Ski resorts across the western U.S. are challenged by global warming, with fewer days of sufficient natural snow during the season and more frequent weather conditions unsuitable for artificial snowmaking early in the season. While this applies to all ski resorts, the magnitude of snow security reduction varies widely. Regional Earth system simulations of sufficient resolution to capture the weather and seasonal snowpack at ski areas in the Rocky Mountain region demonstrate that in the next few decades, high-elevation places, and those historically receiving more snowfall, will be impacted less, while those resorts that currently have relatively low snow security will suffer more. Even though resorts in the Colorado Rockies will experience more warming in the next few decades than ski areas closer to the Pacific coast, they generally are less at risk, including a smaller decrease in the ski season length and in snowmaking potential, and fewer rain-on-snow events.

Climate change and hydrologic alteration by water infrastructure are reshaping river ecosystems globally, threatening freshwater biodiversity. Effective conservation strategies for riverine species depend on understanding how climate-driven changes in temperature and flow interact with water management to regulate population dynamics, yet these interactions remain poorly understood. Here, we use a hierarchical Bayesian stock-recruitment model to quantify how six decades of changing climate and water management (i.e. flow regulation at a large storage dam) jointly influence the productivity of 13 populations of a cold-water fish species of significant conservation and economic importance, Yellowstone cutthroat trout ( Oncorhynchus virginalis bouvieri ) in the upper Snake River watershed, Wyoming, USA. We show that strong seasonal warming had opposing effects on productivity, with declines in warmer winters and springs but increases in warmer summers. The strongest driver of productivity was an interaction between the timing of natural and managed peak spring flows, where matched peak flow events reduced productivity, while mismatched peaks extended the flood pulse and increased productivity. In addition, key flow management periods, including summer irrigation flows and the timing of autumn ramp down, had important but smaller effects on productivity. Future climate projections indicated that continued warming and reduced spring flood duration may threaten trout productivity. However, our results show how adaptive dam management that strategically integrates with natural flow variability could offset climate-driven declines and sustain ecosystem function. Synthesis and applications. Our results identify actionable levers for adaptive water management in the upper Snake River: delaying managed peak releases relative to natural flood peaks to extend the spring flood pulse, and postponing autumn ramp down to maintain side-channel connectivity. While institutional constraints (e.g. water rights obligations) and hydrologic conditions (e.g. drought) may limit implementation, our findings provide key information for both long-term and rapid-response decision making. Quantifying the ecological value of water, as we do here, is essential for elevating fisheries and ecosystems services in water-use negotiations dominated by consumptive demands. Our results provide a transferable framework for adaptive water management to support riverine biodiversity under intensifying warming and water scarcity.

Wind energy is central to the energy transition, but its reliance on atmospheric conditions makes long-term resources planning vulnerable to environmental change. Earth System Model projections suggest uneven regional change, yet coarse resolution limits infrastructure planning in complex terrain, and few multi-model assessments are validated against observations. Here, we address this gap by examining an ensemble of 15 dynamically downscaled CMIP6 Earth System Models at 9 km resolution to assess wind energy potential across the western United States from 1980 to 2099. We evaluate ensemble skill against WRF-ERA5 and MesoWest observations and analyze four decision-relevant indicators: hub-height wind speed, Wind Power Density (WPD), coefficient of variation, and wind-rich level occurrence. Under SSP3-7.0, WPD decreases by 8.77% across the western US by century’s end, but spatial variability is substantial: northern Nevada and southeastern Wyoming experience the largest declines ( -12%), while some high-elevation Colorado sites show minimal change (

