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LQ1 LQ2 LQ3 Health Issues

LQ1 LQ2 LQ3 Animal Loss

LQ1 LQ2 LQ3 Food Shortage uestions(LQ)

LQ1 LQ2 LQ3 Human Prayer

and asked them whether or not they confirm having such a relation or not.

c) Lexical evaluation: This part examines expressiveness, completeness, and clarity of annotations of a given ontology. Expressiveness states the efficacy of the ontology to identify relevant information using natural language processing techniques. Completeness [48] evaluates whether an illustration of a concept using definition and labels adequately define various scenarios in crisis domain. For instance, w.r.t. the given concept "Human Prayer" the definition "prayer is a message to God from victim's relatives and family for protecting their lives and health" is provided along with the labels "send prayers", "heart prayers", "heart praying", "join pray-

ing", "love prayers", "prayers affected", "temple", "church", "prayers city", "prayers families", "prayers involved", etc. Clarity evaluates whether or not the concept name in the ontology is meaningful and easily understandable to human and machine.

d) **Results**: Our survey had 13 participants and the results have been illustrated in figure 4.

The structural evaluation section comprised of seven questions expressed as follows; (SQ1) Concerning a hazard situation, is "Mental Stress" and "Physical Stress" two important concepts under "Health Issues"? (SQ2) Does "No Effect", "Minor", "Major", "Hazardous", "Catastrophic" represents sub-classes of "severity"? (SQ3) Are "Financial Crisis", "Food Shortage", and "Contamination" probable impacts of a Hazard? (SQ4) Are following triples meaningful: "Animal Loss isa Impact", "Communication Lines Failure is-a Infrastructure Damage", "Power Outage is-a Infrastructure Damage", and "Survived People is-a Affected Population"? (SQ5) Do you consider non-government organization's (NGO) report is an expert report? (SQ6) Do health report, service feedback, and weather report define human sensing? (SO7) Can "News Agencies Report" and "Social Media Report24" be categorized under "Media Report", a sub-class of "Report"? Questions SQ1-SQ6 were Yes/No questions, and SQ7 follows Likert Scale. The detailed results are represented in Figure 4a. We observe the average agreement rate above 84.5%.


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a Affected Population " ? ( SQ5 ) Do you consider non - government organization ' s ( NGO ) report is an expert report ? ( SQ6 ) Do health report , service feedback , and weather report define human sensing ? ( SO7 ) Can " News Agencies Report " and " Social Media Report < sup > 24 < / sup > " be categorized under " Media Report " , a sub - class of " Report " ? Questions SQ1 - SQ6 were Yes / No questions , and SQ7 follows Likert Scale . The detailed results are represented in Figure 4a . We observe the average agreement rate above 84 . 5 % .

Regarding evaluation of semantic relations, we designed seven questions as follows: SRQ1: Do you think the following triples make sense; "Event occures in a Place", "Service is offered in a Place", "Each Hazard is associated to a couple (c) of Event" and "each Hazard leads to a couple of Services"? SRQ2: Can concepts, Event, and Service be linked to the concept "Place" using "isLocationAt" relation? SRQ3: Do you think two different types of Hazard can be related concerning Event, Service, and Place? SRQ4: Is the "currentStatus" relation between Facility and Status semantically correct? SRQ5: Is available, offered, requested and unavailable suitable categories for Status? SRQ6: Can the concepts "Service" and "Organization" be concerning the concept "Hazard Type"? SRQ7: Is the relation "needHelp" correctly links "Affected Population" with "Organization" and "Service"? Questions SRQ2-SRQ7 are Yes/No, and SRQ1 follow Likert Scale. From figure 4b, an average agreement of 75.4% was concluded in confirming above facts.

The lexical evaluation of *empathi* was performed by representing definition and synonyms (or labels) describing each concept and asking participants to respond the following questions; (LQ1) Do labels appropriately represent the concept? (LQ2) Are labels complete? (LQ3) Are definitions and labels enough clear? Figure 4c shows the results with the total agreement rate 78.8%.

data/('An ontology for Emergency Managing and', '.pdf')_extraction.md


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The aftermath of the disaster causes agencies/organization to structure a plan for the thoughtful recovery of the area. An

<sup>24http://www.aidforum.org/topics/disaster-relief/top-100-twitter-accounts

opportunity to hasten this process is a need for a knowledge structure containing concepts, constraints, and links that provide before-hand information for disaster preparedness and act. In [49], the author designed a process model of the aftermath of the disasters using the Petri-Net containing inter-linked concepts for efficiency after-disaster execution and adaptation. Furthermore, the cost associated with the recovery of an area urges the need to have a structured source of events, concepts, and relations that define a distressing incident. In [50], the author defines the utility of an ontology providing seismic risk definition, prediction, and management to reduce damages. There is a recent study on crowdsourced emergency event detection in [51]. There, the authors propose the utilization of emerging knowledge from text using concepts and temporal information for events. In a recent work on crisis management [52], there has been a development of a suite of tools which can leverage our ontology for context-aware response generation. CrowdGeoKG [53] is a framework using the entities in the OpenStreetMap and is enriched by Wikidata.

