Journal Cover
Natural Hazards
Journal Prestige (SJR): 0.767
Citation Impact (citeScore): 2
Number of Followers: 286  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-0840 - ISSN (Online) 0921-030X
Published by Springer-Verlag Homepage  [2351 journals]
  • Correction to: A comprehensive flash flood defense system in China:
           overview, achievements, and outlook
    • Abstract: The original article needs the addition of another affiliation.
      PubDate: 2019-09-20
  • Characteristics of drought propagation in South Korea: relationship
           between meteorological, agricultural, and hydrological droughts
    • Abstract: To investigate the propagation of meteorological droughts to agricultural and hydrological droughts, the relationship between droughts was analyzed using observed precipitation and agricultural reservoir and dam storage levels with SPI from 1998 to 2015 in South Korea. For the relationship between different types of droughts, we find that the occurrence of meteorological droughts after concentrated precipitation in the wet season (from June to September) tends not to lead to agricultural or hydrological droughts. A lack of precipitation from April to September, when large volumes of irrigation water are consumed, triggers both meteorological and agricultural droughts. In the case of hydrological droughts propagated from meteorological droughts, precipitation deficits in the dry season (between October and March) caused decrease in dam storage levels only. The occurrence of all different types of droughts is associated with extreme meteorological droughts, which are mainly caused by precipitation deficits in the wet season or prolonged rainfall shortages; in these cases, meteorological droughts led to agricultural and hydrological droughts. An analysis of the seasonal characteristics of storage level changes that in the wet season, agricultural reservoir storage levels are more dependent on precipitation deficits than dam storage levels. On other hand, when precipitation deficits were recorded in the dry season, agricultural reservoir storage levels went up, but dam storage levels dropped. The propagation of meteorological droughts to agricultural and hydrological droughts depends not only on drought severity but also on the drought timing. These findings may contribute to establishing a comprehensive drought monitoring system.
      PubDate: 2019-09-20
  • Analysis of topographic parameters underpinning landslide occurrence in
           Kigezi highlands of southwestern Uganda
    • Abstract: An assessment of the influence of topography on landslide occurrence in the Kigezi highlands of southwestern Uganda was conducted. Whereas the frequency and magnitude of landslides in these highlands are on the increase, the topographic attributes underpinning landslide occurrence are not well understood. Sixty-five landslide scars were surveyed and mapped to produce landslide distribution maps. Specific topographic parameters, namely slope gradient, profile curvature, topographic wetness index (TWI), stream power index (SPI), and topographic position index (TPI), were assessed on landslide slope sites. The attributes were parameterized in the field and GIS environment using a 10-m DEM. Landslides were noted to concentrate along narrow topographic hollows, as opposed to broad concave slopes in the landscape. The occurrence is dominant in slope zones where slope gradient, profile curvature, TWI, TPI, and SPI are 25°–35°, 0.1–5, 8–18, − 1–1, and > 10, respectively. It was established that profile curvature and slope gradient are the most and least significant topographic parameters in landslide occurrence (R2 = 0.802, p value = 0.088 and R2 = 0.5665, p value = 0.057), respectively. An understanding of these topographic underpinnings would serve to identify and predict potential landslide zones within the landscape and enhance landslide hazard mitigation.
      PubDate: 2019-09-20
  • Random forest and artificial neural networks in landslide susceptibility
           modeling: a case study of the Fão River Basin, Southern Brazil
    • Abstract: Empirical models based on machine learning methods have been used for landslide susceptibility mapping. The most accurate model is usually chosen to generate the final map. This paper demonstrates the importance of analyzing the spatial pattern of susceptibility maps, since models with similar performance can produce different output values. The relevance of terrain attributes and the sensitivity of models to input variables are also discussed. The applications of random forest (RF) and artificial neural network (ANN) models to the identification of landslide susceptible areas in the Fão River Basin, Southern Brazil, were evaluated and compared. The following have been included in the methodology: (1) the extraction of predictive attributes (e.g., slope, aspect, curvatures, valley depth) from a digital elevation model; (2) the organization of a landslide scar inventory; (3) the calibration and validation procedures of the models; (4) the analysis of model performance according to accuracy (area under the receiver operating characteristic curve) and parsimony (Akaike Information Criterion); (5) the reclassification of maps into susceptibility categories. All model configurations resulted in an accuracy above 0.9, demonstrating the ability of both techniques in landslide susceptibility mapping. The RF model stood out in this respect, recording the highest accuracy index among all tested configurations (0.949). The ANN model was more parsimonious, obtaining an accuracy of 0.925 with a much smaller number of internal connections. Thus, even with both having high and equivalent accuracy indexes, the models can establish different relationships between the input and the output susceptibility indexes, resulting in various possible landslide occurrence scenarios. These differences, together with the difficulty in defining which model presents more coherent results, reinforce the possibility of extracting spatial statistics, considering multiple configurations of models that combine accuracy and parsimony, in landslide susceptibility mapping.
