Search Results (109)

Climate change is increasingly altering rainfall regimes and hydrological processes, posing major challenges to reservoir operation, flood control, and hydropower production. Understanding its impacts on the Ban Chat reservoir in Northwest Vietnam is therefore crucial for ensuring reliable water resource management under future uncertainties. This study aims to assess potential changes in streamflow to the Ban Chat reservoir under different climate change scenarios. The study employed nine Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Future climate projections were bias-corrected using the Quantile Delta Mapping (QDM) method and used as input for the Hydrological Engineering Center–Hydrological Modeling System (HEC-HMS) to simulate future inflows. Streamflow changes were evaluated for near- (2021–2040), mid- (2041–2060), and late-century (2061–2080) periods relative to the baseline (1995–2014). Results show that under SSP1-2.6, mean annual discharge and flood-season flows steadily increase (up to +6.9% by 2061–2080), while storage deficits persist (−27.7% to −13.1%). Under SSP2-4.5, changes remain small, with flood peaks limited to +4.5% mid-century, but severe dry-season deficits continue (−29.5% to −24.4%). In contrast, SSP5-8.5 projects strong late-century increases in mean flows (+7.5%) and flood peaks (+8.2%), though early-century flood flows decline (−2.1%). These findings provide essential scientific evidence for adaptive reservoir operation, hydropower planning, and flood risk management, underscoring the significance of incorporating climate scenarios into sustainable water resource strategies in mountainous regions. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

This study investigates the impact of climate change on the Mudurnu Stream Basin in northwest Türkiye by analyzing climate parameters in the Mediterranean region. Historical data from EC-Earth2, HadGEM2-ES, and MPI-ESM-MR GCMs from the CMIP5 Euro-CORDEX archive were assessed, and future precipitation and temperature data were derived using five statistical bias correction methods for the selected EC-Earth2 model under RCP4.5 and RCP8.5 scenarios. The SWAT model was employed to simulate future runoff amounts for the Mudurnu Stream Basin. The findings reveal notable changes in precipitation and temperature. The annual and seasonal variations of total precipitation and average, maximum, and minimum temperatures for the RCP4.5 and RCP8.5 scenarios in the Sakarya and Mudurnu regions were analyzed and determined. The projections for future river flow indicate a significant increase in precipitation during the rainy seasons. The Mudurnu Stream mainstem will experience an increase in flow of between 70 and 140% under RCP4.5 and between 80 and 160% under RCP8.5. In the Dinsiz Stream tributary, a 32–55% increase is observed for the spring and summer months. In this context, the rainfall and runoff projections required for the estimation of potential drought and flood risks in the near and distant future were calculated. Full article

Bias correction of global climate model (GCM) simulations is usually required for hydrological impact studies due to the coarse resolution and systematic biases of these simulations. Commonly used bias correction methods are applied at sites independently while ignoring the spatial correlation of variables, which may cause unreasonable hydrological simulation. To solve this problem, a stochastic multisite bias correction (SMBC) method is proposed for hydrological impact studies. It first uses the Daily Bias Correction (DBC) method to correct the distribution of variables, and then the distribution-free shuffle algorithm and Markov chain are used to generate spatial correlation of variables. The performance of this method is compared with the DBC method for hydro-meteorological impact studies in the Xiangjiang River Basin. The results show that the DBC method inherits the bias of the temporal sequence of precipitation and spatial correlation of GCM simulated variables. The mean absolute error (MAE) of the spatial correlation is between 0.25 and 0.38 and between 0.36 and 0.39 for the simulated precipitation occurrence and daily precipitation amount, respectively, while the MAE for the probability of two stations having wet days/dry days simultaneously is around 0.07. The SMBC method effectively reproduces spatial correlation of observations, with the MAE of the above indexes around 0.02. In hydrological simulation, the SMBC method has much better performance in reproducing the return period of the maximum consecutive day for streamflow and the maximum consecutive day’s streamflow. The averaged streamflow process is also well represented. Overall, the SMBC method efficiently reproduces the distribution and spatial correlation of variables, thereby generating more accurate hydrology simulations. Full article

