Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-4651-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4651-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations
Water Resources Science, Oregon State University, Corvallis, OR 97331, USA
Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
David F. Hill
School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA
Katreen Wikstrom Jones
Alaska Division of Geological and Geophysical Surveys, Fairbanks, AK 99709, USA
Gabriel J. Wolken
Alaska Division of Geological and Geophysical Surveys, Fairbanks, AK 99709, USA
International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Anthony A. Arendt
Applied Physics Laboratory, University of Washington, WA 98105, USA
Christina M. Aragon
Water Resources Science, Oregon State University, Corvallis, OR 97331, USA
Christopher Cosgrove
Geography Department, Oregon State University, Corvallis, OR 97331,
USA
Community Snow Observations Participants
Citizen scientists participating in the project Community Snow Observations (CSO)
Related authors
Katrina E. Bennett, Greta Miller, Robert Busey, Min Chen, Emma R. Lathrop, Julian B. Dann, Mara Nutt, Ryan Crumley, Shannon L. Dillard, Baptiste Dafflon, Jitendra Kumar, W. Robert Bolton, Cathy J. Wilson, Colleen M. Iversen, and Stan D. Wullschleger
The Cryosphere, 16, 3269–3293, https://doi.org/10.5194/tc-16-3269-2022, https://doi.org/10.5194/tc-16-3269-2022, 2022
Short summary
Short summary
In the Arctic and sub-Arctic, climate shifts are changing ecosystems, resulting in alterations in snow, shrubs, and permafrost. Thicker snow under shrubs can lead to warmer permafrost because deeper snow will insulate the ground from the cold winter. In this paper, we use modeling to characterize snow to better understand the drivers of snow distribution. Eventually, this work will be used to improve models used to study future changes in Arctic and sub-Arctic snow patterns.
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.
Julia Glaus, Katreen Wikstrom Jones, Perry Bartelt, Marc Christen, Lukas Stoffel, Johan Gaume, and Yves Bühler
EGUsphere, https://doi.org/10.5194/egusphere-2024-771, https://doi.org/10.5194/egusphere-2024-771, 2024
Short summary
Short summary
This study assesses RAMMS::EXTENDED's predictive power in estimating avalanche run-out distances critical for mountain road safety. Leveraging meteorological data and sensitivity analysis, it offers meaningful predictions, aiding near real-time hazard assessments and future model refinement for improved decision-making.
Christina Marie Aragon and David F. Hill
Hydrol. Earth Syst. Sci., 28, 781–800, https://doi.org/10.5194/hess-28-781-2024, https://doi.org/10.5194/hess-28-781-2024, 2024
Short summary
Short summary
A novel snow metric, snow water storage (SwS), is used to characterize the natural reservoir function of snowpacks, quantifying how much water is held in snow reservoirs and for how long. Despite covering only 16 % of US land area, mountainous regions contribute 72 % of the annual SwS. Recent decades show a 22 % decline in annual mountain SwS. Flexible snow metrics such as SwS may become more valuable for monitoring and predicting water resources amidst a future of increased climate variability.
Molly E. Tedesche, Erin D. Trochim, Steven R. Fassnacht, and Gabriel J. Wolken
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-143, https://doi.org/10.5194/tc-2022-143, 2022
Publication in TC not foreseen
Short summary
Short summary
Perennial snowfields in the Brooks Range of Alaska are critical for the ecosystem and provide caribou habitat. Caribou are a crucial food source for rural hunters. The purpose of this research is to map perennial snowfield extents using several remote sensing techniques with Sentinel-1 and 2. These include analysis of Synthetic Aperture Radar backscatter change and of optical satellite imagery. Results are compared with field data and appear to effectively detect perennial snowfield locations.
Katrina E. Bennett, Greta Miller, Robert Busey, Min Chen, Emma R. Lathrop, Julian B. Dann, Mara Nutt, Ryan Crumley, Shannon L. Dillard, Baptiste Dafflon, Jitendra Kumar, W. Robert Bolton, Cathy J. Wilson, Colleen M. Iversen, and Stan D. Wullschleger
The Cryosphere, 16, 3269–3293, https://doi.org/10.5194/tc-16-3269-2022, https://doi.org/10.5194/tc-16-3269-2022, 2022
Short summary
Short summary
In the Arctic and sub-Arctic, climate shifts are changing ecosystems, resulting in alterations in snow, shrubs, and permafrost. Thicker snow under shrubs can lead to warmer permafrost because deeper snow will insulate the ground from the cold winter. In this paper, we use modeling to characterize snow to better understand the drivers of snow distribution. Eventually, this work will be used to improve models used to study future changes in Arctic and sub-Arctic snow patterns.
Claudine Hauri, Cristina Schultz, Katherine Hedstrom, Seth Danielson, Brita Irving, Scott C. Doney, Raphael Dussin, Enrique N. Curchitser, David F. Hill, and Charles A. Stock
Biogeosciences, 17, 3837–3857, https://doi.org/10.5194/bg-17-3837-2020, https://doi.org/10.5194/bg-17-3837-2020, 2020
Short summary
Short summary
The coastal ecosystem of the Gulf of Alaska (GOA) is especially vulnerable to the effects of ocean acidification and climate change. To improve our conceptual understanding of the system, we developed a new regional biogeochemical model setup for the GOA. Model output suggests that bottom water is seasonally high in CO2 between June and January. Such extensive periods of reoccurring high CO2 may be harmful to ocean acidification-sensitive organisms.
Andrew Bliss, Regine Hock, Gabriel Wolken, Erin Whorton, Caroline Aubry-Wake, Juliana Braun, Alessio Gusmeroli, Will Harrison, Andrew Hoffman, Anna Liljedahl, and Jing Zhang
Earth Syst. Sci. Data, 12, 403–427, https://doi.org/10.5194/essd-12-403-2020, https://doi.org/10.5194/essd-12-403-2020, 2020
Short summary
Short summary
Extensive field observations were conducted in the Upper Susitna basin in central Alaska in 2012–2014. This paper describes the weather, glacier mass balance, snow cover, and soils of the basin. We found that temperatures over the glacier are cooler than over land at the same elevation. The glaciers have been losing mass faster in recent years than in the 1980s. Measurements of glacier mass change with traditional methods closely matched radar measurements.
Katherine A. Serafin, Peter Ruggiero, Kai Parker, and David F. Hill
Nat. Hazards Earth Syst. Sci., 19, 1415–1431, https://doi.org/10.5194/nhess-19-1415-2019, https://doi.org/10.5194/nhess-19-1415-2019, 2019
Short summary
Short summary
In coastal environments, extreme water levels driving flooding are often generated by many individual processes like storm surge, streamflow, and tides. To estimate flood drivers along a coastal river, we merge statistical simulations of ocean and river forcing with a hydraulic model to produce water levels. We find both ocean and river forcing are necessary for producing extreme flood levels like the 100-yr event, highlighting the need for considering compound events in flood risk assessments.
David F. Hill, Elizabeth A. Burakowski, Ryan L. Crumley, Julia Keon, J. Michelle Hu, Anthony A. Arendt, Katreen Wikstrom Jones, and Gabriel J. Wolken
The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, https://doi.org/10.5194/tc-13-1767-2019, 2019
Short summary
Short summary
We present a new statistical model for converting snow depths to water equivalent. The only variables required are snow depth, day of year, and location. We use the location to look up climatological parameters such as mean winter precipitation and mean temperature difference (difference between hottest month and coldest month). The model is simple by design so that it can be applied to depth measurements anywhere, anytime. The model is shown to perform better than other widely used approaches.
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.
