The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling

The Yushu County, Qinghai Province, China, April 14, 2010, earthquake triggered thousands of landslides in a zone between 96°20′32.9″E and 97°10′8.9″E, and 32°52′6.7″N and 33°19′47.9″N. This study examines the use of geographic information system (GIS) technology and Bayesian statistics in creating a suitable landslide hazard-zone map of good predictive power. A total of 2,036 landslides were interpreted from high-resolution aerial photographs and multi-source satellite images pre- and post-earthquake, and verified by selected field checking before a final landslide-inventory map of the study area could be established using GIS software. The 2,036 landslides were randomly partitioned into two subsets: a training dataset, which contains 80 % (1,628 landslides), for training the model; and a testing dataset 20 % (408 landslides). Twelve earthquake triggered landslide associated controlling parameters, such as elevation, slope gradient, slope aspect, slope curvature, topographic position, distance from main surface ruptures, peak ground acceleration, distance from roads, normalized difference vegetation index, distance from drainages, lithology, and distance from all faults were obtained from variety of data sources. Landslide hazard indices were calculated using the weight of evidence model. The landslide hazard map was compared with training data and testing data to obtain the success rate and predictive rate of the model, respectively. The validation results showed satisfactory agreement between the hazard map and the existing landslide distribution data. The success rate is 80.607 %, and the predictive rate is 78.855 %. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate, low and very low. The landslide hazard evaluation map should be useful for environmental recovery planning and reconstruction work.     

Ensemble-based landslide susceptibility maps in Jinbu area, Korea

Ensemble techniques were developed, applied and validated for the analysis of landslide susceptibility in Jinbu area, Korea using the geographic information system (GIS). Landslide-occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 70/30 ratio for training and validation of the models, respectively. Topography, geology, soil and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The relationships between the detected landslide locations and the factors were identified and quantified by frequency ratio, weight of evidence, logistic regression and artificial neural network models and their ensemble models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. Then, the four landslide susceptibility maps were used as new input factors and integrated using the frequency ratio, weight of evidence, logistic regression and artificial neural network models as ensemble methods to make better susceptibility maps. All of the susceptibility maps were validated by comparison with known landslide locations that were not used directly in the analysis. As the result, the ensemble-based landslide susceptibility map that used the new landslide-related input factor maps showed better accuracy (87.11% in frequency ratio, 83.14% in weight of evidence, 87.79% in logistic regression and 84.54% in artificial neural network) than the individual landslide susceptibility maps (84.94% in frequency ratio, 82.82% in weight of evidence, 87.72% in logistic regression and 81.44% in artificial neural network). All accuracy assessments showed overall satisfactory agreement of more than 80%. The ensemble model was found to be more effective in terms of prediction accuracy than the individual model.     

Combined analyses of chemometrics and kriging for identifying groundwater contamination sources and origins at the Masan coastal area in Korea

Hydrogeochemical analyses including the basic statistics of chemical components, Piper??s trilinear diagram, and Mazor??s compositional bivariate diagram revealed that the main source and origin of groundwater contamination was seawater intrusion in the study area. However, the other sources and origins of groundwater contamination could be found by the combined analyses of chemometrics and kriging. Cluster analysis was helpful for the classification on the basis of the contamination characteristics of groundwater quality; however, it was not sufficient for the apportionment of groundwater contamination sources. Factor analysis (FA) determined three factors with 81.07% in total variance: Factor 1 for seawater contamination, Factor 2 for nitrate contamination, and Factor 3 for iron contamination. Factor analysis determined the sources of groundwater contamination; however, it could not discover the origins of contaminants except Factor 1. In backward stepwise mode, discriminant analysis decreased the number of parameters from 18 to 6 in discriminating the contaminant type with 96.2% correctness. TDS, Ca, NO₃, Mn, Fe, and Br were the most significant parameters for the discrimination of contaminants. Kriging analysis was very useful for the understanding of correlation and similarity between contaminants and factors of FA, and for the investigation of contaminant origins. It also showed that the similarity between factor scores and contaminant concentrations was proportional to the magnitudes of factor loadings for contaminants. This study represented that the combined analyses of chemometrics and kriging were very indispensable to the identification of groundwater contamination sources and origins, as well as for the spatial classification and assessment of groundwater quality.     

