Articles | Volume 27, issue 21
https://doi.org/10.5194/hess-27-4039-2023
© Author(s) 2023. 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-27-4039-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Nov 2023
Research article |
| 10 Nov 2023
| 10 Nov 2023
Assimilation of airborne gamma observations provides utility for snow estimation in forested environments
Eunsang Cho
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
now at: Ingram School of Engineering, Texas State University, San Marcos, TX, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
now at: Korea Institute of Atmospheric Prediction Systems (KIAPS), Seoul, 07071, South Korea
Sujay V. Kumar
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Carrie M. Vuyovich
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Related authors
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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).
Short summary
Short summary
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).
Short summary
Short summary
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
Short summary
Short summary
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.
Yonghwan Kwon, Sanghee Jun, Hyunglok Kim, Kyung-Hee Seol, In-Hyuk Kwon, Eunkyu Kim, and Sujeong Cho
EGUsphere, https://doi.org/10.2139/ssrn.5148526, https://doi.org/10.2139/ssrn.5148526, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study examines the impact of assimilating satellite-based soil moisture (SM) retrievals from ASCAT backscatter and SMAP brightness temperature measurements into the Korean Integrated Model (KIM) using a weakly coupled data assimilation (DA) framework based on the NASA Land Information System (LIS). Results show that assimilating both ASCAT and SMAP SM data improves KIM’s weather forecasts of specific humidity, air temperature, and precipitation over single-sensor DA.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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 under review for HESS
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
Short summary
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.
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
Short summary
Short summary
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, 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
Short summary
Short summary
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
Short summary
Short summary
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.
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Aoki, T., Kuchiki, K., Niwano, M., Kodama, Y., Hosaka, M., and Tanaka, T.: Physically based snow albedo model for calculating broadband albedos and the solar heating profile in snowpack for general circulation models, J. Geophys. Res., 116, D11114, https://doi.org/10.1029/2010JD015507, 2011.
Augas, J., Abbasnezhadi, K., Rousseau, A. N., and Baraer, M.: What is the Trade-Off between Snowpack Stratification and Simulated Snow Water Equivalent in a Physically-Based Snow Model?, Watero, 12, 3449, https://doi.org/10.3390/w12123449, 2020.
Avanzi, F., De Michele, C., Morin, S., Carmagnola, C. M., Ghezzi, A., and Lejeune, Y.: Model complexity and data requirements in snow hydrology: seeking a balance in practical applications, Hydrol. Process., 30, 2106–2118, 2016.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions, in: Progress in Photosynthesis Research, edited by: Biggins, J., Martinus Nijhoff: Dordrecht, the Netherlands, 221–234, https://doi.org/10.1007/978-94-017-0519-6_48, 1987.
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, 303–309, 2005.
Barrett, A. P.: National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) products at NSIDC, Boulder, CO, National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, p. 19, https://nsidc.org/sites/default/files/nsidc_special_report_11.pdf (last access: 1 September 2023), 2003.
Broxton, P., Dawson, N., and Zeng, X.: Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth, Earth and Space Science, 3, 246–256, https://doi.org/10.1002/2016EA000174, 2016a.
Broxton, P., Zeng, X., and Dawson, N.: Why do global reanalyses and land data assimilation products underestimate snow water equivalent?, J. Hydrometeorol., 17, 2743–2761, https://doi.org/10.1175/JHM-D-16-0056.1, 2016b.
Brutsaert, B.: Evaporation in the Atmosphere: Theory, History and Application, Kluwer Acad., Norwell, Mass, 299 pp., ISBN 9789401714976, https://doi.org/10.1007/978-94-017-1497-6, 1982.
Carroll, T. R.: Airborne Gamma Radiation Snow Survey Program: A User's Guide, Version 5.0, National Operational Hydrologic Remote Sensing Center (NOHRSC), Chanhassen, 14, https://www.nohrsc.noaa.gov/snowsurvey/historical.html (last access: 1 September 2023), 2001.
