Search for: All records

Abstract Snow conditions are changing rapidly across our planet, which has important implications for wildlife managers. In Alaska, USA, the later arrival of snow is challenging wildlife managers' ability to conduct aerial fall (autumn) moose (Alces alces) surveys. Complete snow cover is required to reliably detect and count moose using visual observation from an aircraft. With inadequate snow to help generate high‐quality moose survey data, it is difficult for managers to determine if they are effectively meeting population goals and optimizing hunting opportunities. We quantified past relationships and projected future trends between snow conditions and moose survey success across 7 different moose management areas in Alaska using 32 years (1987–2019) of moose survey data and modeled snow data. We found that modeled mean snow depth was 15 cm (SD = 11) when moose surveys were initiated, and snow depths were greater in years when surveys were completed compared to years when surveys were canceled. Further, we found that mean snow depth toward the beginning of the survey season (1 November) was the best predictor of whether a survey was completed in any given year. Based on modeled conditions, the trend in mean snow depth on 1 November declined from 1980 to 2020 in 5 out of 7 survey areas. These findings, coupled with future projections, indicated that by 2055, the delayed onset of adequate snow accumulation in the fall will prevent the completion of moose surveys over roughly 60% of Alaska's managed moose areas at this time of the year. Our findings can be used by wildlife managers to guide decisions related to the future reliability of aerial fall moose surveys and help to identify timelines for development of alternate measurement and monitoring methods. 

In the natural environment, wildfires affect how water interacts with soil leading to potentially catastrophic phenomena such as flooding, debris flows, and decreased water quality. Wildfires can cause soil sealing from increased soil water repellency, which in turn reduces infiltration and increases flood risk during rainfall. A 2017 meta-analysis found two properties that were affected by soil burning processes: Sorptivity (the capacity of a soil to absorb or desorb liquid by capillarity, S) and hydraulic conductivity (the ability for soil to transmit water when saturated, Kfs). Changes in these properties act synergistically to reduce infiltration, which increases erosion by accelerating and amplifying surface runoff. Thus, this research seeks to understand how soils subjected to severe burning compare to unburned soils. Using a mini-disk infiltrometer, field tests measured hydraulic conductivity of soils burned under slash and burn piles during the winters of 2016-17, 2020-21, and 2023-23 to better understand changes that occur in soil-hydraulic properties over time. These slash and burn piles served as approximate impacts for wildfires. Slash and burn piles also allow for paired measurements of unburned soils immediately adjacent to the burned area. Hydraulic conductivity was not significantly different when comparing burned and unburned soils 1 year after being burned. However, there was a significant difference between the hydraulic conductivity of soils burned 3 years ago compared to both unburned soil and soils burned 1 year ago. This suggests an interim process between 1- and 3-years post-burn that reduces hydraulic conductivity of burned soils. 

This project used cutting-edge soundscape observations and analyses to quantify the influence of changing environmental dynamics and increasing anthropogenic activity on the behavior and phenology of migratory caribou, waterfowl, and songbird communities in Arctic-boreal Alaska and northwestern Canada. We used acoustic and camera-trap monitoring methods to evaluate wildlife responses in novel and non-invasive ways across broad spatial ranges during crucial seasons. Our study combined field observations, modeling, and analyses included (1) soundscape measurements, (2) camera-trap observations, (3) automated soundscape analyses, (4) analyses of camera-trap caribou observations, (5) high-resolution modeling of environmental variables, and (6) statistical analyses of wildlife occupancy, diversity, and phenology. This “Environmental Data” dataset package describes and includes the high-resolution environmental variables used in this study. 

Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice. 

Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 2019–2020, which represent the first such measurements collected across an entire season. Compared with similar historical transects, the snow at MOSAiC was thin (mean depths of approximately 0.1–0.3 m), while the sea ice was relatively thick first-year ice (FYI) and second-year ice (SYI). SYI was of two distinct types: relatively thin level ice formed from surfaces with extensive melt pond cover, and relatively thick deformed ice. On level SYI, spatial signatures of refrozen melt ponds remained detectable in January. At the beginning of winter the thinnest ice also had the thinnest snow, with winter growth rates of thin ice (0.33 m month−1 for FYI, 0.24 m month−1 for previously ponded SYI) exceeding that of thick ice (0.2 m month−1). By January, FYI already had a greater modal ice thickness (1.1 m) than previously ponded SYI (0.9 m). By February, modal thickness of all SYI and FYI became indistinguishable at about 1.4 m. The largest modal thicknesses were measured in May at 1.7 m. Transects included deformed ice, where largest volumes of snow accumulated by April. The remaining snow on level ice exhibited typical spatial heterogeneity in the form of snow dunes. Spatial correlation length scales for snow and sea ice ranged from 20 to 40 m or 60 to 90 m, depending on the sampling direction, which suggests that the known anisotropy of snow dunes also manifests in spatial patterns in sea ice thickness. The diverse snow and ice thickness data obtained from the MOSAiC transects represent an invaluable resource for model and remote sensing product development. 

Abstract Upper Indus Basin (UIB) streamflow originates largely from glacier and snow melt in the upstream Himalaya, Karakoram, and Hindu Kush mountain ranges and is extremely vulnerable because of its projected climate changes, dense populations, and hydropolitical tensions. Accurate knowledge of streamflow constituents is required for resilient water resources management; this is precluded by a paucity of measurement as well as climatological and topographic complexity. Here we integrate citizen scientist acquired geochemical samples, collected from October 2018 through September 2019 in the Shimshal watershed of the Karakoram Mountains of Pakistan, with Sentinel‐1 (S1) synthetic aperture radar (SAR)‐derived wet snow maps, to better understand streamflow constituents for the high altitude and heavily glaciated catchment. We use Bayesian end‐member mixture analysis to separate river flows into baseflow and meltwater constituents, using fixed and time‐variant melt end‐member values. We compare river hydrograph separation results with S1 wet snow time series maps for the same timeframe. We then utilize S1 imagery to inform end‐member mixture analysis to separate meltwaters into snow and glacier melt. For the Shimshal catchment, we find that about 85% of annual river flows are derived from snow and glacier melt; 45% of annual flows are derived from snow melt and 40% glacier melt. Engaged and committed citizen scientists enabled geochemical sample collection and analysis on a significant temporal and spatial scale. In the future, co‐produced knowledge that both implements local expertise and that is also planned and utilized by diverse stakeholders may increase climatological awareness and resilience in the UIB. 