Events mainly characterize a disaster scenario, and it is essential to understand what is happening in an emergency situation. In [54], a given user can utilize GPS data to create a simulation model for predicting human mobility. We assume that identification of core disaster domain-specific concepts can help in annotating the GPS data similar to in OpenStreetMap [55]. In 2012, Hurrican Sandy brought in towering traffic on social sensing sites urging the need of an information filtering mechanism for assisting crisis coordination. A psycholinguistic driven domain-dependent lexicon was created in [56] for assistive crisis response. Moreover, tweets on Hurricane Sandy 2012 identified "power blackout" as one of the implications of the disaster. To identify its associated repercussions, one need a structured domain-model. For instance, crashing out of power affected the medical facility. Hence, there is a need for an ontology to bridge the facilities in emergency situation [39]. In [57], the author created a twitter stream of deceptive and peripheral messages using a knowledge source assisting Public Information Officers (PIOs) to make conscious decisions in an emergency situation.


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psycholinguistic driven domain - dependent lexicon was created in [ 56 ] for assistive crisis response . Moreover , tweets on Hurricane Sandy 2012 identified " power blackout " as one of the implications of the disaster . To identify its associated repercussions , one need a structured domain - model . For instance , crashing out of power affected the medical facility . Hence , there is a need for an ontology to bridge the facilities in emergency situation [ 39 ] . In [ 57 ] , the author created a twitter stream of deceptive and peripheral messages using a knowledge source assisting Public Information Officers ( PIOs ) to make conscious decisions in an emergency situation .

Table 1 motivates creating empathi. Prior ontologies related to crisis management and situational awareness fails to provide structural, lexical and relational benefits needed for extracting situation-specific information extraction from social media content [47]. It is either because these ontologies are diverse in their subject areas or are incomplete concerning referencing, documentation, and expressibility. On analyzing these state-of-the-art vocabularies, we identified subject areas, vocabularies concerning each subject area and used OWL to provide a formal representation. Since ontologies can be domain-specific (crisis domain) or generic (e.g., FOAF), we incorporate concepts relevant in addressing various issues in crisis management. For instance; concept Location was taken from Geonames and iContact, crisis-related concepts from HXL and MOAC. Hence, we addressed all the queries raised in [3]. Human social communication during an emergency event provides real-time insight into various domains such as facility, events, impact, report, surveillance, organization involved and activities carried out during and after the hazard. Extracting actionable information from active social channels is challenges because of 2 reasons: (1) absence of an ontology that map multiple concepts, (2) completeness and expressiveness of the ontology. We provide a utility based case-study (section V) where we used our ontology for mapping 53M tweets to the concepts and sub-concepts in *empathi*. Mapping of social media content to ontology concepts will improve classification and summarization task using state-of-the-art natural language processing and learning techniques.


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We propose *empathi* ontology as a crisis domain archetype that aids crisis management, hazard situational awareness, and hazard events during emergency scenarios. In our study, we demonstrated the prowess of ontology by its integration with relevant and state-of-the-art crisis vocabularies. Moreover, its efficacy was assessed through appropriate evaluation of its quality across three dominant criteria: structure, lexical and semantic relations using the human judge. Furthermore, we illustrated its effectiveness concerning social media domain by linking tweet content to ontology concepts. The ontology has been created using semantic web language (OWL) and links to FOAF, SIOC, DC-terms, and LODE. We aim to extend the ontology in following directions; the first direction is to introduce Internet of Things (IoT) for disaster management. A second possible direction is to improve the ontology with additional ontology quality tools as recommended by the PerfectO methodology25. For instance, use of LODE tool provides automatic documentation. WebVOWL tool provides an automated graph visualization. A third direction is to disseminate more the ontologies on ontology catalogs such as Linked Open Vocabularies (LOV) and LOV4IoT. LOV4IoT could be refined and extended to support the environment domain with various use cases such as flooding, fire, earthquake, tsunami.


