Global Water Basins under Combined Climate Mitigation, Adaptation, and Sustainable Development Targets

Global Water Basins under Combined Climate Mitigation, Adaptation, and Sustainable Development Targets | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global Water Basins under Combined Climate Mitigation, Adaptation, and Sustainable Development Targets Muhammad Awais, Adriano Vinca, Edward Byers, Marina Andrijevic, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4149842/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Water systems are crucial for sustainable development and energy-water-land system-wide climate mitigation and adaptation to changing climate. We explore the dynamic interplay between climate change adaptation, mitigation, and sustainable development within these systems. We demonstrate, how strategic investments in a “Resilience scenario” – climate mitigation and Sustainable Development Goals (SDGs) combined, leads to approximately 20% reduction in water stress for vulnerable populations promoting sustainable water and energy systems. Despite the Resilience scenario targets, about one-third of global population (2.8 billion people) is expected to face adaptation barriers by mid-century, hindered by inadequate governance capacity. We demonstrate that investments in SDGs, combined with climate mitigation strategies, are more beneficial for the water sector than pursuing SDGs alone. Our findings suggest a necessary convergence of sustainable development, climate resilience, and human welfare to create a climate-resilient sustainable future, marking a significant step forward in facilitating integrated environmental policy and decision-making. Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Earth and environmental sciences/Environmental social sciences/Climate-change adaptation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Articles 2 and 4 of the Paris Agreement emphasize the need to integrate climate resilience and greenhouse gas emissions reduction into sustainable development objectives. While the alignment of climate actions with the sustainable development objectives have been somewhat addressed (Bertram et al., 2018; Soergel et al., 2021), the nuanced interplay between adaptation, mitigation and sustainable development often remains underrepresented in the global studies (Andrijevic et al., 2023; van Maanen et al., 2023). The limited focus on integration of multiple societally relevant objectives within sectorally integrated frameworks constrains our ability to understand synergies and tradeoffs that may emerge from simultaneously achieving those multiple objectives. In absence of such an investigation, proposing sustainable and climate-resilient development pathways becomes increasingly challenging. This paper focuses on pathways building resilience in the Energy-Water-Land (EWL) nexus, which requires understanding and management of complex interactions between mitigation, adaptation actions and related Sustainable Development Goals (SDGs) (Mpandeli et al., 2018; Schipper et al., 2022). In this context, multisectoral Integrated Assessment Models (IAMs) can be used to understand pathways to avoid isolated decision-making, effectively capture these complexities across multiple sectors, and identify the trade-offs and synergies between adaptation and mitigation strategies (Andrijevic et al., 2023; van Maanen et al., 2023). The water system remains fundamental in the system transitions for climate adaptation and mitigation, and it necessitates a more nuanced comprehension of its intersection with the energy and land sectors. Depending on the existing and future incentives, policies, and governance, many adaptation and mitigation measures can result in either synergistic or maladaptive effects on water use. It is crucial to manage the substantial water footprint of numerous mitigation measures in a socially, environmentally, and politically acceptable manner, to reduce water intensity and enhance alignment with sustainable development ((IPCC), 2023). Climate mitigation policy assessments have typically focused on energy and land, leaving water unrepresented, a crucial component for achieving local and global climate objectives(Miralles-Wilhelm, 2021). This fragmented focus may impede a comprehensive understanding of climate change impacts and the efficacy of mitigation measures within the constraints of water systems (Douville et al., 2022). Recent advances in IAMs provide the potential for a deeper understanding of water system boundaries under climate policies, but the existing studies are somewhat fragmented in their approaches. (Parkinson et al., 2019) assessed water infrastructure costs for climate mitigation and sustainable development targets but lacks representation of hydrological boundaries, biophysical climate impacts and adaptation. (Kahil et al., 2018) developed a continental scale hydro-economic model albeit with less detailed energy and land policy consideration. Recent studies such as (De Vos et al., 2021) and (Doelman et al., 2022) investigated trade-offs and synergies at various scales, offer valuable insights into the system's intricate dynamics and have the potential to explore the adaptation dimensions of the water system. To fully understand the risks and damages associated with climate change, it is crucial to incorporate biophysical climate impacts from Climate Impact Models (CIMs) into Integrated Assessment Models (IAMs). IAMs emphasize tradeoffs between adaptation and mitigation, aligning with climate targets and sustainable development, fostering resilience, and addressing SDGs in water systems until the year 2100. (Köberle et al., 2021; Piontek et al., 2021; Rising et al., 2022; van Maanen et al., 2023) . In assessing the complex role of water systems and the climate impacts in the IAMs we utilize two IAMs: MESSAGEix-GLOBIOM and IMAGE. In addition to expertly navigating the complexities of global energy systems, the MESSAGEix-GLOBIOM model provides profound techno-economic insights into land use, particularly regarding biomass and emission mitigation and an understanding of water supply system (Awais et al., 2023; Krey et al., 2016). In contrast, IMAGE provides a global perspective on the environmental repercussions of human activities, comprising a variety of challenges ranging from climate change to biodiversity loss (D. van Vuuren et al., 2021). Even though both models provide distinct insights, they are combined to generate common scenarios that provide robust and nuanced perspectives on our multidimensional climate paradigm. Throughout our analysis, results are occasionally derived solely from one model or jointly from both, ensuring a complete and complementary representation of our findings. Towards Climate Resilient Development Pathway We develop an integrated scenario design within two IAMs. One scenario incorporates biophysical climate impacts and examines adaptation responses, and another combines the mitigation targets to keep global warming below 2 0 C and the Sustainable Development Goals (SDGs) (Figure 1). The conventional IAM scenarios, based on Representative Concentration Pathways (RCP) 6.0 and 2.6 that do not incorporate biophysical impacts, are here termed as CurrentPolicy and Mitigation, respectively. Our work combines the biophysical impacts of climate change with SDG targets, for indicators such as the cooling technology capacity factors for thermal power plants, renewable water supply,In addition, in the energy sector, we consider impacts on heating and cooling requirements, as well as hydropower supply curves. In the land sector, we include climate impacts on yields. Model assumptions and details are summarized in Table 1. These biophysical impacts allow the systemic response to climate change from models in form of adaptation such as increased crop yields or reduced dependency on fossil fuels. Scenarios combining RCP 6.0 with biophysical impacts are therefore termed as Adaptation in this study. Table 1 . Included climate impacts and underlying approach/data Biophysical climate impacts Approach Renewable supply (hydro) Renewable costs supply curves based on 0.5x0.5 grid calculations (Gernaat et al., 2021). MESSAGEix -GLOBIOM uses hydropower cost supply curves only. Heating/cooling demand Impact via population-weighted heating and cooling demands based on the work of (Byers et al., 2018; Mastrucci et al., 2021) 0.5 x 0.5 grid Water availability Runoff and groundwater recharge from CWatM calculated at 0.5 x 0.5 grid (Burek et al., 2020) for MESSAGEix-GLOBIOM. IMAGE 3.2 uses LPJmL hydrological model outputs (Schaphoff et al., 2018) Crop yields Climate impacts on crop productivity, nitrogen, and irrigation from the CMIP6 projections of the crop-model EPIC-IIASA are used in GLOBIOM. EPIC-IIASA estimates the impact of climate on rice, maize, wheat, and soy and feeds into GLOBIOM following (Müller & Robertson, 2014) IMAGE uses crop yield outputs from LPJmL (Schaphoff et al., 2018) Cooling technology capacity factor Climate impacts on cooling water discharges for cooling technologies of fossil power plants are used from (Yalew et al., 2020) only in MESSAGEix-GLOBIOM. Desalination potential Desalination potential climate impacts are based on water stress outputs from the combinations of GHMs & GCMs from (Byers et al., 2018) only in MESSAGEix-GLOBIOM 1 IMAGE uses IPSL-CM5A-LR climate model. 2 MESSAGEix-GLOBIOM uses GFDL-ESM2M (for water availability), UKESM1-0-LL (for crop yields), and HadGEM2-ES (for heating/cooling demand). This choice has been motivated by the preferences from different models itself. We integrate SDGs into our scenarios, highlighting their crucial role in developing sustainable and resilient paths. The SDG targets, outlined in Table 2, encompass aims such as diet shifts, minimizing food waste, increasing efficiency of irrigation systems, and advancing electricity production. These targets serve as a framework for aligning environmental and developmental aspirations. The Resilience scenario combines progress towards selected SDGs and mitigation measures. The Resilience scenario considers a holistic pathway pertinent to the energy, water, and land (EWL) sectors, thereby providing a more sustainable development perspective in the context of mitigation and adaptation. It concurs with the definition of Climate Resilient Development Pathway defined in the IPCC AR6 report as " The process of implementing greenhouse gas mitigation and adaptation measures to support sustainable development for all" (Schipper et al., 2022). Table 2 Sustainable Development Goals SDG Measure - IMAGE Measure - MESSAGEix-GLOBIOM SDG2 - Hunger Change towards a healthy diet . Transition towards healthy diets including reduced meat consumption, healthy total calorie intake and increased vegetable intake following (Willett et al., 2019). < 1% undernourishment goal by 2030 Decrease of animal calorie intake to 430 kcal/capita/day by 2030 (USDA recommendations for healthy diets) Reduce food waste. High income countries and middle-income regions reduce food waste to the lowest level among them, in three food supply chain stages (primary, processing, consumption), for six commodities groups (cereals, roots and tubers, oilseeds and pulses, fruit and vegetables, milk, meat) 50% reduction in food waste compared to SSP2 assumptions SDG6 - Water Efficiency improvement for irrigation : Not included Limited irrigation water consumption in agriculture to sustainable removal rates that do not jeopardize ecosystem services and environmental flows (Frank et al., 2021a) Efficiency improvement for electric power generation : -59% in 2050 compared to a baseline case based on (D. P. Van Vuuren et al., 2019) Not included Implement environmental flow constraints. Based on the variable monthly flow (VMF) method developed by (Pastor et al., 2014, 2019) where 60%, 45% and 30% of the mean monthly natural flow is reserved for ecosystems in low, intermediate and high flow periods, respectively. Based on the variable monthly flow (VMF) method developed by (Pastor et al., 2014, 2019)where 60% and 30% of the mean monthly natural flow is reserved for ecosystems in low and high flow periods, respectively. Efficiency improvement for industry : -5% in 2050 compared to a baseline case based on: (D. P. Van Vuuren et al., 2019) Not included Universal piped water access, wastewater collection and improved wastewater treatment capacity Not included Minimum of half all return flows are treated by 2030 for developed regions and 2040 for developing regions. SDG7 - Energy Maximized electricity access On-grid electrification only, based on SSP1 (D. van Vuuren et al., 2021) assumptions (98% in 2030). Results from the MESSAGEix-GLOBIOM are iterated through the MESSAGE-Access-E-USE (end-use services of energy) model by provision of access targets based on income levels and GDP pathways and population with access to modern energy access and the energy demand adjustments are calculated. Minimized traditional bio and coal in cooking and heating Based on SSP1 assumptions (90% reduction of traditional bio in 2050) 90 % access target to modern cooking energy for cooking by 2030 SDG15 (Life on land) Implement protected land maps As in SSP1 (+/-30% protected) (D. van Vuuren et al., 2021) Based on (Frank et al., 2021b), 34% protection in 2030 was assumed and additional area based on the UNEP- WCMC Carbon and Biodiversity Report to identify highly biodiverse areas and prevent their conversion to agriculture or forest management from 2030 onwards. Focusing specifically on the water sector, we examine the trade-offs between measures for adapting to and mitigating the effects of climate change. Based on the evidence documented in more than 50% of accessed literature by (Berrang-Ford et al., 2021), that governance is a stronger barrier than finance for water sectior, we assess basins’ adaptive capacities by combining IAM outcomes with governance projections by (Andrijevic et al., 2020). This helps inn identifying which basins are likely to success or struggle in adapting. We categorize worldwide basins into tiers based on their adaptive capacity by combining IAM results with governance indicators. Figure 1 concisely presents our scenario design, providing a full depiction of the study's framework and improving the clarity and accessibility of our approach. Our study examines how mitigation options might be aligned with SDGs in the energy, water, and land sectors and what the implications are for the water sector. Our analysis highlights how the resilience scenario could shift global water and energy systems towards sustainability. By prioritizing investments that reduce future climatic vulnerabilities and lead to substantial decreases in water withdrawals, we can significantly reduce the population's risk of water stress. Approximately 2.8 billion people would live in environments of governance-driven low adaptive capacity, despite efforts to achieve resilience targets. This highlights the importance of integrating water management into the wider climate policy framework, acknowledging its crucial role in both adaptation and mitigation initiatives. Our findings suggest that prioritizing SDG investments with mitigation efforts is more advantageous for the water sector than pursuing SDGs without a concurrent mitigation strategy. Balancing Synergies and Trade-Offs in Resource Stewardship To achieve both SDGs and meet mitigation targets, the Resilience scenario requires a discernible increase in investments, approximately 22% higher than the CurrentPolicy. 