Hazard Mitigation Strategies Research Articles

Towards ‘strategy as performance’ in hazard mitigation: reflections on Performing City Resilience in New Orleans

ABSTRACT Performing City Resilience is a collaborative research project that investigates interrelations between theories, practices and strategies of city resilience, and those of performance. In this essay, the authors explore ways performance might conceive of and contribute to practices of hazard mitigation strategy to better understand how these might lead to a resilient city. They focus on their research in New Orleans, working with the Office of Homeland Security and Emergency Preparedness during its development of the 2020 Hazard Mitigation Plan. They discuss their interventions, initial impact, and consider performance of strategy as a critical form of ‘strategy as practice’.

A collaborative serious game for water resources planning and hazard mitigation

Hydrological hazards lead to a broad range of socio-economic and environmental risks. The development of a resilient community and risk reduction rely on the adoption of holistic watershed master planning whereby the adaptation options consider the risk associated with individual or multiple hydrological hazards occurring simultaneously at a specific location. Such planning approaches pose multiple challenges for decision-makers including the access and manipulation of high-volume high-variety data. Moreover, modern planning approaches involve the watershed community in decision-making, hence, these approaches have to include elements of social learning and relationship building. This study introduces a serious gaming framework to assist stakeholders in the decision-making process for water resources planning and hazard mitigation. A Multi-Hazard Tournament (MHT) is described that allows the members of a watershed community to evaluate various adaptation options to develop mitigation strategies for multiple water-related hazards (e.g. flood, drought, and water pollution). The tournament offers a competitive and gamified setting for collaborative hazard risk assessment to minimize damages to water quality, riparian habitat, water resources availability, vulnerable populations, and infrastructure. A web-based decision support tool is developed to provide interactive interfaces and engaging visualizations and serve as a one-stop platform to investigate the challenges presented for each damage center within its geographic context and to analyze the cost/benefit relationship for potential hazard mitigation strategies with dynamic analytics tools. Finally, a case study is presented to evaluate the effectiveness of MHT in increasing collective awareness and conceptual and practical understanding of water-related hazards and mitigation strategies.

Flash flood risk assessment using geospatial technology in Shewa Robit town, Ethiopia

Flash flood is an extreme flooding event which is quick, short-lived, hazardous phenomena having negative environmental and socio-economic impacts. Flood risk management is essential to reduce the effects of flood on human lives and livelihoods. The main goals of this research were flash flood risk analysis and risk quantification in terms of land use land cover using geo-spatial technology in Shewa Robit town. In the present study, elevation, slope, drainage density, distance from river, NDVI, land use land cover, topographic wetness index and curvature were used as parameters determining flash flood risk. To realize these research objectives, all these data were reclassified in to five classes and different weights for each of them was assigned using analytic hierarchy process. Weighted Sum Overlay (WSO) analyses of ARC GIS software was employed to produce flood vulnerability map. Then, the accuracy of generated flood risk map was validated using historical flood data. According to the result, about 3.08%, 14.62% and 20.66% of Shewa Robit town are at a very high, high and moderate flash flood risk, respectively. In addition to this, flood risk was also quantified in terms of land use land cover. The result indicates that the settlement land use is the most vulnerable with an estimated area of 12.01 ha, 99.57 ha and 174.16 ha in very high, high and moderate risk classes. The outcome of the study will be applicable in flood hazard management and mitigation strategies.

