Snowpack Variability Research Articles

Multi-locus genomic signatures of local adaptation to snow across the landscape in California populations of a willow leaf beetle.

Organisms living in mountains contend with extreme climatic conditions, including short growing seasons and long winters with extensive snow cover. Anthropogenic climate change is driving unprecedented, rapid warming of montane regions across the globe, resulting in reduced winter snowpack. Loss of snow as a thermal buffer may have serious consequences for animals overwintering in soil, yet little is known about how variability in snowpack acts as a selective agent in montane ecosystems. Here, we examine genomic variation in California populations of the leaf beetle Chrysomela aeneicollis, an emerging natural model system for understanding how organisms respond to climate change. We used a genotype-environment association approach to identify genomic signatures of local adaptation to microclimate in populations from three montane regions with variable snowpack and a coastal region with no snow. We found that both winter-associated environmental variation and geographical distance contribute to overall genomic variation across the landscape. We identified non-synonymous variation in novel candidate loci associated with cytoskeletal function, ion transport and membrane stability, cellular processes associated with cold tolerance in other insects. These findings provide intriguing evidence that variation in snowpack imposes selective gradients in montane ecosystems.

Improvements and Evaluation of the Agro-Hydrologic VegET Model for Large-Area Water Budget Analysis and Drought Monitoring

We enhanced the agro-hydrologic VegET model to include snow accumulation and melt processes and the separation of runoff into surface runoff and deep drainage. Driven by global weather datasets and parameterized by land surface phenology (LSP), the enhanced VegET model was implemented in the cloud to simulate daily soil moisture (SM), actual evapotranspiration (ETa), and runoff (R) for the conterminous United States (CONUS) and the Greater Horn of Africa (GHA). Evaluation of the VegET model with independent data showed satisfactory performance, capturing the temporal variability of SM (Pearson correlation r: 0.22–0.97), snowpack (r: 0.86–0.88), ETa (r: 0.41–0.97), and spatial variability of R (r: 0.81–0.90). Absolute magnitudes showed some biases, indicating the need of calibrating the model for water budget analysis. The seasonal Landscape Water Requirement Satisfaction Index (L-WRSI) for CONUS and GHA showed realistic depictions of drought hazard extent and severity, indicating the usefulness of the L-WRSI for the convergence of an evidence toolkit used by the Famine Early Warning System Network to monitor potential food insecurity conditions in different parts of the world. Using projected weather datasets and landcover-based LSP, the VegET model can be used not only for global monitoring of drought conditions, but also for evaluating scenarios on the effect of a changing climate and land cover on agriculture and water resources.

The Severity of the 2014–2015 Snow Drought in the Oregon Cascades in a Multicentury Context

AbstractThe western United States (US) is a hotspot for snow drought. The Oregon Cascade Range is highly sensitive to warming and as a result has experienced the largest mountain snowpack losses in the western US since the mid‐20th century, including a record‐breaking snow drought in 2014–2015 that culminated in a state of emergency. While Oregon Cascade snowpacks serve as the state's primary water supply, short instrumental records limit water managers' ability to fully constrain long‐term natural snowpack variability prior to the influence of ongoing and projected anthropogenic climate change. Here, we use annually‐resolved tree‐ring records to develop the first multi‐century reconstruction of Oregon Cascade April 1st Snow Water Equivalent (SWE). The model explains 58% of observed snowpack variability and extends back to 1688 AD, nearly quintupling the length of the existing snowpack record. Our reconstruction suggests that only one other multiyear event in the last three centuries was as severe as the 2014–2015 snow drought. The 2015 event alone was more severe than nearly any other year in over three centuries. Extreme low‐to‐high snowpack “whiplash” transitions are a consistent feature throughout the reconstructed record. Multi‐decadal intervals of persistent below‐the‐mean peak SWE are prominent features of pre‐instrumental snowpack variability, but are generally absent from the instrumental period and likely not fully accounted for in modern water management. In the face of projected snow drought intensification and warming, our findings motivate adaptive management strategies that address declining snowpack and increasingly variable precipitation regimes.

