Articles | Volume 28, issue 3
https://doi.org/10.5194/hess-28-631-2024
© Author(s) 2024. 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-28-631-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Melissa L. Wrzesien
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Sujay V. Kumar
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Eunsang Cho
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Current address: Ingram School of Engineering, Texas State University, San Marcos, TX, USA
Kristi R. Arsenault
Science Applications International Corporation, McLean, VA, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Paul R. Houser
Geography and Geoinformation Science Department, George Mason University, Fairfax,VA, USA
Carrie M. Vuyovich
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
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Zachary Fair, Carrie Vuyovich, Thomas Neumann, Justin Pflug, David Shean, Ellyn M. Enderlin, Karina Zikan, Hannah Besso, Jessica Lundquist, Cesar Deschamps-Berger, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3992, https://doi.org/10.5194/egusphere-2024-3992, 2025
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Lidar is commonly used to measure snow over global water reservoirs. However, ground-based and airborne lidar surveys are expensive, so satellite-based methods are needed. In this review, we outline the latest research using satellite-based lidar to monitor snow. Best practices for lidar-based snow monitoring are given, as is a discussion on challenges in this field of research.
Justin M. Pflug, Yiwen Fang, Steven A. Margulis, and Ben Livneh
Hydrol. Earth Syst. Sci., 27, 2747–2762, https://doi.org/10.5194/hess-27-2747-2023, https://doi.org/10.5194/hess-27-2747-2023, 2023
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Wolverine denning habitat inferred using a snow threshold differed for three different spatial representations of snow. These differences were based on the annual volume of snow and the elevation of the snow line. While denning habitat was most influenced by winter meteorological conditions, our results show that studies applying thresholds to environmental datasets should report uncertainties stemming from different spatial resolutions and uncertainties introduced by the thresholds themselves.
Kajsa Holland-Goon, Randall Bonnell, Daniel McGrath, W. Brad Baxter, Tate Meehan, Ryan Webb, Chris Larsen, Hans-Peter Marshall, Megan Mason, and Carrie Vuyovich
EGUsphere, https://doi.org/10.5194/egusphere-2025-2435, https://doi.org/10.5194/egusphere-2025-2435, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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As part of the NASA SnowEx23 campaign, we conducted detailed snowpack experiments in Alaska’s boreal forests and Arctic tundra. We collected ground-penetrating radar measurements of snow depth along 44 short transects. We then excavated the snowpack from below the transects and measured snow depth, noting any vegetation and void spaces. We used the detailed in situ measurements to evaluate uncertainties in ground-penetrating radar and airborne lidar methods for snow depth retrieval.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2025-2550, https://doi.org/10.5194/egusphere-2025-2550, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Edward H. Bair, Dar A. Roberts, David R. Thompson, Philip G. Brodrick, Brenton A. Wilder, Niklas Bohn, Christopher J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
The Cryosphere, 19, 2315–2320, https://doi.org/10.5194/tc-19-2315-2025, https://doi.org/10.5194/tc-19-2315-2025, 2025
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Key to the success of future satellite missions is understanding snowmelt in our warming climate, as this has implications for nearly 2 billion people. An obstacle is that an artifact, called the hook, is often mistaken for soot or dust. Instead, it is caused by three amplifying effects: (1) background reflectance that is too dark, (2) an assumption of level terrain, and (3) differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
Cenlin He, Tzu-Shun Lin, David M. Mocko, Ronnie Abolafia-Rosenzweig, Jerry W. Wegiel, and Sujay V. Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2024-4176, https://doi.org/10.5194/egusphere-2024-4176, 2025
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This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
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We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
Zachary Fair, Carrie Vuyovich, Thomas Neumann, Justin Pflug, David Shean, Ellyn M. Enderlin, Karina Zikan, Hannah Besso, Jessica Lundquist, Cesar Deschamps-Berger, and Désirée Treichler
EGUsphere, https://doi.org/10.5194/egusphere-2024-3992, https://doi.org/10.5194/egusphere-2024-3992, 2025
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Lidar is commonly used to measure snow over global water reservoirs. However, ground-based and airborne lidar surveys are expensive, so satellite-based methods are needed. In this review, we outline the latest research using satellite-based lidar to monitor snow. Best practices for lidar-based snow monitoring are given, as is a discussion on challenges in this field of research.
