Laurie S. Huning

Characterizing the extreme 2015 snowpack deficit in the Sierra Nevada (USA) and the implications for drought recovery

Analysis of the Sierra Nevada (USA) snowpack using a new spatially distributed snow reanalysis data set, in combination with longer term in situ data, indicates that water year 2015 was a truly extreme (dry) year. The range-wide peak snow volume was characterized by a return period of over 600 years (95% confidence interval between 100 and 4400 years) having a strong elevational gradient with a return period at lower elevations over an order of magnitude larger than those at higher elevations. The 2015 conditions, occurring on top of three previous drought years, led to an accumulated (multiyear) snowpack deficit of ~ −22 km3, the highest over the 65 years analyzed. Early estimates based on 1 April snow course data indicate that the snowpack drought deficit will not be overcome in 2016, despite historically strong El Nino conditions. Results based on a probabilistic Monte Carlo simulation show that recovery from the snowpack drought will likely take about 4 years.

Comparison of Methods to Estimate Snow Water Equivalent at the Mountain Range Scale: A Case Study of the California Sierra Nevada

AbstractDespite the importance of snow in global water and energy budgets, estimates of global mountain snow water equivalent (SWE) are not well constrained. Two approaches for estimating total range-wide SWE over Sierra Nevada, California, are assessed: 1) global/hemispherical models and remote sensing and models available for continental United States (CONUS) plus southern Canada (CONUS+) available to the scientific community and 2) regional climate model simulations via the Weather Research and Forecasting (WRF) Model run at 3, 9, and 27 km. As no truth dataset provides total mountain range SWE, these two approaches are compared to a “reference” SWE consisting of three published, independent datasets that utilize/validate against in situ SWE measurements. Model outputs are compared with the reference datasets for three water years: 2005 (high snow accumulation), 2009 (average), and 2014 (low). There is a distinctive difference between the reference/WRF datasets and the global/CONUS+ daily estimates of ...

Investigating the Variability of High-Elevation Seasonal Orographic Snowfall Enhancement and Its Drivers across Sierra Nevada, California

AbstractWhile orographically driven snowfall is known to be important in mountainous regions, a complete understanding of orographic enhancement from the basin to the mountain range scale is often inhibited by limited distributed data and spatial and/or temporal resolutions. A novel, 90-m spatially distributed snow water equivalent (SWE) reanalysis was used to overcome these limitations. Leveraging this SWE information, the interannual variability of orographic gradients in cumulative snowfall (CS) was investigated over 14 windward (western) basins in the Sierra Nevada in California from water years 1985 to 2015. Previous studies have not provided a detailed multidecadal climatology of orographic CS gradients or compared wet-year and dry-year orographic CS patterns, distributions, and gradients across an entire mountain range. The magnitude of seasonal CS gradients range from over 15 cm SWE per 100-m elevation to under 1 cm per 100 m with a 31-yr average of 6.1 cm per 100 m below ~2500 m in the western ba...

Watershed modeling applications with a modular physically-based and spatially-distributed watershed educational toolbox

Introductory hydrology courses traditionally focus on process-based hydrology (i.e. precipitation, evaporation, runoff, etc.) that is ultimately unified through watershed modeling. Although physically-based and spatially-distributed hydrologic models are arguably valuable learning tools, they typically have steep learning curves and high computational costs. To overcome these common drawbacks in hydrology education, we developed the user-friendly, open-access Modular Distributed Watershed Educational Toolbox (MOD-WET). MOD-WET is a compilation of highly modular functions designed for process-based applications, which are integrated into a physically-based and spatially-distributed watershed model. When students use the toolbox to study individual processes, they become familiar with the fundamental components forming the hydrologic model. Student feedback indicates that this approach makes a holistic understanding of the water cycle more accessible to students. Hydrograph sensitivity to watershed characteristics (size, slope, roughness, etc.) is demonstrated. The capability of MOD-WET to model warm and cold land processes is also illustrated for a snow-covered basin. We present an educational physically-based, distributed watershed model (MOD-WET).Process-based hydrologic concepts can be studied with the modular toolbox functions.Hydrograph sensitivity to basin size, shape, slope, and roughness is illustrated.The performance of MOD-WET is demonstrated in snow-dominated watershed applications.MOD-WET makes a holistic comprehension of the water cycle accessible to students.

Climatology of seasonal snowfall accumulation across the Sierra Nevada (USA): Accumulation rates, distributions, and variability: CLIMATOLOGY OF SEASONAL SNOWFALL

A detailed picture of how snowfall varies across high-elevation mountain ranges in both space and time remains a knowledge gap in understanding the montane hydrologic cycle. Previous studies generally used point-scale snow measurements in an attempt to represent the spatial variability of snowfall across a range; however, these traditional approaches provide incomplete insight into the cumulative snowfall (CS) distribution from the basin-scale to the mountain range-scale. In this study, a high-resolution, spatially-distributed snow reanalysis was utilized to characterize 31 winters (water years 1985-2015) of snowfall distributions, snowfall accumulation rates, and snowstorms across the Sierra Nevada (USA). The CS dataset (quantified in units of snow water equivalent) was verified against over 2600 station years of in situ observations. The seasonal CS was found to have mean and root-mean-squared differences of -4 cm and 12 cm, respectively, and a correlation of 0.96 with snow pillow observations. Using this novel CS information, results indicated that the CS accumulates rapidly across all 20 basins examined with, on average, at least 50% of the integrated CS accumulating in less than or equal to six days or three snowstorms over each basin. The largest (or leading) snowstorms each season yield ∼27% of the CS, on average, and most frequently last four days. Across the range, over 40% of the leading snowstorms occur in February. This study showed that the hydroclimatology of the Sierra Nevada is driven by hydrological extremes as manifested in the high inter-annual variability of its seasonally-integrated CS, 4.4-41.3 km3, during the 31 years.