Global hydrological models have become a valuable tool for a range of global impact studies related to water resources. However, glacier parameterization is often simplistic or non-existent in global hydrological models. By contrast, global glacier models do represent complex glacier dynamics and glacier evolution, and as such, they hold the promise of better resolving glacier runoff estimates. In this study, we test the hypothesis that coupling a global glacier model with a global hydrological model leads to a more realistic glacier representation and, consequently, to improved runoff predictions in the global hydrological model. To this end, the Global Glacier Evolution Model (GloGEM) is coupled with the PCRaster GLOBal Water Balance model, version 2.0 (PCR-GLOBWB 2), using the eWaterCycle platform. For the period 2001–2012, the coupled model is evaluated against the uncoupled PCR-GLOBWB 2 in 25 large-scale (>50 000 km2), glacierized basins. The coupled model produces higher runoff estimates across all basins and throughout the melt season. In summer, the runoff differences range from 0.07 % for weakly glacier-influenced basins to 252 % for strongly glacier-influenced basins. The difference can primarily be explained by PCR-GLOBWB 2 not accounting for glacier flow and glacier mass loss, thereby causing an underestimation of glacier runoff. The coupled model performs better in reproducing basin runoff observations mostly in strongly glacier-influenced basins, which is where the coupling has the most impact. This study underlines the importance of glacier representation in global hydrological models and demonstrates the potential of coupling a global hydrological model with a global glacier model for better glacier representation and runoff predictions in glacierized basins.@en

The bargaining power of prosumers in a market can vary significantly. Participants can range from industrial participants to powerful and less powerful citizens. Existing trade mechanisms in such markets, e.g., in rural India’s energy trade market, show occurrences of discrimination, exclusion, and unfairness. We study how discrimination affects market access, efficiency, and demand satisfaction for the discriminating and discriminated groups via an agent-based simulation, incorporating the available real data. We introduce a mechanism for such markets that is designed for the values of inclusion and equal opportunities. The crux of our mechanism is that goods are divided into smaller units, as determined by the market participants’ surplus and demands, and traded anonymously via agents representing the prosumers. We evaluate six hypotheses in a case study about energy trade in rural India, where members of a caste known as Dalits are discriminated by Others. We show that anonymization contributes to the value of inclusion, and the combination of anonymization and inclusion contributes to equal opportunities with respect to market access for both Dalits and Others.

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