Colleen Mortimer, Lawrence Mudryk, Eunsang Cho, Chris Derksen, Mike Brady, and Carrie Vuyovich
The Cryosphere, 18, 5619–5639, https://doi.org/10.5194/tc-18-5619-2024, https://doi.org/10.5194/tc-18-5619-2024, 2024
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Ground measurements of snow water equivalent (SWE) are vital for understanding the accuracy of large-scale estimates from satellites and climate models. We compare two types of measurements – snow courses and airborne gamma SWE estimates – and analyze how measurement type impacts the accuracy assessment of gridded SWE products. We use this analysis to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications.
Peyman Abbaszadeh, Fadji Zaouna Maina, Chen Yang, Dan Rosen, Sujay Kumar, Matthew Rodell, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-280, https://doi.org/10.5194/hess-2024-280, 2024
Revised manuscript accepted for HESS
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To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Preprint archived
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Eunsang Cho, Megan Verfaillie, Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
EGUsphere, https://doi.org/10.5194/egusphere-2024-1530, https://doi.org/10.5194/egusphere-2024-1530, 2024
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Uncrewed Aerial Systems (UAS) lidar and structure-from-motion (SfM) photogrammetry are effective methods for mapping high-resolution snow depths. However, there are limited studies comparing their performance across different surface features and tracking spatial patterns of snowpack changes over time. Our study found that UAS lidar outperformed SfM photogrammetry. With limited wind effects, the snow spatial structure captured by UAS lidar remained temporally stable throughout the snow season.
Zachary Hoppinen, Shadi Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, and Carrie Vuyovich
The Cryosphere, 18, 575–592, https://doi.org/10.5194/tc-18-575-2024, https://doi.org/10.5194/tc-18-575-2024, 2024
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We used changes in radar echo travel time from multiple airborne flights to estimate changes in snow depths across Idaho for two winters. We compared our radar-derived retrievals to snow pits, weather stations, and a 100 m resolution numerical snow model. We had a strong Pearson correlation and root mean squared error of 10 cm relative to in situ measurements. Our retrievals also correlated well with our model, especially in regions of dry snow and low tree coverage.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
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An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere, 17, 3435–3442, https://doi.org/10.5194/tc-17-3435-2023, https://doi.org/10.5194/tc-17-3435-2023, 2023
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This study compares snow depth measurements from two manual instruments in a field and forest. Snow depths measured using a magnaprobe were typically 1 to 3 cm deeper than those measured using a snow tube. These differences were greater in the forest than in the field.
Justin M. Pflug, Yiwen Fang, Steven A. Margulis, and Ben Livneh
Hydrol. Earth Syst. Sci., 27, 2747–2762, https://doi.org/10.5194/hess-27-2747-2023, https://doi.org/10.5194/hess-27-2747-2023, 2023
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Wolverine denning habitat inferred using a snow threshold differed for three different spatial representations of snow. These differences were based on the annual volume of snow and the elevation of the snow line. While denning habitat was most influenced by winter meteorological conditions, our results show that studies applying thresholds to environmental datasets should report uncertainties stemming from different spatial resolutions and uncertainties introduced by the thresholds themselves.
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
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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.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
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The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
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This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
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The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar
Hydrol. Earth Syst. Sci., 26, 2221–2243, https://doi.org/10.5194/hess-26-2221-2022, https://doi.org/10.5194/hess-26-2221-2022, 2022
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Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.
