The Columbia River Treaty (CRT) signed between the United States and Canada in 1961 is known as one of the most successful transboundary water treaties. Under continued cooperation, both countries equitably share collective responsibilities of reservoir operations, and flood control and hydropower benefits from treaty dams. As the balance of benefits is the key factor of cooperation, future cooperation could be challenged by external social and environmental factors which were not originally anticipated, or change in the social preferences of the two actors. To understand the robustness of cooperation dynamics we address two research questions – i) How does social and environmental change influence cooperation dynamics? and ii) How do social preferences influence the probability of cooperation for both actors? We analyzed infrastructural, hydrological, economic, social, and environmental data to inform the development of a socio-hydrological system dynamics model. The model simulates the dynamics of flood control and hydropower benefit sharing as a function of the probability to cooperate, which in turn is affected by the share of benefits. The model is used to evaluate scenarios that represent environmental and institutional change, and changes in political characteristics based on social preferences. Our findings show that stronger institutional capacity ensures equitable sharing of benefits over the long term. Under current CRT, the utility of cooperation is always higher for Canada than non-cooperation which is in contrast to the U.S. The probability to cooperate for each country is lowest when they are self-interested but fluctuates in other social preferences scenarios.

The accuracy of hydrologic and hydrodynamic models, used to study urban hydrology and predict urban flooding, depends on the availability of high-resolution terrain and infrastructure data. Unfortunately, cities often do not have or cannot release complete infrastructure data, and high-resolution terrain data products are not available everywhere. In this study, we quantify how the accuracy and precision of urban hydrologic-hydrodynamic models vary as a function of data completeness and model resolution. For this aim, we apply the one-dimensional (1D) and coupled one- and two-dimensional (1D-2D) versions of the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM) in an urban catchment in the city of Phoenix, Arizona. Here, we have collected detailed infrastructure data, a high-resolution 0.3-m LiDAR-based digital elevation model, and catchment properties data. We tested several model configurations assuming different levels of (i) availability of stormwater infrastructure data (ranging from 5% to 75% of attribute-values missing) and (ii) terrain aggregation (i.e., 4.6 m and 9.7 m). These configurations were generated through random Monte Carlo sampling for SWMM 1D and selective sampling with four cases for SWMM 1D-2D. We ran simulations under the 50-year return period design storm and compared simulated flood metrics assuming the highest-resolution and complete data model configuration as a reference. The study found that the model may over or underestimate flood volume and duration with different levels of missing data depending on the parameters — roughness, diameter or depth, and that model performance is more sensitive to missing data that is downstream and closer to the outfall as opposed to missing data upstream. Errors in flood depth, area and volume estimation are functions of both the data completeness and model resolution. Missing feature data leads to overestimation of flood depth, while lower model resolution results in underestimating flood depth and overestimating flood extent and volume.

Climate change is leading to increasing hydrological extremes and quicker shifts between wet and dry extremes in many regions. These extremes and rapid shifts put pressure on reservoir operations, decreasing the reliability of water supply, flood control and other reservoir benefits. Decision-makers across all levels, from reservoir operators to flood plain residents, turn to heuristics to simplify decisions when faced with complexity and uncertainty, resulting in cognitive biases or systematic errors in decision-making. While cognitive biases are not new, climate change is exacerbating their impact for two reasons: 1) heuristics, just as infrastructure, are based on experience with historic conditions; 2) fragilities created by these cognitive biases can go undetected until extreme events occur. If not acknowledged and managed, these cognitive biases can lead to catastrophic failures of reservoirs and other infrastructure. To minimize risk of such catastrophic failure, we propose a multi-level approach to flood and drought management, one that strikes a balance between centralized and decentralized approaches. Such an approach is better able to cope with uncertain and changing conditions because it creates overlaps and diversity, which can respond to a wide range of conditions and builds checks and balances that mitigate cognitive biases latent in various decision-making units.

Creating sustainable, resilient, and livable cities calls for integrative approaches and collaborative practices across temporal and spatial scales. However, practicability is challenged by institutional, social, and technical complexities and the need to build collective understanding of integrated approaches. Rapid urbanization along the United States-Mexico border, fueled by industrialization, trade, and migration, has resulted in cities confronted with recurrent flooding risk, extended drought, water pollution, habitat destruction and systemic vulnerabilities. The international border, which separates natural and built ecosystems, is both a challenge and an opportunity, making a unique social and institutional setting ideal for testing the integration of urban planning and water management. Our research focuses on fusing multi-functional and multi-scalar green infrastructure to restore ecosystem services through a strategic binational planning process. This paper describes this planning process, including the development and application of both a land suitability analysis and a hydrological model to optimally site green infrastructure in the Nogales, Arizona, United States—Nogales, Sonora, Mexico, cross border region. We draw lessons from this process and stakeholder feedback focused on the potential for urban green infrastructure, to allow for adaptation and even transformation in the face of current and future challenges such as limited resources, underdeveloped governance, bordering, and climate change. In sum, a cross border network of green infrastructure can provide a backbone to connect this transboundary watershed while providing both hydrological and social benefits.