Related subject area
Subject: Snow and Ice | Techniques and Approaches: Modelling approaches
Inferring sediment-discharge event types in an Alpine catchment from sub-daily time series
Debris cover effects on energy and mass balance of Batura Glacier in the Karakoram over the past 20 years
Evaluation of high resolution snowpack simulations from global datasets and comparison with Sentinel-1 snow depth retrievals in the Sierra Nevada, USA
The application and modification of WRF-Hydro/Glacier to a cold-based Antarctic glacier
Spatio-temporal information propagation using sparse observations in hyper-resolution ensemble-based snow data assimilation
Simulated hydrological effects of grooming and snowmaking in a ski resort on the local water balance
Spatial distribution and controls of snowmelt runoff in a sublimation-dominated environment in the semiarid Andes of Chile
Snow data assimilation for seasonal streamflow supply prediction in mountainous basins
Canopy structure, topography, and weather are equally important drivers of small-scale snow cover dynamics in sub-alpine forests
Climate sensitivity of the summer runoff of two glacierised Himalayan catchments with contrasting climate
A snow and glacier hydrological model for large catchments – case study for the Naryn River, central Asia
Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent
Development and parameter estimation of snowmelt models using spatial snow-cover observations from MODIS
Recent hydrological response of glaciers in the Canadian Rockies to changing climate and glacier configuration
Future projections of High Atlas snowpack and runoff under climate change
Trends and variability in snowmelt in China under climate change
Snowpack dynamics in the Lebanese mountains from quasi-dynamically downscaled ERA5 reanalysis updated by assimilating remotely sensed fractional snow-covered area
The evaluation of the potential of global data products for snow hydrological modelling in ungauged high-alpine catchments
Learning about precipitation lapse rates from snow course data improves water balance modeling
Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model
Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada
Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments
Snow processes in mountain forests: interception modeling for coarse-scale applications
Satellite-derived products of solar and longwave irradiances used for snowpack modelling in mountainous terrain
Using Gravity Recovery and Climate Experiment data to derive corrections to precipitation data sets and improve modelled snow mass at high latitudes
The role of liquid water percolation representation in estimating snow water equivalent in a Mediterranean mountain region (Mount Lebanon)
Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering
The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient
Assessment of SWAT spatial and temporal transferability for a high-altitude glacierized catchment
Modeling experiments on seasonal lake ice mass and energy balance in the Qinghai–Tibet Plateau: a case study
A simple model for local-scale sensible and latent heat advection contributions to snowmelt
Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada
A simple temperature-based method to estimate heterogeneous frozen ground within a distributed watershed model
Technical note: Representing glacier geometry changes in a semi-distributed hydrological model
Projected cryospheric and hydrological impacts of 21st century climate change in the Ötztal Alps (Austria) simulated using a physically based approach
Scenario approach for the seasonal forecast of Kharif flows from the Upper Indus Basin
The role of glacier changes and threshold definition in the characterisation of future streamflow droughts in glacierised catchments
Modelling hydrologic impacts of light absorbing aerosol deposition on snow at the catchment scale
Liquid water infiltration into a layered snowpack: evaluation of a 3-D water transport model with laboratory experiments
Assessing glacier melt contribution to streamflow at Universidad Glacier, central Andes of Chile
Modelling liquid water transport in snow under rain-on-snow conditions – considering preferential flow
Developing a representative snow-monitoring network in a forested mountain watershed
Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography
Assessing the benefit of snow data assimilation for runoff modeling in Alpine catchments
Stable oxygen isotope variability in two contrasting glacier river catchments in Greenland
Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover
A conceptual, distributed snow redistribution model
Diagnostic calibration of a hydrological model in a mountain area by hydrograph partitioning
Meltwater run-off from Haig Glacier, Canadian Rocky Mountains, 2002–2013
Modeling the snow surface temperature with a one-layer energy balance snowmelt model
Amalie Skålevåg, Oliver Korup, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 4771–4796, https://doi.org/10.5194/hess-28-4771-2024, https://doi.org/10.5194/hess-28-4771-2024, 2024
Short summary
Short summary
We present a cluster-based approach for inferring sediment discharge event types from suspended sediment concentration and streamflow. Applying it to a glacierised catchment, we find event magnitude and shape complexity to be the key characteristics separating event types, while hysteresis is less important. The four event types are attributed to compound rainfall–melt extremes, high snowmelt and glacier melt, freeze–thaw-modulated snow-melt and precipitation, and late-season glacier melt.
Yu Zhu, Shiyin Liu, Ben W. Brock, Lide Tian, Ying Yi, Fuming Xie, Donghui Shangguan, and Yiyuan Shen
Hydrol. Earth Syst. Sci., 28, 2023–2045, https://doi.org/10.5194/hess-28-2023-2024, https://doi.org/10.5194/hess-28-2023-2024, 2024
Short summary
Short summary
This modeling-based study focused on Batura Glacier from 2000 to 2020, revealing that debris alters its energy budget, affecting mass balance. We propose that the presence of debris on the glacier surface effectively reduces the amount of latent heat available for ablation, which creates a favorable condition for Batura Glacier's relatively low negative mass balance. Batura Glacier shows a trend toward a less negative mass balance due to reduced ablation.
Laura Sourp, Simon Gascoin, Lionel Jarlan, Vanessa Pedinotti, Kat J. Bormann, and Mohamed Wassim Baba
EGUsphere, https://doi.org/10.5194/egusphere-2024-791, https://doi.org/10.5194/egusphere-2024-791, 2024
Short summary
Short summary
An accurate knowledge of the spatial distribution of the snow mass across the landscape is important for water management in mountain catchments. We present a new tool to estimate the snow water resources without ground measurements. We evaluate the output of this tool using accurate airborne measurements in the Sierra Nevada and find that it provides realistic estimates of the snow mass and snow depth at the catchment scale.
Tamara Pletzer, Jonathan P. Conway, Nicolas J. Cullen, Trude Eidhammer, and Marwan Katurji
Hydrol. Earth Syst. Sci., 28, 459–478, https://doi.org/10.5194/hess-28-459-2024, https://doi.org/10.5194/hess-28-459-2024, 2024
Short summary
Short summary
We applied a glacier and hydrology model in the McMurdo Dry Valleys (MDV) to model the start and duration of melt over a summer in this extreme polar desert. To do so, we found it necessary to prevent the drainage of melt into ice and optimize the albedo scheme. We show that simulating albedo (for the first time in the MDV) is critical to modelling the feedbacks of albedo, snowfall and melt in the region. This paper is a first step towards more complex spatial modelling of melt and streamflow.
Esteban Alonso-González, Kristoffer Aalstad, Norbert Pirk, Marco Mazzolini, Désirée Treichler, Paul Leclercq, Sebastian Westermann, Juan Ignacio López-Moreno, and Simon Gascoin
Hydrol. Earth Syst. Sci., 27, 4637–4659, https://doi.org/10.5194/hess-27-4637-2023, https://doi.org/10.5194/hess-27-4637-2023, 2023
Short summary
Short summary
Here we explore how to improve hyper-resolution (5 m) distributed snowpack simulations using sparse observations, which do not provide information from all the areas of the simulation domain. We propose a new way of propagating information throughout the simulations adapted to the hyper-resolution, which could also be used to improve simulations of other nature. The method has been implemented in an open-source data assimilation tool that is readily accessible to everyone.
Samuel Morin, Hugues François, Marion Réveillet, Eric Sauquet, Louise Crochemore, Flora Branger, Étienne Leblois, and Marie Dumont
Hydrol. Earth Syst. Sci., 27, 4257–4277, https://doi.org/10.5194/hess-27-4257-2023, https://doi.org/10.5194/hess-27-4257-2023, 2023
Short summary
Short summary
Ski resorts are a key socio-economic asset of several mountain areas. Grooming and snowmaking are routinely used to manage the snow cover on ski pistes, but despite vivid debate, little is known about their impact on water resources downstream. This study quantifies, for the pilot ski resort La Plagne in the French Alps, the impact of grooming and snowmaking on downstream river flow. Hydrological impacts are mostly apparent at the seasonal scale and rather neutral on the annual scale.
Álvaro Ayala, Simone Schauwecker, and Shelley MacDonell
Hydrol. Earth Syst. Sci., 27, 3463–3484, https://doi.org/10.5194/hess-27-3463-2023, https://doi.org/10.5194/hess-27-3463-2023, 2023
Short summary
Short summary
As the climate of the semiarid Andes is very dry, much of the seasonal snowpack is lost to the atmosphere through sublimation. We propose that snowmelt runoff originates from specific areas that we define as snowmelt hotspots. We estimate that snowmelt hotspots produce half of the snowmelt runoff in a small study catchment but represent about a quarter of the total area. Snowmelt hotspots may be important for groundwater recharge, rock glaciers, and mountain peatlands.
Sammy Metref, Emmanuel Cosme, Matthieu Le Lay, and Joël Gailhard
Hydrol. Earth Syst. Sci., 27, 2283–2299, https://doi.org/10.5194/hess-27-2283-2023, https://doi.org/10.5194/hess-27-2283-2023, 2023
Short summary
Short summary
Predicting the seasonal streamflow supply of water in a mountainous basin is critical to anticipating the operation of hydroelectric dams and avoiding hydrology-related hazard. This quantity partly depends on the snowpack accumulated during winter. The study addresses this prediction problem using information from streamflow data and both direct and indirect snow measurements. In this study, the prediction is improved by integrating the data information into a basin-scale hydrological model.
Giulia Mazzotti, Clare Webster, Louis Quéno, Bertrand Cluzet, and Tobias Jonas
Hydrol. Earth Syst. Sci., 27, 2099–2121, https://doi.org/10.5194/hess-27-2099-2023, https://doi.org/10.5194/hess-27-2099-2023, 2023
Short summary
Short summary
This study analyses snow cover evolution in mountainous forested terrain based on 2 m resolution simulations from a process-based model. We show that snow accumulation patterns are controlled by canopy structure, but topographic shading modulates the timing of melt onset, and variability in weather can cause snow accumulation and melt patterns to vary between years. These findings advance our ability to predict how snow regimes will react to rising temperatures and forest disturbances.
Sourav Laha, Argha Banerjee, Ajit Singh, Parmanand Sharma, and Meloth Thamban
Hydrol. Earth Syst. Sci., 27, 627–645, https://doi.org/10.5194/hess-27-627-2023, https://doi.org/10.5194/hess-27-627-2023, 2023
Short summary
Short summary
A model study of two Himalayan catchments reveals that the summer runoff from the glacierized parts of the catchments responds strongly to temperature forcing and is insensitive to precipitation forcing. The runoff from the non-glacierized parts has the exact opposite behaviour. The interannual variability and decadal changes of runoff under a warming climate is determined by the response of glaciers to temperature forcing and that of off-glacier areas to precipitation perturbations.