Document: An Internet of Things ontology for earthquake emergency evaluation and response
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Abstract—Recent years have seen the fast-diffusion of internetconnected devices and the rise of the Internet of Things (IoT) research and application area. Research works are dealing with technologies that enable the so-called things to communicate among them and with users in order to provide data and/or accomplish tasks. This scenario is posing several challenges so that more and more researchers are dealing with them. Our work deals with the definition of both machine and human understandable descriptions of things by means of ontologies in order to enable the collaboration among physical objects and IT systems. Indeed, recent works highlighted how the IoT technology may be profitably used in several scenarios in order to accompish complex tasks where physical objects are active participants. First, we extend the Semantic Sensor Network ontology defined by the W3C Semantic Sensor Networks Incubator Group with concepts and roles that describe actuators. This leads to the definition of a comprehensive Internet of Things ontology. Then, with the support from domain experts, we analyze the earthquake emergency scenario in order to define for it a domain ontology by means of adding domain-related concepts to the IoT ontology. Furthermore, we compare our work with others that use ontologies to formally describes things.

*Keywords*— Internet of Things, Ontology, Earthquake Emergency.


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Since the term "Internet of Things" (IoT) was introduces by Kevin Ashton in 1999, the number of internet-connected devices had quickly increased. According to CISCO, the number of things connected to the internet exceeded the number of people on earth during 2008; furthermore, the company foresees that the number of internet-connected things will reach 50 billions in 2020. Nowadays, internet-connected devices are involved in many application domains [1]. Now, transportation and logistics, healthcare, smart environment (home, office, plant) can benefit from IoT technologies. Furthermore, the increasing number of new smart things and internet-connected devices developed by companies suggests that, in the near future, futuristic applications (such as robot taxi, city information model, and enhanced game room) will be available.

1"The Internet of Things". http://share.cisco.com/internet-of-things.html. Accessed: 2014-3-4.

Beside an exponential growing of the Internet of Things field, companies started to rethink their core activities including physical objects in their core processes [2], [3]. Indeed, whenever the Internet of Things enables the communication among physical objects and the business IT systems, such objects can be considered as active participants into the business processes of the companies [4]. For example, IoT-enabled objects such as sensors and/or actuators can provide data and/or accomplish operations may be used to execute business process tasks. This brings toward a collaboration among IT systems and physical objects.

Although many efforts have been done in order to connect physical objects to IT systems and enable a collaboration among them, several core challenges arises [5], [6]: scalability, when millions of sensors and/or actuators can be reached to accomplish a given task the amount of information that a system has to manage could be unmanageable; deep heterogeneity, things are very heterogeneous, both regarding physical characteristics and data and/or operations that they provide and/or accomplish, this hinders the use of them in an IoT ecosystem; unknown topology, networks of things may be large and/or things may be unreachable; incomplete or innaccurate metadata, metadata may be used to enrich the description of things and their capabilities but they could be incomplete or inaccurate; and conflict resolution, regarding actuators, conflicts may arise, for instance, when multiple applications attempt to actuate the same device. Furthermore, it is noteworthy the fact that IoT end users are usually non-technical people [7], [3]. They are not experts of the IoT-related technologies, but they know very well the domain of application of the things. An IoT ecosystem should enable these users to use devices even if they do not know IoT implementation details. In order to deal with these challenges, several research works deal with the extension of things descriptions with semantic informations by means of ontologies. Indeed, an ontology is "an explicit specification of a conceptualization" [8] and it is suitable to formally describe a domain of interest, the Internet of Things domain in this case. Ontologies are key elements of the semantic web technologies [9] and were introduced to add semantic machine-understandable information to data on the web, enabling the so-called Web 3.0 [10]. Semantic enriched data can improve search and integration activities; furthermore, they also facilitate the machine capability to reason on data in order to achieve more knowledge of the domain of interest.


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with semantic informations by means of ontologies . Indeed , an ontology is " an explicit specification of a conceptualization " [ 8 ] and it is suitable to formally describe a domain of interest , the Internet of Things domain in this case . Ontologies are key elements of the semantic web technologies [ 9 ] and were introduced to add semantic machine - understandable information to data on the web , enabling the so - called Web 3 . 0 [ 10 ] . Semantic enriched data can improve search and integration activities ; furthermore , they also facilitate the machine capability to reason on data in order to achieve more knowledge of the domain of interest .

Motivations that enabled the use of ontologies on the web lead to utilization of them in the Internet of Things domain on which both systems (mainly, sensors and actuators) and data may be semantically described. In particular, using ontologies things and their capabilities may be modeled at an abstract level which enables non-technical people in using them.

Our work proposes an ontology for Internet of Things in order to deal with the deep heterogeneity challenge of IoT introduced before. Indeed, in order to enable the collaboration among several physical obtects and IT systems, it is needed a common meaning of the involved resources and this can be obtained using semantics [11].

