Study on Model Construction and Extrapolation Accuracy of Surface Branch Moisture Content for Typical Stands in Qipanshan Area, Northeastern China

The Qipanshan area in Northeastern China has diverse stand types and abundant forest resources, but extremely low resistance to external disturbances such as forest fires. Thus, improving the accuracy of understory fuel moisture content prediction is crucial for local forest fire prevention. This study focused on surface branch fuels in four typical stands (Larix gmelinii (Rupr.) Kuzen forest, Betula platyphylla Sukaczev forest, Pinus sylvestris var. Mongholica Litv. forest and cutover land) to evaluate the prediction and extrapolation performance of three hourly scale models (Nelson, Simard, and meteorological element regression models), and analyze their variations with slope positions and stand types, filling the gap in local hourly fuel moisture prediction model application. Results indicated that obvious spatial heterogeneity in fuel moisture content, closely affected by slope, fuel decay degree and microclimate, and thick, badly decayed branches had higher moisture content, with the highest in the Betula platyphylla forest and the lowest in cutover land. In terms of prediction accuracy, the Nelson model performed best, followed by the Simard model, while the meteorological element regression model was the poorest; predictions were more accurate in Pinus sylvestris var. mongholica forest and cutover land, and better on upper slopes than middle and lower slopes. For extrapolation capacity, the Simard model was optimal, followed by the Nelson model, while the meteorological element regression model was unfit for extrapolation due to excessive errors; extrapolation accuracy was best in cutover land and upper slopes. This study clarifies the applicability of the three models, providing methodological support for accurate real-time forest fire danger forecasting in the region.