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Simultaneous Heat and Water (SHAW) input and outputs for four parameterizations of upland deciduous boreal forest at Bonanza Creek, Alaska 2003-2015

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DataCite Commons2021-05-27 更新2025-04-16 收录
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https://arcticdata.io/catalog/view/doi:10.18739/A24M91B8S
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This dataset provides inputs and outputs for the Simultaneous Heat and Water (SHAW) model implemented at the upland birch site (labeled UP1A) at the Bonanza Creek Long Term Ecological Research (BNZ LTER) site. The purpose of this study was to parameterize SHAW for upland boreal deciduous forest. Input climate data includes air temperature, precipitation, wind speed, relative humidity, and downward solar radiation, and was derived from previously published data from the BNZ LTER. Model parameters describing plant structural and water use characteristics were obtained from vegetation surveys at the BNZ LTER and from relevant scientific literature. Soil texture model parameters were also obtained from data published through the BNZ LTER. A Generalized Likelihood Uncertainty Estimation (GLUE) approach was used to assess soil hydraulic parameters. Four sets of soil hydraulic parameters are identified. These sets respectively minimize the root mean squared error of volumetric water content at 50 centimeter (cm) (expanded data), root mean squared error of soil temperature at 1 meter (m) (T1_RMSE) model performance dependence on interannual variability (Case 3), and two sets that minimize the dependence of volumetric water content RMSE on temperature (CSMP_warm) and precipitation (CSMP_wet). The first set of parameter inputs and outputs are uploaded as individual files; inputs and outputs for the other cases are provided as compressed directories with model inputs and outputs for their respective parameters. These data are published in support of the manuscript: Marshall, A.M.; Link, T.E., Flerchinger, G.N., Nicolsky, D.J., Lucash, M.S. (2021). Ecohydrological modeling in a deciduous boreal forest: Model evaluation for application in non-stationary climates. Manuscript submitted to Hydrological Processes.
提供机构:
NSF Arctic Data Center
创建时间:
2021-05-27
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