Articles | Volume 10, issue 1
https://doi.org/10.5194/tc-10-257-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/tc-10-257-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
28 Jan 2016
Research article |
| 28 Jan 2016
Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data
Department of Civil and Environmental Engineering, UC Berkeley, Berkeley, CA, USA
P. B. Kirchner
Joint Institute for Regional Earth System Science and Engineering, Pasadena, CA, USA
Southwest Alaska Network, National Park Service, Anchorage, AK, USA
R. C. Bales
Department of Civil and Environmental Engineering, UC Berkeley, Berkeley, CA, USA
Sierra Nevada Research Institute, UC Merced, Merced, CA, USA
Related authors
Roger C. Bales, Erin M. Stacy, Xiande Meng, Martha H. Conklin, Peter B. Kirchner, and Zeshi Zheng
Earth Syst. Sci. Data, 10, 2115–2122, https://doi.org/10.5194/essd-10-2115-2018, https://doi.org/10.5194/essd-10-2115-2018, 2018
Short summary
Short summary
This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies hydrologic inputs and storage in the mostly undeveloped Wolverton catchment (2180–2750 m) in Sequoia National Park. Two meteorological stations were installed, along with clustered sensors that recorded differences in snow and soil moisture across the landscape with regard to aspect and canopy cover at elevations of 2250 and 2625 m, just above the current rain–snow transition elevation.
Junyan Ding, Polly Buotte, Roger Bales, Bradley Christoffersen, Rosie A. Fisher, Michael Goulden, Ryan Knox, Lara Kueppers, Jacquelyn Shuman, Chonggang Xu, and Charles D. Koven
Biogeosciences, 20, 4491–4510, https://doi.org/10.5194/bg-20-4491-2023, https://doi.org/10.5194/bg-20-4491-2023, 2023
Short summary
Short summary
We used a vegetation model to investigate how the different combinations of plant rooting depths and the sensitivity of leaves and stems to drying lead to differential responses of a pine forest to drought conditions in California, USA. We found that rooting depths are the strongest control in that ecosystem. Deep roots allow trees to fully utilize the soil water during a normal year but result in prolonged depletion of soil moisture during a severe drought and hence a high tree mortality risk.
Tessa Maurer, Francesco Avanzi, Steven D. Glaser, and Roger C. Bales
Hydrol. Earth Syst. Sci., 26, 589–607, https://doi.org/10.5194/hess-26-589-2022, https://doi.org/10.5194/hess-26-589-2022, 2022
Short summary
Short summary
Predicting how much water will end up in rivers is more difficult during droughts because the relationship between precipitation and streamflow can change in unexpected ways. We differentiate between changes that are predictable based on the weather patterns and those harder to predict because they depend on the land and vegetation of a particular region. This work helps clarify why models are less accurate during droughts and helps predict how much water will be available for human use.
Francesco Avanzi, Joseph Rungee, Tessa Maurer, Roger Bales, Qin Ma, Steven Glaser, and Martha Conklin
Hydrol. Earth Syst. Sci., 24, 4317–4337, https://doi.org/10.5194/hess-24-4317-2020, https://doi.org/10.5194/hess-24-4317-2020, 2020
Short summary
Short summary
Multi-year droughts in Mediterranean climates often see a lower fraction of precipitation allocated to runoff compared to non-drought years. By comparing observed water-balance components with simulations by a hydrologic model (PRMS), we reinterpret these shifts as a hysteretic response of the water budget to climate elasticity of evapotranspiration. Our results point to a general improvement in hydrologic predictions across drought and recovery cycles by including this mechanism.
James W. Roche, Robert Rice, Xiande Meng, Daniel R. Cayan, Michael D. Dettinger, Douglas Alden, Sarina C. Patel, Megan A. Mason, Martha H. Conklin, and Roger C. Bales
Earth Syst. Sci. Data, 11, 101–110, https://doi.org/10.5194/essd-11-101-2019, https://doi.org/10.5194/essd-11-101-2019, 2019
Short summary
Short summary
This paper summarizes climate, snow, and soil moisture data for the Tuolumne and Merced river watersheds in California, USA, for water years 2010–2014. Climate data include hourly air temperature and relative humidity, precipitation, wind speed and direction, and solar radiation. Snow depth and soil moisture at three–six points per site are available at four locations. Snow depth and water content are available from instrumented snow pillow sites and manual snow survey locations.
Roger C. Bales, Erin M. Stacy, Xiande Meng, Martha H. Conklin, Peter B. Kirchner, and Zeshi Zheng
Earth Syst. Sci. Data, 10, 2115–2122, https://doi.org/10.5194/essd-10-2115-2018, https://doi.org/10.5194/essd-10-2115-2018, 2018
Short summary
Short summary
This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies hydrologic inputs and storage in the mostly undeveloped Wolverton catchment (2180–2750 m) in Sequoia National Park. Two meteorological stations were installed, along with clustered sensors that recorded differences in snow and soil moisture across the landscape with regard to aspect and canopy cover at elevations of 2250 and 2625 m, just above the current rain–snow transition elevation.