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model

Landslides are one of the most extensive geological disasters in the world. The objective of this study was to assess the performances of different landslide susceptibility models information content method (ICM), analytical hierarchy process (AHP), and random forest (RF) model) and mapping unit (slope unit and grid unit) for landslide susceptibility mapping in the Helong city, Jilin province, northeastern China. First, a total of 159 landslides were mapped in the study area based on a geological hazard survey (1:50,000) of Helong city. Then, the slope units of the study area were divided by using the curvature watershed method. Next, eight influencing factors, namely, lithology, slope angle, slope aspect, rainfall, land use, seismic intensity, distance to river, and distance to fault, were selected to map the landslide susceptibility based on geological data, field survey, and landslide information. Afterward, landslide susceptibility modeling of landslide inventory data is performed for extracting and learning the symmetry latent in data patterns and relationships by three landslide susceptibility models and utilizing it to predict landslide susceptibility. Finally, the receiver operating characteristic (ROC) curve was used to compare the landslide susceptibility models. In addition, results based on grid units were calculated for comparison. The AUC (the area under the curve) result for ICM, AHP, and RF model was 87.1%, 80.5%, and 94.6% for slope units, and 83.4%, 70.9%, and 91.3% for grid units, respectively. Based on the overall assessments, the SU-RF model was the most suitable model for landslide susceptibility mapping. Consequently, these methods can be very useful for landslide hazard mitigation strategies.

Sensitivity of Flood Hazard and Damage to Modelling Approaches

Combination of uncertainties in water level and wave height predictions for extreme storms can result in unacceptable levels of error, rendering flood hazard assessment frameworks less useful. A 2D inundation model, LISFLOOD-FP, was used to quantify sensitivity of flooding to uncertainty in coastal hazard conditions and method used to force the coastal boundary of the model. It is shown that flood inundation is more sensitive to small changes in coastal hazard conditions due to the setup of the regional model, than the approach used to apply these conditions as boundary forcing. Once the threshold for flooding is exceeded, a few centimetres increase in combined water level and wave height increases both the inundation and consequent damage costs. Improved quantification of uncertainty in inundation assessments can aid long-term coastal flood hazard mitigation and adaptation strategies, to increase confidence in knowledge of how coastlines will respond to future changes in sea-level.

Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

Kurseong municipality and its surrounding hill slope are famous high altitudinal residential areas in the Darjeeling Himalayan region of India. In Darjeeling Himalayas, huge landslides occur in the rainy season every year. This paper was aimed to delineate landslide susceptibility zone (LSZ) in Kurseong and its surrounding hill slope in Darjeeling Himalayas. Nine landslide inducing parameters (i.e., slope, altitude, rainfall, geological structure, distance from river channels, distance from lineament, soil type or characteristics, land-use/land-cover and aspect) were used for mapping LSZ applying analytic hierarchy process (AHP), frequency ratio (FR), binary logistic regression (BLR) models and their ensemble (AHP–FR; AHP–BLR; and FR–BLR) with the aid of ArcGIS, SPSS and ERDAS software. The landslide susceptible maps produced in GIS environment by the AHP, FR, BLR and their ensemble models were classified into five susceptibility classes such as very low, low, moderate, high and very high. The area under curve (AUC) of receiver operating characteristics (ROC) and kappa statistics were used to analyze the accuracy of the LSZ maps. The AUC values of prepared maps were 78.86% (AHP), 80.22% (FR), 80.67% (BLR), 83.44% (AHP–FR), 84.39% (AHP–BLR) and 84.73% (FR–BLR). Overall kappa statistics were 0.789 (AHP), 0.812 (FR), 0.799 (BLR), 0.837 (AHP–FR), 0856 (AHP–BLR) and 0.868 (FR–BLR), indicating the good accuracy of the models. The mapping of LSZ will be useful for land-use planning and future landslide hazard mitigation strategies.

Storytelling-Plots of resilience, learning, and discovery in emergency management.

Almost everyone can relate to the experience of telling a story. This article explores how storytelling is being used to identify risks and create hazard mitigation strategies, as well as how it can promote learning within the field of emergency management. Storytelling is both a pedagogical tool and an invaluable resource for practicing emergency managers. This article illustrates the ways in which the process of telling a story enables participates to talk about stressful concerns, internalize complex concepts, and even have fun. The article explores how storytelling drove the public process leading to the adoption of hazard mitigation plans, and how eight types of stories, as defined by the American humorist Kurt Vonnegut, can strengthen emergency management education. This article also explores how research suggests that storytelling can provide an effective way for both the tellers of story and their listeners to find meaning in events, provide context to what is being taught, transmit emotion along with information, develop a professional identity, build empathy and compassion, and help with remembering events and lessons learned. The authors have a long history of utilizing storytelling and present this article in order to share and explore storytelling as applied to the discipline of emergency management.

