Spatial and temporal trends of dust storms across desert regions of Iran

Dust storms are among natural and anthropogenic hazards for socioeconomic resources, especially in desert regions. In recent years, dust storms have become a serious problem, especially in desert regions of Iran. This study investigates temporal and spatial variation of dust storm frequency in desert regions of Iran. The number of dusty days (NDD) are collected from 22 stations across the region. The statistical analysis of NDD time series is carried out to show both spatial and seasonal pattern of dust storm occurrence in the region. The regional map of statistical characteristics indicates a north to south increasing dust storm frequency. The spatial map also reveals higher year-to-year variation in south eastern Iran. The seasonality of NDD shows the highest frequency for summer followed by the spring and autumn seasons. The popular Mann–Kendall and the bootstrap MK test to consider serial correlation are then applied for Trend assessment. Results showed both negative (across the north and northwestern regions) and positive trend (across south and south eastern regions) in the annual and seasonal NDD time series. This north-to-south gradient in the spatial and temporal frequency NDD may arise from harsh dry and gusty winds as well as intense land use change in the south eastern territories of Iran. However, more careful and detailed studies are required to connect environmental conditions to change in NDD frequency.     

Correction to: Assessing real options in urban surface water flood risk management under climate change

Windblown dust originating in China and Mongolia causes health effects and agricultural damage in its source areas and causes Asian dust events in Japan. An early warning system that could be combined with weather forecasts would be helpful in preventing serious damage. However, it is difficult to specify source areas of dust with current dust modeling systems because land surface information, including vegetation coverage and land surface soil water content, is inadequate. To find and monitor dust source regions, a semi-real-time dust erodibility map was developed based on MODIS satellite data that focuses particularly on the threshold wind speed in a target area of northeast Asia including China and Mongolia (35°–50°N, 75°–120°E). The mapping system incorporates satellite data on snow cover, areas of frozen soil, surface soil water content, and vegetation cover.     

Large wood transport modelling by a coupled Eulerian–Lagrangian approach

The paper discusses a model which predicts the trajectory of floating rigid bodies and may be applied to compute the motion of woody “debris” mobilized during floods. The model couples a Discrete Element (DE) Lagrangian approach for the calculation of motion of rigid bodies with the Eulerian solution of the shallow water equations (SWE), in order to simulate the transport of a cylinder in a two-dimensional stream. It differs from existing models since it is based on a dynamic approach, adapting the Basset–Boussinesq–Oseen equation. In a first step, forces are computed from flow and log velocities; then, the equations of dynamics are solved to model the planar roto-translation of the body. Model results and physical reliability are clearly affected by the values of the drag and side coefficients, especially since logs, modelled as cylinders, are able to change their orientation towards the flow. Experimental studies to evaluate drag and side coefficients can be found in the literature for a submerged cylinder, with various orientations. To extend such results to the case of a floating log, the authors performed a series of laboratory tests on partially submerged cylinders, implementing the outcomes in the proposed DE-SWE model. The coupled model is validated against existing laboratory data concerning spheres and wooden cylinder transport.     

Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards

Transportation networks daily provide accessibility and crucial services to societies. However, they must also maintain an acceptable level of service to critical infrastructures in the case of disruptions, especially during natural disasters. We have developed a method for assessing the resilience of transportation network topology when exposed to environmental hazards. This approach integrates graph theory with stress testing methodology and involves five basic steps: (1) establishment of a scenario set that covers a range of seismic damage potential in the network, (2) assessment of resilience using various graph-based metrics, (3) topology-based simulations, (4) evaluation of changes in graph-based metrics, and (5) examination of resilience in terms of spatial distribution of critical nodes and the entire network topology. Our case study was from the city of Kathmandu in Nepal, where the earthquake on April 25, 2015, followed by a major aftershock on May 12, 2015, led to numerous casualties and caused significant damage. Therefore, it is a good example for demonstrating and validating the developed methodology. The results presented here indicate that the proposed approach is quite efficient and accurate in assisting stakeholders when evaluating the resilience of transportation networks based on their topology.     