      PubDate: 2019-09-19
  • Health vulnerability to flood-induced risks of households in flood-prone
           informal settlements in the Coastal City of Mombasa, Kenya
    • Abstract: Floods have serious consequences on community well-being and health. This study was intended to address the health vulnerability of households in flood prone informal settlements in the coastal city of Mombasa in Kenya and their adaptation measures. Mombasa City has a history of floods, in the recent past, significant severe incidences of flooding events have already been experienced. However, there is dearth of evidence regarding vulnerability of households living in informal settlements in the city to the health risks of flooding and households’ coping mechanisms. The study participants were randomly drawn from three purposively selected informal settlements in Mombasa City. Health vulnerability was assessed in terms of flood exposure, flood sensitivity, and flood adaptive capacity. While adaptation measures were explored based on the autonomous steps that household have adapted in response to flooding. Primary data were collected using questionnaires, Key Informant Interviews and Focus Group Discussions. The findings showed that up to 40.8% of the households had a high level of vulnerability, 46.9% had a medium level, while only 12.3% had low level of vulnerability. The findings also showed that household characteristics, water, sanitation and environmental risk factors had an impact on the level of household vulnerability. As coping mechanisms, households had taken some adaptation measures like clearing trenches to unblock drainage channels and piling sand bags around the house. The study concludes that for poor people living in flood prone areas in urban setting, flood early warnings, flood preventive actions and long term mitigation strategies need to be strengthened since they are exposed to greater health problems. The findings of the study are expected to help communities and local support agencies to identify weaknesses, especially in adaptive capacities, and to indicate ways of reducing future health vulnerability of residents of informal settlements to flooding.
      PubDate: 2019-09-19
  • Determination of extreme precipitation threshold and analysis of its
           effective patterns (case study: west of Iran)
    • Abstract: Flash floods caused by extreme rainfalls are one of the most significant natural hazards. In the present study, the precipitation data of 69 meteorological and climatological stations with temporal intervals (1961–2010) were obtained to determine the threshold of extreme precipitation as well as analyzing its significant patterns in the western regions of Iran. To determine the threshold of extreme precipitation, the theory of extreme value method was applied. In this method, precipitation of 22 mm and more than that covers 30% of the area had been identified and extracted as extreme precipitation. Therefore, 119 extreme precipitation events during the study period had been identified. Then, four patterns were analyzed using cluster analysis. After that, network data of geopotential height levels of 200, 300, 400 and 500 hPa for these days, from re-analyzed data series of NCEP/NCAR in the range of 10°–80°E and 0°–70°N and in 13,460 cells 2.5° × 2.5° were extracted by GrADS software. The results of the study showed that the most important humidity source for precipitation was the Mediterranean Sea, the Black Sea and the Red Sea, respectively. The upward vertical speed at different levels, located on the east and southeast cyclones of upper levels, which matches low pressure of the Earth’s surface, indicating unstable conditions in the region. Also, placing cutoff lows due to westerlies activities with warm and humid air advection at the surface and upper-level cold air were the main causes of severe atmospheric instability in the west of Iran.
      PubDate: 2019-09-17
  • Modeling the effect of urbanization on flood risk in Ayamama Watershed,
           Istanbul, Turkey, using the MIKE 21 FM model
    • Abstract: Urbanization is one of the most important factors that affect flood risk. Flood risk is, thus, expected to increase in the future with further urbanization in various parts of the world. In order to overcome flood risk, appropriate measures need to be taken based on a deep understanding of the risk levels under various urban extents as such studies are essential to formulate effective measures. In this study, the dynamic cellular automata-based urbanization model called SLEUTH was used to model urbanization in Ayamama Watershed, a watershed located in Istanbul, Turkey, under three landuse policy scenarios: current trend, east–west-oriented growth trend and growth trend under Project Canal Istanbul (PCIT). The outputs of the urbanization modeling, together with hydrographs of various return periods determined using the Peak-Over-Threshold value method and other required inputs, were used to investigate the effects of urbanization on flood risk using the hydrodynamic two-dimensional flexible mesh model, known as MIKE 21 FM. The results of the study showed that allowing unrestricted urbanization (dense development under PCIT scenario) in Ayamama Watershed will lead to considerable increase in the size of land inundated by flood when compared to the other scenarios. Thus, not allowing further development in the watershed is the best alternative. However, if the implementation of the PCIT scenario is inevitable, limiting the level of development in such a way that it does not result in considerable change in the flood risk is recommended. In addition, improving the drainage system in the watershed could further reduce the flooding risk.
