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Global maps of potential and climatic plant-available soil water

Authors: Gupta, Surya; Lehmann, Peter; Bickel, Samuel; Bonetti, Sara; Or, Dani;

Global maps of potential and climatic plant-available soil water

Abstract

The potential plant-available soil water is the difference between wilting point and field capacity (FC). Soil water content at FC is estimated using the approach developed by Assouline and Or (2014) that considers a dynamic definition of FC based on soil-specific matric potential values beyond which the soil water continuity is substantially reduced or disrupted. For calculations of soil water content at FC we use the van Genuchten soil water characteristic curves from Gupta et al., (2022). Furthermore, we introduce the climatic plant-available soil water that considers potential evaporation and the average number of consecutive dry days between rainfall events. The consecutive dry days were obtained from Bickel and Or (2021). This repository contains four global maps at 1km resolution per 1m soil depth: matric potential at FC [m] water content at FC [m3/m3] potential plant-available soil water [mm] climatic plant-available soil water [mm] For additional details and to cite this dataset please refer to: Gupta, S., Lehmann, P., Bickel, S., Bonetti, S., & Or, D. (2023). Global mapping of potential and climatic plant-available soil water. Submitted to Journal of Advances in Modeling Earth Systems. The study was supported by ETH Zurich (Grant ETH-18 18-1). References: Assouline, S., & Or, D. (2014). The concept of field capacity revisited: Defining intrinsic static and dynamic criteria for soil internal drainage dynamics. Water Resources Research, 50(6), 4787-4802. Bickel, Samuel, & Or, Dani. (2021). Dataset of global climatic soil water contents and consecutive dry days [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5078258 Gupta, Surya, Papritz, Andreas, Lehmann, Peter, Hengl, Tomislav, Bonetti, Sara, & Or, Dani. (2022). Global maps of soil water characteristics parameters developed using the random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution [Data set]. Zenodo. https://doi.org/ 10.5281/zenodo.6343570

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