Roger Bales, Erin Stacy, Mohammad Safeeq, Xiande Meng, Matthew Meadows, Carlos Oroza, Martha Conklin, Steven Glaser, and Joseph Wagenbrenner
Earth Syst. Sci. Data, 10, 1795–1805, https://doi.org/10.5194/essd-10-1795-2018, https://doi.org/10.5194/essd-10-1795-2018, 2018
Short summary
Short summary
Strategically placed, spatially distributed sensors provide representative measures of changes in snowpack and subsurface water storage, plus the fluxes affecting these stores, in a set of nested headwater catchments. We present 8 years of hourly snow-depth, soil-moisture, and soil-temperature data from hundreds of sensors, as well as 14 years of streamflow and meteorological data that detail processes at the rain–snow transition at Providence Creek in the southern Sierra Nevada, California.
Susan L. Brantley, William H. McDowell, William E. Dietrich, Timothy S. White, Praveen Kumar, Suzanne P. Anderson, Jon Chorover, Kathleen Ann Lohse, Roger C. Bales, Daniel D. Richter, Gordon Grant, and Jérôme Gaillardet
Earth Surf. Dynam., 5, 841–860, https://doi.org/10.5194/esurf-5-841-2017, https://doi.org/10.5194/esurf-5-841-2017, 2017
Short summary
Short summary
The layer known as the critical zone extends from the tree tops to the groundwater. This zone varies globally as a function of land use, climate, and geology. Energy and materials input from the land surface downward impact the subsurface landscape of water, gas, weathered material, and biota – at the same time that differences at depth also impact the superficial landscape. Scientists are designing observatories to understand the critical zone and how it will evolve in the future.
A. A. Harpold, J. A. Marshall, S. W. Lyon, T. B. Barnhart, B. A. Fisher, M. Donovan, K. M. Brubaker, C. J. Crosby, N. F. Glenn, C. L. Glennie, P. B. Kirchner, N. Lam, K. D. Mankoff, J. L. McCreight, N. P. Molotch, K. N. Musselman, J. Pelletier, T. Russo, H. Sangireddy, Y. Sjöberg, T. Swetnam, and N. West
Hydrol. Earth Syst. Sci., 19, 2881–2897, https://doi.org/10.5194/hess-19-2881-2015, https://doi.org/10.5194/hess-19-2881-2015, 2015
Short summary
Short summary
This review's objective is to demonstrate the transformative potential of lidar by critically assessing both challenges and opportunities for transdisciplinary lidar applications in geomorphology, hydrology, and ecology. We find that using lidar to its full potential will require numerous advances, including more powerful open-source processing tools, new lidar acquisition technologies, and improved integration with physically based models and complementary observations.
P. B. Kirchner, R. C. Bales, N. P. Molotch, J. Flanagan, and Q. Guo
Hydrol. Earth Syst. Sci., 18, 4261–4275, https://doi.org/10.5194/hess-18-4261-2014, https://doi.org/10.5194/hess-18-4261-2014, 2014
Short summary
Short summary
In this study we present results from LiDAR snow depth measurements made over 53 sq km and a 1600 m elevation gradient. We found a lapse rate of 15 cm accumulated snow depth and 6 cm SWE per 100 m in elevation until 3300 m, where depth sharply decreased. Residuals from this trend revealed the role of aspect and highlighted the importance of solar radiation and wind for snow distribution. Lastly, we compared LiDAR SWE estimations with four model estimates of SWE and total precipitation.
S. Masclin, M. M. Frey, W. F. Rogge, and R. C. Bales
Atmos. Chem. Phys., 13, 8857–8877, https://doi.org/10.5194/acp-13-8857-2013, https://doi.org/10.5194/acp-13-8857-2013, 2013
Related subject area
Snow Hydrology
Towards large-scale daily snow density mapping with spatiotemporally aware model and multi-source data
Drone-based ground-penetrating radar (GPR) application to snow hydrology
Natural climate variability is an important aspect of future projections of snow water resources and rain-on-snow events
Two-dimensional liquid water flow through snow at the plot scale in continental snowpacks: simulations and field data comparisons
Fractional snow-covered area: scale-independent peak of winter parameterization
Seasonal components of freshwater runoff in Glacier Bay, Alaska: diverse spatial patterns and temporal change
Hydrologic flow path development varies by aspect during spring snowmelt in complex subalpine terrain
Snowmelt response to simulated warming across a large elevation gradient, southern Sierra Nevada, California
A continuum model for meltwater flow through compacting snow
Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment
Bias corrections of precipitation measurements across experimental sites in different ecoclimatic regions of western Canada
Observations of capillary barriers and preferential flow in layered snow during cold laboratory experiments
A model for the spatial distribution of snow water equivalent parameterized from the spatial variability of precipitation
Multilevel spatiotemporal validation of snow/ice mass balance and runoff modeling in glacierized catchments
Bulk meltwater flow and liquid water content of snowpacks mapped using the electrical self-potential (SP) method
Inconsistency in precipitation measurements across the Alaska–Yukon border
Precipitation measurement intercomparison in the Qilian Mountains, north-eastern Tibetan Plateau
Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements
Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence
Modeling bulk density and snow water equivalent using daily snow depth observations
Evaluation of the snow regime in dynamic vegetation land surface models using field measurements
Homogenisation of a gridded snow water equivalent climatology for Alpine terrain: methodology and applications
What drives basin scale spatial variability of snowpack properties in northern Colorado?