      PubDate: 2019-09-17
  • Rockfall hazards assessment along the Aswan–Cairo highway, Sohag
           Governorate, Upper Egypt
    • Abstract: The geotechnical evaluation of rockfall hazards along the Aswan–Cairo highway, Sohag Governorate, Upper Egypt has been achieved throughout a variety of field investigations and lab tests on the slopes and rock cut faces of Lower Eocene Plateau limestone. The studied Aswan–Cairo highway is located in a mountainous area and surrounded on both sides by highly sheared limestone plateau possesses steep slopes and swelling clayey hosted bands. The clayey bands are characterized by highly swelling potentiality that considered as instability and weakness planes along which the limestone blocks will be downward fallen. Based on slope height and angle, the studied slopes are mostly considered instable. Kinematically, the Lower Eocene limestone blocks have planer and wedge failure modes. According to the estimated values of the coverage distance and rebound amplitude, ditches must be dug (2 m width and 1.5 m depth) to reduce the rebound amplitude height of falling blocks and catch these blocks to avoid them to reach the highway toe. Ditches can be filled with sands to will absorb the falling blocks kinetic energy as well as rockfall barrier must be constructed at the most hazards sites to retain the falling blocks away from the road toe.
      PubDate: 2019-09-16
  • Assessment of storm surge and structural damage on San Salvador Island,
           Bahamas, associated with Hurricane Joaquin (2015)
    • Abstract: In the afternoon of October 2, 2015, Hurricane Joaquin made landfall on San Salvador Island, the easternmost island in the Bahamian Archipelago. In this study, post-storm surveys that estimated storm surge height and assessed structural damage on the island were evaluated within the context of Joaquin’s meteorological characteristics. The findings from Hurricane Joaquin were then compared to impacts from other notable storms that affected San Salvador in recent decades, namely Hurricane Lili (1996), Hurricane Floyd (1999), and Hurricane Frances (2004). Hurricane Joaquin’s trajectory likely contributed to the extent and distribution of storm surge and damage on San Salvador. Near its peak strength, Joaquin approached the island from the south, which is a climatologically unusual track. Lili also approached San Salvador from the south and exhibited similar patterns of storm surge, overwash, and vegetation disturbance, particularly along the southern end of the island. During Joaquin’s passage, winds of at least tropical storm-force likely impacted San Salvador for 48 consecutive hours, which is twice the duration of such winds associated with the other three hurricanes. Storm surge recorded from Joaquin was more evenly distributed across the island, whereas surge recorded from Floyd and Frances was more concentrated on the western and eastern sides of the island, respectively. While the average surge height on the island was highest from Frances, the percentage of structures with heavy damage was much higher from Joaquin, which may be due to the extended duration of strong winds.
      PubDate: 2019-09-16
  • Assessment of land subsidence susceptibility in Semnan plain (Iran): a
           comparison of support vector machine and weights of evidence data mining
    • Abstract: Land subsidence is a geo-hazard that leads to slow or rapid decrease in ground level. This can result in geological, environmental, hydrogeological, and economic impacts. Land subsidence has already occurred in more than 300 plains in Iran. Semnan plain is one of the most important areas undergoing this phenomenon. In general, miscellaneous methods have been employed around the world to assess land subsidence susceptibility. In this study, support vector machine and weights of evidence Bayesian theory were applied to assess land subsidence susceptibility. In the first step, the required information on the history of subsidence in the study area was provided. Locations of the land subsidence were specified by Landsat 8 satellite images and field surveys. Twelve conditioning factors from different basic layers including topography, geology, land use, and groundwater table were considered for modeling. Spatial correlation between land subsidence locations and effective factors was calculated using weights of evidence Bayesian theory. Land subsidence susceptibility maps were created using support vector machine and weights of evidence models. ROC curve, sensitivity, specificity, Cohen’s Kappa index, and fourfold cross-validation were employed to validate the obtained land subsidence susceptibility maps. In Semnan plain, AUC for the support vector machine and weights of evidence models was 0.748 and 0.726, respectively, demonstrating that the given models hold an acceptable accuracy for land subsidence susceptibility mapping; however, the accuracy of the support vector machine is higher than that of weights of evidence model. Results of this research can help policy makers as well as environmental and urban planners.
