA5 dataset accurately represented the spatial pattern of Level 1 heatwaves, showing widespread occurrence. Historically, Level 1 heatwaves prevailed at 63.0%, followed by Level 2 at 27.7%, concentrated in northwestern states and the enclave between the Tahan and Titiwangsa mountain ranges. During very strong El Niño events in 1982/83, 1997/98, and 2015/16, Level 2 heatwave distributions were 10.4%, 26.8%, and 15.0%, respectively. For future projection, the model ensemble was created by selecting top-performing Global Climate Models (GCMs) using Kling-Gupta efficiency (KGE), ranked re-aggregation with compromise programming index (CPI), and GCM subset selection via Fisher-Jenks. The linear scaling bias-corrected GCMs (BC-GCMs), NorESM2-LM, ACCESS-CM2, MPI-ESM1-2-LR, ACCESS-ESM1-5, and FGOALS-g3, were found to exhibit better performance, and then ensemble. March to May show the highest increase in all scenarios, ranging from 3.3 °C to 4.4 °C for Level 1 heatwaves and 4.1 °C to 10.7 °C for Level 2 heatwaves. In the near future, SSP5-8.5 projects up to a 40.5% spatial increase for Level 1 heatwaves and a 2.3% increase for Level 2 heatwaves, affecting 97.1% and 57.2% of the area, respectively. In the far future, under SSP2-4.5 and SSP5-8.5, Tmax is projected to rise rapidly (1.5–4.5 °C) in the northern, western, and central regions, with increasing population exposure anticipated in the northern and western regions. [ABSTRACT FROM AUTHOR]

Water law aims to establish a legislative framework to meet water governance objectives. However, discrepancies may exist between the current legal provisions and the requirements for effective water quality protection and management. This research aims to investigate these discrepancies by analyzing the gap between actual law coverage and current water legislation, and to identify the necessary legal provisions for effective water management. The study employs an empirical literature review with an exploratory approach, utilizing a gap analysis framework that includes water institution mapping and water legislation mapping to evaluate the extent and efficacy of existing legislation. The gap analysis identified several deficiencies in the current water legislation, highlighting discrepancies and gaps in the legal framework for water quality protection and management. The research concludes that significant gaps in the current water legislation impede effective water quality protection and management. Addressing these gaps through comprehensive legislative reforms is essential to enhance water governance and ensure sustainable water management practices. [ABSTRACT FROM AUTHOR]

Climate change has significantly increased human thermal stress, particularly in tropical regions, exacerbating associated risks and consequences, such as heat-related illnesses, decreased workability, and economic losses. Understanding the changes in human thermal stress and its drivers is crucial to identify adaptation measures. This study aims to assess various meteorological variables' spatial and seasonal impact on Wet Bulb Globe Temperature (WBGT), an indicator of human thermal stress, in Peninsular Malaysia. The Liljegren method is used to estimate WBGT using ERA5 hourly data from 1959 to the present. The trends in WBGT and its influencing factors are evaluated using a modified Mann-Kendall test to determine the region's primary driver of WBGT change. The results indicate that air temperature influences WBGT the most, accounting for nearly 60% of the variation. Solar radiation contributes between 20% and 30% in different seasons. Relative humidity, zenith, and wind speed have relatively lesser impacts, ranging from −5% to 20%. Air temperature has the highest influence in the northern areas (>60%) and the lowest in the coastal regions (40%). On the other hand, solar radiation has the highest influence in the southern areas (20–40%) and the least in the north. The study also reveals a significant annual increase in temperature across all seasons, ranging from 0.06 to 0.24 °C. This rapid temperature rise in the study area region has led to a substantial increase in WBGT. The higher increase in WBGT occurred in the coastal regions, particularly densely populated western coastal regions, indicating potential implications for public health. These findings provide valuable insights into the factors driving WBGT and emphasize the importance of considering air temperature as a key variable when assessing heat stress. [ABSTRACT FROM AUTHOR]

