Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5743847 | Ecological Engineering | 2017 | 15 Pages |
â¢The proposed methodology is straightforward and accessible and project oriented, thus of practical value.â¢The methodology integrates and articulates state-of-the-art tools and techniques, such as GPGPU computing and ANNE.â¢The methodology enhances the efficiency and response time of management processes.â¢The computer routines are implemented in a free platform and made available online.â¢New perspectives in the role of computers in supporting decision-making are given.
Riparian buffer strips (RBS) play an important role in the conservation of water bodies. Yet to date the increasing degradation of riparian vegetation is a major concern due to the inherent risk it poses to quality and quantity of drinkable water. In turn, forest regeneration is known to be a process that evolves in a time-scale of years, such that the regeneration of a forested area after a perturbation can take several decades. In this context, the exploration of models of vegetation growth can contribute to the understanding and acknowledgement of diverse aspects of such phenoma. Further, from the standpoint of conservation projects, the application of models allows the assessment of different future scenarios resulting from the application of different management practices, thus allowing anticipating the likely outcomes of the management process. In this paper, we study the application of the diffusive-logistic model (DLG model) to the problem of forest regeneration. We concentrate in vegetation belonging to permanent preservation areas, such as RBS. The main contribution of this study is to propose and illustrate a thorough methodology applicable as auxiliary assessment and design tool for conservation projects. The study is developed in two steps. We build upon recent results that study on the performance of riparian buffer strips in the removal of nitrogen in agricultural catchments in which an Artificial Neural Network Ensemble (ANNE) was applied to evaluate the riparian buffer strip width needed to accomplish filtering of 90% of the residual nitrogen. On this basis, we calibrate and validate the parameters of the diffusive-logistic model of forest growth to estimate the evolution of regeneration in riparian buffer strips, considering the buffer widths obtained by the ANNE. We implement the solution in GPGPU (General Purpose Graphics Processing Unit) and then simulate for forty years ahead to verify the likely effect of conservation upon the riparian buffer strips in the watershed. The results of calibration and validation show that the diffusive-logistic model provides a rather accurate temporal and spatial account of forest regeneration. The application of the model to a case study of the Ligeiro River watershed, Brazil, shows that the model can provide valuable insights for decision-making in the context of environmental conservation and management.