Abstract

1 Introduction

2 Literature survey

3 Dataset details and investigation protocol

4 Proposed method

5 Experimental results and analysis

6 Conclusion and future work

References

Abstract

     Forest fire poses a serious threat to wildlife, environment, and all mankind. This threat has prompted the development of various intelligent and computer vision based systems to detect forest fire. This article proposes a novel hybrid deep learning model to detect forest fire. This model uses a combination of convolutional neural network (CNN) and recurrent neural network (RNN) for feature extraction and two fully connected layers for final classification. The final feature map obtained from the CNN has been flattened and then fed as an input to the RNN. CNN extracts various low level as well as high level features, whereas RNN extracts various dependent and sequential features. The use of both CNN and RNN for feature extraction is proposed in this article for the first time in the literature of forest fire detection. The performance of the proposed system has been evaluated on two publicly available fire datasets—Mivia lab dataset and Kaggle fire dataset. Experimental results demonstrate that the proposed model is able to achieve very high classification accuracy and outperforms the existing state-of-the-art results in this regard.

Introduction

     Forest fire can potentially result in a large number of environmental disasters, causing vast economical and ecological losses apart from jeopardising human lives. These fires pose a serious threat to people, wildlife, and the environment. To preserve the natural resources and protect the properties and human lives, forest fire detection has become very crucial. It has lead to increasing number of research explorations in this area around the world. Early and accurate detection of forest fires is essential for mitigating the effect of the fire as once a forest fire spreads to a large area it becomes very difficult to control it and might result in a catastrophe. In its early stage any forest fire is relatively small and easy to control. Fire and smoke detector sensors can easily be installed in indoor environments, it is generally not the case for forest areas. Sensors also require the fire to burn for a while before they can be detected. On the contrary, vision based devices can be used to detect fire in real life and they can be deployed in any area by using different means. These systems are also cheap and easy to install.

Conclusion and future work

     This article proposes a combination of CNN and RNN based deep learning method for forest fire detection. The evaluation of the performance of the present system has been done on two different public datasets. The proposed forest fire detection system outperforms the existing studies in this regard. The present work also overcomes various drawbacks of the existing systems. It is evident from the high classification accuracy of the present system that the present system can be employed to detect forest fires in the real world scenarios. The present work shall provide fresh insight to the researchers in carrying out the new researches on fire detection using computer vision based techniques.

     In future, the attempt will be made to carry out the research work in this problem area by employing other sophisticated deep learning techniques. The plan is also there to develop a fire detection system in non-forest areas, especially fires that occur in residential areas and industrial areas. Other possible future directions of this research work include the exploration of the possibility of employing the proposed model for low resolution satellite images covering large geographical areas.

Abstract

     Remote sensing provides crucial support for building damage assessment in the wake of hurricanes. This article proposes a coupled deep learning-based model for damage assessment that leverages a large very high-resolution satellite images dataset and a flexibility of building footprint source. Convolutional Neural Networks were used to generate building footprints from pre-hurricane satellite imagery and conduct a classification of incurred damage. We emphasize the advantages of multiclass classification in comparison with traditional binary classification of damage and propose resolving dataset imbalances due to unequal damage impact distribution with a focal loss function. We also investigate differences between relying on learned features using a deep learning approach for damage classification versus a commonly used shallow machine learning classifier, Support Vector Machines, that requires manual feature engineering. The proposed model leads to an 86.3% overall accuracy of damage classification for a case event of Hurricane Michael and an 11% overall accuracy improvement from the Support Vector Machines classifier, suggesting better applicability of such an open-source deep learning-based workflow in disaster management and recovery. Furthermore, the findings can be integrated into emergency response frameworks for automated damage assessment and prioritization of relief efforts.

