Abstract As flood risks increase, many people struggle to interpret forecasts, visuals, and other risk information designed to guide decision-making. Yet, little is known about how people interpret such graphics and translate them into action. This study surveyed how U.S. adults make flood preparedness decisions, focusing on the influence of climate science literacy, graph interpretation skills, and sociodemographic factors. Participants completed two scenario-based decision tasks featuring extreme precipitation and explained their choices, confidence, and perceived flood likelihood. Results show that stronger graph interpretation skills and higher climate literacy are associated with more protective decisions, greater confidence, and reasoning grounded in economic considerations, flood risk assessment, or precautionary thinking. Others relied on intuition or data interpretation to make decisions, while a subset of responses reflected misconceptions about flood risk, highlighting gaps in public understanding. Participants identifying as more politically conservative reported greater confidence even when choosing less protective options, whereas nonmale participants perceived higher flood likelihood yet expressed lower confidence, indicating a mismatch between confidence and protective behavior. Qualitative responses further suggest that influences beyond the measured variables—such as prior experiences or personal circumstances—may shape how individuals interpret risk and make decisions. Overall, flood risk communication should move beyond a one-size-fits-all approach by pairing clear, accessible information with strategies that support interpretation and reflect diverse audiences and decision contexts. Significance Statement Extreme precipitation and associated flood risks are increasing with climate change, forcing many people to decide how best to stay safe and protect their homes. This study examined how adults in the United States make decisions about flood preparedness using hypothetical scenarios. We found that people who understand climate science and can interpret graphs tend to choose stronger protective actions. Personal factors such as age, political identity, race, and life circumstances also influenced decisions. However, persistent flood myths, such as believing a home on a hill is safe, highlight gaps in understanding that may reduce protective actions. Strengthening people’s climate knowledge and data skills, while tailoring flood messages to diverse communities, can help them make safer and more informed choices.

Extreme precipitation events are becoming more frequent and intense, elevating the risk of floods that threaten lives, property, and infrastructure. Climate scientists often create and use visuals to help communicate these hazards. However, many visuals lack careful consideration of how effectively they are understood and used, especially in supporting decision-making, despite established guidelines for effective graph design. Using an online survey of U.S. adults, we tested how graph type influenced the interpretation, usability, and decision-making related to information about extreme precipitation in flood risk scenarios, following diagnostic design guidelines and the System Usability Scale. Our results show that interpreting extreme precipitation data is challenging for most participants, with simpler graph types} such as bar graphs}leading to better understanding and higher usability scores. However, there was no significant impact of graph type on flood risk decisions, perceived likelihood of flooding, or confidence in decision choices. More complex graph types with detailed features or statistics hindered interpretation, despite offering potentially more comprehensive information. These findings suggest that while simpler visualizations improve usability and interpretation, they may sacrifice important details needed for effective flood risk management. The study emphasizes the importance of balancing simplicity with depth in data visualization and highlights the importance of following design guidelines to support better decision-making in the face of climate risks. SIGNIFICANCE STATEMENT: Extreme precipitation events, like heavy rain and snow, are happening more often and with greater intensity, which increases the risk of floods. This study looks at how different types of graphs impact how people understand extreme precipitation data. This is important because this information is often used to help people and communities make decisions to protect themselves from flood risks. By surveying adults in the United States, we found that simpler graphs are easier to understand and use compared to more complicated ones. This suggests that it is important to balance simplicity and detail in climate graphs. Future research should explore how different graph types affect the understanding of other extreme weather events and survey people outside the United States.

People around the world seek climate risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate risk services should provide, many expect noncommercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: Only four percent of the most-cited peer-reviewed climate risk studies in recent years fully share their data and code although this is a widely accepted minimum standard for transparency. We highlight low-cost measures that noncommercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate risk management.

