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Recently, damage to steep slopes in Korea has been increasing because of abnormal regional torrential rains. The Ministry of Public Administration and Security amended the criteria for a comprehensive plan for natural disaster reduction and selected a technique for determining investment priorities. However, it did not properly reflect the calculation of the damaged area, disaster risk zone, and amount of damage. In this study, the technique for order of preference by similarity to ideal solution (TOPSIS), a decision-making technique considering multiple criteria that are widely used in various fields, was applied to derive the results of priority selection for collapse risk zones. As a result, problems were derived through a comparative analysis of the results of existing investment priority techniques. Furthermore, a priority selection plan for the collapse risk zone improvement project was developed based on the Disaster risk assessment evaluation indicators, and a comprehensive natural disaster plan report was created.

Evapotranspiration (ET) is the amount of water lost from the global surface, and it represents water and Earth's energy cycle. The intensity and frequency of climate variables have been changed because of the ongoing climate crisis, leading to increased climate disasters, such as heat waves and droughts. The abrupt climate crisis affects the variation of ET because climate variables highly influence ET. However, the future potential ET (PET) estimates include various uncertainty resulted from the variations in the projection of climate variables. In this context, the uncertainty in the projected future PETs should be quantified for the high reliability. Therefore, this study projected future global PET using Penman-Monteith (PM) for the near (2031-2065) and far (2066-2100) futures and quantified the corresponding uncertainty. The six climate variables of 14 CMIP6 GCMs were used for estimating historical PET which were compared to those from the NCEP/NCAR reanalysis data using the five evaluation metrics. The changes in PETs for four Shared Socio-economic Pathways (SSPs) scenarios were calculated for the near and far futures compared to the historical period (1980-2014). Subsequently, the uncertainties of PETs were quantified using the reliability ensemble average method. As a result, the future PET in high latitudes showed the most significant variability compared to the other latitudes. The future PET in the southern hemisphere was higher than the historical PET. Especially the PET in the mid-latitudes of southern hemisphere was the highest among the other latitudes. In addition, the uncertainty of PET was the highest in the high latitudes of the northern hemisphere while the mid-latitude in the northern was the lowest. This study provides insight into evaluating the global water cycle based on PET and helps establish appropriate policies for climate impact assessment.

This study aimed to assess the changes in aridity in East Asia (EA) over the next 80 years for the restriction of global warming based on Paris agreement goals. Eight General Circulation Models (GCMs) that provide simulations for 1.5 and 2.0°C global warming scenarios were used for this purpose. The Penman-Monteith Equation was utilized to calculate potential evapotranspiration (PET). The land-use projections data was used to identify the agricultural lands that aridity could impact. The results showed a likely increase in rainfall and PET in EA over the next 80 years. However, the spatial variability of the relative increase in rainfall and PET would cause an aridity shift in 1.2−9.7% of the total land area. Though most of the area would experience a transition to a wetter climate, nearly 2% of the land would experience a transition to a drier climate. It would cause nearly 4.4 and 6.2 thousand km2 of agricultural land to be converted from semi-arid to arid and 31.1 and 42.2 thousand km2 of land from sub-humid to semi-arid in the early period for 1.5 and 2.0°C temperature rise scenarios, respectively. This indicates nearly one and a half times more expansion of aridity on agricultural land in the early period for only a 0.5°C increase in temperature. A decrease in aridity in the far future for both scenarios would cause a reduction of total arid lands and, thus, its impacts on agriculture. Overall, the study revealed a possible reduction of aridity in EA in the long run if the Paris agreement is enforced and global warming is limited.

This study evaluated the inherent uncertainty of future runoff prediction using eleven Coupled Model Intercomparison Project 6 (CMIP6) global climate models (GCMs) and a hydrological model (HM). The soil and water assessment tool (SWAT) model was used as a hydrologic model, and SWAT-CUP was used for parameter calibration. The future runoff projection was simulated utilizing two shared socioeconomic pathways (SSPs) scenarios, SSP2-4.5 and SSP5-8.5, for near (2021–2060) and far (2061–2100) futures. Jensen-Shannon divergence (JS-D) was used to quantify the uncertainties between the past and future probability distributions considering different GCMs and calibrated parameter sets of HM. The JS-D value for each GCM and calibrated HM parameter set was calculated at the range of 0.026–0.075 and 0.035–0.058, respectively. As a result, the uncertainty in the selection of GCMs was found to be greater than in the determination of values for HM parameters. Bayesian model averaging (BMA), which is a statistical approach that can combine estimations from multiple models and produce reliable probabilistic predictions, was applied to quantify the uncertainty by each GCM and HM parameters. When estimating the future runoff, INM-CM4-8 caused the greatest uncertainty, and the calibrated set of HM parameters using the year of high runoff caused the greatest uncertainty. This approach can help the uncertainty analysis in the future runoff estimation.