      PubDate: 2019-09-14
  • An examination of traffic volume during snow events in northeast Ohio
    • Abstract: Snowfall presents a hazard to drivers by reducing visibility and increasing safe stopping distances. Some drivers cancel trips if snowfall is occurring or forecast, and traffic volumes often decrease on snowy days. Lake-effect snow is very localized and is thus hypothesized to have a lesser influence on traffic volume than synoptic-scale snow, which usually covers a broader areal extent. We analyze traffic volume in northeast Ohio during 25 snow events and use a matched-pair analysis to determine whether volumes differ between lake-effect and synoptic-scale snowfall in these regions. We also examine the rate at which traffic volume decreases during snow events by time of day and day of week. Results indicate that there is little difference in mean traffic volume decreases when comparing lake-effect and synoptic-scale snow. Hourly trends suggest that traffic volume is most sensitive to snowfall during the midday on weekdays and late afternoon on weekends and least sensitive to snowfall during the overnight hours. Findings presented herein can assist in transportation planning, risk analysis and roadway safety.
      PubDate: 2019-09-10
  • Numerical simulation of extreme dust storms in east of Iran by the
           WRF-Chem model
    • Abstract: Iran, located in the desert belt, is characterized by frequently increasing sand and dust storms, especially in the eastern and southern areas and creating adverse environmental effects. Efficient management of these devastating events requires an understanding of their features. One way to understand the dust phenomenon is to simulate and predict. The general purpose of the article is to simulate severe storms in the southeast of the country (120-day-old winds) due to the weather conditions of the region and the display of their source and range inside Iran. Aim of this study: Weather Research and Forecasting-Chemistry coupled model (WRF-Chem.3.6.1) is used to simulate, forecast, and design an alert system for sand and dust storm events (east of Iran). Dust concentration data were collected by Environmental Protection Organization, wind speed and direction data were gathered from the Meteorological Organization, MODIS images, and HYSPLIT model forecast was also used to investigate the path of storms and more accurately forecast and time alerting. Results showed that the main dust emission source in Sistan is the dry bed of the Hamoon wetland. Also during the storms that investigated in this study, transport of dust clouds were observed in the southern part of Iran up to Oman sea because of converging currents (north–south winds in the eastern part of Iran, especially in spring and summer) that create strong winds in lower levels of the atmosphere. The WRF-Chem model had reasonable estimations related to spatial and temporal scales in the study area. Using the global forecasted data as model input data, it was expected to observe bias in concentration estimation versus reality. The model was run for 10 and 30 km spatial resolutions, and results revealed storm formation in Sistan was affected by local geographical properties especially topography features. Based on the results obtained and the experience gained, it can be concluded that most dust storms in the Sistan region began in the late spring and will continue until early autumn season.
      PubDate: 2019-09-10
  • Application of nonlinear models and groundwater index to predict
           desertification case study: Sharifabad watershed
    • Abstract: The level of groundwater can also be used in monitoring desertification and land degradation. In this study, three models, namely: partial least square regression, artificial neural networks (ANN), and adaptive neuro-fuzzy inference system, were used to monitor and predict the level of groundwater and the land degradation index via the Iranian Model of Desertification Potential Assessment method. The groundwater data of 24 Piezometric wells from 2002 to 2016 were also collated to predict the groundwater level. In all models, 70% of the data were applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, topographic wetness index, distance of the river (m), latitude and longitude of Piezometers in the Universal Transverse Mercator coordinate system were the inputs, and the level of groundwater was the output of each method. The prediction performance of both training and testing sets is evaluated by R2 and MSE. Looking at statistical inferences, we found that ANN has the highest efficiency (R2 = 0.96, MSE = 0.71 m) which agree with other findings. We combined the results of ANN with ordinary kriging (OK) and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Again between 2002 and 2016, the land area with low degradation risk decreased from 38,030 ha (39% of the study area) to zero ha in 2016. In 2016, there was no moderate land degradation risk. Using ANN, we predicted that around 99% of the area (95,206 ha) was severely degraded in 2017 and according to groundwater level index, the land degradation increased by 100%. This implies that the area deserves urgent care and reclamation. We also used latitude and longitude of Piezometers as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in OK and decreased the total error of two combined models.
