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

Abstract

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.

Global photosynthetic capacity is optimized to the environment

“…The ratio of actual evapotranspiration to equilibrium evapotranspiration (Priestley‐Taylor coefficient, α), which represents the plant‐available surface moisture, was calculated at each 0.5° resolution site using the SPLASH model run at a monthly timescale (Davis et al . ). Soil cation exchange capacity (CEC; cmol c kg −1 ), soil pH, soil C:N ratio, soil silt content (%) and soil clay content (%) at 0–40 cm depth were extracted from 1 km global data provided by ISRIC SoilGrids database (http://www.soilgrids.org).…”

Quantifying leaf‐trait covariation and its controls across climates and biomes

“…Three bioclimate variables adequately represented the controls on vegetation structure and composition across China (Wang et al ., ). These are the accumulated photosynthetically active radiation during the thermal growing season (PAR 0 ), defined as the period when daily temperature is above 0°C; the daily mean temperature during the thermal growing season (mGDD 0 ); and the ratio of mean annual precipitation to annual equilibrium evapotranspiration (moisture index, MI), calculated using Simple Process‐Led Algorithms for Simulating Habitats (S plash ) (Davis et al ., ). The primary data for the calculation of these bioclimatic variables were derived from 1814 meteorological stations (740 stations with data from 1971 to 2000, the rest from 1981 to 1990), interpolated to 1 km resolution with elevation as a covariate using A nusplin v.4.37 (Hutchinson, ).…”

“…We derived climate variables for each site from the nearest 10‐min grid cell in the CRU 2.0 dataset (New et al ., ), which provided long‐term monthly means of temperature, precipitation, and sunshine duration for the standard period 1961–1990. PAR 0 , mGDD 0 , and MI were calculated in the way performed for the sites in China, using S plash to calculate MI (Davis et al ., ).…”

Evaluating the Contribution of Land‐Atmosphere Coupling to Heat Extremes in CMIP5 Models

“…As the surface fluxes required to calculate PET using equation were not available, we approximated CRU‐based PET from equilibrium evapotranspiration (ET 0 ; mm/day) following Priestley and Taylor (): where Δ is the rate of increase of saturated vapor pressure with temperature (Pa/K), γ the psychrometric constant (65 Pa/K), and R n is net radiation (W/m 2 ). ET 0 was calculated from CRU mean monthly air temperature and cloud cover using the R package SPLASH (Davis et al, ). Data for the pixel incorporating each tower were used to calculate AI.…”