Constraining the Sheffield dynamic global vegetation model using stream-flow measurements in the United Kingdom
Article first published online: 10 NOV 2005
Global Change Biology
Volume 11, Issue 12, pages 2196–2210, December 2005
How to Cite
Picard, G., Woodward, F.I., Lomas, M.R., Pellenq, J., Quegan, S. and Kennedy, M. (2005), Constraining the Sheffield dynamic global vegetation model using stream-flow measurements in the United Kingdom. Global Change Biology, 11: 2196–2210. doi: 10.1111/j.1365-2486.2005.01048.x
- Issue published online: 28 NOV 2005
- Article first published online: 10 NOV 2005
- Received 28 January 2005; revised version received 7 July 2005 and accepted 13 July 2005
- carbon flux;
The biospheric water and carbon cycles are intimately coupled, so simulating carbon fluxes by vegetation also requires modelling of the water fluxes, with each component influencing the other. Observations of river streamflow integrate information at the catchment scale and are widely available over a long period; they therefore provide an important source of information for validating or calibrating vegetation models. In this paper, we analyse the performance of the Sheffield dynamic global vegetation model (SDGVM) for predicting river streamflow and quantifying how this information helps to constrain carbon flux predictions.
The SDGVM is run for 29 large catchments in the United Kingdom. Annual streamflow estimates are compared with long time-series observations. In 23 out of the 29 catchments, the bias between model and observations is less than 50 mm, equivalent to less than 10% of precipitation. In the remaining catchments, larger errors are because of combinations of unpredictable causes, in particular various human activities and measurement issues and, in two cases, unidentified causes.
In one of the catchments, we assess to what extent a knowledge of annual streamflow can constrain model parameters and in turn constrain estimates of gross primary production (GPP). For this purpose, we assume the model parameters are uncertain and constrain them by the streamflow observations using the generalized likelihood uncertainty estimation method. Comparing the probability density function of GPP with and without constraint shows that streamflow effectively constrains GPP, mainly by setting a low probability to GPP values below about 1100 g C−1 m2 yr−1. In other words, streamflow observations allow the rejection of low values of GPP, so that the potential range of possible GPP values is almost halved.