The rise in global temperatures and increased extreme weather events, such as heatwaves, underscore the need for accurate regional projections of daily maximum temperature (T_max) to inform effective adaptation strategies. This study develops the CNN-BMA-QDM model, which integrates convolutional neural networks (CNNs), Bayesian model averaging (BMA), and quantile delta mapping (QDM) to downscale and project T_max under future climate scenarios. The CNN-BMA-QDM model stands out for its ability to capture nonlinear relationships between T_max and atmospheric circulation factors, reduce model uncertainty, and correct bias, thus improving simulation accuracy. The CNN-BMA-QDM model is applied to Fujian Province, China, using three CMIP6 GCMs and four shared socioeconomic pathways (SSPs) to project T_max from 2015 to 2100. The results show that CNN-BMA-QDM outperforms CNN-BMA, CNNs, and other downscaling methods (e.g., RF, BPNN, SVM, LS-SVM, and SDSM), particularly in simulating extreme value at the 99% and 95% percentiles. Projections of T_max indicate consistent warming trends across all SSP scenarios, with spatially averaged warming rates of 0.0077 °C/year for SSP126, 0.0269 °C/year for SSP245, 0.0412 °C/year for SSP370, and 0.0526 °C/year for SSP585. Coastal areas experience the most significant warming, with an increase of 4.62–5.73 °C under SSP585 by 2071–2100, while inland regions show a smaller rise of 3.64–3.67 °C. Monthly projections indicate that December sees the largest increase (5.30 °C under SSP585 by 2071–2100), while July experiences the smallest (2.40 °C). On a seasonal scale, winter experiences the highest warming, reaching 4.88 °C under SSP585, whereas summer shows a more modest rise of 3.10 °C. Notably, the greatest discrepancy in T_max rise between the south and north occurs during the summer. These findings emphasize the importance of developing tailored adaptation strategies based on spatial and seasonal variations. The results provide valuable insights for policymakers and contribute to the advancement of regional climate projection research. Full article

The current changing climate requires the development of water–energy–food (WEF) nexus-oriented systems capable of mainstreaming climate-smart innovations into resource management. This study demonstrates the cross-sectoral impacts of climate change on interlinked sectors of water, energy, and food in Narok County, Kenya, and Vhembe District, South Africa. This study used projected hydroclimatic extremes across past, present, and future scenarios to examine potential effects on the availability and accessibility of these essential resources. The projected temperature and rainfall are based on nine dynamically downscaled Coupled Model Intercomparison Project Phase 5 (CMIP 5) of the Global Climate Models (GCMs). The model outputs were derived from two IPCC “Representative Concentration Pathways (RCPs)’’, the RCP 4.5 “moderate scenario”, and RCP 8.5 “business as usual scenario”, also defined as the addition of 4.5 W/m² and 8.5 W/m² radiative forcing in the atmosphere, respectively, by the year 2100. For the climate change projections, outputs from the historical period (1976–2005) and projected time intervals spanning the near future, defined as the period starting from 2036 to 2065, and the far future, spanning from 2066 to 2095, were considered. An ensemble model to increase the skill, reliability, and consistency of output was formulated from the nine models. The statistical bias correction based on quantile mapping using seven ground-based observation data from the South African Weather Services (SAWS) for Limpopo province and nine ground-based observation data acquired from the Trans-African Hydro-Meteorological Observatory (TAHMO) for Narok were used to correct the systematic biases. Results indicate downscaled climate change scenarios and integrate a modelling framework designed to depict the perceptions of future climate change impacts on communities based on questionnaires and first-hand accounts. Furthermore, the analysis points to concerted efforts of multi-stakeholder engagement, the access and use of technology, understanding the changing business environment, integrated government and private sector partnerships, and the co-development of community resilience options, including climate change adaptation and mitigation in the changing climate. The conceptual climate and WEF resource modelling framework confirmed that future climate change will have noticeable interlinked impacts on WEF resources that will impact the livelihoods of vulnerable communities. Building the resilience of communities can be achieved through transformative WEF nexus solutions that are inclusive, sustainable, equitable, and balance adaptation and mitigation goals to ensure a just and sustainable future for all. Full article