The Colorado River supplies water to 40 million people, 4.5 million acres of irrigated land, seven US states, over two dozen federally recognized and sovereign tribes and Mexico. River discharge is strongly controlled by snowpack accumulation and melt in the headwater regions, making upper-basin snow dynamics a primary driver of water availability throughout the basin. Therefore, changes in snow water equivalent (SWE) are critical for water resources planning in the Upper Colorado River Basin (UCRB), yet there is considerable uncertainty even in historical SWE trends. We compare historical UCRB SWE trends across various gridded snow products, including new Weather Research and Forecasting simulations at 45, 9, and 3 km resolution, against snow telemetry station data. We find that SWE trends are systematically amplified with higher model resolution and that high resolution (<10 km) is needed to accurately capture not only SWE amounts but also historical SWE declines across high-elevation mountains. Trend amplification primarily results from higher resolution simulations having higher snowfall due to stronger orographic forcing, an effect further modulated by synoptics. This is confirmed in a set of alternative historical realizations from a downscaled climate model large ensemble, which, despite a tendency for SWE declines, highlight the important role of internal variability in historical UCRB SWE trends. These results demonstrate that model resolution plays an important role in shaping assessments of snow-related climate change, with implications for a wide range of impact studies.

Natural resource management weaves together ecology, collaborative and values-based decision-making, qualitative and quantitative data analysis and interpretation, and on-the-ground management work. Its social and scientific complexity means that natural resource management cases can be transformed into rich, complex, and meaningful instructional phenomena for classroom use. A team made up of a high school science teacher, aquatic ecology researchers, and educational researchers collaborated on a lesson sequence for high school students, centered on the opportunities for students to access, grapple with, and use complex natural resource management data. The central question of this lesson sequence asks, “How can data be used to make fisheries management decisions in our local reservoir ecosystems?” Students’ data grappling includes interpreting and integrating publicly available data (e.g., lake characteristics, fish population and stocking records), student-­gathered data (e.g., fish stomach contents), and recent research (e.g., stable isotope food webs, reservoir data). Their learning culminates with an opportunity to engage in a community townhall meeting in which they develop arguments about fisheries management from the points of view of multiple stakeholder groups. The complexity of this lesson sequence can not only support high school students’ growth in data science but also their experience with complex decision-making and uncertainty.

Snowmelt is a dominant hydrologic process in mountainous watersheds and western river systems, yet automatic logging of snowpack properties such as depth, density, and internal energy fluxes, especially during melt, is not done at many Rocky Mountain surface flux sites. In this study, we show that springtime variations in the surface energy budget imbalance can be used to estimate snowpack energy fluxes. Using observations from the US-GLE AmeriFlux site, we demonstrate a data-driven method to extract springtime energy budget anomalies and show that 83% of this anomaly is explained by the latent heat flux of melting snow when computed at a 6-hour resolution. Additionally, we demonstrate that our model can explain this latent heat flux while preserving the statistical structure of the energy imbalance observed during the rest of the year. Our findings suggest that analysis of the surface energy budget, when tightly constrained, can reveal latent hydrologic processes, improving confidence in surface energy budget measurements and offering a low-cost, data-driven method for estimating snowmelt dynamics at existing flux sites without additional instrumentation.

Uncertainty is foundational to science but often feared in classrooms. Equally scary are big, messy data without clear trends or explanations. Climate data embody both of these daunting traits, but a teacher and a researcher embraced this daunting uncertainty to develop a meaningful learning progression for high school students. The duo collaborated to translate complex climate data into student-accessible formats shared on open platforms. They balanced complexity with accessibility and developed both a dataset and a scaffolded investigation guide for students. In this article, they share their development process, a summary of their implementation, their next steps, and considerations for others interested in bringing messy data into learning spaces.

In the spring of 2026, anomalously low snow conditions in the western United States threatened winter recreation and water supplies. Here, we investigate: to what extent was this snow drought attributable to the climate change that has occurred since the pre-industrial period? We find that a snow drought this severe across the western United States was approximately 4.4 [95% CI: 2.6, 9.4] times more likely in the current climate than in the preindustrial period. In the Upper Colorado River Basin, the snow drought was approximately 14 times more likely [0.09, 4,300]. Given a projected increase in the frequency of snow droughts at least this severe in a moderately high emissions warming scenario, the lived experience of this event may help scientists, resource managers, and the public consider what western US snow might look like if greenhouse gas emissions are not aggressively reduced.

