Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-21-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-27-21-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2023
Research article |
| 02 Jan 2023
| 02 Jan 2023
Estimating spatiotemporally continuous snow water equivalent from intermittent satellite observations: an evaluation using synthetic data
Xiaoyu Ma
Department of Geography, University of California, 90095 Los Angeles, United States
Dongyue Li
CORRESPONDING AUTHOR
Department of Geography, University of California, 90095 Los Angeles, United States
Department of Civil and Environmental Engineering, University of
California, 90095 Los Angeles, United States
Yiwen Fang
Department of Civil and Environmental Engineering, University of
California, 90095 Los Angeles, United States
Steven A. Margulis
Department of Civil and Environmental Engineering, University of
California, 90095 Los Angeles, United States
Dennis P. Lettenmaier
Department of Geography, University of California, 90095 Los Angeles, United States
Department of Civil and Environmental Engineering, University of
California, 90095 Los Angeles, United States
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Manon von Kaenel and Steve Margulis
EGUsphere, https://doi.org/10.5194/egusphere-2024-3389, https://doi.org/10.5194/egusphere-2024-3389, 2024
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Accurate snow water equivalent (SWE) estimates are crucial for water management in snowmelt-dependent regions, but bias and uncertainty in precipitation data make this challenging. Here, we leverage insights from a historical SWE data product to correct these biases and yield more accurate SWE estimates and streamflow predictions. Incorporating snow depth observations further boosts accuracy. This study demonstrates an effective method to downscale and bias-correct global mountain precipitation.
Haorui Sun, Yiwen Fang, Steven Margulis, Colleen Mortimer, Lawrence Mudryk, and Chris Derksen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3213, https://doi.org/10.5194/egusphere-2024-3213, 2024
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The European Space Agency's Snow Climate Change Initiative (Snow CCI) developed a high-quality snow cover extent and snow water equivalent (SWE) Climate Data Record. However, gaps exist in complex terrain due to challenges in using passive microwave sensing and in-situ measurements. This study presents a methodology to fill the mountain SWE gap using Snow CCI Snow Cover Fraction within a Bayesian SWE reanalysis framework, with potential applications in untested regions and with other sensors.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
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We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Yiwen Fang, Yufei Liu, Dongyue Li, Haorui Sun, and Steven A. Margulis
The Cryosphere, 17, 5175–5195, https://doi.org/10.5194/tc-17-5175-2023, https://doi.org/10.5194/tc-17-5175-2023, 2023
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Using newly developed snow reanalysis datasets as references, snow water storage is at high uncertainty among commonly used global products in the Andes and low-resolution products in the western United States, where snow is the key element of water resources. In addition to precipitation, elevation differences and model mechanism variances drive snow uncertainty. This work provides insights for research applying these products and generating future products in areas with limited in situ data.
Justin M. Pflug, Yiwen Fang, Steven A. Margulis, and Ben Livneh
Hydrol. Earth Syst. Sci., 27, 2747–2762, https://doi.org/10.5194/hess-27-2747-2023, https://doi.org/10.5194/hess-27-2747-2023, 2023
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Wolverine denning habitat inferred using a snow threshold differed for three different spatial representations of snow. These differences were based on the annual volume of snow and the elevation of the snow line. While denning habitat was most influenced by winter meteorological conditions, our results show that studies applying thresholds to environmental datasets should report uncertainties stemming from different spatial resolutions and uncertainties introduced by the thresholds themselves.
Yufei Liu, Yiwen Fang, and Steven A. Margulis
The Cryosphere, 15, 5261–5280, https://doi.org/10.5194/tc-15-5261-2021, https://doi.org/10.5194/tc-15-5261-2021, 2021
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We examined the spatiotemporal distribution of stored water in the seasonal snowpack over High Mountain Asia, based on a new snow reanalysis dataset. The dataset was derived utilizing satellite-observed snow information, which spans across 18 water years, at a high spatial (~ 500 m) and temporal (daily) resolution. Snow mass and snow storage distribution over space and time are analyzed in this paper, which brings new insights into understanding the snowpack variability over this region.
Riccardo Tortini, Nina Noujdina, Samantha Yeo, Martina Ricko, Charon M. Birkett, Ankush Khandelwal, Vipin Kumar, Miriam E. Marlier, and Dennis P. Lettenmaier
Earth Syst. Sci. Data, 12, 1141–1151, https://doi.org/10.5194/essd-12-1141-2020, https://doi.org/10.5194/essd-12-1141-2020, 2020
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We present a global collection of satellite-derived time series of surface water volume changes for 347 lakes and reservoirs for 1992–2018. These changes were estimated using a statistical relationship between water surface elevation and area measured from satellite, even during periods when either elevation or area was not available. These records represent the most complete global surface water time series, and they are of fundamental importance to baseline future satellite missions.