Carroll, S. S. and Carroll, T. R.: Effect of forest biomass on airborne snow water equivalent estimates obtained by measuring terrestrial gamma radiation, Remote Sens. Environ., 27, 313–319, 1989a.
Carroll, S. S. and Carroll, T. R.: Effect of uneven snow cover on airborne snow water equivalent estimates obtained by measuring terrestrial gamma radiation, Water Resour. Res., 25, 1505–1510, 1989b.
Carroll, T. R. and Vose, G. D.: Airborne snow water equivalent measurements over a forested environment using terrestrial gamma radiation, in: Proceedings of the Eastern Snow Conference, 29, New Carrolton, Maryland, 7–8 June 1984, 101–115, https://static1.squarespace.com/static/58b98f7bd1758e4cc271d365/t/5e5696e3a6cc764ba89e111e/1582733029376/12+T.R.+Carroll,+G.D.+Vose.pdf (last access: 1 September 2023),1984.
Chang, A., Foster, J., and Hall, D.: Satellite sensor estimates of northern hemisphere snow volume, Int. J. Remote Sens., 11, 167–171, https://doi.org/10.1080/01431169008955009, 1990.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model with the Penn state-NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Cheng, B., Zhang, Z., Vihma, T., Johansson, M., Bian, L., Li, Z., and Huiding, W.: Model experiments on snow and ice thermodynamics in the Arctic Ocean with CHINARE 2003 data, J. Geophys. Res., 113, C09020, https://doi.org/10.1029/2007JC004654, 2008.
Cho, E. and Jacobs, J. M.: Extreme Value Snow Water Equivalent and Snowmelt for Infrastructure Design over the Contiguous United States, Water Resour. Res., 56, e2020WR028126, https://doi.org/10.1029/2020WR028126, 2020.
Cho, E., Jacobs, J. M., Tuttle, S. E., Schroeder, R., and Olheiser, C.: Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture, Remote Sens. Environ., 240, 111668, https://doi.org/10.1016/j.rse.2020.111668, 2020a.
Cho, E., Jacobs, J. M., and Vuyovich, C.: The value of long-term (40 years) airborne gamma radiation SWE record for evaluating three observation-based gridded SWE datasets by seasonal snow and land cover classifications, Water Resour. Res., 56, e2019WR025813, https://doi.org/10.1029/2019WR025813, 2020b.
Cho, E., McCrary, R. R., and Jacobs, J. M.: Future Changes in Snowpack, Snowmelt, and Runoff Potential Extremes Over North America, Geophys. Res. Lett., 48, e2021GL094985, https://doi.org/10.1029/2021GL094985, 2021.
Cho, E., Mortimer, C., Mudryk, L., Derksen, C., Vuyovich, C., and Brady, M.: Quantifying Spatial Representativeness of Airborne Gamma Radiation SWE using Snow Courses and Gridded Reanalysis data, 36th Conference on Hydrology, 102nd American Meteorological Society Annual Meeting, 23 to 27 January 2022, virtual conference, https://ams.confex.com/ams/102ANNUAL/meetingapp.cgi/Paper/400037 (last access: 1 September 2023), 2022.
Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M.: Precipitation biases and snow physics limitations drive the uncertainties in macroscale modeled snow water equivalent, Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, 2022.
Cho, E., Kwon, Y., Kumar, S. V., and Vuyovich, C. M.: Data for “Assimilation of airborne gamma observations provides utility for snow estimation in forested environments”, HydroShare [data set], http://www.hydroshare.org/resource/fc5c757899fb49a5869e597451120a33 (last access: 1 September 2023), 2023.
Clifford, D.: Global estimates of snow water equivalent from passive microwave instruments: History, challenges and future developments, Int. J. Remote Sens., 31, 3707–3726, https://doi.org/10.1080/01431161.2010.483482, 2010.
Cork, H. F. and Loijens, H. S.: The effect of snow drifting on gamma snow survey results, J, Hydrol,, 48, 41–51, 1980.
