10.1594/PANGAEA.909880Yamaguchi, SatoruSatoruYamaguchi0000-0001-9972-0443Hiroyuki, HirashimaHirashimaHiroyukiAvanzi, FrancescoFrancescoAvanzi0000-0003-4235-2373PANGAEA2019Japanlysimetersmachine learningPreferential flowrandom forestsnowSNOWPACKDATE/TIMETemperature, airShort-wave downward (GLOBAL) radiationLong-wave downward radiationSnow water equivalentSnow thicknessPrecipitation, daily totalDensity, snowTemperature, ice/snowPrecipitation, rainPrecipitation, snowDay of water yearSnowfall daysSensible heatLatent heatShort-wave net radiationLong-wave net radiationMelt rate in water equivalent per dayNumber of melt eventsLiquid water contentSnow grain size, meanSnow grain size, differenceDensity, snow, differenceDensity over grain size, differenceNumber of layersLayer thicknessRunoffRunoff, standard deviationCoefficient of variationRunoff, maximum deviationMonitoring station2006-01-05T00:00:00/2018-03-19T00:00:00Supplementary Dataset10.1029/2019WR02482828941 data pointstext/tab-separated-valuesCreative Commons Attribution 4.0 InternationalThis dataset includes daily summaries of weather, snow, and preferential-flow conditions at the Snow and Ice Research Center, Nagaoka (Japan) -- snow seasons 2006 through 2018.

The summary comprises 29 candidate predictors (features) of preferential flow as estimated by a weather-snow station, manual snow pits, and a snow model (SNOWPACK) and four metrics of preferential-flow characteristics measured by a multi-compartment lysimeter made by nine 1-m2 cells (standard deviation of mean snowpack runoff, coefficient of variation of mean snowpack runoff, maximum deviation of mean snowpack runoff, and mean snowpack runoff).

This dataset was produced during the preparation of the paper ''Insights into preferential-flow snowpack runoff using Random Forest'' (WRR, doi:10.1029/2019WR024828).

More details about this dataset can be found in the above mentioned paper.Supplement to: Avanzi, Francesco; Johnson, Ryan Curtis; Oroza, Carlos A; Hirashima, Hiroyuki; Maurer, Tessa; Yamaguchi, Satoru (2019): Insights into preferential-flow snowpack runoff using Random Forest. Water Resources Research138.8833337.41667Japan