[1] Climate change and climate variability, population growth, and land use change drive the need for new hydrologic knowledge and understanding. In the mountainous West and other similar areas worldwide, three pressing hydrologic needs stand out: first, to better understand the processes controlling the partitioning of energy and water fluxes within and out from these systems; second, to better understand feedbacks between hydrological fluxes and biogeochemical and ecological processes; and, third, to enhance our physical and empirical understanding with integrated measurement strategies and information systems. We envision an integrative approach to monitoring, modeling, and sensing the mountain environment that will improve understanding and prediction of hydrologic fluxes and processes. Here extensive monitoring of energy fluxes and hydrologic states are needed to supplement existing measurements, which are largely limited to streamflow and snow water equivalent. Ground-based observing systems must be explicitly designed for integration with remotely sensed data and for scaling up to basins and whole ranges.

Declining mountain snowpack and earlier snowmelt across the western United States has implications for downstream communities. We present a possible mechanism linking snowmelt rate and streamflow generation using a gridded implementation of the Budyko framework. We computed an ensemble of Budyko streamflow anomalies (BSAs) using Variable Infiltration Capacity model‐simulated evapotranspiration, potential evapotranspiration, and estimated precipitation at 1/16° resolution from 1950 to 2013. BSA was correlated with simulated baseflow efficiency (r2 = 0.64) and simulated snowmelt rate (r2 = 0.42). The strong correlation between snowmelt rate and baseflow efficiency (r2 = 0.73) links these relationships and supports a possible streamflow generation mechanism wherein greater snowmelt rates increase subsurface flow. Rapid snowmelt may thus bring the soil to field capacity, facilitating below‐root zone percolation, streamflow, and a positive BSA. Previous works have shown that future increases in regional air temperature may lead to earlier, slower snowmelt and hence decreased streamflow production via the mechanism proposed by this work.

Despite the importance of precipitation phase to global hydroclimate simulations, many land surface models use spatially uniform air temperature thresholds to partition rain and snow. Here we show, through the analysis of a 29-year observational dataset (n = 17.8 million), that the air temperature at which rain and snow fall in equal frequency varies significantly across the Northern Hemisphere, averaging 1.0 °C and ranging from –0.4 to 2.4 °C for 95% of the stations. Continental climates generally exhibit the warmest rain–snow thresholds and maritime the coolest. Simulations show precipitation phase methods incorporating humidity perform better than air temperature-only methods, particularly at relative humidity values below saturation and air temperatures between 0.6 and 3.4 °C. We also present the first continuous Northern Hemisphere map of rain–snow thresholds, underlining the spatial variability of precipitation phase partitioning. These results suggest precipitation phase could be better predicted using humidity and air temperature in large-scale land surface model runs.

[1] Narrow bands of strong atmospheric water vapor transport, referred to as "atmospheric rivers" (ARs), are responsible for the majority of wintertime extreme precipitation events with important contributions to the seasonal water balance. We investigate relationships between snow water equivalent (SWE), precipitation, and surface air temperature (SAT) across the Sierra Nevada for 45 wintertime AR events. Analysis of assimilated and in situ data for water years [2004][2005][2006][2007][2008][2009][2010] indicates that ARs on average generate ∼4 times daily SWE accumulation of non-AR storms. In addition, AR events contributed ∼30-40% of total seasonal SWE accumulation in most years, with the contribution dominated by just 1-2 extreme events in some cases. In situ and remotely sensed observations show that SWE changes associated with ARs are closely related to SAT. These results reveal the previously unexplored significance of ARs with regard to the snowpack and associated sensitivities of AR precipitation to SAT. Citation: Guan, B., N. P. Molotch, D. E. Waliser, E. J. Fetzer, and P. J. Neiman (2010), Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements, Geophys.