Urban flood vulnerability monitoring needs a large amount of socioeconomic and environmental data collected at regular time intervals. Collecting such data volume is a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region. A flood vulnerability index (FVI), estimated from socioeconomic and environmental data for a year, was linked to NTL data using a machine learning algorithm called Adaptive neuro-fuzzy inference system (ANFIS). The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000‒2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient and percentage of foreign nationals. The ANFIS technique showed a good performance in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ~ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data.

Climate change has caused a rise in temperature extremes, particularly heatwaves, in recent decades. Physical-empirical models are developed in this study using two classical machine learning algorithms, namely decision tree (DT) and random forests, and a novel hybrid technique consists of Ada-Boost Regression and decision tree (ABR-DT) for forecasting annual heatwave days (HWDs) of Iran from synoptic predictors. The daily temperature data of Princeton Meteorological Forcing were extracted for 27 points to estimate the annual number of HWDs, and the National Centers for Environmental Prediction reanalysis data were used as predictors. The major synoptic variables were extracted for four pressure levels (e.g., 300, 500, 850, and 1000 hPa) and three monthly time lags. The Principal Component Analysis was employed to reduce the diverse predictors and their features to the most optimal structure. The grid point-based performance evaluation revealed the superiority of ABR-DT, which showed a correlation coefficient (CC) of 0.860 and meant absolute error (MAE) of 6.929, using only specific humidity and wind component as predictors. The spatial performance indices over eight different climate regions of Iran also showed the better performance of ABR-DT, which improved the CC and MAE of its two alternatives up to 185 and 19%. The study identified the optimal parameter combination as the predictors of heatwaves by examining the effects of numerous weather components. The results proved the proposed hybrid forecasting approach’s effectiveness in predicting heatwave days, a devastating hazard, for many regions.

Low-impact development (LID) measures have been popularly installed in many developed countries for urban runoff control and treatment. However, precipitation and temperature changes driven by climate change lead to water cycle deterioration, especially in urban watersheds. It is important to optimize LID implementations aimed at rehabilitating the water cycle in urban watersheds for climate change adaptation. To enhance the water cycle restoration of LID practices according to climate change, an iterative multi-criteria decision making (MCDM) approach is proposed to allocate permeable pavement areas while considering newly released climate change scenarios. Daily precipitation, maximum temperature, and minimum temperature were considered for the future climate environment estimated using 15 coupled model intercomparison project (CMIP) 6 general circulation models (GCMs) according to two scenarios for shared socioeconomic pathways (SSPs). The hydrological impact on the permeable pavement application was simulated using the Storm Water Management Model (SWMM). The priority of each microlevel unit for permeable pavement was robustly determined using combined MCDM methods. According to the derived priorities, every unit area of permeable pavement was iteratively allocated until it converged to the initially planned total permeable pavement area. The results of this study confirmed that different climate predictions for each GCM can affect the priority results. Furthermore, it was confirmed that the iterative process used in this study improved the water cycle restoration more efficiently than when allocating at once according to priority. The results of this study can help determine the robust LID capacity for each sub-watershed under limited resources.

The double gamma quantile mapping (DGQM) can outperform single gamma quantile mapping (SGQM) for bias correction of global circulation models (GCMs) using two gamma functions for two segments based on 90th quantile. However, there are two ambiguous points: the 90th quantile and considering only the Gamma probability function. Therefore, this study introduced a flexible dividing point, δ (%), which can be adjusted to the regionally observed values at the station and considered the combination of various probability distributions, Weibull, lognormal, and Gamma, for two separate segments. The newly proposed method, flexible double distribution quantile mapping (F-DDQM), was employed to correct the bias of 8 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) to correct bias at 22 stations in South Korea. The results clearly showed higher performance of F-DDQM than DGQM and Flexible-DGQM (F-DGQM) by 25 % and 5 %, respectively, in root mean square error. The F-DGQM also showed better performance in replicating probability distribution, spatial variability and extremes of observed precipitation than other methods. This study contributes to improving the bias correction method for the better projection of extreme values.