Micrometeorological processes driving snow ablation in an Alpine catchment
Understanding snow-transport processes shaping the mountain snow-cover
Freshwater flux to Sermilik Fjord, SE Greenland
Huadong Wang, Xueliang Zhang, Pengfeng Xiao, Tao Che, Zhaojun Zheng, Liyun Dai, and Wenbo Luan
The Cryosphere, 17, 33–50, https://doi.org/10.5194/tc-17-33-2023, https://doi.org/10.5194/tc-17-33-2023, 2023
Short summary
Short summary
The geographically and temporally weighted neural network (GTWNN) model is constructed for estimating large-scale daily snow density by integrating satellite, ground, and reanalysis data, which addresses the importance of spatiotemporal heterogeneity and a nonlinear relationship between snow density and impact variables, as well as allows us to understand the spatiotemporal pattern and heterogeneity of snow density in different snow periods and snow cover regions in China from 2013 to 2020.
Eole Valence, Michel Baraer, Eric Rosa, Florent Barbecot, and Chloe Monty
The Cryosphere, 16, 3843–3860, https://doi.org/10.5194/tc-16-3843-2022, https://doi.org/10.5194/tc-16-3843-2022, 2022
Short summary
Short summary
The internal properties of the snow cover shape the annual hygrogram of northern and alpine regions. This study develops a multi-method approach to measure the evolution of snowpack internal properties. The snowpack hydrological property evolution was evaluated with drone-based ground-penetrating radar (GPR) measurements. In addition, the combination of GPR observations and time domain reflectometry measurements is shown to be able to be adapted to monitor the snowpack moisture over winter.
Michael Schirmer, Adam Winstral, Tobias Jonas, Paolo Burlando, and Nadav Peleg
The Cryosphere, 16, 3469–3488, https://doi.org/10.5194/tc-16-3469-2022, https://doi.org/10.5194/tc-16-3469-2022, 2022
Short summary
Short summary
Rain is highly variable in time at a given location so that there can be both wet and dry climate periods. In this study, we quantify the effects of this natural climate variability and other sources of uncertainty on changes in flooding events due to rain on snow (ROS) caused by climate change. For ROS events with a significant contribution of snowmelt to runoff, the change due to climate was too small to draw firm conclusions about whether there are more ROS events of this important type.
Ryan W. Webb, Keith Jennings, Stefan Finsterle, and Steven R. Fassnacht
The Cryosphere, 15, 1423–1434, https://doi.org/10.5194/tc-15-1423-2021, https://doi.org/10.5194/tc-15-1423-2021, 2021
Short summary
Short summary
We simulate the flow of liquid water through snow and compare results to field experiments. This process is important because it controls how much and how quickly water will reach our streams and rivers in snowy regions. We found that water can flow large distances downslope through the snow even after the snow has stopped melting. Improved modeling of snowmelt processes will allow us to more accurately estimate available water resources, especially under changing climate conditions.
Nora Helbig, Yves Bühler, Lucie Eberhard, César Deschamps-Berger, Simon Gascoin, Marie Dumont, Jesus Revuelto, Jeff S. Deems, and Tobias Jonas
The Cryosphere, 15, 615–632, https://doi.org/10.5194/tc-15-615-2021, https://doi.org/10.5194/tc-15-615-2021, 2021
Short summary
Short summary
The spatial variability in snow depth in mountains is driven by interactions between topography, wind, precipitation and radiation. In applications such as weather, climate and hydrological predictions, this is accounted for by the fractional snow-covered area describing the fraction of the ground surface covered by snow. We developed a new description for model grid cell sizes larger than 200 m. An evaluation suggests that the description performs similarly well in most geographical regions.
Ryan L. Crumley, David F. Hill, Jordan P. Beamer, and Elizabeth R. Holzenthal
The Cryosphere, 13, 1597–1619, https://doi.org/10.5194/tc-13-1597-2019, https://doi.org/10.5194/tc-13-1597-2019, 2019
Short summary
Short summary
In this study we investigate the historical (1980–2015) and projection scenario (2070–2099) components of freshwater runoff to Glacier Bay, Alaska, using a modeling approach. We find that many of the historically snow-dominated watersheds in Glacier Bay National Park and Preserve may transition towards rainfall-dominated hydrographs in a projection scenario under RCP 8.5 conditions. The changes in timing and volume of freshwater entering Glacier Bay will affect bay ecology and hydrochemistry.
