The research presented here informs the confidence that can be placed in the
simulations of land-surface models (LSMs).
After introducing a method for simplifying a complex, heterogeneous land-cover
dataset for use in LSMs, I show that LSMs can realistically represent the spatial
distribution of heterogeneous land-cover processes (e.g., biogenic emission of volatile
organic compounds) in Texas. LSM-derived estimates of biogenic emissions are sensitive
(varying up to a factor of 3) to land-cover data, which is not well constrained by
observations. Simulated emissions are most sensitive to land-cover data in eastern and
central Texas, where tropospheric ozone pollution is a concern. I further demonstrate that
interannual variation in leaf mass is at least as important to variation in biogenic
emissions as is interannual variation in shortwave radiation and temperature. Model estimates show that more-humid regions with less year-to-year variation in precipitation
have lower year-to-year variation in biogenic emissions: as modeled mean emissions
increase, their mean-normalized standard deviation decreases.
I evaluate three parameterizations of subsurface hydrology in LSMs (with (1) a
shallow, 10-layer soil; (2) a deeper, many-layered soil; and (3) a lumped aquifer model)
under increasing parameter uncertainty. When given their optimal parameter sets, all
three versions perform equivalently well when simulating monthly change in terrestrial
water storage. The most conceptually realistic model is least sensitive to errant parameter
values. However, even when using the most conceptually realistic model, parameter
interaction ensures that knowing ranges for individual parameters is insufficient to
guarantee realistic simulation.
LSMs are often developed and evaluated at data-rich sites but are then applied in
regions where data are sparse or unavailable. I present a framework for model evaluation
that explicitly acknowledges perennial sources of uncertainty in LSM simulations (e.g.,
parameter uncertainty, meteorological forcing-data uncertainty, evaluation-data
uncertainty) and that evaluates LSMs in a way that is consistent with models’ typical
application. The model performance score quantifies the likelihood that a representative
ensemble of model performance will bracket observations with high skill and low spread.
The robustness score quantifies the sensitivity of model performance to parameter error
or data error. The fitness score ranks models’ suitability for broad application. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/6872 |
| Date | 04 February 2010 |
| Creators | Gulden, Lindsey Elizabeth |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Format | electronic |
| Rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works. |
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