Natural variability of climate considerably affects hydro-climatic trend significance, and therefore, removal of such influence is essential to understand the unidirectional trends due to global warming. The objective of this study was to evaluate the trends in precipitation extremes in the arid province of Pakistan by removing the natural variability of climate to understand the effect of global warming on precipitation extremes during two major cropping seasons, Rabi and Kharif. Daily precipitation data of APHRODITE (Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation) for the period 1951–2015 was used for this purpose. An improved form of classical Mann-Kendall (MK) test known as modified Mann-Kendall (MMK) was used which can estimate trends by discarding the influence of natural cycles present in time series. The results were compared with the classical MK test to show the novelty in the findings of this investigation. The results revealed a large influence of climate fluctuations on the trends in all the extreme precipitation indices for both seasons. The reduction in trend significance was noticed between 25 and 100% for different precipitation indices when MMK instead of MK test was used. The reduction was observed more for the positive trends in the indices compared with negative trends. The results revealed that global warming caused an increase in total annual precipitation at a rate of 2.8–34.8 mm/decade during 1951–2015. Besides, the annual number of extreme precipitation days was found to increase in the north by 0.1–0.84 days/decade and the number of annual precipitation days to decrease in the west for all seasons up to − 8.6 days/decade. An increase in continuous precipitation days was detected by 0.6–1.0 day/decade in the northeast while a decrease by − 0.5 to − 1.0 days/decade in the southwest and northwest. The continuous dry days decreased in the north and the central regions by up to − 6.3 days/decade while a rise in 1-day maximum precipitation by 6.6–35 mm/decade in the central north. Analysis of results revealed that the overestimation of trends by classical MK test is more in the arid region of Pakistan compared with other regions.

This study projected future changes in potential evapotranspiration (PET) over North Korea, which has been exposed to climate change risks. For this purpose, climate change scenarios downscaled at station scale were produced under RCP8.5, which downscale method preserves the long-term trend driven by climate models. Based on the ability to replicate observation, representative climate change scenarios (RCCS) were selected using performance indicators and TOPSIS method. The GCMs having higher spatial resolution were selected as RCCS, and projected that PET would increase in the future. It is found that the inter-model variability of PET in the summer was gradually increased over North Korea and annual mean evapotranspiration would be expected to increase by 1.4 times (F1, 2011–2040), 2.0 times (F2, 2041–2070) and 2.6 times (F3, 2071–2100). In preparation for the deficit of available water due to the increase in evapotranspiration, securing alternative water resources and construction of multi-purpose dams are required.

The possible future changes in temperature over Nigeria were projected in this study. Using Climate Research Unit (CRU) temperature as the reference data, gain ratio (GR), entropy gain (EG), and symmetrical uncertainty (SU) feature selection methods and a multi-criteria decision-making (MCDM) approach were used in selecting the most suitable GCMs for Nigeria from 20 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models (GCMs). The biases in selected GCMs were corrected using power transformation (PT) method. Multi-model ensembles (MMEs) were generated for the selected GCMs for the different temperature classes’ maximum, average, and minimum for all RCPs. The MMEs were used for the projection of temperatures over the country during 2010–2039, 2040–2069, and 2070–2099. The GCMs HadGEM2-ES, CESM1-CAM5, CSIRO-Mk3.6.0, and MRI-CGCM3 were the best performing in replicating temperature characteristics of the observed temperature in Nigeria. The MME mean projections of bias-corrected (BC) GCMs using PT revealed that there will be an increase in temperature of 4.0 °C at the semi-arid and 5.0 °C at the arid regions during dry and wet seasons respectively under RCP 4.5. In the same regions, the maximum temperature is expected to increase up to 5.5 °C under RCP 8.5 during 2070–2099 in the dry season. In the wet season, temperatures are expected to be higher under RCP 8.5, with an increase of 0.0–4.0 °C in the southern region and 3.0–6.9 °C in the northern region.

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.

