Far-future Periods Research Articles

Simulation and projection of photovoltaic energy potential over a tropical region using CMIP6 models

The study examines the potential impact of climate change on photovoltaic energy (PV) in Nigeria. Solar radiation and temperature datasets from 13 regional climate models (CMIP6) for 2015–2099 were used to evaluate the photovoltaic energy under moderate (SSP245) and high (SSP585) emission scenarios for near-future (2023–2053), mid-future (2054–2084), and far-future (2084–2099) periods. The precision of the models for the simulation of PV energy was validated with MERRA-2 reference data using the compromise programming index (CPI). Models with the lowest CPI were selected for regional PV energy projections. The findings showed varying numbers of increase and decrease projected changes across the four regions under SSP245 and SSP585 scenarios for the near, mid and far future timescales. Specifically, in the SSP245 scenario, the model with lowest CPI was the CMCC-CESM2 model, it projected a decrease in PV energy in the Sahel (−2.30), an increase in the Guinea Savannah (+2.80), the Rainforest (+1.20) and the coastal region (+4.80) for the far future period (2085–2099). In the SSP585 scenario, the AWI-CM-1.1-MR model projected a decrease in the Sahel region (−4.60), while the MPI-ESM1-2-LR model projected an increase in the Guinea Savannah region (+1.80), and the ACCESS-CM2 model projected an increase in the Rainforest (+10.20) and Coastal regions (+13.20) for the far-future. All values in the parentheses are measured in watts-hour per square-meters. The projected changes in PV energy revealed the need for a regional-specific approach to the planning and implementation of energy transition mix in Nigeria.

Substantial increase in future land exposure to compound droughts and heatwaves in China dominated by climate change

Increased frequency and magnitude of compound droughts and heatwaves (CDH) under climate warming pose a severe threat to food production on cropland, biodiversity in forests and grasslands, as well as the health of urban populations. However, there is a lack of comprehensive assessments on the different land use types exposed to CDH events. In this study, we explored the changes in cropland, forest, grassland, urban, and bare land exposure to CDH frequency and magnitude (CDHMI) in China under different emission scenarios in the far-future (2070–2099) based on 12 model simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and Land Use Harmonization Version 2 (LUH2) data. The results indicate that with global warming, China is expected to face more frequent and severe CDH events in the future, particularly under high-emission scenarios. Correspondingly, Cropland, forest, grassland, and bare land exposure to CDH frequency and CDHMI show significant upward trends during 2015–2099, increasing at greater rates in high emission scenarios. Although the urban exposure to CDH frequency and CDHMI is projected to decelerate or even decline after 2050, urban exposure to CDH frequency and CDHMI under high-emission scenario will still increase by 605.20% and 207.32% during the far-future period (2070–2099) compared to 1981–2010, respectively. Regionally, the substantial increase in cropland, forest, grassland, urban, and bare land exposure to CDH frequency and CDHMI is concentrated in Northwestern China and Southern China due to the significant rise in frequency and magnitude of CDH events in these areas. The conclusions underline the importance and urgency of taking effective measures to limit emissions and respond to climate change.

Observed and future shifts in climate zone of Borneo based on CMIP6 models

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.

Hydrological response of tropical rivers basins to climate change using the GR2M model: the case of the Casamance and Kayanga-Géva rivers basins