There is a growing concern over the unprecedented shifts in seasonality and extreme rainfall over the last century across the globe due to climate change. However, there is still a lack of basin-scale study in documenting trend, changes, and variation across Peninsular Malaysia and its relation with flood occurrence. Therefore, Johor River basin (JRB) which plays a pivotal role in the country's development was selected as a case study by evaluating 24 rainfall stations for homogeneity over the period 1970–2015 and followed by the analysis of rainfall seasonality and extreme rainfall trend. Mann–Kendall trend test was applied to show the area within JRB that is subjected to change at 95% significance level. Even though 75% of the stations had strong auto-correlation in the time series due to large-scale climate influence, the imputed rainfall stations showed no potential discontinuity with t-test statistics between 0.87 and 0.90 and R2 between 0.995 and 0.999 for the double mass curve of the yearly rainfall time series. The constructed PDF showed the restoration of the normal distribution of the rainfall data with higher peak as the missingness of the high-intensity rainfall has been restored. After that, a homogeneity test was conducted for annual and seasonal rainfall using a hybrid of Standard Normal Homogeneity test, Pettit test, Buishand Range test, and Von Neumann ratio test. The results showed that for the annual series, 43% of the total stations were found to be 'useful', but more 'useful' stations based on the seasonal data, with 67% and 52% of the total stations for NE and SW monsoons, respectively. As no anthropogenic effect can be deduced, the presence of inhomogeneity in some stations is associated with the influence of high climate variability. Although non-significant change was observed, the seasonality index (SI) showed that the annual rainfall regime in JRB is mainly classified as 'rather seasonal with a short drier season' (SI range from 0.39 to 0.47). Comparatively, the rainfall regime during the drier SW monsoon is more irregular (SI range from 0.67 to 0.72) than the wetter NE monsoon (SI range from 0.57 to 0.68). Spatially, increasing SI was observed in the downstream area indicating increasing occurrence of extreme rainfall over a shorter period. For trend analysis, RClimDex was utilised to compute eleven extreme rainfall indices as recommended by ETCCDI, consisting of frequency and intensity extreme rainfall indices. Generally, the results showed that increasing extreme rainfall in the form of frequency indices is more prominent throughout JRB particularly at the end of the NE monsoon. During the NE monsoon, frequency index of R10 showed a significant increasing trend at thirteen stations. Meanwhile, R20 and R25 showed increasing trend at five stations, mainly in the downstream and at the west of the basin. During the SW monsoon, R10 showed a significant increasing trend at six stations, but R20 and R25 showed a significant decreasing trend at two and one station, respectively. Based on the past flood record, increasing trend of R10, R20, R25, and CWD during the NE monsoon across flood prone area in Sayong river, Kota Tinggi town and downstream urbanised area is expected to worsen the flood conditions, which required improved flood mitigation and adaptation strategy. [ABSTRACT FROM AUTHOR]

Water engineering problems are typically nonlinear, multivariable, and multimodal optimization problems. Accurate water engineering problem optimization helps predict these systems' performance. This paper proposes a novel optimization algorithm named enhanced multioperator Runge–Kutta optimization (EMRUN) to accurately solve different types of water engineering problems. The EMRUN's novelty is focused mainly on enhancing the exploration stage, utilizing the Runge–Kutta search mechanism (RK-SM), the covariance matrix adaptation evolution strategy (CMA-ES) techniques, and improving the exploitation stage by using the enhanced solution quality (IESQ) and sequential quadratic programming (SQP) methods. In addition to that, adaptive parameters were included to improve the stability of these two stages. The superior performance of EMRUN is initially tested against a set of CEC-17 benchmark functions. Afterward, the proposed algorithm extracts parameters from an eight-parameter Muskingum model. Finally, the EMRUM is applied to a practical hydropower multireservoir system. The experimental findings show that EMRUN performs much better than advanced optimization approaches. Furthermore, the EMRUN has demonstrated the ability to converge up to 99.99% of the global solution. According to the findings, the suggested method is a competitive algorithm that should be considered in optimizing water engineering problems. [ABSTRACT FROM AUTHOR]

