Stream drying is increasingly recognized as a major driver of economic, geological, and biological processes in freshwater ecosystems worldwide. Understanding how drying affects eco-evolutionary processes has emerged as a key goal in quantifying how biological systems will respond to changes in stream drying during the Anthropocene. Among the challenges to meeting this goal is the need for high-resolution genomic datasets that provide insight into population-level processes at a scale comparable to spatiotemporal patterns of drying, particularly in lotic ecosystems. As part of the StreamCLIMES Project, we are generating population genomic datasets for 12 taxa in 6 hydrologic basins across the USA in order to couple population genomic data with high-resolution hydrological modeling and biological community data. We hypothesize that continental-scale aridity patterns control the location and proportion of intermittent and perennial reaches within stream networks, and that those patterns in turn influence population genetic diversity and structure. Furthermore, we expect that traits and habitat associations of freshwater taxa (e.g., dispersal ability, intermittent specialist vs. generalist) will also influence whether drying acts as a key driver of population genetic diversity. We will present our findings to-date for four taxa from two distinct hydrologic basins: one in the Mazatal Mountains of the arid southwestern USA, and one on the humid Gulf Coastal Plain of the southeastern USA. We quantified population genomic structure and diversity using a double-digest restriction-site associated DNA approach for populations of one Baetinae and one Chironominae species from each basin. We tested for the influence of biological (trait-based), environmental (drying, elevation, and landscape configuration), and spatial (Euclidean and riverine distance) factors on population genetic diversity and structure using landscape genomic approaches. We will compare findings to-date among basins, setting the stage for quantitative comparisons among all taxa in the final dataset.