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset. We calculated the receiver operating characteristic (ROC) curve and used the area under the curve (AUC) for the quantitative evaluation of the landslide susceptibility maps using the testing dataset. Better performance in both the training and testing phases was provided by the RNN algorithm (AUC ​= ​0.88) than by the CNN algorithm (AUC ​= ​0.85). Finally, we calculated areas of susceptibility for each province and found that 6% and 14% of the land area of Iran is very highly and highly susceptible to future landslide events, respectively, with the highest susceptibility in Chaharmahal and Bakhtiari Province (33.8%). About 31% of cities of Iran are located in areas with high and very high landslide susceptibility. The results of the present study will be useful for the development of landslide hazard mitigation strategies.

Integrated Field Surveying and Land Surface Quantitative Analysis to Assess Landslide Proneness in the Conero Promontory Rocky Coast (Italy)

Rock slopes involved in extensive landslide processes are often characterized by complex morphodynamics acting at different scales of space and time, responsible for different evolutionary scenarios. Mass Rock Creep (MRC) is a critical process for long-term geomorphological evolution of slopes and can likewise characterize actively retreating coastal cliffs where, in addition, landslides of different typologies and size superimpose in space and time to marine processes. The rocky coast at the Conero promontory (central Adriatic Sea, Italy) offers a rare opportunity for better understanding the predisposing role of the morphostructural setting on coastal slope instability on a long-time scale. In fact, the area presents several landslides of different typologies and size and state of activity, together with a wide set of landforms and structural features effective for better comprehending the evolution mechanisms of slope instability processes. Different investigation methods were implemented; in particular, traditional geomorphological and structural field surveys were combined with land surface quantitative analysis based on a Digital Elevation Model (DEM) with ground-resolution of 2 m. The results obtained demonstrate that MRC involves the entire coastal slope, which can be zoned in two distinct sectors as a function of a different morphostructural setting responsible for highly differentiated landslide processes. Therefore, at the long-time scale, two different morphodynamic styles can be depicted along the coastal slopes that correspond to specific evolutionary scenarios. The first scenario is characterized by MRC-driven, time-dependent slope processes involving the entire slope, whereas the second one includes force-driven slope processes acting at smaller space–time scales. The Conero promontory case study highlights that the relationships between slope shape and structural setting of the deforming areas are crucial for reaching critical volumes to induce generalized slope collapse as the final stage of the MRC process. The results from this study stress the importance of understanding the role of morphostructures as predisposing conditions for generalized slope failures along rocky coasts involved in MRC. The findings discussed here suggest the importance of the assessment of the slope instability at the long time scale for a better comprehension of the present-day slope dynamics and its major implications for landslide monitoring strategies and the hazard mitigation strategies.

Examining relationships between traditional vulnerability data proxies and hurricane risk perception indicators

Development, social processes, cultures, beliefs, risk perceptions, and attitudes contribute to spatial variations in vulnerability. Risk perceptions directly and indirectly affect how individuals react to and cope with risks, which can affect both pre-disaster vulnerability and post-disaster recovery. Risk perceptions influence how people develop and implement hazard mitigation and adaptation strategies to address risks before a disaster occurs. Understanding how individuals develop risk perceptions and act on them in response to hazards can provide information about existing public risk perception that is essential for the development and implementation of successful mitigation strategies. This paper presents a methodology for identifying and measuring the influence and interdependence of risk perception indicators on vulnerability at the local scale, using Sarasota County, FL as a case study. We use contingency tables to identify interdependence between categorical demographic and risk perception variables and determine ways in which significant pairings support existing literature on risk-reduction behaviours in vulnerable populations, or ways in which this information may be specific to Sarasota County, FL. Results suggest that interdependence exists between risk perceptions and demographics, but the degree of interdependence varies across specific variables, including presence of dependent populations (e.g., children under the age of 5) and duration of residency. Decision makers can use this information to identify non-traditional vulnerability indicators that influence preparation and evacuation behaviour and target customized risk communication to those populations. This information can also help decision makers identify and address attitudes or social processes that cause unequal access to information or risk reduction/mitigation resources in individuals.