Knowledge of the variability and the changes in potential evapotranspiration (PET) is imperative for agriculture and water resource planning and management. There is a growing concern on alteration of PET because of global climate change. The spatial patterns of the changes in PET for annual and two key cropping seasons of Pakistan namely, Kharif and Rabi, are investigated in the present study for the period 1967–2016. The gauge-based gridded PET data of Climatic Research Unit (CRU) is used for this purpose. The rate of changes in PET over various CRU grid cells of Pakistan is assessed with Sen’s Slope estimator, and the significance of the changes is evaluated using a modified version of Mann-Kendall trend test that has the ability to separate the natural variability of PET from unidirectional trend due to global climate change. Besides, trends in annual and seasonal PET for different 30 years with an interval of 10 years are estimated to assess the variations of the trends with time. The results show higher PET in the southern coastal region and lower PET in the northern mountainous regions. The spatial characteristics of annual PET trend showed a significant decrease (0.74 to 1.65 mm/year) in the south and an increase (1.04 to 1.59 mm/year) in the east during 1967–2016. Among the two crop growing seasons, the PET is found to increase (0.51 to 0.66 mm/year) in a small area in the southeast and decrease over a large area in the center (0.87 to 1.29 mm/year) during Kharif, and only increase (0.38 to 0.85 mm/year) in the southeast during Rabi. The time-varying trends in annual and two crop growing seasons reveal that PET has increased significantly at more grid cells in recent years which indicates higher impact of climate change on PET in recent years in Pakistan.

Hydrological drought is assessed through river flow, which depends on river runoff and water withdrawal. This study proposed a framework to project future hydrological droughts considering agricultural water withdrawal (AWW) for shared socioeconomic pathway (SSP) scenarios. The relationship between AWW and potential evapotranspiration (PET) was determined using a deep belief network (DBN) model and then applied to estimate future AWW using projections of the twelve global climate models (GCMs). 12 GCMs were bias-corrected using the quantile mapping method, climate variables were generated, and river flow was estimated using the soil and water assessment tool (SWAT) model. The standardized runoff index (SRI) was used to project the changes in hydrological drought characteristics. The results revealed a higher occurrence of severe droughts in the future. Droughts would be more frequent in the near future (2021-2060) than in the far future (2061-2100) and more severe when AWW is considered. Droughts would also be more severe for SSP5-8.5 than for SSP2-4.5. The study revealed that the increased PET due to rising temperatures is the primary cause of the increased drought frequency and severity. The AWW will accelerate the drought severities in the future in the Yeongsan River basin.

This study compared the performance capabilities of three potential evapotranspiration (PET) methods, Thornthwaite (TW), Hargreaves and Samani (HS), and Penman-Monteith (PM), to simulate historical and future daily PET levels in South Korea using climate variables from Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs). Five evaluation metrics were used to quantify the reproducibility of the climate variables and PETs at ten stations in South Korea for the historical period used here (1985-2014). The changes and uncertainty associated with the changes in PET in the near (2031-2060) and far (2071-2100) futures were calculated for two shared socioeconomic pathways (SSPs) of 2-4.5 and 5-8.5. As a result, PETs estimated using the three methods for the historical period showed high performance in terms of five evaluation metrics. Overall, PETs showed an increase for both the future periods and the SSPs. The PET estimated using the PM method showed the greatest increase, while that estimated using HS showed the most modest increase in the future. The PM method also showed the highest reliability and lowest uncertainty in the PET estimations, while the opposite was true for HS. This study contributes to our understanding of rational PET methods by which to calculate hydrological factors such as drought indexes for future periods via GCM simulations.