A California “megaflood”—a weeks-long cluster of storms with centuries-long return times—would be catastrophic, yet its timing and magnitude under climate change remains uncertain. A central source of uncertainty is whether Earth System Model boundary conditions should be bias corrected before dynamical downscaling. Here, we analyze a large ensemble of downscaled simulations to quantify how this uncertainty shapes California's megaflood risk. Bias correction is necessary to reproduce observed storm statistics and strongly influences both the megafloods historical and future characteristics. Without correction, storm precipitation variability is inappropriately inflated, delaying the emergence of extreme precipitation signals by decades. Further, megaflood intensity estimates based on statistically extended observations are at least 10% drier than explicit computations derived from sufficiently sized storm populations. Our results show that bias correction choices crucially govern projections of rare, high-impact events, with direct implications for infrastructure design/planning and water management in California.

Abrupt snowmelt, triggered by rain-on-snow events or “snow-eater heat waves,” can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850–2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

We implemented an isotope mass balance to estimate evaporation/inflow ratios and evaporation rates from Jackson Lake, a reservoir in the upper Snake River drainage in the Rocky Mountains of North America. We employed a sensitivity analysis to assess the input isotopic values, to identify the influence of reservoir water isotope sampling strategy, and weather station location (lake-buoy vs. land-based site) on evaporation estimates. Furthermore, to assess how antecedent conditions affect evaporation estimates, models were evaluated incorporating air temperature, water temperature and relative humidity data at three time lengths: (1) a full year; (2) a full year excluding the winter ice-on period; and (3) a period representing the summer conditions of the sampling year. Models were developed in a Bayesian framework, allowing us to fully assess uncertainties in parameter estimates. Additionally, we included model simulations with and without d -excess as a calculated parameter. Finally, evaporation rates estimated by the isotope mass balance were compared to those from the Penman–Monteith method. We found that the input isotopic values are sensitive to local evaporation line slope calculations and have a large effect on E / I ratios. The isotope ratio value of water collected at the dam outlet effectively integrated values observed from across the reservoir surface. Models using air temperature and relative humidity from the land-based weather station resulted in higher estimated evaporation rates compared to those using lake-buoy data. The models including d -excess estimated higher evaporation rates and lower turbulence indices than their counterparts excluding d -excess. We found similar model performance using land-based or lake-buoy air temperature and relative humidity compared to Penman–Monteith evaporation rates and found the incorporation of d -excess simulated evaporation rates more similar to Penman–Monteith. These findings highlight the applicability of the isotope mass balance using lake outflow samples and regional weather in complex, high-elevation terrain in the Rocky Mountains.

Assessing local climate change impacts often requires downscaling coarse global climate model (GCM) output to finer resolution. Two main approaches exist: dynamical downscaling using high-resolution regional climate models, and statistical downscaling based on historical relationships between large-scale and local variables. In a recent analysis of five dynamically downscaled simulations over the western United States, Koszuta et al. (2024, https://doi.org/10.1029/2023gl107298) found that warming weakens orographic influence on winter precipitation, damping increases on windward slopes and amplifying them in rain-shadowed regions. Here we show that this effect is robust across seasons and multiple dynamically downscaled ensembles, and is more pronounced at higher model resolutions. However, it is absent in projections from a widely used statistical model (LOCA2), even when trained on high-resolution future simulations (LOCA2-Hybrid). This highlights a key limitation of many statistical downscaling methods: their preservation of parent GCM trends, which usually fail to capture emergent changes in orographic precipitation patterns.

