Journal
HYDROLOGY AND EARTH SYSTEM SCIENCES
Volume 20, Issue 6, Pages 2403-2419Publisher
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/hess-20-2403-2016
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Funding
- Australian Research Council Centre of Excellence for Climate System Science [CE110001028]
- Australian Research Council Linkage grant [LP140100232]
- US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program [DE-FG02-04ER63917, DE-FG02-04ER63911]
- CFCAS
- NSERC
- BIOCAP
- Environment Canada
- NRCan
- CarboEuropeIP
- FAO-GTOS-TCO
- iLEAPS
- Max Planck Institute for Biogeochemistry
- National Science Foundation
- University of Tuscia
- Universite Laval and Environment Canada
- US Department of Energy
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Surface fluxes from land surface models (LSMs) have traditionally been evaluated against monthly, seasonal or annual mean states. The limited ability of LSMs to reproduce observed evaporative fluxes under water-stressed conditions has been previously noted, but very few studies have systematically evaluated these models during rainfall deficits. We evaluated latent heat fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLE) LSM across 20 flux tower sites at sub-annual to inter-annual timescales, in particular focusing on model performance during seasonal-scale rainfall deficits. The importance of key model processes in capturing the latent heat flux was explored by employing alternative representations of hydrology, leaf area index, soil properties and stomatal conductance. We found that the representation of hydrological processes was critical for capturing observed declines in latent heat during rainfall deficits. By contrast, the effects of soil properties, LAI and stomatal conductance were highly site-specific. Whilst the standard model performs reasonably well at annual scales as measured by common metrics, it grossly underestimates latent heat during rainfall deficits. A new version of CABLE, with a more physically consistent representation of hydrology, captures the variation in the latent heat flux during seasonal-scale rainfall deficits better than earlier versions, but remaining biases point to future research needs. Our results highlight the importance of evaluating LSMs under water-stressed conditions and across multiple plant functional types and climate regimes.
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