Twenty years of progresses in oceanic rogue waves: the role played by weakly nonlinear models

Here we discuss some of the progresses that have been made in the last 20 years in the field of oceanic rogue waves, focusing on the role played by leading order equations such as the nonlinear Schrödinger and the Korteweg-De Vries equations. For such equations, it is possible, as shown in Onorato et al. (Origin of heavy tail statistics in equations of the nonlinear Schrödinger type: an exact result, 2016. arXiv:1601.04317), to derive a very simple relation in which the variation of the third (for the KdV) and fourth (for the NLS) moment of the probability density function of the wave field can be related to the variation of the spectral bandwidth. These relations give some new perspectives on the formation of rogue waves in a random sea state.     

Population genetic diversity and structure of a dominant tropical seagrass,Cymodocea rotundata,in the Western Pacific region

Cymodocea rotundata is an ecologically important tropical pioneer seagrass species distributed in the Indo‐Pacific region. The population genetic diversity and structure of this species were analysed at 46 sites spanning the Philippines, Ryukyu Islands (northern limit) and Hainan Island, by using microsatellite simple sequence repeat (SSR) markers. Analyses revealed the persistence of C. rotundata likely relies on local population dynamics and fitness influenced by environmental gradients, with sexual reproduction prevalent in the Philippines while the Ryukyu and Hainan populations were predominantly established by clonal spread. Analysis of molecular variance showed significant genetic differentiation (P < 0.001) among three geographic regions: the Philippines, Ryukyu and Hainan. Furthermore, the mean fixation index value was very high (F_ST = 0.36), indicating poor dispersal potential or limited gene flow. Allelic richness and heterozygosity of C. rotundata was comparable in the Philippines and Ryukyu Islands populations. More private alleles were found in the Philippines and excess heterozygotes in the Ryukyu Islands. STRUCTURE analysis revealed that the Ryukyu and Hainan populations were mosaics of admixed alleles of individuals from the Philippines. An assignment test suggested that recruitment occurs from the Northeast Philippines to Ryukyu Islands. These results suggest that the Philippine populations are tropical seagrass hotspots and perhaps the origin of the populations in the Ryukyu and Hainan Islands. This scenario is most likely driven by the Kuroshio Current and island integration events during plate tectonic activities. The Philippine Archipelago is considered of high importance for conservation objectives and management plan of seagrasses. Likewise, the Ryukyu Islands are also important in terms of conserving regional diversity because locally adapted genotypes have important evolutionary potential in the face of environmental change.     

Revisit the calibration errors on experimental slant total electron content (TEC) determined with GPS

The calibration errors on experimental slant total electron content (TEC) determined with global positioning system (GPS) observations is revisited. Instead of the analysis of the calibration errors on the carrier phase leveled to code ionospheric observable, we focus on the accuracy analysis of the undifferenced ambiguity-fixed carrier phase ionospheric observable determined from a global distribution of permanent receivers. The results achieved are: (1) using data from an entire month within the last solar cycle maximum, the undifferenced ambiguity-fixed carrier phase ionospheric observable is found to be over one order of magnitude more accurate than the carrier phase leveled to code ionospheric observable and the raw code ionospheric observable. The observation error of the undifferenced ambiguity-fixed carrier phase ionospheric observable ranges from 0.05 to 0.11 total electron content unit (TECU) while that of the carrier phase leveled to code and the raw code ionospheric observable is from 0.65 to 1.65 and 3.14 to 7.48 TECU, respectively. (2) The time-varying receiver differential code bias (DCB), which presents clear day boundary discontinuity and intra-day variability pattern, contributes the most part of the observation error. This contribution is assessed by the short-term stability of the between-receiver DCB, which ranges from 0.06 to 0.17 TECU in a single day. (3) The remaining part of the observation errors presents a sidereal time cycle pattern, indicating the effects of the multipath. Further, the magnitude of the remaining part implies that the code multipath effects are much reduced. (4) The intra-day variation of the between-receiver DCB of the collocated stations suggests that estimating DCBs as a daily constant can have a mis-modeling error of at least several tenths of 1 TECU.     

3D-Monitoring Big Geo Data on a seaport infrastructure based on FIWARE

Many organizations of all kinds are using new technologies to assist the acquisition and analysis of data. Seaports are a good example of this trend. Seaports generate data regarding the management of marine traffic and other elements, as well as environmental conditions given by meteorological sensors and buoys. However, this enormous amount of data, also known as Big Data, is useless without a proper system to organize, analyze and visualize it. SmartPort is an online platform for the visualization and management of a seaport data that has been built as a GIS application. This work offers a Rich Internet Application that allows the user to visualize and manage the different sources of information produced in a port environment. The Big Data management is based on the FIWARE platform, as well as “The Internet of Things” solutions for the data acquisition. At the same time, Glob3 Mobile (G3M) framework has been used for the development of map requirements. In this way, SmartPort supports 3D visualization of the ports scenery and its data sources.     

Extreme waves induced by cyclone Nargis at Myanmar coast: numerical modeling versus satellite observations

Natural Hazards - The deltaic coast of Myanmar was severely hit by tropical cyclone Nargis in May 2008. In the present study, a top-down numerical simulation approach using the Weather Research and...     

Long- and short-term volcanic hazard assessment of El Chichón Volcano (Mexico) through Bayesian inference

Natural Hazards - The 1982 eruption of El Chichón volcano constitutes the worst volcanic disaster in Mexico producing more than 2000 fatalities, thousands of displaced people and severe...     

Spatiotemporal patterns of burned areas,fire drivers,and fire probability across the equatorial Andes

Field-based fire studies in the equatorial Andes indicate that fires are strongly associated with biophysical and anthropogenic variables. However, fire controls and fire regimes at the regional scale remain undocumented. Therefore, this paper describes spatial and temporal burned-area patterns, identifies biophysical and anthropogenic fire drivers, and quantifies fire probability across 6° of latitude and 3° of longitude in the equatorial Andes. The spatial and temporal burned-area analysis was carried out based on 18 years(2001-2018) of the MCD64 A1 MODIS burned-area product. Climate, topography, vegetation, and anthropogenic variables were integrated in a logistic regression model to identify the significance of explanatory variables and determine fire occurrence probability. A total of 5779 fire events were registered during the 18 years of this study, located primarily along the western cordillera of the Andes and spreading from North to South. Eighty-eight percent of these fires took place within two fire hotspots located in the northwestern and southwestern corners of the study area. Ninety-nine percent occurred during the second part of the year, between June and December. The largest density of fires was primarily located on herbaceous vegetation and shrublands. Results show that mean monthly temperature, precipitation and NDVI during the prefire season, the location of land cover classes such as forest and agriculture, distance to roads and urban areas, slope, and aspect were the most important determinants of spatial and temporal fire distribution. The logistic regression model achieved a good accuracy in predicting fire probability(80%). Probability was higher in the southwestern and northern corners of the study area, and lower towards the north in the western and eastern piedmonts of the Andes. This analysis contributes to the understanding of fires in mountains within the tropics. The results here presented have the potential to contribute to fire management and control in the region.