85 % of these investments are attributed to energy supply while the rest are to water-related adaptations. These allocations underscore a prioritized strategy aiming at mitigating future climate-related vulnerabilities and uncertainties. Region-specific highlight the highest investments are required in the Middle East (1.75%. of global GDP), Africa (0.9%. of global GDP), and Asia (2.2% of global GDP), reflecting the distinctive socioeconomic and geophysical challenges inherent to these regions (Figure 2). In the context of water withdrawal, the Resilience scenario implies a global reduction of approximately 30-35 % by 2050 thus representing a meaningful resilience pathway. This reduction is primarily attributed to improvements in crop yields and the efficiency of irrigation methods. Notably, there is a gain of 2% in the average global crop yield, with specific crops indicating more substantial growth rates: sugar crops by 5%, cereals by 4%, and oil crops by 4% (Supplementary Figure 6). We also observed a decrease of 35-45% in water use for electricity production in the Resilience scenario (Supplementary Figure 4). The decrease is attributed to the climate impacts of cooling technology of thermal power plants and the fossil fuel phase. Asia, being a hotspot for climate change, having the highest population and the highest amount of global water withdrawals, has comparatively lower level of water withdrawal reductions in the Resilience scenario when compared to other regions, such as OECD 90 countries over the time horizon (Figure 2). This is attributed to the increasing pressures caused by the high population density. However, in the Resilience scenario, a greater abundance of surface water is available globally, although the extent of it varies between different basins. In some basins (such as Ganges Bramaputra, Danube, North Colorado) the hydrological models in the RCP 6.0 scenario demonstrate a rise in water availability as compared to RCP2.6. In general, the regions of Asia, the Middle East, and the Former Soviet Union are exhibiting a steady decrease in surface water availability, with reductions ranging from 5% to a maximum of 20% over the future time horizon. In various geographical areas, the average percentage change often falls within -10% to 10%. These variances mostly rely on the specific geophysical characteristics of each region (Supplementary Figure 1). The results indicate a decrease in the population's exposure to water stress by 40-50% in the Resilience scenario compared to 65-70% in CurrentPolicy (Supplementary Figure 4). The Resilience scenario foresees an increase in access to vital resources such as drinking water (Supplementary Figure 3), clean cooking fuels, sanitation facilities which impacts the combined pathway outlook and implied investments. Financing the Water Transition Considering the increasing challenges for global water systems, it is crucial to understand the often-overlooked costs (investments and operational) associated with water supply from the pathways of IAMs. The Resilience scenario outlines an average yearly investment of 535 billion USD/yr. for global basins, slightly exceeding the 527 billion USD required for Adaptation. The small increase is balanced out by fair allocation of water resources and achieving mitigation targets. The costs of water include renewable water supply, non-renewable groundwater, wastewater reuse, desalination, and cooling technologies for thermal power plants. The relative scale of these investments depends on the socioeconomic context of the region. In the Resilience scenario, North America and Western Europe have the highest absolute per capita investment in water supply with, 141 & 86 USD/capita/yr., respectively. In the future, investments in Africa are projected to rise from 14 USD per capita per year (2020-2050) to 49 USD per capita per year (2060 onwards) (Figure 3). In contrast, South Asia & Sub-Saharan Africa needs to invest additional 0.05% and 0.05% of the regional GDP per year for a Resilient transition. These areas simultaneously have growing demands for water and limitations in adaptive capacity, making resilient pathways difficult to attain. Moreover, some basins, such as the Rift Valley, Africa North Interior, Caspian Sea East Coast, Irrawaddy Salween Sittang, Arabian Sea Coast, Amazon, Australian Sea Cost, Niger, and Ganges Brahmaputra in the Central Asian region, have lower costs of resilience pathway as compared to baseline thus implying economic benefits compared to other sectoral synergies. In the Ganges Brahmaputra basin, growing pressure on freshwater resources drives a shift to increasing levels of wastewater reuse, with up to 50% required for high reliability and an additionally growing share of desalination. Non-renewable groundwater is also exploited but is indicated as a last-resort option because it is unsustainable. These shifts to alternative water sources also require a 70% increase in energy use for water supply compared to the baseline. The Indus River Basin shows insights into diversification of water supply sources under freshwater stress. The significant water stress experienced in this region has consistently resulted in limited access to freshwater resources. The limited availability of water resources necessitates using additional sources, such as desalinated water, estimated to be roughly 0.4 km3/yr in 2050. Notably, the landscape changes according to different scenarios of reliability. In situations characterized by low reliability, desalinated or unsustainable groundwater sources are underutilized. In contrast, high reliability levels indicate a significant shift towards alternative water sources such as desalination and brackish water. These sources are employed to fulfill the water demands, amounting to around 76 km3/yr. Under the Resilience scenario, the North Colorado Basin also presents a different perspective: a shift towards alternative water sources. This is reflected in the significant growth in the utilization of desalination, which is projected to double by the mid-century. The wastewater reuse remains same as the basin is relatively more developed and does not require further increases. For all basins there is a declining trend in investments in power plant cooling systems due to the reduced dependence on fossil fuels within the electricity sector as outlined in the Resilience scenario. For example, in the Danube Basin, the energy-related water consumption decreases by 70% under the Resilience scenario compared to the Baseline. Water distribution costs make up a significant portion of the overall water costs. The costs under the resilience scenario increase due to the targets that ensure reliable and clean drinking water availability, and secure piped connections in both industrial and residential sectors. Governance as barrier to adaptation in water Our study outlines the pivotal role of water system within the overarching framework of global climate narratives and uses multi sectoral IAM scenarios to support the analysis. The increased demand for long-lasting water infrastructure requires increased investements in a scenario that resembles a Climate Resilient Development Pathway. But the long-term benefits of effective water management outweigh the high costs in the short-run. Notably, these anticipated costs could change depending on improvements in governance or advances in technology, both of which could also impact the GDP. Adaptation is not equally feasible in all regions of the world. After financial constraints, the level of governance and quality of institutions are the most important limiting factor for adaptation globally (Berrang-Ford et al., 2021). To link the required investments with levels of governance, we use projections of the Worldwide Governance Index (1 – very good, 0 – very poor) (Andrijevic et al., 2020) as a proxy for adaptive capacity. For example, the Ganges River basin currently has moderate capacity (0.6) to implement the required investments. Comparing African basins such as Lake Chad, Niger, Congo, and Tigris Euphrates with those located in the northern latitudes, the investment needs results should be interpreted carefully. Although the investments per GDP needed in the African basins may seem relatively small, governance ratings of around 0.5 indicate much less capacity for effective implementation. The noticeable deficit in adaptative capacity within these basins serve as a clear indication of their vulnerability to the impacts of climate change and the need for effective strategies to enhance their resilience. The discrepancy in governing abilities (Table 3) highlights a clear disparity in potential for adaptation: Tier 1, with high governance values, encompasses 1.1 billion people more amenable to adaptation. Tier 2, marked by medium governance values, includes 4.7 billion people who encounter moderate adaptation difficulties. Tier 3, characterized by low governance values, implicates 2.8 billion people who will face considerable challenges in adaptation. Our analysis combines conceptual and quantitative explanations laying the groundwork in the nexus of water management and climate change. Prioritizing transformational adaptation alone would be insufficient; strategic investments are essential to actualize the Climate Resilient Development Pathway, enhancing our adaptative potential while mitigating costs. A resilient future can be obtained by the convergence of sustainable development, climatic resilience, and human welfare. While the Adaptation scenario's investments seem modest in comparison to those of the Resilience scenario, it is important to note that the latter's model outputs likely underestimate the cost of climate change impacts in a world that forgoes mitigation strategies. Though relevant, this discussion extends beyond the scope of this paper. It is crucial to note that although our study details increased mitigation costs in the Resilience scenario, the real-world socio-economic impacts of climate change in a Baseline scenario, which the world fails to mitigate, could substantially exceed these costs. This investment-focused overview introduces the complexities of the examined scenarios and highlights the potential economic consequences of insufficient climate change action. This lays the groundwork for a comprehensive analysis of the trade-offs and synergies inherent to each scenario. In conclusion, direct investment comparisons across basins should be undertaken cautiously due to the inherent discrepancies in adaptive capacities. The investment in a basin like the Niger cannot be directly compared to that of the Danube region, given the variable governance scores. The deficiency in adaptive capacity in certain basins poses a barrier to establishing resilient pathways, thus stressing the need for customized approaches that consider the unique challenges and capacities of each basin. Contextualizing this analysis with the basins’ governance levels, offers a perspective on their respective challenges for adaptation. Notably, approximately 2.8 billion people are exposed to adaptation barriers and challenges to pursue resilience pathways due to inadequate governance in 2050. Our findings advocate for the coalescence of adaptation and resilience to capitalize on co-benefits, highlighting the importance of basin-specific socio-economic conditions. This conclusive section elucidates the critical nature of tailored, strategic approaches to enhance the resilience of each basin, ensuring sustainable adaptation and the accrual of significant co-benefits within our global water systems. This study lays the groundwork for a nuanced understanding of the interplay between sustainable water management, climate resilience, and human welfare, charting a course for informed decision-making in the face of climatic uncertainties. Methods MESSAGEix-GLOBIOM Nexus The MESSAGEix-GLOBIOM Integrated Assessment Model, built upon the MESSAGEix framework, optimizes energy systems within an open-source setting, enriched by macro-economic feedback through a stylized computable general equilibrium model (Huppmann et al., 2019).This optimization proceeds iteratively, in concert with the MACRO economic model which forecasts the macroeconomic demand response to the energy system and service costs calculated by MESSAGEix. The MESSAGEix-GLOBIOM Nexus has introduced an innovative water module that encapsulates hydrological variables and an expansive portrayal of water supply and demand. This module accounts for biophysical climate impacts on water and energy supply and demand, bridging the MESSAGEix energy system with the GLOBIOM