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Arctic and subarctic rivers are warming rapidly, with unknown consequences for migratory fishes and the human communities dependent on them. To date, few studies have provided a comprehensive assessment of possible climate change impacts on the hydrology and temperature of Arctic rivers at the regional scale, and even fewer have connected those changes to multiple fish species with input and guidance from Indigenous communities. We used climate, hydrologic, and fish-growth simulations of historical (1990–2021) and future (2034–2065) young-of-year (YOY) growth potential of Chinook salmon (Oncorhynchus tshawytscha) and Dolly Varden (Salvelinus malma) for seven river basins in the Arctic-Yukon-Kuskokwim (AYK) region of Alaska, USA and Yukon Territory, Canada. Historically, summer water temperatures of all river basins remained below thresholds regarded as deleterious for Chinook salmon (14.6 °C) and Dolly Varden (16 °C), even in the warmest years. However, by the mid-century, Chinook salmon growth was limited, with declines in the warmest years in most river basins. Conversely, Dolly Varden are expected to benefit, with a near-doubling in growth projections in all river basins. This suggests that there may be an increase in suitable habitat for Dolly Varden by mid-century. The results highlight species-specific consequences of climate change and can guide future research on refugia for these species of cultural and subsistence importance to Indigenous communities in the AYK region and throughout the Arctic. 

Hydroclimate and terrestrial hydrology greatly influence the local community, ecosystem, and economy in Alaska and Yukon River Basin. A high‐resolution simulation of the historical climate in Alaska can provide an important benchmark for climate change studies. In this study, we utilized the Regional Arctic System Model (RASM) and conducted coupled land‐atmosphere modeling for Alaska and Yukon River Basin at 4‐km grid spacing. In RASM, the land model was replaced with the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. This study aims to maintain good model performance for both hydroclimate and terrestrial hydrology, especially streamflow, which was rarely a priority in coupled models. Therefore, we implemented a strategy of iterative testing and optimization of CTSM. A multi‐decadal climate data set (1990–2021) was generated using RASM with optimized land parameters and manually tuned WRF microphysics. When evaluated against multiple observational data sets, this data set well captures the climate statistics and spatial distributions for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation‐to‐precipitation ratios, and streamflow. The simulated precipitation shows wet bias during the spring season and simulated air temperatures exhibit dampened seasonality with warm biases in winter and cold biases in summer. We used transfer entropy to investigate the discrepancy in connectivity of hydrologic and energy fluxes between the offline CTSM and coupled models, which contributed to their discrepancy in streamflow simulations. 

Habitat loss poses a major threat to global biodiversity. Many studies have explored the potential damages of deforestation to animal populations but few have considered trees as thermoregulatory microhabitats or addressed how tree loss might impact the fate of species under climate change. Using a biophysical approach, we explore how tree loss might affect semi-arboreal diurnal ectotherms (lizards) under current and projected climates. We find that tree loss can reduce lizard population growth by curtailing activity time and length of the activity season. Although climate change can generally promote population growth for lizards, deforestation can reverse these positive effects for 66% of simulated populations and further accelerate population declines for another 18%. Our research underscores the mechanistic link between tree availability and population survival and growth, thus advocating for forest conservation and the integration of biophysical modelling and microhabitat diversity into conservation strategies, particularly in the face of climate change. 

NetCDF files in this dataset can be accessed and downloaded from the ADC directory via: [https://arcticdata.io/data/10.18739/A25M62870/](https://arcticdata.io/data/10.18739/A25M62870/) The Regional Arctic System Model, the combined Weather Research & Forecasting Model and the Community Terrestrial Systems Model for climate and land surface processes, mizuRoute for river routing, and the River Basin Model for river temperature, was used to generate high-resolution spatial and temporal data for 49 major Alaskan river basins. This modeling framework was applied to compare Alaskan hydrology between historical (1990-2021) and mid-century (2035-2064) periods across six future scenarios. These scenarios include six dynamically downscaled projections: two pseudo-global warming simulations based on historical meteorology, and four directly downscaled global climate models under the Shared Socioeconomic Pathway (SSP) SSP2-4.5 and SSP3-7.0 emission pathways. The climate data encompass variables such as snowpack, evapotranspiration, precipitation (rain and snow), groundwater, river temperature and discharge, as well as heat flux to the ocean. 

