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Climate warming in alpine regions is changing patterns of water storage, a primary control on alpine plant ecology, biogeochemistry, and water supplies to lower elevations. There is an outstanding need to determine how the interacting drivers of precipitation and the critical zone (CZ) dictate the spatial pattern and time evolution of soil water storage. In this study, we developed an analytical framework that combines intensive hydrologic measurements and extensive remotely-sensed observations with statistical modeling to identify areas with similar temporal trends in soil water storage within, and predict their relationships across, a 0.26 km 2 alpine catchment in the Colorado Rocky Mountains, U.S.A. Repeat measurements of soil moisture were used to drive an unsupervised clustering algorithm, which identified six unique groups of locations ranging from predominantly dry to persistently very wet within the catchment. We then explored relationships between these hydrologic groups and multiple CZ-related indices, including snow depth, plant productivity, macro- (10 2 ->10 3 m) and microtopography (<10 0 -10 2 m), and hydrological flow paths. Finally, we used a supervised machine learning random forest algorithm to map each of the six hydrologic groups across the catchment based on distributed CZ properties and evaluated their aggregate relationships at the catchment scale. Our analysis indicated that ~40–50% of the catchment is hydrologically connected to the stream channel, lending insight into the portions of the catchment that likely dominate stream water and solute fluxes. This research expands our understanding of patch-to-catchment-scale physical controls on hydrologic and biogeochemical processes, as well as their relationships across space and time, which will inform predictive models aimed at determining future changes to alpine ecosystems. 

Abstract. A critical component of hydrologic modeling in cold andtemperate regions is partitioning precipitation into snow and rain, yetlittle is known about how uncertainty in precipitation phase propagates intovariability in simulated snow accumulation and melt. Given the wide varietyof methods for distinguishing between snow and rain, it is imperative toevaluate the sensitivity of snowpack model output to precipitation phasedetermination methods, especially considering the potential of snow-to-rainshifts associated with climate warming to fundamentally change the hydrologyof snow-dominated areas. To address these needs we quantified thesensitivity of simulated snow accumulation and melt to rain–snowpartitioning methods at sites in the western United States using theSNOWPACK model without the canopy module activated. The methods in thisstudy included different permutations of air, wet bulb and dew pointtemperature thresholds, air temperature ranges, and binary logisticregression models. Compared to observations of snow depth and snow water equivalent (SWE), thebinary logistic regression models produced the lowest mean biases, whilehigh and low air temperature thresholds tended to overpredict andunderpredict snow accumulation, respectively. Relative differences betweenthe minimum and maximum annual snowfall fractions predicted by the differentmethods sometimes exceeded 100 % at elevations less than 2000 m in theOregon Cascades and California's Sierra Nevada. This led to rangesin annual peak SWE typically greater than 200 mm,exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelttiming predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater.Conversely, the three coldest sites in this work were relatively insensitiveto the choice of a precipitation phase method, with average ranges in annualsnowfall fraction, peak SWE, snowmelt timing, and snow cover duration of lessthan 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmeltrate were typically less than 4 mm d−1 and exhibited a smallrelationship to seasonal climate. Overall, sites with a greater proportionof precipitation falling at air temperatures between 0 and4 ∘C exhibited the greatest sensitivity to method selection,suggesting that the identification and use of an optimal precipitation phasemethod is most important at the warmer fringes of the seasonal snow zone. 

Understanding how land cover change will impact water resources in snow‐dominated regions is of critical importance as these locations produce disproportionate runoff relative to their land area. We coupled a land cover evolution model with a spatially explicit, physics‐based, watershed process model to simulate land cover change and its impact on the water balance in a 5.0 km²headwater catchment spanning the alpine–subalpine transition on the Colorado Front Range. We simulated two potential futures both with greater air temperature (+4°C/century) and more precipitation (+15%/century, MP) or less precipitation (−15%/century, LP) from 2000 to 2100. Forest cover in the catchment increased from 72% in 2000 to 84% and 83% in 2050 and to 95% and 92% in 2100 for MP and LP, respectively. Surprisingly, increases in forest cover led to mean increases in annual streamflow production of 12 mm (6%) and 2 mm (1%) for MP and LP in 2050 with an annual control streamflow of 208 mm. In 2100, mean streamflow production increased by 91 mm (44%) and 61 mm (29%) for MP and LP. This result counters previous work as runoff production increased with forested area due to decreases in snow wind‐scour and increases in drifting leeward of vegetation, highlighting the need to better understand the impacts of forest expansion on the spatial pattern of snow scour, deposition and catchment effective precipitation. Identifying the hydrologic response of mountainous areas to climate warming induced land cover change is critically important due to the potential water resources impacts on downstream regions.

 

Abstract. Cold content is a measure of a snowpack's energy deficit and is a linear function of snowpack mass and temperature. Positive energy fluxes into a snowpack must first satisfy the remaining energy deficit before snowmelt runoff begins, making cold content a key component of the snowpack energy budget. Nevertheless, uncertainty surrounds cold content development and its relationship to snowmelt, likely because of a lack of direct observations. This work clarifies the controls exerted by air temperature, precipitation, and negative energy fluxes on cold content development and quantifies the relationship between cold content and snowmelt timing and rate at daily to seasonal timescales. The analysis presented herein leverages a unique long-term snow pit record along with validated output from the SNOWPACK model forced with 23 water years (1991–2013) of quality controlled, infilled hourly meteorological data from an alpine and subalpine site in the Colorado Rocky Mountains. The results indicated that precipitation exerted the primary control on cold content development at our two sites with snowfall responsible for 84.4 and 73.0% of simulated daily gains in the alpine and subalpine, respectively. A negative surface energy balance – primarily driven by sublimation and longwave radiation emission from the snowpack – during days without snowfall provided a secondary pathway for cold content development, and was responsible for the remaining 15.6 and 27.0% of cold content additions. Non-zero cold content values were associated with reduced snowmelt rates and delayed snowmelt onset at daily to sub-seasonal timescales, while peak cold content magnitude had no significant relationship to seasonal snowmelt timing. These results suggest that the information provided by cold content observations and/or simulations is most relevant to snowmelt processes at shorter timescales, and may help water resource managers to better predict melt onset and rate.

 

Growing season length (GSL) is a key unifying concept in ecology that can be estimated from eddy covariance-derived estimates of net ecosystem production (NEP). Previous studies disagree on how increasing GSLs may affect NEP in evergreen coniferous forests, potentially due to the variety of methods used to quantify GSL from NEP. We calculated GSL and GSL-NEP regressions at eleven evergreen conifer sites across a broad climatic gradient in western North America using three common approaches: (1) variable length (3–7 days) regressions of day of year versus NEP, (2) a smoothed threshold approach, and (3) the carbon uptake period, followed by a new approach of a method-averaged ensemble. The GSL and the GSL-NEP relationship differed among methods, resulting in linear relationships with variable sign, slope, and statistical significance. For all combinations of sites and methods, the GSL explained between 6% and 82% of NEP withp-values ranging from 0.45 to < 0.01. These results demonstrate the variability among GSL methods and the importance of selecting an appropriate method to accurately project the ecosystem carbon cycling response to longer growing seasons in the future. To encourage this approach in future studies, we outline a series of best practices for GSL method selection depending on research goals and the annual NEP dynamics of the study site(s). These results contribute to understanding growing season dynamics at ecosystem and continental scales and underscore the potential for methodological variability to influence forecasts of the evergreen conifer forest response to climate variability.