Ryan W. Webb, Steven R. Fassnacht, and Michael N. Gooseff
The Cryosphere, 12, 287–300, https://doi.org/10.5194/tc-12-287-2018, https://doi.org/10.5194/tc-12-287-2018, 2018
Short summary
Short summary
We observed how snowmelt is transported on a hillslope through multiple measurements of snow and soil moisture across a small headwater catchment. We found that snowmelt flows through the snow with less infiltration on north-facing slopes and infiltrates the ground on south-facing slopes. This causes an increase in snow water equivalent at the base of the north-facing slope by as much as 170 %. We present a conceptualization of flow path development to improve future investigations.
Keith N. Musselman, Noah P. Molotch, and Steven A. Margulis
The Cryosphere, 11, 2847–2866, https://doi.org/10.5194/tc-11-2847-2017, https://doi.org/10.5194/tc-11-2847-2017, 2017
Short summary
Short summary
We present a study of how melt rates in the California Sierra Nevada respond to a range of warming projected for this century. Snowfall and melt were simulated for historical and modified (warmer) snow seasons. Winter melt occurs more frequently and more intensely, causing an increase in extreme winter melt. In a warmer climate, less snow persists into the spring, causing spring melt to be substantially lower. The results offer insight into how snow water resources may respond to climate change.
Colin R. Meyer and Ian J. Hewitt
The Cryosphere, 11, 2799–2813, https://doi.org/10.5194/tc-11-2799-2017, https://doi.org/10.5194/tc-11-2799-2017, 2017
Short summary
Short summary
We describe a new model for the evolution of snow temperature, density, and water content on the surface of glaciers and ice sheets. The model encompasses the surface hydrology of accumulation and ablation areas, allowing us to explore the transition from one to the other as thermal forcing varies. We predict year-round liquid water storage for intermediate values of the surface forcing. We also compare our model to data for the vertical percolation of meltwater in Greenland.
Emmy E. Stigter, Niko Wanders, Tuomo M. Saloranta, Joseph M. Shea, Marc F. P. Bierkens, and Walter W. Immerzeel
The Cryosphere, 11, 1647–1664, https://doi.org/10.5194/tc-11-1647-2017, https://doi.org/10.5194/tc-11-1647-2017, 2017
Xicai Pan, Daqing Yang, Yanping Li, Alan Barr, Warren Helgason, Masaki Hayashi, Philip Marsh, John Pomeroy, and Richard J. Janowicz
The Cryosphere, 10, 2347–2360, https://doi.org/10.5194/tc-10-2347-2016, https://doi.org/10.5194/tc-10-2347-2016, 2016
Short summary
Short summary
This study demonstrates a robust procedure for accumulating precipitation gauge measurements and provides an analysis of bias corrections of precipitation measurements across experimental sites in different ecoclimatic regions of western Canada. It highlights the need for and importance of precipitation bias corrections at both research sites and operational networks for water balance assessment and the validation of global/regional climate–hydrology models.
Francesco Avanzi, Hiroyuki Hirashima, Satoru Yamaguchi, Takafumi Katsushima, and Carlo De Michele
The Cryosphere, 10, 2013–2026, https://doi.org/10.5194/tc-10-2013-2016, https://doi.org/10.5194/tc-10-2013-2016, 2016
Short summary
Short summary
We investigate capillary barriers and preferential flow in layered snow during nine cold laboratory experiments. The dynamics of each sample were replicated solving Richards equation within the 1-D multi-layer physically based SNOWPACK model. Results show that both processes affect the speed of water infiltration in stratified snow and are marked by a high degree of spatial variability at cm scale and complex 3-D patterns.
Thomas Skaugen and Ingunn H. Weltzien
The Cryosphere, 10, 1947–1963, https://doi.org/10.5194/tc-10-1947-2016, https://doi.org/10.5194/tc-10-1947-2016, 2016
Short summary
Short summary
In hydrological models it is important to properly simulate the spatial distribution of snow water equivalent (SWE) for the timing of spring melt floods and the accounting of energy fluxes. This paper describes a method for the spatial distribution of SWE which is parameterised from observed spatial variability of precipitation and has hence no calibration parameters. Results show improved simulation of SWE and the evolution of snow-free areas when compared with the standard method.
Florian Hanzer, Kay Helfricht, Thomas Marke, and Ulrich Strasser
The Cryosphere, 10, 1859–1881, https://doi.org/10.5194/tc-10-1859-2016, https://doi.org/10.5194/tc-10-1859-2016, 2016
Short summary
Short summary
The hydroclimatological model AMUNDSEN is set up to simulate snow and ice accumulation, ablation, and runoff for a study region in the Ötztal Alps (Austria) in the period 1997–2013. A new validation concept is introduced and demonstrated by evaluating the model performance using several independent data sets, e.g. snow depth measurements, satellite-derived snow maps, lidar data, glacier mass balances, and runoff measurements.
