• Energy Research

AbstractGlobally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

Climate change may alter ecosystem functioning, as assessed via the net carbon (C) exchange (NEE) with the atmosphere, composed of the biological processes photosynthesis (GPP) and respiration (R(eco)). In addition, in semi-arid Mediterranean ecosystems, a significant fraction of respired CO2 is stored in the vadose zone and emitted afterwards by subsoil ventilation (VE), contributing also to NEE. Such conditions complicate the prediction of NEE for future change scenarios. To evaluate the possible effects of climate change on annual NEE and its underlying processes (GPP, R(eco) and VE) we present, over a climate/altitude range, the annual and interannual variability of NEE, GPP, R(eco) and VE in three Mediterranean sites. We found that annual NEE varied from a net source of around 130 gC m(-2) in hot and arid lowlands to a net sink of similar magnitude for alpine meadows (above 2,000 m a.s.l) that are less water stressed. Annual net C fixation increased because of increased GPP during intermittent and several growth periods occurring even during winter, as well as due to decreased VE. In terms of interannual variability, the studied subalpine site behaved as a neutral C sink (from emission of 49 to fixation of 30 gC m(-2) year(-1)), with precipitation as the main factor controlling annual GPP and R(eco). Finally, the importance of VE as 0-23% of annual NEE is highlighted, indicating that this process could shift some Mediterranean ecosystems from annual C sinks to sources.

[Aims]: This study investigates how precipitation, temperature and seasonality (as a proxy of plant productivity) affect the temporal and spatial variability of soil CO efflux in two dry semiarid grasslands with different degrees of land degradation. [Methods]: We measured soil CO efflux over four years under plant, biological soil crust and bare soil patches and estimated annual soil carbon losses in both, a natural and a degraded grassland, by means of generalised additive mixed models considering temporal autocorrelation in the data. [Results]: Soil CO efflux ranged from 0.08 to 3.70 and from 0.10 to 3.01 μmol CΟ m s in the natural and degraded grasslands, respectively. Daily soil CO efflux was mostly affected by moisture in the degraded grassland (25.4%), while in the natural grassland was affected jointly by seasonality, temperature and moisture (27.5%). Overall, the highest soil carbon fluxes were measured in soils covered by biological soil crusts (1.24 ± 0.02 and 1.10 ± 0.02) and the lowest in bare soils (1.11 ± 0.02 and 0.82 ± 0.02 μmol CΟ s) in the natural and degraded sites, respectively. Cumulative soil carbon fluxes were mainly driven by temperature and previous precipitation (over three months). The highest soil carbon losses were estimated in the driest year (2009) and the lowest in the wettest (2010) with almost twice the amount of rainfall. The main difference between these years was the timing of the events that mostly occurred in the moments of maximum plant activity with optimum temperatures in spring in the dry year. [Conclusions]: Changes in precipitation patterns will affect soil carbon fluxes more than rainfall amount, particularly in degraded grasslands. Therefore, considering all climate drivers together with plant activity is essential to predict how climate change will affect soil biological processes in drylands.

17 pages, 10 figures.-- Printed version published on Sep 15, 2008. Author's version available at: http://www.eeza.csic.es/eeza/documentos/Elsevier_Garcia_2008.pdf There is a need to develop operational land degradation indicators for large regions to prevent losses of biological and economic productivity. Disturbance events press ecosystems beyond resilience and modify the associated hydrological and surface energy balance. Therefore, new indicators for water-limited ecosystems can be based on the partition of the surface energy into latent (λE) and sensible heat flux (H). In this study, a new methodology for monitoring land degradation risk for regional scale application is evaluated in a semiarid area of SE Spain. Input data include ASTER surface temperature and reflectance products, and other ancillary data. The methodology employs two land degradation indicators, one related to ecosystem water use derived from the non-evaporative fraction (NEF = H / (λE + H)), and another related to vegetation greenness derived from the NDVI. The surface energy modeling approach used to estimate the NEF showed errors within the range of similar studies (R^2 = 0.88; RMSE = 0.18 (22%)). To create quantitative indicators suitable for regional analysis, the NEF and NDVI were standardized between two possible extremes of ecosystem status: extremely disturbed and undisturbed in each climatic region to define the NEFS (NEF Standardized) and NDVIS (NDVI Standardized). The procedure was successful, as it statistically identified ecosystem status extremes for both indicators without supervision. Evaluation of the indicators at disturbed and undisturbed (control) sites, and intermediate surface variables such as albedo or surface temperature, provided insights on the main surface energy status controls following disturbance events. These results suggest that ecosystem functional indicators, such as the NEFS, can provide information related to the surface water deficit, including the role of soil properties. This study received financial support from several different research projects: the integrated EU project, DeSurvey (A Surveillance System for Assessing and Monitoring of Desertification) (ref.: FP6-00.950, contract no. 003950), the PROBASE (ref.: CGL2006-11619/HID) and CANOA (ref.: CGL2004-04919-C02-01/HID) projects funded by the Spanish Ministry of Education and Science; and the BACAEMA (‘Balance de carbono y de agua en ecosistemas de matorral mediterráneo en Andalucía: Efecto del cambio climático’, RNM-332) and CAMBIO (‘Efectos del cambio global sobre la biodiversidad y el funcionamiento ecosistémico mediante la identificación de áreas sensibles y de referencia en el SE ibérico’, RNM 1280) projects funded by the Junta de Andalucía (Andalusian Regional Government). Peer reviewed

Water availability controls the functioning of dryland ecosystems, driving a patchy vegetation distribution, unequal nutrient availability, soil respiration in pulses, and limited productivity. Groundwater-dependent ecosystems (GDEs) are acknowledged to be decoupled from precipitation, since their vegetation relies on groundwater sources. Despite their relevance to enhance productivity in drylands, our understanding of how different components of GDEs interconnect (i.e., soil, vegetation, water) remains limited. We studied the GDE dominated by the deep-rooted phreatophyte Ziziphus lotus, a winter-deciduous shrub adapted to arid conditions along the Mediterranean basin. We aimed to disentangle whether the groundwater connection established by Z. lotus will foster soil biological activity and therefore soil fertility in drylands. We assessed (1) soil and vegetation dynamics over seasons (soil CO2 efflux and plant activity), (2) the effect of the patchy distribution on soil quality (properties and nutrient availability), and soil biological activity (microbial biomass and mineralization rates) as essential elements of biogeochemical cycles, and (3) the implications for preserving GDEs and their biogeochemical processes under climate change effects. We found that soil and vegetation dynamics respond to water availability. Whereas soil biological activity promptly responded to precipitation events, vegetation functioning relies on less superficial water and responded on different time scales. Soil quality was higher under the vegetation patches, as was soil biological activity. Our findings highlight the importance of groundwater connections and phreatophytic vegetation to increase litter inputs and organic matter into the soils, which in turn enhances soil quality and decomposition processes in drylands. However, biogeochemical processes are jeopardized in GDEs by climate change effects and land degradation due to the dependence of soil activity on: (1) precipitation for activation, and (2) phreatophytic vegetation for substrate accumulation. Therefore, desertification might modify biogeochemical cycles by disrupting key ecosystem processes such as soil microbial activity, organic matter mineralization, and plant productivity.

Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km2), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R2 0.71 and RMSE 9.99 t·ha−1) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R2 0.70 and RMSE 10.25 t·ha−1), confirming its applicability at coarser resolutions.