Estimating Future Surface Water Availability Through an Integrated Climate‐Hydrology‐Management Modeling Framework at a Basin Scale Under CMIP6 Scenarios

Climate change is one of the most significant challenges of the 21st century, particularly its impact on surface water availability in arid and hyper-arid regions within the Euphrates River Basin. This study aims to analyze the impacts of climate change using five global climate models (GCMs) within the Coupled Model Intercomparison Project Phase 6 (CMIP6, IPCC 2021). Model outputs were statistically downscaled using Statistical Downscaling Model (SDSM 6.1) under three greenhouse gas emission scenarios, and bias correction was performed using Climate Model Data for Hydrologic Modeling (CMhyd 1.02). Performance evaluation was based on data from meteorological stations distributed across five climatic zones within the basin, covering the period from 1982 to 2023. The study results showed that the uncertainty-related biases in CMIP6 models limit the accuracy of climate projections in the basin. Using individual GCM models instead of relying solely on linear trends can reduce bias and uncertainty in estimating climate variables across different regions. Although the SDSM model provides a good fit with GCM outputs, the variability in spatial patterns and emission scenarios is reflected in the limited accuracy of some climate indices. Applying bias correction using the linear scaling method has been shown to improve the accuracy of statistical modeling. Projections indicate that rising temperatures due to increased greenhouse gas emissions may decrease rainfall in arid regions while increasing in humid regions. These results contribute to a better understanding of climate change’s impacts on water resources in river basins and provide a scientific basis for developing effective adaptation strategies and future planning.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-15483-x.

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.RFMIP.MIROC.MIROC6.piClim-histaer' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Earth System Model MIROC6, released in 2017, includes the components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 100 km, atmos: 250 km, land: 100 km, ocean: 100 km, seaIce: 100 km. Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and the results will undoubtedly be relied on by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by World Data Centre for Climate (WDCC) at DKRZ. The project includes simulations from about 90 global climate models and around 40 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.

Climatic changes have altered surface water regimes worldwide, and climate projections suggest that such alterations will continue. To inform management decisions, climate projections must be paired with hydrologic models to develop quantitative estimates of watershed scale water regime changes. Such modeling approaches often involve downscaling climate model outputs, which are generally presented at coarse spatial scales. In this study, Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model projections were analyzed to determine models representing severe and conservative climate scenarios for the study watershed. Based on temperature and precipitation projections, output from GFDL-ESM2G (RCP2.6) and MIROC-ESM (RCP8.5) were selected to represent conservative (ΔC) and severe (ΔS) change scenarios, respectively. Climate data were used as forcing for the Soil and Water Assessment Tool (SWAT) to analyze the potential effects of climate change on hydrologic processes in a mixed-use watershed in central Missouri, USA. Results showed annual streamflow decreases ranging from -5.9% to -26.8% and evapotranspiration (ET) increases ranging from +7.2% to +19.4%. During the mid-21st century, sizeable decreases to summer streamflow were observed under both scenarios, along with large increases of fall, spring, and summer ET under ΔS. During the late 21st century period, large decreases of summer streamflow under both scenarios, and large increases to spring (ΔS), fall (ΔS) and summer (ΔC) ET were observed. This study demonstrated the sensitivity of a Midwestern watershed to future climatic changes utilizing projections from CMIP5 models, and presented an approach that used multiple climate model outputs to characterize potential watershed scale climate impacts.

