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Research Seminar Systems Science
Winter Semester 2015/16
The Research Seminar takes place on Tuesdays from 11:00 AM to 12:00 PM in room 35/E25 (School of Biology/Chemistry, Barbarastr. 11).
Timetable
| Date | Presenter | Title | Target audience * |
|---|---|---|---|
| 10.11.2015 | Fabian Heitmann | New Approaches for Abstraction and Agent-Based Modelling of Common-Pool-Ressource Dilemmas. | USF |
| 24.11.2015 | Matthew Adamson | Partially specified modelling: A new framework for incorporating process uncertainty into biological models. | USF |
| 01.12.2015 | Claudia Pahl - Wostl | Water-Energy-Food Nexus. | USF |
| 03.12.2015 01:30 - 02:15pm Room 66/E16 |
Sebastian Lehmann UFZ |
Fast calibration of dynamic vegetation models. | ASW/TSW EcolMod |
| 02:15 - 03:00pm Room 66/E16 |
Mateus Danta de Paula UFZ |
Consequences of forest fragmentation to ecological and ecosystem processes - Insights from forest models and remote sensing | ASW/TSW EcolMod |
| 15.12.2015 11:30am - 12:30pm Room 66/E16 |
Joanne Vinke - de KruijfClaudia Pahl - Wostl Caroline van Bers |
How to organize a Summer School: experiences from the TIAS-IUSF Autumn School on Comparative Analysis in Water Governance. | USF |
| 05.01.2016 | Vanessa Schakau | Spores, salmon and streams: A modelling approach for salmonid Ceratomyxosis in the Klamath River System. | ASW/TSW |
| 12.01.2016 10:15 - 11:00am |
Edna Rödig UFZ |
Biomass of the Amazon rainforest: regionalization of an individual-based forest gap model | EcolMod, ASW, TSW |
| 11:00 - 11:45am | Gunnar Dreßler UFZ |
The relevance of human decision making for socio-environmental dynamics: using agent-based modelling to assess disaster management performance | REM, EnvEconom, evtl. ASW |
| 19.01.2016 | Philipp Gorris | Network Analysis in Social and Ecological Systems. | USF |
| 26.01.2016 | Jürgen Berlekamp | Introduction to GREAT-ER, I: model concept and geodata processing | ASW/TSW |
| 02.02.2016 | Jörg Klasmeier | Introduction to GREAT-ER, II: applications | ASW/TSW |
| 05.02.2016 11:30-12:30am, 66/E01 |
Dale Rothman, Denver | The pros and cons of working with a large-scale integrated model | USF |
* Target audiences
| USF | general seminar for all members of the Institute |
| specific seminars | |
| ASW | Applied Systems Science |
| EcolMod | Ecological Modelling |
| EnvEconom | Environmental Economics |
| EnvironPhys | Environmental Physics |
| PUM | Projects in Environmental Systems Modelling |
| REM | Resources Management |
| TSW | Theoretical Systems Science |
Abstracts of selected talks
Edna Rödig
Biomass of the Amazon rainforest: regionalization of an individual-based forest gap model
Tropical forests are characterized by their high biodiversity and successional dynamics caused by natural disturbances. These can be captured well on small spatial scales (e.g. with individual-based forest models). By upscaling processes to larger scales, (e.g. the whole Amazon rainforest) important information on forest structures get lost. Here, we introduce a regionalization method for an individual-based forest gap model that does not lose the important structural information. Thereby, we estimated the biomass of the Amazon rainforest.
We applied the individual-based forest gap model FORMIND to several observational, tropical forest sites in central Amazonia in order to reproduce forest structure and biomass of different successional stages. First, we parameterized a generic version of the forest model. Then, the Amazon was divided into regions characterized by different light conditions. The generic forest model was run for each light condition representatively. In a second step, we identified key parameters of the forest model to replicate data from field studies spread over the entire Amazonian rainforest: specific wood density, above-ground biomass and canopy height. This resulted in regionalized forest models. In a third step, canopy heights of model outputs at different successional stages were correlated to estimates of canopy heights derived from lidar measurements.
The generic forest model could reproduce observed biomass and forest structure for the central Amazon. Biomass in the south-west was overestimated, in the north-east it was underestimated. The regionalized forest models with the regionally adapted key parameters could compensate these errors. The correlation of observed canopy heights and simulated successional stages resulted in a biomass map of the Amazon rainforest with a spatial resolution of 1km².