land system.Further enhancements in the model include a detailed sector-specific representation of the water system and climate impacts. The model integrates endogenous water allocation mechanisms, precise in both space and time. These mechanisms orchestrate the interplay between energy, land use, and water requirements, considering how water scarcity might restrict energy and land resource utilization (Awais et al., 2023). The framework's versatility is demonstrated by its ability to integrate outputs from any Global Hydrological Model (GHM), exemplified by its use of the CWatM global hydrological model (Burek et al., 2019).It incorporates a spatial layer with high resolution into the IAM, safeguarding vital hydrological data. Employing the HydroShed Database's basin delineation Lehner & Grill, 2013), the model spans 210 hydrological basins. By aligning water sector insights with the eleven global regions of the MESSAGE energy system IAM, the framework translates hydrological data into energy sector metrics and vice versa. It meticulously matches water supply with demand at the basin level and specifies water needs for energy and land use within the MESSAGE R11 regions. The MESSAGEix-GLOBIOM Nexus tracks municipal and industrial water demands, alongside the water used in power plant cooling, energy extraction, and irrigation. It sources water from diverse origins, including surface, groundwater, and desalination processes. Furthermore, it encompasses wastewater treatment infrastructure to monitor wastewater volumes and calculate the investments needed for various treatment goals. This comprehensive monitoring of water demands facilitates future integrated analysis of climate feedback and adaptation pathways. The IAM incorporates climate feedback concerning water availability, desalination capacity, hydropower potential, and land-use variables (bioenergy, irrigation water) to reflect the impacts of RCP forcings within the system (Awais et al., 2023). IMAGE 3.2 The Integrated Assessment Model (IAM) framework of IMAGE (D. van Vuuren et al., 2021) simulates the global and regional environmental effects of alterations in human activity. The IMAGE model includes comprehensive descriptions of the energy and land use systems. It simulates most socio-economic and environmental parameters for 26 regions based on a geographical grid of 30 by 30 minutes or 5 by 5 minutes (approximately 50 km and 10 km at the equator, respectively). IMAGE is designed to assess large-scale and long-term interactions between human development and the natural environment and to identify response strategies to global environmental change by evaluating mitigation and adaptation options. The IMAGE 3.2 model has been completely calibrated through 2015, with some variables calibrated through even more recent years (2018 for energy system variables and 2020 for renewables capacity and CO2 emission data). The model includes the economic effects of the Covid pandemic in recent years. In IMAGE 3.2, the base year for scenario analysis is 2020, implying that scenarios follow the same trajectory from 2015 to 2020. The modeling of energy demand has been enhanced, particularly in end-use sectors such as transportation, industry, structures, and services. The model contains technology descriptions for each industry segment. In addition, bioenergy modeling has been considerably improved by incorporating dynamic land-use change emission factors based on the IMAGE-land model and introducing biofuel production with carbon capture and storage technology pathways. The number of produce categories in the land use sector was increased to 16, representing all FAO-reported crop production. Based on FAO data and ESA-CCI satellite data, deforestation caused by factors other than agricultural expansion has decreased. Concerning climate change effects and exogenous and endogenous trends in crop yield changes, the link between the agriculture-economic model MAGNET and the IMAGE model was significantly enhanced. The modeling of land-based Mitigation in IMAGE and MAGNET has been improved to account for avoided deforestation and afforestation, as well as the interaction between non-CO2 mitigation and the agriculture and food system. The water modeling in IMAGE that is linked to LPJmL was enhanced by incorporating municipal, energy, and industry water demand into LPJmL, allowing environmental flow requirements to be accounted for. The marginal abatement cost contours for all greenhouse gases other than CO2 have been updated based on recent research. Scenario Assumptions Both models are structured differently, so we present results where they are available; otherwise, show the available ones for each model. However, the scenarios were designed based on common assumptions. Socio-economic Assumptions The Shared Socioeconomic Pathway 2 (SSP2), sometimes referred to as the "Middle of the Road" scenario, depicts a future in which societal, economic, and technological progress advances at a moderate rate, closely following historical patterns (Riahi et al., 2017). This scenario presents a balanced trajectory with moderate challenges for both climate change mitigation and adaptation efforts. It is characterized by intermediate levels of education and income, and does not aggressively pursue either environmental sustainability or reliance on fossil fuels. The population and GDP projections have been upscaled from the gridded data onto the required regional definitions. Time Horizon All scenarios include the time horizon until the end of century. Climate Climate feedback and impacts implementation follows the approach presented in Table 2. Data is based on the Inter-Sectoral Model Intercomparison Project (ISIMIP) (Frieler et al., 2017), to ensure internal consistency across the different indicators and across models. Where relevant, models can differ in terms of impact modules and default climate patterns (e.g., in IMAGE, the IPSL climate pattern is used, while MESSAGEix-GLOBIOM uses the multi-model ensemble mean of 4 models and sometimes specific climate model). The impacts on labor productivity and GDP are not included in this assessment to allow for focusing on biophysical impacts. Water Supply Reliability In MESSAGEix-GLOBIOM, we used a quantile approach to analyze monthly freshwater resources, considering hydro-climate variability and prolonged dry periods. The 10th percentile (Q90) of monthly runoff is calculated from daily data and used to determine a reliable flow for 90% of the time. This methodology is commonly used in water resource and environmental flow assessments to account for seasonal low flows in typical wet and average years. The scale of investments required rises steeply when higher levels of water supply reliability are planned for. While renewable sources typically meet most demands most of the time at a low cost, ensuring more reliable supply typically requires investments in alternative sources. They meet a small proportion of the annual demand, at comparatively high cost. Water Stress Water Stress is calculated by ratio of withdrawals over renewable water availability (runoff). Population under water stress is calculated by cutoff if ratio is greater than 0.4. Adaptive Capacity We spatially calculate Governance projections for SSP2 provided by (Andrijevic et al., 2020) for the basins using weighted average of country population and GDP. This gives us an estimate of the governance indicators since some basins overlap multiple countries. Table 3 Basins categories Governance Indicators Tiers Governance Indicator Tier 1- Easier to adapt >0.8 Tier 2 – Efforts required to adapt 0.66 - 0.8 Tier 3 – Difficult to adapt < 0.66 Limitations Things we are not covering e.g., extreme effects, floods, droughts, economic losses, Input costs doesn't consider GDP trajectories and uniform across region. Declarations Data and Code Availability The MESSAGEix model development done to run the scenarios in this article is described in (Awais et al., 2023). The code and documentation are available on the MESSAGEix public Github page (https://github.com/iiasa/message-ix-models) and documentation (https://docs.messageix.org/projects/models/en/latest/water/index.html ). A public Zenodo data repository also contains all the input data for running MESSAGEix-GLOBIOM (https://zenodo.org/records/7687578). The IMAGE model documentation is available at https://models.pbl.nl/image/index.php/Welcome_to_IMAGE_3.2_Documentation The results specific to this paper from both IAMs will be made available in a public repository and on a public instance of the IIASA Scenario Explorer upon acceptance (e.g. https://iiasa.ac.at/models-tools-data/ar6-scenario-explorer-and-database ) References Andrijevic, M., Crespo Cuaresma, J., Muttarak, R., & Schleussner, C. F. (2020). Governance in socioeconomic pathways and its role for future adaptive capacity. Nat. Sustain. , 3 (1), 35–41. https://doi.org/10.1038/s41893-019-0405-0 Andrijevic, M., Schleussner, C. F., Crespo Cuaresma, J., Lissner, T., Muttarak, R., Riahi, K., Theokritoff, E., Thomas, A., van Maanen, N., & Byers, E. (2023). Towards scenario representation of adaptive capacity for global climate change assessments. Nature Climate Change 2023 , 1–10. https://doi.org/10.1038/s41558-023-01725-1 Awais, M., Vinca, A., Byers, E., Frank, S., Fricko, O., Boere, E., Burek, P., Poblete Cazenave, M., Kishimoto, P. N., Mastrucci, A., Satoh, Y., Palazzo, A., McPherson, M., Riahi, K., & Krey, V. (2023). MESSAGEix Nexus Module: Integrating water sector and climate impacts. In Prep . Berrang-Ford, L., Siders, A. R., Lesnikowski, A., Fischer, A. P., Callaghan, M. W., Haddaway, N. R., Mach, K. J., Araos, M., Shah, M. A. R., Wannewitz, M., Doshi, D., Leiter, T., Matavel, C., Musah-Surugu, J. I., Wong-Parodi, G., Antwi-Agyei, P., Ajibade, I., Chauhan, N., Kakenmaster, W., … Abu, T. Z. (2021). A systematic global stocktake of evidence on human adaptation to climate change. Nature Climate Change , 11 (11), 989–1000. https://doi.org/10.1038/s41558-021-01170-y Burek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., & Wada, Y. (2019). Development of the Community Water Model (CWatM v1.04) A high-resolution hydrological model for global and regional assessment of integrated water resources management. Geoscientific Model Development Discussions , 1–49. https://doi.org/10.5194/gmd-2019-214 Burek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., Zhao, F., & Wada, Y. (2020). Development of the Community Water Model (CWatM v1.04) - A high-resolution hydrological model for global and regional assessment of integrated water resources management. Geoscientific Model Development , 13 (7), 3267–3298. https://doi.org/10.5194/GMD-13-3267-2020 Byers, E., Gidden, M., Leclere, D., Balkovic, J., Burek, P., Ebi, K., Greve, P., Grey, D., Havlik, P., Hillers, A., Johnson, N., Kahil, T., Krey, V., Langan, S., Nakicenovic, N., Novak, R., Obersteiner, M., Pachauri, S., Palazzo, A., … Riahi, K. (2018). Global exposure and vulnerability to multi-sector development and climate change hotspots. Environ. Res. Lett. , 13 (5), 055012. https://doi.org/10.1088/1748-9326/aabf45 De Vos, L., Biemans, H., Doelman, J. C., Stehfest, E., & Van Vuuren, D. P. (2021). Trade-offs between water needs for food, utilities, and the environment—a nexus quantification at different scales. Environmental Research Letters , 16 (11), 115003. https://doi.org/10.1088/1748-9326/AC2B5E Doelman, J. C., Beier, F. D., Stehfest, E., Bodirsky, B. L., Beusen, A. H. W., Humpenöder, F., Mishra, A., Popp, A., Van Vuuren, D. P., De Vos, L., Weindl, I., Van Zeist, W. J., & Kram, T. (2022). Quantifying synergies and trade-offs in the global water-land-food-climate nexus using a multi-model scenario approach. Environmental Research Letters , 17 (4), 045004. https://doi.org/10.1088/1748-9326/AC5766 Douville, H., Allanid, R. P., Ariasid, P. A., Betts, R. A., Caretta, M. A., Cherchiid, A., Mukherji, A., Raghavan, K., & Renwickid, J. (2022). Water remains a blind spot in climate change policies. PLOS Water , 1 (12), e0000058. https://doi.org/10.1371/JOURNAL.PWAT.0000058 Frank, S., Gusti, M., Havlík, P., Lauri, P., Di Fulvio, F., Forsell, N., Hasegawa, T., Krisztin, T., Palazzo, A., & Valin, H. (2021a). Land-based climate change mitigation potentials within the agenda for sustainable development. Environmental Research Letters , 16 (2), 024006. https://doi.org/10.1088/1748-9326/ABC58A Frank, S., Gusti, M., Havlík, P., Lauri, P., Di Fulvio, F., Forsell, N., Hasegawa, T., Krisztin, T., Palazzo, A., & Valin, H. (2021b). Land-based climate change mitigation potentials within the agenda for sustainable development. Environmental Research Letters , 16 (2), 024006. https://doi.org/10.1088/1748-9326/ABC58A Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., … Yamagata, Y. (2017). Assessing the impacts of 1.5ĝ€°C global warming - Simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development , 10 (12), 4321–4345. https://doi.org/10.5194/GMD-10-4321-2017 Gernaat, D. E. H. J., de Boer, H. S., Daioglou, V., Yalew, S. G., Müller, C., & van Vuuren, D. P. (2021). Climate change impacts on renewable energy supply. Nature Climate Change 2021 11:2 , 11 (2), 119–125. https://doi.org/10.1038/s41558-020-00949-9 Huppmann, D., Gidden, M., Fricko, O., Kolp, P., Orthofer, C., Pimmer, M., Kushin, N., Vinca, A., Mastrucci, A., Riahi, K., & Krey, V. (2019). The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. Environmental Modelling and Software , 143–156. https://doi.org/10.1016/j.envsoft.2018.11.012 (IPCC), I. P. on C. C. (2023). Water. In Climate Change 2022 – Impacts, Adaptation and Vulnerability (pp. 551–712). Cambridge University Press. https://doi.org/10.1017/9781009325844.006 Kahil, T., Parkinson, S., Satoh, Y., Greve, P., Burek, P., Veldkamp, T. I. E., Burtscher, R., Byers, E., Djilali, N., Fischer, G., Krey, V., Langan, S., Riahi, K., Tramberend, S., & Wada, Y. (2018). A Continental-Scale Hydroeconomic Model for Integrating Water-Energy-Land Nexus Solutions. Water Resources Research , 54 (10), 7511–7533. https://doi.org/10.1029/2017WR022478 