This study aimed at mapping the Kota Tinggi flood event in 2006/2007 that had caused massive damages to properties and the environment. The flood was associated with unusually high intensity and continuous rainfall, and high tide. Therefore, a reliable technique of floodplain mapping is crucial for the improvement of flood control strategies and for preparing an evacuation plan. The main objective of this study is to incorporate the effect of tide on flood modeling analysis. The inundated areas were mapped for various annual recurrent intervals (ARI) using peak flow data from 1965 to 2010. The study used Light Detection and Ranging data for flood modeling using HEC-HMS, HEC-RAS, and HEC-GeoRAS. The Generalized Extreme Value model was found to be the best fit for the annual flood simulation. The HEC-HMS hydrologic model was calibrated and validated using observed hydrographs in September 2002 and January 2003, respectively. Due to riverbank overflow, the level-discharge rating curve during flood events is not valid which causes underestimation of the peak flow in the observed flow. Therefore, the simulated hydrographs which model the actual peak flow provide more reasonable results of 625.3 m3/s for the December 2006 flood and 743.9 m3/s for the January 2007 flood. The modeling took into account the tidal effect. When the tidal effect was not considered, the simulated flood depth was 43% lower than the observed flood. However, the inclusion of the tidal effect has reduced the simulation error with an average similarity with the observed flood at 91.4% based on site verification. The simulation results show that the river flow starts to over bank for ARIs exceeding 25 years. [ABSTRACT FROM AUTHOR]

Rapid population growth, economic development, land-use modifications, and climate change are the major driving forces of growing hydrological disasters like floods and water stress. Reliable flood modelling is challenging due to the spatiotemporal changes in precipitation intensity, duration and frequency, heterogeneity in temperature rise and land-use changes. Reliable high-resolution precipitation data and distributed hydrological model can solve the problem. This study aims to develop a distributed hydrological model using Machine Learning (ML) algorithms to simulate streamflow extremes from satellite-based high-resolution climate data. Four widely used bias correction methods were compared to select the best method for downscaling coupled model intercomparison project (CMIP6) global climate model (GCMs) simulations. A novel ML-based distributed hydrological model was developed for modelling runoff from the corrected satellite rainfall data. Finally, the model was used to project future changes in runoff and streamflow extremes from the downscaled GCM projected climate. The Johor River Basin (JRB) in Malaysia was considered as the case study area. The distributed hydrological model developed using ML showed Nash–Sutcliffe efficiency (NSE) values of 0.96 and 0.78 and Root Mean Square Error (RMSE) of 4.01 and 5.64 during calibration and validation. The simulated flow analysis using the model showed that the river discharge would increase in the near future (2020–2059) and the far future (2060–2099) for different Shared Socioeconomic Pathways (SSPs). The largest change in river discharge would be for SSP-585. The extreme rainfall indices, such as Total Rainfall above 95th Percentile (R95TOT), Total Rainfall above 99th Percentile (R99TOT), One day Max Rainfall (R × 1day), Five-day Max Rainfall (R × 5day), and Rainfall Intensity (RI), were projected to increase from 5% for SSP-119 to 37% for SSP-585 in the future compared to the base period. The results showed that climate change and socio-economic development would cause an increase in the frequency of streamflow extremes, causing larger flood events. [ABSTRACT FROM AUTHOR]