Introduction

     Hurricanes have been the most expensive type of disaster in the USA (CRED 2019) and historically deathliest, continuing to drive the importance of improving techniques to assess post-hurricane urban damage. The process of such damage assessment involves detection, classification, and evaluation of disaster damage to an economy and society on local, county, state, or tribal levels (FEMA 2016). Remote sensing analysis has proved to be indispensable in aiding these assessment efforts (Adams et al. 2009; Waharte and Trigoni 2010; Stow et al. 2015). In particular, the Federal Emergency Management Agency (FEMA) relies on remote sensing analysis to rapidly assess a large-scale impact and monitor areas that cannot be effectively accessed on the ground. The timeliness, accuracy, and semantic information of such remote sensing analysis results are of key interest (Stow et al. 2015).

Conclusions

     In this study, we proposed a damage assessment workflow from VHR big data imagery, xBD, consisting of two CNNs that delineate building footprints and classify hurricane-incurred damage into four categories: un-damaged, lightly damaged, severely damaged, and destroyed buildings. The coupled model allows users to utilize an existing building footprint, unlike unified models. Another key contribution of this study was addressing a class imbalance problem in the xBD dataset with a focal loss function. We examined a case study of Hurricane Michael in 2018 around the Panama City metropolitan area, where our proposed models achieved an overall accuracy of 84.6% for building footprint segmentation and 86.3% for damage classification tasks. The model successfully identified undamaged buildings with an F1-score of 95.4% and predicts three damage classes (minor, major damage, and destroyed) with 59.3%, 68.7%, and 74.2% F1-score, respectively. An output of this model presents a probability vector of each building belonging to damage classes, thus, creating an opportunity for emergency management and first responders to set a higher or lower threshold for alerting about a hurricane damage presence.

1- Introduction

2- The audiences of earthquake emergency maps

3- Earthquake emergency maps for different audiences

4- Mapping earthquake emergency maps

5- Case study and discussion

6- Conclusions

References

Abstract

Emergency response and rescue is an important way to mitigate the loss caused by earthquakes. Various maps containing information about earthquakes provide effective guidance and references for individuals undertaking such action. However, the production of earthquake emergency maps aimed at the “right” user in an effective manner is challenging. In this study, we focus on two questions: (1) Who are the users of earthquake emergency maps and what information do these individuals need? (2) How can the maps be produced quickly and efficiently according to the requirements of different users? To answer the first question, we classify the users into four categories under the guidance of audience theory: earthquake emergency decision-makers, auxiliary decision-making technicians, emergency rescue workers, and the public. The map contents are also described according to the requirements of different audiences. We depict the methods used for representation in such earthquake emergency maps for different audiences, which places a foundation for answering the second question. Following that, a template-matching mapping method is proposed for the rapid production of emergency maps, including two phases: before the earthquake templates of emergency maps for different audiences are prepared; after an earthquake, the template is updated with information from seismic models, so that the emergency maps are automatically plotted. Finally, a case study is provided to verify the mapping method. We conclude that the application of audience theory and the related template-matching method for map production not only answers the two questions but also will benefit the emergency mapping of other disasters.

Introduction

Earthquakes are natural disasters that usually lead to numerous deaths and injuries, and extensive economic loss [1]. China lies between the circum-Pacific seismic belt and the Eurasian seismic zone and has suffered from some of the most severe earthquake disasters in the world [2]. For example, the Wenchuan earthquake in 2008 caused 69,227 deaths, 374,643 injuries, 17,923 missing people, and more than 10,000 square kilometers of severe damage [3]. A method for the accurate short- and medium-period prediction of earthquakes has not yet been discovered. Earthquake emergency response and rescue is an important means by which mitigation of such disasters can take place after an earthquake, and the effectiveness of this system has often been verified [4]. After the occurrence of an earthquake, maps that include information pertaining to an earthquake and the associated possible disaster can provide a foundation for effective emergency search and rescue, and therefore play an important role in the emergency response and rescue process. These maps are therefore essential thematic maps that display the various types of information needed for earthquake emergency command and rescue. Earthquake emergency maps are important to the leaders of rescue operations and the staff members of governments. These individuals can intuitively understand the information regarding an earthquake disaster, and carry out rescue operations in a timely manner [5]. The three days following an earthquake are known as the “golden period of emergency rescue”; after this time the number of dead sharply increases [6,7]. In China, the government needs approximately 24 h to collect and send emergency rescue materials to a disaster area, which is usually located in the mountain regions of Southwest China. This means that the governmental lead needs to make rescue decisions within half a day following an earthquake. In such a situation, emergency maps that include useful information are crucial in aiding such decision-makers. However, the means by which useful maps in earthquake emergencies can be produced in an effective manner is challenging. Two questions concerning earthquake emergency mapping are discussed in this paper. Many people are involved in emergency response and rescue.