Sarah Shannon, Anthony Payne, Jim Freer, Gemma Coxon, Martina Kauzlaric, David Kriegel, and Stephan Harrison
Hydrol. Earth Syst. Sci., 27, 453–480, https://doi.org/10.5194/hess-27-453-2023, https://doi.org/10.5194/hess-27-453-2023, 2023
Short summary
Short summary
Climate change poses a potential threat to water supply in glaciated river catchments. In this study, we added a snowmelt and glacier melt model to the Dynamic fluxEs and ConnectIvity for Predictions of HydRology model (DECIPHeR). The model is applied to the Naryn River catchment in central Asia and is found to reproduce past change discharge and the spatial extent of seasonal snow cover well.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
Short summary
Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Dhiraj Raj Gyawali and András Bárdossy
Hydrol. Earth Syst. Sci., 26, 3055–3077, https://doi.org/10.5194/hess-26-3055-2022, https://doi.org/10.5194/hess-26-3055-2022, 2022
Short summary
Short summary
In this study, different extensions of the degree-day model were calibrated on snow-cover distribution against freely available satellite snow-cover images. The calibrated models simulated the distribution very well in Baden-Württemberg (Germany) and Switzerland. In addition to reliable identification of snow cover, the melt outputs from the calibrated models were able to improve the flow simulations in different catchments in the study region.
Dhiraj Pradhananga and John W. Pomeroy
Hydrol. Earth Syst. Sci., 26, 2605–2616, https://doi.org/10.5194/hess-26-2605-2022, https://doi.org/10.5194/hess-26-2605-2022, 2022
Short summary
Short summary
This study considers the combined impacts of climate and glacier changes due to recession on the hydrology and water balance of two high-elevation glaciers. Peyto and Athabasca glacier basins in the Canadian Rockies have undergone continuous glacier loss over the last 3 to 5 decades, leading to an increase in ice exposure and changes to the elevation and slope of the glacier surfaces. Streamflow from these glaciers continues to increase more due to climate warming than glacier recession.
Alexandre Tuel, Nabil El Moçayd, Moulay Driss Hasnaoui, and Elfatih A. B. Eltahir
Hydrol. Earth Syst. Sci., 26, 571–588, https://doi.org/10.5194/hess-26-571-2022, https://doi.org/10.5194/hess-26-571-2022, 2022
Short summary
Short summary
Snowmelt in the High Atlas is critical for irrigation in Morocco but is threatened by climate change. We assess future trends in High Atlas snowpack by modelling it under historical and future climate scenarios and estimate their impact on runoff. We find that the combined warming and drying will result in a roughly 80 % decline in snowpack, a 5 %–30 % decrease in runoff efficiency and 50 %–60 % decline in runoff under a business-as-usual scenario.
Yong Yang, Rensheng Chen, Guohua Liu, Zhangwen Liu, and Xiqiang Wang
Hydrol. Earth Syst. Sci., 26, 305–329, https://doi.org/10.5194/hess-26-305-2022, https://doi.org/10.5194/hess-26-305-2022, 2022
Short summary
Short summary
A comprehensive assessment of snowmelt is missing for China. Trends and variability in snowmelt in China under climate change are investigated using historical precipitation and temperature data (1951–2017) and projection scenarios (2006–2099). The snowmelt and snowmelt runoff ratio show significant spatial and temporal variability in China. The spatial variability in snowmelt changes may lead to regional differences in the impact of snowmelt on the water supply.
Esteban Alonso-González, Ethan Gutmann, Kristoffer Aalstad, Abbas Fayad, Marine Bouchet, and Simon Gascoin
Hydrol. Earth Syst. Sci., 25, 4455–4471, https://doi.org/10.5194/hess-25-4455-2021, https://doi.org/10.5194/hess-25-4455-2021, 2021
Short summary
Short summary
Snow water resources represent a key hydrological resource for the Mediterranean regions, where most of the precipitation falls during the winter months. This is the case for Lebanon, where snowpack represents 31 % of the spring flow. We have used models to generate snow information corrected by means of remote sensing snow cover retrievals. Our results highlight the high temporal variability in the snowpack in Lebanon and its sensitivity to further warming caused by its hypsography.
Michael Weber, Franziska Koch, Matthias Bernhardt, and Karsten Schulz
Hydrol. Earth Syst. Sci., 25, 2869–2894, https://doi.org/10.5194/hess-25-2869-2021, https://doi.org/10.5194/hess-25-2869-2021, 2021
Short summary
Short summary
We compared a suite of globally available meteorological and DEM data with in situ data for physically based snow hydrological modelling in a small high-alpine catchment. Although global meteorological data were less suited to describe the snowpack properly, transferred station data from a similar location in the vicinity and substituting single variables with global products performed well. In addition, using 30 m global DEM products as model input was useful in such complex terrain.
Francesco Avanzi, Giulia Ercolani, Simone Gabellani, Edoardo Cremonese, Paolo Pogliotti, Gianluca Filippa, Umberto Morra di Cella, Sara Ratto, Hervè Stevenin, Marco Cauduro, and Stefano Juglair
Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, https://doi.org/10.5194/hess-25-2109-2021, 2021
Short summary
Short summary
Precipitation tends to increase with elevation, but the magnitude and distribution of this enhancement remain poorly understood. By leveraging over 11 000 spatially distributed, manual measurements of snow depth (snow courses) upstream of two reservoirs in the western European Alps, we show that these courses bear a characteristic signature of orographic precipitation. This opens a window of opportunity for improved modeling accuracy and, ultimately, our understanding of the water budget.
Michael Winkler, Harald Schellander, and Stefanie Gruber
Hydrol. Earth Syst. Sci., 25, 1165–1187, https://doi.org/10.5194/hess-25-1165-2021, https://doi.org/10.5194/hess-25-1165-2021, 2021
Short summary
Short summary
A new method to calculate the mass of snow is provided. It is quite simple but gives surprisingly good results. The new approach only requires regular snow depth observations to simulate respective water mass that is stored in the snow. It is called
ΔSNOW model, its code is freely available, and it can be applied in various climates. The method is especially interesting for studies on extremes (e.g., snow loads or flooding) and climate (e.g., precipitation trends).
Fraser King, Andre R. Erler, Steven K. Frey, and Christopher G. Fletcher
Hydrol. Earth Syst. Sci., 24, 4887–4902, https://doi.org/10.5194/hess-24-4887-2020, https://doi.org/10.5194/hess-24-4887-2020, 2020
Short summary
Short summary
Snow is a critical contributor to our water and energy budget, with impacts on flooding and water resource management. Measuring the amount of snow on the ground each year is an expensive and time-consuming task. Snow models and gridded products help to fill these gaps, yet there exist considerable uncertainties associated with their estimates. We demonstrate that machine learning techniques are able to reduce biases in these products to provide more realistic snow estimates across Ontario.
Silvia Terzago, Valentina Andreoli, Gabriele Arduini, Gianpaolo Balsamo, Lorenzo Campo, Claudio Cassardo, Edoardo Cremonese, Daniele Dolia, Simone Gabellani, Jost von Hardenberg, Umberto Morra di Cella, Elisa Palazzi, Gaia Piazzi, Paolo Pogliotti, and Antonello Provenzale
Hydrol. Earth Syst. Sci., 24, 4061–4090, https://doi.org/10.5194/hess-24-4061-2020, https://doi.org/10.5194/hess-24-4061-2020, 2020
Short summary
Short summary
In mountain areas high-quality meteorological data to drive snow models are rarely available, so coarse-resolution data from spatial interpolation of the available in situ measurements or reanalyses are typically employed. We perform 12 experiments using six snow models with different degrees of complexity to show the impact of the accuracy of the forcing on snow depth and snow water equivalent simulations at the Alpine site of Torgnon, discussing the results in relation to the model complexity.
Nora Helbig, David Moeser, Michaela Teich, Laure Vincent, Yves Lejeune, Jean-Emmanuel Sicart, and Jean-Matthieu Monnet
Hydrol. Earth Syst. Sci., 24, 2545–2560, https://doi.org/10.5194/hess-24-2545-2020, https://doi.org/10.5194/hess-24-2545-2020, 2020
Short summary
Short summary
Snow retained in the forest canopy (snow interception) drives spatial variability of the subcanopy snow accumulation. As such, accurately describing snow interception in models is of importance for various applications such as hydrological, weather, and climate predictions. We developed descriptions for the spatial mean and variability of snow interception. An independent evaluation demonstrated that the novel models can be applied in coarse land surface model grid cells.
Louis Quéno, Fatima Karbou, Vincent Vionnet, and Ingrid Dombrowski-Etchevers
Hydrol. Earth Syst. Sci., 24, 2083–2104, https://doi.org/10.5194/hess-24-2083-2020, https://doi.org/10.5194/hess-24-2083-2020, 2020
Short summary
Short summary
In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. Satellite-derived products of incoming radiation were assessed in the French Alps and the Pyrenees and compared to meteorological forecasts, reanalyses and in situ measurements. We showed their good quality in mountains. The different radiation datasets were used as radiative forcing for snowpack simulations with the detailed model Crocus. Their impact on the snowpack evolution was explored.
Emma L. Robinson and Douglas B. Clark
Hydrol. Earth Syst. Sci., 24, 1763–1779, https://doi.org/10.5194/hess-24-1763-2020, https://doi.org/10.5194/hess-24-1763-2020, 2020
Short summary
Short summary
This study used a water balance approach based on GRACE total water storage to infer the amount of cold-season precipitation in four Arctic river basins. This was used to evaluate four gridded meteorological data sets, which were used as inputs to a land surface model. We found that the cold-season precipitation in these data sets needed to be increased by up to 55 %. Using these higher precipitation inputs improved the model representation of Arctic hydrology, particularly lying snow.
