Local and global factors controlling water‐energy balances within the Budyko framework

Abstract

Abstract Quantifying partitioning of precipitation into evapotranspiration (ET) and runoff is the key to assessing water availability globally. Here we develop a universal model to predict water‐energy partitioning ( ϖ parameter for the Fu's equation, one form of the Budyko framework) which spans small to large scale basins globally. A neural network (NN) model was developed using a data set of 224 small U.S. basins (100–10,000 km 2 ) and 32 large, global basins (~230,000–600,000 km 2 ) independently and combined based on both local (slope, normalized difference vegetation index) and global (geolocation) factors. The Budyko framework with NN estimated ϖ reproduced observed mean annual ET well for the combined 256 basins. The predicted mean annual ET for ~36,600 global basins is in good agreement ( R 2 = 0.72) with an independent global satellite‐based ET product, inversely validating the NN model. The NN model enhances the capability of the Budyko framework for assessing water availability at global scales using readily available data.