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.
Stephen Blenkinsop, Hayley J. Fowler, Renaud Barbero, Steven C. Chan, Selma B. Guerreiro, Elizabeth Kendon, Geert Lenderink, Elizabeth Lewis, Xiao-Feng Li, Seth Westra, Lisa Alexander, Richard P. Allan, Peter Berg, Robert J. H. Dunn, Marie Ekström, Jason P. Evans, Greg Holland, Richard Jones, Erik Kjellström, Albert Klein-Tank, Dennis Lettenmaier, Vimal Mishra, Andreas F. Prein, Justin Sheffield, and Mari R. Tye
Adv. Sci. Res., 15, 117–126, https://doi.org/10.5194/asr-15-117-2018, https://doi.org/10.5194/asr-15-117-2018, 2018
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Measurements of sub-daily (e.g. hourly) rainfall totals are essential if we are to understand short, intense bursts of rainfall that cause flash floods. We might expect the intensity of such events to increase in a warming climate but these are poorly realised in projections of future climate change. The INTENSE project is collating a global dataset of hourly rainfall measurements and linking with new developments in climate models to understand the characteristics and causes of these events.
Francina Dominguez, Sandy Dall'erba, Shuyi Huang, Andre Avelino, Ali Mehran, Huancui Hu, Arthur Schmidt, Lawrence Schick, and Dennis Lettenmaier
Earth Syst. Dynam., 9, 249–266, https://doi.org/10.5194/esd-9-249-2018, https://doi.org/10.5194/esd-9-249-2018, 2018
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Atmospheric rivers (ARs) account for most of the extreme flooding events on the northwestern coast of the US. In a warmer climate, ARs in this region are projected to become more frequent and intense. We present an integrated modeling system to quantify atmospheric–hydrologic–hydraulic and economic impacts of an AR event in western Washington. Our integrated modeling tool provides communities in the region with a range of possible future physical and economic impacts associated with AR flooding.
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.
Keith N. Musselman, Noah P. Molotch, and Steven A. Margulis
The Cryosphere, 11, 2847–2866, https://doi.org/10.5194/tc-11-2847-2017, https://doi.org/10.5194/tc-11-2847-2017, 2017
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We present a study of how melt rates in the California Sierra Nevada respond to a range of warming projected for this century. Snowfall and melt were simulated for historical and modified (warmer) snow seasons. Winter melt occurs more frequently and more intensely, causing an increase in extreme winter melt. In a warmer climate, less snow persists into the spring, causing spring melt to be substantially lower. The results offer insight into how snow water resources may respond to climate change.
Wenli Wang, Annette Rinke, John C. Moore, Duoying Ji, Xuefeng Cui, Shushi Peng, David M. Lawrence, A. David McGuire, Eleanor J. Burke, Xiaodong Chen, Bertrand Decharme, Charles Koven, Andrew MacDougall, Kazuyuki Saito, Wenxin Zhang, Ramdane Alkama, Theodore J. Bohn, Philippe Ciais, Christine Delire, Isabelle Gouttevin, Tomohiro Hajima, Gerhard Krinner, Dennis P. Lettenmaier, Paul A. Miller, Benjamin Smith, Tetsuo Sueyoshi, and Artem B. Sherstiukov
The Cryosphere, 10, 1721–1737, https://doi.org/10.5194/tc-10-1721-2016, https://doi.org/10.5194/tc-10-1721-2016, 2016
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The winter snow insulation is a key process for air–soil temperature coupling and is relevant for permafrost simulations. Differences in simulated air–soil temperature relationships and their modulation by climate conditions are found to be related to the snow model physics. Generally, models with better performance apply multilayer snow schemes.
Xiaogang Shi, Tara J. Troy, and Dennis P. Lettenmaier
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-70, https://doi.org/10.5194/tc-2016-70, 2016
Revised manuscript has not been submitted
S. Peng, P. Ciais, G. Krinner, T. Wang, I. Gouttevin, A. D. McGuire, D. Lawrence, E. Burke, X. Chen, B. Decharme, C. Koven, A. MacDougall, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, C. Delire, T. Hajima, D. Ji, D. P. Lettenmaier, P. A. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
The Cryosphere, 10, 179–192, https://doi.org/10.5194/tc-10-179-2016, https://doi.org/10.5194/tc-10-179-2016, 2016
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Soil temperature change is a key indicator of the dynamics of permafrost. Using nine process-based ecosystem models with permafrost processes, a large spread of soil temperature trends across the models. Air temperature and longwave downward radiation are the main drivers of soil temperature trends. Based on an emerging observation constraint method, the total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000.
