Harbin Li

A landscape model (LEEMATH) to evaluate effects of management impacts on timber and wildlife habitat

Abstract Managing forest resources for sustainability requires the successful integration of economic and ecological goals. To attain such integration, land managers need decision support tools that incorporate science, land-use strategies, and policy options to assess resources sustainability at large scales. Landscape Evaluation of Effects of Management Activities on Timber and Habitat (LEEMATH) is a tool for evaluating alternative management strategies from both economic and ecological perspectives. The current version of LEEMATH emphasizes timber production and wildlife habitat in industrial forest landscapes. LEEMATH provides a framework upon which various models can be integrated. It is generic because it is designed to model stand growth, habitat attribute, and habitat suitability as they exist generally throughout the American Southeast. It is dynamic because it examines effects of management strategies on timber production and habitat quality over time, especially the balance between habitat loss and regrowth at the landscape scale. It is spatially explicit because it evaluates landscape configuration for its effects on habitat in terms of adjacency requirements and dispersal potential. It is heuristic because it simulates the dynamics of forest stands under different management scenarios and allows land managers to ask ‘WHAT-IF’ questions to explore management alternatives and their possible effects over time. In this paper, we discuss how to integrate different models into a decision-support system, and how to evaluate habitat suitability at the landscape level. We also discuss the gaps in our knowledge of landscape habitat assessment and the limitations of LEEMATH. Finally, we apply LEEMATH to a forested landscape on the coastal plain of South Carolina, USA, to demonstrate its usefulness in management planning with multiple interests. We show the effects of two management regimes on timber production, habitat attribute dynamics, and habitat quality of three target wildlife species at both the stand and the landscape scales.

A COMPARISON OF MIKE SHE AND DRAINMOD FOR MODELING FORESTED WETLAND HYDROLOGY IN COASTAL SOUTH CAROLINA, USA

Models are widely used to assess hydrologic impacts of land-management, land-use change and climate change. Two hydrologic models with different spatial scales, MIKE SHE (spatially distributed, watershed-scale) and DRAINMOD (lumped, field-scale), were compared in terms of their performance in predicting stream flow and water table depth in a first-order forested watershed in coastal South Carolina. The model performance was evaluated using the coefficient of determination (R2) and Nash-Sutcliffe’s model efficiency (E). Although both models performed reasonably well in predicting monthly and annual average water table depths and stream flow with acceptable E values (0.55-0.99) for the five-year period (2003-2007), MIKE SHE yielded better results than DRAINMOD for daily hydrologic dynamics. Both models, however, showed relatively large uncertainty in simulating stream flow for dry years. The subsurface drainage predicted by MIKE SHE was lower than simulated by DRAINMOD for dry years, higher for extremely wet years and similar for normal climate years. The differences were likely that MIKE SHE employed distributed physical characteristics of the watershed, especially of soil and topography which can substantially affect the subsurface flow, but the spatial average condition was only used by DRAINMOD; the results from both models were, thus, similar for those average (e.g., normal climate) conditions, and different for varying conditions. This study suggests a lumped parameter model could perform equally well at the monthly temporal scale for modeling stream flow under average climatic conditions; however a distributed hydrological model provides more accurate prediction of daily stream flow and water table depth across varying climatic conditions.