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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 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 time scales. 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. SPLASH, as presented here, is designed for application at discrete locations; however, the same methodology can naturally be applied to spatial grids. The climatic drivers include a minimum of three meteorological inputs: precipitation, air temperature, and either fraction of bright sunshine hours or fractional cloud cover. 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. One year of results from a specific location are provided to exemplify the daily and monthly model outputs, following a two-year spin-up of soil moisture content.