1. Introduction

2. Methodology

3. Results and discussion

4. Conclusions

Declaration of Competing Interest

Acknowledgements

References

Abstract

Urban flash flood poses significant hazards on urbanised area, in particular to buildings and infrastructure due to its fast occurrence and high magnitude in financial loss. Risk assessment of the flash flood identify the critically flood-prone areas and provide assistance in improving the resiliency of mitigation plans. In this study, we developed an assessment of flood susceptibility, vulnerability, the impact of socio-economic, and integrated flash flood index based on the historical data of flood events recorded in Kuala Lumpur. Data of rainfall characteristics, inundated location, and areas are extracted from the reports of flood events from the year 2005-2015. Each event is then segregated according to the place of incidence, providing point-based recurrence of flood at each identified location. Indicators of assessment include frequency and month of occurrence, rainfall characteristics (of intensity, duration, and depth), and land use categories. A total of 137 (point) locations have been identified, where each location is colour-coded based on a 5-point rating scale. The point-based flood-prone locations are validated with the watershed based of 50-ARI rainfall modelling, providing comprehensive hotspot maps. Developed interactive colour-coded flood prone maps facilitate relevant agencies for improved coordination in flash flood mitigation, response and early warning.

Introduction

Urban flooding occurs when the capacity of both natural and drainage systems could not cater to the volume of precipitation and runoff discharge within an urbanised area. High surface runoff discharge from heavy rainfall due to impervious surfaces and high building densities escalate the urban flooding (Gaitan et al., 2016; Yao et al., 2016). Although urban flooding is commonly associated with the short duration-high intensity precipitation, such flooding also has been recorded due to prolonged moderate rainfall (Coulthard et al., 2007; Abdullah and Julien, 2014a; Abdullah et al., 2018).

Flash flood is defined as the flood events resulted from high precipitation in short duration, usually less than 6 hours (Suparta et al., 2014). Localised convective storms in a small catchment within short time results in fast-rising of water level, usually with no advance or little time of warning. As such, the flood occurrence in the urbanised area due to insufficient drainage capacity from short duration-high intensity rainfall-induced the urban flash flooding (UFF). Considering the short duration of rainfall, the limb of the hydrograph rose fast to the peak flow whereby the UFF can even be visible just only after 30 minutes of rainfall. The non-alerted and unpredicted flood events caused devastating impacts to the road infrastructures, particularly at the low-lying areas (Doocy et al., 2013). Risk assessment of UFF on the intra-urban transportation network by evaluating the impact of flood depth (obtained through the 2D hydrodynamic modelling) and traffic or probability analysis provide a quantitative impact of UFF (Yin et al., 2016; Li et al., 2018).

Introduction

Urban flood modelling within the DCs

Calibration of flood models

Conclusion – the challenges now for flood modelling?