Inuit hunters and meteorologists alike pay close attention to weather and weather changes, with deep understandings. This paper describes a long-time research project based in Kangiqtugaapik (Clyde River), Nunavut, where a research team of Inuit and visiting scientists have combined information and knowledge from a community-based weather station network, on-going interviews and discussions, and extensive travel (both Arctic fieldwork and visits to southern universities) to co-produce knowledge related to human–weather relationships and weather information needs and uses in one Nunavut community. The project uses the concept of “HREVs”, human-relevant environmental variables — complex, synthesis variables that, used in conjunction with a host of social variables, assist in informing safe land travel and activities. This work, including linking Inuit knowledge and environmental modeling, can be expanded to not only understand human–weather relationships more broadly and in other locations but also provide insights into the process of building diverse research teams and knowledge co-production. Inuit angunasuktiit amma silalirijiit tamarmik ujjiqsuttiasuunguvut silamit amma silaup asijjiqpallianingani, tukisiumaniqarjuaqłutik. Una paippaangujuq unikkaarivuq akuniujumi qaujitasaqtaunirmut piliriangujumi Kangiqtugaapik (Clyde River), Nunavummi, qaujisaqtiujuni katinngajuni Inungni amma pularaqtunut qaujisaqtiujunut katirisimajuni uqausiksani amma qaujimaniujumi nunalingni−tunngavilingmi silalirivvingmi tusaumatittiniujumi, apiqsuqtaunginnaqtuni amma uqallangniujuni, amma aullaaqsimarjuaqłutik (tamakkit Ukiuqtaqtumi iniujumi piliriniujumi amma pulararniujunut qallunaat nunanganni silattuqsarvigjuangujunut) saqqitittiqatigiingnirmut qaujimaniujumi pijjutiqaqtumut inungnut−silamut piliriqatigiingniujuni amma silamut uqausiksani pijariaqarniujunut amma aturniujunut atausirmi Nunavummi nunaliujumi. Piliriangujuq atusuunguvuq isumagijauniujumi “HREVs”, inungnut-atuutilingnut avatimut ajjigiinnginniujunut – nalunaqtuni, katinniujuni isumagijauniujuni aaqqiksinirnut piliri−jusiujumi ajjigiinnginniujuni, atuqatiqaqłuni ilagijaujumi inuuqatigiingujunut ajjigiinnginniujunit, ikajuqsuisuunguvuq aaqqiksuinirmi attananngittumi nunami aullaarniujumi amma qanuiliurniujunut. Una piliriniujuq ilaqaqtumi kasuqatiqarnirmi inuit qaujimajanginni amma avatimut uukturautiqarnirmi, angigligiaqtaujunnaqpuq tukisiumanituangunngittumi inungt-silamut piliriqatigiingniujumi tauvunngaujjiniujumi ammalu asinginni iniujunut, kisiani tunisijunnaqpuq tukisirjuarniujuni piliriniujuni sananirmut ajjigiinngiruluujaqtuni qaujisaqtiujunut katinngajuni amma qaujimanirmut saqqitittiqatigiingniujumi. 

Abstract Migratory birds have the capacity to shift their migration phenology in response to climatic change. Yet the mechanistic underpinning of changes in migratory timing remain poorly understood. We employed newly developed global positioning system (GPS) tracking devices and long-term dataset of migration passage timing to investigate how behavioral responses to environmental conditions relate to phenological shifts in American robins (Turdus migratorius) during spring migration to Arctic-boreal breeding grounds. We found that over the past quarter-century (1994–2018), robins have migrated ca. 5 d/decade earlier. Based on GPS data collected for 55 robins over three springs (2016–2018), we found the arrival timing and likelihood of stopovers, and timing of arrival to breeding grounds, were strongly influenced by dynamics in snow conditions along migratory paths. These findings suggest plasticity in migratory behavior may be an important mechanism for how long-distance migrants adjust their breeding phenology to keep pace with advancement of spring on breeding grounds. 

Abstract With a unique biogeophysical signature relative to other freshwater sources, meltwater from glaciers plays a crucial role in the hydrological and ecological regime of high latitude coastal areas. Today, as glaciers worldwide exhibit persistent negative mass balance, glacier runoff is changing in both magnitude and timing, with potential downstream impacts on infrastructure, ecosystems, and ecosystem resources. However, runoff trends may be difficult to detect in coastal systems with large precipitation variability. Here, we use the coupled energy balance and water routing model SnowModel‐HydroFlow to examine changes in timing and magnitude of runoff from the western Juneau Icefield in Southeast Alaska between 1980 and 2016. We find that under sustained glacier mass loss (−0.57 ± 0.12 m w. e. a⁻¹), several hydrological variables related to runoff show increasing trends. This includes annual and spring glacier ice melt volumes (+10% and +16% decade⁻¹) which, because of higher proportions of precipitation, translate to smaller increases in glacier runoff (+3% and +7% decade⁻¹) and total watershed runoff (+1.4% and +3% decade⁻¹). These results suggest that the western Juneau Icefield watersheds are still in an increasing glacier runoff period prior to reaching “peak water.” In terms of timing, we find that maximum glacier ice melt is occurring earlier (2.5 days decade⁻¹), indicating a change in the source and quality of freshwater being delivered downstream in the early summer. Our findings highlight that even in maritime climates with large precipitation variability, high latitude coastal watersheds are experiencing hydrological regime change driven by ongoing glacier mass loss.