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.
Research article
| 
13 Feb 2024
Research article |
| 13 Feb 2024
| 13 Feb 2024
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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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.
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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
Preprint under review 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
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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, Chris J. Crawford, Nimrod Carmon, Carrie M. Vuyovich, and Jeff Dozier
EGUsphere, https://doi.org/10.5194/egusphere-2024-1681, https://doi.org/10.5194/egusphere-2024-1681, 2024
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Key to the success of future satellite missions is understanding snowmelt in our warming climate, having 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 3 amplifying effects: 1) a background reflectance that is too dark; 2) level terrain assumptions; 3) and differences in optical constants of ice. Sensor calibration and directional effects may also contribute. Solutions are presented.
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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.
Min Huang, Gregory R. Carmichael, James H. Crawford, 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-484, https://doi.org/10.5194/egusphere-2024-484, 2024
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This study uses 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 pollutants’ budgets in this area as local emissions go down.
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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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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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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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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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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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.
Xinxuan Zhang, Viviana Maggioni, Azbina Rahman, Paul Houser, Yuan Xue, Timothy Sauer, Sujay Kumar, and David Mocko
Hydrol. Earth Syst. Sci., 24, 3775–3788, https://doi.org/10.5194/hess-24-3775-2020, https://doi.org/10.5194/hess-24-3775-2020, 2020
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This study assesses the extent to which a land surface model can be optimized via the assimilation of leaf area index (LAI) observations at the global scale. The model performance is evaluated by the model-estimated LAI and five water flux/storage variables. Results show the LAI assimilation reduces errors in the model-estimated LAI. The LAI assimilation also improves the five water variables under wet conditions, but some of the model-estimated variables tend to be worse under dry conditions.
Sujay V. Kumar, Thomas R. Holmes, Rajat Bindlish, Richard de Jeu, and Christa Peters-Lidard
Hydrol. Earth Syst. Sci., 24, 3431–3450, https://doi.org/10.5194/hess-24-3431-2020, https://doi.org/10.5194/hess-24-3431-2020, 2020
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Vegetation optical depth (VOD) is a byproduct of the soil moisture retrieval from passive microwave instruments. This study demonstrates that VOD information can be utilized for improving land surface water budget and carbon conditions through data assimilation.
Shraddhanand Shukla, Kristi R. Arsenault, Abheera Hazra, Christa Peters-Lidard, Randal D. Koster, Frank Davenport, Tamuka Magadzire, Chris Funk, Sujay Kumar, Amy McNally, Augusto Getirana, Greg Husak, Ben Zaitchik, Jim Verdin, Faka Dieudonne Nsadisa, and Inbal Becker-Reshef
Nat. Hazards Earth Syst. Sci., 20, 1187–1201, https://doi.org/10.5194/nhess-20-1187-2020, https://doi.org/10.5194/nhess-20-1187-2020, 2020
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The region of southern Africa is prone to climate-driven food insecurity events, as demonstrated by the major drought event in 2015–2016. This study demonstrates that recently developed NASA Hydrological Forecasting and Analysis System-based root-zone soil moisture monitoring and forecasting products are well correlated with interannual regional crop yield, can identify below-normal crop yield events and provide skillful crop yield forecasts, and hence support early warning of food insecurity.
Kristi R. Arsenault, Sujay V. Kumar, James V. Geiger, Shugong Wang, Eric Kemp, David M. Mocko, Hiroko Kato Beaudoing, Augusto Getirana, Mahdi Navari, Bailing Li, Jossy Jacob, Jerry Wegiel, and Christa D. Peters-Lidard
Geosci. Model Dev., 11, 3605–3621, https://doi.org/10.5194/gmd-11-3605-2018, https://doi.org/10.5194/gmd-11-3605-2018, 2018
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The Earth’s land surface hydrology and physics can be represented in highly sophisticated models known as land surface models. The Land surface Data Toolkit (LDT) software was developed to meet these models’ input processing needs. LDT supports a variety of land surface and hydrology models and prepares the inputs (e.g., meteorological data, satellite observations to be assimilated into a model), which can be used for inter-model studies and to initialize weather and climate forecasts.
