ENSO and IOD analysis on the occurrence of floods in Pakistan

Different techniques have been used to discuss the existence of significant relation between the El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Various studies present their interaction and influence on the natural disasters (i.e. drought, flood, etc.) over large parts of the globe. This study uses a Markov chain method to investigate the relation between the ENSO and IOD for the period of 62 years (1950–2011) and aggregates their influence on the occurrence of floods in Pakistan. Both data sets show similarities in the formation of transition matrices and expected number of visits from one state to another. The strong values of 2-dimensional correlation and high self-communication of the transition states confirm the existence of a possible relation between ENSO and IOD data. Moreover, significant values of dependency and stationary test endorse the applicability of the Markov chain analyses. The independent analysis shows that strong events of both data sets are co-occurred in the same flood years. During the study period maximum number of floods was observed during summer monsoon season. However, further analysis shows that after 1970, Pakistan observed the highest percentage of floods occurred per year during El Nino, Non-ENSO and positive IOD years. These observations and results demonstrate that climate variability especially ENSO and IOD should be incorporated into disaster risk analyses and policies in Pakistan.     

A micro–macro investigation of the capillary strengthening effect in wet granular materials

Wet granular materials are three-dimensionally simulated by the discrete element method with water bridges incorporated between particles. The water bridges are simplified as toroidal shapes, and the matric suction is constantly maintained in the material. A comparison with experimental tests in the literature indicates that the toroidal shape approximation may be one of the best choices with high practicability and decent accuracy. Mechanical behaviours of wet granular materials are studied by triaxial tests. Effects of particle size distributions and void ratios are investigated systematically in this study. The hydraulic limit of the pendular state is also discussed. It gives the capillary cohesion function which is not only determined by the degree of saturation but also positively correlated to relative density and particle size polydispersity and inversely proportional to mean particle size. Furthermore, the capillary strengthening effect is also analysed microscopically in aid of the Stress–Force–Fabric relationship, mainly in fabric anisotropy, coordination number and stress transmission pattern, which revealed the micro-mechanisms of the additional effective stress induced by capillary effect.     

Hydraulic Conductivity Tests for Evaluating Compatibility of Lateritic Soil—Fly Ash Mixtures with Municipal Waste Leachate

Long term competent performance of liner systems is a critical issue in the design and construction of waste repositories due to adverse interactions associated with leachate generated by wastes. This study was conducted to verify the efficacy of fly ash stabilization in enhancing compatibility between lateritic soil and municipal waste leachate. Applications investigated include soil mixtures containing 0, 5, 10, 15, and 20% fly ash compacted at approximately 2% wet of optimum moisture content with modified proctor energy. Baseline hydraulic conductivity was first established at every level of fly ash content by permeating soil mixtures with tap water before permeation with leachate in a compaction mould permeameter using the falling head test method. Results show that the trend in hydraulic conductivity of specimen containing 0% fly ash was characterized by a gradual but erratic decrease which may suggests partial entry of the leachate cations into the double layer. Conversely, specimens containing fly ash showed a general trend consisting of an initial drop in k (up to an order of magnitude) that was followed by slight decrease sustained until k stabilized and later terminated. Above 10% fly ash content, the relatively high values of k observed was not connected with the reactivity of the soil mixtures with leachate, rather it may be attributed to excessive fly ash content that altered their textural and hydraulic properties. The result of this study is potentially significant in the assessment of fly ash as a compatibility enhancing agent which can be admixed in barrier materials that are susceptible to adverse reactions with the liquid to be contained.     

Long-Term Changes in the Extreme Significant Wave Heights on the Western North Pacific: Impacts of Tropical Cyclone Activity and ENSO

Seasonal extreme wave statistics were reproduced by using the 25-km-grid global wave model of WAVEWATCH-III. The results showed that the simulated wave dataset for the present climate (1979-2009) was similar to Climate Forecast System Reanalysis (CFSR) wave data. Statistics such as the root mean squared error (RMSE) and correlation coefficient (CC) over the western North Pacific (WNP) basin were 0.5 m and 0.69 over the analysis domain. The largest trends and standard deviation were around the southern coast of Japan and western edge of the WNP. Linear regression analysis was employed to identify the relationship between the leading principal components (PCs) of significant wave heights (SWHs) in the peak season of July to September and sea surface temperature (SST) anomalies in the equatorial Pacific. The results indicated that the inter-annual variability of SWH can be associated with the El Niño-Southern Oscillation in the peak season. The CC between the first PC of the SWH and anomalies in the Nino 3.4 SST index was also significant at a 99% confidence level. Significant variations in the SWH are affected by tropical cyclones (TCs) caused by increased SST anomalies. The genesis and development of simulated TCs can be important to the variation in SWHs for the WNP in the peak season. Therefore, we can project the variability of SWHs through TC activity based on changes in SST conditions for the equatorial Pacific in the future.     