The main objective of this research is to evaluate the effects of climate change first on precipitation and temperature, and then on the runoff characteristics of two tropical watersheds located in Senegal and Guinea-Bissau. To achieve this, eighteen General Circulation Models (GCMs) were selected to measure various climate change scenarios under the Shared Socioeconomic Pathways (SSP) SSP1-2.6 and SSP5-8.5, using the reference period of 1985–2014. The GR2M hydrological model was employed to replicate past monthly surface runoff patterns for the Casamance and Kayanga-Géva watersheds. After calibrating and validating the GR2M model, the researchers simulated the predictable effect of climate change on the flow for the near future (2021–2040), medium future (2041–2060), and distant future (2081–2100) for each watershed, using the GCM multi-model ensemble mean. The quantile method was used to correct bias in temperature and precipitation data. The results of bias correction give a correlation coefficient greater than 0.9% for temperatures and 0,6% precipitation between the outputs of the multi-model ensemble and observations used. The results indicate also that all watersheds are expected to experience drier conditions in the near-future, mid-future, and far-future periods under both the SSP1-2.6 and SSP5-8.5 scenarios. Furthermore, the predictable temperature trends consistently show a warmer situation with growing radiative making in the future times. However, the primary factor influencing changes in flow for all watersheds is the projected precipitation changes. The anticipated drier conditions in the near-future, mid-future, and far-future horizons under both scenarios would lead to significantly reduced runoff volumes at the beginning and middle of the rainy season. Consequently, the projected seasonal changes in river flow for all catchments (e.g., under SSP5-8.5 scenario, a decline of -34.47%, -56.01%, and -68.01% was noted, respectively, for the horizons 2050, 2070, and 2090 for the Casamance basin) could lead to new frequent occurrences of drought and water scarcity associated with past hydrological regimes. These scenarios enhance the necessity of improving water management, water prizing, and water recycling policies, to ensure water supply and to reduce tensions among regions and countries.

Application of relative importance metrics for CMIP6 models selection in projecting basin-scale rainfall over Johor River basin, Malaysia

The most recent set of General Circulation Models (GCMs) derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) was used in this work to analyse the spatiotemporal patterns of future rainfall distribution across the Johor River Basin (JRB) in Malaysia. A group of 23 GCMs were chosen for comparative assessment in simulating basin-scale rainfall based on daily rainfall from the historical period of the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS). The methodological novelty of this study lies in the application of relative importance metrics (RIM) to rank and select historical GCM simulations for reproducing rainfall at 109 CHIRPS grid points within the JRB. In order to choose the top GCMs, the rankings given by RIM were aggregated using the compromise programming index (CPI) and Jenks optimised classification (JOC). It was found that ACCESS-ESM1–5 and CMCC-ESM2 were ranked the highest in most of the grid. The final GCM was then bias-corrected using the linear scaling method before being ensemble based on the Bayesian model averaging (BMA) technique. The spatiotemporal assessment of the ensemble model for the different months over the near-future period 2021–2060 and far-future period 2061–2100 was compared with those under Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, SSP3–7.0, and SSP5-8.5. Heterogeneous changes in rainfall were projected across the JRB, with both increasing and decreasing trends. In the near-future and far-future scenarios, higher rainfall was projected for December, indicating an elevated risk of flooding during the end of the North East monsoon (NEM). Conversely, August showed a decreasing trend in rainfall, implying an increasing risk of severe drought. The findings of this study provide valuable insights for effective water resource management and climate change adaptation in the region.

Regional-scale flood impacts on a small mountainous catchment in Thailand under a changing climate

Abstract Extreme rainfall and flooding are common during the summer monsoon season in Thailand. In this study, we utilized Robust Empirical Quantile Mapping (RQUANT) to correct the bias in precipitation, and total runoff data obtained from the latest Couple Model Intercomparison Project phase 6 (CMIP6) for the upper Lam Takong river basin. Five different methods were employed to estimate the river discharge and four estimations based on Budyko functions. Our analysis revealed that the ‘Total runoff’ method yielded the most accurate representation of the observed discharge. Impacts of change in land use are examined in terms of compound roughness. The Multi-Model Ensemble (MME) precipitation under medium-emission (SSP2-4.5) and high-emission (SSP5-8.5) scenarios is projected to increase by 5.74 and 10.91%, respectively. Correspondingly, the discharges are expected to increase by 4.57 and 11.05% for the far-future periods. In general, the Flo-2D model satisfactorily simulated the water level in the main channel but it underestimated small inundation depth (<0.5 m) across the floodplain. Comparing inundation maps among different scenarios and timelines, changes in the inundation area were relatively small (0.05%), especially when compared to changes in floodplain storage (6.85%) due to the mountainous nature of the river basin.