Assessment of future water availability from the Himalayan watersheds of Indus Basin (Jhelum, Kabul and upper Indus basin—UIB) is a growing concern for safeguarding the sustainable socioeconomic wellbeing downstream. This requires, before all, robust climate change information from the present-day state-of-the-art climate models. However, the robustness of climate change projections highly depends upon the fidelity of climate modeling experiments. Hence, this study assesses the fidelity of seven dynamically refined (0.44^{circ }) experiments, performed under the framework of the coordinated regional climate downscaling experiment for South Asia (CX-SA), and additionally, their six coarse-resolution driving datasets participating in the coupled model intercomparison project phase 5 (CMIP5). We assess fidelity in terms of reproducibility of the observed climatology of temperature and precipitation, and the seasonality of the latter for the historical period (1971–2005). Based on the model fidelity results, we further assess the robustness or uncertainty of the far future climate (2061–2095), as projected under the extreme-end warming scenario of the representative concentration pathway (RCP) 8.5. Our results show that the CX-SA and their driving CMIP5 experiments consistently feature low fidelity in terms of the chosen skill metrics, suggesting substantial cold (6–10 ^{circ }C) and wet (up to 80%) biases and underestimation of observed precipitation seasonality. Surprisingly, the CX-SA are unable to outperform their driving datasets. Further, the biases of CX-SA and of their driving CMIP5 datasets are higher in magnitude than their projected changes under RCP8.5—and hence under less extreme RCPs—by the end of 21st century, indicating uncertain future climates for the Indus Basin watersheds. Higher inter-dataset disagreements of both CMIP5 and CX-SA for their simulated historical precipitation and for its projected changes reinforce uncertain future wet/dry conditions whereas the CMIP5 projected warming is less robust owing to higher historical period uncertainty. Interestingly, a better agreement among those CX-SA experiments that have been obtained through downscaling different CMIP5 experiments with the same regional climate model (RCM) indicates the RCMs’ ability of modulating the influence of lateral boundary conditions over a large domain. These findings, instead of suggesting the usual skill-based identification of ’reasonable’ global or regional low fidelity experiments, rather emphasize on a paradigm shift towards improving their fidelity by exploiting the potential of meso-to-local scale climate models—preferably of those that can solely resolve global-to-local scale climatic processes—in terms of microphysics, resolution and explicitly resolved convections. Additionally, an extensive monitoring of the nival regime within the Himalayan watersheds will reduce the observational uncertainty, allowing for a more robust fidelity assessment of the climate modeling experiments.

<strong class="journal-contentHeaderColor">Abstract.</strong> NASA has launched a new initiative; the Open-Source Science Initiative (OSSI) to enable and support science towards openness. The OSSI initiative supports open-source software development and dissemination. In this work, we present <em>NASAaccess</em> which is an open-source software package and web-based environmental modeling application for earth observation data accessing, reformatting, and presenting quantitative data products. The main objective of developing the <em>NASAaccess</em> platform is to facilitate exploration, modeling, and understanding of earth data for scientists, stakeholders, and concerned citizens whose objectives align with the new OSSI goals. The <em>NASAaccess</em> platform is available as software packages (i.e., R and conda packages) as well as an interactive format web-based environmental modeling application for earth observation data developed with the Tethys Platform. The <em>NASAaccess</em> has been envisioned to lower the technical barriers and simplify the process of accessing scalable distributed computing resources and leverage additional software for data and computationally intensive modeling frameworks. Specifically, <em>NASAaccess</em> is developed to meet the need for seamless earth observation remote sensing and climate data ingestion into various hydrological modeling frameworks. Moreover, <em>NASAaccess</em> is also contributing to keep interested parties and stakeholders engaged with environmental modeling, accessing the information available in various remote sensing products. <em>NASAaccess</em> current capabilities covers various NASA datasets and products that include the Global Precipitation Measurement (GPM) data products, the Global Land Data Assimilation System (GLDAS) land surface states and fluxes, and the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) Coupled Model Intercomparison Project Phase 5 (CMIP5) &amp; Coupled Model Intercomparison Project Phase 6 (CMIP6) climate change dataset products.

Effect of climate and socioeconomic changes on future surface water availability from mountainous water sources in Pakistan's Upper Indus Basin

The use of CMIP5 data to simulate climate change impacts on flow regime within the Lake Champlain Basin