Systems Theory Principles and Complex Systems Engineering Concepts for Protection and Resilience in Critical Infrastructure: Lessons from the Nuclear Sector

ABSTRACTPart of the Presidential Policy Directive 21 (PPD‐21) (PPD 2013) mandate includes evaluating safety, security, and safeguards (or nonproliferation) mechanisms traditionally implemented within the nuclear reactors, materials, and waste sector of critical infrastructure—including a complex, dynamic set of risks and threats within an all‐hazards approach. In response, research out of Sandia National Laboratories (Sandia) explores the ability of systems theory principles (hierarchy and emergence) and complex systems engineering concepts (multidomain interdependence) to better understand and address these risks and threats. This Sandia research explores the safety, safeguards, and security risks of three different nuclear sector‐related activities—spent nuclear fuel transportation, small modular reactors, and portable nuclear power reactors—to investigate the complex and dynamic risk related to the PPD‐21‐mandated all‐hazards approach. This research showed that a systems‐theoretic approach can better identify inter‐dependencies, conflicts, gaps, and leverage points across traditional safety, security, and safeguards hazard mitigation strategies in the nuclear reactors, materials, and waste sector. As a result, mitigation strategies from applying systems theoretic principles and complex systems engineering concepts can be (1) designed to better capture interdependencies, (2) implemented to better align with real‐world operational uncertainties, and (3) evaluated as a systems‐level whole to better identify, characterize, and manage PPD‐21's all hazards strategies.

Major and trace (including arsenic) groundwater chemistry in central and southern Myanmar

Myanmar, in Southeast Asia, is categorized as a Least Developed Country, facing substantial challenges towards meeting 2030 Sustainable Development Goal targets, including ensuring access to safe water and sanitation. The occurrence of geogenic contaminants, notably arsenic, in Myanmar groundwaters is relatively poorly understood, particularly as compared to other countries in South/Southeast Asia. Improving the understanding of groundwater quality in Myanmar is hence a major concern and relevant to the health of millions of local inhabitants who extensively use groundwater. A groundwater survey was undertaken at 85 sites across five distinctly contrasting zones within three of Myanmar's main river basins (Chindwin-Ayeyarwady, Sittaung, and Salween) to assess the occurrence of arsenic and other potentially harmful elements, and to study the dominant geochemical controls in each zone/basin. A significant number of samples were affected by water quality issues, based on World Health Organization (WHO) guidelines, including elevated arsenic (>10 μg.L−1) in 14% of all samples within the basins studied. Fluoride, nitrate, salinity, iron, manganese, and aluminum also exceeded WHO drinking water guidelines in some locations. Arsenic typically occurred as inorganic AsIII and largely arose from the reductive dissolution of Fe(Mn)-(hydro)xides and was broadly associated with relatively high HCO3 and NH4. Variable dominant geochemical controls on groundwater composition were identified, notably (i) calcite and dolomite dissolution in the Salween basin; (ii) cation exchange, carbonate dissolution and saline intrusion in the Ayeyarwady delta; and (iii) gypsum dissolution, silicate weathering, and cation exchange in Chindwin-Middle Ayeyarwady. This reconnaissance study provides new information on groundwater composition and corresponding geochemical controls across contrasting, and previously underrepresented, areas of Myanmar and may help to inform further hazard assessment, monitoring and/or mitigation strategies.