Drought is identified and quantified using a variety of methods. This study aims to evaluate the performance of several rain-based meteorological droughts, namely, Rainfall Anomaly Index (RAI), Standard Precipitation Index (SPI), China-Z Index (CZI), Percent of Normal (PN), Deciles Index (DI), and Z-Score Index (ZSI) on 1-, 3-, 6-, 9-, 12-, 18-, 24-, 48-, and 72-month time scales based on monthly precipitation totals for 24 Johor River Basin (JRB) stations from 1970 to 2015. After imputation of missing data with Multiple Imputation by Chained Equations (MICE), homogeneity test for the annual time series found that 21 stations were 'useful', 1 station 'doubtful' and 2 stations 'suspect', meanwhile monthly time series found 17 stations 'useful', 1 station 'doubtful' and 6 stations 'suspect'. The performance of CZI, RAI, and SPI were found to be higher compared to others based on the correlation between drought indices, average correlation of a combination of drought indices for all stations (correlation plot and heatmap), scatter plot linearity between drought indices in capturing the wet/dry conditions, and simulating the historical drought events. However, based on the ability to simulate the historical event, spatial assessment, and frequency analysis, it was found that RAI is more responsive in identifying the 'drought prone' area to enable a better spatial mapping of the 3- and 6-months drought condition in JRB. 'Moderately dry' condition is expected to be temporally and spatially widespread as North East (NE) monsoon becomes drier. Meanwhile, a condition of 'very dry' and 'extremely dry' is expected to be more prevalent in the South East area of the basin, particularly due to the drier South West (SW) monsoon period. Study results indicate that RAI can perform comparatively better than other studied indices, and because the calculations of RAI are relatively simple, they can be used as better tools to monitor the conditions of drought in JRB. [ABSTRACT FROM AUTHOR]

Tropical peatlands have high potential function as a major source of atmospheric methane (CH4) and can contribute to global warming due to their large soil carbon stock, high groundwater level (GWL), high humidity and high temperature. In this study, a process-based denitrification–decomposition (DNDC) model was used to simulate CH4 fluxes in a pristine tropical peatland in Sarawak. To test the accuracy of the model, eddy covariance tower datasets were compared. The model was validated for the year 2014, which showed the good performance of the model for simulating CH4 emissions. The monthly predictive ability of the model was better than the daily predictive ability, with a determination coefficient (R2) of 0.67, model error (ME) of 2.47, root mean square error (RMSE) of 3.33, mean absolute error (MAE) of 2.92 and mean square error (MSE) of 11.08. The simulated years of 2015 and 2016 showed the good performance of the DNDC model, although under- and overestimations were found during the drier and rainy months. Similarly, the monthly simulations for the year were better than the daily simulations for the year, showing good correlations at R2 at 0.84 (2015) and 0.87 (2016). Better statistical performance in terms of monthly ME, RMSE, MAE and MSE at − 0.11, 3.38, 3.05 and 11.45 for 2015 and − 1.14, 5.28, 4.93 and 27.83 for 2016, respectively, was also observed. Although the statistical performance of the model simulation for daily average CH4 fluxes was lower than that of the monthly average, we found that the results for total fluxes agreed well between the observed and the simulated values (E = 6.79% and difference = 3.3%). Principal component analysis (PCA) showed that CH4, GWL and rainfall were correlated with each other and explained 41.7% of the total variation. GWL was found to be relatively important in determining the CH4 fluxes in the naturally inundated pristine tropical peatland. These results suggest that GWL is an essential input variable for the DNDC model for predicting CH4 fluxes from the pristine tropical peatland in Sarawak on a monthly basis. [ABSTRACT FROM AUTHOR]

Over the last two decades, several data sets have been developed to assess flood risk at the global scale. In recent years, some of these data sets have become detailed enough to be informative at national scales. The use of these data sets nationally could have enormous benefits in areas lacking existing flood risk information and allow better flood management decisions and disaster response. In this study, we evaluate the usefulness of global data for assessing flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. National flood risk assessments are carried out for each of the five countries using six data sets of global flood hazard, seven data sets of global population, and three different methods for calculating vulnerability. We also conduct interviews with key water experts in each country to explore what capacity there is to use these global data sets nationally. We find that the data sets differ substantially at the national level, and this is reflected in the national flood risk estimates. While some global data sets could be of significant value for national flood risk management, others are either not detailed enough, or too outdated to be relevant at this scale. For the relevant global data sets to be used most effectively for national flood risk management, a country needs a functioning, institutional framework with capability to support their use and implementation. Plain Language Summary: To adequately manage flood risk, it is important to understand where flood risk is greatest. This is done by carrying out a flood risk assessment, which combines flood hazard maps with data for exposure (who or what is flooded) and vulnerability (how affected exposure is by flooding). At the national level, flood risk assessments have typically been limited to countries with the necessary data and expertise to develop such assessments. In recent years, several flood risk data sets have been developed with global coverage, which means national flood risk can now be assessed in any country. We investigate how useful global data sets are for assessing national flood risk in five countries: Colombia, England, Ethiopia, India, and Malaysia. We find that there is significant disagreement between national flood risk estimates, suggesting not all global data sets are up to the task. Beyond that, we show that the usefulness of global flood risk data for national flood risk management is also determined by the national context in which they would be used. Key Points: Sixteen different global data sets are used to calculate national flood risk in Colombia, England, Ethiopia, India, and MalaysiaFlood risk estimates differ substantially at the national level and some data sets are not appropriate for use at the national scaleThe capability of regional and local governance in interpreting and using global data is addressed [ABSTRACT FROM AUTHOR]

Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950–2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172–2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8–94.6%), MSE (57.0–99.5%), NRMSE (42.7–92.1%), PBIAS (100–108%), MD (23.0–85.3%), KGE (21.1–78.1%), and VE (5.1–9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts. • Borneo was classified into several climate zones based on cluster analysis. • Fuzzy C-Mean clustering method can better classify the climate zone of Borneo. • Selection of CMIP6 models was done by employing relative importance metrics. • EC-Earth3-Veg was selected for future climate zones under SSP2-4.5 and SSP5-8.5. • The hotter climate zones, namely, WH and DH, are expected to expand in the future. [ABSTRACT FROM AUTHOR]

Although Borneo Island is one of the most vulnerable tropical regions to climate change, maps depicting the local climate conditions employing climate classification are still not well defined. The present study attempted regional climate classification to divide the Borneo region into several homogenous groups based on long-term average climate behavior. Daily gridded rainfall and temperature (Tavg, Tmax, and Tmin) data at 0.25° resolution spanning 56 years (1960–2016) was used. The classification was done using non-hierarchical k-mean and several hierarchical methods, namely, Single, Complete, McQuitty, Average, Centroid, Median, and two algorithms of Ward's method, wardD, and wardD2. The results showed that k-mean, wardD, and wardD2 were able to classify the climate of Borneo into four zones, namely 'dry and hot' (DH), 'wet and hot' (WH), 'wet' (W), and 'wet and cold' (WC) with a considerable difference at the boundaries. Spatial relevancy, stability, and variability of the clusters based on correlation and compromise programming showed that the wardD method was the most likely to yield acceptable results with optimum 4-cluster to partition the area into four principal climate zones. The constructed cluster plot, centroid plot, and probability distribution function (PDF) showed a distinct climatic characteristic between the climate zones in terms of rainfall, temperature, and seasonality. The proposed climate zonation for Borneo can help in better understanding climate regionality and climate-related development planning. [ABSTRACT FROM AUTHOR]