Abbas Fayad and Simon Gascoin
Hydrol. Earth Syst. Sci., 24, 1527–1542, https://doi.org/10.5194/hess-24-1527-2020, https://doi.org/10.5194/hess-24-1527-2020, 2020
Short summary
Short summary
Seasonal snowpack is an essential water resource in Mediterranean mountains. Here, we look at the role of water percolation in simulating snow mass (SWE), for the first time, in Mount Lebanon. We use SnowModel, a distributed snow model, forced by station data. The main sources of uncertainty were attributed to rain–snow partitioning, transient winter snowmelt, and the subpixel snow cover. Yet, we show that a process-based model is suitable to simulate wet snowpack in Mediterranean mountains.
Joel Fiddes, Kristoffer Aalstad, and Sebastian Westermann
Hydrol. Earth Syst. Sci., 23, 4717–4736, https://doi.org/10.5194/hess-23-4717-2019, https://doi.org/10.5194/hess-23-4717-2019, 2019
Short summary
Short summary
In this paper we address one of the big challenges in snow hydrology, namely the accurate simulation of the seasonal snowpack in ungauged regions. We do this by assimilating satellite observations of snow cover into a modelling framework. Importantly (and a novelty of the paper), we include a clustering approach that permits highly efficient ensemble simulations. Efficiency gains and dependency on purely global datasets, means that this method can be applied over large areas anywhere on Earth.
Keith S. Jennings and Noah P. Molotch
Hydrol. Earth Syst. Sci., 23, 3765–3786, https://doi.org/10.5194/hess-23-3765-2019, https://doi.org/10.5194/hess-23-3765-2019, 2019
Short summary
Short summary
There is a wide variety of modeling methods to designate precipitation as rain, snow, or a mix of the two. Here we show that method choice introduces marked uncertainty to simulated snowpack water storage (> 200 mm) and snow cover duration (> 1 month) in areas that receive significant winter and spring precipitation at air temperatures at and near freezing. This marked uncertainty has implications for water resources management as well as simulations of past and future hydroclimatic states.
Maria Andrianaki, Juna Shrestha, Florian Kobierska, Nikolaos P. Nikolaidis, and Stefano M. Bernasconi
Hydrol. Earth Syst. Sci., 23, 3219–3232, https://doi.org/10.5194/hess-23-3219-2019, https://doi.org/10.5194/hess-23-3219-2019, 2019
Short summary
Short summary
We tested the performance of the SWAT hydrological model after being transferred from a small Alpine watershed to a greater area. We found that the performance of the model for the greater catchment was satisfactory and the climate change simulations gave insights into the impact of climate change on our site. Assessment tests are important in identifying the strengths and weaknesses of the models when they are applied under extreme conditions different to the ones that were calibrated.
Wenfeng Huang, Bin Cheng, Jinrong Zhang, Zheng Zhang, Timo Vihma, Zhijun Li, and Fujun Niu
Hydrol. Earth Syst. Sci., 23, 2173–2186, https://doi.org/10.5194/hess-23-2173-2019, https://doi.org/10.5194/hess-23-2173-2019, 2019
Short summary
Short summary
Up to now, little has been known on ice thermodynamics and lake–atmosphere interaction over the Tibetan Plateau during ice-covered seasons due to a lack of field data. Here, model experiments on ice thermodynamics were conducted in a shallow lake using HIGHTSI. Water–ice heat flux was a major source of uncertainty for lake ice thickness. Heat and mass budgets were estimated within the vertical air–ice–water system. Strong ice sublimation occurred and was responsible for water loss during winter.
Phillip Harder, John W. Pomeroy, and Warren D. Helgason
Hydrol. Earth Syst. Sci., 23, 1–17, https://doi.org/10.5194/hess-23-1-2019, https://doi.org/10.5194/hess-23-1-2019, 2019
Short summary
Short summary
As snow cover becomes patchy during snowmelt, energy is advected from warm snow-free surfaces to cold snow-covered surfaces. This paper proposes a simple sensible and latent heat advection model for snowmelt situations that can be coupled to one-dimensional energy balance snowmelt models. The model demonstrates that sensible and latent heat advection fluxes can compensate for one another, especially in early melt periods.
Fanny Larue, Alain Royer, Danielle De Sève, Alexandre Roy, and Emmanuel Cosme
Hydrol. Earth Syst. Sci., 22, 5711–5734, https://doi.org/10.5194/hess-22-5711-2018, https://doi.org/10.5194/hess-22-5711-2018, 2018
Short summary
Short summary
A data assimilation scheme was developed to improve snow water equivalent (SWE) simulations by updating meteorological forcings and snowpack states using passive microwave satellite observations. A chain of models was first calibrated to simulate satellite observations over northeastern Canada. The assimilation was then validated over 12 stations where daily SWE measurements were acquired during 4 winters (2012–2016). The overall SWE bias is reduced by 68 % compared to original SWE simulations.
Michael L. Follum, Jeffrey D. Niemann, Julie T. Parno, and Charles W. Downer
Hydrol. Earth Syst. Sci., 22, 2669–2688, https://doi.org/10.5194/hess-22-2669-2018, https://doi.org/10.5194/hess-22-2669-2018, 2018
Short summary
Short summary
Spatial patterns of snow and frozen ground within watersheds can impact the volume and timing of runoff. Commonly used snow and frozen ground simulation methods were modified to better account for the effects of topography and land cover on the spatial patterns of snow and frozen ground. When tested using a watershed in Vermont the modifications resulted in more accurate temporal and spatial simulation of both snow and frozen ground.
Jan Seibert, Marc J. P. Vis, Irene Kohn, Markus Weiler, and Kerstin Stahl
Hydrol. Earth Syst. Sci., 22, 2211–2224, https://doi.org/10.5194/hess-22-2211-2018, https://doi.org/10.5194/hess-22-2211-2018, 2018
Short summary
Short summary
In many glacio-hydrological models glacier areas are assumed to be constant over time, which is a crucial limitation. Here we describe a novel approach to translate mass balances as simulated by the (glacio)hydrological model into glacier area changes. We combined the Δh approach of Huss et al. (2010) with the bucket-type model HBV and introduced a lookup table approach, which also allows periods with advancing glaciers to be represented, which is not possible with the original Huss method.
Florian Hanzer, Kristian Förster, Johanna Nemec, and Ulrich Strasser
Hydrol. Earth Syst. Sci., 22, 1593–1614, https://doi.org/10.5194/hess-22-1593-2018, https://doi.org/10.5194/hess-22-1593-2018, 2018
Short summary
Short summary
Climate change effects on snow, glaciers, and hydrology are investigated for the Ötztal Alps region (Austria) using a hydroclimatological model driven by climate projections for the RCP2.6, RCP4.5, and RCP8.5 scenarios. The results show declining snow amounts and strongly retreating glaciers with moderate effects on catchment runoff until the mid-21st century, whereas annual runoff volumes decrease strongly towards the end of the century.
Muhammad Fraz Ismail and Wolfgang Bogacki
Hydrol. Earth Syst. Sci., 22, 1391–1409, https://doi.org/10.5194/hess-22-1391-2018, https://doi.org/10.5194/hess-22-1391-2018, 2018
Marit Van Tiel, Adriaan J. Teuling, Niko Wanders, Marc J. P. Vis, Kerstin Stahl, and Anne F. Van Loon
Hydrol. Earth Syst. Sci., 22, 463–485, https://doi.org/10.5194/hess-22-463-2018, https://doi.org/10.5194/hess-22-463-2018, 2018
Short summary
Short summary
Glaciers are important hydrological reservoirs. Short-term variability in glacier melt and also glacier retreat can cause droughts in streamflow. In this study, we analyse the effect of glacier changes and different drought threshold approaches on future projections of streamflow droughts in glacierised catchments. We show that these different methodological options result in different drought projections and that these options can be used to study different aspects of streamflow droughts.
Felix N. Matt, John F. Burkhart, and Joni-Pekka Pietikäinen
Hydrol. Earth Syst. Sci., 22, 179–201, https://doi.org/10.5194/hess-22-179-2018, https://doi.org/10.5194/hess-22-179-2018, 2018
Short summary
Short summary
Certain particles that have the ability to absorb sunlight deposit onto mountain snow via atmospheric transport mechanisms and then lower the snow's ability to reflect sunlight, which increases snowmelt. Herein we present a model aiming to simulate this effect and model the impacts on the streamflow of a southern Norwegian river. We find a significant difference in streamflow between simulations with and without the effect of light absorbing particles applied, in particular during spring melt.
Hiroyuki Hirashima, Francesco Avanzi, and Satoru Yamaguchi
Hydrol. Earth Syst. Sci., 21, 5503–5515, https://doi.org/10.5194/hess-21-5503-2017, https://doi.org/10.5194/hess-21-5503-2017, 2017
Short summary
Short summary
We reproduced the formation of capillary barriers and the development of preferential flow through snow using a multi-dimensional water transport model, which was then validated using laboratory experiments of liquid water infiltration into layered, initially dry snow. Simulation results showed that the model reconstructs some relevant features of capillary barriers and the timing of liquid water arrival at the snow base.
Claudio Bravo, Thomas Loriaux, Andrés Rivera, and Ben W. Brock
Hydrol. Earth Syst. Sci., 21, 3249–3266, https://doi.org/10.5194/hess-21-3249-2017, https://doi.org/10.5194/hess-21-3249-2017, 2017
Short summary
Short summary
We present an analysis of meteorological conditions and melt for Universidad Glacier in central Chile. This glacier is characterized by high melt rates over the ablation season, representing a mean contribution of between 10 and 13 % of the total runoff observed in the upper Tinguiririca Basin during the November 2009 to March 2010 period. Few studies have quantified the glacier melt contribution to river runoff in Chile, and this work represents a new precedent for the Andes.
