This study combines long-term lake water quality data with statistical modeling to predict coldwater fish habitat conditions from a season-ahead lead. We select a case-study lake in Northern Wisconsin with forty-two years of biological, chemical, and physical data to calculate three summertime oxythermal stress metrics for cisco (Coregonus artedi) conditioned on bi-weekly temperature and dissolved oxygen profiles. Significant intra-and-interannual variability are identified in seasonal oxythermal habitat conditions. Springtime global and local climate variables generally indicate a stronger relationship than within lake variables when correlated with summertime oxythermal habitat, suggesting a potentially robust link between climate patterns and lake water quality in Wisconsin. Leveraging both climate and within lake springtime observations, a principal component regression approach was applied to probabilistically predict summertime oxythermal stress metrics. Models showed skillful prediction of multiple oxythermal habitat metrics across summer months of highest stress, with the best fitting model achieving R2 = 0.42 and RPSS = 0.55. We conclude that characterization and tailored season-ahead prediction of oxythermal habitat conditions may provide prospects for proactive lake management in support of coldwater fish habitat.

Skillful streamflow forecasts are fundamental tools for water managers, particularly in regions where water insecurity is a concern. This study was conducted in a drought-prone Mediterranean river basin, i.e., the Imera-Meridionale River Basin, located in Sicily (Italy). As in much of the region, this area is frequently affected by recurrent droughts, highlighting the need for hydroclimatic forecasts to support informed and proactive water management decisions. To this end, a statistical seasonal streamflow forecasting framework was developed using the oceanic/atmospheric and local hydroclimatic variables as predictors of the hydrological response in the case study area. In particular, two parametric statistical models, i.e., Principal Component Regression (PCR) and Partial Least Squares Regression (PLSR), were implemented in the forecasting framework for fall, winter, and spring seasons to generate ensemble seasonal streamflow forecasts. The models were validated via leave-one-out cross-validation, and the best sets of predictors (components) were selected based on the minimum mean square prediction error of a leave-one-out cross-validation. Model skill was then evaluated using both deterministic and probabilistic performance metrics. The results indicated that PLSR generally outperformed PCR in consistently forecasting seasonal streamflow across all seasons with a parsimonious configuration. PCR also performed well, for example, in the spring season, but required a more complex configuration using six principal components. Although both models performed well during dry years and normal years in the winter and spring seasons, performance in the fall season was generally characterized by lower skill scores.

Dengue fever is a mosquito-borne viral disease rapidly creating a significant global public health burden, particularly in urban areas of tropical and sub-tropical countries. Hydroclimatic variables, particularly local temperature, precipitation, relative humidity, and large-scale climate teleconnections, can influence the prevalence of dengue by impacting vector population development, viral replication, and human-mosquito interactions. Leveraging predictions of these variables at lead times of weeks to months can facilitate early warning system preparatory actions such as allocating funding, acquisition and preparation of medical supplies, or implementation of vector control strategies. We develop hydroclimate-based statistical forecast models for dengue virus (DENV) at 1-, 3-, and 6- month lead times for four cities across Colombia (Cali, Cúcuta, Medellín, and Leticia) and compare with standard autoregressive models conditioned on dengue case counts. Our results indicate that (a) hydroclimate-based models are particularly skillful at 3- and 6- month lead times when autoregressive models often fail, (b) sea surface temperatures are the most skillful predictor at 3- and 6- month leads and (c) application of hydroclimate models are most beneficial when average DENV incidence is low, autoregressive relationships are weak, but outbreaks may still occur.