This study compared the performance of Long Short-Term Memory networks (LSTM) and Soil Water Assessment Tool (SWAT) in simulating observed runoff and projecting future runoff using 11 CMIP6 GCMs. The projected runoff was estimated for two Shared Socioeconomic Pathways (SSPs), 2-4.5 and 5-8.5 for near (2021-2060) and far (2061-2100) futures, respectively. The biases in GCM simulated climate variables were corrected using quantile mapping considering observations at 6 weather stations as reference data over the historical period (1985-2014). Five evaluation metrics were used to quantify the GCM's and hydrological models' capability to reconstruct climate variables and runoff in the Yeongsan Basin of South Korea. Uncertainties in LSTM and SWAT simulated runoff for the historical and projected periods were quantified using Bayesian Model Averaging (BMA) and reliability ensemble averaging (REA), respectively. The results showed significant improvement in bias-corrected GCMs in replicating observations in terms of all evaluation metrics. The extreme runoff estimated using general extreme value (GEV) distribution revealed the better capability of LSTM than SWAT in reproducing observed runoff at all gauging locations. The SWAT projected an increase (17.7%) while LSTM projected a decrease (-13.6%) in the future runoff for both SSPs at most locations. The uncertainty in LSTM simulated runoff was lower than in SWAT runoff at all stations for the historical period. However, the uncertainty in SWAT projected runoff was lower than LSTM projected runoff for both SSPs. This study helps assessing the ability of deep-learning versus physically-based models in hydrological modeling and therefore opens new perspectives for hydrological modeling applications.

This study assessed the performances of 13 GCMs of the CMIP6 in replicating precipitation and maximum and minimum temperatures over Nigeria during 1984–2014 in order to identify the best GCMs for multi model ensemble aggregation for climate projection. The study uses the monthly full reanalysis precipitation product Version 6 of Global Precipitation Climatology Centre and the maximum and minimum temperature CRU version TS v. 3.23 products of Climatic Research Unit as reference data. The study applied five statistical indices namely, normalized root mean square error, percentage of bias, Nash-Sutcliffe efficiency, and coefficient of determination; and volumetric efficiency. Compromise programming (CP) was then used in the aggregation of the scores of the different GCMs for the variables. Spatial assessment, probability distribution function, Taylor diagram, and mean monthly assessments were used in confirming the findings from the CP. The study revealed that CP was able to uniformly evaluate the GCMs even though there were some contradictory results in the statistical indicators. Spatial assessment of the GCMs in relation to the observed showed the highest ranked GCMs by the CP were able to better reproduce the observed properties. The least ranking GCMs were observed to have both spatially overestimated or underestimated precipitation and temperature over the study area. In combination with the other measures, the GCMs were ranked using the final scores from the CP. IPSL-CM6A-LR, NESM3, CMCC-CM2-SR5, and ACCESS-ESM1-5 were the highest ranking GCMs for precipitation. For maximum temperature, INM.CM4-8, BCC-CSM2-MR, MRI-ESM2-0, and ACCESS-ESM1-5 ranked the highest while AWI-CM-1-1-MR, IPSL-CM6A-LR, INM.CM5-0, and CanESM5 ranked the highest for minimum temperature.

This study compared precipitation projections of CMIP5 and CMIP6 GCMs over Yulin City, China. The performance of CMIP5 and CMIP6 GCMs in replicating Global Precipitation Climatology Centre (GPCC) precipitation climatology of the city was evaluated using different statistical metrics. The best performing GCMs common to both CMIP5 and CMIP6 were finally selected and subsequently downscaled to GPCC resolution using linear scaling method to assess spatiotemporal changes in precipitation in the basin. The study revealed BCC.CSM1.1(m), IPSL.CM5A.LR, MRI.CGCM3, and MIROC5 of CMIP5 and their equivalents BCC-CSM2-MR, IPSL-CM6A-LR, MRI.ESM2.0, and MIRCO6 of CMIP6 as the most suitable GCMs for the projection of precipitation in Yulin. This study revealed changes in precipitation in the range of −14.0 to 0.0% and −22.0 to 0.2% during 2021−2060 for RCP4.5 and SSP245 scenarios, respectively. The precipitation was projected to decrease more during 2061–2100 for both the scenarios. The highest decrease of −29.7 to −22.0% was projected by MRI-ESM-2-0 for SSP2-45, while −28.0 to −20.0% by MIROC5 for RCP4.5. For RCP8.5 and SSP5-85 scenarios, precipitation was projected to decrease in the range of −17.0 to −2.0% and −32.0 to 0.0%, respectively, during 2021–2060 by most of the GCMs. An increase in precipitation up to 12.3% was projected only by IPSL-CM5A-LR for RCP85 for this period. A further decrease in precipitation was projected by all GCMs during 2061−2100 for both RCP8.5 and SSP5-85 scenarios. The highest decrease was projected by MIROC5 (−40.2 to −29.0%) for RCP8.5 and IPSL-CM6A-LR (−40.2 to −26.0%) for SSP5-85. Overall, the results revealed a higher decrease in precipitation in Yulin City by CMIP6 GCMs compared to that projected by their corresponding GCMs of CMIP5 for both scenarios. This study can be of significance in the planning and mitigation of climate change as it gives insight into the expected changes in precipitation and the possibility of the choices of the best performing GCMs.