Cuntz, M., Mai, J., Samaniego, L., Clark, M., Wulfmeyer, V., Branch, O., Attinger, S., and Thober, S.: The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model, J. Geophys. Res.-Atmos., 121, 1067610700, https://doi.org/10.1002/2016JD025097, 2016.
Dawson, N., Broxton, P., and Zeng, X.: A new snow density parameterization for land data assimilation, J. Hydrometeorol., 18, 197–207, https://doi.org/10.1175/JHM-D-16-0166.1, 2017.
Dawson, N., Broxton, P., and Zeng, X.: Evaluation of remotely-sensed snow water equivalent and snow cover extent over the contiguous United States, J. Hydrometeorol., 19, 17771791, https://doi.org/10.1175/JHM-D-18-0007.1, 2018.
Derksen, C., Walker, A., and Goodison, B.: Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada, Remote Sens. Environ., 96, 315–327, 2005.
Derksen, C., Toose, P., Rees, A., Wang, L., English, M., Walker, A., and Sturm, M.: Development of a tundra-specific snow water equivalent retrieval algorithm for satellite passive microwave data, Remote Sens. Environ., 114, 1699–1709, https://doi.org/10.1016/j.rse.2010.02.019, 2010.
Dozier, J.: Mountain hydrology, snow color, and the fourth paradigm, EOS T. Am. Geophys. Un., 92, 373–374, https://doi.org/10.1029/2011EO430001, 2011.
Durand, M. and Margulis, S. A.: Feasibility test of multifrequency radiometric data assimilation to estimate snow water equivalent, J. Hydrometeorol., 7, 443–457, https://doi.org/10.1175/JHM502.1, 2006.
Durand, M. and Margulis, S. A.: Correcting first-order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme, J. Geophys. Res., 112, D13121, https://doi.org/10.1029/2006JD008067, 2007.
Durand, M., Kim, E. J., and Margulis, S. A.: Radiance assimilation shows promise for snowpack characterization, Geophys. Res. Lett., 36, L02503, https://doi.org/10.1029/2008GL035214, 2009.
Dziubanski, D. J. and Franz, K. J.: Assimilation of AMSR-E snow water equivalent data in a spatially-lumped snow model, J. Hydrol., 540, 26–39, https://doi.org/10.1016/j.jhydrol.2016.05.046, 2016.
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., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., and Seal, D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Forman, B. A., Reichle, R. H., and Rodell, M.: Assimilation of terrestrial water storage from GRACE in a snow-dominated basin. Water Resour. Res., 48, W01507, https://doi.org/10.1029/2011WR011239, 2012.
Foster, J. L., Chang, A. T. C., Hall, D. K., and Rango, A.: Derivation of snow water equivalent in boreal forests using microwave radiometry, Arctic, 44, 147–152, https://doi.org/10.14430/arctic1581, 1991.
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.
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., da Silva, A. M., 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, 54195454, https://doi.org/10.1175/JCLI-D-16-0758.1, https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/data_access/ (last access: 1 September 2023), 2017.
Glynn, J. E., Carroll, T. R., Holman, P. B., and Grasty, R. L.: An airborne gamma ray snow survey of a forested covered area with a deep snowpack, Remote Sens. Environ., 26, 149–160, 1988.
Goodison, B. E., Banga, A., and Halliday, R. A.: Canada—United States Prairie Snow Cover Runoff Study, Can. Water Resour. J., 9, 99–107, https://doi.org/10.4296/cwrj0901099, 1984.
Hansen, M. and Song, X. P.: Vegetation Continuous Fields (VCF) yearly global 0.05 deg, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MEaSUREs/VCF/VCF5KYR.001, 2018.
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.
Imaoka, K., Kachi, M., Kasahara, M., Ito, N., Nakagawa, K., and Oki, T.: Instrument performance and calibration of AMSR-E and AMSR2, Int. Arch. Photogramm., 38, 13–18, 2010.
