Using GIS to develop a mobile communications network for disaster-damaged areas

Communications network damage resulting from a large disaster causes difficulties in the ability to rapidly understand the current situation and thus make appropriate decisions towards mitigating problems, such as where to send and dispense emergency supplies. The research outlined in this paper focuses on the rapid construction of a network after a disaster occurs. This study suggests ZigBee and geographic information systems (GIS) technologies to resolve these problems and provide an effective communication system. The experimental results of the ZigBee network system are presented, examples are provided of the mapping and analysis undertaken using GIS for the disaster-stricken area of Tsukuba City, Japan, and the communications node arrangements are determined for this region. These results demonstrate the effectiveness of establishing such a communications system for supporting efforts to relieve disaster-damaged areas.     

Three-dimensional mapping of red stingray (Dasyatis akajei) movement with reference to bottom topography

Most demersal fishes maintain strong relations with bottom substrates and bottom depths and/or topography during their lives. It is important to know these relations to for understand their lives. In Tokyo Bay, red stingray, Dasyatis akajei, classified as near-threatened species by IUCN, has increased since the 1980s. It is a top predator and engages in ecosystem engineer by mixing the sand bed surface through burring behavior, and greatly influences a coastal ecosystem. It is reported that this species invades in plage and tidal flats and has sometimes injured beachgoers and people gathering clams in Tokyo bay. Thus, it is necessary to know its behavior and habitat use to avoid accidents and to better conserve the biodiversity of ecosystems. However, previous studies have not examined its relationship with the bottom environment. This study aims to describe its behavior in relation to the bottom environment. We sounded three dimensional bottom topography of their habitat off Kaneda Cove in Tokyo Bay with interferometric sidescan sonar system and traced the movement of red stingrays by attaching a data logger system to survey their migration. The results revealed that red stingray repeated vertical movement between the surface and bottom, and used not only sand beds but also rocky beds.

     

Mechanisms of enhanced ocean surface warming in the Kuroshio region for 1951–2010

Climate Dynamics - Since the early twentieth century, observations have shown that the ocean surface has warmed almost globally, but the rate of sea surface temperature (SST) rise in the Kuroshio...     

Zonation of Sulfate and Sulfide Minerals and Isotopic Composition in the Far Southeast Porphyry and Lepanto Epithermal Cu–Au Deposits,Philippines

The world‐class Far Southeast (FSE) porphyry system, Philippines, includes the FSE Cu–Au porphyry deposit, the Lepanto Cu–Au high‐sulfidation deposit and the Victoria–Teresa Au–Ag intermediate‐sulfidation veins, centered on the intrusive complex of dioritic composition. The Lepanto and FSE deposits are genetically related and both share an evolution characterized by early stage 1 alteration (deep FSE potassic, shallow Lepanto advanced argillic‐silicic, both at ~1.4 Ma), followed by stage 2 phyllic alteration (at ~1.3 Ma); the dominant ore mineral deposition within the FSE porphyry and the Lepanto epithermal deposits occurred during stage 2. We determined the chemical and S isotopic composition of sulfate and sulfide minerals from Lepanto, including stage 1 alunite (12 to 28 permil), aluminum–phosphate–sulfate (APS) minerals (14 to 21 permil) and pyrite (?4 to 2 permil), stage 2 sulfides (mainly enargite–luzonite and some pyrite, ?10 to ?1 permil), and late stage 2 sulfates (barite and anhydrite, 21 to 27 permil). The minerals from FSE include stage 2 chalcopyrite (1.6 to 2.6 permil), pyrite (1.1 to 3.4 permil) and anhydrite (13 to 25 permil). The whole‐rock S isotopic composition of weakly altered syn‐mineral intrusions is 2.0 permil. Stage 1 quartz–alunite–pyrite of the Lepanto lithocap, above about 650 m elevation, formed from acidic condensates of magmatic vapor at the same time as hypersaline liquid formed potassic alteration (biotite) near sea level. The S isotopic composition of stage 1 alunite–pyrite record temperatures of approximately 300–400°C for the vapor condensate directly over the porphyry deposit; this cooled to <250°C as the acidic condensate flowed to the NW along the Lepanto fault where it cut the unconformity at the top of the basement. Stage 1 alunite at the base of the advanced argillic lithocap over FSE contains cores of APS minerals with Sr, Ba and Ca; based on back‐scattered electron images and ion microprobe data, these APS minerals show a large degree of chemical and S‐isotopic heterogeneity within and between samples. The variation in S isotopic values in these finely banded stage 1 alunite and APS minerals (16 permil range), as well as that of pyrite (6 permil range) was due largely to changes in temperature, and perhaps variation in redox conditions (average ~ 2:1 H₂S:SO₄). Such fluctuations could have been related to fluid pulses caused by injection of mafic melt into the diorite magma chamber, supported by mafic xenoliths hosted in diorite of an earlier intrusion. The S isotopic values of stage 2 minerals indicate temperatures as high as 400°C near sea level in the porphyry deposit, associated with a relatively reduced fluid (~10:1 H₂S:SO₄) responsible for deposition of chalcopyrite. Stage 2 fluids were relatively oxidized in the Lepanto lithocap, with an H₂S:SO₄ ratio of about 4. The oxidation resulted from cooling, which was caused by boiling during ascent and then dilution with steam‐heated meteoric water in the lithocap. This cooling also resulted in the sulfidation state of minerals increasing from chalcopyrite stability in the porphyry deposit to that of enargite in the lithocap‐hosted high‐sulfidation deposit. The temperature at the base of the lithocap during stage 2 was ≥300°C, cooling to <250°C within the main lithocap, and about 200°C towards the limit of the Lepanto orebody, approximately 2 km NW of the porphyry deposit. Approximate 300°C and 200°C isotherms, estimated from S isotopic and fluid inclusion temperatures during stage 1 and stage 2, shifted towards the core of the FSE porphyry deposit with time. This general retreat in isotherms was more than 500 m laterally within Lepanto and 500 m vertically within FSE as the magmatic–hydrothermal system evolved and collapsed over the magmatic center. During this evolution, there is also evidence recorded by large S isotopic variations in individual crystals for sharp pulses of higher temperature, relatively reduced fluid injected into the porphyry deposit.     

An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC

Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble.