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60 results for “snowmelt”
Earliest snowmelt estimation dates for Arctic sea ice (2003)
<p>Earliest snowmelt estimation dates calculated for the year 2003 are provided using sea ice brightness temperatures from AMSR-E (Cavalieri et al., 2014) and DMSP SSM/I-SSMIS (Meier et al., 2019), as well as simulated sea ice brightness temperatures from the CESM2 JRA-55 (Danabasoglu et al., 2020; Kobayashi et al., 2015; Tsujino et al., 2018), which were created using the Arctic Ocean Observation Operator (ARC3O; Burgard et al, 2020a,b). Scripts and README files are provided for preparing the model data to act as input to ARC3O. </p>
Impact of Snowmelt Timing and Tree Proximity on Dutchman's Breeches Phenology and Performance in Mont Megantic National Park (Quebec, Canada; 2018-2019)
Data herein were collected in 2018 and 2019 in Mont Megantic National Park, Quebec, Canada, in a sugar maple-dominated temperate deciduous forest. Individuals of Dutchman's breeches (Dicentra cucullaria), a common understory spring ephemeral plant that is only active in the spring, were transplanted into a fully factorial experiment of snowmelt timing (early vs. late) and tree proximity (near vs. far) to determine the role of thaw circle formation in the local clustering of this species near canopy tree trunks. Plant phenology (emergence, senescence, and growing season length) and performance (stem abundance and leaf area) were tracked during two years of snow manipulation. Additionally, microclimate temperature data were collected in a subset of plots in 2018.
Plant and carbon data, snowmelt manipulation experiment, Rocky Mountain Biological Laboratory (RMBL), 2023
These data are from a 2023 snowmelt manipulation experiment in Vera Meadow at the Rocky Mountain Biological Laboratory. We experimentally advanced the snowmelt date in a montane meadow by approximately 12 days using black shade cloths and assessed the effect on plant and carbon dynamics. We measured net ecosystem exchange, gross primary productivity, and soil respiration using a Li-COR 7500 five times biweekly from June to August, plant community composition using the pin-drop method five times biweekly from June to August, and root biomass nine times using bulk soil cores. Using drone imagery, we measured the Normalized Difference Vegetation Index (NDVI). This data package is completed.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
Deschampsia biomass, soil microbes and endophyte root colonization for snowmelt and microbial innoculation transplant experiment in the Green Lakes Valley, 2015-2018
As organisms shift their geographic distributions in response to climate change, biotic interactions have emerged as an important factor driving the rate and success of range expansions. Plant-microbe interactions are an understudied but potentially important factor governing plant range shifts. We studied the distribution and function of microbes present in high-elevation unvegetated soils, areas that plants are colonizing as climate warms, snow melts earlier and the summer growing season lengthens. Using a manipulative snowpack and microbial inoculation transplant experiment, we tested the hypothesis that growing season length and microbial community composition interact to control plant elevational range shifts. We predicted that a lengthening growing season combined with dispersal to patches of soils with more mutualistic microbes and fewer pathogenic microbes would facilitate plant survival and growth in previously unvegetated areas. We identified negative effects on survival of the common alpine bunchgrass Deschampsia cespitosa in both short and long growing seasons, suggesting an optimal growing season length for plant survival in this system that balances time for growth with soil moisture levels. Importantly, growing season length and microbes interacted to affect plant survival and growth, such that microbial community composition increased in importance in suboptimal growing season lengths. Further, plants grown with microbes from unvegetated soils grew as well or better than plants grown with microbes from vegetated soils. These results suggest that the rate and spatial extent of plant colonization of unvegetated soils in mountainous areas experiencing climate change could depend on both growing season length and soil microbial community composition, with microbes potentially playing more important roles as growing seasons lengthen.