Transferability of object-based image analysis approaches for landslide detection in the Himalaya Mountains of northern Pakistan

ABSTRACT A landslide inventory is indispensable for determination of landslide susceptibility, hazard, risk assessment and disaster mitigation strategies. These inventories were traditionally developed using manual digitization of remote sensing images and aerial photographs, and pixel-based image classification. Recently, Object-Based Image Analysis (OBIA) supersedes visual interpretation and pixel-based methods. OBIA utilizes spectral, textural, contextual, morphological and topographical information in remote sensing images. However, OBIA-based landslide detection methods are often designed for specific areas and remote sensing dataset. The aim of this study is to evaluate the transferability of three published OBIA landslide detection methods for semi-automated landslide detection in the Himalaya mountainous region of northern Pakistan. A SPOT-6 multi-spectral image with Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) Digital Elevation Model (DEM) derivatives, i.e. slope, aspect, hill-shade, relief, elevation and stream network are used for landslide detection using eCognition developer software. The three published methods scale parameters for image segmentation and parameter thresholds are evaluated first. It is observed that the aforementioned methods are not directly applicable to our study area and remote sensing datasets. Therefore, an alternate (proposed) method is developed for semi-automated landslide detection. Accuracy assessment of the selected methods and proposed method is assessed by Precision, Recall and F1 measures. Using the proposed method, a total of 357 landslides are detected with 91.46% Precision, 93.31% Recall and 92.38% F1 measure accuracy.

A Historical Geospatial Analysis of Severe Weather Events in Oklahoma State Parks: A Park Management Perspective

The State of Oklahoma, located in the heart of Tornado Alley, frequently experiences severe weather events such as tornadoes, hail, and damaging wind. Weather events such as these inflict injury and, in some cases, death on park visitors as well as damage park infrastructure, amenities, and other natural resources. Park and recreation professionals, especially those in severe weather-prone areas, must prepare for and respond to such events with the appropriate emergency operations and hazard mitigation procedures (National Park Service, 2006; Whitworth & May, 2006). Following the tourism disaster and crisis management frameworks (Faulkner, 2001; Ritchie, 2008), this study focuses on risk analysis and hazard mitigation strategies and implementations applicable to parks and recreation areas. Additionally, this study seeks to remedy the lack of longitudinal historical research by employing geographic information system (GIS)-based approaches to investigate the spatial interaction between severe weather events and state parks. Using geospatial data of tornado, hail, wind, and lightning events from 1950 to 2015, provided by NOAA’s National Weather Service Storm Prediction Center, we quantitatively evaluate the potential risk of severe weather on Oklahoma State Parks. GIS-based kernel density estimation was utilized to create interpolated raster surfaces representing accumulated risk (1950-2015) for each type of severe weather event (similar to Dixon et al., 2011). Results show that tornadoes had the most widespread impact on parks in central to north-central regions of Oklahoma. Conversely, the risk posed by hail is more prevalent in parks located in western Oklahoma while parks in northeastern Oklahoma are most affected by severe winds. Parks located in the southeastern corner of Oklahoma have higher chance of lightning strikes. By combining all types of severe weather as a composite dataset, a ‘severe weather risk’ score was assigned to each state park. Natural hazard mitigation strategies and recommendations include allocate operation and maintenance appropriation to severe weather-prone state parks, strengthen emergency response procedure and training programs, and raise natural hazard awareness through communication and education programs. Although this study emphasizes a state-level administration, these methods are transferable to other scales (i.e., national, local) depending on data availability. Conceptual contributions to the tourism disaster and crisis management framework are also discussed.