Rising temperatures and declining water availability have influenced the ecological function of mountain forests over the past half-century. For instance, warming in spring and summer and shifts towards earlier snowmelt are associated with an increase in wildfire activity and tree mortality in mountain forests in the western United States 1,2 . Temperature increases are expected to continue during the twenty-first century in mountain ecosystems across the globe 3,4 , with uncertain consequences. Here, we examine the influence of interannual variations in snowpack accumulation on forest greenness in the Sierra Nevada Mountains, California, between 1982 and 2006. Using observational records of snow accumulation and satellite data on vegetation greenness we show that vegetation greenness increases with snow accumulation. Indeed, we show that variations in maximum snow accumulation explain over 50% of the interannual variability in peak forest greenness across the Sierra Nevada region. The extent to which snow accumulation can explain variations in greenness varies with elevation, reaching a maximum in the water-limited midelevations, between 2,000 and 2,600 m. In situ measurements of carbon uptake and snow accumulation along an elevational transect in the region confirm the elevation dependence of this relationship. We suggest that mid-elevation mountain forest ecosystems could prove particularly sensitive to future increases in temperature and concurrent changes in snow accumulation and melt.Recent studies have documented a shift from energy to water limitation across forested ecosystems of western North America 5,6 . This transformation has reversed the response of these ecosystems to increases in temperature where before the early 1990s increases in air temperature increased terrestrial carbon uptake. Following the early 1990s an apparent shift from energy to water limitation resulted in reduced carbon uptake with increased temperature and coincident decreases in water availability 5 . In the western United States, increases in regional spring-summer temperatures and earlier snowmelt since the mid-1980s strongly correlate with increases in forest wildfire activity 1 and increases in tree mortality rates 2 . A consistent message has emerged from these studies: the combined effects of increases in temperature and decreases in water availability over the past half-century have impacted the ecological function of mountain forests.The sensitivity of mid-latitude mountain forests to water availability and the associated importance of snowmelt water has been well documented at the plot scale [7][8][9][10] . However, the effects of variations in snowpack accumulation on vegetation activity

Abstract:Regression tree models have been shown to provide the most accurate estimates of distributed snow water equivalent (SWE) when intensive field observations are available. This work presents a comparison of regression tree models using different source digital elevation models (DEMs) and different combinations of independent variables. Different residual interpolation techniques are also compared. The analysis was performed in the 19Ð1 km 2 Tokopah Basin, located in the southern Sierra Nevada of California. Snow depth, the dependent variable of the statistical models, was derived from three snow surveys (April, May and June 1997), with an average of 328 depth measurements per survey. Estimates of distributed SWE were derived from the product of the snow depth surfaces, the average snow density (54 measurements on average) and the fractional snow covered area (obtained from the Landsat Thematic Mapper and the Airborne Visible/Infrared Imaging Spectrometer). Independent variables derived from the standard US Geological Survey DEM yielded the lowest overall model deviance and lowest error in snow depth prediction. Simulations using the Shuttle Radar Topography Mission DEM and the National Elevation Dataset DEM were improved when northness was substituted for solar radiation in five of six cases. Co-kriging with maximum upwind slope and elevation proved to be the best method for distributing residuals for April and June, respectively. Inverse distance weighting was the best residual distribution method for May.

Snowmelt from forested, mountainous environments in the western United States is a critical regional water resource for streamflow and ecological productivity. These landscapes are undergoing rapid changes from the combined effects of forest fires, insect infestation and climate change. Numerous observational studies demonstrate that trees control snowpack accumulation and ablation over scales of tens of metres. Representing forest heterogeneity in models is important for understanding how changes in climate and vegetation cover affect the snowpack; yet, many snow models simplify a forest into two categories: canopy covered and non-canopy covered. We combine existing parameterizations of mass and energy fluxes within a new three-dimensional framework informed by Airborne Laser Swath Mapping (ALSM)-derived canopy maps and evaluated with ALSM-derived snow depth maps to explicitly simulate snow cover in relation to heterogeneous canopy. Model results capture much of the observed snow variability depicted in the 1-m ALSM-derived snow depth maps. Observations and modelled results identify open areas <15 m from tree canopies as having more snow and more snow variability than areas >15 m from tree canopies, and modelled results predict that open areas <15 m from tree canopies have 30-40% more net snow water input than areas that are underneath tree canopies and 10-25% more net snow water input than areas that are >15 m from tree canopies. Furthermore, 1-m simulations give higher estimates for net snow water input than coarser resolution simulations, mainly in areas with fewer trees. These results suggest the importance of explicitly representing canopy edges in snow models.

Peak runoff in streams and rivers of the western United States is strongly influenced by melting of accumulated mountain snowpack. A significant decline in this resource has a direct connection to streamflow, with substantial economic and societal impacts. Observations and reanalyses indicate that between the 1980s and 2000s, there was a 10–20% loss in the annual maximum amount of water contained in the region's snowpack. Here we show that this loss is consistent with results from a large ensemble of climate simulations forced with natural and anthropogenic changes, but is inconsistent with simulations forced by natural changes alone. A further loss of up to 60% is projected within the next 30 years. Uncertainties in loss estimates depend on the size and the rate of response to continued anthropogenic forcing and the magnitude and phasing of internal decadal variability. The projected losses have serious implications for the hydropower, municipal and agricultural sectors in the region.