The World Climate Research Programme (WCRP) has created a new core project: Regional Information for Society (RIfS), which has begun to plan its inaugural activities. Recognizing a gap between core disciplinary projects of WCRP and societal impact, RIfS seeks to foster community exchange around the practices of creating and utilizing climate information. The members of the Scientific Steering Group and International Project Office see this as a collaborative process with stakeholders from various sectors of society. Rather than reproducing more climate services, we are focused on identifying best-practices, building worldwide capacity and equity, and contributing to existing projects at the regional scale, particularly in regions where there are limited resources for such services. This RIfS presentation will focus on the identification of best-practices, particularly in the assessment of climate information. Currently there is no systematic, consistent, or accepted approach to assessing which climate information is robust and actionable, at regional or global scales. This recognizes the multiplicity of non-congruent data and information sources that may be used, the choice of which depends often on subjective selections that can lead to different decision outcomes and the commensurate consequences. At the same time, the volumes of data and demand for information are only growing, and new organizations are emerging offering products to decision-makers with varying levels of transparency about methods. Decisions are being made that affect the global distribution of resources, for example in finance and the insurance sectors. No professional organization has so far managed to establish widely accepted standards and guidelines for what constitutes robust information appropriate for various types of decision-making. This is the central challenge of the moment for the community of climate researchers interested in societal applications. RIfS will begin a process of consensus-building with an expert meeting on robustness of climate information just after the EGU meeting this year, to be followed by additional opportunities to come together around these questions.

Dengue is a mosquito-borne viral disease representing a significant global public health burden, particularly in urban areas of tropical and sub-tropical countries including Colombia. Dengue is spread primarily by the Aedes aegypti and Aedes albopictus mosquitos. Hydroclimatic variables are known to influence the prevalence of dengue by impacting vector population development, viral replication, and human-mosquito interactions. Several variables including large-scale climate phenomena (ENSO), temperature, precipitation, and relative humidity have been identified as important predictors of dengue burden at lead times of weeks to months. Development of dengue forecasts based on hydroclimate variables has shown promise in applications to dengue surveillance and decision making. Little work has been done, however, to assess the predictability of the hydroclimatic conditions associated with increased dengue burden and to establish optimal trigger mechanisms for anticipatory actions to reduce disease risk, particularly in the Amazon region. Building on existing work identifying hydroclimatic drivers of dengue in four cities across Columbia (Cali, Cucuta, Leticia, Medellin), predictability of these hydroclimatic conditions is explored at sub-seasonal to seasonal time scales.

Excessive algae growth can lead to negative consequences for ecosystem function, economic opportunity, and human and animal health. Due to the cost-effectiveness and temporal availability of satellite imagery, remote sensing has become a powerful tool for water quality monitoring. The use of remotely sensed products to monitor water quality related to algae and cyanobacteria productivity during a bloom event may help inform management strategies for inland waters. To evaluate the ability of satellite imagery to monitor algae pigments and dissolved oxygen conditions in a small inland lake, chlorophyll-a, phycocyanin, and dissolved oxygen concentrations are measured using a YSI EXO2 sonde during Sentinel-2 and Sentinel-3 overpasses from 2019 to 2022 on Lake Mendota, WI. Machine learning methods are implemented with existing algorithms to model chlorophyll-a, phycocyanin, and Pc:Chla. A novel machine learning-based dissolved oxygen modeling approach is developed using algae pigment concentrations as predictors. Best model results based on Sentinel-2 (Sentinel-3) imagery achieved R2 scores of 0.47 (0.42) for chlorophyll-a, 0.69 (0.22) for phycocyanin, and 0.70 (0.41) for Pc:Chla. Dissolved oxygen models achieved an R2 of 0.68 (0.36) when applied to Sentinel-2 (Sentinel-3) imagery, and Pc:Chla is found to be the most important predictive feature. Random forest models are better suited to water quality estimations in this system given built in methods for feature selection and a relatively small data set. Use of these approaches for estimation of Pc:Chla and dissolved oxygen can increase the water quality information extracted from satellite imagery and improve characterization of algae conditions among inland waters. Agricultural runoff and wastewater discharge has fueled nutrient pollution in Lake Mendota over the last century. As a result, algae blooms have become a common summertime occurrence on Lake Mendota. Algae blooms are often made up of different algae species. Green algae are typically harmless, but cyanobacteria (blue-green algae) can produce a range of toxins harmful to human and animal health. The ability to discriminate between cyanobacteria and green algae during a bloom may be useful for lake managers and public health officials in making decisions about closing waterfront areas and communicating with the public. In recent years, satellite imagery has become a powerful tool for monitoring water quality. In this study, we build models that use imagery from two satellites to estimate the abundance of cyanobacteria versus green algae in Lake Mendota. We also find that our algae estimates can be used to model dissolved oxygen, an important water quality indicator that cannot be directly measured from satellite imagery. The methods presented for satellite-based monitoring of algae pigments, the Pc:Chla ratio, and dissolved oxygen has the potential to increase the water quality information extracted from satellite imagery, better characterize algae blooms, and inform management strategies for Lake Mendota. Chlorophyll-a and phycocyanin are sampled from 2019 to 2022 on Lake Mendota, WI Sentinel-2 and Sentinel-3 are used to model chlorophyll-a, phycocyanin, and Pc:Chla A model based in situ data allows for satellite-based estimates of dissolved oxygen