This study evaluates the ability of CONUS404, a 4-km resolution historical climate reconstruction that uses a coupled atmosphere–land surface model driven by a global reanalysis, to accurately simulate the precipitation, temperature, and snow water equivalent (SWE) across the mountainous western United States by comparing it against a variety of datasets including observations, statistical interpolations, and data assimilation products. The analysis was performed across four subdomains with distinct hydroclimates over the period from 1985 to 2021. CONUS404 cold-season precipitation generally agrees well with gauge-based gridded estimates, with some exceptions. CONUS404 matches observationally based SWE estimates during the early accumulation period but consistently exhibits earlier-peaking, shallower snowpacks with slower late-season ablation rates. CONUS404’s good agreement in terms of seasonal precipitation and SWE during the accumulation period (early in the cold season) suggests that snowfall is not the primary driver of CONUS404’s negative SWE bias that begins to emerge in January. While the average temperatures are a fairly good representation, CONUS404 cannot adequately simulate the full extent of nighttime cold temperatures (minima are warm biased) or daytime warm temperatures (maxima are cold biased), resulting in a significantly reduced daily temperature range. This study cannot fully attribute the cause of the negative SWE bias in CONUS404 in late winter, and further investigation is required. Significance Statement Streamflow in the interior western United States and other midlatitude semiarid mountainous regions strongly depends on cold-season orographic precipitation and seasonal snowpack. Therefore, an accurate, highly resolved description of these fields, particularly over snow-dominated mountains, is essential for high-fidelity, physics-based watershed hydrologic predictions. Here, the accuracy of a 4-km resolution historical climate reconstruction that uses a coupled atmosphere–land surface model is examined. While precipitation is captured rather well, the peak seasonal snowpack in late winter/early spring is considerably less than observations suggest. This discrepancy calls for a better understanding of the surface energy balance and snow ablation rate at snowpack measurement sites.

Increasing temperatures and extreme weather events challenge agricultural production worldwide by reducing water availability and disrupting operational timelines. Extension educators and technical service providers (TSPs) provide critical assistance to producers. Yet they often face challenges in addressing weather-and climate-related issues due to limited climate literacy training, demanding workloads, and the sensitive nature of the topic. To better understand these challenges and job-specific needs gaps, we conducted a three-phase needs assessment–comprising six focus groups–to identify the needs of Wyoming TSPs in supporting producer decision-making regarding weather, climate, and water availability. Key findings include the need for additional training in climate and environmental sciences, enhanced climate communication skills, expanded place-based monitoring, more locally relevant outreach and education, improved data interpretation, forecasting, and information consolidation, and strengthened professional networks. By situating these findings within global conversations on agricultural resilience, knowledge transfer, and climate change, the study contributes recommendations for building trust, strengthening relationships, and advancing climate literacy and communication in climate-skeptical contexts. This work informs ongoing international efforts in capacity building and extension education, offering insights for Cooperative Extension Services and similar programs, particularly in rural and politically diverse contexts.

The cold-season climatology of cloud supercooled liquid water (SLW) in the western United States is examined by means of a 4-km grid resolution, 43-yr dynamical climate reconstruction. This study is motivated mainly by the uncertainty of effectiveness of glaciogenic cloud seeding, intended to increase precipitation. A better understanding of the climatology of the presence and abundance of SLW is key to narrowing this uncertainty. It is found that SLW below −6°C [cold enough for silver iodide (AgI)-based ice nucleation] is most common just upwind of the mountains in the Pacific Northwest and also rather common over the higher and drier mountains inland from there. SLW is often at levels very close to the ground over these mountains. SLW is far less common over the southwestern United States, with a downward trend over the 43-yr record. On the rare occasions that SLW is present over the Southern California mountains, the abundance of SLW tends to be exceptionally high. Much SLW is common just upwind of the near-coastal mountains in general. In these regions, SLW is distributed over a greater depth and is rarely found very close to the ground on account of a higher freezing level, compared to the inland mountains. The correlation of SLW with various environmental parameters is examined. Integrated vapor transport above the freezing level generally is a strong predictor for SLW, while the Froude number (a measure of flow blocking) is a poor predictor. Uncertainty related to model resolution and to the choice of cloud microphysics parameterization is acknowledged.