The impacts of climate change are accelerating across Pennsylvania, one of the most flood prone states in the U.S., increasing the importance of hazard mitigation for communities bearing the brunt of more frequent flood disasters. Pennsylvania communities are developing hazard mitigation plans (HMPs), a potentially powerful tool for communities seeking to improve community resilience and reduce hazard impacts. At the same time, how climate change information is being included in HMPs has been little studied. This paper qualitatively analyzes 40 FEMA-approved local HMPs from the Susquehanna River watershed in Pennsylvania to assess how these plans address climate change impacts on flood risk. We find that plans authored by external consulting firms included more detailed information about climate change impacts on flooding than plans written by local government officials. None of the local HMPs achieved the best practice standard of including 1) place-relevant climate change information, 2) specific locations with existing, chronic flooding issues, and 3) quantitative information regarding both past and future impacts of climate change on flooding for that county, though several consultant-authored plans came close, lacking only future quantitative information. While plans authored by consulting companies generally include more of these elements of climate change impacts on flooding than plans authored by counties, the plans still vary widely in how they incorporated the information. These findings support FEMA’s recent decision to require that climate change impacts be incorporated into local-level HMPs, but plan authors will need clear guidance on how to do so to facilitate forward-looking mitigation actions.

People respond to climate change and associated risks based on scientific knowledge, lived experiences, worldviews, values, and social relations. People living in flood-prone areas may develop protective practices to mitigate risk. Formal flood resilience goals, such as relocating people from floodplain areas, need to align with local flood protection goals. People often choose to live in flood-prone areas despite the risk because of perceived aesthetic, social, or economic benefits. For flood-risk policy, reducing flood risk generally means implementing policies that aim to reduce overall exposure to flooding. When people acknowledge and manage risk because of a desire to live in a place, exposure-reducing policies may be met with resistance. In this case study analysis, we explore perspectives about flood resilience at different scales to better understand flood risk resilience. Our analysis of flood resilience draws from a framework for considering community resilience to natural disasters and observations made during a multi-year flood resilience co-production initiative with Selinsgrove Borough, Pennsylvania, a small riverine community. Many residents in the study area have developed resilience practices based on their flood experiences over generations. Flood resilience plans may better serve local realities if they provide sufficient flexibility to integrate residents’ resilience practices and knowledge. Local officials need guidance integrating these practices into their formal responsibilities, including developing and implementing flood-risk management plans.

Climate risks are growing. Research is increasingly important to inform the design of risk-management strategies. Assessing such strategies necessarily brings values into research. But the values assumed within research (often only implicitly) may not align with those of stakeholders and decision makers. These misalignments are often invisible to researchers and can severely limit research relevance or lead to inappropriate policy advice. Aligning strategy assessments with stakeholders' values requires a holistic approach to research design that is oriented around those values from the start. Integrating values into research in this way requires collaboration with stakeholders, integration across disciplines, and attention to all aspects of research design. Here we describe and demonstrate a qualitative conceptual tool called a values-informed mental model (ViMM) to support such values-centered research design. ViMMs map stakeholders' values onto a conceptual model of a study system to visualize the intersection of those values with coupled natural-human system dynamics. Through this mapping, ViMMs integrate inputs from diverse collaborators to support the design of research that assesses risk-management strategies in light of stakeholders' values. We define a visual language for ViMMs, describe accompanying practices and workflows, and present an illustrative application to the case of flood-risk management in a small community along the Susquehanna river in the Northeast United States.

Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies.

Designing strategies to manage flood risks is complicated by the often large uncertainty surrounding flood risk projections. Uncertainty surrounding riverine flood risks can stem from choices regarding boundary and initial conditions, model structures, and parameters as well as interactions among hazards, exposures, and vulnerabilities. Here we analyze a case study to rank the drivers of uncertainties surrounding riverine flood hazards and risks. Using Sobol sensitivity analysis with a large number of simulations, we thoroughly explore the interactions among different sources of uncertainty. We find that the projected flood risk is most sensitive to factors associated with flood hazards, rather than exposure and vulnerability: upstream discharge, river bed elevation, channel roughness, and the digital elevation model resolution. Our results highlight the importance of uncertainty quantification in enhancing the reliability of flood models and risk assessments.

