A forest optimization model to reduce the risk of hurricane damage in eastern Nicaragua

Can we successfully propose a forest management plan to reduce the risks of impacts from hurricanes by using historic data of damage caused by hurricanes in the past? Due to their geographical locations and poverty status, Central American countries are vulnerable to extreme meteorological phenomena. This research is located in eastern Nicaragua, specifically located in the Autonomous Region of the Northern Atlantic (RAAN). Nicaragua is the third most highly impacted country in the world by the passage of tropical storms. Moreover, it occupies third place in a ranking of countries most affected by long-term climate risk index (Harmeling and Eckstein 2012). After Hurricane Felix (2007) hit eastern Nicaragua, the potential environmental problems (e.g., forest destruction and flooding) associated with hurricanes became evident. Felix destroyed 1.2 million of hectares of dense and open broadleaf, forest fallow, and pine forests. As a result, this research addresses an important question: what land management strategies (forestry activities and land uses) will be most effective in decreasing hurricane damage? First, this study predicts damage rate related to synthetic hurricanes in four land use classes. The prediction model is based upon Data Model Calibration (DMC) based on observed forest damage from Hurricane Felix (2007). Then, it develops a Forest Optimization Model (FOM) that produces a land management plan which minimizes damages risks posed by hurricanes. In addition, this investigation determines which forestry management activities can best be assigned to a series of spatially predefined management areas (land management allocation solution). The findings of this study suggest that wind speed, distance from the path, elevation, and slope can be employed as explanatory variables to measure potential damage from hurricanes. The results of the sensitivity analysis of the FOM demonstrate that feasible solutions are strongly regulated by the interaction of four factors: (1) the feasible area assigned to each landuse, (2) the minimum and maximum area constraints for each landuse, (3) landuse implementation costs, and (4) the available budget.