Restoring a keystone tree species for the future: American chestnut assisted migration plantings in an adaptive silviculture experiment

Species ranges are forecast to change in response to warming temperatures and altered precipitation patterns, yet tree migration rates fail to track the pace of climate change. In anticipation of these changes, various forest adaptation management strategies have been broadly proposed, including intentionally modifying species composition via assisted migration of future climate adapted species. Despite the potential utility of these adaptation measures, practical evaluations are limited, particularly those applied to meet other ecological objectives such as supporting vulnerable, underrepresented, or degraded populations of foundational species. In this study, we examined the 4-year seedling survival and morpho-physiological response of American chestnut (Castanea dentata (Marsh.) Borkh.; n = 959 seeds sown), a functionally extirpated species. American chestnuts were grown within sixteen replicated 0.1 and 0.4 ha harvest gaps as part of a 160 ha co-developed (manager-scientist designed), operational-scale silvicultural trial (conditions of sufficient scope to be representative of commercial forest operations) in a northern-hardwood forest in the northeastern US. Chestnut restoration and migration potential (e.g., survival, absolute and relative growth rates, photosynthetic capacity) was assessed against the biophysical controls exerted on seedlings (e.g., understory competition, injury associated with browse and extreme cold winter temperatures) and in comparison to seedlings planted from eight other tree species (n = 480 planted per species) identified for assisted migration. Our results show the performance of American chestnut seedlings is controlled by the strength of local competition (odds of survival increased 2.6 times between four understory competition classes, p < 0.001) and cumulative winter shoot injury (relative growth in aboveground biomass adjusted for injury R2 = -0.85, p < 0.001) associated with cold intolerance likely linked to northward movement of chestnut seedlings transferred outside of their parental range. Still, the combined survival-growth response for American chestnuts ranked among the highest (2nd out of 6 possible rankings) relative to the other species tested, and even outperformed other comparable assisted migration species introduced from outside of their parental range. The implications of these findings highlight the potential for American chestnut plantings to be incorporated within both restoration and broader climate adaptation frameworks. Despite these promising outcomes, important biophysical (e.g., vegetative competition, harvest treatment, and variability in insulative snowpack) and climatic barriers for the reestablishment of this species remain. Given the paucity of reproductively viable American chestnuts or disease resistant breeding programs along northern range limits, this may generate a reliance on plant material obtained from outside of historically recognized safe transfer distances; however, increasingly shifting climate and species ranges may lead to better climate matches in the long term. Nevertheless, the broader applicability of this work illustrates the potential for cultural and ecological keystone species restoration efforts to be incorporated within climate adaptation frameworks to assist in the establishment of compositionally diverse and future climate-adapted forests.

Variability in observed stable water isotopes in snowpack across a mountainous watershed in Colorado

AbstractIsotopic information from 81 snowpits was collected over a 5‐year period in a large, Colorado watershed. Data spans gradients in elevation, aspect, vegetation, and seasonal climate. They are combined with overlapping campaigns for water isotopes in precipitation and snowmelt, and a land‐surface model for detailed estimates of snowfall and climate at sample locations. Snowfall isotopic inputs, describe the majority of δ18O snowpack variability. Aspect is a secondary control, with slightly more enriched conditions on east and north facing slopes. This is attributed to preservation of seasonally enriched snowfall and vapour loss in the early winter. Sublimation, expressed by decreases in snowpack d‐excess in comparison to snowfall contributions, increases at low elevation and when seasonal temperature and solar radiation are high. At peak snow accumulation, post‐depositional fractionation appears to occur in the top 25 ± 14% of the snowpack due to melt‐freeze redistribution of lighter isotopes deeper into the snowpack and vapour loss to the atmosphere during intermittent periods of low relative humidity and high windspeed. Relative depth of fractionation increases when winter daytime temperatures are high and winter precipitation is low. Once isothermal, snowpack isotopic homogenization and enrichment was observed with initial snowmelt isotopically depleted in comparison to snowpack and enriching over time. The rate of δ18O increase (d‐excess decrease) in snowmelt was 0.02‰ per day per 100‐m elevation loss. Isotopic data suggests elevation dictates snowpack and snowmelt evolution by controlling early snow persistence (or absence), isotopic lapse rates in precipitation and the ratio of energy to snow availability. Hydrologic tracer studies using stable water isotopes in basins of large topographic relief will require adjustment for these elevational controls to properly constrain stream water sourcing from snowmelt.