A semi-continuous measurement of gaseous ammonia and particulate ammonium concentrations in PM<Subscript>2.5</Subscript> in the ambient atmosphere

Ammonia has a short residence time in the atmosphere and rapidly neutralizes acid gases that occur near its source, requiring a rapid measurement system for ammonia and particulate ammonium concentrations to better understand their sources, temporal variation of ammonia emissions, and the formation of secondary ammonium aerosols. A semi-continuous measurement system, consisting of a diffusion scrubber, a particle growth chamber, an air-liquid separator, and a fluorescent detector, was developed to determine both gaseous ammonia (NH₃) and particulate ammonium (NH ₄ ⁺ ) in PM_2.5 in the ambient atmosphere of Gwangju, South Korea, during the months of March, April, July, and September of 2007. During the sampling periods, the average concentrations of ammonia and ammonium were found to be 2.33?±?1.29 μg/m³ and 1.89?±?0.99 μg/m³, respectively. Although the average gaseous ammonia concentration was highest in March, the particulate ammonium concentration was higher during the warmer season, reaching 2.08?±?1.07 μg/m³ and 2.32?±?0.94 μg/m³ in April and July, respectively, while only 1.68?±?0.61 μg/m³ in March and 1.24?±?0.99 μg/m³ in September. It is proposed that the higher availability of acid species during the warmer months produced a significant amount of particulate ammonium sulfate. Diurnal fluctuation of ammonia and ammonium during the warmer months showed that their peak time occurred at approximately 10:00 am. Both ammonia and ammonium concentrations were better correlated during the warmer months than during the cooler months. Further, the data suggest that the ammonia and ammonium were measured under well dispersed conditions, and multiple sources contributed to the ammonia at the sampling site.     

Seasonality in the relationship between El Nino and Indian Ocean dipole

The seasonal change in the relationship between El Nino and Indian Ocean dipole (IOD) is examined using the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), and the twentieth century simulations (20c3m) from the Geophysical Fluid Dynamics Laboratory Coupled Model, version 2.1. It is found that, both in ERA-40 and the model simulations, the correlation between El Nino (Nino3 index) and the eastern part of the IOD (90?C110°E; 10°S-equator) is predominantly positive from January to June, and then changes to negative from July to December. Correlation maps of atmospheric and oceanic variables with respect to the Nino3 index are constructed for each season in order to examine the spatial structure of their seasonal response to El Nino. The occurrence of El Nino conditions during January to March induces low-level anti-cyclonic circulation anomalies over the southeastern Indian Ocean, which counteracts the climatological cyclonic circulation in that region. As a result, evaporation decreases and the southeastern Indian Ocean warms up as the El Nino proceeds, and weaken the development of a positive phase of an IOD. This warming of the southeastern Indian Ocean associated with the El Nino does not exist past June because the climatological winds there develop into the monsoon-type flow, enhancing the anomalous circulation over the region. Furthermore, the development of El Nino from July to September induces upwelling in the southeastern Indian Ocean, thereby contributing to further cooling of the region during the summer season. This results in the enhancement of a positive phase of an IOD. Once the climatological circulation shifts from the boreal summer to winter mode, the negative correlation between El Nino and SST of the southeastern Indian Ocean changes back to a positive one.     

How predictable is the northern hemisphere summer upper-tropospheric circulation?

The retrospective forecast skill of three coupled climate models (NCEP CFS, GFDL CM2.1, and CAWCR POAMA 1.5) and their multi-model ensemble (MME) is evaluated, focusing on the Northern Hemisphere (NH) summer upper-tropospheric circulation along with surface temperature and precipitation for the 25-year period of 1981–2005. The seasonal prediction skill for the NH 200-hPa geopotential height basically comes from the coupled models’ ability in predicting the first two empirical orthogonal function (EOF) modes of interannual variability, because the models cannot replicate the residual higher modes. The first two leading EOF modes of the summer 200-hPa circulation account for about 84% (35.4%) of the total variability over the NH tropics (extratropics) and offer a hint of realizable potential predictability. The MME is able to predict both spatial and temporal characteristics of the first EOF mode (EOF1) even at a 5-month lead (January initial condition) with a pattern correlation coefficient (PCC) skill of 0.96 and a temporal correlation coefficient (TCC) skill of 0.62. This long-lead predictability of the EOF1 comes mainly from the prolonged impacts of El Niño-Southern Oscillation (ENSO) as the EOF1 tends to occur during the summer after the mature phase of ENSO. The second EOF mode (EOF2), on the other hand, is related to the developing ENSO and also the interdecadal variability of the sea surface temperature over the North Pacific and North Atlantic Ocean. The MME also captures the EOF2 at a 5-month lead with a PCC skill of 0.87 and a TCC skill of 0.67, but these skills are mainly obtained from the zonally symmetric component of the EOF2, not the prominent wavelike structure, the so-called circumglobal teleconnection (CGT) pattern. In both observation and the 1-month lead MME prediction, the first two leading modes are accompanied by significant rainfall and surface air temperature anomalies in the continental regions of the NH extratropics. The MME’s success in predicting the EOF1 (EOF2) is likely to lead to a better prediction of JJA precipitation anomalies over East Asia and the North Pacific (central and southern Europe and western North America).     