We designed an ontology whose goals are the description of an Internet of Things ecosystem. Starting from the work of the W3C Semantic Sensor Networks Incubator Group [11] on semantic sensor networks, we define an ontology that extends sensors description defined by the Incubator Group with a description suitable for actuators. A third part of the IoT ontology is domain related. In particular, we adopt the earthquake emergency evaluation and response domain as domain of interest of our ontology. The goal is to show how the developed ontology may be used to model the things that are involved in the earthquake emergencies.

The rest of the paper is organized as follows. Section II gives an overview of the works that combine Internet of Things with semantics. Section III describes the Internet of Things ontology we propose, focusing on its three-modules architecture and the modeling of sensors and actuators. Next, Section IV completes the description of the ontology depicting a domain-specific ontology suitable for the earthquake emergency evaluation and response. Section V briefly gives information about the implementation of ontologies. Finally, Section VI closes the paper with a discussion on our approach to the semantic Internet of Things and gives a prevision of the future work.


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In the recent years, Internet of Things attracted many researchers. Indeed, due to its pervasiveness and the variety of involved technologies, Internet of Things interests several application areas, resulting in a great number of works in literature. Regarding collaboration, for example, researchers proposed standards that enable the syntactic interoperability among things [12], [13], [14]. Despite that, the formal modeling of things by means of semantics is still an emerging area and there not exists a de facto standard to do that. This obstructs the collaboration among things themselves and among things and IT systems [11]. This leads research groups

to propose their own approaches to semantically describes the Internet of Things domain.

De et al. [15] suggest a service modeling of things using ontologies extendig the Entity, Resource, Service, and Device core elements [16] of a thing model. The W3C Semantic Sensor Network Incubator Group [11] defines an ontology for sensor networks. Such ontology goes deep in the description of devices that are capable to sense features of interest. This work represents a comprehensive description of sensors and the approach is domain independent. Hachem et al. [6] aim at solving the IoT challenges by means of three ontologies that describe in detail devices and physics of the domain of interest. Their work proposes a three-layer view of the IoT-based real world. In detail, the authors identify the following layers: a physical layer, an information layer, and a functional layer. For each of them they design a specific ontology in order to give a proper description of the IoT domain. In this work, some aspects of the information and the functional layers are noteworthy. Indeed, in the respective ontologies there are concepts that rely on reasoning about measurement units and physical concept relations, and they are defined from a mathematical viewpoint. This simplify the searching for and composing of data activities. Wang et al. [7] propose an ontology-based framework to support the discovery and invocation of actuators in home care systems. The goal of their work is to simplify the customization of the home care system policies by non-technical people. A care professional may declare system rules by means of human-understandable concepts defined in a care domain ontology. Then, software handlers are responsible for the translation between ontology-based and device-specific descriptions of sensors and actuators used by the home care system. Discovery and invocation of actuators are supported by querying the system knowledge base on which each device is registered. In a further work, Wang et al. [17] define an ontology for the IoT. Such an ontology aims at giving a comprehensive view on the IoT domain, from sensing and actuating capabilities to the quality of services and information. For this purpose, and in order to keep distinct each area of IoT domain, the description ontology is designed with a modular approach. Each module is responsible for one of the IoT-related areas (e.g., resources, services, QoS) and, in some cases, it is developed starting by and extending third party works on IoT. In this work, a particular attention is given to IoT service modeling and testing, but it overlooks how this ontology may be applied in real world scenarios.

Finally, some considerations have to be done about the domain of application we consider and the related work which deals with it. Emergency management involves many organizations and actors, from national agencies to volunteers. Furthermore, recent works also highlights that citizens may contribute in generating useful data in such mass casualty incidents [18]. It emerges a complex scenario where a large amount of data has to be processed and combined in order to


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by and extending third party works on IoT . In this work , a particular attention is given to IoT service modeling and testing , but it overlooks how this ontology may be applied in real world scenarios . Finally , some considerations have to be done about the domain of application we consider and the related work which deals with it . Emergency management involves many organizations and actors , from national agencies to volunteers . Furthermore , recent works also highlights that citizens may contribute in generating useful data in such mass casualty incidents [ 18 ] . It emerges a complex scenario where a large amount of data has to be processed and combined in order to

better manage the emergency. For this purpose, several works propose the use of ontologies to add semantic information to data and processes involved in the emergency management activities [19], [20]. Despite that, the majority of them does not consider sensors and actuators in the domain ontologies. Thus, our work appear to consider another aspect of the emergency scenario: the things.


