# Tsunami Modeling and Satellite-Based Emergency Mapping: Workflow Integration Opportunities
## 4 Discussion and Conclusions  
The main goal of the research was to identify and evaluate possible operational solutions to further improve the timeliness of SEM services such as the CEMS-RM. To this purpose, one of the key aspects is to establish a closer link between early warning and alert systems, such as GDACS, and CEMS-RM. Section 3.1 demonstrated the benefit of integrating early warning and alert systems in SEM. Even if the first tsunami impact estimate for the Chilean earthquake did not succeed in identifying the areas which suffered the biggest impact in terms of damages (mainly due to the unavailability of the precise finite fault model in the first hours after the event, which led to a latitudinal shift in the estimated maximum weight heights), the possibly affected areas were among those identified. Having access to information on possibly affected areas is crucial for obtaining useful information from SEM. With ongoing research on tsunami modeling (and the availability of faster source mechanisms identification at global level), and through validation of actual events, the accuracy of impact estimates is expected to improve in the near future. Additionally, a Monte Carlo calculation strategy has already been planned to provide not only the expected tsunami wave height but also its uncertainty range, by varying the initial and boundary condition of the simulations. Despite the discussed limitations, the overall goal would be to correctly identify some of the major affected areas, considering that the impact of true positive is operationally of greater importance than possible false positives (leading to acquisition of satellite imagery over non-affected areas). To exploit such alerts in a SEM workflow operationally, the outputs need to be properly integrated with exposure information, as described in Section 3.3. The three proposed approaches are all based on different proxies or estimates of the population living in the impacted areas: the differences in the identified priority areas are due to the different level of accuracy, completeness and update of such datasets. Therefore, the availability of homogeneous and global data sources, focusing not only on population but also on built-up areas, is crucial. Initiatives like the EC-JRC Global Human Settlement (GHS) framework address this need, producing global spatial information about the human presence on the planet over time [6] in the form of built up maps, population density maps and settlement maps. This information is generated with evidence-based analytics and knowledge using new spatial data-mining technologies and is ready to be integrated in the workflow (work in this respect is ongoing). The analysis of the proposed approaches also highlights that the focus should be on integrating different types of exposure information (e.g., a mix of the approaches described in Sections 3.3.2 and 3.3.3), especially considering that a clear understanding of the dynamics of the built environment and how it affects, in case of natural disasters, the life of communities, may lead to a substantial reduction of disaster risk and loss of life [7; 8; 9; 10]. For instance, initiatives like the OpenBuildingMap system ([[http://obm.gfz-potsdam.de/](http://obm.gfz-potsdam.de/)]([http://obm.gfz-potsdam.de/](http://obm.gfz-potsdam.de/))) systematically harvest building footprints from OSM and derives exposure and vulnerability indicators from explicitly provided data (e.g., hospital locations), implicitly provided data (e.g., building shapes and positions), and semantically derived data, i.e., interpretation applying expert knowledge [11]. Unfortunately, the lack of major events for which SEM response was in place and the underlying datasets were available, did not allow the authors to further test the proposed approaches. Additional tests are planned for future major events, with the goal to further evaluate the proposed approaches and to potentially fine tune the underlying methods, identifying a unique operational workflow.