Acknowledgements

References

Abstract

The prevalence of flooding events and the associated risk in the urban areas is an increasingly important issue of global significance, although it is more critical for the developing countries (DCs), such as Nigeria, where the hazard is often poorly understood and understudied. With current predictions of worsening future scenarios, it is important to pursue integrated flood risk management approaches which incorporate flood modelling. This paper is part of a research programme which is assessing and modelling urban flood risks in the DCs and data poor areas. It focuses on the latest science and philosophy in relation to urban flood risk management in the DCs. It reviews the literature around current flood modelling techniques and provides a comprehensive table of the different approaches alongside the strengths and weaknesses of the different models. Indeed, research in the vicinity of flood modelling has been extensive, and over the years has resulted in the development of a wide variety of schema, datasets and methodologies for simulating flood hydrodynamics. However, the actual potential of these developments has not been demonstrated in the management of flood risk within the DCs. To date, a perfect model or generic technique which can capture every aspect of flood hydrodynamics in an optimal fashion within the diversity of study locations is still unrealistic. Thus, to bypass the present flood modelling challenges within the context of the DCs, extensive calibration of state-of-the-art flood models is of significance. Additionally, researchers within the DCs should be fascinated by the prospect of developing bespoke flood models based on simple mathematical formulations which are easy to parameterise using global, open source and freely- accessible datasets.

Introduction

he rate at which flooding occurs in recent times has been unprecedented, with the implication that only few coastal, rural, and urban environments still have their natural states unaltered [67,133,151]. This situation is of global significance, although it seems that the perception of flooding in the developing countries (DCs) for example Nigeria, is being nuanced by obvious limitations in research, economy and policy framework [111]. During flooding, water largely covers land areas not usually covered by water, destroying farmland and critical infrastructure, displacing human populations, disrupting economic activities, and in the worst cases, leading to epidemic and death ([135], pg.309). These incidences, especially those relating to fluvial and coastal flooding often were due to sea-level rise, ice melt, overtopping or destruction of water defences and coastal tsunamis [35,93]. However, pluvial flooding, the focus of this review, appears to be widespread in recent times especially within the urban areas where its impacts are increasingly a major source of concern for urban residents and policy makers [38,75,90,152].

Pluvial flooding is basically due to increased frequency and intensity of rainfall, although there are a number of other potential causative factors which have been identified. In Dawson et al. [39] and Adeloye and Rustum [5] pluvial flooding was problematised on the basis of land use change, geomorphology, failure of urban drainage facilities and poor urban planning. Mark et al. [95] perceived pluvial flooding with regards to the scale of its impacts which in fact seem to correlate positively with the large number of human population and development assets within the urban areas. The study demonstrates that urban areas are the hotspot of large-scale flooding impacts to the same degree that they are typically fundamental to any nations’ sustainable development. Along with the much-discussed global climate change, which triggers heavy storms in recent times, current knowledge of rapid urbanisation and demographic pressures which characterise the DCs underscores the inclusive nature of urban pluvial flooding, and the primacy of galvanizing discussions for the management of its threats within the DCs [58,156,164,168].

1. Introduction

2. Background

3. Methods

4. Results

5. Discussion

6. Conclusions

Declaration of competing interest

Acknowledgements

References

Abstract

The lower 800 km of the Missouri River has been incising for decades. However, the 2011 flood scoured more sediment than the previous 12 yr combined. The river rebounded in the following two years, re-depositing over 70% of the sediment scoured during the 2011 flood. Historic evidence suggests that past Missouri River floods may have followed similar scour-rebound patterns. This paper analyzes 16 channel surveys from 1987 to 2014, including 50,723 total cross sections to document the morphological history of the lower 800 km of the Missouri River over the last thirty years, including flood disturbance and long-term trends. The last thirty years on the Missouri River included two basic morphologic regimes: steady incision during low-to-moderate flow periods and rapid scour-rebound responses to floods. Surveys collected during the 2011 flood demonstrate that the river scoured throughout the flood (including the falling limb) and did not begin a rebound phase until after the flood. The analysis also identifies sediment sinks associated with each of these scour regimes (flood scour and incision between floods) that exceed the total volume eroded from the bed during those periods. Results suggest that floodplain deposition may induce the supply limitation that drives flood scour on this large sand-bed river. Additionally, aggregate mining removes substantially more sediment than the total river incision between floods and the reaches with maximum scour during the 1993 and 2011 floods correspond to the zones of aggregate mining