Hydroclimate and terrestrial hydrology greatly influence the local communities, ecosystems, and economies across Alaska and Yukon River Basin. Therefore, we utilized the Regional Arctic Systems Model (RASM) to model the coupled land-atmosphere, and generated a climate and hydrology dataset at 4-km grid spacing to improve our understanding of the regional hydroclimate and terrestrial hydrology. Our model domain encompasses all of the U.S. State of Alaska, the entire Yukon River Basin, part of Western Canada, and the eastern coastal region of Russia. This dataset includes 1) one simulation of the historical climate (Water Years 1991-2021), which serves as a benchmark for climate change studies, and 2) two future simulations (Equivalent Water Years 2035-2065) using the Pseudo-Global Warming method under future greenhouse gas emission scenario SSP2-4.5. The two future scenarios represent median and high changes derived from ensemble means across different Global Climate Models in the Coupled Model Intercomparison Project Phase 6 within SSP2-4.5 respectively. The microphysics schemes in the Weather Research and Forecast (WRF) atmospheric model were manually tuned for optimal model performance. The land component in RASM was replaced using the Community Terrestrial Systems Model (CTSM) given its comprehensive process representations for cold regions. We conducted optimization for uncoupled CTSM to improve its performance in terrestrial hydrologic simulations, especially streamflow and snow (Cheng et al., 2023). In order to maintain the quality for both hydroclimate and terrestrial hydrologic simulation, we implemented a strategy of iterative testing and re-optimization of CTSM. This dataset was then generated using RASM with optimized CTSM parameters and manually tuned WRF microphysics. The historical simulation was evaluated against multiple observational datasets for five key weather variables and hydrologic fluxes, including precipitation, air temperature, snow fraction, evaporation-to-precipitation ratios, and streamflow. The evaluation details can be found in Cheng et al. (2024). 

This data release includes continuous measurements of stream temperature and specific conductance from 24 sites on the Yukon River and other arctic rivers in Alaska. Measurements were collected at selected U.S. Geological Survey stream gages and in communities along major rivers. 

High spatial and temporal resolution models are essential for understanding future climate impacts and developing effective climate resilience plans. However, existing regional and global river models often lack the resolution needed to accurately capture local conditions. This study uses a series of high-resolution models, including the Regional Arctic System Model, mizuRoute, and the river basin model, to analyze Arctic and sub-Arctic Alaskan hydrology. We compare a historical baseline (1991–2020) with six midcentury (2035–64) futures: two pseudo–global warming scenarios based on historical meteorology and four direct dynamically downscaled global climate models. The six futures reveal significant uncertainty in future annual discharge and peak flows, although a widespread increase in discharge during April (+63%) and October (+31%) is consistently shown across models. Projected increases in rain and shifting weather patterns lead to a transition from snow to rain in spring and autumn, reducing the fraction of snowmelt contributing to river discharge. Rising evapotranspiration moderates discharge changes, particularly in autumn, by offsetting precipitation increases. Average summer river temperatures are projected to increase by approximately 1.5°C, doubling the number of river segments that experience 18°C days, a critical threshold for salmon survival, and intensifying the heat flux to the ocean adding an average of 3.3 × 10¹²MJ yr⁻¹. These changes in the hydrologic cycle could profoundly impact riverine and oceanic ecosystems, posing substantial challenges to communities reliant on these environments. Significance StatementThe purpose of this study is to enhance our understanding of the midcentury climate change impacts on the Alaskan hydrologic cycle. In all six of the potential future scenarios, river flows in spring and autumn are predicted to increase and river temperatures are projected to be warmer throughout the year. These changes are significant as higher river temperatures could jeopardize fish survival. Additionally, the combined effect of increased river water and higher temperatures during spring and autumn will contribute more heat to the ocean, possibly reducing nearshore sea ice. This is crucial because many communities depend on rivers and sea ice for transportation and subsistence activities. 

