Abstract
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.
Highlights
Socio-hydrology aims to study the long term feedbacks between humans and hydrology and tries to explain the phenomena that occur as a result of these feedbacks (Sivapalan and Blöschl, 2015; Sivapalan et al, 2012)
While the sociohydrological models and studies so far show that these models can yield valuable insights into human-flood dynamics, there is a lack of application to real world systems and none of the models so far have been calibrated to represent a specific case study
The aim of this paper is to assess whether the model can be calibrated to these observed data. This is not trivial because the model is highly nonlinear and it is not clear what amount of data is needed for calibration and, more importantly, whether the amount we can imagine to find in a real world case study can be enough
Summary
Socio-hydrology aims to study the long term feedbacks between humans and hydrology and tries to explain the phenomena that occur as a result of these feedbacks (Sivapalan and Blöschl, 2015; Sivapalan et al, 2012). Most of them are general models that are not developed to reproduce and evaluate the dynamics of a specific case study and have not been compared to data. An exception to this is the work of Ciullo et al (2017): they made a qualitative comparison between the model results of Di Baldassarre et al (2015) and the human-flood system of the city of Rome and Bangladesh using data on population density, flood losses and levee heights. It is of interest to assess whether calibrating socio-hydrological models is feasible and understanding what are the data needed and their amount. We develop a socio-hydrological model and explore whether we are able to estimate the parameter values of this model using Bayesian Inference (Gelman et al, 2014) if we would have data available
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