Köberle, A. C., Vandyck, T., Guivarch, C., Macaluso, N., Bosetti, V., Gambhir, A., Tavoni, M., & Rogelj, J. (2021). The cost of mitigation revisited. Nature Climate Change 2021 11:12 , 11 (12), 1035–1045. https://doi.org/10.1038/s41558-021-01203-6 Krey, V., Havlik, P., Fricko, O., J, Z., Forsell N, G. G. M. S. M. K. G. E. T., Obersteiner M, R. M. J. N. K. G. K. P. M. D. P. S. R. S. R. J. V. H., & (2016), K. (2016). MESSAGE-GLOBIOM 1.0 Documentation (1.0). International Institute for Applied Systems Analysis (IIASA). https://docs.messageix.org/projects/global/en/latest/overview/index.html Mastrucci, A., van Ruijven, B., Byers, E., Poblete-Cazenave, M., & Pachauri, S. (2021). Global scenarios of residential heating and cooling energy demand and CO2 emissions. Climatic Change , 168 (3–4), 1–26. https://doi.org/10.1007/S10584-021-03229-3/FIGURES/7 Miralles-Wilhelm, F. (2021). Water is the middle child in global climate policy. Nature Climate Change 2021 12:2 , 12 (2), 110–112. https://doi.org/10.1038/s41558-021-01154-y Mpandeli, S., Naidoo, D., Mabhaudhi, T., Nhemachena, C., Nhamo, L., Liphadzi, S., Hlahla, S., & Modi, A. T. (2018). Climate Change Adaptation through the Water-Energy-Food Nexus in Southern Africa. International Journal of Environmental Research and Public Health 2018, Vol. 15, Page 2306 , 15 (10), 2306. https://doi.org/10.3390/IJERPH15102306 Müller, C., & Robertson, R. D. (2014). Projecting future crop productivity for global economic modeling. Agricultural Economics , 45 (1), 37–50. https://doi.org/10.1111/AGEC.12088 Parkinson, S., Krey, V., Huppmann, D., Kahil, T., McCollum, D., Fricko, O., Byers, E., Gidden, M. J., Mayor, B., Khan, Z., Raptis, C., Rao, N. D., Johnson, N., Wada, Y., Djilali, N., & Riahi, K. (2019). Balancing clean water-climate change mitigation trade-offs. Environmental Research Letters , 14 (1). https://doi.org/10.1088/1748-9326/aaf2a3 Pastor, A. V., Ludwig, F., Biemans, H., Hoff, H., & Kabat, P. (2014). Accounting for environmental flow requirements in global water assessments. Hydrology and Earth System Sciences , 18 (12), 5041–5059. https://doi.org/10.5194/HESS-18-5041-2014 Pastor, A. V., Palazzo, A., Havlik, P., Biemans, H., Wada, Y., Obersteiner, M., Kabat, P., & Ludwig, F. (2019). The global nexus of food–trade–water sustaining environmental flows by 2050. Nature Sustainability 2019 2:6 , 2 (6), 499–507. https://doi.org/10.1038/s41893-019-0287-1 Piontek, F., Drouet, L., Emmerling, J., Kompas, T., Méjean, A., Otto, C., Rising, J., Soergel, B., Taconet, N., & Tavoni, M. (2021). Integrated perspective on translating biophysical to economic impacts of climate change. Nature Climate Change 2021 11:7 , 11 (7), 563–572. https://doi.org/10.1038/s41558-021-01065-y Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O’Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., … Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global Environmental Change , 42 . https://doi.org/10.1016/j.gloenvcha.2016.05.009 Rising, J., Tedesco, M., Piontek, F., & Stainforth, D. A. (2022). The missing risks of climate change. Nature 2022 610:7933 , 610 (7933), 643–651. https://doi.org/10.1038/s41586-022-05243-6 Schaphoff, S., Von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., & Waha, K. (2018). LPJmL4—a dynamic global vegetation model with managed land – Part 1: model description. Geosci. Model Dev. , 11 (4), 1343–1375. https://doi.org/10.5194/gmd-11-1343-2018 Schipper, E. L. F., Revi, A., Preston, B. L., Carr, E. R., Eriksen, S. H., Fernandez-Carril, L. R., Glavovic, B. C., Hilmi, N. J. M., Ley, D., Mukerji, R., Araujo, M. S. M. de, Perez, R., Rose, S. K., & Singh, P. K. (2022). Climate Resilient Development Pathways. In: Impacts, Adaptation and Vulnerability. WGII AR6 IPCC . https://doi.org/10.1017/9781009325844.027 van Maanen, N., Lissner, T., Harmsen, M., Piontek, F., Andrijevic, M., & van Vuuren, D. P. (2023). Representation of adaptation in quantitative climate assessments. Nature Climate Change 2023 13:4 , 13 (4), 309–311. https://doi.org/10.1038/s41558-023-01644-1 Van Vuuren, D. P., Bijl, D. L., Bogaart, P., Stehfest, E., Biemans, H., Dekker, S. C., Doelman, J. C., Gernaat, D. E. H. J., & Harmsen, M. (2019). Integrated scenarios to support analysis of the food–energy–water nexus. Nature Sustainability , 2 (12), 1132–1141. https://doi.org/10.1038/S41893-019-0418-8 van Vuuren, D., Stehfest, E., Gernaat, D., Boer, H.-S. de, Daioglou, V., Doelman, J., Edelenbosch, O., Harmsen, M., Zeist, W.-J. van, Berg, M. van den, Dafnomilis, I., Sluisveld, M. van, Tabeau, A., Vos, L. De, Waal, L. de, Berg, N. van den, Beusen, A., Bos, A., Biemans, H., … Castillo, V. Z. (2021). The 2021 SSP scenarios of the IMAGE 3.2 model . https://doi.org/10.31223/X5CG92 Willett, W., Rockström, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., Garnett, T., Tilman, D., DeClerck, F., Wood, A., Jonell, M., Clark, M., Gordon, L. J., Fanzo, J., Hawkes, C., Zurayk, R., Rivera, J. A., De Vries, W., Majele Sibanda, L., … Murray, C. J. L. (2019). Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. Lancet (London, England) , 393 (10170), 447–492. https://doi.org/10.1016/S0140-6736(18)31788-4 Yalew, S. G., van Vliet, M. T. H., Gernaat, D. E. H. J., Ludwig, F., Miara, A., Park, C., Byers, E., De Cian, E., Piontek, F., Iyer, G., Mouratiadou, I., Glynn, J., Hejazi, M., Dessens, O., Rochedo, P., Pietzcker, R., Schaeffer, R., Fujimori, S., Dasgupta, S., … van Vuuren, D. P. (2020). Impacts of climate change on energy systems in global and regional scenarios. Nature Energy 2020 5:10 , 5 (10), 794–802. https://doi.org/10.1038/s41560-020-0664-z Additional Declarations There is NO Competing Interest. 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Analysis and Graz University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Keywan","middleName":"","lastName":"Riahi","suffix":""}],"badges":[],"createdAt":"2024-03-22 12:45:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4149842/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4149842/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54534282,"identity":"89bc52e3-29b0-44c6-81ba-adb6583cfad3","added_by":"auto","created_at":"2024-04-12 02:03:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":326262,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of how the Adaptation and Resilience scenarios are designed on top of usual scenarios. The CurrentPolicy and Mitigation scenarios also consider climate impacts on renewable energy, water supply, crop yields, heat stress, which allows the models to adjust to these impacts and show adaptive capacity as a response. Adaptation and Resilience scenarios consider the SDG 2,6,7, 13 \u0026amp; 15 assumptions on CurrentPolicy and Mitigation scenarios thus allowing to understand additional adaptation and resilience dimension in the scenarios.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4149842/v1/9dfe9902b1764bf3f5f44207.png"},{"id":54534283,"identity":"803216ba-7e56-4502-ac79-670e989684f7","added_by":"auto","created_at":"2024-04-12 02:03:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158874,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of key indicators (Water Withdrawals, Energy \u0026amp; Water Supply Investment and Primary Energy Fossil use, by region . The plots on the left hand side represent baseline values from MESSAGEix-GLOBIOM across the time horizon aggregated across the regions. The panels on the right shows the absolute changes from CurrentPolicy scenarios as reported by both MESSAGEix-GLOBIOM \u0026amp; IMAGE . The text above each bar shows total percentage difference from the baseline. The results are averaged across 2020-2050 and 2051-2100 to show the outlook of pre and post mid century. The black lines indicate the net global absolute changes.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4149842/v1/71e3918ddcff6db494f7c276.png"},{"id":54534284,"identity":"9454ed2a-14e3-4ce3-b7c2-e753960ac530","added_by":"auto","created_at":"2024-04-12 02:03:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":149204,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the average annual costs (from 2020-2100) in billion US2020$/capita, split by water system type for the Resilient scenario within each region (box plot shows the distribution of basin costs, per capita, within each region). These are the differences from the Baseline thus showing additional cost (or benefits accrued if negative) required to achieve the Resilient pathway, where negative values indicate benefits (i.e. lower costs than the Baseline). The costs include operational and investment costs from MESSAGEix-GLOBIOM. Water distribution includes providing water to municipal and industrial services and access to clean drinking water. Wastewater includes wastewater collection, treatment, recycling and reuse. Desalination includes costs for membrane and distillation desalination technologies. Surface \u0026amp; groundwater refers to the extraction.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4149842/v1/458acff83e8e179329ae87c5.png"},{"id":54534285,"identity":"24a6b9ab-9731-4b51-afc7-e33e04699e0f","added_by":"auto","created_at":"2024-04-12 02:03:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":310458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eProjected Governance and Water-Related Challenges for Selected River Basins under SSP2 for the Year 2050: This figure presents a global overview of governance projections under the Shared Socioeconomic Pathways 2 (SSP2) scenario by 2050, with a specific focus on key water-related indicators. For selected river basins, delineated with red boundaries, the indicators include: i) Average Annual Costs (Avg. Costs), ii) Desalination Capacity (Desal), iii) Energy for Water (E4W), iv) Groundwater Extraction (G. Water), v) Water for Irrigation, vi) Surface Runoff, and vii) Wastewater Reuse (W. reuse). The heatmaps reflect the percentage change from the Current Policy Scenario, offering insights into the Energy-Water-Land (EWL) nexus implications necessary for achieving a resilient scenario. Each heatmap cell color correlates with the magnitude of change, providing a comparative analysis across the basins. The low, medium, high represent reliability levels of renewable water supply (see Methods). This figure underscores the complex interplay between governance, water availability, cost, and sustainability initiatives critical for future water resource management.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4149842/v1/09965f9d36804a545b60eb7f.png"},{"id":54534429,"identity":"b8b111ae-6719-44c8-9686-f9b4a925b11e","added_by":"auto","created_at":"2024-04-12 02:13:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1247679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4149842/v1/7421f70c-7bc4-4eca-9665-b0ee8c832c83.pdf"},{"id":54534286,"identity":"e7cdf351-9d85-452c-a4c6-6ae2a61a9f65","added_by":"auto","created_at":"2024-04-12 02:03:54","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1170784,"visible":true,"origin":"","legend":"","description":"","filename":"wateranalysissupplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-4149842/v1/ca583b59c5a1985a65c89463.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Global Water Basins under Combined Climate Mitigation, Adaptation, and Sustainable Development Targets","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArticles 2 and 4 of the Paris Agreement emphasize the need to integrate climate resilience and greenhouse gas emissions reduction into sustainable development objectives. While the alignment of climate actions with the sustainable development objectives have been somewhat addressed (Bertram et al., 2018; Soergel et al., 2021), the nuanced interplay between adaptation, mitigation and sustainable development often remains underrepresented in the global studies (Andrijevic et al., 2023; van Maanen et al., 2023). The limited focus on integration of multiple societally relevant objectives within sectorally integrated frameworks constrains our ability to understand synergies and tradeoffs that may emerge from simultaneously achieving those multiple objectives. In absence of such an investigation, proposing sustainable and climate-resilient development pathways becomes increasingly challenging.\u003c/p\u003e\n\u003cp\u003eThis paper focuses on pathways building resilience in the Energy-Water-Land (EWL) nexus, which requires understanding and management of complex interactions between mitigation, adaptation actions and related Sustainable Development Goals (SDGs) (Mpandeli et al., 2018; Schipper et al., 2022). In this context, multisectoral Integrated Assessment Models (IAMs) can be used to understand pathways to avoid isolated decision-making, effectively capture these complexities across multiple sectors, and identify the trade-offs and synergies between adaptation and mitigation strategies (Andrijevic et al., 2023; van Maanen et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe water system remains fundamental in the system transitions for climate adaptation and mitigation, and it necessitates a more nuanced comprehension of its intersection with the energy and land sectors. Depending on the existing and future incentives, policies, and governance, many adaptation and mitigation measures can result in either synergistic or maladaptive effects on water use. It is crucial to manage the substantial water footprint of numerous mitigation measures in a socially, environmentally, and politically acceptable manner, to reduce water intensity and enhance alignment with sustainable development ((IPCC), 2023). Climate mitigation policy assessments have typically focused on energy and land, leaving water unrepresented, a crucial component for achieving local and global climate objectives(Miralles-Wilhelm, 2021). This fragmented focus may impede a comprehensive understanding of climate change impacts and the efficacy of mitigation measures within the constraints of water systems (Douville et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent advances in IAMs provide the potential for a deeper understanding of water system boundaries under climate policies, but the existing studies are somewhat fragmented in their approaches. (Parkinson et al., 2019) assessed water infrastructure costs for climate mitigation and sustainable development targets but lacks representation of hydrological boundaries, biophysical climate impacts and adaptation. (Kahil et al., 2018) developed a continental scale hydro-economic model albeit with less detailed energy and land policy consideration. Recent studies such as (De Vos et al., 2021) and (Doelman et al., 2022) investigated trade-offs and synergies at various scales, offer valuable insights into the system's intricate dynamics and have the potential to explore the adaptation dimensions of the water system.