Articles | Volume 18, issue 4
https://doi.org/10.5194/essd-18-2703-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-2703-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
20 Apr 2026
Data description article |
| 20 Apr 2026
| 20 Apr 2026
A harmonized 2000–2024 dataset of daily river ice concentration and annual phenology for major Arctic rivers
Jiahui Qiu
CORRESPONDING AUTHOR
Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu 90014, Finland
Kari Luojus
Space and Earth Observation Centre, Finnish Meteorological Institute, Helsinki 00101, Finland
Harri Kaartinen
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Espoo 02150, Finland
Yubao Qiu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Jari Silander
Quality of Information, Finnish Environment Institute, Helsinki 00790, Finland
Epari Ritesh Patro
Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu 90014, Finland
Björn Klöve
Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu 90014, Finland
Ali Torabi Haghighi
Water, Energy and Environmental Engineering Research Unit, University of Oulu, Oulu 90014, Finland
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Aimad El Issaoui, Veikka Taka, Harri Kaartinen, Antero Kukko, and Juha Hyyppä
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B2-2026, 169–174, https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-169-2026, https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-169-2026, 2026
Susanna Hyyppä, Josef Taher, Matti Hyyppä, Leena Matikainen, Xiaowei Yu, Harri Kaartinen, Oskari Saarinen, Kirsi Karila, Ville Luoma, Marjut Turtiainen, Birgitta Partanen, Roope Näsi, Jyri Maanpää, Eetu Puttonen, Arttu Kivimäki, Mikko Vastaranta, Antero Kukko, Hannu Hyyppä, Matti Vaaja, Fabio Remondino, Narges Takhtkeshha, and Juha Hyyppä
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B1-2026, 443–448, https://doi.org/10.5194/isprs-archives-XLIX-B1-2026-443-2026, https://doi.org/10.5194/isprs-archives-XLIX-B1-2026-443-2026, 2026
Heikki Hyyti, Matias Mäki-Leppilampi, and Harri Kaartinen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B1-2026, 255–261, https://doi.org/10.5194/isprs-archives-XLIX-B1-2026-255-2026, https://doi.org/10.5194/isprs-archives-XLIX-B1-2026-255-2026, 2026
Zuoya Liu, Harri Kaartinen, Antero Kukko, Juha Hyyppa, and Ruizhi Chen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., XI-1-2026, 297–302, https://doi.org/10.5194/isprs-annals-XI-1-2026-297-2026, https://doi.org/10.5194/isprs-annals-XI-1-2026-297-2026, 2026
Marie Korppoo, Inese Huttunen, Markus Huttunen, Maiju Narikka, Jari Silander, Tom Jilbert, Martin Forsius, Pirkko Kortelainen, Niina Kotamäki, Cintia Uvo, and Anna-Kaisa Ronkanen
Hydrol. Earth Syst. Sci., 30, 3095–3119, https://doi.org/10.5194/hess-30-3095-2026, https://doi.org/10.5194/hess-30-3095-2026, 2026
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The development of carbon processes in the water quality model WSFS (Watershed Simulation and Forecasting System)-Vemala presents a significant advancement in simulating both total organic and inorganic carbon dynamics, burial and emissions through a river/lake network. The addition of organic acids to the total alkalinity definition improved pH simulations and thus the simulation of CO2 emissions in the acidic and organic rich waters of Finland. The new Vemala model provides a robust foundation to support water management in the future.
Petra Korhonen, Pertti Ala-Aho, Bjørn Kløve, and Hannu Marttila
Hydrol. Earth Syst. Sci., 30, 3019–3040, https://doi.org/10.5194/hess-30-3019-2026, https://doi.org/10.5194/hess-30-3019-2026, 2026
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We studied how the melting of snow affects the release of dissolved organic carbon (DOC) in a northern peatland. Using detailed aerial surveys of snow cover, landscape moisture, and continuous water quality measurements revealed that DOC is released as snow cover melts, especially in wetter areas. Our results show how the snowmelt patterns control DOC transport, highlighting the sensitivity of northern peatland ecosystems to climate-driven changes in snow cover.