It is crucial to carry out a spatiotemporal evaluation of rainfall concentration in the Johor river basin (JRB) given that erratic rainfall can lead to floods. The study utilized the CHIRPS rainfall dataset from 1983 to 2018 to determine rainfall concentration using the Concentration Index (CI). To further define the rainfall regime, the Seasonality Index (SI) and Precipitation Concentration Index (PCI) were examined. The Mann-Kendall (MK) test was utilized to examine the trend at a 95% level of significance. The results showed the better accuracy of SI than PCI in classifying the rainfall regime in the JRB, defined as "equable but with a definite wetter season" type. The CI values ranged from 0.61 to 0.72 and were typically high all over the basin, with the northwestern and western regions having the highest values. At 27 grid points on the eastern border of the upper and middle parts of the basin, CI has significantly increased as a result of an upsurge in rainfall concentration brought on by climate change. More than 67% and up to 83% of the yearly rainfall for all grid locations was accounted for by the North East monsoon (NEM), with the 25% wettest days caused by high-intensity rainfall events. • Spatiotemporal assessment of CHIRP-based rainfall concentration in JRB. • SI better than PCI for basin-scale rainfall regime classification. • CI values is highest in north-western and western area (0.61–0.72). • Significant increase of CI is expected to cause more flood event in the future. [ABSTRACT FROM AUTHOR]

A multiple variable bias correction approach has been proposed for the projection of the changes in spatial and temporal pattern of rainfall in Borneo Island due to climate change. The ensemble of General Circulation Models (GCMs) was selected through the combination of past performance and envelope approaches. The selected GCMs were downscaled using a Support Vector Machine (SVM) based multiple variable bias correction approach considering that inclusion of multiple circulation variables can provide more information on local climate and able to explicitly account for GCM-inherent error and bias. Finally, an ensemble projection was produced by using Random Forest (RF) regression to simulate the future projection. Four GCMs namely, HadGEM2-AO, HadGEM2-ES, MIROC5 and CCSM4 were found most suitable for the projection of rainfall in Borneo with associated uncertainty. The projected changes in rainfall indicate that the rainfall pattern and variation in Borneo are dominated based on the changes of monsoon season, geographical locations and terrains of the region. Overall, there is an increasing rate of rainfall projections in most parts of Borneo. However, the coastal-western region will become drier in contrast to the eastern region, although rainfall variability remains high. The highest increase in rainfall was projected in East Kalimantan in the range of 11.9% to 50.1% for 2070–2099, while the highest decrease was projected in West Kalimantan in the range of −3.7% to −13.0% for 2010–2039. The rainfall was expected to be more distributed during the Southwest monsoon while shorter Northeast monsoon with higher intensity was projected at most regions. [ABSTRACT FROM AUTHOR]

A study has been conducted to evaluate the changes in temperature of Borneo Island for different representative concentration pathway (RCP) scenarios through statistical downscaling of an ensemble of general circulation models (GCMs) selected using entropy-based methods. A combination of past-performance and envelope approaches was used for the selection of GCM ensemble from a pool of 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs. Ranking of the GCMs was done separately at each grid point by using three entropy-based feature selection methods. Climate Research Unit (CRU) gridded temperature data was used as dependent variable and the simulated temperature of different GCMs for the period 1961–2005 was used as independent variable for the selection of GCMs. The scores obtained by the GCMs at different CRU grids were aggregated using compromise programming method and then ranked for entire Borneo Island using group decision-making method. Linear scaling method was applied to remove the biases in GCMs, followed by random forest (RF)–based regression method to generate multi-model ensemble projections. The results revealed that FIO-ESM, MRI-CGCM3, GFDL-CM3, and IPSL-CM5A-MR are the most suitable GCMs for the projection of temperature in the study area. The projection of temperature using the selected GCMs indicated increase in temperature in the Borneo Island, particularly in the southwest regions. The minimum temperature was projected to increase more (3.3 to 4.7 °C) compared with maximum temperature (3.0 to 4.6 °C) and thus, the diurnal temperature range (DTR) was projected to decrease gradually until the end of the twenty-first century. Increase in average and maximum temperatures was projected more during the southwest (SW) monsoon compared with the northeast (NE) monsoon. The study indicated that large increase in temperature and decrease in DTR would have significant impact on ecology and bio-environment of the island which have one of the dense ecological diversities in the world. [ABSTRACT FROM AUTHOR]

