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This study aimed at mapping the Kota Tinggi flood event in 2006/07 that had caused massive damages to properties and the environment. The flood was associated with unusually high intensity and continuous rainfall. 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 compare the effect of tide on flood modeling analysis. The inundated areas were mapped for various annual recurrent intervals using peak flow data from 1965 to 2010. The study used Light Detection and Ranging (LiDAR) data for flood modeling. HEC-HMS, HEC-RAS, and HEC-GeoRAS were used to develop the flood model. The results reaffirm that the GEV model is the best for fitting the annual flood. The HEC-HMS hydrologic model was calibrated and validated using observed hydrographs in Sep 2002 and Jan 2003, respectively. Upon successful calibration and validation, the model was used to simulate flood hydrograph in Jan 2007. 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 of 91.4%. The simulation results show that the river flow starts to over bank for ARIs exceeded 25 years.

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.

There is a growing concern over the unprecedented shifts in seasonality and extreme rainfall over the last century across the globe. This study investigated 24 rainfall stations in Johor River basin (JRB) for homogeneity over the period 1970–2015 and followed by analysis of rainfall seasonality and extreme rainfall trend. Homogeneity test was conducted for annual and seasonal rainfall using a hybrid of Standard Normal Homogeneity test (SNHT), Pettit test, Buishand Range test (BRT), and Von Neumann ratio (VNR) test. The JRB climate was then divided into several classes using the Seasonality Index (SI). For trend analysis, RClimDex was utilised to compute eleven extreme rainfall indices as recommended by ETCCDI, consisting of frequency indices, namely, number of heavy rainfall days (R10), very heavy rainfall days (R20), extremely heavy rainfall days (R25), maximum number of consecutive dry days (CDD) and maximum number of consecutive wet days (CWD), and intensity indices, namely, daily maximum rainfall (Rx1day), maximum five-day rainfall (Rx5day), annual total wet-day rainfall (PRCPTOT), simple daily intensity index (SDII), very wet day rainfall (R95P) and extremely wet day rainfall (R99P). Mann-Kendall (MK) trend test was applied to show the area within JRB that is subjected to change at 95% significance level. Then, the flood vulnerability area due to significant change in extreme rainfall was assessed based on the past flood record. The results showed that the rainfall series during North East (NE) and South West (SW) monsoon are more homogeneous than annual rainfall series with the presence of inhomogeneity in some stations being associated with the influence of climate variability. Although non-significant, the SI results show increasing trends with rainfall being concentrated in the downstream area. 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. Generally, the results showed that increasing extreme rainfall in the form of frequency indices are more prominent throughout JRB particularly at the end of the NE monsoon. Increasing severity of previously recorded flood prone area is expected, which required improved flood mitigation strategy.

An important factor affecting the climate is the amount of rain that falls in a given year. An imbalanced rainfall distribution results in periods of excessive rainfall over certain location that can increase the risk of flood, and contrarily a periods of drought, which hinder plant and crop growth. Therefore, a spatiotemporal assessment of rainfall concentration is important in case of Johor river basin (JRB) in identifying area that is prone to flood, and to ensure the utilisation of water resource can be optimise and conserve throughout the year. In this study, gridded-based CHIRPS rainfall dataset (period 1983–2018) was used to calculate rainfall concentration based on the concentration index (CI). In addition, the performance of Seasonality Index (SI) and Precipitation Concentration Index (PCI) was investigated to define the rainfall regime in JRB. The mapping of the spatiotemporal distribution of CI was done to understand the pattern and changes in the days of heavy rainfall across JRB. High CI is associated with strong physical processes like convection, while low CI can be construed as the result of regular patterns like the fronts' constant passage. The trend was then analysed with non-parametric Mann–Kendall (MK) at 95% significance level. The results showed that SI can give a better classification of rainfall regime in JRB than PCI, due to broader classification. Under SI classification, the rainfall regime in JRB was mainly characterised by ‘equable but with a definite wetter season’ type. Generally, the CI values is high (≥ 0.60) across the basin, varying from 0.61 to 0.72, with the highest in the north-western and western area. The shift in rainfall concentration was observed, as CI significantly increased at 27 grid points along the eastern border at the upstream and middle part of the basin. 25% rainiest days during the NE monsoon made up more than 67% and up to 83% of the annual rainfall for all grid points. Therefore, it is inferred high-intensity rainfall events contribute by the total rain in the basin, which give the highest overall percentage. The results enhance the knowledge on the local climate in Peninsular Malaysia (PM), and also give considerable important information to water resource management, flood mitigation plan and also valuable for agricultural planning.