Geographically isolated places are often sites of exported environmental risks, intense resource extraction, exploitation and marginalization, and social policy neglect. These conditions create unique challenges related to vulnerability and adaptation that have direct disaster management implications. Our research investigates the relationship between geographic isolation and flood-related social vulnerability across Peru's ecological regions. Ecoregions have different relationships with colonialism and capitalism that shape vulnerability, and we hypothesize that the relationship between vulnerability and geographic isolation varies across ecoregions. Using mapping techniques and spatial regression analysis, we find that relationships between vulnerability and geographic isolation vary regionally, with differences that suggest alignment with regional contexts of extraction. We find notable differences in vulnerability related to public health infrastructure and access to services and between ecoregions with sharply contrasting histories of natural resource extraction and investment and disinvestment.

In recent decades, many inland lakes have seen an increase in the prevalence of potentially harmful algae. In many inland lakes, the peak season for algae abundance (summer and early fall in the northern hemisphere) coincides with the peak season for recreational use. Currently, little information regarding expected algae conditions is available prior to the peak season for productivity in inland lakes. Peak season algae conditions are influenced by an array of pre-season (spring and early summer) local and global scale variables; identifying these variables for forecast development may be useful in managing potential public health threats posed by harmful algae. Using the LAGOS-NE dataset, pre-season local and global drivers of peak-season algae metrics (represented by chlorophyll-a) are identified for 178 lakes across the Northeast and Midwest U.S. from readily available gridded datasets. Forecasting models are built for each lake conditioned on relevant pre-season predictors. Forecasts are assessed for the magnitude, severity, and duration of seasonal chlorophyll concentrations. Regions of pre-season sea surface temperature, and pre-season chlorophyll-a demonstrate the most predictive power for peak season algae metrics, and resulting models show significant skill. Based on categorical forecast metrics, more than 70% of magnitude models and 90% of duration models outperform climatology. Forecasts of high and severe algae magnitude perform best in large mesotrophic and oligotrophic lakes, however, high algae duration performance appears less dependent on lake characteristics. The advance notice of elevated algae biomass provided by these models may allow lake managers to better prepare for challenges posed by algae during the high use season for inland lakes.

Beal MRW, O'Reilly BE, Soley CK, Hietpas KR, Block PJ. 2022. Variability of summer cyanobacteria abundance: can season-ahead forecasts improve beach management? Lake Reserv Manage. XX:XX-XX. As anthropogenic eutrophication and the associated increase of cyanobacteria continue to plague inland waterbodies, local officials are seeking novel methods to proactively manage water resources. Cyanobacteria are of particular concern to health officials due to their ability to produce dangerous hepatotoxins and neurotoxins, which can threaten waterbodies for recreational and drinking-water purposes. Presently, however, there is no cyanobacteria outlook that can provide advance warning of a potential threat at the seasonal time scale. In this study, a statistical model is developed utilizing local and global scale season-ahead hydroclimatic predictors to evaluate the potential for informative cyanobacteria biomass and associated beach closure forecasts across the June-August season for a eutrophic lake in Wisconsin (United States). This model is developed as part of a subseasonal to seasonal cyanobacteria forecasting system to optimize lake management across the peak cyanobacteria season. Model skill is significant in comparison to June-August cyanobacteria observations (Pearson correlation coefficient = 0.62, Heidke skill score = 0.38). The modeling framework proposed here demonstrates encouraging prediction skill and offers the possibility of advanced beach management applications.