Polybrominated diphenyl ethers and polychlorinated biphenyls in sediments of southwest Taiwan: regional characteristics and potential sources

Very little information is available on the contamination of coastal sediments of Taiwan by PBDEs and PCBs. In this study, we determined the concentrations of 19 PBDE and 209 PCB congeners in 57 surface sediment samples to identify the possible sources of PBDEs and PCBs. The total PBDE and PCB concentrations ranged from below detection limit to 7.73 ng/g and 0.88-7.13 ng/g, respectively; these values are within the ranges observed for most coastal sediments worldwide. The PBDE congeners were dominated by BDE-209 (50.7-99.7%), with minor contributions from penta- and octa-BDEs. The signatures of PCB congeners suggested that PCB residues in Kaohsiung coast may be the legacy of past use or the result of ongoing inputs from the maintenance, repair and salvage of old ships. Principal component analysis of the congener-specific composition of PBDEs and PCBs revealed distinct regional patterns that are related to the use of commercial products.     

Long-term simulations of the sulfur concentrations over the China, Japan and Korea: A model comparison study

Three comprehensive acid deposition models were used to simulate the sulfur concentrations over northeast Asia over the period covering entire year of 2002, and discussed the aggregated uncertainties and discrepancies of the three models. The participating models are from the countries participating in the project of Longrange Transboundary Air Pollutants in Northeast Asia (LTP): China, Japan and Korea. The Eulerian Model-3/CMAQ (by China), Regional Air Quality Model (RAQM, by Japan), and Comprehensive Acid Deposition Model (CADM, by Korea) were employed by each country with common emissions data established by the administrative agencies of China, Japan and Korea. The episodic simulation results between 1 to 15, March 2002 are also presented, during which aircraft measurements were carried out over the Yellow sea. The episodic results show both a wide short-term variability in simulations against measurements, and maximum concentration differences of 3～5 times among the three models, requiring that further attention before confidence among the three models can be claimed for short-term simulations. However, the year-long cumulative simulations showed almost the same general features, with lower aggregated uncertainties between the three models, produced by the long term integration over northeast Asia.     

Representation of tropical storms in the northwestern pacific by the Modern-Era Retrospective analysis for research and applications

This study examines the tropical storms simulated in the Modern-Era Retrospective analysis for Research and Applications (MERRA) global atmospheric reanalysis for the recent 12 years (1998–2009), focusing on the tropical storm activity over the Northwestern Pacific. For validation, the International Best Track Archive for Climate Stewardship (IBTrACS) dataset is used as an observational counterpart. Climatological-mean features of the tropical storm genesis, tracks and their maximum intensity are the primary interests in this study. Regarding the genesis location of tropical storms, MERRA is reasonable in resolving major development regions over the South China Sea and the Northwestern Pacific close to the Philippines. The seasonal variation of the number of storms is also reproduced in a realistic way in MERRA, with peak values occurring from July to September. In addition, MERRA tends to reproduce the observed interannual variation of the number of tropical storms during the 12-years, though with a limited accuracy. The simulated paths toward higher latitudes are also reasonable in MERRA, where the reanalysis corresponds well with the observations in resolving frequent paths of westward moving storms and recurving storms toward the northeast. Regarding the intensity, MERRA captures the linear relationship between the minimum center pressure and the maximum wind speed near the surface at the maximum development. Some discrepancies from the observed features are found in the reanalysis, such as less frequent development of storms over the South China Sea and less frequent paths over this region. The reanalysis also does not attain the observed maximum intensity for the resolved tropical storms, particularly underestimating the center pressure. These deficiencies are likely related to limitations in the horizontal resolution and the parameterized physics of the data assimilation system.     

Characteristics of canopy turbulence over a deciduous forest on complex terrain

Canopy turbulence plays an important role in mass and energy exchanges at the canopy-atmosphere interface. Despite extensive studies on canopy turbulence over a flat terrain, less attention has been given to canopy turbulence in a complex terrain. The purpose of this study is to scrutinize characteristics of canopy turbulence in roughness sublayer over a hilly forest terrain. We investigated basic turbulence statistics, conditionally sampled statistics, and turbulence spectrum in terms of different atmospheric stabilities, wind direction and vertical structures of momentum fluxes. Similarly to canopy turbulence over a homogeneous terrain, turbulence statistics showed coherent structure. Both quadrant and spectrum analysis corroborated the role of intermittent and energetic eddies with length scale of the order of canopy height, regardless of wind direction except for shift of peak in vertical wind spectrum to relatively high frequency in the down-valley wind. However, the magnitude of the momentum correlation coefficient in a neutral condition was smaller than typical value over a flat terrain. Further scrutiny manifested that, in the up-valley flow, temperature skewness was larger and the contribution of ejection to both momentum and heat fluxes was larger compared to the downvalley flow, indicating that thermal instability and weaker wind shear in up-valley flow asymmetrically affect turbulent transport within the canopy.