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 0.25o 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.

In this study, a Coupled Model Intercomparison Project Phase 6 (CMIP6), from the newest set of General Circulation Models (GCMs), was used to assess the spatiotemporal variability in rainfall regimes over the Majalaya basin in West Java, Indonesia. Twenty grid points of the gridded-based daily rainfall from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) datasets spanning from 1981 to 2014 were extracted. For the future projection, a set of 23 GCMs from the Shared Socioeconomic Pathways (SSPs) with SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 common to historical period were selected. Initially, the performance of the GCMs in simulating the local rainfall at each grid point was assess by using seven relative importance metrics. Then, compromise programming index (CPI) and Jenks optimised classification (JOC) were used to aggregate the results from the ranking of GCMs to select the best GCMs in simulating the local rainfall. It was found that EC-Earth3-Veg-LR ranked the best as Class I, followed by EC-Earth3-Veg, INM-CM4-8, EC-Earth3, MPI-ESM1-2-HR and MRI-ESM2-0 as Class II. Subsequently, the linear scaling (LS) method was used in bias correcting the selected GCM, EC-Earth3-Veg-LR using CHIRPS as reference observed data. The spatiotemporal assessment of bias-corrected LS-EC-Earth3-Veg-LR for the different months over the mid-future period 2041–2070 and 2071–2100 was compared with those under SSPs 126, 245, 370 and 585. Our results show a generally wetter tendency over Majalaya under all SSPs toward the end of the 21st century, particularly under SSP370 and SSP585. For the mid-future period, the results suggest a worsening drier period for the month of June to October. Meanwhile, the usually high rainfall month of November is projected to receive lower rainfall than normal. For the far-future period, all SSPs in most of the cases projected more increasing rainfall months as the typically drier period become shorter. The changes in rainfall were observed to move toward a more homogeneous increase in rainfall for all rainfall months across the basin from the mid-future to the far-future period. It is projected that rainfall events will become more frequent, more intense, and happen consecutively for the rainy months of December and January and followed by another consecutive rainy month for March and April.

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.

Flooding is a natural disaster with extremely wide-reaching impacts and is a recurring problem in Indonesia. Whilst possible impacts of climate change are expected to aggravate flood risk in already flood-vulnerable areas, many countries struggle to achieve the United Nations’ (UN) 2030 Sustainable Development Goals (SDGs) to achieve a better and more sustainable future for all. Using the case study of Majalaya, Indonesia, the authors investigated the impact of climate change and climate variability on urban flood risk through science-based spatio-temporal flood simulations. Based on the ensemble of 17 General Circulation Models (GCMs) CMIP6, the near-future (2021 to 2050) flood projection under Shared Socioeconomic Pathways (SSPs) 2.6 (low forcing), 4.5 (medium forcing) and 8.5 (high end forcing) common to historical (1981 to 2014) was simulated. The area’s future risk of flooding was then investigated and adaptation measures were suggested for reducing and mitigating worsening flood conditions. A numerical model was developed in HEC-RAS that represented the city of Majalaya and the results were combined with the ensemble of climate projections to enable the assessment of the effects of flooding due to the combined effect of climate change and urbanisation. The model was calibrated using historical stream gauge records and past extreme flood inundation boundaries. Using the model’s output metrics (e.g. flood depth, velocity) and local demographic data, the project aims then to use a vulnerability assessment framework to quantify the impact of climate change on flood risk. The modelling results will allow for spatio-temporal mapping of the flood-prone areas in Majalaya, which will help reduce risk and vulnerability for disadvantaged populations. The development of flood vulnerability maps and future flood risk projections will assist the government in developing land-use and flood prevention management policies. This research area, drawing from the combination of flood modelling and the use of climate projections, allows for an assessment of future flood risk scenarios of the city of Majalaya and paves new avenues towards future research.