Disasters and their associated physical and social risks pose challenges for local, national, and international relief agencies and disaster management organizations globally. Novel approaches are desperately needed for vulnerable communities. Combining season-ahead predictions, proactive management strategies, and communication efforts in disaster planning represents an emerging field in disaster management, particularly when paired with short-term early warnings and post-disaster response. However, these long-lead action protocols are still in their infancy as only a few have been activated - triggering and financing preparedness actions. While these protocols have generally been rigorously established, a holistic evaluation of the framework, including interactions, feedbacks, and dynamic components, is urgently warranted. Open questions across both physical and social aspects remain, including agreeable triggers for action, clarity of roles and responsibilities, and sufficiency of funding. The large number of disparate actors involved and the highly interdisciplinary nature lead to a complex decision-making setting. Further, most protocols are static plans, yet disasters, impacts, infrastructure, communities, and vulnerability states are all dynamic, changing in time; how can protocols better reflect a changing world? Surveys of disaster agencies and communities help to highlight how decision-makers perceive flood-related risk and vulnerability, and how these perceptions impact disaster preparedness and risk communication. These insights, paired with additional holistic analysis for hazard planning and management, are critical for developing proactive preparation and response strategies. We will discuss the current state of anticipatory actions related to disaster management, followed by current barriers and potential opportunities toward reduction of disaster impacts and community vulnerability.

The absence of a basin-wide apportionment agreement on using the Nile River equitably has been a long-standing source of disagreement among Nile riparian states. This study introduces a new approach that the riparian states can consider that quantifies the Nile River’s apportionment. The approach includes (1) developing a basin-wide database of indicators representative of the United Nations Watercourse Convention (UNWC) relevant factors and circumstances, (2) developing an ensemble of indicator weighting scenarios using various weighting methods, and (3) developing six water-sharing methods to obtain a range of apportionments for Egypt, Sudan, Ethiopia and the group of the White Nile Equatorial States for each weighting scenarios. The results illustrate a relatively narrow range of country-level water apportionments, even though some individual factor weights vary from 3% to 26%. Considering the entire Nile River, the water apportionment for Ethiopia ranges from 32% to 38%, Sudan and South Sudan from 25% to 33%, Egypt from 26% to 35%, and the Equatorial States from 5% to 7%. We trust that the six proposed equitable water-sharing methods may aid in fostering basin-wide negotiations toward a mutual agreement and address the dispute over water sharing.

Ethiopia's agriculture-based economy generates highly seasonal outputs, with most production occurring during the long-rains Meher season and a lesser amount during the short-rains Belg season. Despite numerous studies detailing the economic impacts of Meher precipitation, there is relatively little consideration of other seasonal or subseasonal climate impacts in Ethiopia, with most economic models only considering aggregated annual-scale production to simulate the Ethiopian economy. This paper serves to address both seasonal and subseasonal effects of climate using a partial equilibrium agroeconomic model of Ethiopia. First, an annual-scale model is disaggregated into a seasonal time step, corresponding to the two major cropping seasons in Ethiopia. Second, the effect of yield gains from a subseasonal forecast-based planting strategy is considered in the updated model. We found that crop yields and corresponding economic indicators varied widely by season and location, and that subseasonal forecast-based management strategies can, on average, increase gross domestic product (GDP), per capita calorie consumption, and lead toward reduced poverty, warranting the inclusion of seasonal and subseasonal processes in economic models and agricultural planning. (C) 2022American Society of Civil Engineers.

Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experi-ence with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results (n 5 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain "experiential" processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions.

As Ethiopia's population grows, crop yield predictions are becoming increasingly important for national food security. Accurate, easy-to-implement, and computationally efficient forecast approaches are desirable for broad applications in emerging regimes like Ethiopia. In this study, we develop and test an analog approach for preseason crop yield prediction conditioned on antecedent precipitation and planting time soil moisture content indices to guide cultivation decision making. Historical planting time soil moisture at four selected sites were simulated using the Coupled Routing and Excess STorage (CREST) hydrological model and classified into five levels. Likewise, a historical crop yield database for each of the five classes of planting time soil moisture were constructed using the Decision Support System for Agrotechnology Transfer (DSSAT) agricultural model. Adopting maize as a representative crop, analog models based on different indexes or predictors with various lead times were constructed and used to conduct hindcasts during 1979-2014 and real-time forecast in 2018 and 2019. Both the hindcast and real-time forecasts were then evaluated against yield observations. To verify the applicability at locations with various environments, the analog models were then applied in different Agroecological Zones. The analog models were shown to be accurate and easy to implement, which may incentivize adoption by local extension agents and regional agricultural agencies to inform farmers' crop choices.