Jennings, K. S., Kittel, T. G. F., and Molotch, N. P.: Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget, The Cryosphere, 12, 1595–1614, https://doi.org/10.5194/tc-12-1595-2018, 2018.
Jennings, K. S., Winchell, T. S., Livneh, B., and Molotch, N. P.: Spatial variation of the rain-snow temperature threshold across the Northern Hemisphere, Nat. Commun., 9, 1148, https://doi.org/10.1038/s41467-018-03629-7, 2018.
Jordan, R. A.: One-Dimensional Temperature Model for a Snow Cover. Technical Documentation for SNTHERM.89, Technical Report No. 91-16, U.S. Army Corps of Engineers: Washington, DC, USA, https://hdl.handle.net/11681/11677 (last access: 1 September 2023), 1991.
Kang, D. H., Barros, A. P., and Dery, S. J.: Evaluating passive microwave radiometry for the dynamical transition from dry to wet snowpacks, IEEE T. Geosci. Remote, 52, 3–15, https://doi.org/10.1109/TGRS.2012.2234468, 2014.
Kelly, R.: The AMSR-E snow depth algorithm: description and initial results, Journal of The Remote Sensing Society of Japan, 29, 307–317, https://doi.org/10.11440/rssj.29.307, https://www.eorc.jaxa.jp/AMSR/datacatalog/land/#snd (last access: 1 September 2023), 2009.
Kelly, R., Chang, A., Tsang, L., and Foster, J.: A prototype AMSR-E global snow area and snow depth algorithm. IEEE T. Geosci. Remote, 41, 230–242, https://doi.org/10.1109/TGRS.2003.809118, 2003.
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.
Kumar, S., Peters-Lidard, C., Tian, Y., Reichle, R. H., Geiger, J., Alonge, C., Eylander, J., and Houser, P.: An integrated hydrologic modeling and data assimilation framework, Computer, 41, 52–59, https://doi.org/10.1109/MC.2008.475, 2008.
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. Modell. Softw., 21, 1402–1415, https://doi.org/10.1016/j.envsoft.2005.07.004, https://github.com/NASA-LIS/LISF (last access: 1 September 2023), 2006.
Kumar, S. V., Reichle, R. H., Koster, R. D., Crow, W. T., and Peters-Lidard, C. D.: Role of subsurface physics in the assimilation of surface soil moisture observations, J. Hydrometeorol., 10, 1534–1547, https://doi.org/10.1175/2009JHM1134.1, 2009.
Kumar, S. V., Peters-Lidard, C. D., Mocko, D., Reichle, R., Liu, Y., Arsenault, K. R., Xia, Y., Ek, M., Riggs, G., Livneh, B., and Cosh, M.: Assimilation of remotely sensed soil moisture and snow depth retrievals for drought estimation, J. Hydrometeorol., 15, 2446–2469, https://doi.org/10.1175/JHM-D-13-0132.1, 2014.
Kumar, S. V., Zaitchik, B. F., Peters-Lidard, C. D., Rodell, M., Reichle, R., Li, B., Jasinski, M., Mocko, D., Getirana, A., De Lannoy, G., Cosh, M. H., Hain, C. R., Anderson, M., Arsenault, K. R., Xia, Y., and Ek, M.: Assimilation of gridded GRACE terrestrial water storage estimates in the North American Land Data Assimilation System, J. Hydrometeorol., 17, 1951–1972, https://doi.org/10.1175/JHM-D-15-0157.1, 2016.
Kwon, Y. and Koo, B. K.: Simulation of spatio-temporal distributions of winter soil temperature taking account of snow-melting and soil freezing-thawing processes, Journal of Korea Water Resources Association, 47, 945–958, https://doi.org/10.3741/JKWRA.2014.47.10.945, 2014.
Kwon, Y., Toure, A. M., Yang, Z.-L., Rodell, M., and Picard, G.: Error characterization of coupled land surface–radiative transfer models for snow microwave radiance assimilation, IEEE T. Geosci. Remote Sens., 53, 5247–5268, https://doi.org/10.1109/TGRS.2015.2419977, 2015.