Evaluating failure mechanisms of dip slope using a multiscale investigation and discrete element modelling

This study proposed an in-depth multiscale analysis procedure to identify the dip slope area and assess dip slope stability and influencing area. The proposed analysis procedure comprised five stages: (1) identifying the potential area of a dip slope, (2) analyzing the possible failure types of the dip slope, (3) evaluating slope activity, (4) analyzing dip slope stability under the influence of possible triggering factors, and (5) assessment of the deformation and deposition area. To validate the proposed procedure, the Chaochouhu region of Taiwan was analyzed as a dip slope case. In stages (1) to (3), the scale of the utilized map and the precision of associated data were evaluated to identify the potential area of a dip slope, the possible failure types, and the activity of each slope unit. In stages (4) and (5), the processes of deformation and sliding in a high-activity slope unit were simulated using the discrete element method. The numerical model was first verified using in-situ monitoring data obtained from inclinometers. Subsequently, the model was used to predict the slope behavior under conditions of wetting deterioration of geomaterial. The multiscale analysis revealed comprehensive information regarding the dip slope. The application of the proposed approach would be useful to civil engineers for evaluating dip slope stability as well as to relevant authorities for planning hazard mitigation strategies.

Failure mechanisms and deformation processes of a high-locality landslide at Tonghua Town, Li County, China, 2017

High-locality landslides primarily occur in the upper zone of the failed slope, in which the slip surface is considerably higher than the foot of the slope. This paper discusses a high-locality landslide that occurred on August 8, 2017, on the left bank of Zagunao River in Tonghua Town, southwest China. The landslide, called the Tonghua landslide, originated on a 716-m-high hillslope, which was formed by preexisting glacial deposits. The main body of the landslide is currently suspended on hillslope, with a relative relief of 353 m and is undergoing progressive failure. The town of Tonghua is located downslope of this landslide, and hence, the landslide could cause severe damage, including casualties and the destruction of infrastructure. Very few mitigation measures have been implemented to reduce the potentially devastating consequences due to the high locality and continuous movement of the landslide. We conducted comprehensive studies including field investigations, aerial photographic interpretations, pluviometric regime analysis, and superficial displacement monitoring to understand the failure mechanism and evolutionary process of this ongoing landslide. Considering that a Ms 7.0 earthquake occurred in Jiuzhaigou, approximately 220 km northeast of Tonghua Town, on the same day as the landslide, and that sporadic but persistent rainfall occurred before slope failure, we believe that both the rainfall and the earthquake were potential triggers for this landslide. However, further studies are necessary to better understand the characteristics and to develop hazard mitigation strategies for this type of landslide.

Mechanisms of Ash Generation at Basaltic Volcanoes: The Case of Mount Etna, Italy

Basaltic volcanism is the most widespread volcanic activity on Earth and planetary bodies. On Earth, eruptions can impact global and regional climate, and threaten populations living in their shadow, through a combination of ash, gas and lava. Ash emissions are a very typical manifestation of basaltic activity; however, despite their frequency of occurrence, a systematic investigation of basaltic ash sources is currently incomplete. Here, we revise four cases of ash emissions at Mount Etna linked with the most common style of eruptive activity at this volcano: lava fountains (4-5 September 2007), continuous Strombolian activity transitioning to pulsing lava fountaining (24 November 2006), isolated Strombolian explosions (8 April 2010), and continuous to pulsing ash explosions (last phase of 2001 eruption). By combining observations on the eruptive style, deposit features and ash characteristics, we propose three mechanisms of ash generation based on variations in the magma mass flow rate. We then present an analysis of magma residence time within the conduit for both cylindrical and dike geometry, and find that the proportion of tachylite magma residing in the conduit is very small compared to sideromelane, in agreement with observations of ash componentry for lava fountain episodes at Mount Etna. The results of this study are relevant to classify ash emission sources and improve hazard mitigation strategies at basaltic volcanoes where the explosive activity is similar to Mount Etna.