Streamflow forecasts play an important role in water resources operation and management, and skillful seasonal forecasts can significantly facilitate the decision-making process. Streamflow variability is often associated with various large-scale, slowly evolving climate phenomena (e.g., El Nino Southern Oscillation), promoting the value of climate indices for streamflow forecast development, and has been investigated extensively. Separately, global climate models (GCMs), which provide climate forecasts out to 12 months globally, have been demonstrated to enhance seasonal streamflow predictability. However, neither the combination nor the interaction of these two sources of predictability has been given much attention. In this work, we propose a framework that can simultaneously account for antecedent climate indices and GCM forecasts in statistical streamflow forecasts and address how their interactions affect predictability. More specifically, we build a streamflow forecast framework combining statistical forecast models conditioned on climate indices with dynamical North American Multi-Model Ensemble (NMME) precipitation forecasts to generate streamflow forecast ensembles, and merge ensemble members with a Bayesian model averaging with bootstrap aggregating (BMA-bagging) approach. The framework is applied to streamflow on the Blue Nile River upstream of the Grand Ethiopian Renaissance Dam (GERD). Most NMME models tend to improve one-month-ahead GERD inflow forecasts; however, longer lead times prove challenging, and require specific NMME model selection. In contrast, although the value of climate indices varies with forecast lead time, their potential contribution grows at multimonth leads. The GERD inflow forecasts including NMME models and climate indices can simultaneously take advantage of both sources of predictability and prove superior across lead times as compared to including either source individually.

The potential benefits of seasonal streamflow forecasts for the hydropower sector have been evaluated for several basins across the world but with contrasting conclusions on the expected benefits. This raises the prospect of a complex relationship between reservoir characteristics, forecast skill, and value. Here, we unfold the nature of this relationship by studying time series of simulated power production for 735 headwater dams worldwide. The time series are generated by running a detailed dam model over the period 1958–2000 with three operating schemes: basic control rules, perfect forecast-informed operations, and realistic forecast-informed operations. The realistic forecasts are issued by tailored statistical prediction models – based on lagged global and local hydroclimatic variables – predicting seasonal monthly dam inflows. As expected, results show that most dams (94 %) could benefit from perfect forecasts. Yet, the benefits for each dam vary greatly and are primarily controlled by the time-to-fill value and the ratio between reservoir depth and hydraulic head. When realistic forecasts are adopted, 25 % of dams demonstrate improvements with respect to basic control rules. In this case, the likelihood of observing improvements is controlled not only by design specifications but also by forecast skill. We conclude our analysis by identifying two groups of dams of particular interest: dams that fall in regions expressing strong forecast accuracy and having the potential to reap benefits from forecast-informed operations and dams with a strong potential to benefit from forecast-informed operations but falling in regions lacking forecast accuracy. Overall, these results represent a first qualitative step toward informing site-specific hydropower studies.

In their recent paper in ERL, ‘Egypt’s water budget deficit and suggested mitigation policies for the Grand Ethiopian Renaissance Dam (GERD) filling scenarios,’ Heggy et al (2021 Environ. Res. Lett. 16 074022) paint an alarming picture of the water deficits and economic impacts for Egypt that will occur as a consequence of the filling of the GERD. Their median estimate is that filling the GERD will result in a water deficit in Egypt of ∼31 billion m 3 yr −1 . They estimate that under a rapid filling of the GERD over 3 yr, the Egyptian economy would lose US$51 billion and 4.74 million jobs, such that in 2024, Gross Domestic Product (GDP) per capita would be 6% lower than under a counterfactual without the GERD. These and other numbers in Heggy et al (2021 Environ. Res. Lett. 16 074022) article are inconsistent with the best scientific and economic knowledge of the Nile Basin and are not a dependable source of information for policy-makers or the general public. In this response to Heggy et al (2021 Environ. Res. Lett. 16 074022) we draw on high quality peer-reviewed literature and appropriate modeling methods to identify and analyze many flaws in their article, which include (a) not accounting for the current storage level in the High Aswan Dam reservoir (b) inappropriately using a mass-balance approach that does not account for the Nile’s hydrology or how water is managed in Egypt, Sudan and Ethiopia; (c) extreme and unfounded assumptions of reservoir seepage losses from the GERD; and (d) calculations of the economic implications for Egypt during the period of reservoir filling which are based on unfounded assumptions. In contrast to Heggy et al (2021 Environ. Res. Lett. 16 074022), robust scientific analysis has demonstrated that, whilst there is a risk of water shortages in Egypt if a severe drought were to occur at the same time as the GERD reservoir is filling, there is minimal risk of additional water shortages in Egypt during the filling period if flows in the Blue Nile are normal or above average. Moreover, the residual risks could be mitigated by effective and collaborative water management, should a drought occur.

