Articles | Volume 10, issue 2
Geosci. Model Dev., 10, 689–708, 2017
Geosci. Model Dev., 10, 689–708, 2017

Model description paper 14 Feb 2017

Model description paper | 14 Feb 2017

Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture

Tyler W. Davis1,a, I. Colin Prentice1,2,3,4, Benjamin D. Stocker1, Rebecca T. Thomas1, Rhys J. Whitley2,4, Han Wang2,3, Bradley J. Evans4,5, Angela V. Gallego-Sala6, Martin T. Sykes7, and Wolfgang Cramer8 Tyler W. Davis et al.
  • 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

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.

Short summary
This research presents a comprehensive description for calculating necessary, but sparsely observed, factors related to Earth's surface energy and water budgets relevant in, but not limited to, the study of ecosystems. We present the equations, including their derivations and assumptions, as well as example indicators relevant to plant-available moisture. The robustness of these relatively simple equations provides a tool to be used across broad fields of scientific research.