https://hdl.handle.net/10481/41483 2026-04-11T19:11:12Z https://hdl.handle.net/10481/109794 Disparate responses of soil-atmosphere CO2 exchange to biophysical and geochemical factors over a biocrust ecological succession in the Tabernas Desert López Canfín, Clément; Lázaro, Roberto; Pérez Sánchez-Cañete, Enrique There is growing evidence that dryland soils could act as substantial but so far disregarded carbon sinks at the global scale. In drylands, potential abiotic processes of CO2 uptake are still debated while estimates of the biotic contribution of photosynthetizing biocrusts to the net carbon uptake remain uncertain. This uncertainty is partly attributable to a common neglect of the underlying soil and spatiotemporal variability of soil CO2 fluxes. Moreover, it is still unknown how those fluxes evolve during the ecological succession of biocrusts and which factors control them. Therefore, in this study, we aimed to (1) identify those factors and use them for predictions, and (2) explain the inter-annual variation of cumulative CO2 fluxes over the succession. We conducted 2 years of continuous measurements of the topsoil CO2 molar fraction (χs) and microclimatic variables and estimated the soil-atmosphere CO2 flux using the gradient method. Statistical spatiotemporal models were developed of χs dynamics and cumulative annual fluxes. A soil CO2 uptake potentially due to coupled gypsum dissolution-calcite precipitation was consistently detected at night, with cumulative values ranging from −4 to −65 gC m−2 depending on succession stage. In comparison, cumulative soil CO2 emissions ranged from 101 to 307 gC m−2 depending on succession stage. The succession stage, soil water content (ϑw), and temperature (Ts) and the interactions of these variables explained and efficiently predicted the daily averaged χs dynamics. Soil CO2 emissions were more sensitive to ϑw and Ts in late successional stages, apparently because of organic carbon accumulation and higher porosity. Our measurements suggest that CO2 consumption processes were progressively counterbalanced by the increase in biological CO2 production during succession. That is probably why those processes could mainly be detected in early successional stages and more generally in drylands, as they sustain a low biological activity. However, such processes could be ubiquitous in ecosystems but difficult to detect. We discuss the implication of those results for the extensive dryland soils in the context of climate change. This work was funded by the research project CGL2016-78075-P, Biocrust Dynamics (DINCOS), of the Spanish National Program of Scientific and Technical Research, and by the ICAERSA research project (P18-RT-3629) of the Andalusian Regional Government including European Union ERDF funds. The funding for the open access charge was provided by the University of Granada / CBUA. https://hdl.handle.net/10481/109788 Development of a new low-cost device to measure calcium carbonate content, reactive surface area in solid samples and dissolved inorganic carbon content in water samples López Canfín, Clément; Lázaro, Roberto; Pérez Sánchez-Cañete, Enrique Estimates of soil carbonate dynamics are still very scarce, despite their importance in the global carbon budget. Geochemical models used to estimate carbonate precipitation–dissolution rates require important inputs including carbonate content and calcite reactive surface area in soil as well as dissolved inorganic carbon (DIC) content in soil solution. However, most methods currently available to accurately measure these parameters can be time-consuming and/or often require expensive laboratory equipment. To tackle this problem, we aimed to develop a sensitive device to measure these variables at low cost and with little time investment. By taking advantage of the recent development of low-cost CO2 sensors and microcontrollers, a low-cost and easy-to-mount analyzer was developed based on direct measurements of CO2 evolved during an acidic reaction. The new instrument proved to be sensitive, accurate, precise and able to quickly perform the analyses. It was therefore used in a pilot experiment on the inorganic component of CO2 flows from crusted semi-arid soils, and to evaluate the variation in DIC content through a spring-cave-downstream river water continuum. The device could facilitate these analyses for scientists from different fields since it can potentially analyse any solid or aqueous sample. This research was part of the DINCOS project (Biocrust Dynamics, key CGL2016-78075-P) funded by the Spanish State Plan for Scientific and Technical Research and Innovation 2013–2016, which funds the first author. CLC thanks the PhD programme in Earth Sciences (University of Granada) in which he is enrolled, Encarnación Ruiz Agudo and Aurelia Ibañez Velasco (University of Granada) for supervising and providing facilities for the N2-BET analysis, as well as Francisco Contreras for the field expedition in Sorbas caves. The data from the El Cautivo experimental field site were obtained thanks to the courtesy of the Viciana brothers, the landowners. https://hdl.handle.net/10481/108420 CCN estimations at a high-altitude remote site: role of organic aerosol variability and hygroscopicity Rejano Martínez, Fernando; Casans, Andrea; Casquero Vera, Juan Andrés; Castillo, Sonia; Lyamani, Hassan; Cazorla Cabrera, Alberto; Andrews, Elisabeth; Pérez Ramírez, Daniel; Alastuey, Andrés; Gómez-Moreno, Francisco J.; Alados Arboledas, Lucas; Olmo Reyes, Francisco José; Titos Vela, Gloria High-altitude remote sites are unique places to study aerosol–cloud interactions, since they are located at the altitude where clouds may form. At these remote sites, organic aerosols (OAs) are the main constituents of the overall aerosol population, playing a crucial role in defining aerosol hygroscopicity (κ). To estimate the cloud condensation nuclei (CCN) budget at OA-dominated sites, it is crucial to accurately characterize OA hygroscopicity (κOA) and how its temporal variability affects the CCN activity of the aerosol population, since κOA is not well established due to the complex nature of ambient OA. In this study, we performed CCN closures at a high-altitude remote site during summer to investigate the role of κOA in predicting CCN concentrations under different atmospheric conditions. In addition, we performed an OA source apportionment using positive matrix factorization (PMF). Three OA