Kwon, Y., Yang, Z.-L., Zhao, L., Hoar, T. J., Toure, A. M., and Rodell, M.: Estimating snow water storage in North America using CLM4, DART, and snow radiance data assimilation, J. Hydrometeorol., 17, 2853–2874, https://doi.org/10.1175/JHM-D-16-0028.1, 2016.
Kwon, Y., Yang, Z.-L., Hoar, T. J., and Toure, A. M.: Improving the radiance assimilation performance in estimating snow water storage across snow and land-cover types in North America, J. Hydrometeorol., 18, 651–668, https://doi.org/10.1175/JHM-D-16-0102.1, 2017.
Kwon, Y., Forman, B. A., Ahmad, J. A., Kumar, S. V., and Yoon, Y.: Exploring the utility of machine learning-based passive microwave brightness temperature data assimilation over terrestrial snow in High Mountain Asia, Remote Sensing, 11, 2265, https://doi.org/10.3390/rs11192265, 2019.
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.
Larue, F., Royer, A., De Sève, D., Roy, A., and Cosme, E.: Assimilation of passive microwave AMSR-2 satellite observations in a snowpack evolution model over northeastern Canada, Hydrol. Earth Syst. Sci., 22, 5711–5734, https://doi.org/10.5194/hess-22-5711-2018, 2018a.
Larue, F., Royer, A., De Séve, D., Roy, A., Picard, G., Vionnet, V., and Cosme, E.: Simulation and assimilation of passive microwave data using a snowpack model coupled to a calibrated radiative transfer model over northeastern Canada, Water Resour. Res., 54, 4823–4848, https://doi.org/10.1029/2017WR022132, 2018b.
Lecomte, O., Fichefet, T., Vancoppenolle, M., and Nicolaus, M.: A new snow thermodynamic scheme for large-scale sea-ice models, Ann. Glaciol., 52, 337–346, https://doi.org/10.3189/172756411795931453, 2011.
Lemmetyinen, J., Derksen, C., Toose, P., Proksch, M., Pulliainen, J., Kontu, A., Rautiainen, K., Seppänen, J., and Hallikainen, M.: Simulating seasonally and spatially varying snow cover brightness temperature using HUT snow emission model and retrieval of a microwave effective grain size, Remote Sens. Environ., 156, 71–95, https://doi.org/10.1016/j.rse.2014.09.016, 2015.
Letcher, T., Minder, J. R., and Naple, P.: Understanding and Improving Snow Processes in Noah-MP over the Northeast United States via the New York State Mesonet, https://apps.dtic.mil/sti/trecms/pdf/AD1177083.pdf (last access: 1 September 2023), 2022.
Liu, Y., Peters-Lidard, C., Kumar, S., Foster, J., Shaw, M., Tian, Y., and Fall, G.: Assimilating satellite-based snow depth and snow cover products for improving snow predictions in Alaska, Adv. Water Resour., 54, 208–227, https://doi.org/10.1016/j.advwatres.2013.02.005, 2013.
Livneh, B., Xia, Y., Mitchell, K. E., Ek, M. B., and Lettenmaier, D. P.: Noah LSM snow model diagnostics and enhancement, J. Hydrometeorol., 11, 721–738, https://doi.org/10.1175/2009JHM1174.1, 2010.
Marks, D., Domingo, J., Susong, D., Link, T. E., and Garen, D. C.: A spatially distributed energy balance snowmelt model for application in mountain basins, Hydrol. Process., 13, 1935–1959, 1999.
Musselman, K. N., Addor, N., Vano, J. A., and Molotch, N. P.: Winter melt trends portend widespread declines in snow water resources, Nat. Clim. Change, 11, 418–424, 2021.