Yu Zhang, Ming Pan, Justin Sheffield, Amanda L. Siemann, Colby K. Fisher, Miaoling Liang, Hylke E. Beck, Niko Wanders, Rosalyn F. MacCracken, Paul R. Houser, Tian Zhou, Dennis P. Lettenmaier, Rachel T. Pinker, Janice Bytheway, Christian D. Kummerow, and Eric F. Wood
Hydrol. Earth Syst. Sci., 22, 241–263, https://doi.org/10.5194/hess-22-241-2018, https://doi.org/10.5194/hess-22-241-2018, 2018
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A global data record for all four terrestrial water budget variables (precipitation, evapotranspiration, runoff, and total water storage change) at 0.5° resolution and monthly scale for the period of 1984–2010 is developed by optimally merging a series of remote sensing products, in situ measurements, land surface model outputs, and atmospheric reanalysis estimates and enforcing the mass balance of water. Initial validations show the data record is reliable for climate related analysis.
Sujay V. Kumar, Jiarui Dong, Christa D. Peters-Lidard, David Mocko, and Breogán Gómez
Hydrol. Earth Syst. Sci., 21, 2637–2647, https://doi.org/10.5194/hess-21-2637-2017, https://doi.org/10.5194/hess-21-2637-2017, 2017
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Data assimilation deals with the blending of model forecasts and observations based on their relative errors. This paper addresses the importance of accurately representing the errors in the model forecasts for skillful data assimilation performance.
S. V. Kumar, C. D. Peters-Lidard, J. A. Santanello, R. H. Reichle, C. S. Draper, R. D. Koster, G. Nearing, and M. F. Jasinski
Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, https://doi.org/10.5194/hess-19-4463-2015, 2015
Related subject area
Subject: Snow and Ice | Techniques and Approaches: Remote Sensing and GIS
Detecting snowfall events over the Arctic using optical and microwave satellite measurements
Assimilation of airborne gamma observations provides utility for snow estimation in forested environments
Characterizing 4 decades of accelerated glacial mass loss in the west Nyainqentanglha Range of the Tibetan Plateau
Estimating spatiotemporally continuous snow water equivalent from intermittent satellite observations: an evaluation using synthetic data
Development and validation of a new MODIS snow-cover-extent product over China
Processes governing snow ablation in alpine terrain – detailed measurements from the Canadian Rockies
Evaluation of MODIS and VIIRS cloud-gap-filled snow-cover products for production of an Earth science data record
Characterising spatio-temporal variability in seasonal snow cover at a regional scale from MODIS data: the Clutha Catchment, New Zealand
Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product
The recent developments in cloud removal approaches of MODIS snow cover product
Now you see it, now you don't: a case study of ephemeral snowpacks and soil moisture response in the Great Basin, USA
Assessment of a multiresolution snow reanalysis framework: a multidecadal reanalysis case over the upper Yampa River basin, Colorado
Snow cover dynamics in Andean watersheds of Chile (32.0–39.5° S) during the years 2000–2016
A new remote hazard and risk assessment framework for glacial lakes in the Nepal Himalaya
A snow cover climatology for the Pyrenees from MODIS snow products
Cloud obstruction and snow cover in Alpine areas from MODIS products
Application of MODIS snow cover products: wildfire impacts on snow and melt in the Sierra Nevada
LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California
Early 21st century snow cover state over the western river basins of the Indus River system
Validation of the operational MSG-SEVIRI snow cover product over Austria
Reducing cloud obscuration of MODIS snow cover area products by combining spatio-temporal techniques with a probability of snow approach
CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data
Snow cover dynamics and hydrological regime of the Hunza River basin, Karakoram Range, Northern Pakistan
Responses of snowmelt runoff to climatic change in an inland river basin, Northwestern China, over the past 50 years
Assessing the application of a laser rangefinder for determining snow depth in inaccessible alpine terrain
Emmihenna Jääskeläinen, Kerttu Kouki, and Aku Riihelä
Hydrol. Earth Syst. Sci., 28, 3855–3870, https://doi.org/10.5194/hess-28-3855-2024, https://doi.org/10.5194/hess-28-3855-2024, 2024
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Snow cover is an important variable when studying the effect of climate change in the Arctic. Therefore, the correct detection of snowfall is important. In this study, we present methods to detect snowfall accurately using satellite observations. The snowfall event detection results of our limited area are encouraging. We find that further development could enable application over the whole Arctic, providing necessary information on precipitation occurrence over remote areas.