The potential benefits of seasonal streamflow forecasts for the hydropower sector have been evaluated for several basins across the world but with contrasting conclusions on the expected benefits. This raises the prospect of a complex relationship between reservoir characteristics, forecast skill, and value. Here, we unfold the nature of this relationship by studying time series of simulated power production for 735 headwater dams worldwide. The time series are generated by running a detailed dam model over the period 1958–2000 with three operating schemes: basic control rules, perfect forecast-informed operations, and realistic forecast-informed operations. The realistic forecasts are issued by tailored statistical prediction models – based on lagged global and local hydroclimatic variables – predicting seasonal monthly dam inflows. As expected, results show that most dams (94 %) could benefit from perfect forecasts. Yet, the benefits for each dam vary greatly and are primarily controlled by the time-to-fill value and the ratio between reservoir depth and hydraulic head. When realistic forecasts are adopted, 25 % of dams demonstrate improvements with respect to basic control rules. In this case, the likelihood of observing improvements is controlled not only by design specifications but also by forecast skill. We conclude our analysis by identifying two groups of dams of particular interest: dams that fall in regions expressing strong forecast accuracy and having the potential to reap benefits from forecast-informed operations and dams with a strong potential to benefit from forecast-informed operations but falling in regions lacking forecast accuracy. Overall, these results represent a first qualitative step toward informing site-specific hydropower studies.

Climate and weather-related disasters are increasingly expensive and deadly. Hydrologic catastrophes are especially devastating, accounting for over half of all disasters and global disaster victims. Novel approaches are desperately needed for vulnerable communities subject to hydrologic and water-related crises. Post-disaster assistance is a crucial component of disaster relief, however the potential for reducing humanitarian impacts through anticipatory, pre-disaster planning and actions cannot be overstated. Short-term early warning systems are common, yet hydrologic forecasts at monthly or seasonal scales are relatively underused to guide preparatory actions, despite their potential value. Empirical evidence suggests that pre-disaster actions can reduce loss of life and property and result in cost savings for relief and governmental organizations. Such interventions often flow through water management systems, highlighting the central role of water resources decision-making in hazard resilience. Various humanitarian relief agencies have recently developed operational early action protocols, conditioned on forecasts and risk analysis, outlining trigger criteria and identifying early actions. Concurrently, an extensive number of subseasonal-to-seasonal climate forecast products are now available to derive hydrologic forecasts. Thus there exists significant potential to tailor subseasonal-to-seasonal hydrologic forecast products to appropriately trigger a suite of preparedness actions and decisions across multiple lead times. Various frameworks exist to understand pareto trade-offs in actions and financing, including community-based constraints and preferences. These approaches respond to the strong demand for large-scale, multi-sectoral hydrologic forecast and management tools to enable early preparedness for anticipated drought and flood extremes.

In this qualitative study, we analyze the experiences of those living in flood-prone economically constrained communities by exploring relocation, risk perceptions, and communication in the context of extreme seasonal flood disasters. Our study included semi-structured interviews with residents in three communities and unstructured interviews with local experts in Iquitos, Peru. Our results suggest that strategic communication plans and interventions for flood-prone communities should emphasize economic opportunities, rather than trying to emphasize flood risks, since the economic domain appears to be more salient for individuals living in these communities. Conversely, communication in relocated communities, should emphasize safety and overall quality of life, but also consider the economic stresses people face. Ultimately, communication and relief efforts related to addressing problems associated with disasters should start with an understanding of the experiences, perceptions, and communication practices of the communities they are assisting.