Sebastian Würzer, Nander Wever, Roman Juras, Michael Lehning, and Tobias Jonas
Hydrol. Earth Syst. Sci., 21, 1741–1756, https://doi.org/10.5194/hess-21-1741-2017, https://doi.org/10.5194/hess-21-1741-2017, 2017
Short summary
Short summary
We discuss a dual-domain water transport model in a physics-based snowpack model to account for preferential flow (PF) in addition to matrix flow. So far no operationally used snow model has explicitly accounted for PF. The new approach is compared to existing water transport models and validated against in situ data from sprinkling and natural rain-on-snow (ROS) events. Our work demonstrates the benefit of considering PF in modelling hourly snowpack runoff, especially during ROS conditions.
Kelly E. Gleason, Anne W. Nolin, and Travis R. Roth
Hydrol. Earth Syst. Sci., 21, 1137–1147, https://doi.org/10.5194/hess-21-1137-2017, https://doi.org/10.5194/hess-21-1137-2017, 2017
Short summary
Short summary
We present a coupled modeling approach used to objectively identify representative snow-monitoring locations in a forested watershed in the western Oregon Cascades mountain range. The resultant Forest Elevational Snow Transect (ForEST) represents combinations of forested and open land cover types at low, mid-, and high elevations.
Rafael Pimentel, Javier Herrero, and María José Polo
Hydrol. Earth Syst. Sci., 21, 805–820, https://doi.org/10.5194/hess-21-805-2017, https://doi.org/10.5194/hess-21-805-2017, 2017
Short summary
Short summary
This study analyses the subgrid variability of the snow distribution in a Mediterranean region and formulates a parametric approach that includes these scale effects in the physical modelling of snow by means of accumulation–depletion curves associated with snow evolution patterns, by means of terrestrial photography. The results confirm that the use of these on a cell scale provides a solid foundation for the extension of point snow models to larger areas.
Nena Griessinger, Jan Seibert, Jan Magnusson, and Tobias Jonas
Hydrol. Earth Syst. Sci., 20, 3895–3905, https://doi.org/10.5194/hess-20-3895-2016, https://doi.org/10.5194/hess-20-3895-2016, 2016
Short summary
Short summary
In Alpine catchments, snowmelt is a major contribution to runoff. In this study, we address the question of whether the performance of a hydrological model can be enhanced by integrating data from an external snow monitoring system. To this end, a hydrological model was driven with snowmelt input from snow models of different complexities. Best performance was obtained with a snow model, which utilized data assimilation, in particular for catchments at higher elevations and for snow-rich years.
Jacob C. Yde, Niels T. Knudsen, Jørgen P. Steffensen, Jonathan L. Carrivick, Bent Hasholt, Thomas Ingeman-Nielsen, Christian Kronborg, Nicolaj K. Larsen, Sebastian H. Mernild, Hans Oerter, David H. Roberts, and Andrew J. Russell
Hydrol. Earth Syst. Sci., 20, 1197–1210, https://doi.org/10.5194/hess-20-1197-2016, https://doi.org/10.5194/hess-20-1197-2016, 2016
E. Cornwell, N. P. Molotch, and J. McPhee
Hydrol. Earth Syst. Sci., 20, 411–430, https://doi.org/10.5194/hess-20-411-2016, https://doi.org/10.5194/hess-20-411-2016, 2016
Short summary
Short summary
We present a high-resolution snow water equivalent estimation for the 2001–2014 period over the extratropical Andes Cordillera of Argentina and Chile, the first of its type. The effect of elevation on accumulation is confirmed, although this is less marked in the northern portion of the domain. The 3000–4000 m a.s.l. elevation band contributes the bulk of snowmelt, but the 4000–5000 m a.s.l. band is a significant source and deserves further monitoring and research.
S. Frey and H. Holzmann
Hydrol. Earth Syst. Sci., 19, 4517–4530, https://doi.org/10.5194/hess-19-4517-2015, https://doi.org/10.5194/hess-19-4517-2015, 2015
Short summary
Short summary
Temperature index melt models often lead to snow accumulation in high mountainous elevations. We developed a simple conceptual snow redistribution model working on a commonly used grid cell size of 1x1km. That model is integrated in the hydrological rainfall runoff model COSERO. Applying the model to the catchment of Oetztaler Ache, Austria, could prevent the accumulation of snow in the upper altitudes and lead to an improved model efficiency regarding discharge and snow coverage (MODIS).
Z. H. He, F. Q. Tian, H. V. Gupta, H. C. Hu, and H. P. Hu
Hydrol. Earth Syst. Sci., 19, 1807–1826, https://doi.org/10.5194/hess-19-1807-2015, https://doi.org/10.5194/hess-19-1807-2015, 2015
S. J. Marshall
Hydrol. Earth Syst. Sci., 18, 5181–5200, https://doi.org/10.5194/hess-18-5181-2014, https://doi.org/10.5194/hess-18-5181-2014, 2014
Short summary
Short summary
This paper presents a new 12-year glacier meteorological, mass balance, and run-off record from the Canadian Rocky Mountains. This provides insight into the glaciohydrological regime of the Rockies. For the period 2002-2013, about 60% of glacier meltwater run-off originated from seasonal snow and 40% was derived from glacier ice and firn. Ice and firn run-off is concentrated in the months of August and September, at which time it contributes significantly to regional-scale water resources.
J. You, D. G. Tarboton, and C. H. Luce
Hydrol. Earth Syst. Sci., 18, 5061–5076, https://doi.org/10.5194/hess-18-5061-2014, https://doi.org/10.5194/hess-18-5061-2014, 2014
Short summary
Short summary
This paper evaluates three improvements to an energy balance snowmelt model aimed to represent snow surface temperature while retaining the parsimony of a single layer. Surface heat flow is modeled using a forcing term related to the vertical temperature difference and a restore term related to the temporal gradient of surface temperature. Adjustments for melt water refreezing and thermal conductivity when the snow is shallow are introduced. The model performs well at the three test sites.
Cited articles
Anderton, S. P., White, S. M., and Alvera, B.: Evaluation of spatial
variability in snow water equivalent for a high mountain catchment,
Hydrol. Process., 18, 435–453, https://doi.org/10.1002/hyp.1319,
2004.
Baba, M., Gascoin, S., Jarlan, L., Simonneaux, V., and Hanich, L.: Variations
of the Snow Water Equivalent in the Ourika Catchment (Morocco) over
2000–2018 Using Downscaled MERRA-2 Data, Water, 1, 1120,
https://doi.org/10.3390/w10091120, 2018.
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.
Barnes, S. L.: A technique for maximizing details in numerical weather map
analysis, J. Appl. Meteorol., 3, 396–409,
https://doi.org/10.1175/1520-0450(1964)003<0396:ATFMDI>2.0.CO;2, 1964.
Barnes, S. L.: Mesoscale objective map analysis using weighted time-series
observations, Technical Report, National Severe Storms Lab., Norman,
Oklahoma, 1973.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature,
438, 303, https://doi.org/10.1038/nature04141, 2005.
Beamer, J. P., Hill, D. F., Arendt, A., and Liston, G. E.: High-resolution
modeling of coastal freshwater discharge and glacier mass balance in the
Gulf of Alaska watershed, Water Resour. Res., 52, 3888–3909,
https://doi.org/10.1002/2015WR018457, 2016.
Beamer, J. P., Hill, D. F., McGrath, D., Arendt, A., and Kienholz, C.:
Hydrologic impacts of changes in climate and glacier extent in the Gulf of
Alaska watershed, Water Resour. Res., 53, 7502–7520,
https://doi.org/10.1002/2016WR020033, 2017.
Blöschl, G.: Scaling issues in snow hydrology, Hydrol. Process., 13, 2149–2175,
https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2149::AID-HYP847>3.0.CO;2-8, 1999.
Blöschl, G. and Kirnbauer, R.: An analysis of snow cover patterns in a
small alpine catchment, Hydrol. Process., 6, 99–109,
https://doi.org/10.1002/hyp.3360060109, 1992.
Bohr, G. S. and Aguado, E.: Use of April 1 SWE measurements as estimates of
peak seasonal snowpack and total cold-season precipitation, Water Resour.
Res., 37, 51–60, https://doi.org/10.1029/2000WR900256, 2001.
Bonney, R., Cooper, C. B., Dickinson, J., Kelling, S., Phillips, T.,
Rosenberg, K. V., and Shirk, J.: Citizen science: a developing tool for
expanding science knowledge and scientific literacy, BioScience, 59, 977–984,
https://doi.org/10.1525/bio.2009.59.11.9, 2009.
Bühler, Y., Adams, M. S., Bösch, R., and Stoffel, A.: Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 1075–1088, https://doi.org/10.5194/tc-10-1075-2016, 2016.
Buytaert, W., Zulkafli, Z., Grainger, S., Acosta, L., Alemie, T. C.,
Bastiaensen, J., De Bièvre, B., Bhusal, J., Clark, J., Dewulf, A., and
Foggin, M.: Citizen science in hydrology and water resources: opportunities
for knowledge generation, ecosystem service management, and sustainable
development, Front. Earth Sci., 2, 26,
https://doi.org/10.3389/feart.2014.00026, 2014.