Holly Proulx, Jennifer M. Jacobs, Elizabeth A. Burakowski, Eunsang Cho, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, and Cameron Wagner
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-7, https://doi.org/10.5194/tc-2022-7, 2022
Manuscript not accepted for further review
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This study compares snow depth measurements from two manual instruments and an airborne platform in a field and forest. The manual instruments’ snow depths differed by 1 to 3 cm. The airborne measurements , which do not penetrate the leaf litter, were consistently shallower than either manual instrument. When combining airborne snow depth maps with manual density measurements, corrections may be required to create unbiased maps of snow properties.
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021, https://doi.org/10.5194/acp-21-11013-2021, 2021
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This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.
Michiel Maertens, Gabriëlle J. M. De Lannoy, Sebastian Apers, Sujay V. Kumar, and Sarith P. P. Mahanama
Hydrol. Earth Syst. Sci., 25, 4099–4125, https://doi.org/10.5194/hess-25-4099-2021, https://doi.org/10.5194/hess-25-4099-2021, 2021
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In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
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This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
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High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Yifan Zhou, Benjamin F. Zaitchik, Sujay V. Kumar, Kristi R. Arsenault, Mir A. Matin, Faisal M. Qamer, Ryan A. Zamora, and Kiran Shakya
Hydrol. Earth Syst. Sci., 25, 41–61, https://doi.org/10.5194/hess-25-41-2021, https://doi.org/10.5194/hess-25-41-2021, 2021
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South and Southeast Asia face significant food insecurity and hydrological hazards. Here we introduce a South and Southeast Asia hydrological monitoring and sub-seasonal to seasonal forecasting system (SAHFS-S2S) to help local governments and decision-makers prepare for extreme hydroclimatic events. The monitoring system captures soil moisture variability well in most regions, and the forecasting system offers skillful prediction of soil moisture variability 2–3 months in advance, on average.
Anne Sophie Daloz, Marian Mateling, Tristan L'Ecuyer, Mark Kulie, Norm B. Wood, Mikael Durand, Melissa Wrzesien, Camilla W. Stjern, and Ashok P. Dimri
The Cryosphere, 14, 3195–3207, https://doi.org/10.5194/tc-14-3195-2020, https://doi.org/10.5194/tc-14-3195-2020, 2020
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The total of snow that falls globally is a critical factor governing freshwater availability. To better understand how this resource is impacted by climate change, we need to know how reliable the current observational datasets for snow are. Here, we compare five datasets looking at the snow falling over the mountains versus the other continents. We show that there is a large consensus when looking at fractional contributions but strong dissimilarities when comparing magnitudes.
Cited articles
Arsenault, K. R., Wrzesien, M., Gutmann, E. D., Vuyovich, C., Liston, G. E., Mower, R., Reinking, A., Newman, A. J., Kumar, S. V., Wang, S., Navari, M., Forman, B. A., and Jessica, L.: Implementing SnowModel into the Land Information System Framework to Support High Resolution Modeling of Snow Heterogeneity, Presented at the AGU Fall Meeting 2021, AGU, 13–17 December 2021, New Orleans, LA, https://ui.adsabs.harvard.edu/abs/2021AGUFM.C35G0945A/abstract (last access: 9 February 2024), 2021.
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.
Barry, R. G.: The Role of Snow and Ice in the Global Climate System: A Review, Polar Geography, 26, 235–246, https://doi.org/10.1080/789610195, 2002.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Beven, K. J., Kirkby, M. J., Freer, J. E., and Lamb, R.: A history of TOPMODEL, Hydrol. Earth Syst. Sci., 25, 527–549, https://doi.org/10.5194/hess-25-527-2021, 2021.
Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., and Kim, R. S.: Evaluating the utility of active microwave observations as a snow mission concept using observing system simulation experiments, The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, 2023.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B., Cullen, N. J., Kerr, T., Hreinsson, E. Ö., 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.
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., and Luo, L.: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res.-Atmos., 108, 8842, https://doi.org/10.1029/2002JD003118, 2003.