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.
Shuhong Wang, Jintao Liu, Hamish D. Pritchard, Linghong Ke, Xiao Qiao, Jie Zhang, Weihua Xiao, and Yuyan Zhou
Hydrol. Earth Syst. Sci., 27, 933–952, https://doi.org/10.5194/hess-27-933-2023, https://doi.org/10.5194/hess-27-933-2023, 2023
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We assessed and compared the glacier areal retreat rate and surface thinning rate and the effects of topography, debris cover and proglacial lakes in the west Nyainqentanglha Range (WNT) during 1976–2000 and 2000–2020. Our study will help us to better understand the glacier change characteristics in the WNT on a long timescale and will serve as a reference for glacier changes in other regions on the Tibetan Plateau.
Xiaoyu Ma, Dongyue Li, Yiwen Fang, Steven A. Margulis, and Dennis P. Lettenmaier
Hydrol. Earth Syst. Sci., 27, 21–38, https://doi.org/10.5194/hess-27-21-2023, https://doi.org/10.5194/hess-27-21-2023, 2023
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We explore satellite retrievals of snow water equivalent (SWE) along hypothetical ground tracks that would allow estimation of SWE over an entire watershed. The retrieval of SWE from satellites has proved elusive, but there are now technological options that do so along essentially one-dimensional tracks. We use machine learning (ML) algorithms as the basis for a track-to-area (TTA) transformation and show that at least one is robust enough to estimate domain-wide SWE with high accuracy.
Xiaohua Hao, Guanghui Huang, Zhaojun Zheng, Xingliang Sun, Wenzheng Ji, Hongyu Zhao, Jian Wang, Hongyi Li, and Xiaoyan Wang
Hydrol. Earth Syst. Sci., 26, 1937–1952, https://doi.org/10.5194/hess-26-1937-2022, https://doi.org/10.5194/hess-26-1937-2022, 2022
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We develop and validate a new 20-year MODIS snow-cover-extent product over China, which is dedicated to addressing known problems of the standard snow products. As expected, the new product significantly outperforms the state-of-the-art MODIS C6.1 products; improvements are particularly clear in forests and for the daily cloud-free product. Our product has provided more reliable snow knowledge over China and can be accessible freely https://dx.doi.org/10.11888/Snow.tpdc.271387.
Michael Schirmer and John W. Pomeroy
Hydrol. Earth Syst. Sci., 24, 143–157, https://doi.org/10.5194/hess-24-143-2020, https://doi.org/10.5194/hess-24-143-2020, 2020
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The spatial distribution of snow water equivalent (SWE) and melt are important for hydrological applications in alpine terrain. We measured the spatial distribution of melt using a drone in very high resolution and could relate melt to topographic characteristics. Interestingly, melt and SWE were not related spatially, which influences the speed of areal melt out. We could explain this by melt varying over larger distances than SWE.
Dorothy K. Hall, George A. Riggs, Nicolo E. DiGirolamo, and Miguel O. Román
Hydrol. Earth Syst. Sci., 23, 5227–5241, https://doi.org/10.5194/hess-23-5227-2019, https://doi.org/10.5194/hess-23-5227-2019, 2019
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Global snow cover maps have been available since 2000 from the MODerate resolution Imaging Spectroradiometer (MODIS), and since 2000 and 2011 from the Suomi National Polar-orbiting Partnership (S-NPP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), respectively. These products are used extensively in hydrological modeling and climate studies. New, daily cloud-gap-filled snow products are available from both MODIS and VIIRS, and are being used to develop an Earth science data record.