Introduction

Flood disturbance and recovery are critical components of river morphology (Schumm, 1976; Wolman and Gerson, 1978; Lewin, 1989; Knighton, 1998; Simon and Rinaldi, 2006; Phillips and Dyke, 2016) and ecology (Resh 1982; Ward and Stanford, 1983; Lake, 2000). Fluvial form and lotic communities reflect the influence of periodic, powerful events separated by extended periods of low-to-moderate flows. Rivers respond to disturbance events in a variety of ways, on multiple time scales. Additionally, river responses to disturbance-recovery cycles can be complicated by a variety of natural and anthropogenic influences driving long-term incision (e.g., Downs et al., 2013). Parsing transient disturbance-recovery impacts from long-term morphological trends can be particularly challenging on large sand-bed rivers, where noise from rapidly-migrating bedforms can drown out the signal of actual morphological change at individual cross sections. Moreover, remote sensing technologies such as LiDAR, photogrammetry, and structure from motion cannot detect morphological change under meters of turbid water. Currently, only underwater sonar can measure bed shape and change in these deep, turbid systems, which is time consuming and costly over large spatial domains.

Because of the difficulty of large scale morphological studies on large sand-bed rivers, these systems are underrepresented in morphological literature (Hudson, 2002), including the disturbance-recovery literature, despite growing suspicion that their morphologic behaviors differ from those documented in smaller rivers (Latrubesse, 2008). Repeated surveys on the Missouri River present an opportunity to investigate the relative importance of disturbance-recovery cycles and long-term incision on a large sand-bed river.

1. Introduction

2. Flood risk management

3. Flood resilience

4. Conclusions

Appendix A. Supplementary data

Research Data

References

Abstract

Flood risk management has proven successful at reducing the threat of some flooding hazards, preventing loss of life during flooding events and easing the economic burden to communities and regions following floods. It is a useful approach for assessing risks and guiding decisions on implementing protection measures. Recently, in addition to flood risk management, flood resilience is discussed as a new approach in academic literature. This contribution tries to unravel the relation between flood risk management and flood resilience. Therefore, three aspects are discussed: the definition of resilience, its measurement methods and also its possible implementation and embedding in flood risk management.

Introduction

Resilience is a relatively new concept as applied to environmental hazard management [2]. Currently, a risk-based approach is dominating the way society deals with natural hazards, such as floods. Whereas the risk-based approach provides a rational way of balancing the costs of mitigation and adaptation measures [38], resilience embraces the uncertainties associated with natural hazards by focusing on the ability of affected systems to absorb shocks [33].

The idea of resilience follows closely with a modern emphasis on integrated solutions to environmental issues which are becoming increasingly complex as they expand geographically, economically, socially and politically. At the same time, resilience is considered to be too abstract, apolitical and ahistorical in social science [49]. The traditional ways of applying measures which consider closed and simplified boundary conditions are no longer adequate. In a modern world, processes and people are highly dynamic and far-reaching. Therefore, our measures must reflect society by learning to become similarly flexible and inclusive.

1. Introduction

2. Methods

3. Study sites and data

4. Results

5. Discussion and conclusion

Acknowledgements

Appendix A. Supplementary data

References

Abstract

Assessing flood risk using stationary flood frequency analysis techniques is commonplace. However, it is increasingly evident that the stationarity assumption of these analyses does not hold as anthropogenic climate change could shift a site’s hydroclimate beyond the range of historical behaviors. We employ nonstationary flood frequency models using the generalized extreme value (GEV) distribution to model changing flood risk for select seasons at twelve National Parks across the U.S. In this GEV model, the location and/or scale parameters of the distribution are allowed to change as a function of time-variable covariates. We use historical precipitation and modeled flows from the Variable Infiltration Capacity model (VIC), a landsurface model that simulates land–atmosphere fluxes using water and energy balance equations, as covariates to fit a best nonstationary GEV model to each site. We apply climate model projections of precipitation and VIC flows to these models to obtain future flood probability estimates. Our model results project a decrease in flood risk for sites in the southwestern U.S. region and an increase in flood risk for sites in northern and eastern regions of the U.S. for the selected seasons. The methods and results presented will enable the NPS to develop strategies to ensure public safety and efficient infrastructure management and planning in a nonstationary climate.

