Seasonal mountain snowpack holds hydrologic and ecologic significance worldwide. However, observation networks in complex terrain are typically sparse and provide minimal information about prevailing conditions. Snow patterns and processes in this data sparse environment can be characterized with numerical models and satellite-based remote sensing, and thus it is essential to understand their reliability. This research quantifies model and remote sensing uncertainties in snow accumulation, snowmelt, and snow disappearance as revealed through comparisons with unique ground-based measurements. The relationship between snow accumulation uncertainty and model configuration is assessed through a controlled experiment at 154 snow pillow sites in the western United States. To simulate snow water equivalent (SWE), the National Weather Service SNOW-17 model is tested as (1) a traditional "forward" model based primarily on precipitation, (2) a reconstruction model based on total snowmelt before the snow disappearance date, and (3) a combination of (1) and (2). For peak SWE estimation, the reliability of the parent models was indistinguishable, while the combined model was most reliable. A sensitivity analysis demonstrated that the parent models had opposite sensitivities to temperature that tended to cancel in the combined model. Uncertainty in model forcing and parameters significantly controlled model accuracy. Uncertainty in remotely sensed snow cover and snow disappearance in forested areas is enhanced by canopy obstruction but has been ill-quantified due to the lack of sub-canopy observations. To better quantify this uncertainty, dense networks of near-surface temperature sensors were installed at four study areas ( less than or equal to 1 km2) with varying forest cover in the Sierra Nevada, California. Snow presence at each sensor was detected during periods when temperature was damped, which resulted from snow cover insulation. This methodology was verified using time-lapse analysis and high resolution (15m) remote sensing, and then used to test daily 500 m canopy-adjusted MODIS snow cover data. Relative to the ground sensors, MODIS underestimated snow cover by 10-20% in meadows and 10-40% in forests, and showed snow disappearing 12 to 30 days too early in the forested sites. These errors were not detected with operational snow sensors, which have seen frequent use in MODIS validation studies. The link between model forcing and snow model uncertainty is assessed in two studies using measurements at energy balance stations in different snow climates. First, representation of snow surface temperature (T[subscript s]) with temperature and humidity is examined because Ts tracks variations in the snowmelt energy balance. At all sites analyzed, the dew point temperature (T[subscript d]) represented T[subscript s] with lower bias than the dry and wet-bulb temperatures. The potential usefulness of this approximation was demonstrated in a case study where detection of model bias was achieved by comparing daily Tsubscript dand modeled T [subscript s]. Second, the impact of forcing data availability and empirical data estimation is addressed to understand which types of data most impact physically-based snow modeling and need improved representation. An experiment is conducted at four well-instrumented sites with a series of hypothetical weather stations to determine which measurements (beyond temperature and precipitation) most impact snow model behavior. Radiative forcings had the largest impact on model behavior, but these are typically the least often measured.

In-situ measurements and numerical models were used to quantify and improve understanding of the processes governing snowpack dynamics in mountainous terrain. Three studies were conducted in Sequoia National Park in the southern Sierra Nevada, California. The first two studies evaluated and simulated the variability of observed melt rates at the point-scale in a mixed conifer forest. The third study evaluated the accuracy of a distributed snow model run over 1800 km2; a 3600 m elevation gradient that includes ecosystems ranging from semi-arid grasslands to massive sequoia stands to alpine tundra. In the first study, a network of 24 automated snow depth sensors and repeated monthly snow density surveys in a conifer forest were used to measure snow ablation rates for three years. A model was developed to estimate the direct beam solar radiation beneath the forest canopy from upward-looking hemispherical photos and above-canopy measurements. Sub-canopy solar beam irradiance and the bulk canopy metric sky view factor explained the most (58% and 87%, respectively) of the observed ablation rates in years with the least and most cloud cover, respectively; no single metric could explain> 41% of the melt rate variability for all years. In the second study, the time-varying photo-derived direct beam canopy transmissivity and the sky view factor canopy parameter were incorporated into a one-dimensional physically based snowmelt model. Compared to a bulk parameterization of canopy radiative transfer, when the model was modified to accept the time-varying canopy transmissivity, errors in the simulated snow disappearance date were reduced by one week and errors in the timing of soil water fluxes were reduced by 11 days, on average. In the third study, a distributed land surface model was used to simulate snow depth and SWE dynamics for three years. The model was evaluated against data from regional automated SWE measurement stations, repeated catchment-scale depth and density surveys, and airborne LiDAR snow depth data. In general, the model accurately simulated the seasonal maximum snow depth and SWE at lower and middle elevation forested areas. The model tended to overestimate SWE at upper elevations where no precipitation measurements were available. The SWE errors could largely be explained (R2/super” 0.80, p0.01) by distance of the SWE measurement from the nearest precipitation gauge. The results suggest that precipitation uncertainty is a critical limitation on snow model accuracy. Finally, an analysis of seasonal and inter-annual snowmelt patterns highlighted distinct melt differences between lower, middle, and upper elevations. Snowmelt was generally most frequent (70% - 95% of the snow-covered season) at the lower elevations where snow cover was ephemeral and seasonal mean melt rates computed on days when melt was simulated were generally low (3 mm daysuper-1). At upper elevations, melt occurred during less than 65% of the snow-covered period, it occurred later in the season, and mean melt rates were the highest of the region ( 6 mm daysuper-1/super). Middle elevations remained continuously snow covered throughout the winter and early spring, were prone to frequent but intermittent melt, and provided the most sustained period of seasonal mean snowmelt (~ 5 mm day