      PubDate: 2019-09-09
  • Living with chronic contamination: a comparative analysis of divergent
           psychosocial impacts
    • Abstract: Scholarship on contaminated communities has highlighted how residents living with the reality of significant environmental hazards often experience similar negative psychosocial stressors. However, relatively less is known about the mitigating factors that can explain divergence in these impacts such as levels of community efficacy and empowerment. This is critical as insight into these dynamics can provide answers as to why certain communities maintain a sense of efficacy whereas others do not. To address this question, we conduct a comparative analysis of two heavily contaminated communities in Oklahoma and Colorado. Our data come from extensive fieldwork, including in-depth interviews (n = 105) and participant observation. Our findings revealed a set of similar psychosocial outcomes in the two communities, but we argue that specific revitalizing events in one community played a crucial role in sustaining residents’ feelings of empowerment and persistence. Our paper concludes with a discussion of the implications of our research for future work on contaminated communities, technological disasters, and citizen participation.
      PubDate: 2019-09-06
  • Seismically induced snow avalanches at Nubra–Shyok region of Western
           Himalaya, India
    • Abstract: Snow avalanche can be triggered by different mechanisms including metrological conditions, snow pack stability together with external factor such as seismic tremor and explosions. The snow avalanche triggered by seismic event is very important hazard phenomena in the snow covered region. In the present paper, investigation of earthquake-induced snow avalanches is introduced in Nubra–Shyok region of Western Himalaya, India. Compilation of seismogenic snow avalanche and earthquakes occurred in the Nubra–Shyok region during the period of 2010–2012 is made, which reveals that out of 393 natural avalanches, 81 avalanches was triggered due to the earthquake during this period. The local earthquakes occurred in Nubra–Shyok region, recorded by a local seismic network, are utilized for this work. The same date of occurrence of earthquakes and snow avalanches confirm seismogenic snow avalanche in this region. In the present work, avalanches triggered due to natural seismicity during the period of 2010–2012 related with earthquakes of magnitude 1.7 ≤ Mw ≤ 4.4 and distance of induced snow avalanche from epicenter of earthquakes, i.e., 4–92 km. In this study, lower bound limits of earthquake magnitudes, which cause avalanches, are established up to the distance of 92 km. Relation between earthquake magnitude and distance of induced snow avalanche from epicenter reveals that an earthquake of magnitude 1.4 (Mw) can trigger a snow avalanche as distance approaches to zero from earthquake epicenter. The comparison of obtained relation with other similar types of studies, i.e., Keefer (Geol Soc Am Bull 95:406–421, 1984), Podolskiy et al. (J Glaciol 56(197):431–446, 2010a) confirms the reliability of the present work.
      PubDate: 2019-09-06
  • Sedimentation mapping in shallow shoreline of arid environments using
           active remote sensing data
    • Abstract: The applications of remote sensing in monitoring land cover features are an essential tool of natural resources management schemes. The sedimentation mapping of shallow shorelines is insufficient using passive remote sensing images because of the image corrections and weather implications that need to be considered, while active remote sensing data can overcome the difficulties of the weather interference and reach to more reliable results. The current research work took place in the shoreline on Umluj city, west of Saudi Arabia, representing one of the most sensitive wetland habitats within the country. Sentinel-1 images were downloaded and analyzed to delineate the sedimentation process from the European Space Agency. The archive image was acquired in August 2018, while the crisis emerged was acquired in March 2019 after an unusual rainfall event that took place in the vicinity of the study area. Remote sensing techniques of sedimentation mapping end change detection were implemented in the study area to estimate the sedimentation process and its influences on the wetlands. The wetland habitats were decreased by nearly 87% throughout the period of flash floods between November 2018 and March 2019. Meanwhile, sediment deposits along the shoreline of the study area increased by nearly 171%. Therefore, monitoring of the shorelines sedimentation and the wetland habitats using temporal remote sensing data are decision-making priorities to conserve the natural resources within similar arid environments.