\u003c/p\u003e\n\u003cp\u003eTo fully understand the risks and damages associated with climate change, it is crucial to incorporate biophysical climate impacts from Climate Impact Models (CIMs) into Integrated Assessment Models (IAMs). IAMs emphasize tradeoffs between adaptation and mitigation, aligning with climate targets and sustainable development, fostering resilience, and addressing SDGs in water systems until the year 2100. \u003cspan lang=\"ES-NI\"\u003e(Köberle et al., 2021; Piontek et al., 2021; Rising et al., 2022; van Maanen et al., 2023)\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eIn assessing the complex role of water systems and the climate impacts in the IAMs we utilize two IAMs: MESSAGEix-GLOBIOM and IMAGE. In addition to expertly navigating the complexities of global energy systems, the MESSAGEix-GLOBIOM model provides profound techno-economic insights into land use, particularly regarding biomass and emission mitigation and an understanding of water supply system (Awais et al., 2023; Krey et al., 2016). In contrast, IMAGE provides a global perspective on the environmental repercussions of human activities, comprising a variety of challenges ranging from climate change to biodiversity loss (D. van Vuuren et al., 2021). Even though both models provide distinct insights, they are combined to generate common scenarios that provide robust and nuanced perspectives on our multidimensional climate paradigm. Throughout our analysis, results are occasionally derived solely from one model or jointly from both, ensuring a complete and complementary representation of our findings.\u003c/p\u003e"},{"header":"Towards Climate Resilient Development Pathway","content":"\u003cp\u003eWe develop an integrated scenario design within two IAMs. One scenario incorporates biophysical climate impacts and examines adaptation responses, and another combines the mitigation targets to keep global warming below 2\u003csup\u003e0\u003c/sup\u003eC and the Sustainable Development Goals (SDGs) (Figure 1). The conventional IAM scenarios, based on Representative Concentration Pathways (RCP) 6.0 and 2.6 that do not incorporate biophysical impacts, are here termed as \u003cem\u003eCurrentPolicy\u003c/em\u003e and \u003cem\u003eMitigation,\u0026nbsp;\u003c/em\u003erespectively.\u003c/p\u003e\u003cp\u003eOur work combines the biophysical impacts of climate change with SDG targets, for indicators such as the cooling technology capacity factors for thermal power plants, renewable water supply,In addition, in the energy sector, we consider impacts on heating and cooling requirements, as well as hydropower supply curves. In the land sector, we include climate impacts on yields. Model assumptions and details are summarized in Table 1. These biophysical impacts allow the systemic response to climate change from models in form of adaptation such as increased crop yields or reduced dependency on fossil fuels. Scenarios combining RCP 6.0 with biophysical impacts are therefore termed as \u003cem\u003eAdaptation\u003c/em\u003e in this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Included climate impacts and underlying approach/data\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"487\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.58196721311475%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiophysical climate impacts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.41803278688525%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eApproach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.58196721311475%\" valign=\"top\"\u003e\n \u003cp\u003eRenewable supply (hydro)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.41803278688525%\" valign=\"top\"\u003e\n \u003cp\u003eRenewable costs supply curves based on 0.5x0.5 grid calculations (Gernaat et al., 2021). MESSAGEix -GLOBIOM uses hydropower cost supply curves only.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.58196721311475%\" valign=\"top\"\u003e\n \u003cp\u003eHeating/cooling demand\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.41803278688525%\" valign=\"top\"\u003e\n \u003cp\u003eImpact via population-weighted heating and cooling demands based on the work of \u0026nbsp;(Byers et al., 2018; Mastrucci et al., 2021) 0.5 x 0.5 grid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.58196721311475%\" valign=\"top\"\u003e\n \u003cp\u003eWater availability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.41803278688525%\" valign=\"top\"\u003e\n \u003cp\u003eRunoff and groundwater recharge from CWatM calculated at 0.5 x 0.5 grid (Burek et al., 2020) for MESSAGEix-GLOBIOM. IMAGE 3.2 uses LPJmL hydrological model outputs (Schaphoff et al., 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.58196721311475%\" valign=\"top\"\u003e\n \u003cp\u003eCrop yields\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.41803278688525%\" valign=\"top\"\u003e\n \u003cp\u003eClimate impacts on crop productivity, nitrogen, and irrigation from the CMIP6 projections of the crop-model EPIC-IIASA are used in GLOBIOM. EPIC-IIASA estimates the impact of climate on rice, maize, wheat, and soy and feeds into GLOBIOM following (M\u0026uuml;ller \u0026amp; Robertson, 2014)\u003c/p\u003e\n \u003cp\u003eIMAGE uses crop yield outputs from LPJmL (Schaphoff et al., 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.58196721311475%\" valign=\"top\"\u003e\n \u003cp\u003eCooling technology capacity factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.41803278688525%\" valign=\"top\"\u003e\n \u003cp\u003eClimate impacts on cooling water discharges for cooling technologies of fossil power plants are used from (Yalew et al., 2020) only in MESSAGEix-GLOBIOM.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.58196721311475%\" valign=\"top\"\u003e\n \u003cp\u003eDesalination potential\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.41803278688525%\" valign=\"top\"\u003e\n \u003cp\u003eDesalination potential climate impacts are based on water stress outputs from the combinations of GHMs \u0026amp; GCMs from (Byers et al., 2018) only in MESSAGEix-GLOBIOM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e1 IMAGE uses IPSL-CM5A-LR climate model.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2 MESSAGEix-GLOBIOM uses GFDL-ESM2M (for water availability), UKESM1-0-LL (for crop yields), and HadGEM2-ES (for heating/cooling demand). This choice has been motivated by the preferences from different models itself.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWe integrate SDGs into our scenarios, highlighting their crucial role in developing sustainable and resilient paths. \u0026nbsp;The SDG targets, outlined in Table 2, encompass aims such as diet shifts, minimizing food waste, increasing efficiency of irrigation systems, and advancing electricity production. These targets serve as a framework for aligning environmental and developmental aspirations. The \u003cem\u003eResilience\u003c/em\u003e scenario combines progress towards selected SDGs and mitigation measures. The Resilience scenario considers a holistic pathway pertinent to the energy, water, and land (EWL) sectors, thereby providing a more sustainable development perspective in the context of mitigation and adaptation. It concurs with the definition of Climate Resilient Development Pathway defined in the IPCC AR6 report as \"\u003cem\u003eThe process of implementing greenhouse gas mitigation and adaptation measures to support sustainable development for all\"\u003c/em\u003e (Schipper et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Sustainable Development Goals\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSDG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure - IMAGE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure - MESSAGEix-GLOBIOM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSDG2 - Hunger\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange towards a healthy diet\u003c/strong\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTransition towards healthy diets including reduced meat consumption, healthy total calorie intake and increased vegetable intake following (Willett et al., 2019).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cul\u003e\n \u003cli\u003e\u0026lt; 1% undernourishment goal by 2030\u003c/li\u003e\n \u003cli\u003eDecrease of animal calorie intake to 430 kcal/capita/day by 2030 (USDA recommendations for healthy diets)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eReduce food waste.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh income countries and middle-income regions reduce food waste to the lowest level among them, in three food supply chain stages (primary, processing, consumption), for six commodities groups (cereals, roots and tubers, oilseeds and pulses, fruit and vegetables, milk, meat)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50% reduction in food waste compared to SSP2 assumptions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSDG6 - Water\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEfficiency improvement for irrigation\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLimited irrigation water consumption in agriculture to sustainable removal rates that do not jeopardize ecosystem services and environmental flows\u0026nbsp;(Frank et al., 2021a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEfficiency improvement for electric power generation\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-59% in 2050 compared to a baseline case based on (D. P. Van Vuuren et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot included\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImplement environmental flow constraints.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBased on the variable monthly flow (VMF) method developed by\u0026nbsp;(Pastor et al., 2014, 2019)\u0026nbsp;where 60%, 45% and 30% of the mean monthly natural flow is reserved for ecosystems in low, intermediate and high flow periods, respectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBased on the variable monthly flow (VMF) method developed by\u0026nbsp;(Pastor et al., 2014, 2019)where 60% and 30% of the mean monthly natural flow is reserved for ecosystems in low and high flow periods, respectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEfficiency improvement for industry\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5% in 2050 compared to a baseline case based on: (D. P. Van Vuuren et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot included\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eUniversal piped water access, wastewater collection and improved wastewater treatment capacity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNot included\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMinimum of half all return flows are treated by 2030 for developed regions and 2040 for developing regions.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSDG7 - Energy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximized electricity access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOn-grid electrification only, based on SSP1 (D. van Vuuren et al., 2021)\u0026nbsp; assumptions (98% in 2030).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eResults from the MESSAGEix-GLOBIOM are iterated through the MESSAGE-Access-E-USE (end-use services of energy) model by provision of access targets based on income levels and GDP pathways and population with access to modern energy access and the energy demand adjustments are calculated.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimized traditional bio and coal in cooking and heating\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBased on SSP1 assumptions (90% reduction of traditional bio in 2050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90 % access target to modern cooking energy for cooking by 2030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSDG15 (Life on land)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImplement protected land maps\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAs in SSP1 (+/-30% protected) (D. van Vuuren et al., 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBased on\u0026nbsp;(Frank et al., 2021b), 34% protection in 2030 was assumed and additional area based on the UNEP- WCMC Carbon and Biodiversity Report to identify highly biodiverse areas and prevent their conversion to agriculture or forest management from 2030 onwards.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e\n\u003cp\u003eFocusing specifically on the water sector, we examine the trade-offs between measures for adapting to and mitigating the effects of climate change. Based on the evidence documented in more than 50% of accessed literature by (Berrang-Ford et al., 2021), that governance is a stronger barrier than finance for water sectior, we assess basins’ adaptive capacities by combining IAM outcomes with governance projections by (Andrijevic et al., 2020). This helps inn identifying which basins are likely to success or struggle in adapting. \u0026nbsp; We categorize worldwide basins into tiers based on their adaptive capacity by combining IAM results with governance indicators. Figure 1 concisely presents our scenario design, providing a full depiction of the study's framework and improving the clarity and accessibility of our approach. \u0026nbsp; Our study examines how mitigation options might be aligned with SDGs in the energy, water, and land sectors and what the implications are for the water sector.