Sarah S. Thompson, Bernd Kulessa, Richard L. H. Essery, and Martin P. Lüthi
The Cryosphere, 10, 433–444, https://doi.org/10.5194/tc-10-433-2016, https://doi.org/10.5194/tc-10-433-2016, 2016
Short summary
Short summary
We show that strong electrical self-potential fields are generated in melting in in situ snowpacks at Rhone Glacier and Jungfraujoch Glacier, Switzerland. We conclude that the electrical self-potential method is a promising snow and firn hydrology sensor, owing to its suitability for sensing lateral and vertical liquid water flows directly and minimally invasively, complementing established observational programs and monitoring autonomously at a low cost.
L. Scaff, D. Yang, Y. Li, and E. Mekis
The Cryosphere, 9, 2417–2428, https://doi.org/10.5194/tc-9-2417-2015, https://doi.org/10.5194/tc-9-2417-2015, 2015
Short summary
Short summary
The bias corrections show significant errors in the gauge precipitation measurements over the northern regions. Monthly precipitation is closely correlated between the stations across the Alaska--Yukon border, particularly for the warm months. Double mass curves indicate changes in the cumulative precipitation due to bias corrections over the study period. Overall the bias corrections lead to a smaller and inverted precipitation gradient across the border, especially for snowfall.
R. Chen, J. Liu, E. Kang, Y. Yang, C. Han, Z. Liu, Y. Song, W. Qing, and P. Zhu
The Cryosphere, 9, 1995–2008, https://doi.org/10.5194/tc-9-1995-2015, https://doi.org/10.5194/tc-9-1995-2015, 2015
Short summary
Short summary
The catch ratio of Chinese standard precipitation gauge vs. wind speed relationship for different precipitation types was well quantified by cubic polynomials and exponential functions using 5-year field data in the high-mountain environment of the Tibetan Plateau. The daily precipitation measured by shielded gauges increases linearly with that of unshielded gauges. The pit gauge catches the most local precipitation in rainy season and could be used as a reference in most regions of China.
A. Hedrick, H.-P. Marshall, A. Winstral, K. Elder, S. Yueh, and D. Cline
The Cryosphere, 9, 13–23, https://doi.org/10.5194/tc-9-13-2015, https://doi.org/10.5194/tc-9-13-2015, 2015
J. Revuelto, J. I. López-Moreno, C. Azorin-Molina, and S. M. Vicente-Serrano
The Cryosphere, 8, 1989–2006, https://doi.org/10.5194/tc-8-1989-2014, https://doi.org/10.5194/tc-8-1989-2014, 2014
J. L. McCreight and E. E. Small
The Cryosphere, 8, 521–536, https://doi.org/10.5194/tc-8-521-2014, https://doi.org/10.5194/tc-8-521-2014, 2014
E. Kantzas, S. Quegan, M. Lomas, and E. Zakharova
The Cryosphere, 8, 487–502, https://doi.org/10.5194/tc-8-487-2014, https://doi.org/10.5194/tc-8-487-2014, 2014
S. Jörg-Hess, F. Fundel, T. Jonas, and M. Zappa
The Cryosphere, 8, 471–485, https://doi.org/10.5194/tc-8-471-2014, https://doi.org/10.5194/tc-8-471-2014, 2014
G. A. Sexstone and S. R. Fassnacht
The Cryosphere, 8, 329–344, https://doi.org/10.5194/tc-8-329-2014, https://doi.org/10.5194/tc-8-329-2014, 2014
R. Mott, L. Egli, T. Grünewald, N. Dawes, C. Manes, M. Bavay, and M. Lehning
The Cryosphere, 5, 1083–1098, https://doi.org/10.5194/tc-5-1083-2011, https://doi.org/10.5194/tc-5-1083-2011, 2011
R. Mott, M. Schirmer, M. Bavay, T. Grünewald, and M. Lehning
The Cryosphere, 4, 545–559, https://doi.org/10.5194/tc-4-545-2010, https://doi.org/10.5194/tc-4-545-2010, 2010
S. H. Mernild, I. M. Howat, Y. Ahn, G. E. Liston, K. Steffen, B. H. Jakobsen, B. Hasholt, B. Fog, and D. van As
The Cryosphere, 4, 453–465, https://doi.org/10.5194/tc-4-453-2010, https://doi.org/10.5194/tc-4-453-2010, 2010
Cited articles
Anderson, H. W.: Managing California's Snow Zone Lands for Water, USDA Forest Service
Research Paper PSW-6, USDA Forest Service, 1963.
Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., and
Dozier, J.: Mountain hydrology of the western United States, Water Resour.
Res., 42, W08432, https://doi.org/10.1029/2005WR004387, 2006.
Bales, R. C., Hopmans, J. W., O'Geen, A. T., Meadows, M., Hartsough, P. C.,
Kirchner, P., Hunsaker, C. T., and Beaudette, D.: Soil Moisture Response to
Snowmelt and Rainfall in a Sierra Nevada Mixed-Conifer Forest, Vadose Zone
J., 10, 786–799, https://doi.org/10.2136/vzj2011.0001, 2011.
