Commonly comprised of cyanobacteria, algae, bacteria and fungi, hypolithic communities inhabit the underside of cobblestones and pebbles in diverse desert biomes. Notwithstanding their abundance and widespread geographic distribution and their growth in the driest regions on Earth, the source of water supporting these communities remains puzzling. Adding to the puzzle is the presence of cyanobacteria that require liquid water for net photosynthesis. Here we report results from six-year monitoring in the Negev Desert (with average annual precipitation of similar to 90 mm) during which periodical measurements of the water content of cobblestone undersides were carried out. We show that while no effective wetting took place following direct rain, dew or fog, high vapor flux, induced by a sharp temperature gradient, took place from the wet subsurface soil after rain, resulting in wet-dry cycles and wetting of the cobblestone undersides. Up to 12 wet-dry cycles were recorded following a single rain event, which resulted in vapor condensation on the undersides of the cobblestones, with the daily wet phase lasting for several hours during daylight. This 'concealed mechanism' expands the distribution of photoautotrophic organisms into hostile regions where the abiotic conditions limit their growth, and provides the driving force for important evolutionary processes not yet fully explored.

Constructing an interpretable model for the long-term deformation Structural Health Monitoring (SHM) of earthrock dams is of great significance for improving the safety state evaluation and monitoring effect. In this paper, a physics -data -driven model for the deformation SHM of earth -rock dams is proposed based on deep mechanism knowledge distillation. Firstly, the deterministic model is established based on the Finite Element Model (FEM) and outputs the hydraulic load component curve and aging component curve. Then a regression prediction model (HTSGAN) between influencing factors and deformation measurements at multiple measurement points is established based on the Graph Convolutional Network (GCN) and attention mechanism. Finally, the TeacherHydraulic -Time -Seepage Graph Attention Networks (T-HTSGAN) model is established based on the featurebased multi -teacher knowledge distillation using the knowledge of hydraulic loading physics and soil -rock creep physics of the FEM for mechanism constraints. The model effectively solves the problems of poor model interpretability and lack of physics knowledge constraints in previous earth -rock dam SHM models. The research results are applied to a project of a 185.5 -meter -high concrete -faced rockfill dam, and the predictive performance of the model is more effective and stable through the comparison of six baseline models. The comparative analysis of the component curves proves the effectiveness of the proposed knowledge distillation method for mechanism constraints and improves the interpretability of the neural network model. Therefore, the model is more suitable for engineering applications.