Item Infomation
Title: | Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output |
Authors: | Tol, C.V.D. Raj, R. |
Issue Date: | 2017 |
Citation: | Geoscientific Model DevelopmentVolume 11, pp. 83 - 101 |
Abstract: | In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the orest. The calibration improved the root mean square error and enhanced Nash–Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated Biome-BGC. |
URI: | http://tailieuso.tlu.edu.vn/handle/DHTL/4490 |
Source: | https://www.geosci-model-dev.net/11/83/2018/gmd-11-83-2018.pdf |
ISSN: | 1991-9603 |
Appears in Collections: | Tài liệu mở |
ABSTRACTS VIEWS
9
VIEWS & DOWNLOAD
0
Files in This Item:
There are no files associated with this item.
Bạn đọc là cán bộ, giáo viên, sinh viên của Trường Đại học Thuỷ Lợi cần đăng nhập để Xem trực tuyến/Tải về
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.