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The following section describes the architecture of the Internet of Things ontology we propose. Such architecture is composed by three modules, namely Sensors Module, Actuators Module, and Domain Module, divided in two layers (Figure 1). On one hand, the first layer contains Sensors and Actuators modules and formally describes both sensors and actuators in an IoT ecosystem by means of two ontologies, one for sensors and one for actuators. On the other hand the second layer contains the Domain Module. It details the generic descriptions of things given by the first layer adding ontology elements that describe specific-domain devices and features of interest involved in the application scenario, in this case the earthquake emergency evaluation and response scenario. This modular two-layers architecture provides both flexibility and compatibility of the whole ontology: all the specific-domain concepts and roles can be added to the Domain Module without requiring the modification of other modules; furthermore, in order to describe the domain scenario, other ontologies may be used in the domain-related layer without violating the whole IoT ontology design principles.


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Figure

Image /page/2/Picture/3 description: A block diagram illustrating the components of an "IoT Ontology". The diagram consists of a large, light gray, rounded rectangle labeled "IoT Ontology" at the top. Inside this main rectangle, there are three smaller, light blue, rounded rectangles. The top half of the main rectangle contains two of these smaller rectangles placed side-by-side. The one on the left is labeled "Sensor" and the one on the right is labeled "Actuator". Below these two, a single, wider, light blue rectangle spans the bottom half of the main container and is labeled "Domain".


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Figure: Figure 1. Internet of Things ontology architecture.

Figure 1. Internet of Things ontology architecture.


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In the following, we illustrate the three modules of the ontology.


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This module is responsible for the description of sensors. Since our work aims at the compatibility with other ontology implementations, we decided to use the ontology designed by the W3C Semantic Sensor Network Incubator Group *et al.* [11] to semantically describe the sensors. This ontology uses the *Stimulus-Sensor-Observation pattern* [21] as starting point to define the description of sensors. According to such pattern: a stimulus is a change or a state in an environment that a sensor can detect and use to measure a property; a sensor is anything that senses and, usually, it consists in an object that observes, transforming incoming stimuli into

another representation; then, an observation is the result of a measurement operation and connects a stimulus that a sensor measures and the sensor that observes such stimulus, it focuses on a single measurement that a sensor takes at a precise instant and in a precise location. By means of this pattern and the definition of the three related core concepts, the W3C Semantic Sensor Network Incubator Group designed the ontology for the semantic sensor networks. The above pattern may be analyzed through four different perspectives which are shown in the following.

Sensor Perspective. It describes the property the sensor measures (ssn:Property) and how (ssn:Sensing and ssn:Stimulus) measurement taken. Furthermore, this perspective deals with sensor capabilities (ssn:MeasurementProperty) in terms of accuracy (ssn:Accuracy), resolution (ssn:Resolution), (ssn:Precision), and other similar sensor characteristics. These characteristics are related to particular conditions (ssn:Condition), consequently several measurement capabilities (ssn:MeasurementCapability) can be associated with each sensor.

System Perspective. It focuses on the platform (ssn:Platform) description. In the semantic sensor network, a system (ssn:System) may be composed by several subsystems (ssn:hasSubSystem). Furthermore, each sensing device must be considered a system as well (ssn:SensingDevice ⊑ ssn:Device ⊑ ssn:System). This allows the description of complex platforms on which several sensors are aggregated in a single device. Finally, each system has its own operating range (ssn:OperatingRange) and survival range (ssn:SurvivalRange).

Feature and Property Perspective. This perspective focuses on the properties (ssn:Property) that may be sensed and the related observations that are related to them. Properties are grouped in features of interest (ssn:FeatureOfInterest) that represents homogeneous collections.

Observation Perspective. Finally, the fourth perspective proposed by W3C SSN Incubator Group deals with observations and related metadata. Observations are taken by means of a sensor (ssn:Sensor) at a certain moment, measuring a stimulus (ssn:Stimulus), using a sensing method (ssn:Sensing), and observing a property (ssn:Property) of a particular feature of interest (ssn:FeatureOfInterest). The result of the observation corresponds to the output of the sensor (ssn:SensorOutput).


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At the best of our knowledge, there not exist generic purpose ontologies for actuators networks; as a consequence, we propose a new ontology of which goal is the modeling of actuators networks at a generic description level. leaving apart the domain-related details. Our ontology is based on the work of the W3C Semantic Sensor Network Incubator Group described above and uses several concepts defined in the SSN ontology. Reasons for this choice rely on the compatibility, the the high level of flexibility, and the level of standardization of that ontology. Taking advantage of the SSN ontology and extending it, we propose the Semantic Actuator Network ontology (SAN ontology) (Figure 2) that formally describes concepts regarding actuators.