Pluvial flooding in urban regions is a natural hazard that has been rarely investigated. Here, we evaluate the utility of three radar (Stage IV, Multi-Radar Multi-Sensor or MRMS, and gauge-corrected MRMS or GCMRM) quantitative precipitation estimates (QPEs) and the Storm Water Management Model (SWMM) hydrologic–hydraulic model to simulate pluvial flooding during the North American monsoon in Phoenix. We focus on an urban catchment of 2.38 km² and, for four storms, we simulate a set of flooding metrics using the original QPEs and an ensemble of 100 QPEs characterizing radar uncertainty through a statistical error model. We find that Stage IV QPEs are the most accurate, while MRMS QPEs are positively biased and their utility to simulate flooding increases with the gauge correction done for GCMRMS. For all radar products, simulated flood metrics have lower uncertainty than QPEs as a result of rainfall–runoff transformation. By relying on extensive precipitation and basin datasets, this work provides useful insights for urban flood predictions.

Flooding is the most common natural hazard, leading to property damage, injuries, and death. Despite the potential for major consequences, urban flooding remains difficult to forecast, largely due to a lack of data availability at fine spatial scales and associated predictive capabilities. Crowdsourcing of public webcams, social media, and citizen science represent potentially important data sources for obtaining fine-scale hydrological data, but also raise novel challenges related to data reliability and consistency. We provide a review of literature and analysis of existing databases regarding the availability and quality of these unconventional sources that then drives a discussion of their potential to support fine-grained urban flood modelling and prediction. Our review and analysis suggest that crowdsourced data are increasingly available in urban contexts and have considerable potential. Integration of crowdsourced data could help ameliorate quality and completeness issues in any one source. Yet, substantial weaknesses and challenges remain to be addressed.

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.

Water stress prompts a durable reduction in water demand under some circumstances. This demand reduction has the potential to alter the benefits and costs of demand- and supply-side alternatives in water supply planning. This paper takes a socio-hydrological approach to assess the implications of this feedback, in the context of Las Vegas, Nevada. This application demonstrates feasibility first by developing and testing a novel model of water salience as a function of proximity to water supply thresholds, and then linking modules to account for feedback between subsystems. Lastly, by comparing this model to a water use scenario model to assess system performance under a range of future conditions and potential responses, this work illustrates the trade-offs between scenarios and the socio-hydrological approach. This model, while specific to Las Vegas, demonstrates a prototypical modeling framework capable of examining water supply–demand interactions by incorporating water stress-driven conservation.

Infrastructure are at the center of three trends: accelerating human activities and impacts, increasing uncertainty in social, technological, and climatological factors, and increasing complexity of the systems and environments in which they operate. Infrastructure – the engineered systems that deliver basic and critical services – have largely been designed for rigidity, and the changing conditions that define their environments are incompatible. Resilience theory is well-positioned to support restructuring how we approach infrastructure, but engineering framings of resilience will need to evolve beyond rebound and robustness to consider adaptation and transformation. Resilience theory can help infrastructure managers navigate through increasingly complex environments, and in doing so will need to emphasize agility and flexibility in both physical assets and governance, and sensemaking capabilities, to give meaning to how the environment is changing. The restructuring of infrastructure technologies, governance, and education to build resilience capacity for the future is a monumental but necessary undertaking.

Reservoir storage helps manage hydrological variability, increasing predictability and productivity of water supply. However, there are inevitable tradeoffs, with control of high frequency variability coming at the expense of robustness to low frequency variability. Tightly controlling variability can reduce incentives to maintain adaptive capacity needed during events that exceed design thresholds. With multiple dimensions of change projected for many water supply systems globally, increased knowledge on the role of design and operational choices in balancing short-term control and long-term adaptability is needed. Here we investigated how the scale of reservoir storage (relative to demands and streamflow variability) and reservoir operating rules interact to mitigate shortage risk under changing supplies and/or demands. To address these questions, we examined three water supply systems that have faced changing conditions: the Colorado River in the Western United States, the Melbourne Water Supply System in Southeastern Australia, and the Western Cape Water Supply System in South Africa. Moreover, we parameterize a sociohydrological model of reservoir dynamics using time series from the three case studies above. We then used the model to explore the impacts of storage and operational rules. We found that larger storage volumes lead to a greater time before the shortage is observed, but that this time is not consistently used for adaptation. Additionally, our modeling results show that operating rules that trigger withdrawal decreases sooner tend to increase the probability of an adaptive response; the findings from this model are bolstered by the three case studies. While there are many factors influencing the response to water stress, our results demonstrate the importance of: i) evaluating design and operational choices in concert, and ii) examining the role of information salience in adapting water supply systems to changing conditions.