\u0026nbsp;\u003c/p\u003e\u003cp\u003eOur analysis highlights how the resilience scenario could shift global water and energy systems towards sustainability. By prioritizing investments that reduce future climatic vulnerabilities and lead to substantial decreases in water withdrawals, we can significantly reduce the population's risk of water stress. Approximately 2.8 billion people would live in environments of governance-driven low adaptive capacity, despite efforts to achieve resilience targets. This highlights the importance of integrating water management into the wider climate policy framework, acknowledging its crucial role in both adaptation and mitigation initiatives. Our findings suggest that prioritizing SDG investments with mitigation efforts is more advantageous for the water sector than pursuing SDGs without a concurrent mitigation strategy.\u003c/p\u003e"},{"header":"Balancing Synergies and Trade-Offs in Resource Stewardship","content":"\u003cp\u003eTo achieve both SDGs and meet mitigation targets, the Resilience scenario requires a discernible increase in investments, approximately 22% higher than the CurrentPolicy. 85 % of these investments are attributed to energy supply while the rest are to water-related adaptations. These allocations underscore a prioritized strategy aiming at mitigating future climate-related vulnerabilities and uncertainties. Region-specific highlight the highest investments are required in the Middle East (1.75%. of global GDP), Africa (0.9%. of global GDP), and Asia (2.2% of global GDP), reflecting the distinctive socioeconomic and geophysical challenges inherent to these regions (Figure 2).\u003c/p\u003e\u003cp\u003eIn the context of water withdrawal, the Resilience scenario implies a global reduction of approximately 30-35 % by 2050 thus representing a meaningful resilience pathway. This reduction is primarily attributed to improvements in crop yields and the efficiency of irrigation methods. Notably, there is a gain of 2% in the average global crop yield, with specific crops indicating more substantial growth rates: sugar crops by 5%, cereals by 4%, and oil crops by 4% (Supplementary Figure 6). We also observed a decrease of 35-45% in water use for electricity production in the Resilience scenario (Supplementary Figure 4). The decrease is attributed to the climate impacts of cooling technology of thermal power plants and the fossil fuel phase.\u003c/p\u003e\u003cp\u003eAsia, being a hotspot for climate change, having the highest population and the highest amount of global water withdrawals, has comparatively lower level of water withdrawal reductions in the Resilience scenario when compared to other regions, such as OECD 90 countries over the time horizon (Figure 2). This is attributed to the increasing pressures caused by the high population density. However, in the Resilience scenario, a greater abundance of surface water is available globally, although the extent of it varies between different basins. In some basins (such as Ganges Bramaputra, Danube, North Colorado) the hydrological models in the RCP 6.0 scenario demonstrate a rise in water availability as compared to RCP2.6. In general, the regions of Asia, the Middle East, and the Former Soviet Union are exhibiting a steady decrease in surface water availability, with reductions ranging from 5% to a maximum of 20% over the future time horizon. In various geographical areas, the average percentage change often falls within -10% to 10%. These variances mostly rely on the specific geophysical characteristics of each region (Supplementary Figure 1).\u003c/p\u003e\u003cp\u003eThe results indicate a decrease in the population's exposure to water stress by 40-50% in the Resilience scenario compared to 65-70% in CurrentPolicy (Supplementary Figure 4). The Resilience scenario foresees an increase in access to vital resources such as drinking water (Supplementary Figure 3), clean cooking fuels, sanitation facilities which impacts the combined pathway outlook and implied investments.\u003c/p\u003e"},{"header":"Financing the Water Transition","content":"\u003cp\u003eConsidering the increasing challenges for global water systems, it is crucial to understand the often-overlooked costs (investments and operational) associated with water supply from the pathways of IAMs. The Resilience scenario outlines an average yearly investment of 535 billion USD/yr. for global basins, slightly exceeding the 527 billion USD required for Adaptation. The small increase is balanced out by fair allocation of water resources and achieving mitigation targets. The costs of water include renewable water supply, non-renewable groundwater, wastewater reuse, desalination, and cooling technologies for thermal power plants.\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe relative scale of these investments depends on the socioeconomic context of the region. In the Resilience scenario, North America and Western Europe have the highest absolute per capita investment in water supply with, 141 \u0026amp; 86 USD/capita/yr., respectively. In the future, investments in Africa are projected to rise from 14 USD per capita per year (2020-2050) to 49 USD per capita per year (2060 onwards) (Figure 3). In contrast, South Asia \u0026amp; Sub-Saharan Africa needs to invest additional 0.05% and 0.05% of the regional GDP per year for a Resilient transition. These areas simultaneously have growing demands for water and limitations in adaptive capacity, making resilient pathways difficult to attain. Moreover, some basins, such as the Rift Valley, Africa North Interior, Caspian Sea East Coast, Irrawaddy Salween Sittang, Arabian Sea Coast, Amazon, Australian Sea Cost, Niger, and Ganges Brahmaputra in the Central Asian region, have lower costs of resilience pathway as compared to baseline thus implying economic benefits compared to other sectoral synergies.\u003c/p\u003e\u003cp\u003eIn the Ganges Brahmaputra basin, growing pressure on freshwater resources drives a shift to increasing levels of wastewater reuse, with up to 50% required for high reliability and an additionally growing share of desalination. Non-renewable groundwater is also exploited but is indicated as a last-resort option because it is unsustainable. These shifts to alternative water sources also require a 70% increase in energy use for water supply compared to the baseline.\u003c/p\u003e\u003cp\u003eThe Indus River Basin shows insights into diversification of water supply sources under freshwater stress. The significant water stress experienced in this region has consistently resulted in limited access to freshwater resources. The limited availability of water resources necessitates using additional sources, such as desalinated water, estimated to be roughly 0.4 km3/yr in 2050. Notably, the landscape changes according to different scenarios of reliability. In situations characterized by low reliability, desalinated or unsustainable groundwater sources are underutilized. In contrast, high reliability levels indicate a significant shift towards alternative water sources such as desalination and brackish water. These sources are employed to fulfill the water demands, amounting to around 76 km3/yr.\u003c/p\u003e\u003cp\u003eUnder the Resilience scenario, the North Colorado Basin also presents a different perspective: a shift towards alternative water sources. This is reflected in the significant growth in the utilization of desalination, which is projected to double by the mid-century. The wastewater reuse remains same as the basin is relatively more developed and does not require further increases.\u003c/p\u003e\u003cp\u003eFor all basins there is a declining trend in investments in power plant cooling systems due to the reduced dependence on fossil fuels within the electricity sector as outlined in the Resilience scenario. For example, in the Danube Basin, the energy-related water consumption decreases by 70% under the Resilience scenario compared to the Baseline. Water distribution costs make up a significant portion of the overall water costs. The costs under the resilience scenario increase due to the targets that ensure reliable and clean drinking water availability, and secure piped connections in both industrial and residential sectors.\u003c/p\u003e"},{"header":"Governance as barrier to adaptation in water","content":"\u003cp\u003eOur study outlines the pivotal role of water system within the overarching framework of global climate narratives and uses multi sectoral IAM scenarios to support the analysis. The increased demand for long-lasting water infrastructure requires increased investements in a scenario that resembles a Climate Resilient Development Pathway. But the long-term benefits of effective water management\u0026nbsp;outweigh the high costs in the short-run. Notably, these anticipated costs could change depending on improvements in governance or advances in technology, both of which could also impact the GDP.\u003c/p\u003e\u003cp\u003eAdaptation is not equally feasible in all regions of the world. After financial constraints, the level of governance and quality of institutions are the most important limiting factor for adaptation globally (Berrang-Ford et al., 2021). To link the required investments with levels of governance, we use projections of the Worldwide Governance Index (1 – very good, 0 – very poor) (Andrijevic et al., 2020) as a proxy for adaptive capacity. For example, the Ganges River basin currently has moderate capacity (0.6) to implement the required investments. Comparing African basins such as Lake Chad, Niger, Congo, and Tigris Euphrates with those located in the northern latitudes, the investment needs results should be interpreted carefully. Although the investments per GDP needed in the African basins may seem relatively small, governance ratings of around 0.5 indicate much less capacity for effective implementation. The noticeable deficit in adaptative capacity within these basins serve as a clear indication of their vulnerability to the impacts of climate change and the need for effective strategies to enhance their resilience.\u0026nbsp;\u003c/p\u003e\u003cp\u003eThe discrepancy in governing abilities (Table 3) highlights a clear disparity in potential for adaptation:\u003c/p\u003e\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eTier 1, with high governance values, encompasses 1.1 billion people more amenable to adaptation.\u003c/li\u003e\n \u003cli\u003eTier 2, marked by medium governance values, includes 4.7 billion people who encounter moderate adaptation difficulties.\u003c/li\u003e\n \u003cli\u003eTier 3, characterized by low governance values, implicates 2.8 billion people who will face considerable challenges in adaptation.\u003c/li\u003e\n\u003c/ul\u003e\u003cp\u003eOur analysis\u0026nbsp;combines conceptual and quantitative explanations laying the groundwork in the nexus of water management and climate change. Prioritizing transformational adaptation alone would be insufficient; strategic investments are essential to actualize the Climate Resilient Development Pathway, enhancing our adaptative potential while mitigating costs. A resilient future can be obtained\u0026nbsp;by the convergence of sustainable development, climatic resilience, and human welfare.\u003c/p\u003e\u003cp\u003eWhile the Adaptation scenario's investments seem modest in comparison to those of the Resilience scenario, it is important to note that the latter's model outputs likely underestimate the cost of climate change impacts in a world that forgoes mitigation strategies. Though relevant, this discussion extends beyond the scope of this paper. It is crucial to note that although our study details increased mitigation costs in the Resilience scenario, the real-world socio-economic impacts of climate change in a Baseline scenario, which the world fails to mitigate, could substantially exceed these costs. This investment-focused overview introduces the complexities of the examined scenarios and highlights the potential economic consequences of insufficient climate change action. This lays the groundwork for a comprehensive analysis of the trade-offs and synergies inherent to each scenario.\u0026nbsp;\u003c/p\u003e\u003cp\u003eIn conclusion, direct investment comparisons across basins should be undertaken cautiously due to the inherent discrepancies in adaptive capacities. The investment in a basin like the Niger cannot be directly compared to that of the Danube region, given the variable governance scores. The deficiency in adaptive capacity in certain basins poses a barrier to establishing resilient pathways, thus stressing the need for customized approaches that consider the unique challenges and capacities of each basin. Contextualizing this analysis with the basins’ governance levels, offers a perspective on their respective challenges for adaptation. Notably, approximately 2.8 billion people are exposed to adaptation barriers and challenges to pursue resilience pathways due to inadequate governance in 2050.\u003c/p\u003e\u003cp\u003eOur findings advocate for the coalescence of adaptation and resilience to capitalize on co-benefits, highlighting the importance of basin-specific socio-economic conditions. This conclusive section elucidates the critical nature of tailored, strategic approaches to enhance the resilience of each basin, ensuring sustainable adaptation and the accrual of significant co-benefits within our global water systems. This study lays the groundwork for a nuanced understanding of the interplay between sustainable water management, climate resilience, and human welfare, charting a course for informed decision-making in the face of climatic uncertainties.