Todd A. N. Redpath, Pascal Sirguey, and Nicolas J. Cullen
Hydrol. Earth Syst. Sci., 23, 3189–3217, https://doi.org/10.5194/hess-23-3189-2019, https://doi.org/10.5194/hess-23-3189-2019, 2019
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Spatio-temporal variability of seasonal snow cover is characterised from 16 years of MODIS data for the Clutha Catchment, New Zealand. No trend was detected in snow-covered area. Spatial modes of variability reveal the role of anomalous winter airflow. The sensitivity of snow cover duration to temperature and precipitation variability is found to vary spatially across the catchment. These findings provide new insight into seasonal snow processes in New Zealand and guidance for modelling efforts.
Andri Gunnarsson, Sigurður M. Garðarsson, and Óli G. B. Sveinsson
Hydrol. Earth Syst. Sci., 23, 3021–3036, https://doi.org/10.5194/hess-23-3021-2019, https://doi.org/10.5194/hess-23-3021-2019, 2019
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In this study a gap-filled snow cover product for Iceland is developed using MODIS satellite data and validated with both in situ observations and alternative remote sensing data sources with good agreement. Information about snow cover extent, duration and changes over time is presented, indicating that snow cover extent has been increasing slightly for the past few years.
Xinghua Li, Yinghong Jing, Huanfeng Shen, and Liangpei Zhang
Hydrol. Earth Syst. Sci., 23, 2401–2416, https://doi.org/10.5194/hess-23-2401-2019, https://doi.org/10.5194/hess-23-2401-2019, 2019
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This paper is a review article on the cloud removal methods of MODIS snow cover products.
Rose Petersky and Adrian Harpold
Hydrol. Earth Syst. Sci., 22, 4891–4906, https://doi.org/10.5194/hess-22-4891-2018, https://doi.org/10.5194/hess-22-4891-2018, 2018
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Ephemeral snowpacks are snowpacks that persist for less than 2 months. We show that ephemeral snowpacks melt earlier and provide less soil water input in the spring. Elevation is strongly correlated with whether snowpacks are ephemeral or seasonal. Snowpacks were also more likely to be ephemeral on south-facing slopes than north-facing slopes at high elevations. In warm years, the Great Basin shifts to ephemerally dominant as rain becomes more prevalent at increasing elevations.
Elisabeth Baldo and Steven A. Margulis
Hydrol. Earth Syst. Sci., 22, 3575–3587, https://doi.org/10.5194/hess-22-3575-2018, https://doi.org/10.5194/hess-22-3575-2018, 2018
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Montane snowpacks are extremely complex to represent and usually require assimilating remote sensing images at very fine spatial resolutions, which is computationally expensive. Adapting the grid size of the terrain to its complexity was shown to cut runtime and storage needs by half while preserving the accuracy of ~ 100 m snow estimates. This novel approach will facilitate the large-scale implementation of high-resolution remote sensing data assimilation over snow-dominated montane ranges.
Alejandra Stehr and Mauricio Aguayo
Hydrol. Earth Syst. Sci., 21, 5111–5126, https://doi.org/10.5194/hess-21-5111-2017, https://doi.org/10.5194/hess-21-5111-2017, 2017
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In Chile there is a lack of hydrological data, which complicates the analysis of important hydrological processes. In this study we validate a remote sensing product, i.e. the MODIS snow product, in Chile using ground observations, obtaining good results. Then MODIS was use to evaluated snow cover dynamic during 2000–2016 at five watersheds in Chile. The analysis shows that there is a significant reduction in snow cover area in two watersheds located in the northern part of the study area.
David R. Rounce, Daene C. McKinney, Jonathan M. Lala, Alton C. Byers, and C. Scott Watson
Hydrol. Earth Syst. Sci., 20, 3455–3475, https://doi.org/10.5194/hess-20-3455-2016, https://doi.org/10.5194/hess-20-3455-2016, 2016
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Glacial lake outburst floods pose a significant threat to downstream communities and infrastructure as they rapidly unleash stored lake water. Nepal is home to many potentially dangerous glacial lakes, yet a holistic understanding of the hazards faced by these lakes is lacking. This study develops a framework using remotely sensed data to investigate the hazards and risks associated with each glacial lake and discusses how this assessment may help inform future management actions.