De Lannoy, G. J. M., Reichle, R. H., Arsenault, K. R., Houser, P. R., Kumar, S., Verhoest, N. E. C., and Pauwels, V. R. N.: Multiscale assimilation of Advanced Microwave Scanning Radiometer–EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado, Water Resour. Res., 48, W01522, https://doi.org/10.1029/2011WR010588, 2012.
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Interannual Consistency in Fractal Snow Depth Patterns at Two Colorado Mountain Sites, J. Hydrometeor., 9, 977–988, https://doi.org/10.1175/2008JHM901.1, 2008.
Derksen, C., Lemmetyinen, J., Toose, P., Silis, A., Pulliainen, J., and Sturm, M.: Physical properties of arctic versus subarctic snow: Implications for high latitude passive microwave snow water equivalent retrievals, J. Geophys. Res.-Atmos., 119, 7254–7270, https://doi.org/10.1002/2013JD021264, 2014.
Durand, M., Johnson, J. T., Dechow, J., Tsang, L., Borah, F., and Kim, E. J.: Retrieval of snow water equivalent from dual-frequency radar measurements: using time series to overcome the need for accurate a priori information, The Cryosphere, 18, 139–152, https://doi.org/10.5194/tc-18-139-2024, 2024.
Copernicus Climate Change Service: ERA5 data, European Centre for Medium-Range Weather Forecasts climate data store [data set], https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 (last access: 9 February 2024), 2024.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Errico, R. M., Yang, R., Masutani, M., and Woollen, J. S.: The estimation of analysis error characteristics using an observation systems simulation experiment, Meteorol. Z., 16, 695–708, 2007.
Fang, Y., Liu, Y., and Margulis, S. A.: A western United States snow reanalysis dataset over the Landsat era from water eyars 1985 to 2021, Sci. Data, 9, 677, https://doi.org/10.1038/s41597-022-01768-7, 2022.
Foster, J. L., Sun, C., Walker, J. P., Kelly, R., Chang, A., Dong, J., and Powell, H.: Quantifying the uncertainty in passive microwave snow water equivalent observations, Remote Sens. Environ., 94, 187–203, https://doi.org/10.1016/j.rse.2004.09.012, 2005.
Franz, K. J., Butcher, P., and Ajami, N. K.: Addressing snow model uncertainty for hydrologic prediction, Adv. Water Resour., 33, 820–832, https://doi.org/10.1016/j.advwatres.2010.05.004, 2010.
Garnaud, C., Bélair, S., Carrera, M. L., Derksen, C., Bilodeau, B., Abrahamowicz, M., Gauthier, N., and Vionnet, V.: Quantifying Snow Mass Mission Concept Trade-Offs Using an Observing System Simulation Experiment, J. Hydrometeorol., 20, 155–173, https://doi.org/10.1175/JHM-D-17-0241.1, 2019.
Garousi-Nejad, I. and Tarboton, D. G.: A comparison of National Water Model retrospective analysis snow outputs at snow telemetry sites across the Western United States, Hydrol. Process., 36, e14469, https://doi.org/10.1002/hyp.14469, 2022.
Gelaro, R., McCarty, W., Suárez, M.J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., Silva, A. M. da, Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: 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.
GES DISC: MERRA-2 forcing data, Goddard Earth Sciences Data and Information Services Center [data set], https://disc.gsfc.nasa.gov/ (last access: 9 February 2024), 2024.
Henn, B., Newman, A. J., Livneh, B., Daly, C., and Lundquist, J. D.: An assessment of differences in gridded precipitation datasets in complex terrain, J. Hydrol., 556, 1205–1219, https://doi.org/10.1016/j.jhydrol.2017.03.008, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hiemstra, C. A., Liston, G. E., and Reiners, W. A.: Snow Redistribution by Wind and Interactions with Vegetation at Upper Treeline in the Medicine Bow Mountains, Wyoming, U.S.A., Arct. Antarct. Alp. Res., 34, 262–273, https://doi.org/10.1080/15230430.2002.12003493, 2002.