This study uses 25 CMIP6 global climate model simulations, bias-corrected and dynamically downscaled to 9 km, to examine regional changes in extreme precipitation, and predictive uncertainty, in the western United States under global warming levels (GWL) of 2°C and 3°C. This resolution is needed to capture orographic precipitation enhancement. Most models agree on significant increases in both the Rx1day and R99p indices. The largest increases in extreme precipitation are anticipated in California, both in an absolute sense, with Rx1day increases up to ∼10 mm/day, and in a relative sense, with up to a doubling of R99p in the more arid parts of the state for GWL 3°C. The most significant reductions in return intervals of extreme precipitation events are anticipated in the Rocky Mountain region. For instance, 50-year Rx1day events become 3 to 4 times more frequent under GWL 2°C and 6 to 8 times more frequent under GWL 3°C.

Landscapes are composed of habitat patches and conditions that vary across space and time. While habitat variability and complexity can support important ecological processes and ecosystem services, the dynamic nature of habitats can also constrain organismal growth and production as optimal conditions are fleeting. In riverine ecosystems, groundwater discharge to streams stabilises water temperature and flow regimes, thus mediating how habitat complexity is expressed. Yet, how stable habitats structure growth and production within the broader landscape matrix is not well understood.

Many of America’s cherished national parks are seeing a domino effect of ecological change triggered by climatic shifts. Long-term monitoring offers an opportunity to document baseline conditions, detect change, and make informed decisions about how to address our uncertain future. Given the National Park Service’s commitment to embedding science-informed practices into all aspects of the agency’s work, we recently established a multi-entity partnership to improve direct experiences with water science in park-based youth programs that conduct monitoring, for the sake of both youth science literacy and long-term monitoring. Here, Grand Teton National Park, Teton Science Schools, and the University of Wyoming Science & Mathematics Teaching Center share an approach and lessons learned from an ongoing project to foster engagement of 5th-graders via water quality monitoring opportunities along the Snake River. We forged a partnership that evolved, much like the ebb and flow of the braided channel of the Snake. Insights include some of the challenges in identifying meaningful project elements and creating age-appropriate scientific monitoring protocols that meet converging goals and values.

As water reservoirs in the western United States reach historic lows, students are increasingly confronted with complex environmental challenges stemming from water scarcity that directly affect their lives and communities. This article highlights how three high school teachers used a free online platform called WY-Adapt—which includes more than ten interactive dashboards featuring temperature, precipitation, snowpack, streamflow, and real-time sensor data—to help students explore past climate trends and future projections and connect them to changes in their own communities. These teachers turned complex, place-relevant climate data into meaningful learning experiences that supported student sensemaking around NGSS-aligned ideas connected to evidencebased forecasts of Earth’s systems and relationships among Earth’s systems and human activity. Their classroom examples and insights illustrate how tools like WY-Adapt can support data-driven, place-relevant, and sensemaking-focused learning.

Record fire years in recent decades have challenged post-fire forest recovery in the western United States and beyond. To improve management responses, it is critical that we understand the conditions under which management can mitigate severe wildfire impacts, and when it cannot. Here, we evaluated the influence of top-down and bottom-up fire severity forcings on 17 wildfires occurring during two consecutive record-setting years in the eastern Cascade Mountains of Washington State. Despite much of the area having been burned after an extended period of fire exclusion, nearly one-third of the forested area burned at low severity.

Downscaled climate projections provide valuable information needed to better understand the impacts of climate change on health outcomes and to inform adaptation and mitigation strategies at local to regional scales. Because downscaled climate products vary in their representations of fine-scale spatiotemporal patterns, due to of multiple interacting factors, epidemiologic analyses need to consider how differences across downscaling approaches impact projections of health impacts into the future. We evaluate the projected seasonality of coccidioidomycosis in response to projected temperature and precipitation estimated using global climate models from CMIP6 included in California's Fifth Climate Change Assessment, downscaled using two approaches: (a) dynamical downscaling using the Weather Research and Forecasting model; and (b) hybrid statistical downscaling using the Localized Constructed Analogs approach. Our results indicate that by end of century, coccidioidomycosis transmission is projected to start earlier, end later, and last longer across the California endemic region; however, the magnitude of these changes varies by downscaling method. Specifically, LOCA2-hybrid projected a season onset that is 4.2 weeks earlier and an end that is 4.1 weeks later than historical conditions, while the dynamical approach projected a 4 week earlier onset and a 3.8 week later end compared to the historical period. Overall, the LOCA2-hybrid product estimates that the transmission season will last about 0.3 weeks longer than what is projected using dynamical downscaling by end of century. This analysis highlights the sensitivity of coccidioidomycosis seasonality projections to choice of downscaling product, underscoring the need to account for these differences in mitigation and adaptation planning.