Potential effects of projected climate variability on base flow and groundwater storage in the North Fork Red River aquifer, Oklahoma (USA), were estimated using downscaled climate model data coupled with a numerical groundwater-flow model. The North Fork Red River aquifer discharges groundwater to the North Fork Red River, which provides inflow to Lake Altus. To approximate future conditions, Coupled Model Intercomparison Project Phase 5 climate data were downscaled to the watershed and a time-series of scaling factors were developed and interpolated for three climate scenarios (central tendency, warmer and drier, and less warm and wetter) representing future climate conditions for the period 2045–2074. These scaling factors were then applied to a soil-water-balance model to produce groundwater recharge and evapotranspiration estimates. A MODFLOW groundwater-flow model of the North Fork Red River aquifer used the scaled recharge and evapotranspiration data to estimate changes in base flow and water-surface elevation of Lake Altus. Compared to a baseline scenario, the mean percent change in annual base flow during 2045–2074 was −10.8 and −15.9% for the central tendency and warmer/drier scenarios, respectively; the mean percent change in annual base flow for the less-warm/wetter scenario was +15.7%. The mean annual percent change in groundwater storage for the central tendency, warmer/drier, and less-warm/wetter climate scenarios and the baseline are −2.7, −3.2, and +3.0%, respectively. The range of outcomes from the climate scenarios may be influenced by variability in the downscaled climate data for precipitation more than for temperature.

Advances in weather and climate research have demonstrated the role of the stratosphere in the Earth system across a wide range of temporal and spatial scales. Stratospheric ozone loss has been identified as a key driver of Southern Hemisphere tropospheric circulation trends, affecting ocean currents and carbon uptake, sea ice, and possibly even the Antarctic ice sheets. Stratospheric variability has also been shown to affect short-term and seasonal forecasts, connecting the tropics and midlatitudes and guiding storm-track dynamics. The two-way interactions between the stratosphere and the Earth system have motivated the World Climate Research Programme's (WCRP) Stratospheric Processes and their Role in Climate's (SPARC) activity on Modelling the Dynamics and Variability of the Stratosphere-Troposphere System (DynVar) to investigate the impact of stratospheric dynamics and variability on climate. This assessment will be made possible by two new multimodel datasets. First, roughly 10 models with a well-resolved stratosphere are participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), providing the first multimodel ensemble of climate simulations coupled from the stratopause to the sea floor. Second, the Stratosphere Resolving Historical Forecast Project (Strat-HFP) of WCRP's Climate Variability and Predictability (CLIVAR) program is forming a multimodel set of seasonal hind-casts with stratosphere-resolving models, revealing the impact of both stratospheric initial conditions and dynamics on intraseasonal prediction. The CMIP5 and Strat-HFP model datasets will offer an unprecedented opportunity to understand the role of the stratosphere in the natural and forced variability of the Earth system and to determine whether incorporating knowledge of the middle atmosphere improves seasonal forecasts and climate projections.

Leveraging climate and remote sensing metrics for predicting forest carbon stock using Bayesian geostatistical modelling under a projected climate warming in Zimbabwe

Climate change significantly alters the low-flow regimes of river basins worldwide and presents significant challenges to water-scarce regions, especially in dry regions. This current study investigates the impact of climate change on projected low-flow variations in the Maduru Oya River Basin in Sri Lanka, focusing on the reach to the Padiyathalawa stream-gauge station. The study utilizes a lumped hydrological modeling framework, which used the HEC-HMS rainfall-runoff model to simulate streamflow behavior considering anticipated climate scenarios. Projections for future precipitation were obtained from the CNRM-CM6-1 Global Climate Model (GCM), which is part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), and subsequently downscaled through the Long Ashton Research Station Weather Generator (LARS-WG) according to two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The precipitation data, downscaled to the local scale, were integrated into the HEC-HMS model to forecast future river discharge and investigate possible changes in low-flow characteristics. The 7Q10 low-flow index, which is defined as the minimum average flow in a continuous seven-day period with a recurrence interval of ten years was used for estimating and comparing low-flow characteristics. The model parameters were calibrated and validated using historical data from 1997 to 2019. Three objective functions namely: Nash-Sutcliffe Efficiency (NSE), Mean Relative Absolute Error (MRAE), and Percent Error in Peak Flow (PEPF) were used for optimizing model parameters. Future precipitation was projected for short-term (2021-2040), medium-term (2041-2060), and long-term (2061-2080) durations. The projected precipitation data was subsequently input into the developed HEC-HMS model to obtain future streamflow projections for the specified periods. The results of the climate change scenario analysis showed that precipitation may vary due to climate change within the range of -16 % to -5 % for the 2021-2040 period, - 4 % to 1 % for the 2041-2060 period, and 1 % to 21 % for the 2061-2080 period. The results indicated a likely increase in low-flow values across both SSP scenarios. The flow-duration analysis showed that the Q90 flow, representing the flow level that exceeds 90% of the time, is expected to increase, reflecting an upward change in streamflow for low-flow conditions. These findings are important for water resource managers working in the area to plan for and adapt to the impacts of altered low-flow regimes that can impact water supply, agriculture, and overall ecosystem health. Further studies should consider incorporating the use of hydrological models coupled with diverse climate scenarios to better capture the uncertainties related to climate predictions and land-use changes. These would provide a better understanding of the impacts of climate change on river basin hydrology in dry regions like the Maduru Oya River Basin.