Introduction

Anthropogenic climate change has increased global mean annual land-surface air temperatures and evidence supports a change in the behavior of precipitation (Hartmann et al., 2013) and streamflow extremes (Hirsch and Ryberg, 2012; Mallakpour and Villarini, 2015; Ahn and Palmer, 2016). Given the non-stationary nature of our climate system at present, the common assumption in traditional flood frequency analysis techniques that flood risk will remain stationary into the future must be questioned – climate change is anticipated to continue to shift hydroclimate beyond the range of historical behaviors (Milly et al., 2008).

As temperatures rise, we expect an increase in total precipitable water in the atmosphere (Trenberth et al., 2003), which was already observed over much of North America (Ross and Elliott, 1996). Consequently, Hartmann et al. (2013) suggest a likely observed increase in either the frequency or intensity of heavy precipitation events across North America, particularly in central North America. Studies using extreme value theory and precipitation-temperature scaling also generally support this claim (DeGaetano, 2009; Wasko and Sharma, 2017).

1- Introduction

2- Data and analysis

3- Discussion and results

4- Conclusions

References

Abstract

The enhancement of the atmospheric greenhouse effect due to the increase in the atmospheric greenhouse gases is often considered as responsible for global warming (known as greenhouse hypothesis of global warming). In this context, the temperature field of global troposphere and lower stratosphere over the period 12/1978–07/2018 is explored using the recent Version 6 of the UAH MSU/AMSU global satellite temperature dataset. Our analysis did not show a consistent warming with gradual increase from low to high latitudes in both hemispheres, as it should be from the global warming theory. In addition, in the lower stratosphere the temperature cooling over both poles is lower than that over tropics and extratropics. To study further the thermal field variability we investigated the long-range correlations throughout the global lower troposphere-lower stratosphere region. The results show that the temperature field displays power-law behaviour that becomes stronger by going from the lower troposphere to the tropopause.This power-law behaviour suggests that the fluctuations in global tropospheric temperature at short intervals are positively correlated with those at longer intervals in a power-law manner. The latter, however, does not apply to global temperature in the lower stratosphere. This suggests that the investigated intrinsic properties of the lower stratospheric temperature are not related to those of the troposphere, as is expected by the global warming theory.

Introduction

Over the last decades, the rise in surface air temperature in regions of our planet has led to a debate in the scientific community about the causes and impacts of this temperature rise, especially if it comes from anthropogenic activities or is of natural origin. We must bear in mind that by definition the climate system is part of the wider global system. In particular, it is composed of five subsystems the atmosphere, the cryosphere, the hydrosphere, the biosphere and the lithosphere, which interact with each other with mostly non-linear processes in space and time (e.g., IPCC, 2014; Lovejoy and Varotsos, 2016). Therefore, a change in a parameter of a climatic subsystem (e.g., atmospheric temperature) does not predict a climate change, as all other parameters of the atmosphere but also of other subsystems (known and measurable or not) are not necessarily known and stable. Also, by definition, the climate is a complicated (displaying many degrees of freedom) and a complex (non-linear, dynamical, sensitive) system (e.g. Lucarini, 2011). Therefore, it is a truism that climate has always been changing, and it will always be changing. Which subsystem dominates the climate change depends, for instance, on the time window, namely: For t < 10yrs the atmospheric degrees of freedom are active and the other sub-systems are frozen. For 100 < t < 1000yrs the ocean dominates, and for t > 5000yrs cryosphere dominates. Several analyses have been made on the key issues of scientific understanding of contemporary global climate change (e.g. Christy et al., 2007). The focus of most of these analyses is to discuss the uncertainties associated with existing observation data and the results of numerical modelling. These emphasize the need to analyze the ability of current models to simulate real climate change. As mentioned above, real climate change results from the non-linear interactions between numerous components of the climatic system. In these should also be taken into consideration and possible contributions by external forcings e.g., cosmic factors, such as solar activity. Despite the projected exponential growth in computer power, these processes will not be adequately resolved in numerical climate models in the near future (Franzke et al., 2015). Stochastic methods for numerical climate prediction may allow for an adequate representation of uncertainties, the reduction of systematic biases and improved representation of longterm climate variability (e.g., Droegemeier, 2009).