Models with high-dimensional parameter spaces are common in many applications. Global sensitivity analyses can provide insights on how uncertain inputs and interactions influence the outputs. Many sensitivity analysis methods face nontrivial challenges for computationally demanding models. Common approaches to tackle these challenges are to (i) use a computationally efficient emulator and (ii) sample adaptively. However, these approaches still involve potentially large computational costs and approximation errors. Here we compare the results and computational costs of four existing global sensitivity analysis methods applied to a test problem. We sample different model evaluation time and numbers of model parameters. We find that the emulation and adaptive sampling approaches are faster than Sobol' method for slow models. The Bayesian adaptive spline surface method is the fastest for most slow and high-dimensional models. Our results can guide the choice of a sensitivity analysis method under computational resources constraints.

Abstract Flooding drives considerable risks. Designing strategies to manage these risks is complicated by the often-large uncertainty surrounding flood risk projections. Uncertainty surrounding riverine flood risks can stem, for example, from choices regarding boundary conditions, model structures, and parameters as well as interactions among hazards, exposures, and vulnerabilities. A quantitative understanding of which factors drive uncertainties surrounding flood hazards and risks can inform the design of mission-oriented research. Here we analyze a case study to (i) characterize key uncertainties impacting flood risk projections and (ii) characterize the most important drivers of the uncertainties surrounding riverine flood hazards and risks. We find that the projected flood risk is most sensitive to factors associated with flood hazards: upstream discharge, riverbed elevation, channel roughness, and the digital elevation model resolution. Our framework and results can help to improve flood-hazard and risk projections.

Forest managers must balance multiple objectives and consider tradeoffs when developing a management plan. Complex interactions between successional dynamics and natural disturbances make it challenging, especially when decisions play out under the deep and dynamic uncertainties associated with climate change. Here we explored a suite of management strategies to maximize multiple management objectives and minimize tradeoffs under future climate projections and quantified the greatest sources of uncertainty. We used a spatially-explicit forest simulation model (LANDIS-II) to simulate the effects of wind, management, and climate change in central Wisconsin and calculated benefits and tradeoffs among six management objectives (maximize aboveground carbon (C), soil C, harvested C, C stored in species of cultural importance to the Menominee tribe, tree diversity, and age diversity). We found that uneven-aged management achieves more ecosystem benefits (except for harvested C) than the other harvest strategies, but it was the business-as-usual harvest scenario that minimized tradeoffs among objectives. Climate change made it more difficult to store C in soils and have diverse forests and the management strategies we considered were unable to regain these lost benefits. Climate change reduced harvested C and C stored in culturally-important species, but the management strategies were able to at least partially compensate for this effect. The uncertainty surrounding the climate projections generated the largest variation in all benefits except harvested C. Managers seeking to maximize benefits and minimize tradeoffs should consider a range of silvicultural strategies while recognizing that climate change may shrink the operating space for achieving foresters’ management goals.

The increasingly urgent need to develop knowledge and practices to manage flood risks drives innovative information design. However, experts often disagree about design practices. As a result, flood-risk estimates can diverge, leading to different conclusions for decision-making. Using examples of household-scale fluvial (riverine) flood-risk information in the United States, we assess design features and risk communication approaches that may lead to more actionable information for decision-making. We argue that increased attention to uncertainty characterization and model diagnostics is a critical intermediate step for developing simpler approaches for designing flood-risk information. Simpler frameworks are desirable because flood risks change over time, and simpler frameworks are less costly to update. Developing frameworks for large spatial domains require collaboration grounded in principles of open science. Finally, systematically evaluating how decision-makers access and use information can provide new insights to guide risk communication and information design.

