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Understanding the process of precipitation partitioning into evapotranspiration and streamflow is fundamental for water resource planning. The Budyko framework has been widely used to evaluate the factors influencing this process. Still, its application has primarily focused on studying watersheds with minimal human influence and on a relatively small number of factors. Furthermore, there are discrepancies in the literature regarding the effects of climatic factors and land use changes on this process. To address these gaps, this study aims to quantify the influence of climate and anthropogenic activities on streamflow generation in the contiguous United States. To accomplish this, we calibrated an analytical form of the Budyko curve from 1990 to 2020 for 383 watersheds. We developed regional models of , a free parameter introduced to account for controls of precipitation partitioning not captured in the original Budyko equation, within different climate zones. We computed 49 climatic and landscape factors that were related to using correlation analysis and stepwise multiple linear regression. The findings of this study show that human activities explained a low variance of the spatial heterogeneity of compared with the watershed slope and the synchronization between precipitation and potential evapotranspiration, nevertheless, urban development emerged as a factor in temperate climates, whereas irrigated agriculture emerged in cold climates. In arid climates, mean annual precipitation explains less than 20% of the spatial variability in mean annual streamflow; furthermore, this climate is the most responsive to changes in . These results provide valuable insights into how land use and climate interact to impact streamflow generation differently in the contiguous United States contingent on the regional climate, explaining discrepancies in the literature. 

The Urban Water Infrastructure Investment Model (UWIIM) is a discrete time dynamical systems model designed to reflect the general flow of water, investment, and information in a stylized urban water coupled infrastructure system. The model couples operational considerations regarding the use of infrastructure, including storage, processing, and delivery infrastructure to meet annual demand given varying, user-defined, hydrologic inflows with political-economic considerations at play in three annual decisions: short-term (with a year) curtailment of demand, investment in infrastructure, and rate-setting. We demonstrate the model with representative configurations for three Phoenix Metropolitan Area (PMA) cities: Phoenix, Scottsdale, and Queen Creek. A detailed description of the model can be found in the attached Supporting Information document. The model uses the Julia programming language (version 1.8.4). The resources published here allow users to (i.) run the UWIIM for each of the three PMA cities and vary the parameters or initial conditions used and (ii.) replicate the sensitivity analysis performed in the referenced manuscript. Both tasks can be performed with the Jupyter notebooks or Julia code contained in the source code file. We also provide the raw outputs from the sensitivity analysis and R scripts used to produce the analysis figures displayed in the manuscript. 

Urban water systems across the United States are facing a variety of challenges to existing supply and demand dynamics. Responding to these challenges and working toward sustainability in these complex socio-environmental systems (SES) requires integrating various types of information – ranging from hydrologic data to political considerations and beyond – into policy and management decisions. However, the design of institutions, i.e. the formal rules in which urban water utilities are embedded, impact the flow of various types of information, especially across diverse actor groups critical to developing and implementing policy. Drawing on a neuroscience-informed Bayesian application of the Robustness of Coupled Infrastructure Systems (CIS) Framework, this study examines the institutional designs of two urban U.S. water systems. It aims to advance our understanding of these systems by: A) theoretically linking cognitive science and its action-oriented predictive processing approach to the institutional configurations that shape collective-action; and B) identifying potential institutional dependencies and voids that may limit the use of formalized climate-related guidance in systems facing increased risks. We utilize process-tracing along with an institutional analysis approach called the Institutional Grammar Tool (IGT) to parse the institutions into their semantic and syntactic components, identifying institutional dependencies, voids, or conflicts which may influence long-range performance of the systems. Our findings have important implications for the (re)design of institutions that better facilitate the flow of information among key policy actors and support policy changes that promote sustainable long-term urban water supply.