Huang, H., Tsang, L., Colliander, A., and Yueh, S. H.: Propagation of Waves in Randomly Distributed Cylinders Using Three-Dimensional Vector Cylindrical Wave Expansions in Foldy–Lax Equations, IEEE Journal on Multiscale and Multiphysics Computational Techniques 4, 214–226, https://doi.org/10.1109/JMMCT.2019.2948022, 2019.
Kim, R. S., Kumar, S., Vuyovich, C., Houser, P., Lundquist, J., Mudryk, L., Durand, M., Barros, A., Kim, E. J., Forman, B. A., Gutmann, E. D., Wrzesien, M. L., Garnaud, C., Sandells, M., Marshall, H.-P., Cristea, N., Pflug, J. M., Johnston, J., Cao, Y., Mocko, D., and Wang, S.: Snow Ensemble Uncertainty Project (SEUP): quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling, The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, 2021.
Koster, R. D., Suarez, M. J., Ducharne, A., Stieglitz, M., and Kumar, P.: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure. J. Geophys. Res.-Atmos., 105, 24809–24822, https://doi.org/10.1029/2000JD900327, 2000.
Koster, R. D., Mahanama, S. P. P., Livneh, B., Lettenmaier, D. P., and Reichle, R. H.: Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow, Nat. Geosci., 3, 613–616, https://doi.org/10.1038/ngeo944, 2010.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F., and Sheffield, J.: Land information system: An interoperable framework for high resolution land surface modeling, Environ. Model. Softw., 21, 1402–1415, https://doi.org/10.1016/j.envsoft.2005.07.004, 2006.
Kumar, S. V., Peters-Lidard, C. D., Mocko, D., and Tian, Y.: Multiscale Evaluation of the Improvements in Surface Snow Simulation through Terrain Adjustments to Radiation, J. Hydrometeorol., 14, 220–232, https://doi.org/10.1175/JHM-D-12-046.1, 2013.
Kumar, S. V., Kolassa, J., Reichle, R., Crow, W., de Lannoy, G., de Rosnay, P., MacBean, N., Girotto, M., Fox, A., Quaife, T., Draper, C., Forman, B., Balsamo, G., Steele-Dunne, S., Albergel, C., Bonan, B., Calvet, J.-C., Dong, J., Liddy, H., and Ruston, B.: An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space, J. Adv. Model. Earth Sy., 14, e2022MS003259, https://doi.org/10.1029/2022MS003259, 2022.
Kwon, Y., Yoon, Y., Forman, B. A., Kumar, S. V., and Wang, L.: Quantifying the observational requirements of a space-borne LiDAR snow mission, J. Hydrol., 601, 126709, https://doi.org/10.1016/j.jhydrol.2021.126709, 2021.
Lahmers, T. M., Kumar, S. V., Rosen, D., Dugger, A., Gochis, D. J., Santanello, J. A., Gangodagamage, C., and Dunlap, R.: Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF-Hydro System, Water Resour. Res., 58, e2021WR029867, https://doi.org/10.1029/2021WR029867, 2022.
Latifovic, R., Pouliot, D., and Olthof, I.: Circa 2010 Land Cover of Canada: Local optimization methodology and product development, Remote Sens., 9, 11, https://doi.org/10.3390/rs9111098, 2017.
Le Moigne, J., Dabney, P., de Weck, O., Foreman, V., Grogan, P., Holland, M., Hughes, S., and Nag, S.: Tradespace analysis tool for designing constellations (TAT-C), in: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, 1181–1184, https://doi.org/10.1109/IGARSS.2017.8127168, 2017.
Lehning, M., Grünewald, T., and Schirmer, M.: Mountain snow distribution governed by an altitudinal gradient and terrain roughness, Geophys. Res. Lett., 38, L19504, https://doi.org/10.1029/2011GL048927, 2011.
Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.: How much runoff originates as snow in the western United States, and how will that change in the future?, Geophys. Res. Lett., 44, 6163–6172, https://doi.org/10.1002/2017GL073551, 2017.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Lievens, H., Demuzere, M., Marshall, H. P., Reichle, R. H., Brucker, L., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W., Jonas, T., Kim, E. J., Koch, I., Marty, C., Saloranta, T., Schober, J., and De Lannoy, G. J. M.: Snow depth variability in the Northern Hemisphere mountains observed from space, Nat. Commun., 10, 4629, https://doi.org/10.1038/s41467-019-12566-y, 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, 2006.
Liston, G. E., Perham, C. J., Shideler, R. T., and Cheuvront, A. N.: Modeling snowdrift habitat for polar bear dens, Ecol. Model., 320, 114–134, https://doi.org/10.1016/j.ecolmodel.2015.09.010, 2016.
Liu, Y., Fang, Y., Li, D., and Margulis, S. A.: How well do global snow products characterize snow storage in high mountain Asia?, Geophys. Res. Lett., 49, e2022GL100082, https://doi.org/10.1029/2022GL100082, 2022.
Livneh, B. and Badger, A. M.: Drought less predictable under declining future snowpack, Nat. Clim. Chang., 10, 452–458, https://doi.org/10.1038/s41558-020-0754-8, 2020.
Lundquist, J. D., Dickerson-Lange, S., Gutmann, E., Jonas, T., Lumbrazo, C., and Reynolds, D.: Snow interceptions modelling: Isolated observations have led to many land surface models lacking appropriate temperature sensitivities, Hydrol. Process., 35, 7, https://doi.org/10.1002/hyp.14274, 2021.
Mahoney, P. J., Liston, G. E., LaPoint, S., Gurarie, E., Mangipane, B., Wells, A. G., Brinkman, T. J., Eitel, J. U. H., Hebblewhite, M., Nolin, A. W., Boelman, N., and Prugh, L. R.: Navigating snowscapes: scale-dependent responses of mountain sheep to snowpack properties, Ecol. Appl., 28, 1715–1729, https://doi.org/10.1002/eap.1773, 2018.
McGrath, D., Sass, L., O'Neel, S., McNeil, C., Candela, S. G., Baker, E. H., and Marshall, H.-P.: Interannual snow accumulation variability on glaciers derived from repeat, spatially extensive ground-penetrating radar surveys, The Cryosphere, 12, 3617–3633, https://doi.org/10.5194/tc-12-3617-2018, 2018.
Mernild, S. H., Liston, G. E., Hiemstra, C., and Wilson, R.: The Andes Cordillera. Part III: glacier surface mass balance and contribution to sea level rise (1979–2014), Int. J. Climatol., 37, 3154–3174, https://doi.org/10.1002/joc.4907, 2017.
Minder, J. R., Durran, D. R., Roe, G. H., and Anders, A. M.: The climatology of small-scale orographic precipitation over the Olympic Mountains: Patterns and processes, Q. J. Roy. Meteor. Soc., 134, 817–839, https://doi.org/10.1002/qj.258, 2008.
Montomoli, F., Macelloni, G., Brogioni, M., Lemmetyinen, J., Cohen, J., and Rott, H.: Observations and simulation of multifrequency SAR data over a snow-covered boreal forest, IEEE J. Sel. Top. Appl., 9, 1216–1228, 2015.
NASA: LIS Framework, https://lis.gsfc.nasa.gov/ (last access: 9 February 2024), 2024.
NASA: Vehicle management space air ground Trade-space Analysis Tool for designing Constellations (TAT-C) – Version 2.0 (GSC-18399-1) https://software.nasa.gov/software/GSC-18399-1 last access: 9 February 2024), 2024.
