Cabon A., Martínez-Vilalta J., Martínez de Aragón J., Poyatos R., De Cáceres M. (2018) Applying the eco-hydrological equilibrium hypothesis to model root distribution in water-limited forests. Ecohydrology. : 0-0.EnllaçDoi: 10.1002/eco.2015
Drought is a key driver of vegetation dynamics, but plant water-uptake patterns and consequent plant responses to drought are poorly understood at large spatial scales. The capacity of vegetation to use soil water depends on its root distribution (RD). However, RD is extremely variable in space and difficult to measure in the field, which hinders accurate predictions of water fluxes and vegetation dynamics. We propose a new method to estimate RD within water balance models, assuming that vegetation is at eco-hydrological equilibrium (EHE). EHE conditions imply that vegetation optimizes RD such that transpiration is maximized within the limits of bearable drought stress, characterized here by species-specific hydraulic thresholds. Optimized RD estimates were validated against RD estimates obtained by model calibration from sap flow or soil moisture from 38 forest plots in Catalonia (NE Spain). In water-limited plots, optimized RD was similar to calibrated RD, but estimates diverged with higher water availability, suggesting that the EHE may not be assumed when water is not limiting. Thereafter, we applied the optimization procedure at the regional scale, to estimate RD for the water-limited forests of Catalonia. Regional variations of optimum RD reproduced many expected patterns in response to climate, soil physical properties, forest structure, and species hydraulic traits. We conclude that RD optimization, based on the EHE hypothesis and a simple description of plant hydraulics, produces realistic estimates of RD that can be used for model parameterization and shows promise to improve our ability to forecast vegetation dynamics under increased drought. © 2018 John Wiley & Sons, Ltd.
De Cáceres M., Martin-StPaul N., Turco M., Cabon A., Granda V. (2018) Estimating daily meteorological data and downscaling climate models over landscapes. Environmental Modelling and Software. 108: 186-196.EnllaçDoi: 10.1016/j.envsoft.2018.08.003
High-resolution meteorological data are necessary to understand and predict climate-driven impacts on the structure and function of terrestrial ecosystems. However, the spatial resolution of climate reanalysis data and climate model outputs is often too coarse for studies at local/landscape scales. Additionally, climate model projections usually contain important biases, requiring the application of statistical corrections. Here we present ‘meteoland’ an R package that integrates several tools to facilitate the estimation of daily weather over landscapes, both under current and future conditions. The package contains functions: (1) to interpolate daily weather including topographic effects; and (2) to correct the biases of a given weather series (e.g., climate model outputs). We illustrate and validate the functions of the package using weather station data from Catalonia (NE Spain), re-analysis data and climate model outputs for a specific county. We conclude with a discussion of current limitations and potential improvements of the package. © 2018
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