Guangxin He, Wang Zhang, Xiaoran Zhuang, Yuxuan Feng, Juanzhen Sun, Yubao Qiu, Lei Lei, and Jingjia Luo
EGUsphere, https://doi.org/10.5194/egusphere-2025-6488, https://doi.org/10.5194/egusphere-2025-6488, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Accurate weather forecasting is vital for safety but faces challenges with calculation errors. We developed a new predictive model that enhances accuracy by reducing data redundancy and optimizing information flow. Experiments with real radar data show our approach significantly outperforms existing methods, particularly for heavy rainfall. This model maintains clear details over time, offering a robust tool for timely severe weather warnings and effective disaster prevention.
Zhengxin Jiang, Yubao Qiu, Matti Leppäranta, Xiaoting Li, Peng Yao, Guoqiang Jia, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-721, https://doi.org/10.5194/essd-2025-721, 2026
Preprint under review for ESSD
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Lake ice profoundly impacts regional climate and ecosystems in cold climate regions. The long-term daily lake ice coverage, annual ice phenology, and the probability of complete ice-cover occurrence were produced for 32800 global lakes using gap-filled MODIS data from 2002 to 2024. Patterns and trends of ice phenology and ice-cover status quantitatively revealed that how lakes respond to the climate change. The dataset provides a valuable resource for hydrology, ecology and climate research.
Andreas Güntner, Ehsan Sharifi, Julian Haas, Eva Boergens, Feifei Cao, Christoph Dahle, Neda Darbeheshti, Henryk Dobslaw, Inés Dussaillant, Wouter Dorigo, Frank Flechtner, Adrian Jäggi, Miriam Kosmale, Martin Lasser, Kari Luojus, Ulrich Meyer, Adam Pasik, Wolfgang Preimersberger, Claudia Ruz Vargas, and Michael Zemp
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-797, https://doi.org/10.5194/essd-2025-797, 2026
Revised manuscript under review for ESSD
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We provide a data set that illustrates how the amount of water that is stored in the subsurface as groundwater varies in time over the continents of the Earth. A main source of the data are observations with satellites that weigh the changing amount of water by its mass attraction effect. The data allow for assessing how groundwater as the most important freshwater resource for mankind and ecosystems is affected by climate variability, climate change and withdrawal by human activities.
Pinja Venäläinen, Colleen Mortimer, Kari Luojus, Lawrence Mudryk, Matias Takala, and Jouni Pulliainen
The Cryosphere, 19, 6301–6318, https://doi.org/10.5194/tc-19-6301-2025, https://doi.org/10.5194/tc-19-6301-2025, 2025
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Satellite data-based estimation of large snow water equivalent (SWE) values can be improved with bias correction. This study updates the bias correction method by using updated snow course data, extending correction to two new months. Additionally, bias correction is expanded from a monthly to a daily time scale. The daily bias correction offers more accurate hemispheric snow mass estimation, aligning well with reanalysis data.
Abolfazl Jalali Shahrood, Amirhossein Ahrari, and Ali Torabi Haghighi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2982, https://doi.org/10.5194/egusphere-2025-2982, 2025
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We analyzed 57 years of river, snow, and temperature records from a sub-Arctic watershed in Finland to understand how seasonal freezing and thawing have changed due to climate warming. Our findings show that spring events like snowmelt and ice break-up are happening earlier, while autumn changes remain unpredictable. This loss of seasonal coordination affects river behavior, water resources, and future planning under ongoing climate change.