Baltsavias, E.: Airborne laser scanning: basic relations and formulas, ISPRS
J. Photogramm. Remote Sens., 54, 199–214, https://doi.org/10.1016/S0924-2716(99)00015-5, 1999.
Barret, A. P.: National Operational Hydrologic Remote Sensing Center Snow Data Assimilation
System (SNODAS) Products at NSIDC, NSIDC Special Report 11, National Snow and
Ice Data Center, Boulder, CO, 2003.
Berris, S. N. and Harr, R. D.: Comparative snow accumulation and melt during
rainfall in forested and clear-cut plots in the Western Cascades of Oregon,
Water Resour. Res., 23, 135–142, https://doi.org/10.1029/WR023i001p00135, 1987.
Breiman, L.: Random forest, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
California Department of Water Resources: California's Flood Future:
Recommendations for Managing the State's Flood Risk, US Army Corps of Engineers, 2013.
Cleveland, W. S.: Robust Locally Weighted Regression and Smoothing
Scatterplots, J. Am. Stat. Assoc., 74, 829–836, https://doi.org/10.2307/2286407, 1979.
Clow, D. W., Nanus, L., Verdin, K. L., and Schmidt, J.: Evaluation of SNODAS
snow depth and snow water equivalent estimates for the Colorado Rocky
Mountains, USA, Hydrol. Process., 26, 2583–2591, https://doi.org/10.1002/hyp.9385, 2012.
Colle, B. A.: Sensitivity of Orographic Precipitation to Changing Ambient
Conditions and Terrain Geometries: An Idealized Modeling Perspective, J.
Atmos. Sci., 61, 588–606, https://doi.org/10.1175/1520-0469(2004)061<0588:SOOPTC>2.0.CO;2, 2004.
Courbaud, B., De Coligny, F., and Cordonnier, T.: Simulating radiation
distribution in a heterogeneous Norway spruce forest on a slope, Agr. Forest
Meteorol., 116, 1–18, https://doi.org/10.1016/S0168-1923(02)00254-X, 2003.
Deems, J. S. and Painter, T. H.: Lidar measurement of snow depth: accuracy
and error sources, Proc. 2006 Int. Snow Sci. Work. Telluride, Colorado, USA,
Int. Snow Sci. Work., 330, 330–338, 2006.
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Fractal Distribution of
Snow Depth from Lidar Data, J. Hydrometeorol., 7, 285–297, 2006.
Deems, J. S., Painter, T. H. and Finnegan, D. C.: Lidar measurement of snow
depth: a review, J. Glaciol., 59, 467–479, https://doi.org/10.3189/2013JoG12J154, 2013.
Dubayah, R. C.: Modeling a solar radiation topoclimatology for the Rio
Grande River Basin, J. Veg. Sci., 5, 627–640, https://doi.org/10.2307/3235879, 1994.
Erickson, T. A., Williams, M. W., and Winstral, A.: Persistence of
topographic controls on the spatial distribution of snow in rugged mountain
terrain, Colorado, United States, Water Resour. Res., 41, 1–17, https://doi.org/10.1029/2003WR002973, 2005.
Erxleben, J., Elder, K., and Davis, R.: Comparison of spatial interpolation
methods for estimating snow distribution in the Colorado Rocky Mountains,
Hydrol. Process., 16, 3627–3649, https://doi.org/10.1002/hyp.1239, 2002.
Essery, R., Bunting, P., Rowlands, A., Rutter, N., Hardy, J., Melloh, R.,
Link, T., Marks, D. and Pomeroy, J.: Radiative Transfer Modeling of a
Coniferous Canopy Characterized by Airborne Remote Sensing, J.
Hydrometeorol., 9, 228–241, https://doi.org/10.1175/2007JHM870.1, 2008.
Gelfan, A. N., Pomeroy, J. W., and Kuchment, L. S.: Modeling Forest Cover
Influences on Snow Accumulation, Sublimation, and Melt, J. Hydrometeorol.,
5, 785–803, https://doi.org/10.1175/1525-7541(2004)005<0785:MFCIOS>2.0.CO;2, 2004.
Golding, D. L. and Swanson, R. H.: Snow distribution patterns in clearings
and adjacent forest, Water Resour. Res., 22, 1931, https://doi.org/10.1029/WR022i013p01931, 1986.
Goulden, M. L., Anderson, R. G., Bales, R. C., Kelly, A. E., Meadows, M., and
Winston, G. C.: Evapotranspiration along an elevation gradient in
California's Sierra Nevada, J. Geophys. Res.-Biogeo., 117, 1–13, https://doi.org/10.1029/2012JG002027, 2012.
Grünewald, T., Stötter, J., Pomeroy, J. W., Dadic, R., Moreno
Baños, I., Marturià, J., Spross, M., Hopkinson, C., Burlando, P., and
Lehning, M.: Statistical modelling of the snow depth distribution in open
alpine terrain, Hydrol. Earth Syst. Sci., 17, 3005–3021, https://doi.org/10.5194/hess-17-3005-2013, 2013.