In the architecture of our Internet of Things ontology, the Semantic Actuator Network constitutes the actuator module. In conjunction with the SSN ontology it gives a comprehensive formalization for the representation of a thing network. Thus, by means of those two ontology, we can model any complex device composed by sensors and actuators.

As SSN proposes the Sensor-Stimulus-Observation pattern in order to describe how a sensing activity works, we propose the *Actuator-Stimulus-Operation pattern* in order to describe the actuators. According to this pattern, an Actuator is an object capable to modify a property in the environment where it operates. To do that, it produces a Stimulus that is able to modify a property. Finally, an Operation represents the activity that is accomplished by the actuator.

In order to better describe the proposed pattern, similarly to the the description of SSN ontology, we define two perspectives as follows.

Actuator Perspective. This perspective gives a static description of the actuator and highlights the properties that the actuator may manage and/or modify. Similarly to the sensor perspective, this perspective also defines actuator specifications. An actuator (san:Actuator) is able to modify a property (ssn:Property) by means of generating a stimulus (ssn:Stimulus). In this case, a stimulus is the change that an actuator may actuate (san:models) in order to modify (san:modify) a property. It is worthy of note that a stimulus does not necessarily refers to the property that the actuator modifies. For instance, let us consider an electric motor as actuator. The property that we want to manage is its rotational speed. In order to modify it, we vary the rotor current that, in this case, is the stimulus on which the actuator acts. Furthermore, the actuator implements (san:implements) an acting process (san:Acting  $\square$  ssn:Process) that describes the process implemented by the actuator in order to manage the property of interest. As described for the sensors, in order to describe the actuator working specifications we define the change property concept (san:ChangePropery ≡ ssn:MeasurementProperty), related with properties (such as ssn:Accuracy, ssn:Resolution, etcetera) defined in the SSN ontology. Furthermore the concept san:ChangeCapability is defined. It links (ssn:inCondition) a set of working specifications with a specific condition (ssn:Condition).

III. THE IOT ONTOLOGY
B. Actuator Module


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( san : implements ) an acting process ( san : Acting $ \ square $ ssn : Process ) that describes the process implemented by the actuator in order to manage the property of interest . As described for the sensors , in order to describe the actuator working specifications we define the change property concept ( san : ChangePropery ≡ ssn : MeasurementProperty ) , related with properties ( such as ssn : Accuracy , ssn : Resolution , etcetera ) defined in the SSN ontology . Furthermore the concept san : ChangeCapability is defined . It links ( ssn : inCondition ) a set of working specifications with a specific condition ( ssn : Condition ) .

Operation Perspective. It provides the dynamic description of actuators. Indeed, it focuses on the operation (san:Operation) that a given actuator accomplishes in a given instant (san:operationResultTime). The operation is accomplished (san:operatedBy) by an actuator in order to modify (san:controlledProperty) a property of a specific feature of interest (san:featureOfInterest). Then, this perspective defines both the stimulus that the actuators receives and the method it uses (san:actingMethodUsed). Obviously, the operation is related with the result we want to obtain. Such result is the actuator input (san:ActuatorInput) since it consists on the information on which depends the property modification that is desired. Returning to the above example of the electric motor, the new value of the rotational speed is the input of the actuator.

Both the *System perspective* and the *Feature and Property perspective* can be defined for the SAN ontology, as well. On the other hand, such perspectives have already been defined in the SSN ontology. Therefore, regarding the *System perspetive*, we simply introduced a concept to model an acting device (san:ActingDevice  $\sqsubseteq$  san:Actuator, ssn:Device) and define its relations with ssn:Device. As a consequence, according to our IoT ontology (obtained without modifying the SSN ontology), a system is a platform that may include both sensors and actuators. Regarding the *Feature and Property perspective*, no extensions are required since features of interest and properties are the same described for the SSN ontology. The only difference is that features and properties must be changed by actuators instead of being observed by sensors.


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The domain module is responsible for the description of domain-specific things. Since it uses the Sensors and Actuators modules in order to define its own concepts and relations, we choosed to create a second layer for it in the our IoT ontology architecture. By means of it, the resulting architecture is flexible and ontology designers may focus on the specific application domain without taking care of the complex structures of the ontologies for sensors and actuators, but only using their concepts to define the new ones required by the domain of interest. The next section illustrates an example for the earthquake emergency evaluation and response domain.


