Near-term Forecasts Research Articles

Recent and forecasted increases in coccidioidomycosis incidence in California linked to hydroclimatic swings.

Coccidioidomycosis, or Valley fever, is an infectious disease caused by inhalation of Coccidioides spp., fungi found primarily in soils of the southwestern United States. Prior work showed that coccidioidomycosis cases in California sharply increase by nearly 2-fold following wet winters that occur one- and two-years following drought. Statewide drought between 2020-2022 followed by heavy precipitation during the 2022-2023 winter raised concerns over potential increases in coccidioidomycosis cases in the fall of 2023, prompting California Department of Public Health (CDPH) to issue public health alerts. As anticipated, California saw a near record number of cases in 2023, with 9,054 provisional cases reported. During the 2023-2024 California wet season, precipitation was 115% the long-term average, furthering concerns about continued high coccidioidomycosis risk. We developed an ensemble model to forecast coccidioidomycosis cases in California in 2024-2025. Using this model, we predicted a total of 11,846 cases (90% PI: 10,056-14,094) in California between April 1, 2023, and March 31, 2024, encompassing the preliminary state report of 10,593. Our model forecasted 12,244 cases statewide between April 1, 2024, and March 31, 2025 - a 62% increase over the cases reported during the same period two years prior, and on par with the high incidence seen in 2023. The Southern San Joaquin Valley (5,398 cases, 90% PI: 4,556-6,442), Southern Coast (3,322, 90% PI: 2,694-3,961), and Central Coast (1,207 cases, 90% PI: 867-1,585) regions are expected to see the largest number of infections. Our model forecasts that disease incidence will exhibit pronounced seasonality, particularly in endemic regions, with cases rising in June and peaking in November at 1,411 (90% PI: 815-2,172) cases statewide - 98% higher than the peak two years prior (714) and nearly as high as the peak in 2023 (1,462). Near-term forecasts have the potential to inform public health messaging to enhance provider and patient awareness, encourage risk reduction practices, and improve recognition and management of coccidioidomycosis.

Near-term fire weather forecasting in the Pacific Northwest using 500-hPa map types

Background Near-term forecasts of fire danger based on predicted surface weather and fuel dryness are widely used to support the decisions of wildfire managers. The incorporation of synoptic-scale upper-air patterns into predictive models may provide additional value in operational forecasting. Aims In this study, we assess the impact of synoptic-scale upper-air patterns on the occurrence of large wildfires and widespread fire outbreaks in the US Pacific Northwest. Additionally, we examine how discrete upper-air map types can augment subregional models of wildfire risk. Methods We assess the statistical relationship between synoptic map types, surface weather and wildfire occurrence. Additionally, we compare subregional fire danger models to identify the predictive value contributed by upper-air map types. Key results We find that these map types explain variation in wildfire occurrence not captured by fire danger indices based on surface weather alone, with specific map types associated with significantly higher expected daily ignition counts in half of the subregions. Conclusions We observe that incorporating upper-air map types enhances the explanatory power of subregional fire danger models. Implications Our approach provides value to operational wildfire management and provides a template for how these methods may be implemented in other regions.

Decomposing Uncertainty in Macro-Finance Term Structure Models

Abstract This paper studies the extent to which macro-finance term structure models are susceptible to predictive uncertainty. We propose a general form of arbitrage-free models and quantify the relative importance of unpredictable priced risk variance, as well as macro-finance model uncertainty and learning uncertainty in predictability. Predictive performance and relative contributions of uncertainty sources are dynamically measured based on Bayesian methods, revealing dominating priced risk variance and other important uncertainty sources at different points in time. Macro-finance model uncertainty is high for near-term forward spread forecasts and contributes up to 87% of predictive uncertainty prior to recessions, implying strong dispersion in the information content of macro variables when forming near-term monetary policy expectations. (JEL C1, C3, C5, D8, E4, G1)

Functional traits shape plant-plant interactions and recruitment in a hotspot of woody plant diversity.

Understanding and predicting recruitment in species-rich plant communities requires identifying functional determinants of both density-independent performance and interactions. In a common-garden field experiment with 25 species of the woody plant genus Protea, we varied the initial spatial and taxonomic arrangement of seedlings and followed their survival and growth during recruitment. Neighbourhood models quantified how six key functional traits affect density-independent performance, interaction effects and responses. Trait-based neighbourhood models accurately predicted individual survival and growth from the initial spatial and functional composition of species-rich experimental communities. Functional variation among species caused substantial variation in density-independent survival and growth that was not correlated with interaction effects and responses. Interactions were spatially restricted but had important, predominantly competitive, effects on recruitment. Traits increasing the acquisition of limiting resources (water for survival and soil P for growth) mediated trade-offs between interaction effects and responses. Moreover, resprouting species had higher survival but reduced growth, likely reinforcing the survival-growth trade-off in adult plants. Resource acquisition of juvenile plants shapes Protea community dynamics with acquisitive species with strong competitive effects suffering more from competition. Together with functional determinants of density-independent performance, this makes recruitment remarkably predictable, which is critical for efficient restoration and near-term ecological forecasts of species-rich communities.