Summer ecosystem structure in mountain lakes linked to interannual variability of lake ice, snowpack, and landscape attributes

AbstractMountain lakes experience interannual variability in spring snowpack and ice cover that can lead to differences in physical, chemical, and biological properties in the succeeding summer. Lake studies that capture extreme years of snow and ice would be useful to understand and anticipate effects of climate change, but such data are rare for remote mountain lakes. Monitoring of lakes in Olympic, North Cascades, and Mount Rainier National Parks from 2007 to 2018 allowed us to examine limnological differences along interannual and elevation‐driven climate gradients that included unusually high (2011–2012) and 100‐yr record low (2015) snowpack years. Years with lower spring snowpack had earlier ice‐out. Across lakes, our analysis suggested an average of 0.075°C lake warming per day of lost ice duration (0.525°C per week), giving rise to other ecosystem changes linked to temperature such as lower dissolved oxygen, higher total dissolved N, higher chlorophyll, and higher abundance of cladoceran zooplankton. Conversely, in years with higher snowpack and a shorter ice‐free season, lakes were colder and clearer (1 m deeper Secchi depth for every 1 m May snow water equivalent), with more dilute ions as well as lower algal biomass and zooplankton abundance. These results add to evidence that changes in snowpack or ice‐out dates alter mountain lake ecology through multiple processes associated with hydrology, terrestrial‐aquatic connection, water temperature, productivity, ion composition, and plankton communities.

Towards hybrid modeling of the global hydrological cycle

Abstract. State-of-the-art global hydrological models (GHMs) exhibit large uncertainties in hydrological simulations due to the complexity, diversity, and heterogeneity of the land surface and subsurface processes, as well as the scale dependency of these processes and associated parameters. Recent progress in machine learning, fueled by relevant Earth observation data streams, may help overcome these challenges. But machine learning methods are not bound by physical laws, and their interpretability is limited by design. In this study, we exemplify a hybrid approach to global hydrological modeling that exploits the data adaptivity of neural networks for representing uncertain processes within a model structure based on physical principles (e.g., mass conservation) that form the basis of GHMs. This combination of machine learning and physical knowledge can potentially lead to data-driven, yet physically consistent and partially interpretable hybrid models. The hybrid hydrological model (H2M), extended from Kraft et al. (2020), simulates the dynamics of snow, soil moisture, and groundwater storage globally at 1∘ spatial resolution and daily time step. Water fluxes are simulated by an embedded recurrent neural network. We trained the model simultaneously against observational products of terrestrial water storage variations (TWS), grid cell runoff (Q), evapotranspiration (ET), and snow water equivalent (SWE) with a multi-task learning approach. We find that the H2M is capable of reproducing key patterns of global water cycle components, with model performances being at least on par with four state-of-the-art GHMs which provide a necessary benchmark for H2M. The neural-network-learned hydrological responses of evapotranspiration and grid cell runoff to antecedent soil moisture states are qualitatively consistent with our understanding and theory. The simulated contributions of groundwater, soil moisture, and snowpack variability to TWS variations are plausible and within the ranges of traditional GHMs. H2M identifies a somewhat stronger role of soil moisture for TWS variations in transitional and tropical regions compared to GHMs. With the findings and analysis, we conclude that H2M provides a new data-driven perspective on modeling the global hydrological cycle and physical responses with machine-learned parameters that is consistent with and complementary to existing global modeling frameworks. The hybrid modeling approaches have a large potential to better leverage ever-increasing Earth observation data streams to advance our understandings of the Earth system and capabilities to monitor and model it.

Aquifer recharge in the Piedmont Alpine zone: historical trends and future scenarios