1- Introduction

2- Weather perils in Europe

3- Disaster statistics for Europe

4- Risk assessment and reduction

5- Concluding remarks

References

Abstract

Over the last decades, the damage caused by weather events has increased dramatically and ubiquitously. In Europe, weather catastrophes constitute a growing burden on national economies and insurance companies, not least because of the costs of precautionary measures. For a long time, the insurance sector has flagged that weather disasters are on the rise, both in terms of the number of occurrences and material damage caused. The main reasons for this are: increase in the number and area of settlements in exposed areas, the accumulation of ever more valuable and vulnerable assets in these areas, as well as the climate and environmental changes that have already taken place. This paper examines observed changes in risk of various categories of weather disasters in Europe, backed by statistical analyses of relevant, updated information originating from a valuable and quite unique source, Munich Re’s NatCatSERVICE database, that is of considerable interest and value to the scientific community and beyond (e.g. in the reinsurance and insurance industries). The paper also calls for partnership in the reduction of risk of weather extremes and discusses the role of the insurance industry

Introduction

There have been many weather disasters in Europe in the last two decades. They include numerous floods, such as those in northern Italy, France and Switzerland in 2000, in the upper Elbe and Danube catchments in 2002 and 2013, along the lower Danube in 2006, in the United Kingdom in 2007, in the Adriatic region in 2014, and in Germany and France in 2016. There have also been severe heat waves and droughts in the summers of 2003, 2010 and 2018; wildfires in southern and eastern Europe in 2007, 2010 and 2017; hailstorms in Germany (1984 and 2013); winter storms Kyrill (2007) and Xynthia (2010); as well as the extreme snowpack in the northern Alps in 2006 and at the beginning of 2019, just to mention a few examples. Munich Re has been systematically collecting information on natural disasters since 1974. The NatCatSERVICE (NCS) database run by the company, covering losses caused by natural extreme events, is among the world’s largest and contains more than 40,000 entries. The number of disastrous weather events included in the database is growing much faster than the number of geophysical events (earthquakes, volcanic eruptions) (Kundzewicz et al., 2014). This finding holds for several periods of analysis. This could have something to do with climate change, but also with non-climatic factors, such as landuse and land-cover change. Exposure has been growing with the intensification of human settlement of unsafe areas, such as flood plains. Impacts from severe weather are not as devastating to European societies (in particular to the population of industrialised countries of the European Union and OECD) as to those in some other parts of the world, where whole countries are sometimes thrown back years in their development by the occurrence of disastrous weather extremes. On the whole, wealthy European Union countries make efforts to manage (and reduce) risk of weather extremes and to improve societal resilience (Hegger et al., 2016; Priest et al., 2016). But European states do have a significant burden not only from natural disasters themselves, but also from the costly measures that citizens demand from their governments to protect themselves and their properties, especially against the flood hazard. De Bruijn et al. (2017) examined resilience in practice, reviewing principles to enable societies to cope with extreme weather events.