Niu, G.-Y. and Yang, Z.-L.: The effects of canopy processes on snow surface energy and mass balances, J. Geophys. Res., 109, D23111, https://doi.org/10.1029/2004JD004884, 2004.
Niu, G.-Y. and Yang, Z.-L.: Effects of frozen soil on snowmelt runoff and soil water storage at a continental scale, J. Hydrometeorol., 7, 937–952, https://doi.org/10.1175/JHM538.1, 2006.
Niu, G.-Y., Yang, Z.-L., Dickinson, R. E., Gulden, L. E., and Su, H.: Development of a simple groundwater model for use in climate models and evaluation with Gravity Recovery and Climate Experiment data, J. Geophys. Res., 112, D07103, https://doi.org/10.1029/2006JD007522, 2007.
Niu, G.-Y., Yang, Z.-L., 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., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Offenbacher, E. L. and Colbeck, S. C.: Remote sensing of snow covers using the gamma‐ray technique (No. AD‐A‐238016/0/XAB; CRREL–91‐9), Cold Regions Research and Engineering Lab., Hanover, NH (United States), https://apps.dtic.mil/sti/pdfs/ADA238016.pdf (last access: 1 September 2023), 1991.
Peck, E. L., Carroll, T. R., and VanDemark, S. C.: Operational aerial snow surveying in the United States/Etude de neige aérienne effectuée aux Etats Unis, Hydrolog. Sci. J., 25, 51–62, 1980.
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015.
Reichle, R. H., Walker, J. P., Koster, R. D., and Houser, P. R.: Extended versus ensemble Kalman filtering for land data assimilation, J. Hydrometeorol., 3, 728–740, https://doi.org/10.1175/1525-7541(2002)003<0728:EVEKFF>2.0.CO;2, 2002.
Reichle, R. H., Crow, W. T., and Keppenne, C. L.: An adaptive ensemble Kalman filter for soil moisture data assimilation, Water Resour. Res., 44, W03423, https://doi.org/10.1029/2007WR006357, 2008.
Roy, A., Royer, A., Wigneron, J.-P., Langlois, A., Bergeron, J., and Cliche, P.: A simple parameterization for a boreal forest radiative transfer model at microwave frequencies, Remote Sens. Environ., 124, 371–383, https://doi.org/10.1016/j.rse.2012.05.020, 2012.
Saha, S. K., Sujith, K., Pokhrel, S., Chaudhari, H. S., and Hazra, A.: Effects of multilayer snow scheme on the simulation of snow: Offline Noah and coupled with NCEP CFSv2, J. Adv. Model. Earth Sy., 9, 271–290, https://doi.org/10.1002/2016MS000845, 2017.
Smyth, E. J., Raleigh, M. S., and Small, E. E.: Particle filter data assimilation of monthly snow depth observations improves estimation of snow density and SWE, Water Resour. Res., 55, 1296–1311, 2019.
Smyth, E. J., Raleigh, M. S., and Small, E. E.: Improving SWE Estimation with Data Assimilation: The Influence of Snow Depth Observation Timing and Uncertainty, Water Resour. Res., 56, e2019WR026853, https://doi.org/10.1029/2019WR026853, 2020.
Sturm, M., Taras, B., Liston, G. E., 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.
Sturm, M. and Liston, G. E.: Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications, J. Hydrometeorol., 22, 2917–2938, 2021.
Sturm, M., Goldstein, M. A., and Parr, C.: Water and life from snow: A trillion dollar science question, Water Resour. Res., 53, 3534–3544, 2017.
Suzuki, K. and Zupanski, M.: Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models, Front. Earth Sci., 12, 672–682, https://doi.org/10.1007/s11707-018-0691-2, 2018.
Terzago, S., Andreoli, V., Arduini, G., Balsamo, G., Campo, L., Cassardo, C., Cremonese, E., Dolia, D., Gabellani, S., von Hardenberg, J., Morra di Cella, U., Palazzi, E., Piazzi, G., Pogliotti, P., and Provenzale, A.: Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments, Hydrol. Earth Syst. Sci., 24, 4061–4090, https://doi.org/10.5194/hess-24-4061-2020, 2020.