Convergence research is driven by specific and compelling problems and requires deep integration across disciplines. The potential of convergence research is widely recognized, but questions remain about how to design, facilitate, and assess such research. Here we analyze a seven-year, twelve-million-dollar convergence project on sustainable climate risk management to answer two questions. First, what is the impact of a project-level emphasis on the values that motivate and tie convergence research to the compelling problems? Second, how does participation in convergence projects shape the research of postdoctoral scholars who are still in the process of establishing themselves professionally? We use an interview-based approach to characterize what the project specifically enabled in each participant’s research. We find that (a) the project pushed participants’ research into better alignment with the motivating concept of convergence research and that this effect was stronger for postdoctoral scholars than for more senior faculty. (b) Postdocs’ self-assessed understanding of key project themes, however, appears unconnected to metrics of project participation, raising questions about training and integration. Regarding values, (c) the project enabled heightened attention to values in the research of a large minority of participants. (d) Participants strongly believe in the importance of explicitly reflecting on values that motivate and pervade scientific research, but they question their own understanding of how to put value-focused science into practice. This mismatch of perceived importance with poor understanding highlights an unmet need in the practice of convergence science.

This white paper provides an overview of priorities related to community resilience to flooding that emerged during a 27 September 2019 meeting with local, regional and state representatives in Selinsgrove, Pennsylvania. The document compiles workshop details, participants and a summary of discussions and outcomes. It does not, however, attempt to provide a comprehensive listing of every topic raised by participants. In addition, this workshop was held before the advent of covid-19; the impacts of this pandemic are not addressed in this document.

There is an increasingly urgent need to develop knowledge and practices to manage climate risks. For example, flood-risk information can inform household decisions such as purchasing a home or flood insurance. However, flood-risk estimates are deeply uncertain, meaning that they are subject to sizeable disagreement. Available flood-risk estimates provide inconsistent and incomplete information and pose communication challenges. The effects of different choices of design and communication options can create confusion in decision-making processes. The climate services literature includes insights into desirable features for producing information that is credible and relevant. Using examples of riverine (fluvial) flood-risk information products and studies in the United States, we assess how existing risk characterizations integrate desirable features outlined in the climate services literature. Improved characterization and communication of decision-relevant (and often deep) uncertainties, including those arising from human decisions, is a crucial next step. We argue that producing relevant flood-risk information requires applying principles of open science and co-production.

Flood-related risks to people and property are expected to increase in the future due to environmental and demographic changes. It is important to quantify and effectively communicate flood hazards and exposure to inform the design and implementation of flood risk management strategies. Here we develop an integrated modeling framework to assess projected changes in regional riverine flood inundation risks. The framework samples climate model outputs to force a hydrologic model and generate streamflow projections. Together with a statistical and hydraulic model, we use the projected streamflow to map the uncertainty of flood inundation projections for extreme flood events. We implement the framework for rivers across the state of Pennsylvania, United States. Our projections suggest that flood hazards and exposure across Pennsylvania are overall increasing with future climate change. Specific regions, including the main stem Susquehanna River, lower portion of the Allegheny basin and central portion of Delaware River basin, demonstrate higher flood inundation risks. In our analysis, the climate uncertainty dominates the overall uncertainty surrounding the flood inundation projection chain. The combined hydrologic and hydraulic uncertainties can account for as much as 37% of the total uncertainty. We discuss how this framework can provide regional and dynamic flood-risk assessments and help to inform the design of risk-management strategies.