Abstract Earth System Models (ESM)are crucial for quantifying climate impacts across Earth's interconnected systems and supporting science‐based adaptation and mitigation. However, not including end‐users, especially decision‐makers representing communities vulnerable to climate change, can limit model utility, increase epistemic risks, and lead to information misuse in decision‐making. While the ESM community increasingly values broad community engagement, end‐users may not initially perceive models as useful for local planning. Co‐designing models with end‐users fosters two‐way learning: users better understand models and their outputs, while modelers gain insights into fine‐scale local processes like monitoring practices and management priorities. Higher‐level co‐design can lead to more customized, priority‐driven, and useful modeling products. Despite these benefits, modelers often struggle to initiate meaningful partnerships with local communities. Therefore, this paper explores model co‐design from the perspective of modelers. This study presents two case studies where modelers and social scientists collaborated with Indigenous communities' decision‐makers to reflect their priorities in model design and application. In the Arctic Rivers Project, high‐resolution climate and hydrology data sets for Alaska were developed with guidance from an Indigenous Advisory Council, using optimized, coupled land‐atmosphere models. In the Mid‐Klamath Project, we partnered with the Karuk Tribe's Department of Natural Resources to assess climate change and prescribed burning impacts on terrestrial hydrology in the Klamath River Basin. Drawing from these studies, we introduce a four‐level framework: (a) Co‐design Configuration; (b) Model Tuning; (c) Incorporate Contextual Knowledge; (d) Co‐develop New Model Functions. We aim to help researchers consider and compare co‐design across diverse modeling projects systematically and coherently. 

Abstract Winters in snow-covered regions have warmed, likely shifting the timing and magnitude of nutrient export, leading to unquantified changes in water quality. Intermittent, seasonal, and permanent snow covers more than half of the global land surface. Warming has reduced the cold conditions that limit winter runoff and nutrient transport, while cold season snowmelt, the amount of winter precipitation falling as rain, and rain-on-snow have increased. We used existing geospatial datasets (rain-on-snow frequency overlain on nitrogen and phosphorous inventories) to identify areas of the contiguous United States (US) where water quality could be threatened by this change. Next, to illustrate the potential export impacts of these events, we examined flow and turbidity data from a large regional rain-on-snow event in the United States’ largest river basin, the Mississippi River Basin. We show that rain-on-snow, a major flood-generating mechanism for large areas of the globe (Berghuijs et al 2019 Water Resour. Res. 55 4582–93; Berghuijs et al 2016 Geophys. Res. Lett. 43 4382–90), affects 53% of the contiguous US and puts 50% of US nitrogen and phosphorus pools (43% of the contiguous US) at risk of export to groundwater and surface water. Further, the 2019 rain-on-snow event in the Mississippi River Basin demonstrates that these events could have large, cascading impacts on winter nutrient transport. We suggest that the assumption of low wintertime discharge and nutrient transport in historically snow-covered regions no longer holds. Critically, however, we lack sufficient data to accurately measure and predict these episodic and potentially large wintertime nutrient export events at regional to continental scales. 

Climate change projections consistently demonstrate that warming temperatures and dwindling seasonal snowpack will elicit cascading effects on ecosystem function and water resource availability. Despite this consensus, little is known about potential changes in the variability of ecohydrological conditions, which is also required to inform climate change adaptation and mitigation strategies. Considering potential changes in ecohydrological variability is critical to evaluating the emergence of trends, assessing the likelihood of extreme events such as floods and droughts, and identifying when tipping points may be reached that fundamentally alter ecohydrological function. Using a single-model Large Ensemble with sophisticated terrestrial ecosystem representation, we characterize projected changes in the mean state and variability of ecohydrological processes in historically snow-dominated regions of the Northern Hemisphere. Widespread snowpack reductions, earlier snowmelt timing, longer growing seasons, drier soils, and increased fire risk are projected for this century under a high-emissions scenario. In addition to these changes in the mean state, increased variability in winter snowmelt will increase growing-season water deficits and increase the stochasticity of runoff. Thus, with warming, declining snowpack loses its dependable buffering capacity so that runoff quantity and timing more closely reflect the episodic characteristics of precipitation. This results in a declining predictability of annual runoff from maximum snow water equivalent, which has critical implications for ecosystem stress and water resource management. Our results suggest that there is a strong likelihood of pervasive alterations to ecohydrological function that may be expected with climate change.