Abstract. The spatial and temporal variability of air temperature, precipitation, actual evapotranspiration (AET) and their related water balance components, as well as their responses to anthropogenic climate change, provide fundamental information for an effective management of water resources and for a proactive involvement of users and stakeholders, in order to develop and apply adaptation and mitigation strategies at the local level. In this study, using an interdisciplinary research approach tailored to water management needs, we evaluate the past, present and future quantity of water potentially available for drinking supply in the water catchments feeding the about 2.3 million inhabitants of the Turin metropolitan area (the former Province of Turin, north-western Italy), considering climatologies at the quarterly and yearly timescales. Observed daily maximum surface air temperature and precipitation data from 1959 to 2017 were analysed to assess historical trends, their significance and the possible cross-correlations between the water balance components. Regional climate model (RCM) simulations from a small ensemble were analysed to provide mid-century projections of the difference between precipitation and AET for the area of interest in the future CMIP5 scenarios RCP4.5 (stabilization) and RCP8.5 (business as usual). Temporal and spatial variations in recharge were approximated with variations of drainage. The impact of irrigation, and of snowpack variability, on the latter was also assessed. The other terms of water balance were disregarded because they are affected by higher uncertainty. The analysis over the historical period indicated that the driest area of the study region displayed significant negative annual (and spring) trends of both precipitation and drainage. Results from field experiments were used to model irrigation, and we found that relatively wetter watersheds in the northern and in the southern parts behave differently, with a significant increase of AET due to irrigation. The analysis of future projections suggested almost stationary conditions for annual data. Regarding quarterly data, a slight decrease in summer drainage was found in three out of five models in both emission scenarios. The RCM ensemble exhibits a large spread in the representation of the future drainage trends. The large interannual variability of precipitation was also quantified and identified as a relevant risk factor for water management, expected to play a major role also in future decades.

Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas &gt; 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.

Pattern-based downscaling of snowpack variability in the western United States

The decline in snowpack across the western United States is one of the most pressing threats posed by climate change to regional economies and livelihoods. Earth system models are important tools for exploring past and future snowpack variability, yet their coarse spatial resolutions distort local topography and bias spatial patterns of accumulation and ablation. Here, we explore pattern-based statistical downscaling for spatially-continuous interannual snowpack estimates. We find that a few leading patterns capture the majority of snowpack variability across the western US in observations, reanalyses, and free-running simulations. Pattern-based downscaling methods yield accurate, high resolution maps that correct mean and variance biases in domain-wide simulated snowpack. Methods that use large-scale patterns as both predictors and predictands perform better than those that do not and all are superior to an interpolation-based “delta change” approach. These findings suggest that pattern-based methods are appropriate for downscaling interannual snowpack variability and that using physically meaningful large-scale patterns is more important than the details of any particular downscaling method.

Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset

Abstract. Seasonal snowpack is an essential component in the hydrological cycle and plays a significant role in supplying water resources to downstream users. Yet the snow water equivalent (SWE) in seasonal snowpacks, and its space–time variation, remains highly uncertain, especially over mountainous areas with complex terrain and sparse observations, such as in High Mountain Asia (HMA). In this work, we assessed the spatiotemporal distribution of seasonal SWE, obtained from a new 18-year HMA Snow Reanalysis (HMASR) dataset, as part of the recent NASA High Mountain Asia Team (HiMAT) effort. A Bayesian snow reanalysis scheme previously developed to assimilate satellite-derived fractional snow-covered area (fSCA) products from Landsat and MODIS platforms has been applied to develop the HMASR dataset (at a spatial resolution of 16 arcsec (∼500 m) and daily temporal resolution) over the joint Landsat–MODIS period covering water years (WYs) 2000–2017. Based on the results, the HMA-wide total SWE volume is found to be around 163 km3 on average and ranges from 114 km3 (WY2001) to 227 km3 (WY2005) when assessed over 18 WYs. The most abundant snowpacks are found in the northwestern basins (e.g., Indus, Syr Darya and Amu Darya) that are mainly affected by the westerlies, accounting for around 66 % of total seasonal SWE volume. Seasonal snowpack in HMA is depicted by snow accumulating through October to March and April, typically peaking around April and depleting in July–October, with variations across basins and WYs. When examining the elevational distribution over the HMA domain, seasonal SWE volume peaks at mid-elevations (around 3500 m), with over 50 % of the volume stored above 3500 m. Above-average amounts of precipitation causes significant overall increase in SWE volumes across all elevations, while an increase in air temperature (∼1.5 K) from cooler to normal conditions leads to an redistribution in snow storage from lower elevations to mid-elevations. This work brings new insight into understanding the climatology and variability of seasonal snowpack over HMA, with the regional snow reanalysis constrained by remote-sensing data, providing a new reference dataset for future studies of seasonal snow and how it contributes to the water cycle and climate over the HMA region.