This study aims to evaluate the usefulness and effectiveness of four machine learning (ML) models for modelling cyanobacteria blue-green algae (CBGA) at two rivers located in the USA. The proposed modelling framework was based on establishing a link between five water quality variables and the concentration of CBGA. For this purpose, artificial neural network (ANN), extreme learning machine (ELM), random forest regression (RFR), and random vector functional link (RVFL) are developed. First, the four models were developed using only water quality variables. Second, based on the results of the first, a new modelling strategy was introduced based on preprocessing signal decomposition. Hence, the empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) were used for decomposing the water quality variables into several subcomponents, and the obtained intrinsic mode functions (IMFs) and multiresolution analysis (MRA) components were used as new input variables for the ML models. Results of the present investigation show that (i) using single models, good predictive accuracy was obtained using the RFR model exhibiting an R and NSE values of ≈0.914 and ≈0.833 for the first station, and ≈0.944 and ≈0.884 for the second station, while the others models, i.e., ANN, RVFL, and ELM, have failed to provide a good estimation of the CBGA; (ii) the decomposition methods have contributed to a significant improvement of the individual models performances; (iii) among the thee decomposition methods, the EMD was found to be superior to the VMD and EWT; and (iv) the ANN and RFR were found to be more accurate compared to the ELM and RVFL models, exhibiting high numerical performances with R and NSE values of approximately ≈0.983, ≈0.967, and ≈0.989 and ≈0.976, respectively.

Due to global warming, the existing IDF curves that were derived from historical data may no longer valid for estimating future rainfall. On the other hand, the alternative future rainfall data generated from Community Climate System Model version 3 (CCSM3) cannot be used directly due to systematic biases caused by non-inclusion of local features. Quantile mapping was used to bias correct the rainfall data generated by CCSM3 model based on the properties of observed rainfall. Only 4 out of 16 stations in the state of Kelantan have shown an increasing significant trend for 1-h annual maximum rainfall. The best transfer function used to correct bias in CCSM3 data is the second order polynomial function. The quantile mapping for bias correction is satisfactory as the highest RMSE was only 4.045, a reduction by 15% compared to the original CCSM3 data. Subsequently, a unique transfer function was developed to represent the behavior of CCSM3 and observed data for each station. The future intensity was found to increase for short and medium return periods of 2-, 5-, 10- and 25-year, where most of the stations have Climate Change Factor (CCF) larger than 1. On the contrary, for a longer return period of 50- and 100-year, the intensities are predicted to be lower.

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.

Monitoring hourly river flows is indispensable for flood forecasting and disaster risk management. The objective of the present study is to develop a suite of hourly river flow forecasting models for the Albert river, located in Queensland, Australia using various machine learning (ML) based models including a relatively new and novel artificial intelligent modeling technique known as emotional neural network (ENN). Hourly river flow data for the period 2011–2014 is employed for the development and evaluation of the predictive models. The performance of the ENN model in forecasting hourly stage river flow is compared with other well-established ML-based models using a number of statistical metrics and graphical evaluation methods. The ENN showed an outstanding performance in terms of their forecasting accuracies, in comparison with other ML models. In general, the results clearly advocate the ENN as a promising artificial intelligence technique for accurate forecasting of hourly river flow in the form of real-time.

Tropical peatlands have high potential function as a major source of atmospheric methane (CH

This study assesses the water resources and environmental challenges of Lagos mega city, Nigeria, in the context of climate change. Being a commercial hub, the Lagos population has grown rapidly causing an insurmountable water and environmental crisis. In this study, a combined field observation, sample analysis, and interviews were used to assess water challenges. Observed climate, general circulation model (GCM) projections and groundwater data were used to assess water challenges due to climate change. The study revealed that unavailability of sufficient water supply provision in Lagos has overwhelmingly compelled the population to depend on groundwater, which has eventually caused groundwater overdraft. Salt water intrusion and subsidence has occurred due to groundwater overexploitation. High concentrations of heavy metals were observed in wells around a landfill. Climate projections showed a decrease in rainfall of up to 140 mm and an increase in temperature of up to 8 °C. Groundwater storage is projected to decrease after the mid-century due to climate change. Sea level rise will continue until the end of the century. As the water and environmental challenges of Lagos are broad and the changing characteristics of the climate are expected to intensify these as projected, tackling these challenges requires a holistic approach from an integrated water resources management perspective.

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.

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.

At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.

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.

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.

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.