Emerald ash borer (EAB; Agrilus planipennis Farimaire) has been found in 35 US states and five Canadian provinces. This invasive beetle is causing widespread mortality to ash trees ( Fraxinus spp.), which are an important timber product and ornamental tree, as well as a cultural resource for some Tribes. The damage will likely continue despite efforts to impede its spread. Further, widespread and rapid ash mortality as a result of EAB is expected to alter forest composition and structure, especially when coupled with the regional effects of climate change in post-ash forests. Thus, we forecasted the long-term effects of EAB-induced ash mortality and preemptive ash harvest (a forest management mitigation strategy) on forested land across a 2-million-hectare region in northern Wisconsin. We used a spatially explicit and spatially interactive forest simulation model, LANDIS-II, to estimate future species dominance and biodiversity assuming continued widespread ash mortality. We ran forest disturbance and succession simulations under historic climate conditions and three downscaled CMIP5 climate change projections representing the upper bound of expected changes in precipitation and temperature. Our results suggest that although ash loss from EAB or harvest resulted in altered biodiversity patterns in some stands, climate change will be the major driver of changes in biodiversity by the end of century, causing increases in the dominance of southern species and homogenization of species composition across the landscape.

CONTEXT: To meet the nutritional and environmental needs of a growing population, dairy producers must increase milk production while minimizing the farm-gate environmental impact and adapting to the effects of climate change. OBJECTIVE: Here we comprehensively assess the effects of climate change on the environmental performance and productivity of three typical US dairy farms, and evaluate the potential benefits of adaptation strategies and implementation of Beneficial Management Practices (BMPs) for mitigating these effects and the potential in-creases in environmental impact. METHODS: Using the Integrated Farm System Model (IFSM), we predicted the productivity and environmental impact of these baseline farms under current emission scenarios and climate projections of 6 general circulation models (GCM), for high and low emission scenarios. We simulated farm-specific BMPs for current and future climate conditions for both unadapted and 'adapted' field cultivation plans, based on experiences from other climate locations. Finally, the IFSM predictions were compared to those of two other process-based models to test result robustness. RESULTS AND CONCLUSIONS: We find that the environmental impact of the three northern US dairy farms (New York, Pennsylvania, and Wisconsin) generally increases by mid-century, if no mitigation measures are taken. Overall, feed production is maintained, as decreased corn grain yields are compensated by increased forage yields. Adoption of farm-specific Beneficial Management Practices can substantially reduce the GHG emissions and nutrient losses from dairy farms under current climate conditions and stabilize the environmental impact in future climate conditions, while maintaining farm productivity (milk and feed production). A comparison of three models corroborates the estimated reductions in methane and ammonia emissions associated with BMPs, as well as the relative trend in P-loss reduction. SIGNIFICANCE: This study provides a holistic assessment of the impacts of climate change on dairy production systems focusing on both feed production and environmental impacts. It demonstrates the interest of BMPs to both reduce GHG emissions and contribute to more resilient farming systems in a changing climate.

Abstract Current approaches to design flood‐sensitive infrastructure typically assume a stationary rainfall distribution and neglect many uncertainties. These assumptions are inconsistent with observations that suggest intensifying extreme precipitation events and the uncertainties surrounding projections of the coupled natural‐human systems. Here we demonstrate a safety factor approach to designing urban infrastructure in a changing climate. Our results show that assuming climate stationarity and neglecting deep uncertainties can drastically underestimate flood risks and lead to poor infrastructure design choices. We find that climate uncertainty dominates the socioeconomic and engineering uncertainties that impact the hydraulic reliability in stormwater drainage systems. We quantify the upfront costs needed to achieve higher hydraulic reliability and robustness against the deep uncertainties surrounding projections of rainfall, surface runoff characteristics, and infrastructure lifetime. Depending on the location, we find that adding safety factors of 1.4–1.7 to the standard stormwater pipe design guidance produces robust performance to the considered deep uncertainties. The insights gained from this study highlights the need for updating traditional engineering design strategies to improve infrastructure reliability under socioeconomic and environmental changes.

Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence. Historical annual maize yields in the U.S. are overestimated by CMIP5 models and underestimated by bias-corrected and downscaled models due to differences in temperature and precipitation hindcasts, according to a multi-model ensemble comparison.