Comparing tree-ring based reconstructions of snowpack variability at different scales for the Navajo Nation

Snowpack in the western U.S. is on the decline, largely attributed to increasing temperatures in the region. This is a critical issue for many Native American communities who disproportionately rely on local snow-fed water supplies. In light of a combined ongoing drought and limited climate information for the Navajo Nation, Navajo water managers face decision-making challenges complicated by past and future climate uncertainty. Developed in partnership with the Navajo Nation Water Management Branch, this study documents two snowpack reconstruction options to address Navajo concerns about the amount and variability of snowpack in the Chuska Mountains. We used two separate snowpack datasets with tree rings collected in northern Arizona to develop and evaluate reconstructions of Chuska snowpack and their potential relevance and usefulness to Navajo water managers’ decision-making. We found that both reconstructions skillfully estimated snowpack, though there were differences that may have meaningful implications for water managers. Major snow droughts occurred roughly once per century over the last 300 years, with droughts in 1728–1744, 1818–1834, 1950–1977, and 1999–2006. Extremely dry individual years in each reconstruction punctuate multi-year drought periods in a way that has not been recognized from instrumental data alone and that can have a large influence on the overall intensity of a given drought. The reconstruction that is most representative of Chuska snowpack has less explanatory power than the regionally representative reconstruction, but the Chuska reconstruction effectively captures snowpack extremes and snow drought timing unique to the Chuska Mountains, and may hold greater relevance to Navajo water management.

Snowpack signals in North American tree rings

Climate change has contributed to recent declines in mountain snowpack and earlier runoff, which in turn have intensified hydrological droughts in western North America. Climate model projections suggest that continued and severe snowpack reductions are expected over the 21st century, with profound consequences for ecosystems and human welfare. Yet the current understanding of trends and variability in mountain snowpack is limited by the relatively short and strongly temperature forced observational record. Motivated by the urgent need to better understand snowpack dynamics in a long-term, spatially coherent framework, here we examine snow-growth relationships in western North American tree-ring chronologies. We present an extensive network of snow-sensitive proxy data to support high space/time resolution paleosnow reconstruction, quantify and interpret the type and spatial density of snow related signals in tree-ring records, and examine the potential for regional bias in the tree-ring based reconstruction of different snow drought types (dry versus warm). Our results indicate three distinct snow-growth relationships in tree-ring chronologies: moisture-limited snow proxies that include a spring temperature signal, moisture-limited snow proxies lacking a spring temperature signal, and energy-limited snow proxies. Each proxy type is based on distinct physiological tree-growth mechanisms related to topographic and climatic site conditions, and provides unique information on mountain snowpack dynamics that can be capitalized upon within a statistical reconstruction framework. This work provides a platform and foundational background required for the accelerated production of high-quality annually resolved snowpack reconstructions from regional to high (12 km) spatial scales in western North America and, by extension, will support an improved understanding of the vulnerability of snowmelt-derived water resources to natural variability and future climate warming.

Multi-scale snowdrift-permitting modelling of mountain snowpack

Abstract. The interaction of mountain terrain with meteorological processes causes substantial temporal and spatial variability in snow accumulation and ablation. Processes impacted by complex terrain include large-scale orographic enhancement of snowfall, small-scale processes such as gravitational and wind-induced transport of snow, and variability in the radiative balance such as through terrain shadowing. In this study, a multi-scale modelling approach is proposed to simulate the temporal and spatial evolution of high-mountain snowpacks. The multi-scale approach combines atmospheric data from a numerical weather prediction system at the kilometre scale with process-based downscaling techniques to drive the Canadian Hydrological Model (CHM) at spatial resolutions allowing for explicit snow redistribution modelling. CHM permits a variable spatial resolution by using the efficient terrain representation by unstructured triangular meshes. The model simulates processes such as radiation shadowing and irradiance to slopes, blowing-snow transport (saltation and suspension) and sublimation, avalanching, forest canopy interception and sublimation, and snowpack melt. Short-term, kilometre-scale atmospheric forecasts from Environment and Climate Change Canada's Global Environmental Multiscale Model through its High Resolution Deterministic Prediction System (HRDPS) drive CHM and are downscaled to the unstructured mesh scale. In particular, a new wind-downscaling strategy uses pre-computed wind fields from a mass-conserving wind model at 50 m resolution to perturb the mesoscale HRDPS wind and to account for the influence of topographic features on wind direction and speed. HRDPS-CHM was applied to simulate snow conditions down to 50 m resolution during winter 2017/2018 in a domain around the Kananaskis Valley (∼1000 km2) in the Canadian Rockies. Simulations were evaluated using high-resolution airborne light detection and ranging (lidar) snow depth data and snow persistence indexes derived from remotely sensed imagery. Results included model falsifications and showed that both wind-induced and gravitational snow redistribution need to be simulated to capture the snowpack variability and the evolution of snow depth and persistence with elevation across the region. Accumulation of windblown snow on leeward slopes and associated snow cover persistence were underestimated in a CHM simulation driven by wind fields that did not capture lee-side flow recirculation and associated wind speed decreases. A terrain-based metric helped to identify these lee-side areas and improved the wind field and the associated snow redistribution. An overestimation of snow redistribution from windward to leeward slopes and subsequent avalanching was still found. The results of this study highlight the need for further improvements of snowdrift-permitting models for large-scale applications, in particular the representation of subgrid topographic effects on snow transport.