NASA-LIS: LISF, GitHub [code], https://github.com/NASA-LIS/LISF (last access: 9 February 2024), 2024.
NASM: National Academies of Sciences, Engineering, and Medicine: Thriving on our changing planet: A decadal strategy for Earth observation from space, Washington, DC, The National Academies Press, https://doi.org/10.17226/24938, 2018.
Niu, G., Yang, Z., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Niu, G.-Y. and Yang, Z.-L.: Effects of vegetation canopy processes on snow surface energy and mass balances, J. Geophys. Res.-Atmos., 109, D23111, https://doi.org/10.1029/2004JD004884, 2004.
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., Mattmann, C., McGurk, B., Ramirez, P., Richardson, M., Skiles, S. M., Seidel, F. C., and Winstral, A.: 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.
Pflug, J. M.: Pflug et al. (2023) – Model configuration and outputs, HydroShare [data set], http://www.hydroshare.org/resource/e0ad80f818bf4062a335e9e0d7362834 (last access: 9 February 2024), 2023.
Pflug, J. M., Hughes, M., and Lundquist, J. D.: Downscaling snow deposition using historic snow depth patterns: Diagnosing limitations from snowfall biases, winter snow losses, and interannual snow pattern repeatability, Water Resour. Res., e2021WR029999, https://doi.org/10.1029/2021WR029999, 2021.
Pflug, J. M., Margulis, S. A., and Lundquist, J. D.: Inferring watershed-scale mean snowfall magnitude and distribution using multidecadal snow reanalysis patterns and snow pillow observations, Hydrol. Process., 36, e14581, 2022.
Raleigh, M. S. and Lundquist, J. D.: Comparing and combining SWE estimates from the SNOW-17 model using PRISM and SWE reconstruction, Water Resour. Res., 48, W01506, https://doi.org/10.1029/2011WR010542, 2012.
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.
Rott, H., Yueh, S. H., Cline, D. W., Duguay, C., Essery, R., Haas, C., Hélière, F., Kern, M., Macelloni, G., Malnes, E., and Thompson, A.: Cold regions hydrology high-resolution observatory for snow and cold land processes, P. IEEE, 98, 752–765, 2010.
Rott, H., Duguay, C., Etchevers, P., Essery, R., Hajnsek, I., Macelloni, G., Malnes, E., and Pulliainen, J.: CoReH2O Report for mission selection: An Earth Explorer to observe snow and ice, Tech. rep., European Space Agency, https://earth.esa.int/eogateway/documents/20142/37627/CoReH 2O-Report-for-Mission-Selection-An-Earth-Explorer-to-observe-snow-and-ice.pdf (last access: 9 Feburary 2024), 2012.
Ruiz, J. J., Lemmetyinen, J., Kontu, A., Tarvainen, R., Vehmas, R., Pulliainen, J., and Praks, J.: Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval, IEEE T. Geosci. Remote, 60, 1–15, https://doi.org/10.1109/TGRS.2022.3223760, 2022.
Schirmer, M., Wirz, V., Clifton, A., and Lehning, M.: Persistence in intra-annual snow depth distribution: 1. Measurements and topographic control: Persistent Snow Depth Development, 1., Water Resour. Res., 47, W09516, https://doi.org/10.1029/2010WR009426, 2011.
Singh, S., Durand, M., Kim, E., and Barros, A. P.: Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic-aperture-radar demonstration using airborne SnowSAR in SnowEx17, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-1987, 2023.
Sturm, M. and Liston, G. E.: Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications, J. Hydrometeorol., 22, 2917–2938, https://doi.org/10.1175/JHM-D-21-0070.1, 2021.
Sturm, M. and Wagner, A. M.: Using repeated patterns in snow distribution modeling: An Arctic example, Water Resour. Res., 46, W12559, https://doi.org/10.1029/2010WR009434, 2010.