M. Navari, S. A. Margulis, S. M. Bateni, M. Tedesco, P. Alexander, and X. Fettweis
The Cryosphere, 10, 103–120, https://doi.org/10.5194/tc-10-103-2016, https://doi.org/10.5194/tc-10-103-2016, 2016
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An ensemble batch smoother was used to assess the feasibility of generating a reanalysis estimate of the Greenland ice sheet (GrIS) surface mass fluxes (SMF) via integrating measured ice surface temperatures with a regional climate model estimate. The results showed that assimilation of IST were able to overcome uncertainties in meteorological forcings that drive the GrIS surface processes. We showed that the proposed methodology is able to generate posterior reanalysis estimates of the SMF.
X. Chen, T. J. Bohn, and D. P. Lettenmaier
Biogeosciences, 12, 6259–6277, https://doi.org/10.5194/bg-12-6259-2015, https://doi.org/10.5194/bg-12-6259-2015, 2015
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We used a process-based model to investigate the sensitivities of pan-Arctic wetland methane emissions to climate factors, as a function of climate. Over the period 1960-2006, temperature was the dominant driver of trends in emissions. Wetlands north of 60N were temperature-limited, and wetlands south of 60N latitude were water-limited. Projected future warming will cause water-limited wetlands to expand northward over the next century, lessening the role of temperature in the future.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
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We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
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
Extending the utility of space-borne snow water equivalent observations over vegetated areas with data assimilation
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
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.
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
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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, 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.
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
Cited articles
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., and
Arshad, H.: State-of-the-art in artificial neural network applications, A
survey, Heliyon, 4, 3–6, https://doi.org/10.1016/j.heliyon.2018.e00938, 2018.
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, https://doi.org/10.1038/nature04141, 2005.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Chakraborty, D., Başağaoğlu, H., Gutierrez, L., and Mirchi, A.:
Explainable AI reveals new hydroclimatic insights for ecosystem-centric
groundwater management, Environ. Res. Lett., 16, 114024,
https://doi.org/10.1088/1748-9326/ac2fde, 2021.
Clark, M. P., Hendrikx, J., Slater, A. G., Kavetski, D., Anderson, B.,
Cullen, N. J., Kerr, T., Örn Hreinsson, E., and Woods, R. A.:
Representing spatial variability of snow water equivalent in hydrologic and
land-surface models: A review, Water Resour. Res., 47, 17–18,
https://doi.org/10.1029/2011WR010745, 2011.
Cline, D. W., Bales, R. C., and Dozier, J.: Estimating the spatial
distribution of snow in mountain basins using remote sensing and energy
balance modeling, Water Resour. Res., 34, 1275–1285,
https://doi.org/10.1029/97WR03755, 1998.
Costa, M. A., de Pádua Braga, A., and de Menezes, B. R.: Improving
generalization of MLPs with sliding mode control and the
Levenberg–Marquardt algorithm, Neurocomputing, 70, 1342–1347,
https://doi.org/10.1016/j.neucom.2006.09.003, 2007.
Deschamps-Berger, C., Gascoin, S., Berthier, E., Deems, J., Gutmann, E., Dehecq, A., Shean, D., and Dumont, M.: Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data, The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, 2020.
Dikshit, A. and Pradhan, B.: Explainable AI in drought forecasting, Machine
Learning with Applications, 6, 100192,
https://doi.org/10.1016/j.mlwa.2021.100192, 2021a.
Dikshit, A. and Pradhan, B.: Interpretable and explainable AI (XAI) model
for spatial drought prediction, Sci. Total. Environ., 801, 149797,
https://doi.org/10.1016/j.scitotenv.2021.149797, 2021b.
Dong, C.: Remote sensing, hydrological modeling and in situ observations in
snow cover research: A review, J. Hydrol., 561, 573–583,
https://doi.org/10.1016/j.jhydrol.2018.04.027, 2018.
Dozier, J.: Mountain hydrology, snow color, and the fourth paradigm,
Transactions American Geophysical Union, EOS, 92, 373–374,
https://doi.org/10.1029/2011EO430001, 2011.
Fang, Y., Liu, Y., and Margulis, S. A.: A western United States snow
reanalysis dataset over the Landsat era from water years 1985 to 2021, Sci.
Data, 9, 677, https://doi.org/10.1038/s41597-022-01768-7, 2022.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005RG000183, 2007.