Estimating the Effects of Forest Structure Changes From Wildfire on Snow Water Resources Under Varying Meteorological Conditions

AbstractModeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine‐scale changes in structure following a disturbance. We applied a 1 m2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire‐mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed significant decreases, resulting in higher snowpack variability. The spatial differences in snow were correlated with the change in several direction‐based forest structure metrics (aspect‐based canopy edginess and gap area). Locations with decreases in snow following the fire were on southern aspects that transitioned to south facing canopy edges, canopy gaps that increased in size to the south, or where large trees were removed. Locations with largest increases in snow occurred where all canopy was removed. Changes in canopy density metrics, typically used in snow models to represent the forest, did not fully explain the effects of fire on snow distribution. This explains why many models are not able to represent greater postfire variability in snow distribution and tend to predict only increases in snowpack following a canopy disturbance event despite observational studies showing both increases and decreases.

Three hundred years of snowpack variability in southwestern British Columbia reconstructed from tree‐rings

AbstractRecent snow droughts in southwestern British Columbia (BC), Canada, have reduced seasonal streamflow during the typically dry late‐spring and summer months, leading to socio‐economic and ecological impacts that draw attention to the impending consequences of climate change. Knowledge of annual winter snowfall variability within this region is largely derived from a sparse network of short‐duration (≤50 years) snow survey stations. In this paper, we develop an annual April 1 snow water equivalent (SWE) reconstruction from living tree‐ring chronologies that offer a perspective on long‐term natural snowpack variability. The dendrohydrological model estimates the first principal component April 1 SWE for the southwestern regions of BC to 1711. Spectral analysis identified dominant multidecadal April 1 SWE variability over the pre‐instrumental period. The reconstruction successfully captures known instrumental period influences of La Niña oscillations on reconstructed SWE, suggesting that our tree‐ring based the reconstruction has the potential to provide insights on pre‐instrumental ocean–atmosphere links with southwestern BC snowpack dynamics. Runs analysis suggests pre‐instrumental snow droughts have been more than twice as long in duration and severity than during the observed period which indicates the instrumental record may not capture the full range of April 1 SWE variability. The reconstruction provides the first high‐resolution description of SWE over the past 300 years in southwestern BC and is of immediate use to regional water resource managers.

2,200-Year tree-ring and lake-sediment based snowpack reconstruction for the northern Rocky Mountains highlights the historic magnitude of recent snow drought

In recent decades, Rocky Mountain accumulated snowpack levels have experienced rapid declines, yet long-term records of snowpack prior to the installation of snowpack observation stations in the early and mid 20th century are limited. To date, a small number of tree-ring based reconstructions of April 1 Snow Water Equivalent (SWE) in the northern Rocky Mountains have extended modern records of snowpack variability to ∼1200 C.E. Carbonate isotope lake sediment records, provide an opportunity to further extend tree-ring based reconstructions through the Holocene, providing a millennial-scale temporal record that allows for an evaluation of multi-scale drivers of snowpack variability, from internal climate dynamics to orbital-scale forcings. Here we present a ∼2200 year preliminary reconstruction of northern Rockies snowpack based on δ18O measurements of sediment carbonates collected from Foy Lake, Montana. We explore the statistical calibration of lake sediment δ18O to an annually resolved snowpack reconstruction from tree rings, and develop an approach to assess and quantify potential sources of error in this reconstruction approach. The sediment-based snowpack reconstruction shows strong low-frequency variability in snowpack over the last two millennia with few snow droughts approaching the magnitude of recent snowpack declines. Given the growing availability of high-resolution, carbonate-rich lake sediment records, such reconstructions could help improve our understanding of how snowpack conditions varied under previous climatic events (mid-Holocene climate optimum ca. 9−6 ka), providing critical insights for anticipating future snowpack conditions.