Changes in the Seasonal Cycle of Heatwaves, Dry and Wet Spells over West Africa Using CORDEX Simulations

This study analyzes the potential response of the seasonal cycle of heatwaves (HWDI) and dry (CDD) and wet (CWD) spell indices over West Africa for the near- (2031–2060) and the far-future periods (2071–2100) under RCP4.5 and RCP8.5 scenarios using Coordinated Regional Climate Downscaling Experiment (CORDEX) simulations. Despite the fact that some relative biases (an underestimation of 30% for CDD, an overestimation of about 60% for CWD, and an overestimation of about 50% for HWDI) exist, during the historical period (1976–2005) in general, the CORDEX simulations and their ensemble mean outperform the seasonal variability in the above-mentioned indices over three defined subregions of West Africa (i.e., the Gulf of Guinea and Western and Eastern Sahel). They show high correlation coefficients (0.9 in general) and less RMSE. They project an increase (about 10 and 20 days) in heatwave days for both the near- and far-future periods over the whole West African region under both RCP scenarios. In addition, projections indicate that the Sahel regions will experience a decrease (about 5 days) in wet spell days from March to November, while in the Gulf of Guinea, a decrease (about 3 days) is projected throughout the year, except in the CCCLM simulation, which indicates an increase (about 5 days) during the retreat phase of the monsoon (October to December). Our results also highlight an increase (about 80%) in dry spells over the Sahel regions that are more pronounced during the March–November period, while over the Gulf of Guinea, an increase (about 40%) is projected over the entire year. Moreover, the months of increasing dry spells and decreasing wet spells coincide, suggesting that countries in these regions could be simultaneously exposed to dry seasons associated with a high risk of drought and heatwaves under future climate conditions.

Future projections of worst floods and dam break analysis in Mahanadi River Basin under CMIP6 climate change scenarios.

This study provides a comprehensive analysis of the hydrological effects and flood risks of the Hirakud Reservoir, considering different CMIP6 climate change scenarios. Using the HEC-HMS and HEC-RAS models, the study evaluates future flow patterns and the potential repercussions of dam breaches. The following summary of the work: firstly, the HEC-HMS model is calibrated and validated using daily stage-discharge observations from the Basantpur station. With coefficient of determination (R2) values of 0.764 and 0.858 for calibration and validation, respectively, the model demonstrates satisfactory performance. Secondly, The HEC-HMS model predicts future flow for the Hirakud Reservoir under three climate change scenarios (SSP2-4.5, SSP3-7.0 and SSP5-8.5) and for three future periods (near future, mid future and far future). Thirdly, by analyzing time-series hydrographs, the study identifies peak flooding events. In addition, the HEC-RAS model is used to assess the effects of dam breaches. Downstream of the Hirakud Dam, the analysis highlights potential inundation areas and depth variations. The study determines the following inundation areas for the worst flood scenarios: 3651.52 km2, 2931.46 km2 and 4207.6 km2 for the near-future, mid-future and far-future periods, respectively. In addition, the utmost flood depths for these scenarios are determined to be 31 m, 29 m and 39 m for the respective future periods. The study area identifies 105 vulnerable villages and several towns. This study emphasizes the importance of contemplating climate change scenarios and implementing proactive measures to mitigate the peak flooding events in the Hirakud reservoir region.

Precipitation-based climate change hotspots across India through a Multi-model assessment from CMIP6

This study presents an analysis to identify precipitation-based climate change hotspots and key-vulnerable cities using multi-model, multi-scenario, high-resolution (0.25°×0.25°), bias-corrected precipitation dataset across India from 14 state-of-the-art General Circulation Models (GCMs), participating in Coupled Model Intercomparison Project version 6 (CMIP6). Preliminary analysis indicates an overall wetter future across India with 290 ± 150 mm to 530 ± 260 mm increase in mean annual precipitation towards end of this century under various climate change scenarios. Apart from the mean precipitation, the extremes are also found to be increasing by alarmingly higher rates. However, the spatio-temporal variations of such increments are notably diverse over different seasons in a year and across different Homogeneous Precipitation Zones (HPZs) in the country. Therefore, a new and more inclusive index, named as Precipitation-based Hotspot Index (PHI), is developed to identify the ‘precipitation-based hotspots’, i.e. the places with most pronounced changes in precipitation characteristics. The PHI considers the changes in various aspects of precipitation, such as mean, variability, and characteristics of extremes (magnitude, frequency and intensity). Based on the PHI values in the far-future period (2061–2100) and for the worst climate change scenario, the hotspot regions are identified mostly in the northwest, west-central, west coast, and northeast parts of India. Further, considering two important socio-economic vulnerability factors (population density and human development index) along with PHI, the key-vulnerable tier-I cities are identified across India. The analysis reveals four out of ten tier-I cities will be highly vulnerable towards end of this century. The findings of this analysis (hotspot maps) and the data products (high-resolution, bias-corrected precipitation dataset) are expected to be highly beneficial for impact assessments, hydrologic modelling, and formulating suitable adaptation and mitigation strategies for India over future.

