Development of a multi-objective decision support system for eco-hydrological forest management that quantifies and...

Sustainable forest management (FOMA) requires explicit knowledge of the ecosystem services (ES) provided by forests and how they can be improved by different FOMA alternatives, especially when threatened by climate change and the productivity/profitability is low. Decision Support Systems (DSS) have evolved as powerful tools that facilitate decision-making in multi-objective FOMA. However, their use is often limited, as they are mono-objective, are based on empirical relationships, consider limited ES or do not define which silvicultural actions can be deployed, where and when. Thus, decision-makers and forest planners cannot adjust them to local conditions or specific criteria. CAFE (Carbon, Aqua, Fire & Eco-resilience) is a multi-objective DSS for FOMA that quantifies and optimizes different ES that stem from forest management. Its main contribution is coupling eco-hydrological process-based models and multi-objective optimization with genetic evolutionary algorithms. The output of the DSS, shown in a user-friendly interface, is a selection of the best-performing solutions (Pareto Optimal Front) of FOMA that optimize the user-selected ES. This tool allows designing and planning silvicultural operations such as thinning or planting required for meeting multiple objectives in FOMA by answering four fundamental questions: How much (thinning intensity or plantation density), where (spatial allocation), when (year of next intervention) and how (target forest strata in which the stand is vertically divided) thinning. This paper presents the design and components of CAFE, and a practical demonstration in contrasted forests. CAFE might contribute to addressing current challenges of meeting global environmental policy goals by stating baselines and additionality in FOMA.