Forest fire modeling often requires estimates of fuel moisture status. Among the various fuel variables used for fire modeling studies, the 10-h fuel moisture content (10-h FMC) is a promising predictor since it can be automatically measured in real time at study sites, yielding more information for fire models. Here, the performance of 10-h FMC models based on three different approaches, including regression (M_REG), machine learning algorithms (M_ML) with random forest and support vector machine, and a process-based model (M_FSMM), were compared. In addition, whole-year models of each type were compared with their respective seasonal models to explore whether the development of separate seasonal models yielded better estimates. Meteorological conditions and 10-h FMC were measured each minute for 18 months in and near a forest site and used for constructing and examining the 10-h FMC models. In the assessments, M_ML showed the best performance (R² = 0.77–0.82 and root mean squared error [RMSE] = 2.05–2.84%). The introduction of the correction coefficient into M_REG improved its estimates (R² improved from 0.56–0.58 to 0.68–0.70 and RMSE improved from 3.13–3.85% to 2.64–3.27%) by reducing the errors associated with high 10-h FMC values. M_FSMM showed the worst performance (R² = 0.41–0.43 and RMSE = 3.70–4.39%), which could possibly be attributed to the lack of radiation input from the study sites as well as the particular fuel moisture stick sensor that was used. Whole-year models and seasonal models showed almost equal performance because 10-h FMC varied in response to atmospheric moisture conditions rather than specific seasonal patterns. The adoption of a hybrid modeling approach that blends machine-learning and process-based approaches may yield better predictability and interpretability. This study provides additional evidence of the lagged response of 10-h FMC after rainfall, and suggests a new way of accounting for this response in a regression model. Our approach using comparisons among models can be utilized for other fire modeling studies, including those involving fire danger ratings.

Tree species vary in how they invest resources to different functions throughout their life histories, and investigating the detailed patterns of ontogenetic changes in key functional traits will aid in predicting forest dynamics and ecosystem processes. In this context, we investigated size-dependent changes in key leaf functional traits and nitrogen (N) allocation trade-offs in black locust (Robinia pseudoacacia L., an N-fixing pioneer species) and giant dogwood (Cornus controversa Hemsl., a mid-successional species), which have different life-history strategies, especially in their light use. We found that the leaf mass per area and leaf carbon concentrations increased linearly with tree size (diameter at breast height, DBH), whereas leaf N concentrations decreased nonlinearly, with U- and hump-shaped patterns in black locust and giant dogwood, respectively. We also discovered large differences in N allocation between the two species. The fraction of leaf N invested in cell walls was much higher in black locust than in giant dogwood, while the opposite was true for the light harvesting N fraction. Furthermore, these fractions were related to DBH to varying degrees: the cell wall N fraction increased with DBH for both species, whereas the light harvesting N fraction of giant dogwood decreased nonlinearly and that of black locust remained constant. Instead, black locust reduced the fraction of leaf N invested in other N pools, resulting in a smaller fraction compared to that of giant dogwood. On the other hand, both species had similar fraction of leaf N invested in ribulose-1,5-bisphosphate carboxylase/oxygenase across tree size. This study indicated that both species increased leaf mechanical toughness through characteristic changes in N allocation trade-offs over the lifetimes of the trees.

The spatial contribution to the global land‐atmosphere carbon dioxide (CO₂) exchange is crucial in understanding and projecting the global carbon cycle, yet different studies diverge on the dominant regions. Informing land models with observational data is a promising way to reduce the parameter and structural uncertainties and advance our understanding. Here, we develop a parsimonious diagnostic process‐based model of land carbon cycles, constraining parameters with observation‐based products. We compare CO₂ flux estimates from our model with observational constraints and Trends in Net Land‐Atmosphere Carbon Exchange (TRENDY) model ensemble to show that our model reasonably reproduces the seasonality of net ecosystem exchange (NEE) and gross primary productivity (GPP) and interannual variability (IAV) of NEE. Finally, we use the developed model, TRENDY models, and observational constraints to attribute variability in global NEE and GPP to regional variability. The attribution analysis confirms the dominance of Northern temperate and boreal regions in the seasonality of CO₂ fluxes. Regarding NEE IAV, we identify a significant contribution from tropical savanna regions as previously perceived. Furthermore, we highlight that tropical humid regions are also identified as at least equally relevant contributors as semi‐arid regions. At the same time, the largest uncertainty among ensemble members of NEE constraint and TRENDY models in the tropical humid regions underscore the necessity of better process understanding and more observations in these regions. Overall, our study identifies tropical humid regions as key regions for global land‐atmosphere CO₂ exchanges and the inter‐model spread of its modeling.