ACS_Bayelva_class: 302 high-resolution snow cover maps covering the 2012-2017 snowmelt seasons in the Bayelva catchment (Svalbard, Norway)
<p>The ACS_Bayelva_class dataset contains 302 high-resolution binary snow cover images that were obtained by classifying orthrorectified photographs of a 1.77 km^2 area of interest in the Bayelva catchment. This latest version (2.0) of the dataset includes the orthorectified photographs that were used to classify the binary snow cover images. The catchment is close to Ny-Ålesund, the northernmost permanent civilian settlement in the world and a major hub for polar research, in the Norwegian high-Arctic Svalbard archipelago. The imagery has a (roughly) daily temporal resolution and a ground sampling distance (pixel spacing) of 0.5 m. The dataset spans 6 snowmelt seasons, covering the months May-August for the period 2012-2017. The orthophotos were obtained by processing oblique time-lapse photographs taken by a terrestrial automatic camera system (ACS) mounted at 562 m a.s.l. near the summit of Scheteligfjellet (719 m a.s.l.) a few kilometers west of Ny-Ålesund. The orthophotos were manually classified into binary snow cover images (0=no snow, 1=snow) by iteratively selecting a (visually) optimal threshold on the intensity in the blue-band for each image. More details are provided in the study of Aalstad et al. (2020) [a copy is available in this repository] where this dataset was created. The ACS was maintained by scientists from the group of Sebastian Westermann at the Section for Physical Geography and Hydrology in the Department of Geosciences at the University of Oslo, Oslo, Norway. </p>
Raw Data and Scripts for manuscript submitted to Oikos as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in a Subalpine Grassland Ecosystems'
<p>Raw Data and Scripts for manuscript submitted to PCI as 'Early Spring Snowmelt and Summer Droughts Strongly Impair the Resilience of Key Microbial Communities in Subalpine Grassland Ecosystems'</p>
Data for "Hatching phenology is lagging behind an advancing snowmelt pattern in a high-alpine bird"
<p><strong>Abstract</strong></p> <p>To track peaks in resource abundance, temperate-zone animals use predictive environmental cues to rear their offspring when conditions are most favourable. However, climate change threatens the reliability of such cues when an animal and its resource respond differently to a changing environment. This is especially problematic in alpine environments, where climate warming exceeds the Holarctic trend and may thus lead to rapid asynchrony between peaks in resource abundance and periods of increased resource requirements such as reproductive period of high-alpine specialists. We therefore investigated interannual variation and long-term trends in the breeding phenology of a high-alpine specialist, the white-winged snowfinch, <em>Montifringilla nivalis</em>, using a 20-year dataset from Switzerland. We found that two thirds of broods hatched during snowmelt. Hatching dates positively correlated with April and May precipitation, but changes in mean hatching dates did not coincide with earlier snowmelt in recent years. Our results offer a potential explanation for recently observed population declines already recognisable at lower elevations. We discuss non-adaptive phenotypic plasticity as potential causes for the asynchrony between changes in snowmelt and hatching dates of snowfinches, but the underlying causes are subject to further research.</p>
Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements
<p>The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017).</p> <p> </p> <p>Software versions used:</p> <ul> <li>Image Processing Workbench v2.2.0 (Marks et al., 2017)</li> <li>Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019)</li> </ul> <p> </p> <p><strong>NOTE:</strong> Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file.</p> <p> </p> <p><strong>topo.nc</strong> – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are:</p> <ul> <li>dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017)</li> <li>mask – basin mask for the Boise River Basin</li> <li>veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015)</li> <li>veg_type – vegetation type from the National Land Cover Database</li> <li>veg_tau – vegetation fractional transmissivity derived from the vegetation type</li> <li>veg_k – vegetation emissivity derived from the vegetation type</li> </ul> <p> </p> <p><strong>maxus.nc</strong> – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002)</p> <p> </p> <p><strong>Station data:</strong></p> <ul> <li>Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF.</li> <li>metadata.csv – metadata for 40 stations</li> <li>air_temp.csv – 38 stations</li> <li>cloud_factor.csv – 7 stations</li> <li>precip.csv – 21 stations</li> <li>vapor_pressure.csv – 19 stations</li> <li>wind_direction.csv – 14 stations</li> <li>wind_speed.csv – 14 stations</li> </ul> <p> </p> <p><strong>smrf_config.ini</strong> – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.</p>
"Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest" -- data sets
<p>These files contain the data used in the analysis and production of graphs reported in a manuscript titled "Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest," by Stefan Gronsdahl, R. Dan Moore, Jordan Rosenfeld, Rich McCleary, Rita Winkler. The paper will be published in the journal Hydrological Processes. The file named "readme.txt" explains the contents of the files.</p>
Early snowmelt and warming experiments to study plant phenology
Phenology - the timing of life events - determines how a species’ life cycle aligns with the abiotic and biotic environment, however, climate change has altered the environmental cues organisms use to track climate leading to shifts in phenology. In high latitude environments, phenological shifts in plants are associated with both temperature and the timing of snowmelt, but the mechanism underlying the effect of snowmelt on phenology remains unclear. Here we aim to disentangle the effects of experimental warming and earlier snowmelt on the phenology of three long lived perennial wildflowers. In the summer of 2019, we factorially crossed passive warming with early snowmelt timing within a subalpine plant community in the Colorado Rocky Mountains at the Rocky Mountain Biological Laboratory to understand the individual effects of these aspects of climate change.