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Several works deal with the use of semantics in the emergency management fields (e.g., see [19], [20]). Nevertheless, they usually do not focus on "things" that are involved in the emergency domain. Here, we propose the *Earthquake Emergency (EEM) ontology* that formally models sensors and actuators, the former used to evaluate the earthquake entity and the latter used to respond to the emergency. In order to design and develop this ontology, we


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Figure

Image /page/4/Figure/0 description: A concept map or ontology diagram illustrating relationships between various entities, primarily centered around an 'Actuator'. The diagram uses rounded rectangles for concepts and labeled arrows for relationships. Concepts in green include 'ActingDevice', 'Actuator', 'Acting', 'ActuatorInput', 'OperationValue', 'Operation', 'ChangeCapability', and 'ChangeProperty'. Concepts in blue include 'Device', 'Stimulus', 'Process', 'Property', 'FeatureOfInterest', 'Condition', 'Accuracy', 'Resolution', and a box with ellipses. Solid lines indicate 'is a' relationships: 'ActingDevice' is a 'Device', 'Acting' is a 'Process', and 'ChangeProperty' is a 'Property'. 'Accuracy', 'Resolution', and others are types of 'ChangeProperty'. Dashed lines represent other relationships. An 'Actuator' is connected to a 'Device', 'implements some' 'Acting', 'models only' a 'Stimulus', is 'operatedBy only' an 'Operation', and 'hasChangeCapability only' a 'ChangeCapability'. An 'Operation' is linked to 'Actuator' ('operatedBy'), 'Acting' ('actingMethodUsed'), 'Stimulus' ('includesEvent'), 'Property' ('controlledProperty'), 'FeatureOfInterest', and 'ActuatorInput' ('operationDesiredResult'). A 'Property' is modified by 'Acting', is a proxy for 'Stimulus', and has a 'FeatureOfInterest'.


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Figure: Figure 2. The Semantic Actuator Network ontology (the light blue concepts belong to SSN).

Figure 2. The Semantic Actuator Network ontology (the light blue concepts belong to SSN).


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Section: IV. THE IOT EARTHQUAKE ONTOLOGY
Type: text

benefited from the help of earthquake domain experts: we consulted civil engineers that work in this kind of emergency and interviewed them to get a proper overview over the sensors and actuators involved in this scenario.

In our domain analysis, we first identify five features of interest and related properties2 (Figure 3):

- emergency site (eem:SiteFeature) that has ground acceleration and the displacement the properties (eem:SiteAccelerationProperty and as eem:SiteDisplacementProperty);
- earthquake (eem:EarthquakeFeature) magnitudo, the epicenter, and the distance the has (eem:EarthquakeMagnitudoProperty, eem:EarthquakeDistanceProperty, eem:EarthquakeEpicenterProperty);
- the evaluation of cracks (eem:CrackFeature) that has their widths as property (eem:CrackWidthProperty);
- the localization (eem:LocalizationFeature) that latitude-longitude pair as property eem:GPSLocalizationProperty);
- the alarm (eem:AlarmFeature) that aims at notifying people of the earthquake entity and of additional information in order to reach a safe place (denoted by properties such as eem:AlarmNotificationProperty, eem:AlarmLightProperty, and eem:AlarmSoundProperty).


Document: I. Introduction
Section: IV. THE IOT EARTHQUAKE ONTOLOGY
Type: figure
Figure

Image /page/4/Figure/10 description: A line of black text on a white background, which appears to be a citation. The text reads: "²See http://earthquake.usgs.gov/learn/glossary/. Accessed: 2014-3-3." The number '2' at the beginning is a superscript.


Document: I. Introduction
Section: IV. THE IOT EARTHQUAKE ONTOLOGY
Type: figure
Figure

Image /page/4/Figure/11 description: A diagram illustrating a conceptual model or ontology, organized into three main columns connected by arrows indicating relationships. The first column, on the left, is titled 'FeatureOf Interest' in a light green box. Below it, a vertical line with 'is a' connects to a list of light blue boxes: 'SiteFeature', 'Earthquake Feature', 'CrackFeature', 'Localization Feature', and 'AlarmFeature'. The middle column has a light green box labeled 'Property'. Below it, a list of light blue boxes are all connected to 'Property' with arrows labeled 'is a'. These boxes are: 'Site Acceleration Property', 'Site Displacement Property', 'Earthquake Distance Property', 'Epicenter Property', 'Magnitudo Property', 'Crack Width Property', 'GPS Localization Property', 'AlarmNotification Property', 'AlarmLight Property', and 'AlarmSound Property'. The third column, on the right, is titled 'Quantity Kind' in a yellow box. Below it, a vertical line with 'is a' connects to a list of yellow boxes: 'Acceleration', 'Distance', 'Geographic Coordinates', 'Magnitudo', and 'Dimensionless'. Dashed arrows labeled 'hasProperty' connect features from the first column to their corresponding properties in the second column. For example, 'SiteFeature' has properties 'Site Acceleration Property' and 'Site Displacement Property'. Solid arrows connect properties from the second column to their quantity kinds in the third column. For example, 'Site Acceleration Property' points to 'Acceleration', and 'Epicenter Property' points to 'Geographic Coordinates'.