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.
Gardner, M. W. and Dorling, S. R.: Artificial neural networks (the multilayer perceptron) – a review of applications in the atmospheric sciences, Atmos. Environ., 32, 2627–2636, https://doi.org/10.1016/S1352-2310(97)00447-0, 1998.
Garrison, J. L., Piepmeier, J., Shah, R., Vega, M. A., Spencer, D. A.,
Banting, R., Firman, C. M., Nold, B., Larsen, K., and Bindlish, R.: SNOOPI:
A Technology Validation Mission for P-band Reflectometry using Signals of
Opportunity, in: IGARSS 2019, IEEE Int. Geosci. Remote. Se. Symposium,
5082–5085, https://doi.org/10.1109/IGARSS.2019.8900351, 2019.
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, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Guan, B., Molotch, N. P., Waliser, D. E., Jepsen, S. M., Painter, T. H., and Dozier, J.: Snow water equivalent in the Sierra Nevada: Blending snow sensor observations with snowmelt model simulations, Water Resour. Res., 49, 5029–5046, https://doi.org/10.1002/wrcr.20387, 2013.
Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., and Klambauer, G.:
NeuralHydrology – Interpreting LSTMs in Hydrology, in: Explainable AI:
Interpreting, Explaining and Visualizing Deep Learning, edited by: Samek,
W., Montavon, G., Vedaldi, A., Hansen, L. K., and Müller, K.-R.,
Springer International Publishing, Cham, 347–362,
https://doi.org/10.1007/978-3-030-28954-6_19, 2019.
Kuter, S.: Completing the machine learning saga in fractional snow cover estimation from MODIS Terra reflectance data: Random forests versus support vector regression, Remote Sens. Environ., 255, 112294, https://doi.org/10.1016/j.rse.2021.112294, 2021.
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., and
Wood, E. F.: Inroads of remote sensing into hydrologic science during the
WRR era, Water Resour. Res., 51, 7309–7342,
https://doi.org/10.1002/2015WR017616, 2015.
Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.: How
much runoff originates as snow in the western United States, and how will
that change in the future?, Geophys. Res. Lett., 44, 6163–6172,
https://doi.org/10.1002/2017GL073551, 2017a.
Li, D., Durand, M., and Margulis, S. A.: Estimating snow water equivalent in
a Sierra Nevada watershed via spaceborne radiance data assimilation, Water
Resour. Res., 53, 647–671, https://doi.org/10.1002/2016WR018878, 2017b.
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, 2022.
Liston, G. E.: Representing Subgrid Snow Cover Heterogeneities in Regional
and Global Models, J. Climate., 17, 1381–1397,
https://doi.org/10.1175/1520-0442(2004)017<1381:RSSCHI>2.0.CO;2, 2004.
Liu, C., Huang, X., Li, X., and Liang, T.: MODIS Fractional Snow Cover Mapping Using Machine Learning Technology in a Mountainous Area, Remote Sens.-Basel., 12, 962, https://doi.org/10.3390/rs12060962, 2020.
Luce, C. H., Lopez-Burgos, V., and Holden, Z.: Sensitivity of snowpack
storage to precipitation and temperature using spatial and temporal analog
models, Water. Resour. Res., 50, 9447–9462,
https://doi.org/10.1002/2013WR014844, 2014.
Magnusson, J., Gustafsson, D., Hüsler, F., and Jonas, T.: Assimilation
of point SWE data into a distributed snow cover model comparing two
contrasting methods, Water. Resour. Res., 50, 7816–7835,
https://doi.org/10.1002/2014WR015302, 2014.
Margulis, S. A., Girotto, M., Cortés, G., and Durand, M.: A Particle
Batch Smoother Approach to Snow Water Equivalent Estimation, J.
Hydrometeorol., 16, 1752–1772, https://doi.org/10.1175/JHM-D-14-0177.1,
2015.
Ma, X., Li, D., Fang, Y., Margulis, S. A., and Lettenmaier, D. P.: Datasets of estimating spatiotemporally continuous snow water equivalent from intermittent satellite track observations using machine learning methods, figshare [data set], https://doi.org/10.6084/m9.figshare.20044424.v1, 2022.
Molotch, N. P. and Bales, R. C.: Scaling snow observations from the point to
the grid element: Implications for observation network design, Water.
Resour. Res., 41, 1–2, https://doi.org/10.1029/2005WR004229, 2005.
Molotch, N. P. and Bales, R. C.: SNOTEL representativeness in the Rio Grande
headwaters on the basis of physiographics and remotely sensed snow cover
persistence, Hydrol. Process., 20, 723–739,
https://doi.org/10.1002/hyp.6128, 2006.
