Climate impact of transportation A model comparison

Transportation contributes to a significant and rising share of global energy use and GHG emissions. Therefore modeling future travel demand, its fuel use, and resulting CO₂ emission is highly relevant for climate change mitigation. In this study we compare the baseline projections for global service demand (passenger-kilometers, ton-kilometers), fuel use, and CO₂ emissions of five different global transport models using harmonized input assumptions on income and population. For four models we also evaluate the impact of a carbon tax. All models project a steep increase in service demand over the century. Technology change is important for limiting energy consumption and CO₂ emissions, the study also shows that in order to stabilise or even decrease emissions radical changes would be required. While all models project liquid fossil fuels dominating up to 2050, they differ regarding the use of alternative fuels (natural gas, hydrogen, biofuels, and electricity), because of different fuel price projections. The carbon tax of 200 USD/tCO₂ in 2050 stabilizes or reverses global emission growth in all models. Besides common findings many differences in the model assumptions and projections indicate room for further understanding long-term trends and uncertainty in future transport systems.     

Determination of coronal magnetic fields from 10 to 26R ⊙ using the density compression ratios of CME-driven shocks

Recently, the estimation of coronal magnetic field using new methods, such as standoff distance method or density compression ratio method has been reported. In the present work, we utilized the density compression ratio of CME-driven shocks for 10 events at 29 different locations in the upper solar corona (10–26R _⊙) and determined the coronal magnetic field for two different adiabatic indices (γ=4/3 and 5/3). In addition, radial dependence of shock parameters in the corona is studied. It is found that the magnetic field estimated in the above range agree with the general trend. In addition, we obtained a radial profile of magnetic field [B(R)=623R ^?1.4] in the entire upper corona (3–30R _⊙) by combining the magnetic field estimated by Kim et al. (Astrophys. J. 746:118, 2012) in the range 3–15R _⊙ and that estimated in the present study in the range (10–26R _⊙). The power-law indices are nearly in agreement with recent results of CME-driven shocks reported in the literature. The results are discussed with the comparison of newly reported coronal magnetic field values obtained by different techniques and found that the power-law relation closely follow the literature values.     

Active-Region Monitoring and Flare Forecasting – I. Data Processing and First Results

This paper discusses a near real-time approach to solar active-region monitoring and flare prediction using the Big Bear Solar Observatory Active Region Monitor (ARM). Every hour, ARM reads, calibrates, and analyses a variety of data including: full-disk Hα images from the Global Hα Network; EUV, continuum, and magnetogram data from the Solar and Heliospheric Observatory (SOHO); and full-disk magnetograms from the Global Oscillation Network Group (GONG). For the first time, magnetic gradient maps derived from GONG longitudinal magnetograms are now available on-line and are found to be a useful diagnostic of flare activity. ARM also includes a variety of active-region properties from the National Oceanic and Atmospheric Administration's Space Environment Center, such as up-to-date active-region positions, GOES 5-min X-ray data, and flare-to-region identifications. Furthermore, we have developed a Flare Prediction System which estimates the probability for each region to produce C-, M-, or X-class flares based on nearly eight years of NOAA data from cycle 22. This, in addition to BBSO's daily solar activity reports, has proven a useful resource for activity forecasting. Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1023/A:1020950221179     

Storm‐coverage effect on dynamic flood‐frequency analysis: empirical data analysis

In this study, a dynamic flood‐frequency analysis model considering the storm coverage effect is proposed and applied to six sub‐basins in the Pyungchang River basin, Korea. The model proposed is composed of the rectangular pulse Poisson process model for rainfall, the Soil Conservation Service curve number method for infiltration and the geomorphoclimatic instantaneous unit hydrograph for runoff estimation. Also, the model developed by Marco and Valdes is adopted for quantifying the storm‐coverage characteristics. By comparing the results from the same model with and without the storm‐coverage effect consideration, we could quantify the storm‐coverage effect on the flood‐frequency analysis. As a result of that, we found the storm‐coverage effect was so significant that overestimation of the design flood was unavoidable without its consideration. This also becomes more serious for larger basins where the probability of complete storm coverage is quite low. However, for smaller basins, the limited number of rain gauges is found to hamper the proper quantification of the storm‐coverage characteristics. Provided with a relationship curve between the basin size and the storm coverage (as in this study), this problem could be overcome with an acceptable accuracy level. Copyright © 2003 John Wiley & Sons, Ltd.     