The accurate prediction of river discharge is essential in water resource management, particularly under variability due to climate change. Traditional hydrological models commonly struggle to capture the complex, nonlinear relationships between climate variables and river discharge, leading to uncertainties in long-term projections. To mitigate these challenges, this research integrates machine learning (ML) and deep learning (DL) techniques to predict discharge in the Subernarekha River Basin (India) under future climate scenarios. Global climate models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) are assessed for their ability to reproduce historical discharge trends. The selected CNRM-M6-1 model is bias-corrected and downscaled before being used to simulate future discharge patterns under SSP585 (a high-emission scenario). Various AI-driven models, such as a temporal convolutional network (TCN), a gated recurrent unit (GRU), a support vector regressor (SVR), and a novel DL network named the Temporal Enhanced Attention Network (TeaNet), are implemented by integrating the maximum and minimum daily temperatures and precipitation as key input parameters. The performance of the models is evaluated using the mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R²). Among the evaluated models, TeaNet demonstrates the best performance, with the lowest error rates (RMSE: 2.34–3.04; MAE: 1.13–1.52 during training) and highest R² (0.87–0.95), outperforming the TCN (R²: 0.79–0.88), GRU (R²: 0.75–0.84), SVR (R²: 0.68–0.80), and RF (R²: 0.72–0.82) by 8–15% in accuracy across four gauge stations. The efficacy of the proposed model lies in its enhanced attention mechanism, which successfully identifies temporal relationships in hydrological information. In determining the most relevant predictors of river discharge, the feature importance is analyzed using the proposed TeaNet model. The findings of this research strengthen the role of DL architectures in improving long-term discharge prediction, providing valuable knowledge for climate adaptation and strategic planning in the Subernarekha region. Full article

Climate change presents considerable challenges to hydrological stability by modifying precipitation patterns and exacerbating the frequency and intensity of extreme rainfall events. This research evaluates the prospective alterations in rainfall quantiles in South Korea by employing a multi-model ensemble (MME) derived from 23 Global Climate Models (GCMs) associated with the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5). Historical rainfall data from simulations (1985–2014) and future projections (2015–2044, 2043–2072, and 2071–2100) were analyzed across a total of 615 sites. Statistical Quantile Mapping (SQM) bias correction significantly enhanced the accuracy of projections (RMSE reduction of 63.0–85.3%, Pbias reduction of 93.6%, and R² increase of 0.73). An uncertainty analysis revealed model uncertainty to be the dominant factor (approximately 71.87–70.49%) in the near- to mid-term periods, and scenario uncertainty increased notably (up to 5.94%) by the end of the century. The results indicate substantial temporal and spatial changes, notably including increased precipitation in central inland and eastern coastal regions, with peak monthly increases exceeding 40 mm under high-emission scenarios. Under the SSP2-4.5 and SSP5-8.5 scenarios, the 100-year rainfall quantile is projected to increase by over 40% across significant portions of the country, emphasizing growing challenges for water resource management and infrastructure planning. These findings provide critical insights for water resource management, disaster mitigation, and climate adaptation strategies in South Korea. Full article

Bangladesh, one of the most vulnerable countries to climate change, has been experiencing significant climate change-induced risks. Particularly, the northwest region of the country has been severely affected by climate extremes, including droughts and heat waves. Therefore, proper understanding and assessment of future climate change scenarios is crucial for the adaptive management of water resources. The current study used the statistical downscaling model (SDSM) to downscale and analyze climate change-induced future changes in temperature and precipitation based on multiple global climate models (GCMs), including HadCM3, CanESM2, and CanESM5. A quantitative approach was adopted for both calibration and validation, showing that the SDSM is well-suited for downscaling mean temperature and precipitation. Furthermore, bias correction was applied to enhance the accuracy of the downscaled climate variables. The downscaled projections revealed an upward trend in mean annual temperatures, while precipitation exhibited a declining trend up to the end of the century for all scenarios. The observed data periods for the CanESM5, CanESM2, and HadCM3 GCMs used in SDSM were 1985–2014, 1975–2005, and 1975–2001, respectively. Based on the aforementioned periods, the projections for the next century indicate that under the CanESM5 (SSP5-8.5 scenario), temperature is projected to increase by 0.98 °C, with a 12.4% decrease in precipitation. For CanESM2 (RCP8.5 scenario), temperature is expected to rise by 0.94 °C, and precipitation is projected to decrease by 10.3%. Similarly, under HadCM3 (A2 scenario), temperature is projected to increase by 0.67 °C, with a 7.0% decrease in precipitation. These downscaled pathways provide a strong basis for assessing the potential impacts of future climate change across the northwestern region of Bangladesh. Full article

