Field‐chamber measurements of soil respiration from 17 different forest and shrubland sites in Europe and North America were summarized and analyzed with the goal to develop a model describing seasonal, interannual and spatial variability of soil respiration as affected by water availability, temperature, and site properties. The analysis was performed at a daily and at a monthly time step. With the daily time step, the relative soil water content in the upper soil layer expressed as a fraction of field capacity was a good predictor of soil respiration at all sites. Among the site variables tested, those related to site productivity (e.g., leaf area index) correlated significantly with soil respiration, while carbon pool variables like standing biomass or the litter and soil carbon stocks did not show a clear relationship with soil respiration. Furthermore, it was evidenced that the effect of precipitation on soil respiration stretched beyond its direct effect via soil moisture. A general statistical nonlinear regression model was developed to describe soil respiration as dependent on soil temperature, soil water content, and site‐specific maximum leaf area index. The model explained nearly two thirds of the temporal and intersite variability of soil respiration with a mean absolute error of 0.82 μmol m⁻² s⁻¹. The parameterized model exhibits the following principal properties: (1) At a relative amount of upper‐layer soil water of 16% of field capacity, half‐maximal soil respiration rates are reached. (2) The apparent temperature sensitivity of soil respiration measured as Q₁₀ varies between 1 and 5 depending on soil temperature and water content. (3) Soil respiration under reference moisture and temperature conditions is linearly related to maximum site leaf area index. At a monthly timescale, we employed the approach by Raich et al. [2002] that used monthly precipitation and air temperature to globally predict soil respiration (T&P model). While this model was able to explain some of the month‐to‐month variability of soil respiration, it failed to capture the intersite variability, regardless of whether the original or a new optimized model parameterization was used. In both cases, the residuals were strongly related to maximum site leaf area index. Thus, for a monthly timescale, we developed a simple T&P&LAI model that includes leaf area index as an additional predictor of soil respiration. This extended but still simple model performed nearly as well as the more detailed time step model and explained 50% of the overall and 65% of the site‐to‐site variability. Consequently, better estimates of globally distributed soil respiration should be obtained with the new model driven by satellite estimates of leaf area index. Before application at the continental or global scale, this approach should be further tested in boreal, cold‐temperate, and tropical biomes as well as for non‐woody vegetation.

Mechanistic water balance models can be used to predict soil moisture dynamics and drought stress in individual forest stands. Predicting current and future levels of plant drought stress is important not only at the local scale, but also at larger, landscape to regional, scales, because these are the management scales at which adaptation and mitigation strategies are implemented. To obtain reliable predictions of soil moisture and plant drought stress over large extents, water balance models need to be complemented with detailed information about the spatial variation of vegetation and soil attributes. We designed, calibrated and validated a water balance model that produces annual estimates of drought intensity and duration for all plant cohorts in a forest stand. Taking Catalonia (NE Spain) as a case study, we coupled this model with plot records from two Spanish forest inventories in which species identity, diameter and height of plant cohorts were available. Leaf area index of each plant cohort was estimated from basal area using species-specific relationships. Vertical root distribution for each species in each forest plot was estimated by determining the distribution that maximized transpiration in the model, given average climatic conditions, soil attributes and stand density. We determined recent trends (period 1980-2010) in drought stress for the main tree species in Catalonia; where forest growth and densification occurs in many areas as a result of rural abandonment and decrease of forest management. Regional increases in drought stress were detected for most tree species, although we found high variation in stress changes among individual forest plots. Moreover, predicted trends in tree drought stress were mainly due to changes in leaf area occurred between the two forest inventories rather than to climatic trends. We conclude that forest structure needs to be explicitly considered in assessments of plant drought stress patterns and trends over large geographic areas, and that forest inventories are useful sources of data provided that reasonably good estimates of soil attributes and root distribution are available. Our approach coupled with recent improvements in forest survey technologies may allow obtaining spatially continuous and precise assessments of drought stress. Further efforts are needed to calibrate drought-related demographic processes before water balance and drought stress estimates can be fully used for the accurate prediction of drought impacts. © 2015 Elsevier B.V.This study has received financial support from the projects CGL2011-29539, CGL2010-16373, CGL2012-39938-C02-01, CGL2013-46808-R and MONTES-Consolider CSD2008-00040 granted by the Spanish Ministry of Education and Science (MEC). This is a contribution to the ERA-NET INFORMED project. Additional support to M.D.C came from research contract granted by the Spanish Ministry of Economy and Competitiveness (RYC-2012-11109).Peer Reviewe