Assessment of impact of climate change on the streamflow of Idamalayar River Basin, Kerala

Abstract This study investigates the impacts of climate change on water availability in the Idamalayar basin, Kerala. Soil and Water Assessment Tool (SWAT), Hydrologic Engineering Centre – Hydrologic Modelling System (HEC-HMS), and bias-corrected climate change data were used to simulate future streamflows. The performances of SWAT and HEC-HMS were assessed using four statistical indices (R2, Nash–Sutcliffe efficiency, percentage bias, and RSR). SWAT slightly outperformed HEC-HMS. The CMIP6 general circulation models (GCMs) were selected using PROMETHEE-2. The variations in climate variables and streamflows were studied for three future periods, i.e., near-future (2031–2040), mid-future (2051–2060), and far-future (2071–2080) under three shared socio-economic pathway (SSP) scenarios (SSP126, SSP245, and SSP585). The projections of GCMs show different patterns in variation of precipitation. Generally, there is a slight increase in annual precipitation. However, there was a notable decline in peak in July. An additional peak was often seen in October. The maximum and minimum temperatures showed a decreasing trend. The average annual streamflow reduction under SSP126 was approximately 25.63, 27.92, and 26.24% in the near, mid, and far future, respectively. Under SSP245, the average decrease was 30.71, 16.06, and 19.06% for the near, mid and far future, respectively. For SSP585, there was 12.73% increase in the far-future period.

Evaluating uncertainty in climate change impacts on peak discharge and flood volume in the Qaran Talar watershed, Iran

Abstract This study evaluates the effects of climate change (CC) on runoff properties over the case study of the Qaran Talar watershed located in Iran. To consider the two main sources of uncertainty, i.e., Greenhouse Gases emission (GHG) scenarios and outputs of Atmosphere-Ocean General Circulation Models (AOGCMs), a daily rainfall time series was generated for two future periods (2021-2050 and 2070-2099) at three risk levels of 0.1, 0.25 and 0.50. 22 AOGCMs outputs following two emission scenarios (RCP4.5 and RCP8.5) were used. The results showed that the uncertainty of climate change scenarios was primarily owing to the uncertainty of GCMs outputs. Regarding the 2021-2050 period, under both emission scenarios, the increases in peak discharge and flood volume (FV) were estimated to reach 70, 50, and 30% at three risk levels of 0.1, 0.25 and 0.50, respectively, compared to the recent past period. As the current century draws to a close, the difference between the results of the two emission scenarios becomes apparent so that in the far-future period (2070-2099), the RCP8.5 scenario estimates and FV more than the RCP4.5 scenario does. Additionally, it was found that the uncertainty caused by AOGCMs was more than that by GHG emission scenarios.

Future Changes in Thermal Bioclimate Conditions over West Bengal, India, Based on a Climate Model

Changes in extreme human bioclimate conditions are accepted evidence for and serve as a broad measure of anthropogenic climate change. The essential objective of the current study was to investigate past and future thermal bioclimate conditions across West Bengal (WB), India. The daily physiologically equivalent temperature (PET) was calculated by considering definite climate variables as inputs. These meteorological variables were captured from the Coordinated Regional Downscaling Experiment (CORDEX)-South Asia. The initial results from this research work present the mean monthly distribution of each PET class over the considered stations of WB during the period (1986–2005) and three future time periods: (i) near future (2016–2035), (ii) mid-future (2046–2065), and (iii) far future (2080–2099). It was observed that the months from April to June comprise heat stress months in terms of human thermal perception, whereas thermally acceptable conditions begin in November and continue until March for most stations. Results from future PET changes over WB in the context of the reference period (1986–2005) reveal a prominent increase in warm and hot PETs for all future time periods in two different greenhouse gas emission scenarios. During the far-future time period, stations within a kilometer of the Bay of Bengal such as Digha, Diamond Harbour, Canning, and Baruipur account for the highest percentage in the warm PET class (35.7–43.8 °C) in high-end emission scenarios. Simultaneously, during the period from 2080 to 2099, Kolkata, Dum Dum, Kharagpur, and Siliguri will experience a PET greater than 43.8 °C for close to 10% of the days in the year and more than 10% in Sriniketan, Malda, Asansol, and Birbhum. During the far-future period, a negative change in the very cool PET class (<3.3 °C) indicating a decrease in cold days was the largest for Darjeeling.