Grünewald, T., Bühler, Y., and Lehning, M.: Elevation dependency of
mountain snow depth, The Cryosphere, 8, 2381–2394, https://doi.org/10.5194/tc-8-2381-2014, 2014.
Guan, B., Molotch, N. P., Waliser, D. E., Jepsen, S. M., Painter, T. H., and
Dozier, J.: Snow water equivalent in the Sierra Nevada: Blending snow sensor
observations with snowmelt model simulations, Water Resour. Res., 49,
5029–5046, https://doi.org/10.1002/wrcr.20387, 2013.
Hedstrom, N. R. and Pomeroy, J. W.: Measurements and modelling of snow
interception in the boreal forest, Hydrol. Process., 12, 1611–1625,
https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4, 1998.
Hodgson, M. E. and Bresnahan, P.: Accuracy of Airborne Lidar-Derived
Elevation: Empirical Assessment and Error Budget, Photogramm. Eng. Remote
Sens., 70, 331–339, 2004.
Hopkinson, C., Sitar, M., Chasmer, L., Gynan, C., Agro, D., Enter, R.,
Foster, J., Heels, N., Hoffman, C., Nillson, J., and St Pierre, R.: Mapping
the spatial distribution of snowpack depth beneath a variable forest canopy
using airborne laser altimetry, Proc. 58th Annu. East. Snow Conf., Ottawa,
Ontario, Canada, 2001.
Hopkinson, C., Sitar, M., Chasmer, L., and Treitz, P.: Mapping snowpack depth
beneath forest canopies using airborne lidar, Photogramm. Eng. Remote
Sens., 70, 323–330, 2004.
Howat, I. M. and Tulaczyk, S.: Trends in spring snowpack over a half-century
of climate warming in California, USA, Ann. Glaciol., 40, 151–156,
https://doi.org/10.3189/172756405781813816, 2005.
Hunsaker, C. T., Whitaker, T. W., and Bales, R. C.: Snowmelt Runoff and Water
Yield Along Elevation and Temperature Gradients in California's Southern
Sierra Nevada1, JAWRA J. Am. Water Resour. Assoc., 48, 667–678, https://doi.org/10.1111/j.1752-1688.2012.00641.x, 2012.
Julander, R. P., Wilson, G. R., and Nault, R.: The Franklin basin problem,
66th Annual Western Snow Conference, Snowbird, Utah, 1998.
Kirchner, P. B.: Dissertation for the degree of Doctor of Philosophy,
University of California, Merced., 2013.
Kirchner, P. B., Bales, R. C., Molotch, N. P., Flanagan, J., and Guo, Q.:
LiDAR measurement of seasonal snow accumulation along an elevation gradient
in the southern Sierra Nevada, California, Hydrol. Earth Syst. Sci., 18,
4261–4275, https://doi.org/10.5194/hess-18-4261-2014, 2014.
Lehning, M., Grünewald, T., and Schirmer, M.: Mountain snow distribution
governed by an altitudinal gradient and terrain roughness, Geophys. Res.
Lett., 38, 1–5, https://doi.org/10.1029/2011GL048927, 2011.
Mahat, V. and Tarboton, D. G.: Representation of canopy snow interception,
unloading and melt in a parsimonious snowmelt model, Hydrol. Process., 28,
6320–6336, https://doi.org/10.1002/hyp.10116, 2013.
Marks, K. and Bates, P.: Integration of high-resolution topographic data
with floodplain flow models, Hydrol. Process., 14, 2109–2122,
https://doi.org/10.1002/1099-1085(20000815/30)14:11/12<2109::AID-HYP58>3.0.CO;2-1, 2000.
McMillen, R. T.: An eddy correlation technique with extended applicability
to non-simple terrain, Bound.-Lay. Meteorol., 43, 231–245, https://doi.org/10.1007/BF00128405, 1988.
Molotch, N. P. and Margulis, S. a.: Estimating the distribution of snow
water equivalent using remotely sensed snow cover data and a spatially
distributed snowmelt model: A multi-resolution, multi-sensor comparison,
Adv. Water Resour., 31, 1503–1514, https://doi.org/10.1016/j.advwatres.2008.07.017, 2008.
Molotch, N. P., Colee, M. T., Bales, R. C., and Dozier, J.: Estimating the
spatial distribution of snow water equivalent in an alpine basin using
binary regression tree models: The impact of digital elevation data and
independent variable selection, Hydrol. Process., 19, 1459–1479, https://doi.org/10.1002/hyp.5586, 2005.
Musselman, K. N., Molotch, N. P., and Brooks, P. D.: Effects of vegetation on
snow accumulation and ablation in a mid-latitude sub-alpine forest, Hydrol.
Process., 22, 2767–2776, https://doi.org/10.1002/hyp.7050, 2008.
