Socio-Economic Impact Workflow

Digital Earth Flood Event Explorer – The Socio-Economic Impact Workflow

Floods impact individuals and communities and may have significant social, economic and environmental consequences. Understanding the controls of flood impacts is crucial to mitigate consequences and reduce vulnerability. The key question of the socio-economic impact workflow is: What are useful indicators to assess flood impacts? To answer this question, the interactions of complex flood generation and impact processes across the boundaries of 'climate and atmosphere', 'catchment and river network', and 'socio-economy' compartments are investigated and flood impact indicators are devised.

Picking the Link between Society and Natural Hazards

The Socio-Economic Impact workflow is a part of the Digital Earth Flood Explorer focusing on indicators to assess flood impacts. The approach integrates data from the different compartments in a new flood data set, which gives a comprehensive view to flood controls, flood impacts and their interrelationships. Flood controls, or driving factors, are variables that influence the generation and the characteristics of floods, for instance, precipitation, snow cover and soil moisture. Flood impacts comprise the intensity of inundations in terms of the affected area, inundation depth, as well as adverse consequences. The region of interest is the Elbe catchment in Germany. The period considered follows the availability of data and the occurrence of past floods. The data sources comprise of hindcast simulations from the hydrological model mHM, the regional climate downscaling model REMO, and the Regional Flood Model (RFM), which represents hydrological, hydraulic and damage processes. Further data sources are climate and precipitation stations, and water level and discharge gauges. Besides, inundation maps for past floods are derived by fusing data from multiple sensors including in-situ stations, remote sensing, and volunteered geographic information. Flood controls and impacts are explored using data science methods, e.g. clustering, classification and correlation, to derive flood indicators. The flood impact indicators represent individual or aggregated controls and allow for evaluating flood events. For current flood events, the indicators enable a quick classification of expected impacts. For future floods, they provide a means to assess future changes in flood impacts.


Kai Schröter, Section Hydrology GFZ: kai.schroeter(at)