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  1. 防災科研関係論文

Insights Into Preferential Flow Snowpack Runoff Using Random Forest

https://nied-repo.bosai.go.jp/records/4599
https://nied-repo.bosai.go.jp/records/4599
43b198c7-7cf6-48c2-91d7-b980df234aaf
Item type researchmap(1)
公開日 2023-04-27
タイトル
言語 en
タイトル Insights Into Preferential Flow Snowpack Runoff Using Random Forest
言語
言語 eng
著者 Francesco Avanzi

× Francesco Avanzi

en Francesco Avanzi

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Ryan Curtis Johnson

× Ryan Curtis Johnson

en Ryan Curtis Johnson

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Carlos A. Oroza

× Carlos A. Oroza

en Carlos A. Oroza

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Hiroyuki Hirashima

× Hiroyuki Hirashima

en Hiroyuki Hirashima

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Tessa Maurer

× Tessa Maurer

en Tessa Maurer

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Satoru Yamaguchi

× Satoru Yamaguchi

en Satoru Yamaguchi

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抄録
内容記述タイプ Other
内容記述 Using 12 seasons of data from a multicompartment snow lysimeter and a statistical learning algorithm (Random Forest), we investigated to what extent preferential flow snowpack runoff can be predicted from concurrent weather and snow conditions, as well as the relative importance of factors affecting this process. We found that preferential flow development can be partially predicted based on concurrent weather and snow conditions. In this case study where snow is generally wet and coarse, the most important predictors of standard and maximum deviation from mean spatial snowpack runoff are related to weather inputs and their interaction with the snowpack (rainfall, longwave radiation, and snow-surface temperature) and to more season-specific snow properties (number of macroscopic snow layers and snowfall days to date, the latter being a feature we included to account for microstructural heterogeneity developing at smaller scales than macroscopic layers). This combination between weather and season-specific snow factors and the fact that several of these important features are correlated with other processes result in significant seasonal variability of the Random Forest algorithm's accuracy. All versions of the Random Forest algorithm underestimated seasonal peaks in preferential flow, which points to these peaks being either undersampled in our data set or caused by poorly understood redistribution processes acting at larger spatial scales than the size of our multicompartment lysimeter (e.g., dimples).
言語 en
書誌情報 en : WATER RESOURCES RESEARCH

巻 55, 号 12, p. 10727-10746, 発行日 2019-12
出版者
言語 en
出版者 AMER GEOPHYSICAL UNION
ISSN
収録物識別子タイプ EISSN
収録物識別子 1944-7973
DOI
関連識別子 10.1029/2019WR024828
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