Musselman, K. N., Molotch, N. P., Margulis, S. A., Kirchner, P. B., and
Bales, R. C.: Influence of canopy structure and direct beam solar irradiance
on snowmelt rates in a mixed conifer forest, Agric. Forest Meteorol., 161,
46–56, https://doi.org/10.1016/j.agrformet.2012.03.011, 2012.
Musselman, K. N., Margulis, S. A., and Molotch, N. P.: Estimation of solar
direct beam transmittance of conifer canopies from airborne LiDAR, Remote
Sens. Environ., 136, 402–415, https://doi.org/10.1016/j.rse.2013.05.021, 2013.
Nolan, M., Larsen, C., and Sturm, M.: Mapping snow depth from manned aircraft
on landscape scales at centimeter resolution using structure-from-motion
photogrammetry, The Cryosphere, 9, 1445–1463, https://doi.org/10.5194/tc-9-1445-2015, 2015.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O.,
Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos,
A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn:
Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011
Pomeroy, J. W., Parviainen, J., Hedstrom, N., and Gray, D. M.: Coupled
modelling of forest snow interception and sublimation, Hydrol. Process.,
12, 2317–2337, https://doi.org/10.1002/(SICI)1099-1085(199812)12:15<2317::AID-HYP799>3.0.CO;2-X, 1998.
Pomeroy, J. W., Gray, D. M., Hedstrom, N. R., and Janowicz, J. R.: Prediction
of seasonal snow accumulation in cold climate forests, Hydrol. Process.,
16, 3543–3558, https://doi.org/10.1002/hyp.1228, 2002.
Raupach, M. R.: Vegetation-atmosphere interaction in homogeneous and
heterogeneous terrain: some implications of mixed-layer dynamics, Vegetatio,
91, 105–120, https://doi.org/10.1007/BF00036051, 1991.
Revuelto, J., Lopez-Moreno, J. I., Azorin-Molina, C., and
Vicente-Serrano, S. M.: Canopy influence on snow depth distribution in a pine stand
determined from terrestrial laser data, Water Resour. Res., 51, 3476–3489,
https://doi.org/10.1002/2014WR016496, 2015.
Rice, R. and Bales, R. C.: Embedded-sensor network design for snow cover
measurements around snow pillow and snow course sites in the Sierra Nevada
of California, Water Resour. Res., 46, 1–13, https://doi.org/10.1029/2008WR007318, 2010.
Rice, R., Bales, R. C., Painter, T. H., and Dozier, J.: Snow water equivalent
along elevation gradients in the Merced and Tuolumne River basins of the
Sierra Nevada, Water Resour. Res., 47, W08515, https://doi.org/10.1029/2010WR009278, 2011.
Roe, G. H.: Orographic Precipitation, Annu. Rev. Earth Planet. Sci., 33,
645–671, https://doi.org/10.1146/annurev.earth.33.092203.122541, 2005.
Roe, G. H. and Baker, M. B.: Microphysical and Geometrical Controls on the
Pattern of Orographic Precipitation, J. Atmos. Sci., 63, 861–880, https://doi.org/10.1175/JAS3619.1, 2006.
Rosenberg, E. A., Wood, A. W., and Steinemann, A. C.: Statistical
applications of physically based hydrologic models to seasonal streamflow
forecasts, Water Resour. Res., 47, W00H14, https://doi.org/10.1029/2010WR010101, 2011.
Rotach, M. W. and Zardi, D.: On the boundary-layer structure over highly
complex terrain: Key findings from MAP, Q. J. Roy. Meteorol. Soc., 133,
937–948, https://doi.org/10.1002/qj.71, 2007.
Schmidt, R. A. and Gluns, D. R.: Snowfall interception on branches of three
conifer species, Can. J. Forest Res., 21, 1262–1269, https://doi.org/10.1139/x91-176, 1991.
Smith, R. B. and Barstad, I.: A Linear Theory of Orographic Precipitation,
J. Atmos. Sci., 61, 1377–1391, https://doi.org/10.1175/1520-0469(2004)061<1377:ALTOOP>2.0.CO;2, 2004.
Sturm, M.: Snow distribution and heat flow in the taiga, Arctic, Antarct.
Alp. Res., 24, 145–152, 1992.
Teti, P.: Relations between peak snow accumulation and canopy density, Forest
Chron., 79, 307–312, 2003.
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed
hydrology-vegetation model for complex terrain, Water Resour. Res., 30,
1665–1680, https://doi.org/10.1029/94WR00436, 1994.
Short summary
By analyzing high-resolution lidar products and using statistical methods, we quantified the snow depth dependency on elevation, slope and aspect of the terrain and also the surrounding vegetation in four catchment size sites in the southern Sierra Nevada during snow peak season. The relative importance of topographic and vegetation attributes varies with elevation and canopy, but all these attributes were found significant in affecting snow distribution in mountain basins.
By analyzing high-resolution lidar products and using statistical methods, we quantified the...
