Journal Article

FZJ-2017-03490

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites

Post, H. (Corresponding author) ; Vrugt, J. A. ; Fox, A. ; Vereecken, H.FZJ* ; Hendricks Franssen, H.-J.FZJ*

2017
[Washington, DC]

This record in other databases:    

Please use a persistent id in citations: http://hdl.handle.net/2128/20696  doi:10.1002/2015JG003297

Abstract: The Community Land Model (CLM) contains many parameters whose values are uncertain and thus require careful estimation for model application at individual sites. Here we used Bayesian inference with the DiffeRential Evolution Adaptive Metropolis (DREAM(zs)) algorithm to estimate eight CLM v.4.5 ecosystem parameters using 1 year records of half-hourly net ecosystem CO2 exchange (NEE) observations of four central European sites with different plant functional types (PFTs). The posterior CLM parameter distributions of each site were estimated per individual season and on a yearly basis. These estimates were then evaluated using NEE data from an independent evaluation period and data from “nearby” FLUXNET sites at ~600 km distance to the original sites. Latent variables (multipliers) were used to treat explicitly uncertainty in the initial carbon-nitrogen pools. The posterior parameter estimates were superior to their default values in their ability to track and explain the measured NEE data of each site. The seasonal parameter values reduced with more than 50% (averaged over all sites) the bias in the simulated NEE values. The most consistent performance of CLM during the evaluation period was found for the posterior parameter values of the forest PFTs, and contrary to the C3-grass and C3-crop sites, the latent variables of the initial pools further enhanced the quality-of-fit. The carbon sink function of the forest PFTs significantly increased with the posterior parameter estimates. We thus conclude that land surface model predictions of carbon stocks and fluxes require careful consideration of uncertain ecological parameters and initial states.

---

Contributing Institute(s):

1. Agrosphäre (IBG-3)
2. JARA - HPC (JARA-HPC)

Research Program(s):

1. 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) (POF3-255)
2. Better predictions with environmental simulation models: optimally integrating new data sources (jicg41_20100501) (jicg41_20100501)

Appears in the scientific report 2017

---

Database coverage:
; ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Physical, Chemical and Earth Sciences ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

---