Papua New Guinea (PNG) is saddled with frequent natural disasters like earthquake, volcanic eruption, landslide, drought, flood etc. Flood, as a hydrological disaster to humankind's niche brings about a powerful and often sudden, pernicious change in the surface distribution of water on land, while the benevolence of flood manifests in restoring the health of the thalweg from excessive siltation by redistributing the fertile sediments on the riverine floodplains. In respect to social, economic and environmental perspective, flood is one of the most devastating disasters in PNG. This research was conducted to investigate the usefulness of remote sensing, geographic information system and the frequency ratio (FR) for flood susceptibility mapping. FR model was used to handle different independent variables via weighted-based bivariate probability values to generate a plausible flood susceptibility map. This study was conducted in the Markham riverine precinct under Morobe province in PNG. A historical flood inventory database of PNG resource information system (PNGRIS) was used to generate 143 flood locations based on "create fishnet" analysis. 100 (70%) flood sample locations were selected randomly for model building. Ten independent variables, namely land use/land cover, elevation, slope, topographic wetness index, surface runoff, landform, lithology, distance from the main river, soil texture and soil drainage were used into the FR model for flood vulnerability analysis. Finally, the database was developed for areas vulnerable to flood. The result demonstrated a span of FR values ranging from 2.66 (least flood prone) to 19.02 (most flood prone) for the study area. The developed database was reclassified into five (5) flood vulnerability zones segmenting on the FR values, namely very low (less that 5.0), low (5.0-7.5), moderate (7.5-10.0), high (10.0-12.5) and very high susceptibility (more than 12.5). The result indicated that about 19.4% land area as 'very high' and 35.8% as 'high' flood vulnerable class. The FR model output was validated with remaining 43 (30%) flood points, where 42 points were marked as correct predictions which evinced an accuracy of 97.7% in prediction. A total of 137292 people are living in those vulnerable zones. The flood susceptibility analysis using this model will be very useful and also an efficient tool to the local government administrators, researchers and planners for devising flood mitigation plans.

Papua New Guinea is blessed with a plethora of enviable natural resources, but at the same time it is also cursed by quite a few natural disasters like volcanic eruptions, earthquakes, landslide, floods, droughts etc. Floods happen to be a natural process of maintaining the health of the rivers and depth of its thalweg; it saves the river from becoming morbid while toning up the fertility of the riverine landscape. At the same time, from human perspective, all these ecological goodies are nullified when flood is construed overwhelmingly as one of the most devastating events in respect to social and economic consequences. The present investigation was tailored to assess the use of multi-criteria decision approach (MCDA) in inland flood risk analysis. Categorization of possible flood risk zones was accomplished using geospatial data sets, like elevation, slope, distance to river, and land use/land cover, which were derived from digital elevation model (DEM) and satellite image, respectively. A pilot study area was selected in the lower part of Markham River in Morobe Province, Papua New Guinea. The study area is bounded by 146˝31 1 to 146˝58 1 east and 6˝33 1 to 6˝46 1 south; covers an area of 758.30 km 2 . The validation of a flood hazard risk map was carried out using past flood records in the study area. This result suggests that MCDA within GIS techniques is very useful in accurate and reliable flood risk analysis and mapping. This approach is convenient for the assessment of flood in any region, specifically in no-data regions, and can be useful for researchers and planners in flood mitigation strategies.

No abstract

Weighted linear combination (WLC) method was used to assess landslides vulnerability of the Simbu Province, Papua New Guinea within the GIS environment of ArcGIS. This multi-criteria evaluation method allows flexibility and tradeoffs amongst all parameters used. Ranks and weights are assigned depending on their influence on the occurrence of landslides. Parameters selected for the study include slope angle, elevation, rainfall, vegetation cover, land use/land cover, landform, proximity to roads, proximity to rivers and proximity to lineaments. Restricted in some sense in terms of data, WLC was appropriate in using existing metadata of the country; Papua New Guinea Resource Information System and Forest Information Management System. The landslide susceptibility map provides valuable information of the risk at hand in the province and district levels to better manage and plan mitigation measures. The slope factor was assigned a weighted of 4 as having greater influence on landslides in the region followed by rainfall weighted of 2 and the other having uniform influence of 1. The study area shows the distribution of the five vulnerability/susceptibility classes ranking from very low (1) to very high (5). Areas with very high landslide vulnerability zones are found in the northern and western parts of Simbu Province. Comparatively, southern parts have very low vulnerability areas. From the calculations done, 6.21 % of area is at very low risk, 20.24 % at low risk, 32.27 % of moderate risk, 26.88 % of high risk and 14.41 % of very high risk area coverage.

This research established an empirical methodology to estimate potential soil erosion rate based on revised universal soil loss equation (RUSLE) and E 30 model. The study was conducted on a highly precipitated, rugged, tropical forested with steep slope watershed during 1992 to 2009. The fourth (4th) largest river of Papua New Guinea, and its catchment area was considered for this research. Lots of commercial mining and logging activities are the ongoing processes in the upper catchment area without proper conservation measures. Digital elevation model (DEM), landsat satellite images, average annual rainfall, soil texture data base were used to derived mandatory input factors into the RUSLE and E 30 model. Raster calculator of ArcGIS spatial analyst was used to generate all input factors and final pixel-by-pixel based computation of soil loss pattern. The average potential soil erosion rate were calculated in the range of 20.34 mm/year to 23.70 mm/year through RSULE model and in the other hand the rate varies from 21.07 mm/year to 26.78 mm/year through E 30 model during 1992 to 2009 respectively. The erosion rate through both model indicates extremely severe rate of erosion in the upper catchment area are required immediate attention of soil conservation practices.