Hydrological Response of Tropical Catchments to Climate Change as Modeled by the GR2M Model: A Case Study in Costa Rica

This study aimed to assess the impacts of climate change on streamflow characteristics of five tropical catchments located in Costa Rica. An ensemble of five General Circulation Models (GCMs), namely HadGEM2-ES, CanESM2, EC-EARTH, MIROC5, MPI-ESM-LR dynamically downscaled by two Regional Climate Models (RCMs), specifically HadRM3P and RCA4, was selected to provide an overview of the impacts of different climate change scenarios under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 using the 1961–1990 baseline period. The GR2M hydrological model was used to reproduce the historical monthly surface runoff patterns of each catchment. Following calibration and validation of the GRM2 model, the projected impact of climate change on streamflow was simulated for a near-future (2011–2040), mid-future (2041–2070) and far-future (2071–2100) for each catchment using the bias-corrected GCM-RCM multimodel ensemble-mean (MEM). Results anticipate wetter conditions for all catchments in the near-future and mid-future periods under RCPs 2.6 and 4.5, whereas dryer conditions are expected for the far-future period under RCP 8.5. Projected temperature trends indicate consistently warmer conditions with increasing radiative forcing and future periods. Streamflow changes across all catchments however are dominated by variations in projected precipitation. Wetter conditions for the near-future and mid-future horizons under RCPs 2.6 and 4.5 would result in higher runoff volumes, particularly during the late wet season (LWS). Conversely, dryer conditions for the far-future period under RCP8.5 would result in considerably lower runoff volumes during the early wet season (EWS) and the Mid-Summer Drought (MSD). In consequence, projected seasonal changes on streamflow across all catchments may result in more frequent flooding, droughts, and water supply shortage compared to historical hydrological regimes.

Using High-Resolution Climate Models to Identify Climate Change Hotspots in the Middle East: A Case Study of Iran

The adverse effects of climate change will impact all regions around the world, especially Middle Eastern countries, which have prioritized economic growth over environmental protection. However, these impacts are not evenly distributed spatially, and some locations, namely climate change hotspots, will suffer more from climate change consequences. In this study, we identified climate change hotspots over Iran—a developing country in the Middle East that is facing dire economic situations—in order to suggest pragmatic solutions for vulnerable regions. We used a statistical index as a representative of the differences in climatic parameters for the RCP8.5 and RCP4.5 forcing pathways between historical data (1975–2005), near-future data (2030–2060) and far-future data (2070–2100). More specifically, we used downscaled high-resolution (0.25°) meteorological data from five GCMs of the CMIP5 database to calculate the statistical metric. Results indicate that for the far-future period and RCP4.5, regions stretching from the northwest to southeast of Iran, namely the Hotspot Belt, are the most vulnerable areas, while, for RCP8.5, almost the whole country is vulnerable to climate change. The highest and lowest differences in temperature for RCP8.5 in 2070–2100 are observed during summer in the northwestern and central parts and during winter in the northern and northeastern parts. Moreover, the maximum increase and decrease in precipitation are identified over the western parts of Iran during fall and winter, respectively. Overall, western provinces (e.g., Lorestan and Kermanshah), which are mostly reliant on rainfed agriculture and other climate-dependent sectors, will face the highest change in climate in the future. As these regions have less adaptive capacity, they should be prioritized through upstream policy change and special budget allocation from the government to increase their resiliency against climate change.