S. Gascoin, O. Hagolle, M. Huc, L. Jarlan, J.-F. Dejoux, C. Szczypta, R. Marti, and R. Sánchez
Hydrol. Earth Syst. Sci., 19, 2337–2351, https://doi.org/10.5194/hess-19-2337-2015, https://doi.org/10.5194/hess-19-2337-2015, 2015
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There is a good agreement between the MODIS snow products and observations from automatic stations and Landsat snow maps in the Pyrenees. The optimal thresholds for which a MODIS pixel is marked as snow-covered are 40mm in water equivalent and 150mm in snow depth.
We generate a gap-filled snow cover climatology for the Pyrenees. We compute the mean snow cover duration by elevation and aspect classes. We show anomalous snow patterns in 2012 and consequences on hydropower production.
P. Da Ronco and C. De Michele
Hydrol. Earth Syst. Sci., 18, 4579–4600, https://doi.org/10.5194/hess-18-4579-2014, https://doi.org/10.5194/hess-18-4579-2014, 2014
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The negative impacts of cloud obstruction in snow mapping from MODIS and a new reliable cloud removal procedure for the Italian Alps.
P. D. Micheletty, A. M. Kinoshita, and T. S. Hogue
Hydrol. Earth Syst. Sci., 18, 4601–4615, https://doi.org/10.5194/hess-18-4601-2014, https://doi.org/10.5194/hess-18-4601-2014, 2014
P. B. Kirchner, R. C. Bales, N. P. Molotch, J. Flanagan, and Q. Guo
Hydrol. Earth Syst. Sci., 18, 4261–4275, https://doi.org/10.5194/hess-18-4261-2014, https://doi.org/10.5194/hess-18-4261-2014, 2014
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In this study we present results from LiDAR snow depth measurements made over 53 sq km and a 1600 m elevation gradient. We found a lapse rate of 15 cm accumulated snow depth and 6 cm SWE per 100 m in elevation until 3300 m, where depth sharply decreased. Residuals from this trend revealed the role of aspect and highlighted the importance of solar radiation and wind for snow distribution. Lastly, we compared LiDAR SWE estimations with four model estimates of SWE and total precipitation.
S. Hasson, V. Lucarini, M. R. Khan, M. Petitta, T. Bolch, and G. Gioli
Hydrol. Earth Syst. Sci., 18, 4077–4100, https://doi.org/10.5194/hess-18-4077-2014, https://doi.org/10.5194/hess-18-4077-2014, 2014
S. Surer, J. Parajka, and Z. Akyurek
Hydrol. Earth Syst. Sci., 18, 763–774, https://doi.org/10.5194/hess-18-763-2014, https://doi.org/10.5194/hess-18-763-2014, 2014
V. López-Burgos, H. V. Gupta, and M. Clark
Hydrol. Earth Syst. Sci., 17, 1809–1823, https://doi.org/10.5194/hess-17-1809-2013, https://doi.org/10.5194/hess-17-1809-2013, 2013
T. Y. Lakhankar, J. Muñoz, P. Romanov, A. M. Powell, N. Y. Krakauer, W. B. Rossow, and R. M. Khanbilvardi
Hydrol. Earth Syst. Sci., 17, 783–793, https://doi.org/10.5194/hess-17-783-2013, https://doi.org/10.5194/hess-17-783-2013, 2013
A. A. Tahir, P. Chevallier, Y. Arnaud, and B. Ahmad
Hydrol. Earth Syst. Sci., 15, 2275–2290, https://doi.org/10.5194/hess-15-2275-2011, https://doi.org/10.5194/hess-15-2275-2011, 2011
J. Wang, H. Li, and X. Hao
Hydrol. Earth Syst. Sci., 14, 1979–1987, https://doi.org/10.5194/hess-14-1979-2010, https://doi.org/10.5194/hess-14-1979-2010, 2010
J. L. Hood and M. Hayashi
Hydrol. Earth Syst. Sci., 14, 901–910, https://doi.org/10.5194/hess-14-901-2010, https://doi.org/10.5194/hess-14-901-2010, 2010
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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...