In recent decades, Europe has warmed rapidly while a prominent ‘warming hole’ has muted changes in eastern North America. Paradoxically, the pattern coincides with cooling in the North Atlantic, even though ocean cooling is commonly expected to chill Europe, not North America. The regional departures from global trends and expectations may have informative precedents in millennial-scale climate anomalies during the Holocene. Here, circum-Atlantic records reveal that counter-intuitive European warming developed amid rapid cooling of the North Atlantic and North America from 5.8-4.8 ka and after 2.2 ka. The anomalies track rapid reductions in the flow of North Atlantic Deep Water southwest of Iceland and appear in independent but highly correlated records of mean sea-surface temperatures, continental temperatures, and hydroclimate changes from both sides of the Atlantic. The patterns confirm the importance of millennial climate variability during the Holocene and the risk of climate disruptions around the North Atlantic today.

Lakes Mead and Powell in the Colorado River Basin underpin water and hydropower supply for the western United States. While the policies currently regulating the basin will expire by 2026, planning remains challenging due to intertwined climate variability and policy uncertainties. Based on streamflow projections from 10 dynamically downscaled CMIP6 global climate models and unique methods that add and remove internal variability, we evaluate future conditions at Powell and Mead under existing and alternative policies. Due to projected streamflow declines, under existing policy, both reservoirs will face substantial risks (>80% likelihood) of reaching dead pool before 2060. Adopting recently proposed alternative policies reduces but doesn’t eliminate such risks. All policies also exhibit tipping points where reservoir levels can change rapidly with a slight change in streamflow. A sustainable policy may require larger reductions to further reduce the reservoirs’ dead pool risks and provide better buffers from sudden changes.

Grazing lands in the United States are the foundation of a $140 billion ruminant livestock production industry in the United States (U.S.) and management of these range and pasture lands holds strong support from government programs. This paper reviews current published literature to identify which land management practices have beneficial ecosystem and climate outcomes for carbon sequestration and greenhouse gas drawdown. We examined practices defined by the National Resource Conservation Service (NRCS) to provide an assessment for conservation planners and ranchers in the U.S., while noting that the application of these practices can vary across landscapes. Data needs are extensive and outcomes for some management practices are mixed, but practices including wetland restoration and pasture and hay planting have clear climate benefits and should be incentivized.

Climate change impacts vary depending on social and biophysical vulnerabilities, and the ability of society to respond, resist, or adapt to change/stress (i.e. adaptive capacity). In the field of climate science, funding for applied and engaged research is growing rapidly. Variously termed ‘knowledge co-production,’ ‘collaborative,’ ‘convergent,’ or ‘transdisciplinary’ research is hoped to improve outcomes in complex and dynamic social-ecological systems. Collaborative processes like knowledge co-production (KCP) may enhance adaptive capacity by facilitating social learning and engaging diverse worldviews. However, to date there is limited evaluation connecting processes of KCP to indicators of adaptive capacity. We use an interdisciplinary research approach to determine measurable dimensions of adaptive capacity and iterate between adaptive capacity and KCP literatures to expand the dimensions to more effectively encompass processes, outcomes, and associations. We also identify a gap related to identity and relationality and include this as a new dimension of adaptive capacity. Lastly, we present a new framework to evaluate the efficacy of engaged research, aimed at enhancing the adaptive capacity of individuals and groups, we call it the Wheel. We also share how the Wheel can be tailored to various contexts and makes strides to weave in power and different ways of knowing, although there is room to expand consideration of these elements.