Predicting spring phenology in deciduous broadleaf forests: NEON phenology forecasting community challenge

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative's National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate when, where, and for what model type phenology forecast skill is highest. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts.

Short-Term (7 Day) Beaufort Sea Ice Extent Forecasting with Deep Learning

Abstract Ships inside the Arctic basin require high-resolution (1–5 km), near-term (days to semimonthly) forecasts for guidance on scales of interest to their operations where forecast model predictions are insufficient due to their coarse spatial and temporal resolutions. Deep learning techniques offer the capability of rapid assimilation and analysis of multiple sources of information for improved forecasting. Data from the National Oceanographic and Atmospheric Administration’s Global Forecast System, Multi-scale Ultra-high Resolution Sea Surface Temperature (MEaSUREs), and the National Snow and Ice Data Center’s Multisensor Analyzed Sea ice Extent (MASIE) were used to develop the sea ice extent deep learning forecast model, over the freeze-up periods of 2016, 2018, 2019, and 2020 in the Beaufort Sea. Sea ice extent forecasts were produced for 1–7 days in the future. The approach was novel for sea ice extent forecasting in using forecast data as model input to aid in the prediction of sea ice extent. Model accuracy was assessed against a persistence model. While the average accuracy of the persistence model dropped from 97% to 90% for forecast days 1–7, the deep learning model accuracy dropped only to 93%. A k-fold (fourfold) cross-validation study found that on all except the first day, the deep learning model, which includes a U-Net architecture with an 18-layer residual neural network (Resnet-18) backbone, does better than the persistence model. Skill scores improve the farther out in time to 0.27. The model demonstrated success in predicting changes in ice extent of significance for navigation in the Amundsen Gulf. Extensions to other Arctic seas, seasons, and sea ice parameters are under development. Significance Statement Ships traversing the Arctic require timely, accurate sea ice location information to successfully complete their transits. After testing several potential candidates, we have developed a short-term (7 day) forecast process using existing observations of ice extent and sea surface temperature, with operational forecasts of weather and oceanographic variables, and appropriate machine learning models. The process included using forecasts of atmospheric and oceanographic conditions, as a human forecaster/analyst would. The models were trained for the Beaufort Sea north of Alaska using data and forecasts from 2016 combined with 2018–20. The results showed improvement in short-term forecasts of ice locations over current methods and also demonstrated correctly predicted changes in the sea ice that are important for navigation.

The Case for Administrative Benchmarks (and Some Challenges)

Summary Most U.S. Medicare beneficiaries are in population-based programs where spending targets, known as benchmarks, are used to determine payments. These include both the Medicare Advantage (MA) program, which relies on private health plans to manage care, and Accountable Care Organization (ACO) models, where providers are rewarded if they efficiently deliver care. When well designed, these models can incentivize reductions in spending and improve patient outcomes. A crucial aspect of these models, however, is how the benchmarks are determined. Policy makers have multiple approaches at their disposal, and each comes with unique strengths and weaknesses. The most common benchmark setting approach is empirical in that benchmarks are tied to actual spending or near-term forecasts based on lagged spending in fee-for-service traditional Medicare (TM). Empirical benchmarks reduce the risk that plans and providers face from fluctuations in costs that are beyond their control. That risk is absorbed by Medicare, so program spending will rise or fall with TM spending. Empirical benchmark systems have several significant weaknesses — including a ratchet effect and a collective success problem — that make it difficult for plans and providers to share in savings over the long run. This limits the long-term success of these population-based models. In contrast, administrative benchmarks are set by taking a base rate (such as historical spending) and growing it by an administrative factor that can reflect policy goals, anticipated volume, and intensity growth, or a socially agreed-upon rate, such as gross domestic product growth. The key feature is that, without policy action, the benchmark trajectory is not influenced by realized spending trends. An example of the administrative benchmark approach is the Accountable Care Prospective Trend component of the benchmarks for ACOs in the Medicare Shared Savings Program, which uses an initial benchmark, based on baseline spending, that grows based on spending growth that was forecast when the program was launched rather than realized spending growth. This administrative benchmark allows benchmark growth to diverge from, rather than track with, realized spending growth, which avoids shortcomings of empirical benchmarks and allows for greater incentives to deliver care efficiently, providing predictability over time. The key to success for administrative benchmarks is that the administrative growth rate needs to be set low enough so that Medicare saves money relative to what would happen without ACOs or MA, but high enough to still encourage participation.

