Refugees – Solvency – Hurricanes

Refugees – Solvency – Hurricanes

The Atmospheric Special Interest Group (SIG) was formed in 2003 and established a dialog between ESRI and the atmospheric sciences community about data representation issues. The Atmospheric SIG focused on two areas: temporal data management and improved raster data support. This involves the development of Network Common Data Format (NetCDF) converters for ingesting data into ArcGIS, and developing combined support for NetCDF, HDF, and GRIB formats through a single API. Significant progress is seen in various areas of GIS use with weather and climate data. Examples include the use of GIS with radar data (Berkowitz, D. and R. Steadham, 2005; Shipley et al., 2005), climate data (Higgins, 2005), weather warnings (Waters et al., 2005), watershed modeling (Wasson et al., 2002), weather-related business problems (Sznaider, 2002), hydro meteorological applications (Yates et al, 2002). The list of applications is longer and software tools were developed to address various aspects of GIS use with weather data for practical applications.

In addition to applications that use weather and climate data, other GIS applications were developed to be used with population evacuation and hurricane disaster management. L'S Corps of Engineers working with Federal Emergency Management Agency (FEMA) after hurricane Katrina, employed GIS in various projects: assessment of post-disaster damage; rescuing and recovering; building temporary homes; removing debris, pumping fioodwater; identify impacted communities (Castanga, 2005). L'SGS National Wetlands Research Center use remote sensing and GIS to analyze land-water change caused by Katrina and Rita hurricanes, and future work includes hurricane recovery, and restoration of land (Barras, 2006). Another significant area of GIS application is in connection with population evacuation models. The hurricane and evaluation (HL'RREVAC) program uses GIS in formation to correlate demographic data with shelter locations and their proximity to evacuation routes to improve evacuation decisions (Wolhson et al., 2001). Significant GIS effort is also ongoing for rebuilding after Katrina related projects, in which multiple disciplines and agencies are involved (Hart et al., 2006).

Intense rainfall, high surge and levees failure created unprecedented flooding in about 80% of the city (as seen in Landsat image above on September 7, 2005, Figure 11). The floods that buried New Orleans had noticeably subsided by September 15, 2005, when the top image was taken by the Landsat 7 satellite. In the two and a half weeks that had passed since Hurricane Katrina flooded the city, pumps had been working nonstop to return the water to Lake Pontchartrain. The progress in draining the city is evident when the September 15 image is compared with an image taken one week earlier. In the lower image, taken by the Landsat 5 satellite on September 7, black flood water covers much of the city while by September 15, most of the dark flood water disappeared. Note also that September 7 image does not show the full extent of the flooding. We use Census data 2000 to illustrate some demographic characteristics in the New Orleans, especially in the area impacted by severe flood. We illustrate our results at census tract level, while current census data and ArcGIS allow investigation at a higher spatial resolution, which is necessary for decision makers. We must note first that New Orleans population increased significantly over the last century (Figure 12), which is in trend with the population increase in the coastal regions of the United States.

Locations of earthquakes can be quite accurately predicted since they occur exclusively along fault lines; however, predicting when an earthquake will occur is a major challenge. The literature is rich with information about how satellite remote sensing can be used to monitor land, oceanic and atmospheric precursors to earthquake activity, largely attributed to an early start in research in this area; yet, because of the particular challenges posed by the oceans, research into the use of satellite remote sensing for predicting submarine earthquakes is rather limited. In fact, land precursors can be entirely excluded from this study. Instead, earthquake warnings for coastal areas rely on perturbations of gravity waves in the ionosphere, and more recently, on fluctuations of chlorophyll concentrations in the oceans.

Imminent earthquakes can produce gravity perturbations in the ionosphere that are detectable by dedicated satellite sensors from one to five days in advance (Pulinets, 2006). These perturbations occur because nonstationary Joules are heated in the ionosphere by the electric field generated above the epicenter (Haegai and Kim et al., 2006; Pulinets, 2006). Figure 4.8 illustrates this relationship in detail. Examples of such satellite missions that have detected the ionospheric precursor include CHAMP (German), GRACE (American), GOCE (European), INTERCOSMOS-BULGARIA-1300 (Bulgarian), GEOS-2 (European), BeiDou- 2 (Chinese), QuakeSat (American), DEMETER (French), ESPERIA (Italian) and Ukrainian Variant (Gousheva and Glavcheva et al., 2006; Pulinets, 2006; Tralli and Blom et al., 2005). These satellites can detect the variations in gravity waves because they are different from other high frequency variations, such as those produced as a result of tides (Tralli and Blom et al., 2005) and of solar-magnetospheric origin (Gousheva and Glavcheva et al., 2006). 18 GRACE, launched in 2002, was able to detect large changes in the gravity fields prior to the 2002 Alaska and 2003 Hokkaido earthquakes (Tralli and Blom et al., 2005). Grousheva and Glavcheva (2006) observed the following: • effects of increase (bulge) in the quasi-static electric fields up to 7 mV, days and hours before the earthquake, for low- and mid-latitudes; • effects of increase (bulge) in the quasi-static electric fields, 7 and more hours after the earthquake occurrence, as post-effect (these effects are well observed for shallow earthquakes with depths down to 33 km and less for intermediate earthquakes with depths 70–300 km); and • sometimes the above mentioned effects cannot be observed due to the limited threshold of the instruments.

In addition to the ionospheric precursor just discussed, submarine earthquakes also seem to be lead by an oceanic precursor; that is an increase in chlorophyll-a concentrations around the epicenter. The mechanism stems from the thermal energy released prior to an earthquake, which is transported to the surface, because its density is less relative to the surrounding water, and causes an increase in sea surface temperature and surface latent heat flux. This enhances upwelling and transport of nutrient-rich water to the surface which promotes an algal bloom. Furthermore, this sequence of events leading to an algal bloom will begin earlier and occur faster in the face of higher magnitude earthquakes (Singh and Dey, 2006). What’s more, algal blooms have long been recorded using satellites, such as MODIS and Landsat, which are capable of imaging in the visible spectrum; therefore, the technology is not new and it can be used for a multitude of applications, making it a more desirable option.

Earthquakes are potentially devastating natural events which threaten lives, destroy property, and disrupt life-sustaining services and societal functions. In 1986, the National Science Foundation established the National Center for Earthquake Engineering Research to carry out systems integrated research to mitigate earthquake hazards in vulnerable communities and to enhance implementation efforts through technology transfer, outreach, and education. Since that time, our Center has engaged in a wide variety of multidisciplinary studies to develop solutions to the complex array of problems associated with the development of earthquake-resistant communities.

This chapter examines the role of GIS in emergency management through the lens of comprehensive emergency management (CEM) and its four phases: mitigation, preparedness, response, and recovery. The primary concern before a potential disaster is mitigating the impact of a hazard. Here GIS is gaining favour in risk assessment and the development of long-term mitigation strategies. In the preparedness and response phases, GIS may serve either as the integrating centrepiece for a comprehensive disaster preparedness and response system or as a portable, on-site source of spatial information. In the wake of a disaster, GIS is becoming integral in supporting damage assessment, rebuilding, and public education. The chapter concludes with an example application of GIS in emergency planning: evacuation vulnerability mapping.