Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model
- 1ARC Centre of Excellence for Climate System Science, University of New South Wales, Kensington, NSW 2052, Australia
- 2Climate Change Research Centre, University of New South Wales, Kensington, NSW 2052, Australia
- 3Department of Biological Sciences, Macquarie University, Balaclava Road, North Ryde, NSW 2109, Australia
- 4Murdoch University, School of Veterinary and Life Sciences, Environmental and Conservation Sciences, Murdoch, WA 6150, Australia
- 5CSIRO Ocean and Atmosphere, Aspendale, VIC 3195, Australia
Abstract. 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.