1AXA Chair of Biosphere and Climate Impacts, Grand Challenges in Ecosystems and the Environment and Grantham Institute – Climate Change
and the Environment, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, UK
2Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia
3State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, College of Forestry, Northwest Agriculture & Forestry University, Yangling 712100, China
4Terrestrial Ecosystem Research Network (TERN) Ecosystem Modelling and Scaling Infrastructure (eMAST), Sydney, New South Wales, Australia
5Faculty of Agriculture and Environment, Department of Environmental Sciences, The University of Sydney, Sydney, New South Wales, Australia
6Department of Geography, University of Exeter, Exeter, Devon, UK
7Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
8Mediterranean Institute of marine and terrestrial Biodiversity and Ecology (IMBE), Aix Marseille University, CNRS, IRD, Avignon University, Aix-en-Provence, France
anow at: United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, USA
Received: 29 Feb 2016 – Discussion started: 15 Apr 2016
Abstract. Bioclimatic indices for use in studies of ecosystem function, species distribution, and vegetation dynamics under changing climate scenarios depend on estimates of surface fluxes and other quantities, such as radiation, evapotranspiration and soil moisture, for which direct observations are sparse. These quantities can be derived indirectly from meteorological variables, such as near-surface air temperature, precipitation and cloudiness. Here we present a consolidated set of simple process-led algorithms for simulating habitats (SPLASH) allowing robust approximations of key quantities at ecologically relevant timescales. We specify equations, derivations, simplifications, and assumptions for the estimation of daily and monthly quantities of top-of-the-atmosphere solar radiation, net surface radiation, photosynthetic photon flux density, evapotranspiration (potential, equilibrium, and actual), condensation, soil moisture, and runoff, based on analysis of their relationship to fundamental climatic drivers. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and fraction of bright sunshine hours. Indices, such as the moisture index, the climatic water deficit, and the Priestley–Taylor coefficient, are also defined. The SPLASH code is transcribed in C++, FORTRAN, Python, and R. A total of 1 year of results are presented at the local and global scales to exemplify the spatiotemporal patterns of daily and monthly model outputs along with comparisons to other model results.
Revised: 22 Dec 2016 – Accepted: 16 Jan 2017 – Published: 14 Feb 2017
Davis, T. W., Prentice, I. C., Stocker, B. D., Thomas, R. T., Whitley, R. J., Wang, H., Evans, B. J., Gallego-Sala, A. V., Sykes, M. T., and Cramer, W.: Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture, Geosci. Model Dev., 10, 689-708, doi:10.5194/gmd-10-689-2017, 2017.