Articles | Volume 379
Proc. IAHS, 379, 193–198, 2018
Proc. IAHS, 379, 193–198, 2018

Pre-conference publication 05 Jun 2018

Pre-conference publication | 05 Jun 2018

Estimating parameter values of a socio-hydrological flood model

Marlies Holkje Barendrecht1, Alberto Viglione1, Heidi Kreibich2, Sergiy Vorogushyn2, Bruno Merz2, and Günter Blöschl1 Marlies Holkje Barendrecht et al.
  • 1Vienna University of Technology, Institute of Hydraulic Engineering and Water Resources Management, Vienna, Austria
  • 2GeoForschungsZentrum Potsdam (GFZ), Section Engineering Hydrology, Telegrafenberg, Potsdam, Germany

Abstract. Socio-hydrological modelling studies that have been published so far show that dynamic coupled human-flood models are a promising tool to represent the phenomena and the feedbacks in human-flood systems. So far these models are mostly generic and have not been developed and calibrated to represent specific case studies. We believe that applying and calibrating these type of models to real world case studies can help us to further develop our understanding about the phenomena that occur in these systems. In this paper we propose a method to estimate the parameter values of a socio-hydrological model and we test it by applying it to an artificial case study. We postulate a model that describes the feedbacks between floods, awareness and preparedness. After simulating hypothetical time series with a given combination of parameters, we sample few data points for our variables and try to estimate the parameters given these data points using Bayesian Inference. The results show that, if we are able to collect data for our case study, we would, in theory, be able to estimate the parameter values for our socio-hydrological flood model.

Short summary
The aim of this paper is to assess whether a Socio-Hydrological model can be calibrated to data artificially generated from it. This is not trivial because the model is highly nonlinear and it is not clear what amount of data would be needed for calibration. We demonstrate that, using Bayesian inference, the parameters of the model can be estimated quite accurately from relatively few data, which could be available in real case studies.