Tree-ring intra-annual stable isotopes (delta C-13 and delta O-18) are powerful tools for revealing plant ecophysiological responses to climatic extremes. We analyzed interannual and fine-scale intra-annual variability of tree-ring delta C-13 and delta O-18 in Chinese red pine (Pinus massoniana) from southeastern China to explore environmental drivers and potential trade-offs between the main physiological controls. We show that wet season relative humidity (May-October RH) drove interannual variability of delta O-18 and intra-annual variability of tree-ring delta O-18. It also drove intra-annual variability of tree-ring delta C-13, whereas interannual variability was mainly controlled by February-May temperature and September-October RH. Furthermore, intra-annual tree-ring delta O-18 variability was larger during wet years compared with dry years, whereas delta C-13 variability was lower during wet years compared with dry years. As a result of these differences in intra-annual variability amplitude, process-based models (we used the Roden model for delta O-18 and the Farquhar model for delta C-13) captured the intra-annual delta O-18 pattern better in wet years compared with dry years, whereas intra-annual delta C-13 pattern was better simulated in dry years compared with wet years. This result suggests a potential asymmetric bias in process-based models in capturing the interplay of the different mechanistic processes (i.e., isotopic source and leaf-level enrichment) operating in dry versus wet years. We therefore propose an intra-annual conceptual model considering a dynamic trade-off between the isotopic source and leaf-level enrichment in different tree-ring parts to understand how climate and ecophysiological processes drive intra-annual tree-ring stable isotopic variability under humid climate conditions.

Accurate quantification of the distribution and characteristics of frozen soil is critical for evaluating the impacts of climate change on the ecological and hydrological systems in high-latitude and-altitude regions, such as the Tibetan Plateau (TP). However, field observations have been limited by the harsh climate and complex terrain on the plateau, which greatly restricts our ability to predict the existence of and variations in frozen soils, especially at the regional scale. Here, we present a study relying solely on satellite data to drive process-based simulation of soil freeze-thaw processes. Modifications are made to an existing process-based model (Geomorphology-Based Eco-Hydrological Model, GBEHM) such that the model is fully adaptable to remote sensing inputs. The developed model fed with a combination of MODIS, TRMM and AIRX3STD satellite products is applied in the upper Yellow River Basin (coverage of similar to 2.54 x 10(5) km(2)) in the northeast TP and validated against field observations of freezing and thawing front depths (D-ft) and soil temperature (T-soil) at 54 China Meteorological Administration (CMA) stations, as well as frozen-ground types at 22 boreholes. Results indicate that the developed model performs reasonably well in simulating D-ft (R-2 = 0.69; mean bias = -0.03 m) and T-soil (station averaged R-2 and mean bias range between 0.90-0.96 and -0.51 similar to -0.14 degrees C at eight observational depths, respectively), and outperforms the original GBEHM forced with ground-measured meteorological variables. The frozen-ground types are also (in general) accurately identified by the satellite-based approach, except for a few permafrost boreholes located near the permafrost boundary regions. Additionally, we also demonstrate the importance of considering dynamic soil water content in frozen soil simulation: We find that a static-soil-moisture assumption (as used in previous studies) would lead to biased soil temperature estimates by > 0.5 degrees C. Our study demonstrates the value of using satellite data in frozen-soil simulation over complex landscapes, potentially leading to a greater understanding of soil freeze-thaw processes at the regional scale.