Combining snow, streamflow, and precipitation gauge observations to infer basin‐mean precipitation

Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; McGurk, B. J.; Painter, T. H.; Lundquist, Jessica D.

doi:10.1002/2015wr018564

Cited by 23 publications

(19 citation statements)

References 62 publications

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“…Mountain regions are considered as the ''water towers'' of the surrounding low lands [Liniger et al, 1998;Viviroli et al, 2007] with snow melt often being of major importance for its fresh water supply [Sturm, 2015]. Snow state data from both ground-based measurements and remote sensing products are increasingly used for snow hydrological applications including multiobjective calibration of hydrological models [Kirnbauer et al, 1994;Finger et al, 2011Finger et al, , 2015Sch€ ober et al, 2014;Berezowski et al, 2015;Revuelto et al, 2016], reverse modeling of basin-scale precipitation [Shrestha et al, 2014;Henn et al, 2016], and data assimilation [Slater and Clark, 2006;Thirel et al, 2013;Magnusson et al, 2014].…”

Section: Introductionmentioning

confidence: 99%

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Schattan

Baroni

Oswald

et al. 2017

Water Resources Research

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The characteristics of an aboveground cosmic‐ray neutron sensor (CRNS) are evaluated for monitoring a mountain snowpack in the Austrian Alps from March 2014 to June 2016. Neutron counts were compared to continuous point‐scale snow depth (SD) and snow‐water‐equivalent (SWE) measurements from an automatic weather station with a maximum SWE of 600 mm (April 2014). Several spatially distributed Terrestrial Laser Scanning (TLS)‐based SD and SWE maps were additionally used. A strong nonlinear correlation is found for both SD and SWE. The representative footprint of the CRNS is in the range of 230–270 m. In contrast to previous studies suggesting signal saturation at around 100 mm of SWE, no complete signal saturation was observed. These results imply that CRNS could be transferred into an unprecedented method for continuous detection of spatially averaged SD and SWE for alpine snowpacks, though with sensitivity decreasing with increasing SWE. While initially different functions were found for accumulation and melting season conditions, this could be resolved by accounting for a limited measurement depth. This depth limit is in the range of 200 mm of SWE for dense snowpacks with high liquid water contents and associated snow density values around 450 kg m−3 and above. In contrast to prior studies with shallow snowpacks, interannual transferability of the results is very high regardless of presnowfall soil moisture conditions. This underlines the unexpectedly high potential of CRNS to close the gap between point‐scale measurements, hydrological models, and remote sensing of the cryosphere in alpine terrain.

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Section: Introductionmentioning

confidence: 99%

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Schattan

Baroni

Oswald

et al. 2017

Water Resources Research

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“…Evaluating the credibility of simulated climate and snow dynamics through a decision‐relevant lens and attributing sources of bias across multiple processes, scales of analysis, and models poses a significant challenge to the scientific community. The WUS is a well‐studied region with a vast literature in observed snowpack dynamics (Bales et al, ; Cristea et al, ; Henn et al, ; Kapnick & Hall, ; Margulis, Cortés, Girotto, & Durand, ; Mote, ; Mote et al, ; Painter et al, ; Serreze et al, ; Trujillo & Molotch, ) and modeling approaches (Ashfaq et al, , ; Giorgi et al, ; Huang et al, ; Liu et al, ; McCrary et al, ; Minder et al, ; Naz et al, ; Pierce & Cayan, ; Rasmussen et al, ; Rhoades et al, , , ; Sun et al, ; Wu et al, ) dating back decades (Anderson, ; Palmer, ). Consequently, a staggering growth in publicly available observational and regional climate data sets have been produced, yet a lag in expert direction regarding their applicability for regional planning (Hall, ).…”

Section: Introductionmentioning

confidence: 99%

Assessing Mountains as Natural Reservoirs With a Multimetric Framework

2018

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Anthropogenic climate change will continue to diminish the unique role that mountains perform as natural reservoirs and alter long‐held assumptions of water management. Climate models are important tools to help constrain uncertainty and understand processes that shape this decline. To ensure that climate model estimates provide stakeholder relevant information, the formulation of multimetric model evaluation frameworks informed by stakeholder interactions are critical. In this study, we present one such multimetric framework to evaluate snowpack data sets in the California Sierra Nevada: the snow water equivalent (SWE) triangle. SWE triangle metrics help to describe snowpack characteristics associated with total water volume buildup, peak water availability, and the rate of water release. This approach highlights compensating errors that would not be reflected in conventional large‐scale spatiotemporal analysis. To test our multimetric evaluation framework, we evaluate several publicly available snow products including the Sierra Nevada Snow Reanalysis, Livneh (L15), and the North American Land Data Assimilation System version 2 data sets. We then evaluate regional climate model skill within the North American Coordinated Regional Climate Downscaling Experiment. All data sets analyzed show variation across the various SWE triangle metrics, even within observationally constrained snow products. This spread was especially shown in spring season melt rates. Melt rate biases were prevalent throughout most regional climate model simulations, regardless of snow accumulation dynamics, and will need to be addressed to improve their utility for water stakeholders.

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“…ASO SWE data validated against weighing snow pillows in the Tuolumne watershed suggested unbiased estimates of SWE per grid cell with uncertainties of 5% for moderate to deeper snowpacks and up to 20% for shallow snowpacks (Painter et al, ). Prior work comparing ASO SWE estimates against a regional data set of snow pillows and courses (Henn et al, ) showed that the spatially‐averaged ASO SWE amounts tend to be less than that of pillows and courses at a given elevation interval, a finding that is likely due to the preferential siting of these types of in situ observations in locations with greater snowpack (Molotch & Bales, ; Rice et al, ).…”

Section: Datamentioning

confidence: 99%

High‐Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance

Henn

Painter

Bormann

et al. 2018

Water Resources Research

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Hydrologic variables such as evapotranspiration (ET) and soil water storage are difficult to observe across spatial scales in complex terrain. Streamflow and lidar‐derived snow observations provide information about distributed hydrologic processes such as snowmelt, infiltration, and storage. We use a distributed streamflow data set across eight basins in the upper Tuolumne River region of Yosemite National Park in the Sierra Nevada mountain range, and the NASA Airborne Snow Observatory (ASO) lidar‐derived snow data set over 3 years (2013–2015) during a prolonged drought in California, to estimate basin‐scale water balance components. We compare snowmelt and cumulative precipitation over periods from the ASO flight to the end of the water year against cumulative streamflow observations. The basin water balance residual term (snow melt plus precipitation minus streamflow) is calculated for each basin and year. Using soil moisture observations and hydrologic model simulations, we show that the residual term represents short‐term changes in basin water storage over the snowmelt season, but that over the period from peak snow water equivalent (SWE) to the end of summer, it represents cumulative basin‐mean ET. Warm‐season ET estimated from this approach is 168 (85–252 at 95% confidence), 162 (0–326) and 191 (48–334) mm averaged across the basins in 2013, 2014, and 2015, respectively. These values are lower than previous full‐year and point ET estimates in the Sierra Nevada, potentially reflecting reduced ET during drought, the effects of spatial variability, and the part‐year time period. Using streamflow and ASO snow observations, we quantify spatially‐distributed hydrologic processes otherwise difficult to observe.

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Combining snow, streamflow, and precipitation gauge observations to infer basin‐mean precipitation

Cited by 23 publications

References 62 publications

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Continuous monitoring of snowpack dynamics in alpine terrain by aboveground neutron sensing

Assessing Mountains as Natural Reservoirs With a Multimetric Framework

High‐Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance

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