湘江中下游农田土壤和蔬菜的重金属污染

A total of 219 agricultural soil and 48 vegetable samples were collected from the midstream and downstream of the Xiangjiang River（the Hengyang-Changsha section）in Hunan Province.The accumulation characteristics,spatial distribution and potential risk of heavy metals in the agricultural soils and vegetables were depicted.There are higher accumulations of heavy metals such as As,Cd,Cu,Ni,Pb and Zn in agricultural soils,and the contents of Cd（2.44 mg kg^-1 ）,Pb（65.00 mg kg^-1 ）and Zn（144.13 mg kg^-1 ）are 7.97,3.69 and 1.63 times the corresponding background contents in soils of Hunan Province,respectively. 13.2%of As,68.5%of Cd,2.7%of Cu,2.7%of Ni,8.7%of Pb and 15.1%of Zn in soil samples from the investigated sites exceeded the maximum allowable heavy metal contents in the China Environmental Quality Standard for Soils（GB15618-1995,Grade Ⅱ）.The pollution characteristics of multi-metals in soils are mainly due to Cd.The contents of As,Cd,Cu,Pb and Zn in vegetable soils are significantly higher than the contents in paddy soils.95.8%, 68.8%,10.4%and 95.8%of vegetable samples exceeded the Maximum Levels of Contaminants in Foods（GB2762-2005）for As,Cd,Ni and Pb concentrations,respectively.There are significantly positive correlations between the concentrations of Cd,Pb and Zn in vegetables and the concentrations in the corresponding vegetable soils（p〈0.01）.It is very necessary to focus on the potential risk of heavy metals for food safety and human health in agricultural soils and vegetables in the midstream and downstream of the Xiangjiang River,Hunan Province of China.     

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area,China

Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.     

Impact of satellite observed microwave SST on the simulation of tropical cyclones

The impact of realistic representation of sea surface temperature (SST) on the numerical simulation of track and intensity of tropical cyclones formed over the north Indian Ocean is studied using the Weather Research and Forecast (WRF) model. We have selected two intense tropical cyclones formed over the Bay of Bengal for studying the SST impact. Two different sets of SSTs were used in this study: one from TRMM Microwave Imager (TMI) satellite and other is the weekly averaged Reynold’s SST analysis from National Center for Environmental Prediction (NCEP). WRF simulations were conducted using the Reynold’s and TMI SST as model boundary condition for the two cyclone cases selected. The TMI SST which has a better temporal and spatial resolution showed sharper gradient when compared to the Reynold’s SST. The use of TMI SST improved the WRF cyclone intensity prediction when compared to that using Reynold’s SST for both the cases studied. The improvements in intensity were mainly due to the improved prediction of surface latent and sensible heat fluxes. The use of TMI SST in place of Reynold’s SST improved cyclone track prediction for Orissa super cyclone but slightly degraded track prediction for cyclone Mala. The present modeling study supports the well established notion that the horizontal SST gradient is one of the major driving forces for the intensification and movement of tropical cyclones over the Indian Ocean.     

Analysis of decade-long time series of GPS-based polar motion estimates at 15-min temporal resolution

We present results from the generation of 10-year-long continuous time series of the Earth’s polar motion at 15-min temporal resolution using Global Positioning System ground data. From our results, we infer an overall noise level in our high-rate polar motion time series of 60 \(\upmu \hbox {as}\) (RMS). However, a spectral decomposition of our estimates indicates a noise floor of 4 \(\upmu \hbox {as}\) at periods shorter than 2 days, which enables recovery of diurnal and semidiurnal tidally induced polar motion. We deliberately place no constraints on retrograde diurnal polar motion despite its inherent ambiguity with long-period nutation. With this approach, we are able to resolve damped manifestations of the effects of the diurnal ocean tides on retrograde polar motion. As such, our approach is at least capable of discriminating between a historical background nutation model that excludes the effects of the diurnal ocean tides and modern models that include those effects. To assess the quality of our polar motion solution outside of the retrograde diurnal frequency band, we focus on its capability to recover tidally driven and non-tidal variations manifesting at the ultra-rapid (intra-daily) and rapid (characterized by periods ranging from 2 to 20 days) periods. We find that our best estimates of diurnal and semidiurnal tidally induced polar motion result from an approach that adopts, at the observation level, a reasonable background model of these effects. We also demonstrate that our high-rate polar motion estimates yield similar results to daily-resolved polar motion estimates, and therefore do not compromise the ability to resolve polar motion at periods of 2–20 days.     

Respiration of Recently-Fixed Plant Carbon Dominates Mid-Winter Ecosystem CO2 Production in Sub-Arctic Heath Tundra

Arctic ecosystems could provide a substantial positive feedback to global climate change if warming stimulates below-ground CO₂ release by enhancing decomposition of bulk soil organic matter reserves.Ecosystem respiration during winter is important in this context because CO₂ release from snow-covered tundra soils is a substantial component of annual net carbon (C) balance, and because global climate models predict that the most rapid rises in regional air temperature will occur in the Arctic during winter. In this manipulative field study, the relative contributions of plant and bulk soil organic matter C pools to ecosystem CO₂ production in mid-winter were investigated. We measured CO₂ efflux rates in Swedish sub-arctic heath tundra from control plots and from plots that had been clipped in the previous growing season to disrupt plant activity. Respiration derived from recently-fixed plant C (i.e., plant respiration, and respiration associated with rhizosphere exudates and decomposition of fresh litter) was the principal source of CO₂ efflux, while respiration associated with decomposition of bulk soil organic matter was low, and appeared relatively insensitive to temperature. These results suggest that warmer mid-winter temperatures in the Arctic may have a much greater impact on the cycling of recently-fixed, plant-associated C pools than on the depletion of tundra bulk soil C reserves, and consequently that there is a low potential for significant initial feedbacks from arctic ecosystems to climate change during mid-winter.