factors were identified from the PMF analysis: hydrocarbon-like OA (HOA), less-oxidized oxygenated OA (LO-OOA), and more-oxidized oxygenated OA (MO-OOA), with average contributions of 5 %, 36 %, and 59 % of the total OA, respectively. This result highlights the predominance of secondary organic aerosol (SOA) with a high degree of oxidation at this high-altitude site. To understand the impact of each OA factor on the overall OA hygroscopicity, we defined three κOA schemes that assume different hygroscopicity values for each OA factor. Our results show that the different κOA schemes lead to similar CCN closure results between observations and predictions (slope and correlation ranging between 1.08–1.40 and 0.89–0.94, respectively). However, the predictions were not equally accurate across the day. During the night, CCN predictions underestimated observations by 6 %–16 %, while, during morning and midday hours, when the aerosol was influenced by vertical transport of particles and/or new particle formation events, CCN concentrations were overestimated by 0 %–20 %. To further evaluate the role of κOA in CCN predictions, we established a new OA scheme that uses the OA oxidation level (parameterized by the f44 factor) to calculate κOA and predict CCN. This method also shows a large bias, especially during midday hours (up to 40 %), indicating that diurnal information about the oxygenation degree does not improve CCN predictions. Finally, we used a neural network model with four inputs to predict CCN: N80 (number concentration of particles with diameter > 80 nm), OA fraction, f44, and solar global irradiance. This model matched the observations better than the previous approaches, with a bias within ± 10 % and with no daily variation, reproducing the CCN variability throughout the day. Therefore, neural network models seem to be an appropriate tool to estimate CCN concentrations using ancillary parameters accordingly. This work has been supported by University of Granada Plan Propio through the Visiting Scholars (PPVS2018-04) and Singular Laboratory (AGORA, LS2022-1) programs. Fernando Rejano acknowledges support from an FPU grant (FPU19/05340, Ministerio de Universidades). Elisabeth Andrews acknowledges support from NOAA cooperative agreement NA22OAR4320151.; This research was funded by the Spanish Ministry of Science and Innovation through projects NUCLEUS (grant no. PID2021-128757OB-I00) funded by MICIU/AEI/10.13039/501100011033, and ERDF – “A way of mak ing Europe”, BioCloud (grant no. RTI2018.101154.A.I00) funded by MCIN/AEI/10.13039/501100011033 from ERDF – “A way of making Europe”, ELPIS (grant no. PID2020-120015RB-100) funded by MCIN/AEI/10.13039/501100011033, and ACTRISEspaña RED2022-134824-E. Also, this research has received support from the European Union’s Horizon 2020 research and innovation program through projects ACTRIS.IMP (grant no. 871115) and ATMO_ACCESS (grant no. 101008004). Andrea Casans is funded by the Spanish Ministry of Science and Innovation under the predoctoral program FPI (grant no. PRE2019-090827) funded by MCIN/AEI/10.13039/501100011033, FSE – “El FSE invierte en tu futuro” https://hdl.handle.net/10481/105599 Classifying the content of social media images to support cultural ecosystem service assessments using deep learning models Cardoso, Ana Sofía; Renna, Francesco; Moreno-Llorca, Ricardo; Alcaraz-Segura, Domingo; Tabik, Siham; Ladle, Richard; Vaz, Ana Sofía Crowdsourced social media data has become popular for assessing cultural ecosystem services (CES). Nevertheless, social media data analyses in the context of CES can be time consuming and costly, particularly when based on the manual classification of images or texts shared by people. The potential of deep learning for automating the analysis of crowdsourced social media content is still being explored in CES research. Here, we use freely available deep learning models, i.e., Convolutional Neural Networks, for automating the classification of natural and human (e.g., species and human structures) elements relevant to CES from Flickr and Wikiloc images. Our approach is developed for Peneda-Gerês (Portugal) and then applied to Sierra Nevada (Spain). For Peneda-Gerês, image classification showed promising results (F1-score ca. 80%), highlighting a preference for aesthetics appreciation by social media users. In Sierra Nevada, even though model performance decreased, it was still satisfactory (F1-score ca. 60%), indicating a predominance of people’s pursuit for cultural heritage and spiritual enrichment. Our study shows great potential from deep learning to assist in the automated classification of human-nature interactions and elements from social media content and, by extension, for supporting researchers and stakeholders to decode CES distributions, benefits, and values. https://hdl.handle.net/10481/105598 Remote Sensing in Sierra Nevada: From Abiotic Processes to Biodiversity and Ecosystem Functions and Services Alcaraz-Segura, Domingo; Cabello, Javier; Arenas-Castro, Salvador; Peñas De Giles, Julio; Vaz, Ana Sofía During the last decades, remote sensing has changed the way humans observe and understand the Earth system. The repeated and increasingly detailed observations made from satellite platforms and other remote sensing procedures have revolutionized research, particularly in the atmospheric and oceanographic sciences but also in the biophysical sciences. This chapter presents a systematic literature review of the different ways in which remote sensing has been applied in Sierra Nevada, Spain. Studies ranged from basic research to how remote sensing is actually contributing to management in this mountain biosphere reserve. The chapter is structured using the ecosystem services cascade as a framework, i.e., from studies on abiotic (i.e., geophysical, atmospheric, cryospheric, and hydrological) processes to research on biodiversity and ecosystem functions and services. The number of remote sensing studies in Sierra Nevada is quickly growing but still relatively scarce (only 65 records). Most of this research was either applied or use-oriented research and found to be potentially useful to assess biodiversity conservation status and ecosystem services, indeed it frequently contained recommendations for the management of the protected area. Hence, there is an expected increase in the interdisciplinary and transdisciplinary application of remote sensing to research in Sierra Nevada.