Impact of future climate change on river discharge and groundwater recharge: a case study of Ho Chi Minh City, Vietnam

Abstract Climate change (CC) is likely to have a long-term influence on regional water resources, including surface water and groundwater. Therefore, quantifying the CC influence is indispensable for proper management of water resources. This study scrutinized the influence of CC on river discharge and groundwater recharge (GWR) in Ho Chi Minh City (HCMC), Vietnam, utilizing the Soil and Water Assessment Tool (SWAT). The calibrated SWAT was utilized to simulate the discharge and GWR under projected climate scenarios in reliance on an ensemble of seven General Circulation Models (GCMs) derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways (SSPs), including SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results pointed out that the climate of HCMC is warmer and wetter in the 21st century. Under the CC influence, the future discharge is envisaged to rise from 0.1 to 4.5% during the near-future period of 2030s (2021–2045), 8.1 to 11.6% during the mid-future period of 2055s (2046–2070), and 7.7 to 19.6% during the far-future period of 2080s (2071–2095) under the three SSP scenarios. In addition, the GWR is prognosticated to have rising trends of 0.9–4.9%, 5.3–7.9%, and 5.7– 13.5% during the near-future, mid-future, and far-future periods, respectively. Furthermore, uncertainties in the discharge and GWR projections connected with SSP scenarios and CMIP6 GCMs are considerable.

Investigation of the Effects of Climate Variability, Anthropogenic Activities, and Climate Change on Streamflow Using Multi-Model Ensembles

Streamflow is a very important component of the hydrological cycle, and variation in the streamflow can be an indication of hydrological disaster. Thus, the accurate quantification of streamflow variation is a core concern in water resources engineering. In this study, we evaluated the factors influencing streamflow and decomposed their effects in three large rivers: the Buk Han River (BHR), the Nam Han River (NHR), and the Lower Han River (LHB). The Pettit test was used to investigate breakpoints in conjunction with the climate elasticity approach and decomposition framework to quantify and decompose the effects of climate variability and anthropogenic activity. The abrupt breakpoints in the streamflow and precipitation data were detected in 1997 and 1995. Considering these breakpoints, we divided the time series into two periods: the baseline period and the post-baseline period. Climate elasticity approaches were used to quantify the effects of climate variability and anthropogenic activity during the baseline period, post-baseline period, and future periods (2031–2060 and 2071–2100) under the Representative Concentration Pathways’ 4.5 and 8.5 scenarios. The results revealed that climate variability was the leading cause of alteration in the streamflow in the BHR and NHR, accounting for 76.52% to 80.51% of the total change, respectively. Meanwhile, the LHR remained more sensitive to anthropogenic activity, which accounted for 56.42% of the total variation in streamflow. Future climate change also showed an increase in precipitation and temperature in both scenarios, especially during the far-future period (2071–2100). This variation in the climatic factor was shown to affect the future streamflow by 22.14% to 27.32%. These findings can play a very important role in future planning for large river basins, considering the impacts of increasing anthropogenic activity and climate change to reduce the risks of hydrological hazards.

Estimation of future changes in photovoltaic potential in Australia due to climate change

Solar photovoltaic (PV) energy is one of the fastest growing renewable energy sources globally. However, the dependency of PV generation on climatological factors such as the intensity of radiation, temperature, wind speed, cloud cover, etc can impact future power generation capacity. Considering the future large-scale deployment of PV systems, accurate climate information is essential for PV site selection, stable grid regulation, planning and energy output projections. In this study, the long-term changes in the future PV potential are estimated over Australia using regional climate projections for the near-future (2020–2039) and far-future (2060–2079) periods under a high emission scenario that projects 3.4 °C warming by 2100. The effects of projected changes in shortwave downwelling radiation, temperature and wind speed on the future performance of PV systems over Australia is also examined. Results indicate decline in the future PV potential over most of the continent due to reduced insolation and increased temperature. Northern coastal Australia experiences negligible increase in PV potential during the far future period due to increase in radiation and wind speed in that region. On further investigation, we find that the cell temperatures are projected to increase in the future under a high emission scenario (2.5 °C by 2079), resulting in increased degradation and risks of failure. The elevated cell temperatures significantly contribute to cell efficiency losses, that are expected to increase in the future (6–13 d yr−1 for multi-crystalline silicon cells) mostly around Western and central Australia indicating further reductions in PV power generation. Therefore, long-term PV power projections can help understand the variations in future power generation and identify regions where PV systems will be highly susceptible to losses in Australia.