Nolin, A. W.: Recent advances in remote sensing of seasonal snow, J.
Glaciol., 56, 1141–1150, https://doi.org/10.3189/002214311796406077, 2010.
Pflug, J. M. and Lundquist, J. D.: Inferring Distributed Snow Depth by
Leveraging Snow Pattern Repeatability: Investigation Using 47 Lidar
Observations in the Tuolumne Watershed, Sierra Nevada, California, Water
Resour. Res., 56, e2020WR027243, https://doi.org/10.1029/2020WR027243, 2020.
Raleigh, M. S. and Lundquist, J. D.: Comparing and combining SWE estimates
from the SNOW-17 model using PRISM and SWE reconstruction, Water Resour.
Res., 48, p. 13, https://doi.org/10.1029/2011WR010542, 2012.
Schneider, D. and Molotch, N. P.: Real-time estimation of snow water
equivalent in the Upper Colorado River Basin using MODIS-based SWE
Reconstructions and SNOTEL data, Water Resour. Res., 52, 7892–7910, https://doi.org/10.1002/2016WR019067, 2016.
Segal, M. R.: Machine Learning Benchmarks and Random Forest Regression, UCSF: Center for Bioinformatics and Molecular Biostatistics, https://escholarship.org/uc/item/35x3v9t4 (last access: 23 December 2022), 2004.
Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C.,
Kim, D.-H., Collins, K. M., Channan, S., DiMiceli, C., and Townshend, J. R.:
Global, 30 m resolution continuous fields of tree cover: Landsat-based
rescaling of MODIS vegetation continuous fields with lidar-based estimates
of error, Int. J. Digit. Earth, 6, 427–448,
https://doi.org/10.1080/17538947.2013.786146, 2013.
Shah, R., Yueh, S., Xu, X., Elder, K., Huang, H., and Tsang, L.:
Experimental Results of Snow Measurement Using P-Band Signals of
Opportunity, in: IGARSS 2018–2018 IEEE International Geoscience and Remote
Sensing Symposium, IGARSS 2018–2018, IEEE Geosci. Remote Se. Symposium,
6280–6283, https://doi.org/10.1109/IGARSS.2018.8517749, 2018.
Sicart, J. E., Pomeroy, J. W., Essery, R. L. H., and Bewley, D.: Incoming
longwave radiation to melting snow: observations, sensitivity and estimation
in Northern environments, Hydrol. Process., 20, 3697–3708,
https://doi.org/10.1002/hyp.6383, 2006.
Sun, S. and Xue, Y.: Implementing a new snow scheme in Simplified Simple
Biosphere Model, Adv. Atmos. Sci., 18, 335–354,
https://doi.org/10.1007/BF02919314, 2001.
Tanaka, M. and Okutomi, M.: A novel inference of a restricted boltzmann
machine, IEEE, 2014 22nd Int. C. Patt. Recog., 1526–1531,
https://doi.org/10.1109/ICPR.2014.271, 2014.
Trujillo, E., Molotch, N. P., Goulden, M. L., Kelly, A. E., and Bales, R.
C.: Elevation-dependent influence of snow accumulation on forest greening,
Nat. Geosci., 5, 705–709, https://doi.org/10.1038/ngeo1571, 2012.
Vapnik, V. N.: Estimation of Dependences Based on Empirical Data, Addendum 1, New York: Springer-Verlag, 1982.
Vapnik, V.: The Support Vector Method of Function Estimation, in: Nonlinear Modeling: Advanced Black-Box Techniques, edited by: Suykens, J. A. K. and Vandewalle, J., Springer US, Boston, MA, 55–85, https://doi.org/10.1007/978-1-4615-5703-6_3, 1998.
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.
Xue, Y., Sun, S., Kahan, D. S., and Jiao, Y.: Impact of parameterizations in
snow physics and interface processes on the simulation of snow cover and
runoff at several cold region sites, J. Geophys. Res-Atmos., 108, 8859,
https://doi.org/10.1029/2002JD003174, 2003.
Yueh, S. H., Shah, R., Xu, X., Stiles, B., and Bosch-Lluis, X.: A Satellite
Synthetic Aperture Radar Concept Using P-Band Signals of Opportunity, IEEE
J. Sel. Top. Appl., 14, 2796–2816, https://doi.org/10.1109/JSTARS.2021.3059242, 2021.
Short summary
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.
We explore satellite retrievals of snow water equivalent (SWE) along hypothetical ground tracks...
Special issue