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin. Full article

The amplitude and interval of drought events are expected to enhance in upcoming years resulting from global warming and climate alterations. Understanding future drought events’ potential impacts is important for effective regional adaptation and mitigation approaches. The main goal of this research is to study the impacts of climate change on drought in the Büyük Menderes Basin located in the Aegean region of western Türkiye by using the outcomes of three general circulation models (GCMs) from CMIP6 considering two different emission scenarios (SSP2-4.5 and SSP5-8.5). Following a bias correction using a linear scaling method, daily precipitation and temperature projections are used to compute the Standardized Precipitation Evapotranspiration Index (SPEI). The effectiveness of the GCMs in projecting precipitation and temperature is evaluated using observational data from the reference period (1985–2014). Future drought conditions are then assessed based on drought indices for three periods: 2015–2040 (near future), 2041–2070 (mid-term future), and 2071–2100 (late future). Consequently, the number of dry months is projected and expected to elevate, informed by SSP2-4.5 and SSP5-8.5 scenarios, during the late-century timeframe (2071–2100) in comparison to the baseline period (1985–2014). The findings of this study offer an important understanding for crafting adaptation strategies aimed at reducing future drought impacts in the Büyük Menderes Basin in the face of changing climate conditions. Full article

Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. Traditional flood forecasting in Central Asia still relies on statistical models developed during the Soviet era, which are limited in their ability to incorporate non-stationary climate and anthropogenic influences. This study addresses this gap by applying the Soil and Water Integrated Model (SWIM) to project climate-driven changes in the hydrological regime of the Zhabay River. The study employs a process-based, high-resolution hydrological model to simulate flood dynamics under future climate conditions. Historical hydrometeorological data were used to calibrate and validate the model at the Atbasar gauge station. Future flood scenarios were simulated using bias-corrected outputs from an ensemble of General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the periods 2011–2040, 2041–2070, and 2071–2099. This approach enables the assessment of seasonal and interannual variability in flood magnitudes, peak discharges, and their potential recurrence intervals. Findings indicate a substantial increase in peak spring floods, with projected discharge nearly doubling by mid-century under both climate scenarios. The study reveals a 1.8-fold increase in peak discharge between 2010 and 2040, and a twofold increase from 2041 to 2070. Under the RCP 4.5 scenario, extreme flood events exceeding a 100-year return period (2000 m³/s) are expected to become more frequent, whereas the RCP 8.5 scenario suggests a stabilization of extreme event occurrences beyond 2071. These findings underscore the growing flood risk in the region and highlight the necessity for adaptive water resource management strategies. This research contributes to the advancement of climate-resilient flood forecasting in Central Asian river basins. The integration of process-based hydrological modelling with climate projections provides a more robust framework for flood risk assessment and early warning system development. The outcomes of this study offer crucial insights for policymakers, hydrologists, and disaster management agencies in mitigating the adverse effects of climate-induced hydrological extremes in Kazakhstan. Full article