Tuttle, S. E., Jacobs, J. M., Vuyovich, C. M., Olheiser, C., and Cho, E.: Intercomparison of snow water equivalent observations in the Northern Great Plains, Hydrol. Process., 32, 817–829, https://doi.org/10.1002/hyp.11459, 2018.
Vuyovich, C. M., Jacobs, J. M., and Daly, S. F.: Comparison of passive microwave and modeled estimates of total watershed SWE in the continental United States, Water Resour. Res., 50, 9088–9102, 2014.
Walker, A. E. and Goodison, B. E.: Discrimination of a wet snow cover using passive microwave satellite data, Ann. Glaciol., 17, 307–311, https://doi.org/10.3189/S026030550001301X, 1993.
Welty, J. and Zeng, X.: Characteristics and causes of extreme snowmelt over the conterminous United States, B. Am. Meteorol. Soc., 102, E1526–E1542, 2021.
Wilson, M. F. J., O'Connell, B., Brown, C., Guinan, J. C., and Grehan, A. J.: Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope, Mar. Geod., 30, 3–35, 2007.
Xia, Y., Mocko, D., Huang, M., Li, B., Rodell, M., Mitchell, K. E., Cai, X., and Ek, M. B.: Comparison and assessment of three advanced land surface models in simulating terrestrial water storage components over the United States, J. Hydrometeorol., 18, 625–649, https://doi.org/10.1175/JHM-D-16-0112.1, 2017.
Yang, R. and Friedl, M. A.: Modeling the effects of three-dimensional vegetation structure on surface radiation and energy balance in boreal forests, J. Geophys. Res., 108, 8615, https://doi.org/10.1029/2002JD003109, 2003.
Yang, Z.-L. and Dickinson, R. E.: Description of the Biosphere-Atmosphere Transfer Scheme (BATS) for the soil moisture workshop and evaluation of its performance, Global Planet. Change, 13, 117–134, https://doi.org/10.1016/0921-8181(95)00041-0, 1996.
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011.
Yoon, Y., Kumar, S. V., Forman, B. A., Zaitchik, B. F., Kwon, Y., Qian, Y., Rupper, S., Maggioni, V., Houser, P., Kirschbaum, D., Richey, A., Arendt, A., Mocko, D., Jacob, J., Bhanja, S. and Mukherjee, A.: Evaluating the Uncertainty of Terrestrial Water Budget Components Over High Mountain Asia, Front. Earth Sci., 7, 120, https://doi.org/10.3389/feart.2019.00120, 2019.
You, Y., Huang, C., Yang, Z., Zhang, Y., Bai, Y., and Gu, J.: Assessing Noah-MP parameterization sensitivity and uncertainty interval across snow climates, J. Geophys. Res.-Atmos., 125, e2019JD030417, https://doi.org/10.1029/2019JD030417, 2020.
Zeng, X., Broxton, P., and Dawson, N.: Snowpack change from 1982 to 2016 over conterminous United States, Geophys. Res. Lett., 45, 12940–12947, https://doi.org/10.1029/2018GL079621, https://nsidc.org/data/nsidc-0719 (last access: 1 September 2023), 2018.
Zhang, X. Y., Jin, J., Zeng, X., Hawkins, C. P., Neto, A. A., and Niu, G. Y.: The Compensatory CO2 Fertilization and Stomatal Closure Effects on Runoff Projection From 2016–2099 in the Western United States, Water Resour. Res., 58, e2021WR030046, https://doi.org/10.1029/2021WR030046, 2022.
Zhang, Y.-F., Hoar, T. J., Yang, Z.-L., Anderson, J. L., Toure, A. M., and Rodell, M.: Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4, J. Geophys. Res.-Atmos., 119, 7091–7103, https://doi.org/10.1002/2013JD021329, 2014.
Short summary
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.
An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in...