Parsing Synthetic Aperture Radar Measurements of Snow in Complex Terrain: Scaling Behaviour and Sensitivity to Snow Wetness and Landcover

This study investigates the spatial signatures of seasonal snow in Synthetic Aperture Radar (SAR) observations at different spatial scales and for different physiographic regions. Sentinel-1 C-band (SAR) backscattering coefficients (BSC) were analyzed in the Swiss Alps (SA), in high elevation forest and grasslands in Grand Mesa (GM), Colorado, and in North Dakota (ND) croplands. GM BSC exhibit 10 dB sensitivity to wetness at small scales (~100 m) over homogeneous grassland. Sensitivity decreases to 5 dB in the presence of trees, and it is demonstrated that VH BSC sensitivity enables wet snow mapping below the tree-line. Area-variance scaling relationships show minima at ~100 m and 150–250 m, respectively, in barren and grasslands in SA and GM, increasing up to 1 km and longer in GM forests and ND agricultural fields. The spatial organization of BSC (as described by 1D-directional BSC wavelength spectra) exhibits multi-scaling behavior in the 100–1000 m range with a break at (180–360 m) that is also present in UAVSAR L-band measurements in GM. Spectral slopes in GM forested areas steepen during accumulation and flatten in the melting season with mirror behavior for grasslands reflecting changes in scattering mechanisms with snow depth and wetness, and vegetation mass and structure. Overall, this study reveals persistent patterns of SAR scattering variability spatially organized by land-cover, topography and regional winds with large inter-annual variability tied to precipitation. This dynamic scaling behavior emerges as an integral physical expression of snowpack variability that can be used to model sub-km scales and for downscaling applications.

Snowmelt Detection with Calibrated, Enhanced‐Resolution Brightness Temperatures (CETB) in Colorado Watersheds

AbstractUnderstanding the timing of snowmelt is critical for water resources management in snow‐dominated watersheds. Passive microwave remote sensing can be used to estimate snowmelt events through brightness temperature satellite observations. Previous studies were limited to lower resolution (~25 km or coarser) datasets, making it difficult to quantify snowpack variability in heterogeneous, high‐relief areas. This study investigates the use of recently available Calibrated, Enhanced‐Resolution Passive Microwave Daily EASE‐Grid 2.0 Brightness Temperature ESDR (CETB) to estimate snowmelt timing at much higher spatial resolution (~3‐6 km) than has been previously available. This study investigates the effectiveness of the CETB product for snowmelt detection in several locations in Colorado (North Park, Rabbit Ears, Fraser) that were the sites of previous ground/airborne surveys during the NASA Cold Land Processes Field Experiment (CLPX 2002‐2003), along with data for the Senator Beck Basin from the Center for Snow and Avalanche Studies (CSAS). We compare melt variability with nearby air temperature and stream discharge to show that the new CETB product allows detection of hydrologic processes in mountainous watersheds. We show that the higher resolution CETB product can detect snowmelt in heterogeneous terrain more accurately than the coarser resolution product in terms of the number of winter melt events and seasonal melt onset date. This work lays the foundation for the utilization of higher resolution reprocessed CETB data for snowpack evolution more broadly in a range of environments.

Empirical Application of Rubinstein Bargaining Model in Western U.S. Water Transactions

Water transactions are proposed as a policy tool for increasing regional drought resilience and improving allocative efficiency. However, water trading deviates considerably from classical competitive markets and alternative theoretical approaches are useful for understanding water market structure and performance. In this paper, we model a bilateral water transaction using a non-cooperative bargaining model. Then we empirically test predictions regarding time preference, outside options, and water supply risk. The econometric analysis encompasses two transaction regions: Colorado’s Front Range and New Mexico’s Middle Rio Grande Basin. We find evidence suggesting that larger cities pay lower prices in water transactions, faster growing and higher income cities pay higher prices and that prices are higher when snowpack variability is higher. Findings suggest that a stronger time preference results in a less favorable price for urban buyers and that buyer risk-aversion is a greater disadvantage in bargaining in locations with greater water supply variability.