The rapid expansion of Earth system model (ESM) data available from the Coupled Model Intercomparison Project Phase 6 (CMIP6) necessitates new methods to evaluate the performance and suitability of ESMs used for hydroclimate applications as these extremely large data volumes complicate stakeholder efforts to use new ESM outputs in updated climate vulnerability and impact assessments. We develop an analysis framework to inform ESM sub‐selection based on process‐oriented considerations and demonstrate its performance for a regional application in the US Pacific Northwest. First, a suite of global and regional metrics is calculated, using multiple historical observation datasets to assess ESM performance. These metrics are then used to rank CMIP6 models, and a culled ensemble of models is selected using a trend‐related diagnostics approach. This culling strategy does not dramatically change climate scenario trend projections in this region, despite retaining only 20% of the CMIP6 ESMs in the final model ensemble. The reliability of the culled trend projection envelope and model response similarity is also assessed using a perfect model framework. The absolute difference in temperature trend projections is reduced relative to the full ensemble compared to the model for each SSP scenario, while precipitation trend errors are largely unaffected. In addition, we find that the spread of the culled ensemble temperature and precipitation trends includes the trend of the “truth” model ∼83%‐92% of the time. This analysis demonstrates a reliable method to reduce ESM ensemble size that can ease use of ESMs for creating and understanding climate vulnerability and impact assessments.

<strong class="journal-contentHeaderColor">Abstract.</strong> Root-zone soil moisture is a key variable representing water cycle dynamics that strongly interacts with ecohydrological, atmospheric, and biogeochemical processes. Recently, it was proposed as the control variable for the green water planetary boundary, suggesting that widespread and considerable deviations from baseline variability now predispose destabilization of Earth System functions critical to an agriculture-based civilisation. However, the global extent and severity of root-zone soil moisture changes under future scenarios remains to be scrutinized. Here, we analyzed root-zone soil moisture departures from the pre-industrial climate variability for a multi-model ensemble of 14 Earth System Models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in four climate scenarios as defined by the Shared Socioeconomic Pathways (SSP), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, between 2021 and 2100. The analyses were done for 43 ice-free climate reference regions used by the Intergovernmental Panel on Climate Change (IPCC).We defined &lsquo;permanent departures&rsquo; when a region&rsquo;s soil moisture exits the regional variability envelope of the pre-industrial climate and does not fall back into the range covered by the baseline envelope until 2100. Permanent dry departures (i.e. lower soil moisture than pre-industrial variability) were found to be most pronounced in Central America, southern Africa, the Mediterranean region, and most of South America, whereas permanent wet departures are most pronounced in southeastern South America, northern Africa, and southern Asia. In the Mediterranean region, permanent departure may have already happened according to some models. By 2100, there is permanent departure in the Mediterranean in 60 % of the ESMs in SSP1-2.6, the most mitigated situation, and 100 % in SSP3-7.0 and SSP5-8.5, the medium-high and worst-case scenarios. North-Eastern Africa is projected to experience permanent departures in 50 % of the ESMs under SSP1-2.6, and 86 % under SSP5-8.5. The percentage of ice-free land area with departures increases in all SSP scenarios as time goes by.Wet departure are more widespread than dry departures throughout the studied timeframe, except in SSP1-2.6. In most regions, the severity of the departures increases with the severity of global warming. In 2050, permanent departures (ensemble median) occur in 10 % of global ice-free land areas in SSP1-2.6, and in 25 % in SSP3-7.0. By the end of the 21st century, the occurrence of permanent departures in SSP1-2.6 increases to 34 %, and in SSP3-7.0, 45 %. Our findings underscore the importance of mitigation to avoid further degrading the Earth System functions upheld by soil moisture.

