Estimation of Seasonal Snow Mass Balance all over Japan Using a High-Resolution Atmosphere-Snow Model Chain

Niwano, Michio; Suya, Masami; Nagaya, Koichi; Yamaguchi, Satoru; Matoba, Sumito; Harada, Ikuo; Ohkawara, Nozomu

doi:10.2151/sola.2022-031

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…The spatially and temporally consistent framework of IT-SNOW can aid not only climatological and hydrologic studies, for example, by providing highly necessary validation datasets of snow cover and SWE, but also operational water resources managers who routinely use SWE as an estimate of freshet volume (Harrison and Bales, 2016). Similar efforts have already been made in other regions like California, USA, where Margulis et al (2016) estimate about 20 Gm 3 of mean peak water volume in snow across the Sierra Nevada, or Japan, where Niwano et al (2022) estimate 42.2 Gm 3 on average during the 2017-2022 winters. These estimates tally with those of IT-SNOW across the Italian mountain ranges (Fig.…”

Section: Examples Of Usementioning

confidence: 92%

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

Avanzi

Gabellani

Delogu

et al. 2023

Earth Syst. Sci. Data

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Abstract. We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 103 km2) – a transitional continental-to-Mediterranean region where snow plays an important but still poorly constrained societal and ecological role. IT-SNOW provides ∼ 500 m daily maps of snow water equivalent (SWE), snow depth, bulk snow density, and liquid water content for the initial period 1 September 2010–31 August 2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel-2, MODIS (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 350 on-the-ground snow depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in situ snow data from three focus regions (Aosta Valley, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 mm for in situ, measured snow depth and snow water equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available at https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate.

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Section: Examples Of Usementioning

confidence: 92%

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

Avanzi

Gabellani

Delogu

et al. 2023

Earth Syst. Sci. Data

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Operation, Expansion, and Improvement of the Snow Load Alert System “YukioroSignal”

Hirashima,

Kawashima,

Motoya

et al. 2024

JDR

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The “YukioroSignal” system, which provides snow load alerts, was developed to inform decision-making regarding snow removal from house roofs. It was launched in Niigata Prefecture in 2018 and expanded to cover all special heavy snowfall areas in Japan, including the Hokkaido, Tohoku, and Hokuriku regions in 2024. The system uses the SNOWPACK model to estimate high-accuracy snow weight from real-time snow depth data published online at observation points. At locations where snow depth gauges are not installed, such as in mountainous areas, snow weight is estimated using inverse distance-weighted interpolation, but accuracy is reduced. To overcome this problem, this study attempted to integrate this information with the snow water equivalent distribution calculated using the simple-layer snow distribution model. To validate this improvement, manual observations of snow weight were performed at 98 sites and compared with simulation results. The accuracy of snow weight estimation at distances far away from snow depth stations was improved. The six-year operation of YukioroSignal showed the additional required information that is vulnerable to damage even with less snowfall, such as vacant houses, and caution of changes in hazard levels by an increase in snowburst in a short period.

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Assessments of long-term precipitation and temperature variations during snow cover periods using high-elevation AMeDAS data in central Japan

Ueno

2023

Bulletin of Glacier Research

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Long-term hourly data records of the Automated Meteorological Data Acquisition System (AMeDAS) with snow depth measurements at three high elevation sites were analyzed in central Japan to assess the decadal scale winter climate variabilities with the effects of observation biases. Year-to-year variations of persistent snow cover periods (PSCPs) did not show a significant shortening trend with the delays of starting the PSCP associated with El Niño in December and forward to the ending of the PSCP with nationwide warming in March. Significant warming trends in the surface air temperature averaged in PSCPs were not identified, and length of the PSCP strongly contributed to the year-to-year variations of monthly averaged winter air temperatures. Long-term variabilities of precipitation records were primarily affected by changing the gauge type and data resolutions. After a simple correction of gauge undercatch by air temperature and wind speed, the amount of precipitation increased by around 20 % at the northern station but only by 3 % in the south due to frequent rain-on-snow events. In the two northern stations, snowfall amounts on the order of around 10 % were estimated to be missing in the precipitation records, where a long-term decreasing trend in occurrences of freezing precipitation was also found. The statistics calculated by AMeDAS data were verified using the data of the Vaisala Present Weather Detector in the Sugadaira Highland.

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Estimation of Seasonal Snow Mass Balance all over Japan Using a High-Resolution Atmosphere-Snow Model Chain

Abstract: This pre-publication manuscript may be downloaded, distributed and used under the provisions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license. It may be cited using the DOI below.

Cited by 4 publications

References 28 publications

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

Operation, Expansion, and Improvement of the Snow Load Alert System “YukioroSignal”

Assessments of long-term precipitation and temperature variations during snow cover periods using high-elevation AMeDAS data in central Japan

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