@article{KreibichBottoMerzetal.2016,
  author    = {Kreibich, Heidi and Botto, Anna and Merz, Bruno and Schr{\"o}ter, Kai},
  title     = {Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO},
  series = {Risk analysis},
  volume    = {37},
  journal   = {Risk analysis},
  number    = {4},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0272-4332},
  doi       = {10.1111/risa.12650},
  pages     = {774 -- 787},
  year      = {2016},
  abstract  = {Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.},
  language  = {en}
}
@misc{DormannSchymanskiCabraletal.2012,
  author    = {Dormann, Carsten F. and Schymanski, Stanislaus J. and Cabral, Juliano Sarmento and Chuine, Isabelle and Graham, Catherine and Hartig, Florian and Kearney, Michael and Morin, Xavier and R{\"o}mermann, Christine and Schr{\"o}der-Esselbach, Boris and Singer, Alexander},
  title     = {Correlation and process in species distribution models: bridging a dichotomy},
  series = {Journal of biogeography},
  volume    = {39},
  journal   = {Journal of biogeography},
  number    = {12},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {0305-0270},
  doi       = {10.1111/j.1365-2699.2011.02659.x},
  pages     = {2119 -- 2131},
  year      = {2012},
  abstract  = {Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.},
  language  = {en}
}