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eMESSAGEix-GLOBIOM Nexus\u003c/h2\u003e\n\u003cp\u003eThe MESSAGEix-GLOBIOM Integrated Assessment Model, built upon the MESSAGEix framework, optimizes energy systems within an open-source setting, enriched by macro-economic feedback through a stylized computable general equilibrium model\u0026nbsp;(Huppmann et al., 2019).This optimization proceeds iteratively, in concert with the MACRO economic model which forecasts the macroeconomic demand response to the energy system and service costs calculated by MESSAGEix.\u003c/p\u003e\n\u003cp\u003eThe MESSAGEix-GLOBIOM Nexus has introduced an innovative water module that encapsulates hydrological variables and an expansive portrayal of water supply and demand. This module accounts for biophysical climate impacts on water and energy supply and demand, bridging the MESSAGEix energy system with the GLOBIOM land system.Further enhancements in the model include a detailed sector-specific representation of the water system and climate impacts. The model integrates endogenous water allocation mechanisms, precise in both space and time. These mechanisms orchestrate the interplay between energy, land use, and water requirements, considering how water scarcity might restrict energy and land resource utilization (Awais et al., 2023).\u003c/p\u003e\n\u003cp\u003eThe framework's versatility is demonstrated by its ability to integrate outputs from any Global Hydrological Model (GHM), exemplified by its use of the CWatM global hydrological model (Burek et al., 2019).It incorporates a spatial layer with high resolution into the IAM, safeguarding vital hydrological data. Employing the HydroShed Database's basin delineation Lehner \u0026amp; Grill, 2013), the model spans 210 hydrological basins.\u003c/p\u003e\n\u003cp\u003eBy aligning water sector insights with the eleven global regions of the MESSAGE energy system IAM, the framework translates hydrological data into energy sector metrics and vice versa. It meticulously matches water supply with demand at the basin level and specifies water needs for energy and land use within the MESSAGE R11 regions.\u003c/p\u003e\n\u003cp\u003eThe MESSAGEix-GLOBIOM Nexus tracks municipal and industrial water demands, alongside the water used in power plant cooling, energy extraction, and irrigation. It sources water from diverse origins, including surface, groundwater, and desalination processes. Furthermore, it encompasses wastewater treatment infrastructure to monitor wastewater volumes and calculate the investments needed for various treatment goals. This comprehensive monitoring of water demands facilitates future integrated analysis of climate feedback and adaptation pathways. The IAM incorporates climate feedback concerning water availability, desalination capacity, hydropower potential, and land-use variables (bioenergy, irrigation water) to reflect the impacts of RCP forcings within the system (Awais et al., 2023).\u003c/p\u003e\n\u003ch2\u003eIMAGE 3.2\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe Integrated Assessment Model (IAM) framework of IMAGE (D. van Vuuren et al., 2021) simulates the global and regional environmental effects of alterations in human activity. The IMAGE model includes comprehensive descriptions of the energy and land use systems. It simulates most socio-economic and environmental parameters for 26 regions based on a geographical grid of 30 by 30 minutes or 5 by 5 minutes (approximately 50 km and 10 km at the equator, respectively). IMAGE is designed to assess large-scale and long-term interactions between human development and the natural environment and to identify response strategies to global environmental change by evaluating mitigation and adaptation options.\u003c/p\u003e\n\u003cp\u003eThe IMAGE 3.2 model has been completely calibrated through 2015, with some variables calibrated through even more recent years (2018 for energy system variables and 2020 for renewables capacity and CO2 emission data). The model includes the economic effects of the Covid pandemic in recent years. In IMAGE 3.2, the base year for scenario analysis is 2020, implying that scenarios follow the same trajectory from 2015 to 2020. The modeling of energy demand has been enhanced, particularly in end-use sectors such as transportation, industry, structures, and services.\u003c/p\u003e\n\u003cp\u003eThe model contains technology descriptions for each industry segment. In addition, bioenergy modeling has been considerably improved by incorporating dynamic land-use change emission factors based on the IMAGE-land model and introducing biofuel production with carbon capture and storage technology pathways.\u003c/p\u003e\n\u003cp\u003eThe number of produce categories in the land use sector was increased to 16, representing all FAO-reported crop production. Based on FAO data and ESA-CCI satellite data, deforestation caused by factors other than agricultural expansion has decreased. Concerning climate change effects and exogenous and endogenous trends in crop yield changes, the link between the agriculture-economic model MAGNET and the IMAGE model was significantly enhanced.\u003c/p\u003e\n\u003cp\u003eThe modeling of land-based Mitigation in IMAGE and MAGNET has been improved to account for avoided deforestation and afforestation, as well as the interaction between non-CO2 mitigation and the agriculture and food system. The water modeling in IMAGE that is linked to LPJmL was enhanced by incorporating municipal, energy, and industry water demand into LPJmL, allowing environmental flow requirements to be accounted for. The marginal abatement cost contours for all greenhouse gases other than CO2 have been updated based on recent research.\u003c/p\u003e\n\u003ch2\u003eScenario Assumptions\u003c/h2\u003e\n\u003cp\u003eBoth models are structured differently, so we present results where they are available; otherwise, show the available ones for each model. However, the scenarios were designed based on common assumptions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSocio-economic Assumptions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Shared Socioeconomic Pathway 2 (SSP2), sometimes referred to as the \"Middle of the Road\" scenario, depicts a future in which societal, economic, and technological progress advances at a moderate rate, closely following historical patterns (Riahi et al., 2017). \u0026nbsp; This scenario presents a balanced trajectory with moderate challenges for both climate change mitigation and adaptation efforts. It is characterized by intermediate levels of education and income, and does not aggressively pursue either environmental sustainability or reliance on fossil fuels. The population and GDP projections have been upscaled from the gridded data onto the required regional definitions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTime Horizon\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll scenarios include the time horizon until the end of century.\u003c/p\u003e\n\u003ch3\u003eClimate\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eClimate feedback and impacts implementation follows the approach presented in Table 2. Data is based on the Inter-Sectoral Model Intercomparison Project (ISIMIP) (Frieler et al., 2017), to ensure internal consistency across the different indicators and across models. Where relevant, models can differ in terms of impact modules and default climate patterns (e.g., in IMAGE, the IPSL climate pattern is used, while MESSAGEix-GLOBIOM uses the multi-model ensemble mean of 4 models and sometimes specific climate model). The impacts on labor productivity and GDP are not included in this assessment to allow for focusing on biophysical impacts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWater Supply Reliability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn MESSAGEix-GLOBIOM, we used a quantile approach to analyze monthly freshwater resources, considering hydro-climate variability and prolonged dry periods. The 10th percentile (Q90) of monthly runoff is calculated from daily data and used to determine a reliable flow for 90% of the time. This methodology is commonly used in water resource and environmental flow assessments to account for seasonal low flows in typical wet and average years. The scale of investments required rises steeply when higher levels of water supply reliability are planned for. While renewable sources typically meet most demands most of the time at a low cost, ensuring more reliable supply typically requires investments in alternative sources. They meet a small proportion of the annual demand, at comparatively high cost.\u003c/p\u003e\n\u003cp\u003eWater Stress\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWater Stress is calculated by ratio of withdrawals over renewable water availability (runoff). Population under water stress is calculated by cutoff if ratio is greater than 0.4.\u003c/p\u003e\n\u003cp\u003eAdaptive Capacity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe spatially calculate Governance projections for SSP2 provided by (Andrijevic et al., 2020) for the basins using weighted average of country population and GDP. This gives us an estimate of the governance indicators since some basins overlap multiple countries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Basins categories \u0026nbsp;Governance Indicators\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTiers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGovernance Indicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eTier 1- Easier to adapt\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026gt;0.8\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eTier 2 – Efforts required to adapt\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;0.66 - 0.8\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003eTier 3 – Difficult to adapt\u003c/p\u003e\n \u003c/td\u003e\u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026lt; 0.66\u003c/p\u003e\n \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\n\n"},{"header":"Limitations ","content":"\u003cp\u003eThings we are not covering e.g., extreme effects, floods, droughts, economic losses,\u0026nbsp;\u003c/p\u003e\u003cp\u003eInput costs doesn't consider GDP trajectories and uniform across region.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData and Code Availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe MESSAGEix model development done to run the scenarios in this article is described in (Awais et al., 2023). The code and documentation are available on the MESSAGEix public Github page (https://github.com/iiasa/message-ix-models) and documentation (https://docs.messageix.org/projects/models/en/latest/water/index.html\u0026nbsp;). A public Zenodo data repository also contains all the input data for running MESSAGEix-GLOBIOM (https://zenodo.org/records/7687578). The IMAGE model documentation is available at https://models.pbl.nl/image/index.php/Welcome_to_IMAGE_3.2_Documentation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results specific to this paper from both IAMs will be made available in a public repository and on a public instance of the IIASA Scenario Explorer upon acceptance (e.g. https://iiasa.ac.at/models-tools-data/ar6-scenario-explorer-and-database )\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndrijevic, M., Crespo Cuaresma, J., Muttarak, R., \u0026amp; Schleussner, C. F. (2020). Governance in socioeconomic pathways and its role for future adaptive capacity. \u003cem\u003eNat. Sustain.\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 35\u0026ndash;41. https://doi.org/10.1038/s41893-019-0405-0\u003c/li\u003e\n\u003cli\u003eAndrijevic, M., Schleussner, C. F., Crespo Cuaresma, J., Lissner, T., Muttarak, R., Riahi, K., Theokritoff, E., Thomas, A., van Maanen, N., \u0026amp; Byers, E. (2023). Towards scenario representation of adaptive capacity for global climate change assessments. \u003cem\u003eNature Climate Change 2023\u003c/em\u003e, 1\u0026ndash;10. https://doi.org/10.1038/s41558-023-01725-1\u003c/li\u003e\n\u003cli\u003eAwais, M., Vinca, A., Byers, E., Frank, S., Fricko, O., Boere, E., Burek, P., Poblete Cazenave, M., Kishimoto, P. N., Mastrucci, A., Satoh, Y., Palazzo, A., McPherson, M., Riahi, K., \u0026amp; Krey, V. (2023). MESSAGEix Nexus Module: Integrating water sector and climate impacts. \u003cem\u003eIn Prep\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eBerrang-Ford, L., Siders, A. R., Lesnikowski, A., Fischer, A. P., Callaghan, M. W., Haddaway, N. R., Mach, K. J., Araos, M., Shah, M. A. R., Wannewitz, M., Doshi, D., Leiter, T., Matavel, C., Musah-Surugu, J. I., Wong-Parodi, G., Antwi-Agyei, P., Ajibade, I., Chauhan, N., Kakenmaster, W., \u0026hellip; Abu, T. Z. (2021). A systematic global stocktake of evidence on human adaptation to climate change. \u003cem\u003eNature Climate Change\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(11), 989\u0026ndash;1000. https://doi.org/10.1038/s41558-021-01170-y\u003c/li\u003e\n\u003cli\u003eBurek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., \u0026amp; Wada, Y. (2019). Development of the Community Water Model (CWatM v1.04) A high-resolution hydrological model for global and regional assessment of integrated water resources management. \u003cem\u003eGeoscientific Model Development Discussions\u003c/em\u003e, 1\u0026ndash;49. https://doi.org/10.5194/gmd-2019-214\u003c/li\u003e\n\u003cli\u003eBurek, P., Satoh, Y., Kahil, T., Tang, T., Greve, P., Smilovic, M., Guillaumot, L., Zhao, F., \u0026amp; Wada, Y. (2020). Development of the Community Water Model (CWatM v1.04) - A high-resolution hydrological model for global and regional assessment of integrated water resources management. \u003cem\u003eGeoscientific Model Development\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(7), 3267\u0026ndash;3298. https://doi.org/10.5194/GMD-13-3267-2020\u003c/li\u003e\n\u003cli\u003eByers, E., Gidden, M., Leclere, D., Balkovic, J., Burek, P., Ebi, K., Greve, P., Grey, D., Havlik, P., Hillers, A., Johnson, N., Kahil, T., Krey, V., Langan, S., Nakicenovic, N., Novak, R., Obersteiner, M., Pachauri, S., Palazzo, A., \u0026hellip; Riahi, K. (2018). Global exposure and vulnerability to multi-sector development and climate change hotspots. \u003cem\u003eEnviron. Res. Lett.