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This dataset contains: Values for 42 variables related to topography, climate, vegetation, geology, and anthropogenic activities collected from 2001 to 2020. These variables were used to assess the drivers of streamflow drought deficit and duration across 2,550 stream gauges in the contiguous United States, including both natural and human-impacted sites. The computation method for each factor is detailed in Table 1. The dataset also includes trend analyses of drought duration and deficit from 1980 to 2020, performed using the Mann-Kendall test under three conditions: independence, short-term persistence, and long-term persistence. The complete analysis and findings are presented in Vicario, S.A., Hornberger, G.M., Mazzoleni, M., Garcia, M. (2025), "Drivers and trends of streamflow droughts in natural and human-impacted basins across the contiguous United States," Journal of Hydrology, DOI: https://doi.org/10.1016/j.jhydrol.2025.132908. 

This resource contains 49 factors categorized into five groups: climatic (14), topographic (11), vegetation-related (5), anthropogenic (12), and geologic (7) factors, concerning 383 watersheds within the GAGES-II gages dataset (https:// water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml) across the contiguous United States (CONUS). The selection of the 383 watersheds out of the 9067 (in the CONUS) from GAGES-II was determined by the availability of daily streamflow data from 1990 to 2020 and its anthropogenic influence. For further details, refer to section 2.1 of https://doi.org/10.1016/j.jhydrol.2024.130984. The factors represent average values for each watershed spanning 1990 to 2020, calculated using publicly available data. Detailed information on these factors, including their sources and calculation methods, is provided in Tables 1 and 2 of the PDF document (Methodology_factors.pdf). The Excel file (Factors.xlsx) contains the classification of the gages and their associated factor values. The computation of these factors was conducted for the manuscript authored by Sara Alonso Vicario, George M. Hornberger, Maurizio Mazzoleni, and Margaret Garcia, titled "The Importance of Climate and Anthropogenic Influence in Precipitation Partitioning in the Contiguous United States," published in the Journal of Hydrology, Volume 633 (2024). The manuscript is accessible at https://doi.org/10.1016/j.jhydrol.2024.130984. 

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. 

Abstract Designing urban water systems to respond to the accelerating and unpredictable changes of the Anthropocene will require changes not only to built infrastructure and operating rules, but also to the governance arrangements responsible for investing in them. Yet, inclusion of thispolitical‐economic feedbackin dynamic models of infrastructure systems and socio‐hydrology has lagged behindoperationalfeedback concerns. We address this gap through a dynamical systems application of the Coupled Infrastructure Systems (CIS) Framework, which provides the conceptual building blocks for analyzing social‐ecological systems through various classes of infrastructure and the flows of material and information among them. In the model, political‐economic feedback involves three decisions—infrastructure investment, rate‐setting, and short‐term demand curtailment—and each decision is constrained by institutional friction, the aggregation of decision and transaction costs associated with taking action. We apply the model to three cities in the Phoenix Metropolitan Area to compare how institutional friction interacts with a city's water resource portfolio and financial position to determine its sensitivity, or the degree to which its performance (e.g., providing sufficient supply to meet demand) changes given reductions in Colorado River water availability. We find that the slowing effect of institutional friction on investment and rate‐setting decisions can increase the sensitivity of a city's supply, but it can also promote objectives that compete with over‐response (e.g., rate burden). The effect is dependent on the initial operating capacity of the CIS and flexibility within the institutions, highlighting the need to consider political‐economic and operational feedback together when evaluating infrastructure systems. 

Abstract Urban water supply systems in the United States are designed to be robust to a wide range of historical hydrological conditions in both their physical infrastructure and in the institutional arrangements that govern their use. However, these systems vary greatly in their capacity to respond to new and evolving stressors on water supplies, such as those associated with climate change. Developing a more precise understanding of the complexity of interactions between the environmental and human components of urban water systems, specifically via their institutions, has the potential to help identify institutional design choices that can foster proactive transitions to more sustainable operating states. This article adapts the Institutional Grammar (IG) within the Robustness of Coupled Infrastructure Systems Framework to assess how a heavily engineered system's institutional configuration may impact its ability to transition to more sustainable management practices. While use of the IG has historically been limited in larger‐N studies, our application demonstrates its flexibility in revealing variation in specific components across cases. The analysis finds the structure of formal institutions shape the interactions between actors differently, and that institutional diversity exists across environmental contexts. The extent to which this institutional diversity drives transitions remains an open question. The results highlight both the importance of and challenges involved with developing longitudinal data on social and natural system interactions.