The presence of long-term persistence (LTP) in hydroclimatic time series can lead to considerable change in the significance of trend. Therefore, past findings of climatic trend analysis without considering LTP in time series has become a disputable issue. The objective of this study was to assess the spatial patterns in the trends of annual and seasonal rainfall amounts and extremes in Peninsular Malaysia considering LTP. Daily rainfall data of APHRODITE (Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation) for the period 1951–2007 was used to assess the trends using the classical Mann–Kendall (MK) test and the modified version of the MK test, which can remove the influence of LTP in the significance of trends. The results indicate that significant trends in different rainfall indices of Peninsular Malaysia obtained using the MK test reduced drastically when LTP was taken into consideration. There was almost no change in annual and seasonal rainfall amounts, which contradicts the findings of previous studies. The field significance of regional trends revealed an increase in wet spells at an average rate of 4.8 and 4.9 days/decade in the southeast and the southwest respectively during the northeast monsoon, and a decrease in rainy days by −1.4 days/decade in the north and an increase both in dry spells by 1.0 day/decade in the southeast and in the maximum 1 day rainfall by 1.7 mm/decade on the west coast during the southwest monsoon. The results indicate that the trends in rainfall indices reported in the maritime continent in previous studies should be re-evaluated as most of them are due to LTP.

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.

The objective of this study was to assess the changes in the severity and return periods of meteorological droughts during major cropping seasons of Nigeria for the period 1901–2010 in order to understand the impacts of climate variation on seasonal droughts. Gauge-based gridded global precipitation climatology center (GPCC) rainfall and climate research unit (CRU) potential evapotranspiration (PET) data having a spatial resolution of 0.5° were used for the reconstruction of droughts using standardized precipitation evapotranspiration index (SPEI). SPEI values were fitted with the best distribution function to estimate seasonal droughts return periods and the modified Mann-Kendall test was used to assess the secular changes in climate variables and drought index. The temporal variations in droughts and their interrelations with rainfall and temperature trends were assessed using a 50-year moving window with a 10-year time step. The results showed that meteorological drought severity has increased for all the cropping seasons of Nigeria. Temperature was found to be the dominating factor for defining droughts in semi-arid regions in the north while rainfall in the monsoon and tropical savanna climatic zones dominates in the south. Rises in temperature in the range of 0.14–0.42 °C/decade and almost no change in rainfall have caused decreases in SPEI up to −0.25/decade in some regions. The number of grid points with decreasing SPEI was found to vary between 50 out of 323 for millet and 152 for yam growing season. The moderate droughts were found to become more frequent compared to other classes of droughts. Amongst the cropping seasons, the highest increases in drought frequency were observed during corn growing season. The highest decrease in the median value of the return period of moderate, severe and extreme droughts during the two corn growing seasons were observed to be 6.25, 12.86 to 13.20, 23.15 to 24.31 years in 1901–1950 to 6.25, 12.92 to 12.94, 23.51 to 23.64 in 1961–2010. Compared to other drought severities, the return periods of the moderate droughts were found to decrease more. A general decrease in the return periods of droughts indicates more frequent droughts during all cropping seasons of Nigeria. Therefore, it can be concluded that the rising temperature due to global warming would increase drought severity and frequency in all the cropping seasons of Nigeria.

