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
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Governance Indicator
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Tier 1- Easier to adapt
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>0.8
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Tier 2 – Efforts required to adapt
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0.66 - 0.8
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Tier 3 – Difficult to adapt
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< 0.66
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