Towards an Open Information Infrastructure for Disaster Research and Management: Data Management and Information Systems Inside DFNK

The development of new and better methods for preventing and managing natural disasters requires a variety of different data sets, covering the range from referenced data, e.g. topographic data and digital elevation models, to various kinds of thematic data, e.g. data about soil, vegetation and land usage as well as monitoring data like precipitation and water levels. So a well-organised data and information management and the implementation of a modern processing environment to acquire, store, analyse and visualise data were decisive for the success of the German Research Network Natural Disasters (DFNK). An information infrastructure was established to support data management and information flows inside the network. A web-based portal offers general information to the public and internal documents, data and software tools to the project community. A catalog service allows the overview of existing but distributed data scombined with flexible data retrieval. Based on internet technology and global standards these concepts contribute to a superior information infrastructure and finally substantiate to the development of Spatial Data Infrastructures (SDI). SDIs aim is to improve availability and usability of spatial data for the manifold application areas. Disaster management is one field depending on a very high level on high-quality data equipment, and on a working SDI.     

Assessment of Water Availability in a Central-European River Basin (Elbe) Under Climate Change

 The Elbe region is representative of humid to semi-humid landscapes in Central Europe, where water availability during the summer season is the limiting factor for plant growth and crop yields, especially in the loess areas with high crop productivity having annual precipitation lower than 500 mm. This paper summarizes the results of the first phase of the GLOWA (GLObal WAter)-Elbe project and tries to assess the reliability of water supply in the German part of the Elbe river basin for the next 50 years, a time scale relevant for the implementation of water and land use management plans. One focus of the study was developing scenarios which are consistent with climate and land use changes considering possible uncertainties. The concluding result of the study is that nature and communities in parts of Central Europe will have to deal with considerably lower water resources under scenario conditions.     

Continuous Hidden Markov Models: Application to automatic earthquake detection and classification at Las Canãdas caldera,Tenerife

A possible interaction of (volcano-) tectonic earthquakes with the continuous seismic noise recorded in the volcanic island of Tenerife was recently suggested. Also recently the zone close to Las Canadas caldera shows unusual high number of near (< 25 km), possibly volcano-tectonic, earthquakes indicating signs of reawakening of the volcano putting high pressure on the risk analyst. Certainly for both tasks consistent earthquake catalogues provide valuable information and thus there is a strong demand for automatic detection and classification methodologies generating such catalogues. Therefore we adopt methodologies of speech recognition where statistical models, called Hidden Markov Models (HMMs), are widely used for spotting words in continuous audio data. In this study HMMs are used to detect and classify volcano-tectonic and/or tectonic earthquakes in continuous seismic data. Further the HMM detection and classification is evaluated and discussed for a one month period of continuous seismic data at a single seismic station. Being a stochastic process, HMMs provide the possibility to add a confidence measure to each classification made, basically evaluating how “sure” the algorithm is when classifying a certain earthquake. Moreover, this provides helpful information for the seismological analyst when cataloguing earthquakes. Combined with the confidence measure the HMM detection and classification can provide precise enough earthquake statistics, both for further evidence on the interaction between seismic noise and (volcano-) tectonic earthquakes as well as for incorporation in an automatic early warning system.     

Mass fraction spatiotemporal geostatistics and its application to map atmospheric polycyclic aromatic hydrocarbons after 9/11

This work proposes a space/time estimation method for atmospheric PM_2.5 components by modelling the mass fraction at a selection of space/time locations where the component is measured and applying the model to the extensive PM_2.5 monitoring network. The method we developed utilizes the nonlinear Bayesian maximum entropy framework to perform the geostatistical estimation. We implemented this approach using data from nine carcinogenic, particle-bound polycyclic aromatic hydrocarbons (PAHs) measured from archived PM_2.5 samples collected at four locations around the World Trade Center (WTC) from September 22, 2001 to March 27, 2002. The mass fraction model developed at these four sites was used to estimate PAH concentrations at additional PM_2.5 monitors. Even with limited PAH data, a spatial validation showed the application of the mass fraction model reduced the mean squared error (MSE) by 7–22%, while in the temporal validation there was an exponential improvement in MSE positively associated with the number of days of PAH data removed. Our results include space/time maps of atmospheric PAH concentrations in the New York area after 9/11.