\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(5), 055012. https://doi.org/10.1088/1748-9326/aabf45\u003c/li\u003e\n\u003cli\u003eDe Vos, L., Biemans, H., Doelman, J. C., Stehfest, E., \u0026amp; Van Vuuren, D. P. (2021). Trade-offs between water needs for food, utilities, and the environment\u0026mdash;a nexus quantification at different scales. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(11), 115003. https://doi.org/10.1088/1748-9326/AC2B5E\u003c/li\u003e\n\u003cli\u003eDoelman, J. C., Beier, F. D., Stehfest, E., Bodirsky, B. L., Beusen, A. H. W., Humpen\u0026ouml;der, F., Mishra, A., Popp, A., Van Vuuren, D. P., De Vos, L., Weindl, I., Van Zeist, W. J., \u0026amp; Kram, T. (2022). Quantifying synergies and trade-offs in the global water-land-food-climate nexus using a multi-model scenario approach. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(4), 045004. https://doi.org/10.1088/1748-9326/AC5766\u003c/li\u003e\n\u003cli\u003eDouville, H., Allanid, R. P., Ariasid, P. A., Betts, R. A., Caretta, M. A., Cherchiid, A., Mukherji, A., Raghavan, K., \u0026amp; Renwickid, J. (2022). Water remains a blind spot in climate change policies. \u003cem\u003ePLOS Water\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(12), e0000058. https://doi.org/10.1371/JOURNAL.PWAT.0000058\u003c/li\u003e\n\u003cli\u003eFrank, S., Gusti, M., Havl\u0026iacute;k, P., Lauri, P., Di Fulvio, F., Forsell, N., Hasegawa, T., Krisztin, T., Palazzo, A., \u0026amp; Valin, H. (2021a). Land-based climate change mitigation potentials within the agenda for sustainable development. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 024006. https://doi.org/10.1088/1748-9326/ABC58A\u003c/li\u003e\n\u003cli\u003eFrank, S., Gusti, M., Havl\u0026iacute;k, P., Lauri, P., Di Fulvio, F., Forsell, N., Hasegawa, T., Krisztin, T., Palazzo, A., \u0026amp; Valin, H. (2021b). Land-based climate change mitigation potentials within the agenda for sustainable development. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(2), 024006. https://doi.org/10.1088/1748-9326/ABC58A\u003c/li\u003e\n\u003cli\u003eFrieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., \u0026hellip; Yamagata, Y. (2017). Assessing the impacts of 1.5ĝ\u0026euro;\u0026deg;C global warming - Simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). \u003cem\u003eGeoscientific Model Development\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(12), 4321\u0026ndash;4345. https://doi.org/10.5194/GMD-10-4321-2017\u003c/li\u003e\n\u003cli\u003eGernaat, D. E. H. J., de Boer, H. S., Daioglou, V., Yalew, S. G., M\u0026uuml;ller, C., \u0026amp; van Vuuren, D. P. (2021). Climate change impacts on renewable energy supply. \u003cem\u003eNature Climate Change 2021 11:2\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 119\u0026ndash;125. https://doi.org/10.1038/s41558-020-00949-9\u003c/li\u003e\n\u003cli\u003eHuppmann, D., Gidden, M., Fricko, O., Kolp, P., Orthofer, C., Pimmer, M., Kushin, N., Vinca, A., Mastrucci, A., Riahi, K., \u0026amp; Krey, V. (2019). The MESSAGEix Integrated Assessment Model and the ix modeling platform (ixmp): An open framework for integrated and cross-cutting analysis of energy, climate, the environment, and sustainable development. \u003cem\u003eEnvironmental Modelling and Software\u003c/em\u003e, 143\u0026ndash;156. https://doi.org/10.1016/j.envsoft.2018.11.012\u003c/li\u003e\n\u003cli\u003e(IPCC), I. P. on C. C. (2023). Water. In \u003cem\u003eClimate Change 2022 \u0026ndash; Impacts, Adaptation and Vulnerability\u003c/em\u003e (pp. 551\u0026ndash;712). Cambridge University Press. https://doi.org/10.1017/9781009325844.006\u003c/li\u003e\n\u003cli\u003eKahil, T., Parkinson, S., Satoh, Y., Greve, P., Burek, P., Veldkamp, T. I. E., Burtscher, R., Byers, E., Djilali, N., Fischer, G., Krey, V., Langan, S., Riahi, K., Tramberend, S., \u0026amp; Wada, Y. (2018). A Continental-Scale Hydroeconomic Model for Integrating Water-Energy-Land Nexus Solutions. \u003cem\u003eWater Resources Research\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(10), 7511\u0026ndash;7533. https://doi.org/10.1029/2017WR022478\u003c/li\u003e\n\u003cli\u003eK\u0026ouml;berle, A. C., Vandyck, T., Guivarch, C., Macaluso, N., Bosetti, V., Gambhir, A., Tavoni, M., \u0026amp; Rogelj, J. (2021). The cost of mitigation revisited. \u003cem\u003eNature Climate Change 2021 11:12\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(12), 1035\u0026ndash;1045. https://doi.org/10.1038/s41558-021-01203-6\u003c/li\u003e\n\u003cli\u003eKrey, V., Havlik, P., Fricko, O., J, Z., Forsell N, G. G. M. S. M. K. G. E. T., Obersteiner M, R. M. J. N. K. G. K. P. M. D. P. S. R. S. R. J. V. H., \u0026amp; (2016), K. (2016). \u003cem\u003eMESSAGE-GLOBIOM 1.0 Documentation\u003c/em\u003e (1.0). International Institute for Applied Systems Analysis (IIASA). https://docs.messageix.org/projects/global/en/latest/overview/index.html\u003c/li\u003e\n\u003cli\u003eMastrucci, A., van Ruijven, B., Byers, E., Poblete-Cazenave, M., \u0026amp; Pachauri, S. (2021). Global scenarios of residential heating and cooling energy demand and CO2 emissions. \u003cem\u003eClimatic Change\u003c/em\u003e, \u003cem\u003e168\u003c/em\u003e(3\u0026ndash;4), 1\u0026ndash;26. https://doi.org/10.1007/S10584-021-03229-3/FIGURES/7\u003c/li\u003e\n\u003cli\u003eMiralles-Wilhelm, F. (2021). Water is the middle child in global climate policy. \u003cem\u003eNature Climate Change 2021 12:2\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(2), 110\u0026ndash;112. https://doi.org/10.1038/s41558-021-01154-y\u003c/li\u003e\n\u003cli\u003eMpandeli, S., Naidoo, D., Mabhaudhi, T., Nhemachena, C., Nhamo, L., Liphadzi, S., Hlahla, S., \u0026amp; Modi, A. T. (2018). Climate Change Adaptation through the Water-Energy-Food Nexus in Southern Africa. \u003cem\u003eInternational Journal of Environmental Research and Public Health 2018, Vol. 15, Page 2306\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(10), 2306. https://doi.org/10.3390/IJERPH15102306\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller, C., \u0026amp; Robertson, R. D. (2014). Projecting future crop productivity for global economic modeling. \u003cem\u003eAgricultural Economics\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(1), 37\u0026ndash;50. https://doi.org/10.1111/AGEC.12088\u003c/li\u003e\n\u003cli\u003eParkinson, S., Krey, V., Huppmann, D., Kahil, T., McCollum, D., Fricko, O., Byers, E., Gidden, M. J., Mayor, B., Khan, Z., Raptis, C., Rao, N. D., Johnson, N., Wada, Y., Djilali, N., \u0026amp; Riahi, K. (2019). Balancing clean water-climate change mitigation trade-offs. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1). https://doi.org/10.1088/1748-9326/aaf2a3\u003c/li\u003e\n\u003cli\u003ePastor, A. V., Ludwig, F., Biemans, H., Hoff, H., \u0026amp; Kabat, P. (2014). Accounting for environmental flow requirements in global water assessments. \u003cem\u003eHydrology and Earth System Sciences\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(12), 5041\u0026ndash;5059. https://doi.org/10.5194/HESS-18-5041-2014\u003c/li\u003e\n\u003cli\u003ePastor, A. V., Palazzo, A., Havlik, P., Biemans, H., Wada, Y., Obersteiner, M., Kabat, P., \u0026amp; Ludwig, F. (2019). The global nexus of food\u0026ndash;trade\u0026ndash;water sustaining environmental flows by 2050. \u003cem\u003eNature Sustainability 2019 2:6\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(6), 499\u0026ndash;507. https://doi.org/10.1038/s41893-019-0287-1\u003c/li\u003e\n\u003cli\u003ePiontek, F., Drouet, L., Emmerling, J., Kompas, T., M\u0026eacute;jean, A., Otto, C., Rising, J., Soergel, B., Taconet, N., \u0026amp; Tavoni, M. (2021). Integrated perspective on translating biophysical to economic impacts of climate change. \u003cem\u003eNature Climate Change 2021 11:7\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(7), 563\u0026ndash;572. https://doi.org/10.1038/s41558-021-01065-y\u003c/li\u003e\n\u003cli\u003eRiahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O\u0026rsquo;Neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J. C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., \u0026hellip; Tavoni, M. (2017). The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. \u003cem\u003eGlobal Environmental Change\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e. https://doi.org/10.1016/j.gloenvcha.2016.05.009\u003c/li\u003e\n\u003cli\u003eRising, J., Tedesco, M., Piontek, F., \u0026amp; Stainforth, D. A. (2022). The missing risks of climate change. \u003cem\u003eNature 2022 610:7933\u003c/em\u003e, \u003cem\u003e610\u003c/em\u003e(7933), 643\u0026ndash;651. https://doi.org/10.1038/s41586-022-05243-6\u003c/li\u003e\n\u003cli\u003eSchaphoff, S., Von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., J\u0026auml;germeyr, J., Knauer, J., Langerwisch, F., Lucht, W., M\u0026uuml;ller, C., Rolinski, S., \u0026amp; Waha, K. (2018). LPJmL4\u0026mdash;a dynamic global vegetation model with managed land \u0026ndash; Part 1: model description. \u003cem\u003eGeosci. Model Dev.\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(4), 1343\u0026ndash;1375. https://doi.org/10.5194/gmd-11-1343-2018\u003c/li\u003e\n\u003cli\u003eSchipper, E. L. F., Revi, A., Preston, B. L., Carr, E. R., Eriksen, S. H., Fernandez-Carril, L. R., Glavovic, B. C., Hilmi, N. J. M., Ley, D., Mukerji, R., Araujo, M. S. M. de, Perez, R., Rose, S. K., \u0026amp; Singh, P. K. (2022). \u003cem\u003eClimate Resilient Development Pathways. In: Impacts, Adaptation and Vulnerability. WGII AR6 IPCC\u003c/em\u003e. https://doi.org/10.1017/9781009325844.027\u003c/li\u003e\n\u003cli\u003evan Maanen, N., Lissner, T., Harmsen, M., Piontek, F., Andrijevic, M., \u0026amp; van Vuuren, D. P. (2023). Representation of adaptation in quantitative climate assessments. \u003cem\u003eNature Climate Change 2023 13:4\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 309\u0026ndash;311. https://doi.org/10.1038/s41558-023-01644-1\u003c/li\u003e\n\u003cli\u003eVan Vuuren, D. P., Bijl, D. L., Bogaart, P., Stehfest, E., Biemans, H., Dekker, S. C., Doelman, J. C., Gernaat, D. E. H. J., \u0026amp; Harmsen, M. (2019). Integrated scenarios to support analysis of the food\u0026ndash;energy\u0026ndash;water nexus. \u003cem\u003eNature Sustainability\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(12), 1132\u0026ndash;1141. https://doi.org/10.1038/S41893-019-0418-8\u003c/li\u003e\n\u003cli\u003evan Vuuren, D., Stehfest, E., Gernaat, D., Boer, H.-S. de, Daioglou, V., Doelman, J., Edelenbosch, O., Harmsen, M., Zeist, W.-J. van, Berg, M. van den, Dafnomilis, I., Sluisveld, M. van, Tabeau, A., Vos, L. De, Waal, L. de, Berg, N. van den, Beusen, A., Bos, A., Biemans, H., \u0026hellip; Castillo, V. Z. (2021). \u003cem\u003eThe 2021 SSP scenarios of the IMAGE 3.2 model\u003c/em\u003e. https://doi.org/10.31223/X5CG92\u003c/li\u003e\n\u003cli\u003eWillett, W., Rockstr\u0026ouml;m, J., Loken, B., Springmann, M., Lang, T., Vermeulen, S., Garnett, T., Tilman, D., DeClerck, F., Wood, A., Jonell, M., Clark, M., Gordon, L. J., Fanzo, J., Hawkes, C., Zurayk, R., Rivera, J. A., De Vries, W., Majele Sibanda, L., \u0026hellip; Murray, C. J. L. (2019). Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. \u003cem\u003eLancet (London, England)\u003c/em\u003e, \u003cem\u003e393\u003c/em\u003e(10170), 447\u0026ndash;492. https://doi.org/10.1016/S0140-6736(18)31788-4\u003c/li\u003e\n\u003cli\u003eYalew, S. G., van Vliet, M. T. H., Gernaat, D. E. H. J., Ludwig, F., Miara, A., Park, C., Byers, E., De Cian, E., Piontek, F., Iyer, G., Mouratiadou, I., Glynn, J., Hejazi, M., Dessens, O., Rochedo, P., Pietzcker, R., Schaeffer, R., Fujimori, S., Dasgupta, S., \u0026hellip; van Vuuren, D. P. (2020). Impacts of climate change on energy systems in global and regional scenarios. \u003cem\u003eNature Energy 2020 5:10\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(10), 794\u0026ndash;802. https://doi.org/10.1038/s41560-020-0664-z\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4149842/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4149842/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Water systems are crucial for sustainable development and energy-water-land system-wide climate mitigation and adaptation to changing climate. We explore the dynamic interplay between climate change adaptation, mitigation, and sustainable development within these systems. We demonstrate, how strategic investments in a “Resilience scenario” – climate mitigation and Sustainable Development Goals (SDGs) combined, leads to approximately 20% reduction in water stress for vulnerable populations promoting sustainable water and energy systems. Despite the Resilience scenario targets, about one-third of global population (2.8 billion people) is expected to face adaptation barriers by mid-century, hindered by inadequate governance capacity. We demonstrate that investments in SDGs, combined with climate mitigation strategies, are more beneficial for the water sector than pursuing SDGs alone. Our findings suggest a necessary convergence of sustainable development, climate resilience, and human welfare to create a climate-resilient sustainable future, marking a significant step forward in facilitating integrated environmental policy and decision-making.","manuscriptTitle":"Global Water Basins under Combined Climate Mitigation, Adaptation, and Sustainable Development Targets","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-12 02:03:49","doi":"10.21203/rs.3.rs-4149842/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"69fe152b-fb4a-469f-8202-ca92958c83bc","owner":[],"postedDate":"April 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":30186845,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":30186846,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change adaptation"}],"tags":[],"updatedAt":"2024-06-19T18:45:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-12 02:03:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4149842","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4149842","identity":"rs-4149842","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}