Origin of Coronal Shocks without Mass Ejections

We present an analysis of all the events (around 400) of coronal shocks for which the shock-associated metric type IIs were observed by many spectrographs during the period April 1997– December 2000. The main objective of this analysis is to give evidence for the type IIs related to only flare-blast waves, and thus to find out whether there are any type II-associated coronal shocks without mass ejections. By carefully analyzing the data from multi-wavelength observations (Radio, GOES X-ray, Hα, SOHO/LASCO and SOHO/EIT-EUV data), we have identified only 30 events for which there were actually no reports of CMEs. Then from the analysis of the LASCO and EIT running difference images, we found that there are some shocks (nearly 40%, 12/30) which might be associated with weak and narrow mass ejections. These weak and narrow ejections were not reported earlier. For the remaining 60% events (18/30), there are no mass ejections seen in SOHO/LASCO. But all of them are associated with flares and EIT brightenings. Pre-assuming that these type IIs are related to the flares, and from those flare locations of these 18 cases, 16 events are found to occur within the central region of the solar disk (longitude ≤45^∘). In this case, the weak CMEs originating from this region are unlikely to be detected by SOHO/LASCO due to low scattering. The remaining two events occurred beyond this longitudinal limit for which any mass ejections would have been detected if they were present. For both these events, though there are weak eruption features (EIT dimming and loop displacement) in the EIT images, no mass ejection was seen in LASCO for one event, and a CME appeared very late for the other event. While these two cases may imply that the coronal shocks can be produced without any mass ejections, we cannot deny the strong relationship between type IIs and CMEs.     

Variationsl solution of long-period oscillations of the Earth

Summary The linearized equation of motion for the slightly elliptical rotating earth is obtained and using Phinney & Burridge's generalized spherical harmonics, the variational principle is derived for the normal mode oscillations of the Earth. The numerical solutions of two earth models 1066B and B1S6 are searched by minimizing the energy functional for the terrestrial spectral range longer than the lowest order free oscillation. The periods of core modes computed for the earth model B1S6, with stably stratified outer core, ranges from about 4 to 13hr and the periods for the 1066B are much more spread without clustering around the periods of 6 and 12 hr as in B1S6. The results for the earth model 1066B indicate that an outer core can support long-period oscillations even when it is not stably stratified. The Chandler wobble periods obtained are 402.3 day for B1S6 and 402.7 day for 1066B.     

Seismically induced changes in groundwater levels and temperatures following the ML5.8 (ML5.1) Gyeongju earthquake in South Korea

Hydrogeological responses to earthquakes such as changes in groundwater level, temperature, and chemistry, have been observed for several decades. This study examines behavior associated with M_L 5.8 and M_L 5.1 earthquakes that occurred on 12 September 2016 near Gyeongju, a city located on the southeast coast of the Korean peninsula. The M_L 5.8 event stands as the largest recorded earthquake in South Korea since the advent of modern recording systems. There was considerable damage associated with the earthquakes and many aftershocks. Records from monitoring wells located about 135 km west of the epicenter displayed various patterns of change in both water level and temperature. There were transient-type, step-like-type (up and down), and persistent-type (rise and fall) changes in water levels. The water temperature changes were of transient, shift-change, and tendency-change types. Transient changes in the groundwater level and temperature were particularly well developed in monitoring wells installed along a major boundary fault that bisected the study area. These changes were interpreted as representing an aquifer system deformed by seismic waves. The various patterns in groundwater level and temperature, therefore, suggested that seismic waves impacted the fractured units through the reactivation of fractures, joints, and microcracks, which resulted from a pulse in fluid pressure. This study points to the value of long-term monitoring efforts, which in this case were able to provide detailed information needed to manage the groundwater resources in areas potentially affected by further earthquakes.

     

Source and remediation for heavy metals of soils at an iron mine of Ulsan City,Korea

Ulsan mine produced the iron ore minerals of magnetite, arsenopyrite, and scheelite in 1992, and serpentine was developed from 1977 to 2002. The soils of the mine were contaminated by heavy metals such as As, Zn, Ni, and Cd. Heavy metals of Ni and Zn came mostly from serpentinite, and As was derived mainly from arsenopyrite in the scan-type iron ore body. As, Zn, and Ni were major contaminants, but Cd was a minor contaminant on a basis of Korean standard. The heavy metals in the deep depth (>?5 m) came from the host rocks, and those in the shallow depth (<?5 m) were derived from the organic–mineral complexation soil. The remediation plan was a soil washing for highly contaminated soils and the containment of clay materials for less contaminated soils. Even though the remediation methods were successful, the continuous monitoring and the analysis of monitoring data are still necessary for the conservation of soil and groundwater around the study area.     

Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines

This study developed an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A (S1A) data. The data were processed through four steps: (1) data pre-processing, (2) constructing smooth time series VH backscatter data, (3) rice crop classification using random forests (RF) and support vector machines (SVM) and (4) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of rice-cropping patterns in the study region. The comparisons between the classification results and the ground reference data indicated that the overall accuracy and Kappa coefficient achieved from RF were 86.1% and 0.72, respectively, which were slightly more accurate than SVM (overall accuracy of 83.4% and Kappa coefficient of 0.67). These results were reaffirmed by the government’s rice area statistics with the relative error in area (REA) values of 0.2 and 2.2% for RF and SVM, respectively.