| || || Paquette, Jessy.|
| || || A GIS approach to flood vulnerability modeling in the Nadi River basin, Fiji|
Institution: University of the South Pacific.
Subject: Flood forecasting -- Fiji -- Nadi River -- Mathematical models, Floods -- Fiji -- Nadi River, Hydrologic models -- Fiji -- Nadi River
Call No.: pac GB 1399 .5 .F5 P26 2011
Copyright:Under 10% of this thesis may be copied without the authors written permission
Abstract: The January 2009 floods in Fiji were reported to be amongst the worst in the history of the country. Nationwide, 11,458 individuals were evacuated, 11 people died, and economic losses exceed F$ 113 million (Holland 2009). The Nadi River Basin, a small and very reactive watershed, was one of the worst hit with flood heights up to 3.5 meters. Little is known about the hydrology of the basin and the inhabitants living in the area are vulnerable seeing as there is no flood model to warn them of approaching floods. This paper presents a simple and affordable approach to flood hazard assessment in a region where primary data are scarce. The objectives of this project were to collect precise topographical and hydrological data, develop an accurate flood model and to create a variety of detailed datasets that could be intergraded in a flood warning system. The resulting multicriteria decision analysis (MCDA) flood vulnerably model incorporates six parameters: elevation, catchments, land-use, slopes, distance (from channel) and soil types. The Analytical Hierarchy Process (AHP) matrices where calculated in MS Excel 2003 and the GIS manipulations were done in ArcGIS 9.3. The final output, a flood vulnerability GIS, was linked to the 2007 census data to evaluate the total risk and exposure of seven focus zones in the greater Nadi area. The GIS model has revealed that 2% of the buildings are in extreme hazard, 10% are in very high hazard, 11% are in high hazard 23% are in moderate hazard, 40% are in low hazard and 14% are in very low hazard. Several interviews were conducted to verify the model’s accuracy and some minor imprecision’s were noted by the participants. However, many of these inaccuracies were caused by the models limitations or by issues that were not identified in the field survey. For that reason, it was determined