Giant palaeotsunami in Kiribati: Converging evidence from geology and oral history

For tsunami science within Oceania, the vast Central and Western Pacific (CEWEP) is an anomalous region because of the scarcity of historical tsunami observations and the complete absence of dated palaeotsunami evidence. This paper therefore records the first dated high-magnitude palaeotsunami event within the CEWEP region. A combination of both geological data and oral history is provided for a palaeotsunami that struck remote Makin island, northernmost of the Gilbert Islands in Kiribati, toward the end of the 16th century. A previously undocumented oral tradition of giant waves is well known to the people of Makin. Narration of this legend by the Wiin te Maneaba, traditional storyteller on Makin, provided important details supporting a tsunami hypothesis. The legend preserves credible information surrounding the giant-wave origin of Rebua and Tokia, two prominent subaerial megaclasts of blade and block geometry that were transported 80–130 m shorewards from the reef-edge source and deposited in sideways and inverted orientations. From available hydrodynamic flow transport equations, minimum flow velocities of 7.3–16.3 m s⁻¹ were generated, depending on whether the reefblocks were rotated or lifted onto the reef platform. The youngest U-Th age-dates for fossil corals retrieved from the reefblocks give a maximum age for the palaeotsunami of circa AD 1576. Several far-field Pacific Rim and regional possibilities exist for tsunamigenesis. These include subduction-zone seismicity and catastrophic volcanic eruption, both of which have been linked to earlier (late 15th century) palaeotsunami events recorded elsewhere in the Pacific Islands. However, the available evidence here suggests that the ~AD 1576 Makin palaeotsunami was more likely to have been locally generated by tsunamigenic offshore submarine slope failure close to Makin's western reef, associated with the giant arcuate bight structure that characterizes the northern rim of Butaritari atoll.     

The effects of space and scale on unconditional beta convergence: test results from the United States, 1970–2004

Beta convergence theorizes that regions with low levels of income will grow faster than regions with higher incomes as investment is moved to regions where returns can be maximized. The formal test for beta convergence is an OLS regression with income change as the dependent variable and initial income as the predictor. Beta convergence is present when the coefficient on initial income is negative. Empirical evidence of convergence is mixed, however. We contend that omission of spatial effects, and the confounding problem of geographic scale, help explain inconsistent empirical findings. We, therefore, examine the role of spatial effects on the standard convergence model at three spatial scales—states, Economic Areas, and counties—in the United States from 1970 to 2004. Our results indicate failure to account for spatial and scalar effects can lead to conflicting results. Convergence evidence is strongest at smaller levels of spatial aggregation, yet model fit is better at larger levels. In addition, accounting for spatial effects improves model diagnostic performance but weakens convergence evidence.     

A STATISTICAL RELATIONSHIP BETWEEN EL NIÑO-SOUTHERN OSCILLATION AND FLORIDA WILDFIRE OCCURRENCE

Wildfires have long been an important environmental concern in Florida. Recent wildfires along the urban-rural interface of large cities in Florida have pointed to the need for an increased understanding of the physical and biological mechanisms that govern wildfire occurrence. Increased awareness of the relationships between global climate changes occurring in the tropics and their teleconnections outside the tropics dictate a reevaluation of the role of phenomena such as El Niño-Southern Oscillation on the risk of wildfire. One simple way of illustrating the relationship between ENSO and wildfire occurrence is the use of an Analysis of Variance (ANOVA) on wildfire data that has been categorized according to ENSO status (El Niño, La Niña, and neutral years). This research shows that a statistically significant relationship exists between ENSO conditions and annual wildfire occurrence in Florida when ENSO conditions are treated as a potential precursor to wildfire conditions. In particular, a statistically significant relationship exists between both acreage burned and average fire size, when the data are separated into El Niño and La Niña categories according to the previous year's ENSO status. This supports the idea that the climate from previous years has a measurable effect upon fire statistics in the years following the climate measurements, and that it may be possible to create a regional fire prediction model that incorporates ENSO information. [Key words: wildfire, natural hazards, ANOVA, Florida, ENSO.]     

The 2dF Galaxy Redshift Survey: the nature of the relative bias between galaxies of different spectral type

We present an analysis of the relative bias between early- and late-type galaxies in the Two-degree Field Galaxy Redshift Survey (2dFGRS) – as defined by the η parameter of Madgwick et al., which quantifies the spectral type of galaxies in the survey. We calculate counts in cells for flux-limited samples of early- and late-type galaxies, using approximately cubical cells with sides ranging from 7 to  42 h ⁻¹ Mpc  . We measure the variance of the counts in cells using the method of Efstathiou et al., which we find requires a correction for a finite volume effect equivalent to the integral constraint bias of the autocorrelation function. Using a maximum-likelihood technique we fit lognormal models to the one-point density distribution, and develop methods of dealing with biases in the recovered variances resulting from this technique. We then examine the joint density distribution function,   f (δ_E, δ_L)  , and directly fit deterministic bias models to the joint counts in cells. We measure a linear relative bias of ≈1.3, which does not vary significantly with ℓ. A deterministic linear bias model is, however, a poor approximation to the data, especially on small scales  (ℓ≤ 28  h ⁻¹ Mpc)  where deterministic linear bias is excluded at high significance. A power-law bias model with index   b ₁≈ 0.75  is a significantly better fit to the data on all scales, although linear bias becomes consistent with the data for  ℓ≳ 40  h ⁻¹ Mpc  .     

An evaluation of Radarsat-2 individual and combined image dates for land use/cover mapping

Various land use/cover types exhibit seasonal characteristics which can be captured in remotely sensed imagery. This study examined how different seasons of Radarsat-2 data influence land use/cover classification accuracies for two study sites. Two dates of Radarsat-2 C-band quad-polarised images were obtained for Washington, DC, USA and Wad Madani, Sudan. Spectral signatures were extracted and used with a maximum likelihood decision rule for classification and thematic accuracies were then determined. Both despeckled radar and derived texture measures were examined. Thematic accuracies for the two despeckled image dates were similar with a difference of 3% for Washington and 6% for Sudan. Merging the despeckled images for both seasons increased overall accuracy by 2% for Washington and 9% for Sudan. Further combining the original radar for both seasons with derived texture measures increased overall accuracies by 9% for Washington and 16% for Sudan for final overall accuracy values of 73 and 82%.