This study evaluates the ability of 15 CMIP6 global climate models (GCMs), dynamically downscaled to a 9-km grid, to effectively simulate the observed regional hydroclimate across the complex terrain of the western United States. The evaluation focuses on orographic precipitation, surface temperature, and snow water equivalent (SWE), evaluated over a 33-yr period (1981–2014) using gridded gauge- and station-based datasets and a snowpack reanalysis product. Additional comparisons are made against two ERA5-driven climate reconstructions: one at 9-km resolution, with the same physical choices, and one at 4-km resolution. The latter better captures the terrain and orographic processes and uses different physics. Model performance is evaluated in four geographic regions, and mountains are contrasted against the surrounding plains. The evaluation is challenged by the fact that gridded observational estimates of climate parameters in complex terrain have a poorly quantified uncertainty related to measurement and/or representativeness issues. The ensemble mean of the downscaled GCMs overestimates cold-season precipitation, its orographic enhancement, and peak SWE in the mountains. Its diurnal temperature cycle and its mountain–plain temperature contrast are suppressed, compared to observations: its temperature estimates err toward the mean on both sides of both distributions. But the same applies to the identically downscaled (9 km) ERA5, indicating that these biases are due to model physics, not a misrepresentation of the climate system. The 4-km ERA5-based reconstruction has smaller biases in terms of precipitation and temperature, especially over mountains, but underestimates SWE. Model performance differs between mountains and plains, and the differences vary by region.

Warming temperatures and increasingly variable precipitation patterns are reducing winter snowpack and critical late-season streamflows. Here, we used two models (LANDIS-II and DHSVM) in linked simulations to evaluate the effects of wildfire and forest management scenarios on future snowpack and streamflow dynamics. We characterized the biophysical attributes of the areas with the greatest potential for treatments to improve hydrologic functioning and we examined projected trends in flow regimes over the 21st century. We found that, despite a projected increase in total annual flows, there was a steep decline in snowpack and late-season flows. Wildfire was an important factor influencing streamflow and snowpack dynamics, with increasing burned area partially offsetting climate-driven declines in snowpack and spring flows. Forest thinning treatments contributed modest increases to annual flows, although effects were overshadowed by the influences of climate and wildfire. Warming winter temperatures extinguished snowmelt-driven flows in low- and mid-elevation watersheds, causing a transition from spring snowmelt- to autumn rain-dominated streamflow regimes. Our results complement prior empirical studies showing that forest treatments can improve snowpack retention and annual streamflow, and they emphasize the importance of wildfire as a primary factor governing landscape hydrology. We found that neither land management practices nor wildfire could completely compensate for the top–down controls of future climate on landscape hydrology. Declines in snowpack retention and a regime shift in the timing of peak flows will have dramatic consequences for forest health, human water resources, and Pacific salmon populations.

Ecological disturbances driving state changes in ecosystems are likely to be exacerbated with climate shifts during this century. Temperate montane ecosystems of the Western United States (Mountain West) are especially vulnerable due to their low fertility, heterogeneous landscapes, and tight coupling between terrestrial and aquatic components. We review how catchment level pulse and press disturbances will intensify, and how they are reflected by coupled measurements of stream water chemistry and flow. Detecting effects on watershed processes can be complex and depend on the type and extent of disturbance. Within this context, we discuss the impacts of wildfire (pulse), bark beetle outbreaks (pulse), snowpack shifts (press), and progressive vegetation community shifts (press) on streamflow and chemistry dynamics (hydrochemographs). We used long-term data from three mid- to high-elevation watersheds as examples of how disturbances may influence stream hydrochemographs, including increased variability in winter nitrate export in more recent years with extremes in snowmelt runoff export; variable nitrate export when snow water equivalent was abnormally high or low; and high nitrate flux in the years immediately following sudden forest loss. These examples illustrate the need for long-term continuous monitoring to fill the gap in our understanding of the short- and long-term consequences of climate change-induced disturbances to watersheds. As disturbances increase in severity and frequency and induce ecological state changes, it is critical that we develop our understanding of impacts on downstream communities that depend sociologically and economically on water availability and quality.

