Remotely sensed data for the observation and analysis of natural hazards is becoming increasingly commonplace and accessible. Furthermore, the accuracy and coverage of such data is rapidly improving. In parallel with this growth are ongoing developments in computational methods to store, process, and analyze these data for a variety of geospatial needs. One such use of this geospatial data is for input and calibration for the modeling of natural hazards, such as the spread of wildfires, flooding, tidal inundation, and landslides. Computational models for natural hazards show increasing real-world applicability, and it is only recently that the full potential of using remotely sensed data in these models is being understood and investigated. Some examples of geospatial data required for natural hazard modeling include: • elevation models derived from RADAR and Light Detection and Ranging (LIDAR) techniques for flooding, landslide, and wildfire spread models • accurate vertical datum calculations from geodetic measurements for flooding and tidal inundation models • multispectral imaging techniques to provide land cover information for fuel types in wildfire models or roughness maps for flood inundation studies Accurate modeling of such natural hazards allows a qualitative and quantitative estimate of risks associated with such events. With increasing spatial and temporal resolution, there is also an opportunity to investigate further value-added usage of remotely sensed data in the disaster modeling context. Improving spatial data resolution allows greater fidelity in models allowing, for example, the impact of fires or flooding on individual households to be determined. Improving temporal data allows short and long-term trends to be incorporated into models, such as the changing conditions through a fire season or the changing depth and meander of a water channel.
Mahesh Prakash, James Hilton, Claire Miller, Vincent Lemiale, Raymond Cohen, and Yunze Wang
Guy J.-P. Schumann
For about 40 years, with a proliferation over the last two decades, remote sensing data, primarily in the form of satellite and airborne imagery and altimetry, have been used to study floods, floodplain inundation, and river hydrodynamics. The sensors and data processing techniques that exist to derive information about floods are numerous. Instruments that record flood events may operate in the visible, thermal, and microwave range of the electromagnetic spectrum. Due to the limitations posed by adverse weather conditions during flood events, radar (microwave range) sensors are invaluable for monitoring floods; however, if a visible image of flooding can be acquired, retrieving useful information from this is often more straightforward. During recent years, scientific contributions in the field of remote sensing of floods have increased considerably, and science has presented innovative research and methods for retrieving information content from multi-scale coverages of disastrous flood events all over the world. Progress has been transformative, and the information obtained from remote sensing of floods is becoming mature enough to not only be integrated with computer simulations of flooding to allow better prediction, but also to assist flood response agencies in their operations. Furthermore, this advancement has led to a number of recent and upcoming satellite missions that are already transforming current procedures and operations in flood modeling and monitoring, as well as our understanding of river and floodplain hydrodynamics globally. Global initiatives that utilize remote sensing data to strengthen support in managing and responding to flood disasters (e.g., The International Charter, The Dartmouth Flood Observatory, CEOS, NASA’s Servir and the European Space Agency’s Tiger-Net initiatives), primarily in developing nations, are becoming established and also recognized by many nations that are in need of assistance because traditional ground-based monitoring systems are sparse and in decline. The value remote sensing can offer is growing rapidly, and the challenge now lies in ensuring sustainable and interoperable use as well as optimized distribution of remote sensing products and services for science as well as operational assistance.
Snow- and ice-related hazardous processes threaten society in tropical to high-latitude mountain areas worldwide and at highly variable time scales. On the one hand, small snow avalanches are recorded in high numbers every winter. On the other hand, glacial lake outburst floods (GLOFs) or large-scale volcano–ice interactions occur less frequently but may evolve into destructive process chains resulting in major disasters. These extreme examples document the huge field of types, magnitudes, and frequencies of snow- and ice-related hazardous processes. Mountain societies have learned to cope with natural hazards for centuries, guided by personal experiences and oral and written tradition. Historical records are today still important as a basis to mitigate snow- and ice-related hazards. They are complemented by a broad array of observation and modeling techniques. These techniques differ among themselves with regard to (1) the type of process under investigation and (2) the scale and purpose of investigation. Multi-scale monitoring and warning systems for snow avalanches are in operation in densely populated mid-latitude mountain areas. They build on meteorological and snow profile data in combination with a large pool of expert knowledge. In contrast, ice-related processes such as ice- or rock-ice avalanches, GLOFs, or associated process chains cause damage less frequently in space and time, so that societies are less well adapted. Even though the hazard sources are often far from the society—making field observation challenging—flows travelling for tens of kilometers sometimes impact populated areas. These hazards are strongly influenced by climate change–induced glacier and permafrost dynamics. On the regional or national scale, the evolution of such hazards has to be monitored at short intervals through aerial and satellite imagery and terrain data, employing geographic information systems (GIS). Known hazardous situations have to be monitored in the field. Physical models—applied either in the laboratory or at real-world sites—are employed to explore the mobility of hazardous processes. Since the 1950s, however, computer models have increasingly gained importance in exploring possible travel distances, impact areas, velocities, and impact forces of events. While simple empirical-statistical approaches are used at broad scales in combination with GIS, advanced numeric models are applied to analyze specific case studies. However, the input parameters for these models are uncertain so that (1) the model results have to be validated with observations and (2) appropriate strategies to deal with the uncertainties have to be applied before using the model results for hazard zoning or dimensioning of protective structures. Due to rapid atmospheric warming and related changes in the cryosphere, hazard situations beyond historical experiences are expected to be increasingly relevant in the future. Scenario-based modeling of complex systems and process chains therefore represents an emerging research direction.