Causes and consequences of changing oxygen availability in lakes

ABSTRACT Changing oxygen availability in lakes and reservoirs is a fundamental limnological challenge of our time, with massive consequences for freshwater ecosystem functioning and water quality. Cross-lake surveys, paleolimnological studies, and long-term monitoring records indicate that many lakes are exhibiting declines in both surface- and bottom-water oxygen availability due to climate and land use change, although a few lakes are exhibiting increases in oxygen. By analyzing time series of oxygen monitoring data from ∼400 lakes, I found that some lakes may be experiencing a decoupling of surface and bottom oxygen dynamics; variability in surface oxygen concentrations is decreasing in some lakes while variability in bottom oxygen concentrations is increasing. Changes in both oxygen concentrations and variability have many implications for lake functioning because oxygen concentrations control many ecosystem processes. Consequently, lake ecosystem provisioning and cultural services (e.g., drinking water, fisheries, recreation) will likely be impaired by declining oxygen, whereas the effects of changing oxygen on regulatory and supporting ecosystem services (e.g., nitrate removal through denitrification, carbon burial, sediment fluxes of phosphorus) may be more equivocal. These challenges motivate a research agenda focused on expanding the geographical range, temporal duration, and spatial extent of lake oxygen monitoring, as well as new approaches for studying and managing lakes (whole-ecosystem experiments, near-term oxygen forecasts). Looking ahead, advances in sensor technology, monitoring networks, data sharing, and forecasting, as well as the demonstrated success of environmental legislation in decreasing hypoxia, provide important opportunities for guiding restoration and science on lake oxygen.

Soil surface treatments and precipitation timing determine seedling development across southwestern US restoration sites.

Restoration in dryland ecosystems often has poor success due to low and variable water availability, degraded soil conditions, and slow plant community recovery rates. Restoration treatments can mitigate these constraints but, because treatments and subsequent monitoring are typically limited in space and time, our understanding of their applicability across broader environmental gradients remains limited. To address this limitation, we implemented and monitored a standardized set of seeding and soil surface treatments (pits, mulch, and ConMod artificial nurse plants) designed to enhance soil moisture and seedling establishment across RestoreNet, a growing network of 21 diverse dryland restoration sites in the southwestern USA over 3 years. Generally, we found that the timing of precipitation relative to seeding and the use of soil surface treatments were more important in determining seeded species emergence, survival, and growth than site-specific characteristics. Using soil surface treatments in tandem with seeding promoted up to 3× greater seedling emergence densities compared with seeding alone. The positive effect of soil surface treatments became more prominent with increased cumulative precipitation since seeding. The seed mix type with species currently found within or near a site and adapted to the historical climate promoted greater seedling emergence densities compared with the seed mix type with species from warmer, drier conditions expected to perform well under climate change. Seed mix and soil surface treatments had a diminishing effect as plants developed beyond the first season of establishment. However, we found strong effects of the initial period seeded and of the precipitation leading up to each monitoring date on seedling survival over time, especially for annual and perennial forbs. The presence of exotic species exerted a negative influence on seedling survival and growth, but not initial emergence. Our findings suggest that seeded species recruitment across drylands can generally be promoted, regardless of location, by (1) incorporation of soil surface treatments, (2) employment of near-term seasonal climate forecasts, (3) suppression of exotic species, and (4) seeding at multiple times. Taken together, these results point to a multifaceted approach to ameliorate harsh environmental conditions for improved seeding success in drylands, both now and under expected aridification.

Progress and opportunities in advancing near-term forecasting of freshwater quality.