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in
the geosciences: An overview of methods, issues, and perspectives, WIRES Clim. Change, 9, e535,
https://doi.org/10.1002/wcc.535, 2018.
Carroll, T., Cline, D., Fall, G., Nilsson, A., Li, L., and Rost, A.: NOHRSC
operations and the simulation of snow cover properties for the coterminous
US, in: Proc. 69th Annual Meeting of the Western Snow Conf., Sun Valley Idaho, 1–14, 2001.
Carter, S., Carter, P., and Levison, J.: Skier triggered surface hoar: A
discussion of avalanche involvements during the 2006 Valdez Chugach
helicopter ski season, in: Proceedings of International Snow Science Workshop,
860–867, 2006.
Clark, M. P., Slater, A. G., Barrett, A. P., Hay, L. E., McCabe, G. J.,
Rajagopalan, B., and Leavesley, G. H.: Assimilation of snow covered area
information into hydrologic and land-surface models, Adv. Water
Resour., 29, 1209–1221,
https://doi.org/10.1016/j.advwatres.2005.10.001, 2006.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.: Representing spatial
variability of snow water equivalent in hydrologic and land-surface models:
A review, Water Resour. Res., 47, W07539,
https://doi.org/10.1029/2011WR010745, 2011.
Community Snow Observations Data Portal: http://app.communitysnowobs.org/, last access: 20 August 2021.
Contosta, A. R., Adolph, A., Burchsted, D., Burakowski, E., Green, M.,
Guerra, D., Albert, M., Dibb, J., Martin, M., McDowell, W. H., and Routhier,
M.: A longer vernal window: the role of winter coldness and snowpack in
driving spring transitions and lags, Glob. Change Biol., 23,
1610–1625, https://doi.org/10.1111/gcb.13517, 2017.
Cooper, C. B., Dickinson J., Phillips, T., and Bonney, R.: Citizen science
as a tool for conservation in residential ecosystems, Ecol. Soc.,
12, 11, http://www.ecologyandsociety.org/vol12/iss2/art11/ (last access:
5 May 2020), 2007.
Cosgrove, C. L., Wells, J., Nolin, A. W., Putera, J., and Prugh, L. R.: Seasonal
influence of snow conditions on Dall's sheep productivity in Wrangell-St
Elias National Park and Preserve, PloS One, 16, e0244787, https://doi.org/10.1371/journal.pone.0244787, 2021.
Crumley, R. and Hill D.; Preprocessing model inputs from Google Earth Engine tool, Zenodo [code], https://doi.org/10.5281/zenodo.5188622, 2021.
Crumley, R. L., Hill, D. F., Beamer, J. P., and Holzenthal, E. R.: Seasonal components of freshwater runoff in Glacier Bay, Alaska: diverse spatial patterns and temporal change, The Cryosphere, 13, 1597–1619, https://doi.org/10.5194/tc-13-1597-2019, 2019.
Deems, J. S. and Painter, T. H.: Lidar measurement of snow depth: accuracy and
error sources, in: Proceedings of the 2006 International Snow Science
Workshop: Telluride, Colorado, USA, International Snow Science Workshop,
330–338, 2006.
Dickinson, J. L., Zuckerberg, B., and Bonter, D. N.: Citizen science as an
ecological research tool: challenges and benefits, Annu. Rev. Ecol.
Evol. S., 41, 149–172,
https://doi.org/10.1146/annurev-ecolsys-102209-144636, 2010.
Dixon, D. and Boon, S.: Comparison of the SnowHydro snow sampler with
existing snow tube designs, Hydrol. Process., 26, 2555–2562,
https://doi.org/10.1002/hyp.9317, 2012.
Dressler, K. A., Fassnacht, S. R., and Bales, R. C.: A comparison of snow
telemetry and snow course measurements in the Colorado River basin, J. Hydrometeorol.,
7, 705–712, https://doi.org/10.1175/JHM506.1, 2006.
Elder, K., Rosenthal, W., and Davis, R. E.: Estimating the spatial
distribution of snow water equivalence in a montane watershed, Hydrol. Process., 12, 1793–1808,
https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1793::AID-HYP695>3.0.CO;2-K, 1998.
Fayad, A., Gascoin, S., Faour, G., López-Moreno, J. I., Drapeau, L., Le
Page, M., and Escadafal, R.: Snow hydrology in Mediterranean mountain
regions: A review, J. Hydrol., 551, 374–396,
https://doi.org/10.1016/j.jhydrol.2017.05.063, 2017.
Fienen, M. N. and Lowry, C. S.: Social. Water – A crowdsourcing tool for
environmental data acquisition, Comput. Geosci., 49, 164–169,
https://doi.org/10.1016/j.cageo.2012.06.015, 2012.
Fletcher, S. J., Liston, G. E., Hiemstra, C. A., and Miller, S. D.: Assimilating
MODIS and AMSR-E snow observations in a snow evolution model, J. Hydrometeorol., 13,
1475–1492, https://doi.org/10.1175/JHM-D-11-082.1, 2012.
Garnett, R. and Stewart, R.: Comparison of GPS units and mobile Apple GPS
capabilities in an urban landscape, Cartogr. Geogr. Inform., 42, 1–8, https://doi.org/10.1080/15230406.2014.974074, 2015.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., and Wargan,
K.: The modern-era retrospective analysis for research and applications,
version 2 (MERRA-2), J. Climate, 30, 5419–5454,
https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D.: The national elevation dataset, Photogramm. Eng. Rem. S., 68, 5–32, 2002.
Haberkorn, A.: European Snow Booklet – an Inventory of Snow Measurements in
Europe, EnviDat, https://doi.org/10.16904/envidat.59, 2019.
Hall, D. K. and Riggs, G. A., and Salomonson V. V.: MODIS/Terra Snow Cover Daily L3
Global 500 m Grid, Version 6. Boulder, CO, NASA National Snow and Ice Data
Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MOD10A1.006, 2016.
Han, E., Merwade, V., and Heathman, G. C.: Implementation of surface soil
moisture data assimilation with watershed scale distributed hydrological
model, J. Hydrol., 416, 98–117,
https://doi.org/10.1016/j.jhydrol.2011.11.039, 2012.
Hedrick, A. R., Marks, D., Havens, S., Robertson, M., Johnson, M., Sandusky,
M., Marshall, H. P., Kormos, P. R., Bormann, K. J., and Painter, T. H.: Direct
insertion of NASA Airborne Snow Observatory-derived snow depth time series
into the iSnobal energy balance snow model, Water Resour. Res.,
54, 8045–8063, https://doi.org/10.1029/2018WR023190, 2018.
Helmert, J., Lange, M., Dong, J., De Rosnay, P., Gustafsson, D., Churulin,
E., Kurzeneva, E., Müller, R., Trentmann, J., Souverijns, N., and Koch,
R.: 1st Snow Data Assimilation Workshop in the framework of COST HarmoSnow
ESSEM 1404, Meteorol. Z., 27, 325–333,
https://doi.org/10.1127/metz/2018/0906, 2018.
Hendrikx, J., Johnson, J., and Shelly, C.: Using GPS tracking to explore
terrain preferences of heli-ski guides, Journal of outdoor recreation and
tourism, 13, 34–43, https://doi.org/10.1016/j.jort.2015.11.004, 2016.
Hill, D. and Aragon, C.: Snow density calculator, Zenodo [code], https://doi.org/10.5281/zenodo.5225097, 2021.
Hill, D., Wolken, G., Wikstrom Jones K., Crumley, R., and Arendt, A.:
Crowdsourcing snow depth data with citizen scientists, Eos, 99,
https://doi.org/10.1029/2018EO108991, 2018.
Hill, D., Aragon, C., and Crumley, R.: Preprocessing Micromet inputs from Google Earth Engine, Zenodo [code], https://doi.org/10.5281/zenodo.5224852, 2021.
Hill, D. F., Burakowski, E. A., Crumley, R. L., Keon, J., Hu, J. M., Arendt, A. A., Wikstrom Jones, K., and Wolken, G. J.: Converting snow depth to snow water equivalent using climatological variables, The Cryosphere, 13, 1767–1784, https://doi.org/10.5194/tc-13-1767-2019, 2019.
Holko, L., Gorbachova, L., and Kostka, Z.: Snow hydrology in central Europe,
Geography Compass, 5, 200–218,
https://doi.org/10.1111/j.1749-8198.2011.00412.x, 2011.
Homer, C., Dewitz, J., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston,
J., Herold, N., Wickham, J., and Megown, K.: Completion of the 2011 National
Land Cover Database for the conterminous United States–representing a
decade of land cover change information. Photogrammetric Engineering &
Remote Sensing, 81, 345–354, 2015.
Jonas, T., Marty, C., and Magnusson, J.: Estimating the snow water equivalent
from snow depth measurements in the Swiss Alps, J. Hydrology,
378, 161–167, https://doi.org/10.1016/j.jhydrol.2009.09.021, 2009.
Johnson, J. B.: A theory of pressure sensor performance in snow, Hydrol.
Process., 18, 53–64, https://doi.org/10.1002/hyp.1310, 2003.
Johnson, J. B. and Schaefer, G. L.: The influence of thermal, hydrologic, and
snow deformation mechanisms on snow water equivalent pressure sensor
accuracy, Hydrol. Process., 16, 3529–3542,
https://doi.org/10.1002/hyp.1236, 2002.