Hubbard Brook Nitrogen Oligotrophication (HBNO): Snowmelt Manipulation study, 2021-2023
In seasonally snow-covered ecosystems such as northern hardwood forests of the northeastern U.S., spring snowmelt is a critical transition period for plant and microbial communities, as well as for the biogeochemical cycling of nitrogen (N). However, it remains unknown how shifting snowmelt dynamics influence soil and plant processing and uptake of N in these forests, which are experiencing reductions in N availability relative to demand, a process known as oligotrophication. We determined the role of changing spring snowmelt timing on root production and N pools and fluxes by manipulating snowmelt timing along a climate elevation gradient at the Hubbard Brook Experimental Forest in New Hampshire. We manually halved or doubled snow water equivalent (SWE) in experimental plots in March of 2022 and 2023 to accelerate or delay by an average of one week, respectively, the onset of spring snowmelt. Earlier snowmelt led to reduced snowpack depth and duration, as well as deeper, more sustained soil frost during the snowmelt period in 2022, but soil freezing did not occur in 2023. Soil nitrate and net nitrification rates were significantly lower with shallower snowpack and earlier snowmelt compared to plots with deeper snow and later snowmelt. Shallower snowpack and early snowmelt were also associated with decreased foliar N concentrations and 15N values, indications that earlier snowmelt contributes to lower N availability relative to plant N uptake and demand. Our study provides evidence that early snowmelt resulting from shallower snowpack contributes to N oligotrophication, primarily through impacts on soil nitrate supply and uptake of N by trees. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Warming acts through earlier snowmelt to advance but not extend alpine community flowering
<p>Large-scale warming will alter multiple local climate factors in alpine tundra, yet very few experimental studies examine the combined yet distinct influences of earlier snowmelt, higher temperatures and altered soil moisture on alpine ecosystems. This limits our ability to predict responses to climate change by plant species and communities. To address this gap, we used infrared heaters and manual watering in a fully factorial experiment to determine the relative importance of these climate factors on plant flowering phenology, and response differences among plant functional groups. Heating advanced snowmelt and flower initiation, but exposed plants to colder early-spring conditions in the period prior to first flower, indicating that snowmelt timing, not temperature, advances flowering initiation in the alpine community. Flowering duration was largely conserved; heating did not extend average species flowering into the latter part of the growing season but instead flowering was completed earlier in heated plots. Although passive warming experiments have resulted in warming-induced soil drying suggested to advance flower senescence, supplemental water did not counteract the average species advance in flowering senescence caused by heating or extend flowering in unheated plots, and variation in soil moisture had inconsistent effects on flowering periods. Functional groups differed in sensitivity to earlier snowmelt, with flower initiation most advanced for early-season species and flowering duration lengthened only for graminoids and forbs. We conclude that earlier snowmelt, driven by increased radiative heating, is the most important factor altering alpine flowering phenology. Studies that only manipulate summer temperature will err in estimating the sensitivity of alpine flowering phenology to large-scale warming. The wholesale advance in flowering phenology with earlier snowmelt suggests that alpine communities will track warming, but only alpine forbs and graminoids appear able to take advantage of an extended snow-free season. </p>
Earlier spring snowmelt drives arrowleaf balsamroot phenology in montane meadows
<p>Climate change is causing global shifts in phenology, altering when and how species respond to environmental cues such as temperature and the timing of snowmelt. These shifts may result in phenological mismatches among interacting species, creating cascading effects on community and ecosystem dynamics. Using passive warming structures and snow removal, we examined how experimentally increased temperatures, earlier spring snowmelt, and the poorly understood interaction between warming and earlier spring snowmelt affected flower onset, flowering duration, and maximum floral display of the spring flowering montane species, Arrowleaf Balsamroot (<em>Balsamorhiza sagittata</em>), over a seven-year period. Additionally, potential cumulative effects of treatments were evaluated over the study duration. The combination of heating with snow removal led to earlier flower onset, extended flowering duration, and increased maximum floral display. While there was year-to-year variation in floral phenology, the effect of heating with snow removal on earlier onset and maximum floral display strengthened over time. This suggests that short-term studies likely underestimate the potential for climate change to influence phenological plant traits. Overall, this research indicates that<em> Balsamorhiza sagittata</em>'s flowering onset responded more strongly to snow removal than to heating, but the combination of heating with snow removal allowed plants to bloom earlier, longer, and more profusely, providing more pollinator resources in spring. If warming and early snowmelt cause similar responses in other plant species besides<em> Balsamorhiza sagittata</em>, these patterns could mitigate phenological mismatches with pollinators by providing a wider window of time for interaction and resiliency in the face of change. This example demonstrates that a detailed understanding of how spring-flowering plants respond to specific aspects of predicted climatic scenarios will improve our understanding of the effects of climate change on native plant-pollinator interactions in montane ecosystems. Studies like this help elucidate long-term physiological effects of climate-induced stressors on plant phenology in long-lived forbs.</p>
Data from: Snowmelt and laying date shape the parental care strategy of a high-Arctic shorebird
<p>Parental care varies across animal taxa, from uniparental to biparental care, driven by ecological and social factors such as weather, food availability, predation, and partner availability. Understanding this diversity within species can reveal biotic and abiotic conditions allowing uniparental versus biparental strategies. This study examines the impact of biotic and abiotic factors on parental care strategies in Sanderlings (<em>Calidris alba</em>), one of the few species that uses both types of care. Using long-term data from Greenland (2011-2023), path analyses revealed that laying date and snowmelt influence parental care strategies, with indirect climatic effects during migration and on breeding grounds. We observed a greater proportion of uniparental nests in years with delayed laying dates, and a greater proportion of biparental nests in years with delayed snowmelt. These findings underscore the complex interplay between environmental factors and parental care strategies, offering insights into how these strategies may evolve under changing ecological conditions.</p>
Earlier spring snowmelt drives arrowleaf balsamroot phenology in montane meadows
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Warming acts through earlier snowmelt to advance but not extend alpine community flowering
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Data from: Snowmelt and laying date shape the parental care strategy of a high-Arctic shorebird
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Snowmelt lysimeter chemistry data from Soddie, Subnivean, Saddle and TB site, 1993 - 2013.
