Digital Earth Flood Event Explorer – The Smart Monitoring Workflow
A deeper understanding of the Earth system as a whole and its interacting sub-systems depends, perhaps more than ever, not only on accurate mathematical approximations of the physical processes but also on the availability of environmental data across time and spatial scales. Even though advanced numerical simulations and satellite-based remote sensing in conjunction with sophisticated algorithms such as machine learning tools can provide 4D environmental datasets, local and mesoscale measurements continue to be the backbone in many disciplines such as hydrology.
The Challenge of Big Data Handling
Considering the limitations of human and technical resources, monitoring strategies for these types of measurements should be well designed to increase the information gain provided. One helpful set of tools to address these tasks are visual-analytical data exploration frameworks providing qualified data from different sources and tailoring available computational and visual methods to explore and analyse multi-parameter datasets. In this context, we developed a smart monitoring workflow to determine the most suitable time and location for event-driven, ad-hoc monitoring in hydrology using soil moisture measurements as our target variable.
Step by Step to find the Best Spot
The SMART Monitoring workflow consists of three main steps. First is the identification of the region of interest, either via user selection or recommendation based on spatial environmental parameters provided by the user. Statistical filters and different colour schemes can be applied to highlight different regions. The second step is to access time-dependent environmental parameters (e.g. rainfall and soil moisture estimates of the recent past, weather predictions from numerical weather models and swath forecasts from Earth observation satellites) for the region of interest and visualizing the results. Lastly, a detailed assessment of the region of interest is conducted by applying filter and weight functions in combination with multiple linear regressions on selected input parameters. Depending on the measurement objective (e.g highest/lowest values, highest/lowest change), most suitable areas for monitoring will subsequently be visually highlighted. In combination with the provided background map, an efficient route for monitoring can be planned directly in the visual-analytical environment.
Erik Nixdorf, UFZ: erik.nixdorf(at)ufz.de