<strong class="journal-contentHeaderColor">Abstract.</strong> Root-zone soil moisture is a key variable representing water cycle dynamics that strongly interacts with ecohydrological, atmospheric, and biogeochemical processes. Recently, it was proposed as the control variable for the green water planetary boundary, suggesting that widespread and considerable deviations from baseline variability now predispose destabilization of Earth System functions critical to an agriculture-based civilisation. However, the global extent and severity of root-zone soil moisture changes under future scenarios remains to be scrutinized. Here, we analyzed root-zone soil moisture departures from the pre-industrial climate variability for a multi-model ensemble of 14 Earth System Models (ESMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in four climate scenarios as defined by the Shared Socioeconomic Pathways (SSP), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, between 2021 and 2100. The analyses were done for 43 ice-free climate reference regions used by the Intergovernmental Panel on Climate Change (IPCC).We defined &lsquo;permanent departures&rsquo; when a region&rsquo;s soil moisture exits the regional variability envelope of the pre-industrial climate and does not fall back into the range covered by the baseline envelope until 2100. Permanent dry departures (i.e. lower soil moisture than pre-industrial variability) were found to be most pronounced in Central America, southern Africa, the Mediterranean region, and most of South America, whereas permanent wet departures are most pronounced in southeastern South America, northern Africa, and southern Asia. In the Mediterranean region, permanent departure may have already happened according to some models. By 2100, there is permanent departure in the Mediterranean in 60 % of the ESMs in SSP1-2.6, the most mitigated situation, and 100 % in SSP3-7.0 and SSP5-8.5, the medium-high and worst-case scenarios. North-Eastern Africa is projected to experience permanent departures in 50 % of the ESMs under SSP1-2.6, and 86 % under SSP5-8.5. The percentage of ice-free land area with departures increases in all SSP scenarios as time goes by.Wet departure are more widespread than dry departures throughout the studied timeframe, except in SSP1-2.6. In most regions, the severity of the departures increases with the severity of global warming. In 2050, permanent departures (ensemble median) occur in 10 % of global ice-free land areas in SSP1-2.6, and in 25 % in SSP3-7.0. By the end of the 21st century, the occurrence of permanent departures in SSP1-2.6 increases to 34 %, and in SSP3-7.0, 45 %. Our findings underscore the importance of mitigation to avoid further degrading the Earth System functions upheld by soil moisture.

Climate change has emerged as a critical issue that demands the attention of global communities, becoming a primary concern for many nations. Sri Lanka's Central Province is particularly vulnerable to these shifts. This study aims to outline the expected patterns of temperature and rainfall in the Central Province under different climatic scenarios, using various models across diverse socio-economic pathways. The data for this analysis were obtained from the global coordination system established by the Coupled Model Intercomparison Project Phase 6. Utilizing carbon emission scenarios SSP2-4.5 and SSP5-8.5, a range of models were examined, and overarching conclusions were drawn using MiniTab 17 software. Under the SSP2-4.5 scenario, the consolidated results from the various models suggest that the average projected temperature for the Central Province from 2020 to 2100 will rise by 1.17°C, with May identified as the warmest month, experiencing a temperature increase of 1.33°C. Average rainfall is expected to increase by 105.56 mm, with November showing the most significant rise at 124.62 mm. In the context of the SSP5-8.5 scenario, the combined results indicate that the average temperature is projected to rise by 1.94°C during the same period, with May again exhibiting the highest temperature increase at 2.17°C. Average rainfall is expected to increase by 125.01 mm, with November encountering the greatest uplift at 184.77 mm. By understanding the projected temperature and rainfall changes in the Central Province, sustainable development in the region can be strengthened, thereby contributing to Sri Lanka's broader economic advancement.