This paper describes CyberGIS-based research and development aimed at improving geospatial data integration and visual analytics to better understand the impact of regional climate change on water availability in the U.S. Rocky Mountains. Two Web computing applications are presented. DEVISE - Derived Environmental Variability Indices Spatial Extractor, streamlines utilization of environmental data for better-informed wildlife decisions by biologists and game managers. The WY-Adapt platform aims to enhance predictive understanding of climate change impacts on water availability through two modules: “Current Conditions” and “Future Scenarios”. It integrates high-resolution models of the biophysical environment and human interactions, providing a robust framework for professionals and the public to visualize and explore climate data through a user-friendly map-based interface. Both tools represent significant strides in bridging the gap between CI-enabled science and on-the-ground application, especially in the context of regional climate change, by streamlining both data integration methods and customized spatial queries.

Agricultural producers and their local technical service providers (TSPs) need timely, science-based, and region-specific information and tools to enable them to make climate-informed decisions about their operations. This bulletin reports the results of six focus groups of TSPs serving the Wyoming agricultural community to better understand their knowledge, skills, and resource needs around changes in weather and climate variability and water availability.

The climate in Jackson Hole, and in the surrounding Greater Yellowstone Area (GYA), is anticipated to change more rapidly in the 21st century, compared to the last few decades. Here, we provide some quantitative information about anticipated changes in surface air temperature for the 21st century. The CMIP6 member climate models, built by climate research centers across the world, represent the physical processes underlying interactions between the biosphere, atmosphere, and oceans in slightly different ways, and the representation of these complex processes has inherent uncertainty.

Temperature is expected to rise in future decades, at a rate of as much as 1ºF per decade later in the century. This prediction is based on the CMIP6 climate simulations, and this climate information sheet uses the same WUSD3 dataset to examine predicted changes in precipitation and snowpack. The WUSD3 dataset consists of 15 CMIP6 climate models, bias-corrected with historical data and dynamically downscaled to a grid resolution of 9 km (5.6 miles).

Jackson Hole’s climate has been changing. NASA reports that global average temperatures in 2023 were 1.8°F (1.0° C) higher than the average from 1950-1981, but local changes are not always the same as global averages. Jackson Hole is unique. Our weather has tracked the global trends in surprising ways that is already affecting this iconic landscape. The Nature Conservancy’s Wildflower Watch, building on the pioneering observations of Frank Craighead, has found that flowers in Jackson Hole are blooming three weeks earlier now than in the 1970s. Our weather stations help to show why.

This paper describes CyberGIS-based research and development aimed at improving geospatial data integration and visual analytics to better understand the impact of regional climate change on water availability in the U.S. Rocky Mountains. Two Web computing applications are presented. DEVISE - Derived Environmental Variability Indices Spatial Extractor, streamlines utilization of environmental data for better-informed wildlife decisions by biologists and game managers. The WY-Adapt platform aims to enhance predictive understanding of climate change impacts on water availability through two modules: “Current Conditions” and “Future Scenarios”. It integrates high-resolution models of the biophysical environment and human interactions, providing a robust framework for professionals and the public to visualize and explore climate data through a user-friendly map-based interface. Both tools represent significant strides in bridging the gap between CI-enabled science and on-the-ground application, especially in the context of regional climate change, by streamlining both data integration methods and customized spatial queries.

Rangeland and wildlife managers often have the field knowledge needed to make timely decisions but lack accessible tools for turning complex environmental datasets into management-relevant information. •DEVISE ( https://devise.uwyo.edu ) and WY-Adapt ( https://future.wyadapt.org/places ) are web-based tools that translate raw weather, phenology, and water datasets into decision-ready products that can support management across the western United States, including applications such as harvest planning, irrigation timing, and climate adaptation. •By allowing users to select a biological unit such as a wildlife seasonal range or ranch pasture and access summarized environmental information without GIS software or programming skills, these tools reduce technical barriers between environmental data and management decisions. •Co-production with end users highlighted three priorities for successful decisionsupport tools: engage users early and throughout development, keep interfaces genuinely simple, and invest in computational infrastructure that can support increasingly complex data and user expectations.