Update December 2021
Scientific publications (peer-reviewed):
- Berzaghi, F., Wright, I. J., Kramer, K., Oddou-Muratorio, S., Bohn, F. J., Reyer, C. P., … & Hartig, F. (2020). Towards a new generation of trait-flexible vegetation models. Trends in Ecology & Evolution, 35(3), 191-205.
- Oberpriller, J., Cameron, D. R., Dietze, M. C., & Hartig, F. (2021). Towards robust statistical inference for complex computer models. Ecology Letters, 24(6), 1251-1261.
- Hülsmann, L., Chisholm, R. A., & Hartig, F. (2021). Is Variation in conspecific negative density dependence driving tree diversity patterns at large scales? Trends in Ecology & Evolution, 36(2), 151-163.
- Oberpriller, J., Herschlein, C., Anthoni, P., Arneth, A., Krause, A., Rammig, A., … & Hartig, F. (2021). Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0). Geoscientific Model Development Discussions, 1-34.
Publications:
Oberpriller, J., Arneth, A., Herschlein, C., Rammig, A. & Hartig, F. (2021 im Druck). Modellunsicher-heiten in Klimafolgeprojektionen – eine Einführung. In: Mitteilungen der Fränkischen Geographischen Gesellschaft Band 67: Herausforderungen des Klimawandels in Bayern. Erlangen.
In preperation:
Oberpriller, J., Trotsiuk, V., Heiland, L., Hülsmann, L. & Hartig, F. (in preparation) Local Adaptation Bayesian Calibration of a physiological forest ecosystem model shows evidence of biological adaptations across Europe
Goals of the subproject
To assess the consequences of mitigation and adaptation strategies, public and private decision-makers need not only detailed forecasts of the most likely impacts of climate change, but also estimates of the uncertainties associated with such forecasts.
What are the uncertainties of various management options under climate change? Subproject 5 calculates forecasting uncertainties for the predictions made by BLIZ. (Photo: Rammig)
Subproject 5 of BLIZ (BayRisk) at the University of Regensburg deals with quantifying uncertainties in model projections. BayRisk will create uncertainty estimates of the ecological models used in BLIZ, using Monte-Carlo simulations and Bayesian statistical methods. The calculated uncertainties are then used in cooperation with the socio-economic subprojects of BLIZ to derive probability distributions and risks for economically relevant parameters. Hence, BayRisk forms a link between the ecological subprojects 1-3 and the socio-economic subprojects 4 & 6. The latter conduct research on land use decisions and the evaluation of risks and options for action.