Kalnay, E.: Atmospheric modeling, data assimilation and predictability,
Cambridge University Press, Cambridge, United Kingdom, 2003.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., and Zhu, Y.: The NCEP/NCAR
40-year reanalysis project, B. Am. Meteorol. Soc.,
77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Kapnick, S. and Hall, A.: Causes of recent changes in western North American
snowpack, Clim. Dynam., 38, 1885–1899, https://doi.org/10.1007/s00382-011-1089-y,
2012.
King, J. M., Cabrera, A. R., and Kelly, R. E.: The Snowtweets Project:
Communicating snow depth measurements from specialists and non-specialists
via mobile communication technologies and social networks, AGU Fall Meeting
Abstracts, Bibcode: 2009AGUFMED11A0562K, 2009.
Lader, R., Bhatt, U. S., Walsh, J. E., Rupp, T. S., and Bieniek, P. A.: Two-meter
temperature and precipitation from atmospheric reanalysis evaluated for
Alaska, J. Appl. Meteorol. Clim., 55, 901–922,
https://doi.org/10.1175/JAMC-D-15-0162.1, 2016.
Lehning, M., Bartelt, P., Brown, B., Russi, T., Stöckli, U., and
Zimmerli, M.: SNOWPACK model calculations for avalanche warning based upon a
new network of weather and snow stations, Cold Reg. Sci. Technol., 30, 145–157,
https://doi.org/10.1016/S0165-232X(99)00022-1, 1999.
Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T. A., Stähli, M.,
and Zappa, M.: ALPINE3D: a detailed model of mountain surface processes and its
application to snow hydrology, Hydrol. Process., 20, 2111–2128, https://doi.org/10.1002/hyp.6204, 2006.
Li, D., Wigmore, O., Durand, M. T., Vander-Jagt, B., Margulis, S. A., Molotch,
N. P., and Bales, R. C.: Potential of Balloon Photogrammetry for Spatially
Continuous Snow Depth Measurements, IEEE Geosci. Remote S., 17, 1667–1671, https://doi.org/10.1109/LGRS.2019.2953481, 2019.
Liston, G. E. and Elder, K.: A distributed snow-evolution modeling system
(SnowModel), J. Hydrometeorol., 7, 1259–1276,
https://doi.org/10.1175/JHM548.1, 2006a.
Liston, G. E. and Elder, K.: A meteorological distribution system for
high-resolution terrestrial modeling (MicroMet), J.
Hydrometeorol., 7, 217–234, https://doi.org/10.1175/JHM486.1, 2006b.
Liston, G. E. and Hiemstra, C. A.: A simple data assimilation system for
complex snow distributions (SnowAssim), J. Hydrometeorol., 9,
989–1004, https://doi.org/10.1175/2008JHM871.1, 2008.
Liston, G. E. and Hiemstra, C. A.: The changing cryosphere: Pan-Arctic snow
trends (1979–2009), J. Climate, 24, 5691–5712,
https://doi.org/10.1175/JCLI-D-11-00081.1, 2011.
Liston, G. E., Haehnel, R. B., Sturm, M., Hiemstra, C. A., Berezovskaya, S., and Tabler, R. D.: Simulating complex snow distributions in windy environments
using SnowTran-3D, J. Glaciol., 53, 241–256,
https://doi.org/10.3189/172756507782202865, 2007.
López-Moreno, J. I., Fassnacht, S. R., Heath, J. T., Musselman, K. N.,
Revuelto, J., Latron, J., Morán-Tejeda, E., and Jonas, T.: Small scale
spatial variability of snow density and depth over complex alpine terrain:
Implications for estimating snow water equivalent, Adv. Water Resour., 55, 40–52,
https://doi.org/10.1016/j.advwatres.2012.08.010, 2013.
Lowry, C. S. and Fienen, M. N.: CrowdHydrology: crowdsourcing hydrologic data
and engaging citizen scientists, GroundWater, 51, 151–156,
https://doi.org/10.1111/j.1745-6584.2012.00956.x, 2013.
Luce, C. H., Tarboton, D. G., and Cooley, K. R.: The influence of the spatial
distribution of snow on basin-averaged snowmelt, Hydrol. Process., 12, 1671–1683,
https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1671::AID-HYP688>3.0.CO;2-N, 1998.
Luojus, K., Pulliainen, J., Takala, M., Derksen, C., Rott, H., Nagler, T.,
Solberg, R., Wiesmann, A., Metsamaki, S., Malnes, E., and Bojkov, B.:
Investigating the feasibility of the GlobSnow snow water equivalent data for
climate research purposes, in: 2010 IEEE International Geoscience and Remote
Sensing Symposium, 4851–4853,
https://doi.org/10.1109/IGARSS.2010.5741987, 2010.
Magnusson, J., Gustafsson, D., Hüsler, F., and Jonas, T.: Assimilation of
point SWE data into a distributed snow cover model comparing two contrasting
methods, Water Resour. Res., 50, 7816–7835,
https://doi.org/10.1002/2014WR015302, 2014.
Magnusson, J., Winstral, A., Stordal, A. S., Essery, R., and Jonas, T.:
Improving physically based snow simulations by assimilating snow depths
using the particle filter, Water Resour. Res., 53, 1125–1143,
https://doi.org/10.1002/2016WR019092, 2017.
Mankin, J. S., Viviroli, D., Singh, D., Hoekstra, A. Y., and Diffenbaugh, N. S.:
The potential for snow to supply human water demand in the present and
future, Environ. Res. Lett., 10, 114016, https://doi.org/10.1088/1748-9326/10/11/114016, 2015.
Margulis, S. A., Girotto, M., Cortés, G., and Durand, M.: A particle batch
smoother approach to snow water equivalent estimation, J.
Hydrometeorol., 16, 1752–1772, https://doi.org/10.1175/JHM-D-14-0177.1, 2015.
Marks, D., Domingo, J., Susong, D., Link, T., and Garen, D.: A spatially
distributed energy balance snowmelt model for application in mountain
basins, Hydrol. Process., 13, 1935–1959,
https://doi.org/10.1002/(SICI)1099-1085(199909)13:12/13<1935::AID-HYP868>3.0.CO;2-C, 1999.
Massey, Jr., F. J.: The Kolmogorov-Smirnov test for goodness of fit, J. Am. Stat. Assoc.,
46, 68–78, 1951.
McCreight, J. L., Small, E. E., and Larson, K. M.: Snow depth, density, and SWE
estimates derived from GPS reflection data: Validation in the western US,
Water Resour. Res., 50, 6892–6909,
https://doi.org/10.1002/2014WR015561, 2014.
McGuire, M., Wood, A. W., Hamlet, A. F., and Lettenmaier, D. P.: Use of
satellite data for streamflow and reservoir storage forecasts in the Snake
River Basin, J. Water Resour. Plan. Man., 132, 97–110,
https://doi.org/10.1061/(ASCE)0733-9496(2006)132:2(97), 2006.
McKinley, D. C., Miller-Rushing, A. J., Ballard, H. L., Bonney, R., Brown, H.,
Cook-Patton, S. C., Evans, D. M., French, R. A., Parrish, J. K., Phillips, T. B., and Ryan, S. F.: Citizen science can improve conservation science, natural
resource management, and environmental protection, Biol. Conserv., 208, 15–28,
https://doi.org/10.1016/j.biocon.2016.05.015, 2017.
McLaughlin, D.: An integrated approach to hydrologic data assimilation:
interpolation, smoothing, and filtering, Adv. Water Resour., 25,
1275–1286, https://doi.org/10.1016/S0309-1708(02)00055-6, 2002.
McMillan, H. K., Hreinsson, E. Ö., Clark, M. P., Singh, S. K., Zammit, C., and Uddstrom, M. J.: Operational hydrological data assimilation with the recursive ensemble Kalman filter, Hydrol. Earth Syst. Sci., 17, 21–38, https://doi.org/10.5194/hess-17-21-2013, 2013.
Mernild, S. H., Liston, G. E., Hasholt, B., and Knudsen, N.T.: Snow
distribution and melt modeling for Mittivakkat Glacier, Ammassalik Island,
southeast Greenland, J. Hydrometeorol., 7, 808–824, https://doi.org/10.1175/JHM522.1,
2006.
Mernild, S. H., Liston, G. E., Hiemstra, C. A., Malmros, J. K., Yde, J. C., and
McPhee, J.: The Andes Cordillera. Part I: snow distribution, properties, and
trends (1979–2014), Int. J. Climatol., 37,
1680–1698, https://doi.org/10.1002/joc.4804, 2017a.
Mernild, S. H., Liston, G. E., Hiemstra, C. A., Yde, J. C., McPhee, J., and
Malmros, J.K.: The Andes Cordillera. Part II: Rio Olivares Basin snow
conditions (1979–2014), central Chile, Int. J.
Climatol., 37, 1699–1715, https://doi.org/10.1002/joc.4828, 2017b.
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C.,
Ebisuzaki, W., Jović, D., Woollen, J., Rogers, E., Berbery, E. H., and Ek,
M. B.: North American regional reanalysis, B. Am.
Meteorol. Soc., 87, 343–360,
https://doi.org/10.1175/BAMS-87-3-343, 2006.
Molotch, N. P. and Bales, R. C.: Scaling snow observations from the point to
the grid element: Implications for observation network design, Water Resour. Res., 41, W11421,
https://doi.org/10.1029/2005WR004229, 2005.
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.
Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M., and Engel, R.: Dramatic
declines in snowpack in the western US, Npj Climate and Atmospheric Science, 1, 1–6,
https://doi.org/10.1038/s41612-018-0012-1, 2018.