Climate change impact on major crop yield and water footprint under CMIP6 climate projections in repeated drought and flood areas in Thailand

Understanding crop yield and water requirements in response to the future climate at the local scale is essential to develop more precise and appropriate adaptation strategies. From this perspective, repeated drought and flood events in the lower north of Thailand were investigated. The objectives of the study were to evaluate the impact of climate change on major crop yields and the water footprint (WF). Five global circulation model datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), known as Shared Socioeconomic Pathways (SSPs), were selected. Three future periods: near (2015–2039), mid (2040–2069), and far future (2070–2100) under SSP245 and SSP585 scenarios were used to predict the major crop yields and WF changes in the future. The precipitation and maximum and minimum temperatures were projected to increase in all periods under both scenarios. Rice yields in irrigated areas were predicted to rise gradually over the three projection periods under SSP245 but decline in mid and far-future periods under SSP585. There was a predicted reduction of first and second rice crop yields by −6.0% to −17.7% under SSP585. Fortunately, those rice yields were expected to increase in the near-future period under SSP245 by 3.0% to 4.3%. Growing maize, soybean, or mung bean instead of a second rice crop will have a less negative impact on future climate change. Changing from growing rice to be planting maize twice per year and growing cassava had increased favorability in rain-fed areas. The WF changes in the future were associated with future crop yield changes; therefore, the decrease in WFs was due to an increase in crop yield and vice-versa. The total WFs of maize, soybean, mung bean, and cassava production were roughly half that of rice production, indicating that these crops are suitable alternatives in the dry season.

Projected drought conditions by CMIP6 multimodel ensemble over Southeast Asia

Abstract Southeast Asia (SEA) is vulnerable to climate extremes due to its large and growing population, long coastlines with low-lying areas, reliance on agricultural sector developments. Here, the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) was employed to examine future climate change and drought in this region under two SSP–RCP (shared socioeconomic pathway–representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The CMIP6 multimodel ensemble mean projects a warming (wetting) of 1.99–4.29 °C (9.62–18.43%) in the 21st century. The Standardized Precipitation Evapotranspiration Index at 12-month time scales (SPEI-12) displays moderate-to-severe dry conditions over all countries during the near-future period, then the wet condition is projected from mid-future to far-future periods. The projected drought characteristics show relatively longer durations, higher peak intensities, and more severities under SSP5-8.5, while the higher number of events are projected under SSP2-4.5. Overall, the SPEI-12 over SEA displays significant regional differences with decreasing dryness trend toward the 21st century. All these findings have important implications for policy intervention to water resource management under a changing climate over SEA.

Management of vegetative land for more water yield under future climate conditions in the over-utilized water resources regions: A case study in the Xiong’an New area

Seeking more water yield in the over-utilized water resources regions that are home to nearly 35% of the population all over the world is a great challenge under future climate change. Impacts of land conversion (i.e., land conversion from cropland to grassland and forest) and climate change during near-future 2030s (2021–2040), mid-future 2050s (2041–2060), and far-future 2080s (2071–2090) periods on vegetation dynamics (LAI) and water budgets (evapotranspiration and runoff) were investigated in a typical over-utilized water resources area of China (Xiong’an New Area, XNA) using the WAVES ecohydrological model. Future climate change impacts were assessed with statistically downscaled forcing of 18 global circulation models (GCMs) under three representative concentration pathways (RCPs) using secondary bias-correction technique. Projected future climate indicated that XNA would become warmer and wetter in future. The WAVES model was calibrated against remotely sensed LAI and ET data. Modeling results during the historical period of 1982–2012 showed that forest evaporated more water (32.0 mm yr−1) than cropland, while grassland used almost the same amount of water as cropland. Under future climate conditions, both water use and water yield would increase in cropland and grassland due to increased precipitation. Forest was predicted to use 9.5–14.6% (36.4–55.8 mm yr−1) more water and to have 2.6-52.4% (2.6–51.1 mm yr−1) more water yield in the 2050s and 2080s under RCP4.5 and in all three future periods (2030s, 2050s, and 2080s) under RCP8.5 due to greater increases in precipitation. Afforestation is not recommended as a land management practice for more water yield in near-future, but could be implemented to achieve a win-win situation for more water yield and more forest coverage in mid- and far-future periods in XNA due to increased precipitation. This study highlights that uncertainties in projected future climate pose formidable challenges in management of vegetative land for more water yield in the over-utilized water resources regions.