Matias Mäki-Leppilampi, Heikki Hyyti, and Harri Kaartinen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-W2-2025, 125–132, https://doi.org/10.5194/isprs-annals-X-2-W2-2025-125-2025, https://doi.org/10.5194/isprs-annals-X-2-W2-2025-125-2025, 2025
Evgeny Lopatin, Kari Väätäinen, Harri Kaartinen, Heikki Hyyti, Lauri Sikanen, Yrjö Nuutinen, and Mauricio Acuna
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-W2-2025, 117–123, https://doi.org/10.5194/isprs-annals-X-2-W2-2025-117-2025, https://doi.org/10.5194/isprs-annals-X-2-W2-2025-117-2025, 2025
Joonas Kousa, Teemu Hakala, Antero Kukko, and Harri Kaartinen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-W2-2025, 101–108, https://doi.org/10.5194/isprs-annals-X-2-W2-2025-101-2025, https://doi.org/10.5194/isprs-annals-X-2-W2-2025-101-2025, 2025
Ella Kivimäki, Maria Tenkanen, Tuula Aalto, Michael Buchwitz, Kari Luojus, Jouni Pulliainen, Kimmo Rautiainen, Oliver Schneising, Anu-Maija Sundström, Johanna Tamminen, Aki Tsuruta, and Hannakaisa Lindqvist
Biogeosciences, 22, 5193–5230, https://doi.org/10.5194/bg-22-5193-2025, https://doi.org/10.5194/bg-22-5193-2025, 2025
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We study how environmental variables influencing natural methane fluxes explain the seasonal variability in satellite-observed methane in Northern Hemisphere high-latitude wetland areas. Using two atmospheric model set-ups, we assess consistency with satellite data. Methane loss through reaction with hydroxyl radicals and links with snow cover, temperature, and snowmelt had the strongest influence. Our study highlights the value of satellite observations for understanding large-scale wetland emissions.
Zuoya Liu, Harri Kaartinen, Teemu Hakala, Heikki Hyyti, Antero Kukko, Juha Hyyppa, and Ruizhi Chen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-G-2025, 551–557, https://doi.org/10.5194/isprs-annals-X-G-2025-551-2025, https://doi.org/10.5194/isprs-annals-X-G-2025-551-2025, 2025
Tamás Faitli, Heikki Hyyti, Juha Hyyppä, Antero Kukko, and Harri Kaartinen
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W4-2025, 37–42, https://doi.org/10.5194/isprs-archives-XLVIII-1-W4-2025-37-2025, https://doi.org/10.5194/isprs-archives-XLVIII-1-W4-2025-37-2025, 2025
Filip Muhic, Pertti Ala-Aho, Matthias Sprenger, Björn Klöve, and Hannu Marttila
Hydrol. Earth Syst. Sci., 28, 4861–4881, https://doi.org/10.5194/hess-28-4861-2024, https://doi.org/10.5194/hess-28-4861-2024, 2024
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The snowmelt event governs the hydrological cycle of sub-arctic areas. In this study, we conducted a tracer experiment on a forested hilltop in Lapland to identify how high-volume infiltration events modify the soil water storage. We found that a strong tracer signal remained in deeper soil layers after the experiment and over the winter, but it got fully displaced during the snowmelt. We propose a conceptual infiltration model that explains how the snowmelt homogenizes the soil water storage.
Umer Saleem, Ali Torabi Haghighi, Björn Klöve, and Mourad Oussalah
EGUsphere, https://doi.org/10.5194/egusphere-2024-1170, https://doi.org/10.5194/egusphere-2024-1170, 2024
This preprint is open for discussion and under review for The Cryosphere (TC).
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This paper discusses the impact of citizen science and remote sensing on water quality monitoring. It explores applications combining citizen science with tools like microwave and optical systems, assessing parameters and techniques via apps such as EyeOnWater and HydroColor. It highlights the transformative potential in addressing water quality research gaps.
Getnet Demil, Ali Torabi Haghighi, Björn Klöve, and Mourad Oussalah
EGUsphere, https://doi.org/10.5194/egusphere-2024-1158, https://doi.org/10.5194/egusphere-2024-1158, 2024
This preprint is open for discussion and under review for The Cryosphere (TC).