This study assesses the spatial pattern of changes in rainfall extremes of Sarawak in recent years (1980–2014). The Mann–Kendall (MK) test along with modified Mann–Kendall (m-MK) test, which can discriminate multi-scale variability of unidirectional trend, was used to analyze the changes at 31 stations. Taking account of the scaling effect through eliminating the effect of autocorrelation, m-MK was employed to discriminate multi-scale variability of the unidirectional trends of the annual rainfall in Sarawak. It can confirm the significance of the MK test. The annual rainfall trend from MK test showed significant changes at 95% confidence level at five stations. The seasonal trends from MK test indicate an increasing rate of rainfall during the Northeast monsoon and a decreasing trend during the Southwest monsoon in some region of Sarawak. However, the m-MK test detected an increasing trend in annual rainfall only at one station and no significant trend in seasonal rainfall at any stations. The significant increasing trends of the 1-h maximum rainfall from the MK test are detected mainly at the stations located in the urban area giving concern to the occurrence of the flash flood. On the other hand, the m-MK test detected no significant trend in 1- and 3-h maximum rainfalls at any location. On the contrary, it detected significant trends in 6- and 72-h maximum rainfalls at a station located in the Lower Rajang basin area which is an extensive low-lying agricultural area and prone to stagnant flood. These results indicate that the trends in rainfall and rainfall extremes reported in Malaysia and surrounding region should be verified with m-MK test as most of the trends may result from scaling effect. [ABSTRACT FROM AUTHOR]

This study assesses the possible changes in rainfall patterns of Sarawak in Borneo Island due to climate change through statistical downscaling of General Circulation Models (GCM) projections. Available in-situ observed rainfall data were used to downscale the future rainfall from ensembles of 20 GCMs of Coupled Model Intercomparison Project phase 5 (CMIP5) for four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Model Output Statistics (MOS) based downscaling models were developed using two data mining approaches known as Random Forest (RF) and Support Vector Machine (SVM). The SVM was found to downscale all GCMs with normalized mean square error (NMSE) of 48.2–75.2 and skill score (SS) of 0.94–0.98 during validation. The results show that the future projection of the annual rainfalls is increasing and decreasing on the region-based and catchment-based basis due to the influence of the monsoon season affecting the coast of Sarawak. The ensemble mean of GCMs projections reveals the increased and decreased mean of annual precipitations at 33 stations with the rate of 0.1% to 19.6% and one station with the rate of − 7.9% to − 3.1%, respectively under all RCP scenarios. The remaining 15 stations showed inconsistency neither increasing nor decreasing at the rate of − 5.6% to 5.2%, but mainly showing a trend of decreasing rainfall during the first period (2010–2039) followed by increasing rainfall for the period of 2070–2099. [ABSTRACT FROM AUTHOR]

Climate change may not change the rainfall mean, but the variability and extremes. Therefore, it is required to explore the possible distributional changes of rainfall characteristics over time. The objective of present study is to assess the distributional changes in annual and northeast monsoon rainfall (November-January) and river flow in Sarawak where small changes in rainfall or river flow variability/distribution may have severe implications on ecology and agriculture. A quantile regression-based approach was used to assess the changes of scale and location of empirical probability density function over the period 1980-2014 at 31 observational stations. The results indicate that diverse variation patterns exist at all stations for annual rainfall but mainly increasing quantile trend at the lowers, and higher quantiles for the month of January and December. The significant increase in annual rainfall is found mostly in the north and central-coastal region and monsoon month rainfalls in the interior and north of Sarawak. Trends in river flow data show that changes in rainfall distribution have affected higher quantiles of river flow in monsoon months at some of the basins and therefore more flooding. The study reveals that quantile trend can provide more information of rainfall change which may be useful for climate change mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]