General Circulation Models (GCMs) provide vital information on the likely future climate, much needed for the effective planning and management of water resources. The performance assessment of GCMs has received significant attention in recent years for reliable estimation of future climate. Even though many approaches have been trialled in the ranking of GCMs for the selection of an appropriate ensemble, there is still a need to explore the potential of the state-of-the-art ranking approaches for a more dependable selection of GCMs suitable for a given task, over a region of interest. The present study assessed the potential of a state-of-the-art feature selection method known as Symmetrical Uncertainty (SU) in ranking 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs based on their ability to simulate monthly precipitation and the monthly average of daily maximum and minimum temperature for annual, monsoon and winter seasons. The performance of GCMs was assessed using gridded climate data obtained from Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU) and Princeton Global Meteorological Forcing dataset (Prin) over the period 1961–2005 considering Pakistan as the study area. The ranks obtained with SU were compared with those obtained using two well-established ranking approaches; (1) Compromise Programming (CP) and (2) Wavelet-based Skill Score (WSS). According to the results of this study, for the simulation of all the three climate variables in all seasons; CESM1-CAM5, HadGEM2-AO, NorESM1-M and HadGEM2-ES were identified as the best GCMs by SU, whereas CESM1-CAM5, HadGEM2-AO, NorESM1-M and GFDL-CM3 were identified as the best GCMs by CP, and CCSM4, CESM1-CAM5, GFDL-ESM2G and HadGEM2-ES by WSS. The comparison of ranks of GCMs obtained using the same ranking approach but based on different gridded data products showed more or less similar ranks for a given GCM. However, the differences were noticeable when the ranking was conducted with the same gridded data but employing different ranking approaches. The approach presented in this study can be extended to any number of GCMs and can be applied over any region, for the identification of the best performing ensemble of GCMs for a set of climate variables.

This study uses a multi-model ensemble (MME) for the assessment of the spatial and temporal variations of rainfall in peninsular Malaysia under climate change scenarios. The past performance approach was used for the selection of GCM ensemble from a pool of Coupled Model Intercomparison Project Phase 5 (CMIP5) GCMs. The performances of four bias correction methods, namely, scaling, gamma quantile mapping, generalized quantile mapping, and power transformation were assessed to select the most suitable method for the downscaling of daily rainfall of selected GCMs based on APHRODITE rainfall at a spatial resolution of 0.25° × 0.25°. The downscaling model was used for the projections of daily rainfall for the period 2010–2099 for four representative concentration pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0, and RCP8.5. Random forest regression algorithm was used to develop the multi-model ensemble (MME) mean of GCM-projected rainfall for different RCPs in order to show the changes in rainfall for three future periods, 2010–2039, 2040–2069, and 2070–2099. The results revealed four GCMs, BCC-CSM1.1(M), CCSM4, CSIRO-Mk3.6.0, and HadGEM2-ES as the most suitable GCMs for the projection of daily rainfall of peninsular Malaysia. The power transformation was found as the most suitable method for the correction of biases in GCM daily rainfall. The MME mean of projected rainfall showed the increase in rainfall in peninsular Malaysia for all the scenarios and future periods. The maximum increase in annual rainfall was projected by 15.72% during 2070–2099 for RCP8.5. The variability of future rainfall was also found to increase along with mean rainfall. The increase in rainfall variability was projected by 26.15% for RCP8.5 during 2070–2099. The spatial pattern of rainfall changes revealed more variability in future rainfall in the northeast where frequency of hydro-climatic disasters is higher compared to other regions. The results indicate the possible increase in hydro-climatic disaster in peninsular Malaysia due to climate change.

Changes in the temperature and precipitation have significantly affected water resources and agricultural productions in many countries across the world. The objective of the present study is to analyze the changing patterns of annual and seasonal precipitation and temperature in Iraq for the period 1961–2010. Monthly gridded precipitation and temperature data of Global precipitation climate center (GPCC) and climate research unit (CRU) respectively having a spatial resolution of 0.5° were used in this study to show the spatial pattern in trends. The rate of change in rainfall and temperature was estimated using Sen’s slope method while the significance of change was confirmed using Mann-Kendal test (MK) and the modified Mann-Kendall test (mMK). The results revealed large differences in the number of grid points showing significant changes in rainfall and temperature using MK and mMK methods. The mMK method revealed that the annual rainfall is decreasing at a rate of −1.0 to −5.0 mm/year in the northwest part of Iraq. The seasonal precipitations were found to decrease in spring (−0.4 to −2.56 mm/year) and winter (−0.4 to −2.0 mm/year), increase in summer (0.06 to 0.21 mm/year) at a few grid points and no change in autumn. On the other hand, a sharp rise in annual average of daily mean (0.42 to 0.64 °C/decade), maximum (0.39 to 0.65 °C/decade) and minimum (0.36 to 0.69 °C/decade) temperature was observed.