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This review explores using advanced image-based methods to estimate snow parameters for water resource management. Deep learning and satellite imagery improve accuracy in predicting snowmelt and depth. Challenges like data availability persist; addressing them requires novel deep learning architectures and better data synchronization. Integration of image-based approaches can revolutionize snow hydrology modeling and environmental management.
Danny Croghan, Pertti Ala-Aho, Jeffrey Welker, Kaisa-Riikka Mustonen, Kieran Khamis, David M. Hannah, Jussi Vuorenmaa, Bjørn Kløve, and Hannu Marttila
Hydrol. Earth Syst. Sci., 28, 1055–1070, https://doi.org/10.5194/hess-28-1055-2024, https://doi.org/10.5194/hess-28-1055-2024, 2024
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The transport of dissolved organic carbon (DOC) from land into streams is changing due to climate change. We used a multi-year dataset of DOC and predictors of DOC in a subarctic stream to find out how transport of DOC varied between seasons and between years. We found that the way DOC is transported varied strongly seasonally, but year-to-year differences were less apparent. We conclude that the mechanisms of transport show a higher degree of interannual consistency than previously thought.
Kerttu Kouki, Kari Luojus, and Aku Riihelä
The Cryosphere, 17, 5007–5026, https://doi.org/10.5194/tc-17-5007-2023, https://doi.org/10.5194/tc-17-5007-2023, 2023
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We evaluated snow cover properties in state-of-the-art reanalyses (ERA5 and ERA5-Land) with satellite-based datasets. Both ERA5 and ERA5-Land overestimate snow mass, whereas albedo estimates are more consistent between the datasets. Snow cover extent (SCE) is accurately described in ERA5-Land, while ERA5 shows larger SCE than the satellite-based datasets. The trends in snow mass, SCE, and albedo are mostly negative in 1982–2018, and the negative trends become more apparent when spring advances.
Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4363–4380, https://doi.org/10.5194/tc-17-4363-2023, https://doi.org/10.5194/tc-17-4363-2023, 2023
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Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere, 17, 4343–4362, https://doi.org/10.5194/tc-17-4343-2023, https://doi.org/10.5194/tc-17-4343-2023, 2023
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Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
T. Faitli, T. Hakala, H. Kaartinen, J. Hyyppä, and A. Kukko
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 145–150, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-145-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-145-2023, 2023
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
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Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
P. Rönnholm, S. Wittke, M. Ingman, P. Putkiranta, H. Kauhanen, H. Kaartinen, and M. T. Vaaja
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 633–639, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-633-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-633-2022, 2022
Kerttu Kouki, Petri Räisänen, Kari Luojus, Anna Luomaranta, and Aku Riihelä
The Cryosphere, 16, 1007–1030, https://doi.org/10.5194/tc-16-1007-2022, https://doi.org/10.5194/tc-16-1007-2022, 2022
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We analyze state-of-the-art climate models’ ability to describe snow mass and whether biases in modeled temperature or precipitation can explain the discrepancies in snow mass. In winter, biases in precipitation are the main factor affecting snow mass, while in spring, biases in temperature becomes more important, which is an expected result. However, temperature or precipitation cannot explain all snow mass discrepancies. Other factors, such as models’ structural errors, are also significant.
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Short summary
We developed a 24-year record revealing how river ice on the six largest Arctic rivers has changed under a warming climate. Using satellite images from the MODIS Terra and Aqua sensors, we monitored daily ice cover and seasonal freeze-up and breakup timing. On average, over 65 % of river segments show later freeze-up (~ 9 d), earlier breakup (~ 8 d), and shorter ice seasons (~ 14 d), revealing a clear signal of rapid warming across Arctic river systems.
We developed a 24-year record revealing how river ice on the six largest Arctic rivers has...
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