The multi-model ensemble (MME) approaches are highly regarded in climate prediction and risk assessment for their capacity to integrate multiple global climate models (GCMs) and minimize uncertainties associated with individual models. However, the quantitative impacts of spatial scale, weighted ensemble, and bias correction on the spatiotemporal comprehensive performance of MME remain unknown. In this study, we comprehensively assessed the historical simulation capabilities of 41 CMIP6 GCMs at national, basin, and grid scales. Additionally, we investigated the impact of bias correction and weighted ensemble on enhancing climate simulation performance. The results indicate that CMIP6 models exhibit notable differences in simulating regional climate characteristics of China across different scales. Weighted multi-model ensemble schemes incorporating better-performing models consistently outperform equal-weight approaches, achieving an average 20.67% reduction in the DISO (distance between indices of simulation and observation) index, with temporal performance improvements being particularly pronounced. Bias correction played a critical role in the enhancement of MMEs, reducing DISO values by 41.60% on average, particularly in the spatial dimension. Among all MMEs, the grid-scale optimized ensemble (GBQ), combining bias correction, model selection, and performance-based weighting, demonstrated superior comprehensive performance, achieving the lowest DISO values across spatial and temporal dimensions. These findings provide new insights for enhancing regional climate simulation and evaluation, and they provide more reliable scientific information for investigating climate change and formulating adaptation strategies in China. Full article

The Huaihe River Basin is particularly vulnerable to climate change. This paper first evaluated interpolation methods for different meteorological elements, followed by an assessment of the simulation performance of various Coupled Model Intercomparison Project 5 (CMIP5) Global Climate Models (GCMs) for these elements. We then applied the Improved Quantile Mapping (IQM) method for bias correction of the GCMs. Finally, we analyzed the characteristics of future climate change in the Huaihe River Basin. The results show the following: (1) The radial basis function interpolation method is the most effective for rainfall, while Kriging performs best for air temperature. (2) The HadGEM2-AO model provides the most accurate rainfall simulations, MIROC-ESM best simulates maximum air temperature, and HadGEM2-ES is most effective for minimum air temperature. (3) The IQM method outperforms other approaches for bias correction of climate variables in the basin. (4) Future projections show an increase in both rainfall and air temperature, with more pronounced rises under the RCP8.5 scenario. Additionally, rainfall and maximum air temperature show considerable spatial variation across emission scenarios, while minimum air temperature consistently exhibits an upward trend. Full article

This work aims to examine the effect of bias correction (BC) methods on the development of Intensity–Duration–Frequency (IDF) curves under climate change at multiple temporal scales. Daily outputs from a 9-member CORDEX-CA GCM-RCM multi-model ensemble (MME) under RCP 8.5 were used to represent future precipitation. Two stationary BC methods, empirical quantile mapping (EQM) and gamma-pareto quantile mapping (GPM), along with three non-stationary BC methods, detrended quantile mapping (DQM), quantile delta mapping (QDM), and robust quantile mapping (RQM), were selected to adjust daily biases between MME members and observations from the SJO weather station located in Costa Rica. The equidistant quantile-matching (EDQM) temporal disaggregation method was applied to obtain future sub-daily annual maximum precipitation series (AMPs) based on daily projections from the bias-corrected ensemble members. Both historical and future IDF curves were developed based on 5 min temporal resolution AMP series using the Generalized Extreme Value (GEV) distribution. The results indicate that projected future precipitation intensities (2020–2100) vary significantly from historical IDF curves (1970–2020), depending on individual GCM-RCMs, BC methods, durations, and return periods. Regardless of stationarity, the ensemble spread increases steadily with the return period, as uncertainties are further amplified with increasing return periods. Stationary BC methods show a wide variety of trends depending on individual GCM-RCM models, many of which are unrealistic and physically improbable. In contrast, non-stationary BC methods generally show a tendency towards higher precipitation intensities as the return period increases for individual GCM-RCMs, despite differences in the magnitude of changes. Precipitation intensities based on ensemble means are found to increase with the change factor (CF), ranging between 2 and 25% depending on the temporal scale, return period, and non-stationary BC method, with moderately smaller increases for short-durations and long-durations, and slightly higher for mid-durations. In summary, it can be concluded that stationary BC methods underperform compared to non-stationary BC methods. DQM and RQM are the most suitable BC methods for generating future IDF curves, recommending the use of ensemble means over ensemble medians or individual GCM-RCM outcomes. Full article