Uncertain human consequences in asteroid risk analysis and the global catastrophe threshold

This paper studies the risk of collision between asteroids and Earth. It focuses on uncertainty in the human consequences of asteroid collisions, with emphasis on the possibility of global catastrophe to human civilization. A detailed survey of the asteroid risk literature shows that while human consequences are recognized as a major point of uncertainty, the studies focus mainly on physical and environmental dimensions of the risk. Some potential human consequences are omitted entirely, such as the possibility of asteroid explosions inadvertently causing nuclear war. Other human consequences are modeled with varying degrees of detail. Direct medical effects are relatively well-characterized, while human consequences of global environmental effects are more uncertain. The latter are evaluated mainly in terms of a global catastrophe threshold, but such a threshold is deeply uncertain and may not even exist. To handle threshold uncertainty in asteroid policy, this paper adapts the concept of policy boundaries from literature on anthropogenic global environmental change (i.e., planetary boundaries). The paper proposes policy boundaries of 100 m asteroid diameter for global environmental effects and 1 m for inadvertent nuclear war. Other policy implications include a more aggressive asteroid risk mitigation policy and measures to avoid inadvertent nuclear war. The paper argues that for rare events like large asteroid collisions, the absence of robust data means that a wide range of possible human consequences should be considered. This implies humility for risk analysis and erring on the side of caution in policy.     

Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data

With the acceleration of urbanisation in China, preventing and reducing the economic losses and casualties caused by urban rainstorm waterlogging disasters have become a critical and difficult issue that the government is concerned about. As urban storms are sudden, clustered, continuous, and cause huge economic losses, it is difficult to conduct emergency management. Developing a more scientific method for real-time disaster identification will help prevent losses over time. Examining social media big data is a feasible method for obtaining on-site disaster data and carrying out disaster risk assessments. This paper presents a real-time identification method for urban-storm disasters using Weibo data. Taking the June 2016 heavy rainstorm in Nanjing as an example, the obtained Weibo data are divided into eight parts for the training data set and two parts for the testing data set. It then performs text pre-processing using the Jieba segmentation module for word segmentation. Then, the term frequency–inverse document frequency method is used to calculate the feature items weights and extract the features. Hashing algorithms are introduced for processing high-dimensional sparse vector matrices. Finally, the naive Bayes, support vector machine, and random forest text classification algorithms are used to train the model, and a test set sample is introduced for testing the model to select the optimal classification algorithm. The experiments showed that the naive Bayes algorithm had the highest macro-average accuracy.     

Review of comparative studies on market mechanisms for carbon emission reduction: a bibliometric analysis

This study uses the bibliometric method to analyze the comparative studies on different market mechanisms applied to carbon reduction between 1970 and 2016 based on the online databases of Science Citation Index Expanded (1970–2016) and Social Science Citation Index (2002–2016). We found by observing the characteristics of publications that such studies belong to a multidisciplinary field that has been continuously developing since the 1990s. The USA and the UK have maintained their leading research strengths in the field, whereas China entered late but has developed rapidly. The most productive journal, institution, and author in this field are Energy Policy, Resources for the Future, and Pizer from the USA, respectively. The auctorial and institutional cooperation degrees are growing and still have a broad collaboration space, but the cooperation among countries is fluctuating at a lower level. Nonetheless, the USA keeps its significant international cooperation ties. The bibliographic coupling and co-citation analysis reveals that articles in this field are closely related to one another. The basic literature in the field was written by Weitzman and published in 1974, and the article by Nordhaus published in Science in 1992 is the most cited in the field. The analysis of keywords and abstract shows that the hot spots include policy choice, price-versus-quantity analysis, and mechanism design.     

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while one-third of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.     

Mining exploitation influence range

Mining exploitation has a negative impact on the natural environment. Voids created in the rockmass result in displacements and deformations of land surface. During planning and conducting the exploitation, the range of exploitation influence in the form of linear deformations is being determined. On the basis of mining-geological parameters of exploitation, the exploitation range of influences is calculated. According to the literature, many different ranges of exploitation influences can be determined depending on what has been the purpose of it. Different types of exploitation influence ranges can be distinguished, such as theoretical, damage or measurable. In the paper, the matters connected with determining those three types of the influence range are taken under consideration. The comparison of magnitudes of determined influence ranges is illustrated with two practical examples.