NOHRSC: Snow Data Assimilation System (SNODAS) Data Products at NSIDC,
Version 1, Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center,
https://doi.org/10.7265/N5TB14TC, 2004.
Pagano, T., Garen, D., Perkins, T., and Pasteris, P.: Daily updating of
operational statistical seasonal water supply forecasts for the western
U.S., J. Am. Water Resour. As., 45, 767–778,
https://doi.org/10.1111/j.1752-1688.2009.00321.x, 2009.
Painter, T. H., Berisford, D. F., Boardman, J. W., Bormann, K. J., Deems, J. S.,
Gehrke, F., Hedrick, A., Joyce, M., Laidlaw, R., Marks, D., and Mattmann, C.:
The Airborne Snow Observatory: Fusion of scanning lidar, imaging
spectrometer, and physically-based modeling for mapping snow water
equivalent and snow albedo, Remote Sens. Environ., 184, 139–152,
https://doi.org/10.1016/j.rse.2016.06.018, 2016.
Park, S. K. and Xu, L. (Eds.): Data assimilation for atmospheric, oceanic and
hydrologic applications, Vol. 2, Springer Science & Business Media, Heidelberg, Germany,
2013.
Pistocchi, A.: Simple estimation of snow density in an Alpine region,
J. Hydrol.: Regional Studies, 6, 82–89,
https://doi.org/10.1016/j.ejrh.2016.03.004, 2016.
Pomeroy, J. W., Gray, D. M., and Landine, P. G. The prairie blowing snow model:
characteristics, validation, operation, J. Hydrol., 144,
165–192, https://doi.org/10.1016/0022-1694(93)90171-5, 1993.
Rabier, F.: Overview of global data assimilation developments in numerical
weather-prediction centres, Q. J. Roy. Meteor.
Soc., 131, 3215–3233,
https://doi.org/10.1256/qj.05.129, 2005.
Reges, H. W., Doesken, N., Turner, J., Newman, N., Bergantino, A., and
Schwalbe, Z.: COCORAHS: The evolution and accomplishments of a volunteer
rain gauge network, B. Am. Meteorol. Soc., 97,
1831–1846, https://doi.org/10.1175/BAMS-D-14-00213.1, 2016.
Reichle, R. H.: Data assimilation methods in the Earth sciences, Adv.
Water Resour., 31, 1411–1418,
https://doi.org/10.1016/j.advwatres.2008.01.001, 2008.
Reichle, R. H., McLaughlin, D. B., and Entekhabi, D.: Hydrologic data
assimilation with the ensemble Kalman filter, Mon. Weather Rev.,
130, 103–114, https://doi.org/10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2, 2002.
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, W03537, https://doi.org/10.1029/2008WR007318, 2010.
Riemann, R., Wilson, B. T., Lister, A., and Parks, S.: An effective assessment
protocol for continuous geospatial datasets of forest characteristics using
USFS Forest Inventory and Analysis (FIA) data, Remote Sens.
Environ., 114, 2337–2352,
https://doi.org/10.1016/j.rse.2010.05.010, 2010.
Rivington, M., Matthews, K. B., Bellocchi, G., and Buchan, K.: Evaluating
uncertainty introduced to process-based simulation model estimates by
alternative sources of meteorological data, Agr. Syst., 88,
451–471, https://doi.org/10.1016/j.agsy.2005.07.004, 2006.
Saha, S., Moorthi, S., Pan, H. L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., and Liu, H.: The NCEP climate
forecast system reanalysis, B. Am. Meteorol. Soc.,
91, 1015–1058, https://doi.org/10.1175/2010BAMS3001.1, 2010.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Research Data Archive [data set], https://doi.org/10.5065/D61C1TXF, 2011.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer,
D., Hou, Y. T., Chuang, H. Y., Iredell, M., and Ek, M.: The NCEP climate
forecast system version 2, J. Climate, 27, 2185–2208,
https://doi.org/10.1175/JCLI-D-12-00823.1, 2014.
Schmucki, E., Marty, C., Fierz, C., and Lehning, M.: Evaluation of modelled
snow depth and snow water equivalent at three contrasting sites in
Switzerland using SNOWPACK simulations driven by different meteorological
data input, Cold Reg. Sci. Technol., 99, 27–37,
https://doi.org/10.1016/j.coldregions.2013.12.004, 2014.
Schaefer, M. and Woodyer, T.: Assessing absolute and relative accuracy of
recreation-grade and mobile phone GNSS devices: a method for informing
device choice, Area, 47, 185–196, https://doi.org/10.1111/area.12172,
2015.
Schlögl, S., Marty, C., Bavay, M., and Lehning, M.: Sensitivity of
Alpine3D modeled snow cover to modifications in DEM resolution, station
coverage and meteorological input quantities, Environ. Modell.
Softw., 83, 387–396, https://doi.org/10.1016/j.envsoft.2016.02.017,
2016.
Schneider, C., Laizé, C. L. R., Acreman, M. C., and Flörke, M.: How will climate change modify river flow regimes in Europe?, Hydrol. Earth Syst. Sci., 17, 325–339, https://doi.org/10.5194/hess-17-325-2013, 2013.
Seibert, J., Strobl, B., Etter, S., Hummer, P., and van Meerveld, H.J.:
Virtual staff gauges for crowd-based stream level observations, Front.
Earth Sci., 7, 70,
https://doi.org/10.3389/feart.2019.00070, 2019.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and Pulwarty,
R. S.: Characteristics of the western United States snowpack from snowpack
telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160,
https://doi.org/10.1029/1999WR900090, 1999.
Shulski, M. and Wendler, G.: The climate of Alaska, University of Alaska
Press, Fairbanks, Alaska, USA, 2007.
Silvertown, J.: A new dawn for citizen science, Trends Ecol.
Evol., 24, 467–471, https://doi.org/10.1016/j.tree.2009.03.017,
2009.
Sturm, M.: White water: Fifty years of snow research in WRR and the outlook
for the future, Water Resour. Res., 51, 4948–4965,
https://doi.org/10.1002/2015WR017242, 2015.
Sturm, M. and Wagner, A. M.: Using repeated patterns in snow distribution
modeling: An Arctic example, Water Resour. Res., 46, W12549, https://doi.org/10.1029/2010WR009434,
2010.
Sturm, M., Holmgren, J., and Liston, G. E.: A seasonal snow cover
classification system for local to global applications, J. Climate,
8, 1261–1283, https://doi.org/10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2, 1995.
Sturm, M., Taras, B., Liston, G., Derksen, C., Jonas, T., and Lea, J.:
Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes,
J. Hydrometeorol., 11, 1380–1394,
https://doi.org/10.1175/2010JHM1202.1, 2010.
Trujillo, E., Molotch, N. P., Goulden, M. L., Kelly, A. E., and Bales, R. C.:
Elevation-dependent influence of snow accumulation on forest greening,
Nat. Geosci., 5, 705–709, https://doi.org/10.1038/ngeo1571, 2012.
Natural Resources Conservation Service, Upper Tsaina SNOTEL Station Data Porta: https://wcc.sc.egov.usda.gov/nwcc/site?sitenum=1055, last access: 20 August 2021.
van Meerveld, H. J. I., Vis, M. J. P., and Seibert, J.: Information content of stream level class data for hydrological model calibration, Hydrol. Earth Syst. Sci., 21, 4895–4905, https://doi.org/10.5194/hess-21-4895-2017, 2017.
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M., and Weingartner, R.:
Mountains of the world, water towers for humanity: Typology, mapping, and
global significance, Water Resour. Res., 43, W07447,
https://doi.org/10.1029/2006WR005653, 2007.
Wagner, W.: Investigating the snow climate of Turnagain Pass, Alaska, in:
Proceedings of the International Snow Science Workshop, Anchorage, AK,
913–917, 2012.
Wiggins, A. and Crowston, K.: From conservation to crowdsourcing: A typology
of citizen science, in: 2011 44th Hawaii international conference on system
sciences, 1–10, https://doi.org/10.1109/HICSS.2011.207, 2011.
Wrzesien, M. L., Durand, M. T., Pavelsky, T. M., Howat, I. M., Margulis, S. A., and Huning, L. S.: Comparison of methods to estimate snow water equivalent at
the mountain range scale: a case study of the California Sierra Nevada,
J. Hydrometeorol., 18, 1101–1119, https://doi.org/10.1175/JHM-D-16-0246.1, 2017.
Yeeles, A.: Citizen snow-scientists trek into the back country, Nat.
Clim. Change, 8, 944, https://doi.org/10.1038/s41558-018-0329-0,
2018.
Young, J. C., Pettit, E., Arendt, A., Hood, E., Liston, G. E., and Beamer, J.:
A changing hydrological regime: Trends in magnitude and timing of glacier
ice melt and glacier runoff in a high latitude coastal watershed, Water
Resour. Res., 57, e2020WR027404, https://doi.org/10.1029/2020WR027404, 2020.
Short summary
In this study, we use a new snow data set collected by participants in the Community Snow Observations project in coastal Alaska to improve snow depth and snow water equivalence simulations from a snow process model. We validate our simulations with multiple datasets, taking advantage of snow telemetry (SNOTEL), snow depth and snow water equivalence, and remote sensing measurements. Our results demonstrate that assimilating citizen science snow depth measurements can improve model performance.
In this study, we use a new snow data set collected by participants in the Community Snow...