Unconventional natural gas production has significantly expanded in North America, owing to combined use of horizontal drilling and hydraulic fracturing (HF) techniques. These technical advances have enabled exploitation of unconventional natural gas reservoirs, such as low-permeability organic-rich shale formations and “tight-gas” reservoirs, redrawing the domestic energy landscape in Canada (Rivard et al., 2014). In 2012, Canada was the third largest producer of natural gas in the world and exported $15.6 billion worth of natural gas. The unconventional gas industry was responsible for about 15% of total natural gas production. By 2025, Canadian natural gas production is projected to be at least 25% greater than current levels, and the increase will be primarily attributed to unconventional natural gas production (NEB, 2013). In Canada, unconventional natural gas production has been distributed within several major shale plays, the most important plays include the Muskwa-Otter Park shale members of the Horn River Basin of Northern British Columbia (BC) and the adjacent Montney Basin, spanning the BC and Alberta border, as well as the Duvernay Formation in West-central Alberta (NRC, 2016). As a major natural gas producing province, BC is particularly well poised to benefit from the recent overhaul of the natural gas industry. As of 2014, over a thousand wells have been drilled for shale gas exploration or production in BC, and in most cases, completion processes have involved HF (FracFocus, 2014). The HF process involves the pumping of large volumes of fracturing fluid, consisting of a base fluid (primarily water), proppants (typically quartz sand), and various additives, under high pressure into a perforated wellbore to initiate and expand fractures within the adjacent geological formation (Vidic et al., 2013). The fractures increase the permeability of the formations, allowing for the previously trapped natural gas to flow through the formations into the wellbore (Ferrer and Thurman, 2015).

Distillery industries (DIs) are the key contributors to the world’s economy, but these industries also considered as one of the major source of environmental pollution worldwide. In India, there are ~ 319 distilleries with annual production of 3.25 × 109 L of alcohol and 40.4 × 1010 L of wastewater (Chandra et al., 2012; Uppal, 2004). The global production of bioethanol in 2007 was 50 billion liters whereas, in 2008, it reached 60 billion liters representing almost 4% of the 62 world’s gasoline consumption (Mussatto et al., 2010; Balat and Balat, 2009). DIs generate a huge volume of dark colored wastewater characterized by its dark brown color, acidic pH (5.4-4.5), high BOD (40,000- 50,000 mg/L), COD (80,000-100,000 mg/L), total dissolved solids (TDS), total solids (TS), total suspended solid (TSS), with high nitrogen, potassium, phosphates, calcium, and sulfate content (Table 1). The high BOD and COD values of DWW are mainly due to the presence of high organic content such as proteins, reduced sugars, polysaccharides, lignin, melanoidins, and waxes along with a complex mixture of recalcitrant organic pollutants (Chowdhary et al., 2017). In distillery wastewater (DWW), melanoidins are the major coloring pollutants causing serious environmental problems and health threats in human and animals (Tamanna and Mahmood, 2015; Saranraj and Dtella, 2014). Melanoidins are recalcitrant compounds of sugar and amino acids, formed during the processing of sugar cane juice in sugar factories and molasses in distillery industries (Arimi et al., 2015; Saranraj and Dtella, 2014; Onyango et al., 2012; Wang et al., 2011; Agarwal et al., 2010; Mohana et al., 2009; Plavsic et al., 2006).

مقاله سال 2017

تغييرات آب و هوايي درگذشته و پیش‌بینی‌شده در افغانستان به‌طور منظم و با توجه به مناطق مختلف آب‌وهوای آن تجزیه‌وتحلیل شده است تا ابتكارات كمي براي آسیب‌پذیری افغانستان به تغييرات آب‌وهوای جاري و آينده به دست آورده شود. برای این منظور، درجه حرارت، بارندگی و پنج شاخص آب و هوایی دیگر برای ارزیابی کشاورزی (بارندگی سنگین، بارش بهار، طول فصل رشد (GSL)، شاخص میزان گرما (HWMI) و شاخص تبخیر تعرق استاندارد (SPEI)) از داده‌های باز آزمایی می باشند که برای انطباق آن‌ها جهت شناسایی تغییرات درگذشته (اطلاعات از سال 1950) موردبررسی قرارگرفته است. برای تغییرات آتی (تا سال 2100)، پارامترهای مشابه از مجموعه‌ای از 12 مدل آب‌وهوای منطقه‌ای (RCM) و مدل‌سازی با پروژه (CORDEX) در آسیای جنوبی برای سناریوهای با انتشار کم‌وزیاد (مسیرهای تمرکز 4.5 و 8.5) مورداستفاده شده است.