Terzago, S., Bongiovanni, G., and von Hardenberg, J.: Seasonal forecasting of snow resources at Alpine sites, Hydrol. Earth Syst. Sci., 27, 519–542, https://doi.org/10.5194/hess-27-519-2023, 2023.
Trujillo, E., Ramírez, J. A., and Elder, K. J.: Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields, Water Resour. Res., 43, W07409, https://doi.org/10.1029/2006WR005317, 2007.
Tsang, L., Durand, M., Derksen, C., Barros, A. P., Kang, D.-H., Lievens, H., Marshall, H.-P., Zhu, J., Johnson, J., King, J., Lemmetyinen, J., Sandells, M., Rutter, N., Siqueira, P., Nolin, A., Osmanoglu, B., Vuyovich, C., Kim, E., Taylor, D., Merkouriadi, I., Brucker, L., Navari, M., Dumont, M., Kelly, R., Kim, R. S., Liao, T.-H., Borah, F., and Xu, X.: Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing, The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, 2022.
Wayand, N. E., Hamlet, A. F., Hughes, M., Feld, S. I., and Lundquist, J. D.: Intercomparison of Meteorological Forcing Data from Empirical and Mesoscale Model Sources in the North Fork American River Basin in Northern Sierra Nevada, California, J. Hydrometeorol., 14, 677–699, https://doi.org/10.1175/JHM-D-12-0102.1, 2013.
Woodruff, C. D. and Qualls, R. J.: Recurrent Snowmelt Pattern Synthesis using Principal Component Analysis of Multi-Year Remotely Sensed Snow Cover, Water Resour. Res., 55, 6869–6885, https://doi.org/10.1029/2018WR024546, 2019.
Wrzesien, M., Kumar, S. V., Vuyovich, C., Kim, R. S., Cho, E., Pflug, J. M., Konapala, G., and Arsenault, K. R.: Merging remote sensing and models to improve performance and accessibility of snow information, AGU Fall Meeting, Conference on Hydrology, 12–16 December 2022, Chicago, IL, 2022.
Wrzesien, M. L., Kumar, S., Vuyovich, C., Gutmann, E. D., Kim, R. S., Forman, B. A., Durand, M., Raleigh, M. S., Webb, R., and Houser, P.: Development of a “Nature Run” for Observing System Simulation Experiments (OSSEs) for Snow Mission Development, J. Hydrometeorol., 23, 351–375, https://doi.org/10.1175/JHM-D-21-0071.1, 2022.
Ying, Y.: Assimilating Observations with Spatially Correlated Errors Using a Serial Ensemble Filter with a Multiscale Approach, Mon. Weather Rev., 148, 3397–3412, https://doi.org/10.1175/MWR-D-19-0387.1, 2020.
Yu, L., Fennel, K., Wang, B., Laurent, A., Thompson, K. R., and Shay, L. K.: Evaluation of nonidentical versus identical twin approaches for observation impact assessments: an ensemble-Kalman-filter-based ocean assimilation application for the Gulf of Mexico, Ocean Sci., 15, 1801–1814, https://doi.org/10.5194/os-15-1801-2019, 2019.
Yueh, S. H., Dinardo, S. J., Akgiray, A., West, R., Cline, D. W., and Elder, K.: Airborne Ku-band polarimetric radar remote sensing of terrestrial snow cover, IEEE T. Geosci. Remote, 47, 3347–3364, 2009.
Zhu, J., Tan, S., King, J., Derksen, C., Lemmetyinen, J., and Tsang, L.: Forward and inverse radar modeling of terrestrial snow using SnowSAR Data, IEEE T. Geosci. Remote, 56, 7122–7132, https://doi.org/10.1109/TGRS.2018.2848642, 2018.
Zhu, J., Tan, S., Tsang, L., Kang, D. K., and Kim, E.: Snow water equivalent retrieval using active and passive microwave observations, Water Resour. Res., 57, e2020WR027563, https://doi.org/10.1029/2020WR027563, 2021.
Short summary
Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by...