The Sustainable Development Goals (SDGs) of the United Nations Agenda 2030 represent an ambitious blueprint to reduce inequalities globally and achieve a sustainable future for all mankind. Meeting the SDGs for water requires an integrated approach to managing and allocating water resources, by involving all actors and stakeholders, and considering how water resources link different sectors of society. To date, water management practice is dominated by technocratic, scenario‐based approaches that may work well in the short‐term, but can result in unintended consequences in the long‐term due to limited accounting of dynamic feedbacks between the natural, technical and social dimensions of human‐water systems. The discipline of socio‐hydrology has an important role to play in informing policy by developing a generalizable understanding of phenomena that arise from interactions between water and human systems. To explain these phenomena, socio‐hydrology must address several scientific challenges to strengthen the field and broaden its scope. These include engagement with social scientists to accommodate social heterogeneity, power relations, trust, cultural beliefs, and cognitive biases, which strongly influence the way in which people alter, and adapt to, changing hydrological regimes. It also requires development of new methods to formulate and test alternative hypotheses for the explanation of emergent phenomena generated by feedbacks between water and society. Advancing socio‐hydrology in these ways therefore represents a major contribution towards meeting the targets set by the SDGs, the societal grand challenge of our time.

From 2007 to 2017, the state of California experienced two major droughts that required significant governmental action to decrease urban water demand. The purpose of this study is to isolate and explore the effects of these policy changes on water use during and after these droughts, and to see how these policies interact with hydroclimatic variability. The results of the city level water demand models indicate that implementation of mandatory policies that target water use behaviors effectively reduce water use. The findings suggest that drought-related policies impact per capita urban water use along with temperature, income, unemployment, and water stress. The mathematically significant relationships identified in this study offer a path forward for more complex water demand models to include policy changes as a driver of water use. The policy coding methodology offers a start to the complicated task of categorizing drought policies and identifying what qualities make them effective at reducing urban water use. 

Open science practices such as publishing data and code are transforming water science by enabling synthesis and enhancing reproducibility. However, as research increasingly bridges the physical and social science domains (e.g., socio‐hydrology), there is the potential for well‐meaning researchers to unintentionally violate the privacy and security of individuals or communities by sharing sensitive information. Here, we identify the contexts in which privacy violations are most likely to occur, such as working with high‐resolution spatial data (e.g., from remote sensing), consumer data (e.g., from smart meters), and/or digital trace data (e.g., from social media). We also suggest practices for identifying and addressing privacy concerns at the individual, institutional, and disciplinary levels. We strongly advocate that the water science community continue moving toward open science and socio‐environmental research and that progress toward these goals be rooted in open and ethical data management.

Infrastructure are increasingly being recognized as too rigid to quickly adapt to a changing climate and a non-stationary future. This rigidness poses risks to infrastructure service delivery and public welfare. Adaptivity in infrastructure is critical for managing uncertainties to continue providing services, yet little is known about how infrastructure can be made more agile and flexible for improved adaptive capacity. A literature review identified approximately fifty examples of novel infrastructure and technologies which support adaptivity through one or more of ten theoretical characteristics of adaptive infrastructure. From these examples, several infrastructure forms and possible strategies for adaptivity emerged, including smart technologies, combined centralized / decentralized organizational structures, and renewable electricity generation. With institutional and cultural support, such novel structures and systems have the potential to transform infrastructure provision and management.

Complex socio-environmental challenges require interdisciplinary, team-based research capacity. Graduate students are fundamental to building such capacity, yet formal opportunities for graduate students to develop these capacities and skills are uncommon. This paper presents an assessment of the Graduate Pursuit (GP) program, a formal interdisciplinary team science graduate research and training program administered by the National Socio-Environmental Synthesis Center (SESYNC). Quantitative and qualitative assessment of the program’s first cohort revealed that participants became significantly more comfortable with interdisciplinary research and team science approaches, increased their capacity to work across disciplines, and were enabled to produce tangible research outcomes. Qualitative analysis of four themes—(1) discipline, specialization, and shared purpose, (2) interpersonal skills and personality, (3) communication and teamwork, and (4) perceived costs and benefits—encompass participants’ positive and negative experiences and support findings from past assessments. The findings also identify challenges and benefits related to individual personality traits and team personality orientation, the importance of perceiving a sense of autonomy and independence, and the benefit of graduate training programs independent of the university and graduate program environment.

Over the past twenty-five years, per capita water use has declined in many US cities. Technological and policy changes partially explain this decline, but variables beyond control of water managers also influence water use including external (e.g. drought) and emergent (e.g. public attention) changes. Importantly, these variables interact and the relationships between these variables and water use are non-stationary. However, many models assume fixed relationships between water use and its drivers, and limited interaction between variables. Here we present a flexible socio-hydrological approach to model how conservation strategies, and external and emergent changes, interact to influence per capita water use. We apply this approach to Las Vegas and find that marginal water rates, code changes coupled with population growth, and conservation response to water stress are the key drivers of the observed decline. Critically, modeling these strategies in absence of their connections to population growth and water stress cannot fully account for observed changes.

The expansion of reservoirs to cope with droughts and water shortages is hotly debated in many places around the world. We argue that there are two counterintuitive dynamics that should be considered in this debate: supply–demand cycles and reservoir effects. Supply–demand cycles describe instances where increasing water supply enables higher water demand, which can quickly offset the initial benefits of reservoirs. Reservoir effects refer to cases where over-reliance on reservoirs increases vulnerability, and therefore increases the potential damage caused by droughts. Here we illustrate these counterintuitive dynamics with global and local examples, and discuss policy and research implications.