Near-term freshwater forecasts, defined as sub-daily to decadal future predictions of a freshwater variable with quantified uncertainty, are urgently needed to improve water quality management as freshwater ecosystems exhibit greater variability due to global change. Shifting baselines in freshwater ecosystems due to land use and climate change prevent managers from relying on historical averages for predicting future conditions, necessitating near-term forecasts to mitigate freshwater risks to human health and safety (e.g., flash floods, harmful algal blooms) and ecosystem services (e.g., water-related recreation and tourism). To assess the current state of freshwater forecasting and identify opportunities for future progress, we synthesized freshwater forecasting papers published in the past 5 years. We found that freshwater forecasting is currently dominated by near-term forecasts of water quantity and that near-term water quality forecasts are fewer in number and in the early stages of development (i.e., non-operational) despite their potential as important preemptive decision support tools. We contend that more freshwater quality forecasts are critically needed and that near-term water quality forecasting is poised to make substantial advances based on examples of recent progress in forecasting methodology, workflows, and end-user engagement. For example, current water quality forecasting systems can predict water temperature, dissolved oxygen, and algal bloom/toxin events 5 days ahead with reasonable accuracy. Continued progress in freshwater quality forecasting will be greatly accelerated by adapting tools and approaches from freshwater quantity forecasting (e.g., machine learning modeling methods). In addition, future development of effective operational freshwater quality forecasts will require substantive engagement of end users throughout the forecast process, funding, and training opportunities. Looking ahead, near-term forecasting provides a hopeful future for freshwater management in the face of increased variability and risk due to global change, and we encourage the freshwater scientific community to incorporate forecasting approaches in water quality research and management.

Ecological Forecasting and Operational Information Systems Support Sustainable Ocean Management

In times of rapid change and rising human pressures on marine systems, information about the future state of the ocean can provide decision-makers with time to avoid adverse impacts and maximise opportunities. An ecological forecast predicts changes in ecosystems and its components due to environmental forcing such as climate variability and change, extreme weather conditions, pollution, or habitat change. Here, we summarise examples from several sectors and a range of locations. We describe the need, approach, forecast performance, delivery system, and end user uptake. This examination shows that near-term ecological forecasts are needed by end users, decisions are being made based on forecasts, and there is an urgent need to develop operational information systems to support sustainable ocean management. An operational information system is critical for connecting to decision makers and providing an enduring approach to forecasting and proactive decision making. These operational systems require significant investment and ongoing maintenance but are key to delivering ecological forecasts for societal benefits. Iterative forecasting practices could provide continuous improvement by incorporating evaluation and feedback to overcome the limitations of the imperfect model and incomplete observations to achieve better forecast outcomes and accuracy.

Big data forecasting of South African inflation.

The online version contains supplementary material available at 10.1007/s00181-022-02329-y.

The Climate Science for Ecological Forecasting symposium

The Climate Science for Ecological Forecasting symposium

Multi-season climate projections forecast declines in migratory monarch butterflies.

Climate change poses a unique threat to migratory species as it has the potential to alter environmental conditions at multiple points along a species' migratory route. The eastern migratory population of monarch butterflies (Danaus plexippus) has declined markedly over the last few decades, in part due to variation in breeding-season climate. Here, we combined a retrospective, annual-cycle model for the eastern monarch population with climate projections within the spring breeding grounds in eastern Texas and across the summer breeding grounds in the midwestern U.S. and southern Ontario, Canada to evaluate how monarchs are likely to respond to climate change over the next century. Our results reveal that projected changes in breeding-season climate are likely to lead to decreases in monarch abundance, with high potential for overwintering population size to fall below the historical minimum three or more times in the next two decades. Climatic changes across the expansive summer breeding grounds will also cause shifts in the distribution of monarchs, with higher projected abundances in areas that become wetter but not appreciably hotter (e.g., northern Ohio) and declines in abundance where summer temperatures are projected to increase well above those observed in the recent past (e.g., northern Minnesota). Although climate uncertainties dominate long-term population forecasts, our analyses suggest that we can improve precision of near-term forecasts by collecting targeted data to better understand relationships between breeding-season climate variables and local monarch abundance. Overall, our results highlight the importance of accounting for the impacts of climate changes throughout the full-annual cycle of migratory species.

Near-term phytoplankton forecasts reveal the effects of model time step and forecast horizon on predictability.

As climate and land use increase the variability of many ecosystems, forecasts of ecological variables are needed to inform management and use of ecosystem services. In particular, forecasts of phytoplankton would be especially useful for drinking water management, as phytoplankton populations are exhibiting greater fluctuations due to human activities. While phytoplankton forecasts are increasing in number, many questions remain regarding the optimal model time step (the temporal frequency of the forecast model output), time horizon (the length of time into the future a prediction is made) for maximizing forecast performance, as well as what factors contribute to uncertainty in forecasts and their scalability among sites. To answer these questions, we developed near-term, iterative forecasts of phytoplankton 1-14 days into the future using forecast models with three different time steps (daily, weekly, fortnightly), that included a full uncertainty partitioning analysis at two drinking water reservoirs. We found that forecast accuracy varies with model time step and forecast horizon, and that forecast models can outperform null estimates under most conditions. Weekly and fortnightly forecasts consistently outperformed daily forecasts at 7-day and 14-day horizons, a trend that increased up to the 14-day forecast horizon. Importantly, our work suggests that forecast accuracy can be increased by matching the forecast model time step to the forecast horizon for which predictions are needed. We found that model process uncertainty was the primary source of uncertainty in our phytoplankton forecasts over the forecast period, but parameter uncertainty increased during phytoplankton blooms and when scaling the forecast model to a new site. Overall, our scalability analysis shows promising results that simple models can be transferred to produce forecasts at additional sites. Altogether, our study advances our understanding of how forecast model time step and forecast horizon influence the forecastability of phytoplankton dynamics in aquatic systems and adds to the growing body of work regarding the predictability of ecological systems broadly.

Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S.: data, forecasts, and scores

Near-term forecasts of NEON lakes reveal gradients of environmental predictability across the U.S.: data, forecasts, and scores

A multi-method framework for global real-time climate attribution

Abstract. Human-driven climate change has caused a wide range of extreme weather events to become more frequent in recent decades. Although increased and intense periods of extreme weather are expected consequences of anthropogenic climate warming, it remains challenging to rapidly and continuously assess the degree to which human activity alters the probability of specific events. This study introduces a new framework to enable the production and communication of global real-time estimates of how human-driven climate change has changed the likelihood of daily weather events. The framework's multi-method approach implements one model-based and two observation-based methods to provide ensemble attribution estimates with accompanying confidence levels. The framework is designed to be computationally lightweight to allow attributable probability changes to be rapidly calculated using forecasts or the latest observations. The framework is particularly suited for highlighting ordinary weather events that have been altered by human-caused climate change. An example application using daily maximum temperature in Phoenix, AZ, USA, highlights the framework's effectiveness in estimating the attributable human influence on observed daily temperatures (and deriving associated confidence levels). Global analyses show that the framework is capable of producing worldwide complementary observational- and model-based assessments of how human-caused climate change changes the likelihood of daily maximum temperatures. For instance, over 56 % of the Earth's total land area, all three framework methods agree that maximum temperatures greater than the preindustrial 99th percentile have become at least twice as likely in today's human-influenced climate. Additionally, over 52 % of land in the tropics, human-caused climate change is responsible for at least five-fold increases in the likelihood of preindustrial 99th percentile maximum temperatures. By systematically applying this framework to near-term forecasts or daily observations, local attribution analyses can be provided in real time worldwide. These new analyses create opportunities to enhance communication and provide input and/or context for policy, adaptation, human health, and other ecosystem/human system impact studies.

Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density.

Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (1‐ to 4‐week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4‐week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1‐week‐ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.

People-focused and near-term public transit performance analysis

Public transit ridership forecasts have long played a role in understanding the potential success of a policy or investment, but their limitations have led researchers and practitioners to identify other performance analysis approaches. Accessibility, or the ease of reaching opportunities, has become very popular and widely used for this purpose. But commonly used accessibility measures also embody weaknesses that are seldom acknowledged; these limit their utility for truly understanding the benefits of transit investments. In this paper, we identify the pros and cons of these competing approaches and offer a third strategy. Specifically, we describe how revealed travel behavior data, potentially combined with near-term forecasts, can provide information about how current public transit users will be affected by a new project. While acknowledging the limitations of this approach, we demonstrate how accessibility can be misleading when applied without an understanding of ridership patterns.

Managing multiple species with conflicting needs in the Greater Everglades

Given limited funding, natural resources decision making is riddled with tradeoffs, including which species or landscapes to prioritize for management action. Florida’s Everglades wetland is home to numerous indicator species, some of which are endangered. But with a multitude of species comes differing hydrologic requirements to yield appropriate foraging and breeding conditions for each. The Everglades ecosystem is highly managed, with water being moved across the landscape to meet the habitat and reproductive needs of species of concern. Predictive modeling can help water managers understand potential consequences to targeted water conditions. EverForecast is a novel spatially explicit, hydrologic, and ecological operational forecast developed to inform conservation management decisions. Not only does EverForecast provide probable near-term water conditions, but also predicted species responses to those hydrologic conditions. Using examples from two focal regions of the Everglades, we show the magnitude of impacts to a suite of species and an almost 70% decline in suitable conditions for one species when prioritizing water management to meet the needs of another species. Although EverForecast is a relatively new decision support tool, its hydrologic outputs are already commonly used to make water management recommendations because it provides near-term hydrologic forecasts that scientists and managers need for water operations decision making. Because species management decisions have historically been made to target a single species at a time, it may take longer for full utility of EverForecast’s ability to quantify tradeoffs among species to become integrated into decision making.