We live on a dynamic Earth shaped by both natural processes and the impacts of humans on their environment. It is in our collective interest to observe and understand our planet, and to predict future behavior to the extent possible, in order to effectively manage resources, successfully respond to threats from natural and human-induced environmental change, and capitalize on the opportunities â€" social, economic, security, and more â€" that such knowledge can bring. By continuously monitoring and exploring Earth, developing a deep understanding of its evolving behavior, and characterizing the processes that shape and reshape the environment in which we live, we not only advance knowledge and basic discovery about our planet, but we further develop the foundation upon which benefits to society are built. Thriving on Our Changing Planet presents prioritized science, applications, and observations, along with related strategic and programmatic guidance, to support the U.S. civil space Earth observation program over the coming decade.

Mountainous snow-covered landscapes in the Western United States behave like natural reservoirs, storing water during cold winter periods and sustaining snowmelt-driven streamflow vital for agriculture, municipalities, hydropower generation, and local ecosystems. In these regions, the timing and duration of spring snowmelt, and the resulting streamflow, are driven by both total snow volume and its spatial distribution across the landscape. Yet, our ability to model mountainous snow magnitude at hillslope spatial scales ( 100 m resolution) is hindered by uncertainties in snowfall and misrepresentations of snow processes like wind redistribution, preferential deposition, and avalanching. Fortunately, snow deposition in mountainous landscapes is driven by the interaction between prevailing snowstorm characteristics and static features like terrain and vegetation, often resulting in interannually repeatable snow distribution patterns. This dissertation investigated the value of repeatable snow patterns in the California Sierra Nevada using an unprecedented collection of ground-based, airborne, and satellite-based snow observations. We investigated how historic information about snow distribution and real-time snowpack observations could be combined to infer snow magnitude at hillslope spatial scales using both statistical and numerical modeling approaches. In Chapter 2, we began by calculating snow depth pattern repeatability at 25 m spatial resolution using a set of 47 airborne lidar snow depth observations in the Upper Tuolumne river watershed spanning water-years 2013 through 2019. Our results showed that normalized snow depth patterns between observation dates with similar relative amounts of snow accumulation and depletion, similar seasonal timing, and similar snow extents, were well-correlated in space (median r 0.84). This pattern repeatability could be used to infer watershed-scale snow depth distribution using the relationship between a snow depth pattern from a different year and a small subset of real-time observations covering

The contributions in this volume consider the uncertainties in the end-to-end prediction of hydrological variables, beginning with the atmospheric driving, and ending with the hydrological calculations for scientifically-sound decisions in sustainable water management.

The Sierra Nevada, California’s iconic mountain range, harbors thousands of remote high-elevations lakes from which water flows to sustain agriculture and cities. As climate and air quality in the region change, so do the watershed processes upon which these lakes depend. In order to understand the future of California’s ecology and natural resources, we need an integrated account of the environmental processes that underlie these aquatic systems. Synthesizing over three decades of research on the lakes and watersheds of the Sierra Nevada, this book develops an integrated account of the hydrological and biogeochemical systems that sustain them. With a focus on Emerald Lake in Sequoia National Park, the book marshals long-term limnological and ecological data to provide a detailed and synthetic account, while also highlighting the vulnerability of Sierra lakes to changes in climate and atmospheric deposition. In so doing, it lays the scientific foundations for predicting and understanding how the lakes and watersheds will respond.