      PubDate: 2019-09-06
  • Simulation of snowmelt runoff and sensitivity analysis in the Nyang River
           Basin, southeastern Qinghai-Tibetan Plateau, China
    • Abstract: The water-runoff in the plateau mountainous areas is mainly contributed by precipitation, snowmelt and glacial meltwater; the different runoff components result from different mechanism of runoff generation. Plateau mountainous areas have not only a unique hydrological cycle mechanism but also are sensitive to climate change. Glacier and snow meltwater in the plateau mountainous areas have a large proportion in runoff and are a main water resources for industrial, agricultural and domestic water use in the basin. Two commonly used model, HBV and SRM, were selected for the quantitative analysis of snowmelt runoff contribution and the hydrological response to climate change scenarios in the Nyang River Basin in the southeastern part of the Qinghai-Tibet Plateau. Based on the characteristics of the models, the HBV model was used to analyze the runoff composition, while the SRM model was used to analyze the runoff in climate change scenarios. The results showed that both models have a good performance in modeling the hydrological processes in the basin. The snow melts mainly concentrate in May, in the average annual precipitation, rainfall and snowfall accounted for 85% and 15%, respectively. From the results of sensitivity analysis, the increase in temperature would accelerate the melting of snow in April and May and turns the snowfall into rainfall in October. However, the change in precipitation mainly affects the runoff in July, August and September, when precipitation is dominated by rain. The results indicate that the timing of the effects of temperature and precipitation on the runoff process is different.
      PubDate: 2019-09-05
  • A review of flexible protection in rockfall protection
    • Abstract: Natural hazards, such as high winds, heavy rains and ice melting, can easily trigger the rockfall which usually leads to great personal injuries and property loss; therefore, the rockfall protection is of great significance and necessity. Among the types of protection, the flexible protection occupies a beneficial condition of application. This paper indicates the basics of flexible protection which includes its classification and advantages, subsequently, analyzing the mechanism of both active protection and passive protection, and then systematically summarizing the research accomplishments, and puts forward the research direction of the flexible protection, including: (1) apart from the traditional rigid protection, the flexible protection has a wide range of advantages, which makes the flexible protection a new and effective protective structure in the rockfall protection; (2) the current researches reveal that though the scholars have done a variety of achievements of flexible protection, there is still a lack of precise simulation of the whole model and local test of the component; (3) putting forward the prospective research direction minutely in both active protection and passive protection.
      PubDate: 2019-09-05
  • Urbanization and CO 2 emissions in resource-exhausted cities: evidence
           from Xuzhou city, China
    • Abstract: In this paper, we discussed the different impacts of urbanization, technical factors and resource utilization on CO2 emissions. Specifically, we investigated the urbanization process of a typical resource-based city, Xuzhou, in China to learn more about how the urbanization development of resource-exhausted cities can affect the urban economy and environment. We examined the urbanization speed and quality and then employed the STIRPAT model to analyze the actual relationship between urbanization and CO2 emissions. The results indicate that there are inverted U-shaped relationships between CO2 emissions and economic growth, the urbanization rate (UR) and urbanization quality (UQ). This proves the existence of an Environmental Kuznets Curve in the case of a resource-exhausted city. In addition, a decoupling development has gradually occurred between urbanization and CO2 emissions, and the UR has a greater influence on CO2 emissions relative to the UQ. Besides, the positive effect of industrial development on CO2 emissions gradually weakened from 2014 and may even be offset by the suppression effect of energy intensity in the future. Finally, the negative effect of the utilization rate of coal capacity indicates that optimizing energy utilization by cutting excess capacity is not only an effective way for urbanization transformation but also for improving the urban environment. These results have important implications for governmental policy decisions pertaining to the sustainable development of resource-exhausted cities.
      PubDate: 2019-09-05
  • Extreme rainfall and vulnerability assessment: case study of Uttarakhand
    • Abstract: The torrential rains in June 2013 combined with melting of snow caused voluminous floods in the rivers of Uttarakhand and subsequently triggered widespread mud, landslides and debris deposition. The event caused instability of the channel by shifting the banks. Erosion rendered many locations along the banks vulnerable to economic and human loss. The shifts in reaches are calculated by digitizing the bank line using satellite imageries of year 2005, 2010 and 2015. The extent and magnitude of risks have been assessed based on information of past events, rapid field assessments, current mitigation measures and interactions with the locals. The findings from these interactions, and secondary data based on geospatial analysis of bank line changes have been used in the identification of vulnerable reaches along the major rivers. Criteria to identify the vulnerable reaches are based on risk, exposure and hazards in that area. The magnitude of risks due to flood hazards on various exposures along the riverbank is calculated based on qualitatively derived scores. River basins focusing on rainfall, topography, drainage pattern, soil, landslide and exiting infrastructure in relation to vulnerability of the region using GIS data are discussed in details. A fuller understanding will enable decision makers towards more efficient resources management for prevention and protection of river banks due to flood events. In addition to this, an official online decision support system ( with collaborating partners and organizations for relevant data, information and document has been created.
      PubDate: 2019-09-04
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