Probabilistic performance assessment of power distribution infrastructure under wind events

The electric power delivery infrastructure directly contributes to essential societal functions, the economy, and the general quality of life. Recent extreme wind events, such as Hurricanes Irma and Maria, and more recently Hurricane Michael resulted in power outages that affected millions of customers and led to major social and economic disruptions throughout communities. The longer the outage duration, the greater the incurred losses. Power distribution systems have proven to be highly vulnerable to such events and responsible for 90% of the outages. Distribution structures are built according to safety standards to ensure the safety of assets in operational and extreme conditions, but the dynamic nature of the wind loading is often overlooked or only considered empirically. Therefore, in this paper, such effects are included in a comprehensive risk-informed framework to assess the performance of electric power distribution components. This methodology explicitly accounts for the inevitable uncertainty in predictions of the component response. The focus is on characterizing wind events and the component-level risk analysis of physical components of the electric power system. For this purpose, a power pole–conductor system is modeled in which wind events are simulated as one-dimensional multivariable stochastic processes along the height of the poles and the conductors. Three-second peak gust wind speeds are used as a modeling reference. A probabilistic framework is developed to estimate the capacity of the distribution poles under different aging mechanisms. The response of the power distribution poles due to the simulated wind speeds is then computed using a finite element analysis. Fragility functions are generated to estimate the probability of exceedance for different damage states. A set of hazard mitigation strategies, the associated costs, and estimated benefits are implemented, and the improved fragility functions for elements are generated. A probabilistic life-cycle cost analysis is used to assess the long-term benefits of investing in different components of the power distribution system.

Drought hazard in Kazakhstan in 2000-2016: a remote sensing perspective.

Droughts have significant negative impacts on livelihoods and economy of Kazakhstan. In this study, we assessed and characterized drought hazard events in Kazakhstan using satellite Remote Sensing time series for the period between 2000 and 2016. First, we calculated Vegetation Condition Index (VCI) and Standardized Enhanced Vegetation Index anomalies (ZEVI) based on 250m Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series. Second, we assessed vegetation cover changes for the observation period. Third, we analyzed different characteristics of the drought hazard as well as spatial distribution of the drought-affected areas within the country. The results confirmed that drought was one of the environmental challenges for Kazakhstan in 2000-2016. The obtained maps showed that drought hazard conditions were observed every year, though the areal coverage of the drought conditions largely varied between the analyzed years. The calculated drought indices indicated that in years 2000, 2008, 2010, 2011, 2012, and 2014, more than 50% of the area of the country were affected by drought conditions of different severity with the largest droughts in terms of the areal spread occurring in 2012 and 2014. We concluded that the pre-requisite of successful implementation of drought hazard and risk mitigation strategies is availability of spatially explicit, timely, and reliable information on drought hazard. This suggests the necessity of incorporation of remote sensing-based drought information, as was demonstrated in this paper, in the national drought monitoring system of Kazakhstan.

First in-situ observation of a moving natural pyroclastic density current using Doppler radar

Pyroclastic density currents are one of the most devastating volcanic hazards. Understanding their dynamics is a key to develop successful hazard mitigation strategies. The hazard associated with pyroclastic density currents is commonly investigated a posteriori from their deposits or a priori using analogue and numerical experiments. Despite the low probability of observing a natural moving pyroclastic density current, we present the first in-situ analysis of the internal particle velocities of pyroclastic density currents at Volcán de Colima using a Doppler radar. Our data show two Vulcanian explosions, immediately followed by column collapse and a first pyroclastic density current travelling down the south flank with an average speed of 30 m/s (>50 m/s maximum speed) to a distance of 3 km from the crater rim. The direction of the pyroclastic density current coincided with that of the radar beam enabling measurement of velocity spectra (histogram of particle velocities within the radar beam). The measurement geometry enables the simultaneous measurement of the dense undercurrent at the crater rim (with <20 m/s and an increasing echo power over 20 s) and the dilute cloud higher above the topography approaching the radar (with >20 m/s and approximately constant echo power). The presented data set may be used as a benchmark for future experimental and numerical models that simulate the dynamics of pyroclastic density currents. Using the measured velocities of the collapsing column as input for numerical models will permit the validation of the models for the prediction of the true run-out distance, and thus provide valuable information for hazard assessments.