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Hydro-economic modeling (HEM) addresses research and policy questions from socioeconomic and biophysical perspectives under a broad range of water-related topics. Applications of HEM include economic evaluations of existing and new water projects, alternative water management actions or policies, risk assessments from hydro-climatic uncertainty (e.g., climate change), and the costs and benefits of mitigation and/or adaptation to such events. This paper reviews applications of HEM in five different categories: (1) climate change impacts and adaptation, (2) water–food–energy–ecosystems nexus management, (3) capability to link to other models, (4) innovative water management options, and (5) the ability to address and integrate uncertainty. We find that (i) the increasing complexity and heterogeneity of water resource management problems due to the growing demand and competition for water across economic sectors, (ii) limited availability and high costs of developing additional supplies, and (iii) emerging recognition and consideration of environmental water demands and value, have inspired new integrated hydro-economic problems and models to address issues of water–food–energy nexus sustainability, resilience, reliability through water (re)allocation based on the relative “value” of water uses. In the past decade, the field of HEM has improved the integration of ecosystem needs, but their representation is still insufficient and mostly ineffective. HEM studies address how to sustainably manage water resources, including groundwater which has become an area of particular interest in climate change adaptation. The current most used spatial and temporal resolutions (basin-scale and yearly time-step) are appropriate for planning but not for operational decisions and could be underestimating impacts from extreme events (e.g., flood risk) captured only by sub-monthly time scales. In addition, HEM primarily focuses on biophysical and economic indicators but often overlooks p

This work aims to describe the spatial distribution of flow from characteristics of the underlying pore structure in heterogeneous porous media. Thousands of two-dimensional samples of polydispersed granular media are used to (1) obtain the velocity field via direct numerical simulations, and (2) conceptualize the pore network as a graph in each sample. Analysis of the flow field allows us to distinguish preferential from stagnant flow regions and to quantify how channelized the flow is. Then, the graph’s edges are weighted by geometric attributes of their corresponding pores to find the path of minimum resistance of each sample. Overlap between the preferential flow paths and the predicted minimum resistance path determines the accuracy in individual samples. An evolutionary algorithm is employed to determine the “fittest” weighting scheme (here, the channel’s arc length to pore throat ratio) that maximizes accuracy across the entire dataset while minimizing over-parameterization. Finally, the structural similarity of neighboring edges is analyzed to explain the spatial arrangement of preferential flow within the pore network. We find that connected edges within the preferential flow subnetwork are highly similar, while those within the stagnant flow subnetwork are dissimilar. The contrast in similarity between these regions increases with flow channelization, explaining the structural constraints to local flow. The proposed framework may be used for fast characterization of porous media heterogeneity relative to computationally expensive direct numerical simulations. Article Highlights: 1.A quantitative assessment of flow channeling is proposed that distinguishes pore-scale flow fields into preferential and stagnant flow regions.2.Geometry and topology of the pore network are used to predict the spatial distribution of fast flow paths from structural data alone.3.Local disorder of pore networks provides structural constraints for flow separation into pre