Snowmelt lysimeters were located at the soddie and subnivean sites on Niwot Ridge from 1993 to 2013, with the larger array located at the soddie site. The soddie lysimeter array is a collection of 105 snowmelt lysimeters that collect discharge measurements at the base of the snowpack. Each lysimeter is a polyethylene pan with 22 cm high vertical walls and 47 cm by 39.5 cm in horizontal extent resulting in a footprint area of ~1,850 cm2. The height of the lysimeter walls prevent both the loss of water out of the lysimeters and the influx of water from saturated zones along the snow-soil interface [Kattelmann, 2000]. Many lysimeters were functional at the soddie site during spring snowmelt (April through July) for water years 1993 through 2013. The 1998 water year had only 36 lysimeters installed. From 1999 through 2001, the array was organized as a two meter grid nested within a four meter grid. In the 2002 water year, 15 of lysimeters on the perimeter were relocated to increase the density of the inner grid. Lysimeters were moved from locations of low snow accumulation on the eastern edge and a region that collects debris during melt on the western edge. By 2010 the number of snowmelt lysimeters at the soddie was reduced to 6. Meltwater collected by the lysimeters flowed to an underground laboratory through flexible PVC piping into Rainwise Rainew Tipping Bucket Rain Gauges. 250ml sample bottles were placed above the tipping buckets at some locations to collect snowmelt from the lysimeters for chemical analysis. Samples were collected once to twice daily depending on snowmelt rates. Upon sample collection some of the snowmelt sample collected in the 250ml bottle was poured off into a 30ml glass bottle for oxygen isotope analysis. Snowmelt samples were collected and analyzed for cations and anions at the Mountain Research Station's Kiowa Laboratory previously, and INSTAAR's Arikaree lab currently.
Spatial variability of the snowmelt-albedo feedback in Antarctica
<p>This dataset accompanies the manuscript "Spatial variability of the snowmelt-albedo feedback in Antarctica" by C.L. Jakobs et al., submitted to JGR: Earth Surface.</p> <p>File naming convention:</p> <p>Modelversion_domain_parameter_timeresolution_beginyear_endyear_experiment.nc</p> <p>where "experiment" is optional.</p> <p> </p> <p>Parameter names (note that precipitation and snowmelt are provided in units <em>per second</em>):</p> <ul> <li>LWin: downward longwave radiation [W/m<sup>2</sup>]</li> <li>LWout: upward longwave radiation [W/m<sup>2</sup>]</li> <li>precip: precipitation (liquid and solid) [kg/m<sup>2</sup>/s]</li> <li>QG: ground heat flux [W/m<sup>2</sup>]</li> <li>QL: latent heat flux [W/m<sup>2</sup>]</li> <li>QS: sensible heat flux [W/m<sup>2</sup>]</li> <li>snowmelt: surface melt rate [kg/m<sup>2</sup>/s]</li> <li>SWin: incoming shortwave radiation [W/m<sup>2</sup>]</li> <li>SWout: reflected shortwave radiation [W/m<sup>2</sup>]</li> <li>T2m: 2-meter temperature [K]</li> </ul> <p> </p> <p><strong>Note!</strong></p> <p>This dataset might not represent the latest version of these data. Please contact the Institute for Marine and Atmospheric Research Utrecht (imau@science.uu.nl) for information about the latest available version of these data.</p>
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