Document: I. Introduction
Section: IV. THE IOT EARTHQUAKE ONTOLOGY
Type: text

The Earthquake Emergency Management ontology: Features of Interest and Properties (green concepts belong to SSN, yellow concepts belong to QU).

We used the *Quantities Kinds and Units ontology* (QU)3

<sup>3"Library for Quantity Kinds and Units: schema, based on QUDV model OMG SysML(TM), Version 1.2", http://purl.org/NET/ssnx/qu/qu\#. Accessed: 2013-11-23.

to represent units of measurement of such properties. This ontology defines two main concepts: qu:QuantityKind denotes the kind of the measurement, and qu:Unit denotes the unit of measurement. The two concepts are linked through the property qu:unitKind. In EEM, the properties we defined extend (is-a) both ssn:Property and qu:QuantityKind. In this manner, we can specify the unit of measurement for each property. Furthermore, as some units of measurement we need are not defined in QU, we directly added them in EEM. For instance, we introduced the magnitudo (eem:Magnitudo) and the geographic coordinates (eem:GeographicCoordinates).

After property identification, we analyzed the devices involved in the earthquake emergency scenario (see Figure 4).

We identify the following sensing devices: the fissurometer (eem:Fissurometer) that measures the width of cracks; the seismometer (eem:Seismometer), the accelerometer (eem:Accelerometer), and the GPS (eem:GPS), they all are needed to evaluate an earthquake; the seismograph station (eem:SeismographStation), that is composed by the previous three sensors; and the seismograph network (eem:SeismographNetwork), that is composed by three seismograph stations at least and is used to evaluate the earthquake epicenter. Then, we identify the following actuators: the alarm siren (eem:AlarmSiren) that informs people to evacuate a building; the signaling escape light (eem:SignalingEscapeLight) that highlights the escape path during a building evacuation; and, finally, the alarm message notifier (eem:AlarmMessageNotifier) that sends information about safe places after an earthquake to citizens' mobile devices.


Document: I. Introduction
Section: V. IMPLEMENTATION
Type: text

As mentioned above, our IoT ontology is composed by three main ontologies: the Semantic Sensor Network (SSN) and the Semantic Actuator Network (SAN) ontologies describe the infrastructure of a network of things, whereas the Earthquake Emergency Management (EEM) ontology describes the domain of interest we evaluate in this work. We implemented the last two ontologies using the OWL 2 language [22]. It results in two files, san.owl and eem.owl, that define respectively the SAN ontology and the EEM ontology. These files are available at the following URL: https://bitbucket.org/gilbertotcc/iot-ontology.


Document: I. Introduction
Section: VI. DISCUSSION AND CONCLUSION
Type: text

This work proposes an ontology for Internet of Things extending the ontology for semantic sensor networks developed by the W3C Semantic Sensor Network Incubator Group and applies it to the earthquake emergency domain.

Although several works deal with semantic description of things, we focus on flexibility and compatibility proposing

a three modules ontology in two layers: one dealing with a generic model for the things and one dealing with specific-domain things. Indeed, De et al. [15] gave a detailed description of things but their approach is oriented to the users of those devices and do not focus on modeling such sensors and actuators as, for example, the Semantic Sensor Network ontology [11] does about sensors. Instead, our work extends the SSN ontology and proposes a detailed description model for the actuators too. The work by Hachem et al. [6] shows an interesting analysis of the IoT domain and proposes an ontology composed by three modules. Their work focuses on the combination of things, not only on their description, up to include the mathematical laws involved in the domain. This goes beyond the goals of our work which deals only with devices and physics of the domain of interest. In our opinions, our approach results more easy to learn for end users of the ontology. In comparison with the work of Wang et al. [7], our approach is more flexible. Due to its modularity, the ontology we propose is not domain specific but is suitable for each scenario on which things are involved. The work proposed by Wang et al. [17] shows a comprehensive description of the IoT domain which includes also services and testing areas and it goes beyond the goal of our work, as well. Furthermore, their work does not show any example and/or case study in order to evaluate differences with our approach.

In conclusion, our work represents a starting point to the development of an Internet of Things ontology also suitable for users, adding a semantic description for thing interfaces. By adding a semantic description to the physical objects, such ontology eases the collaboration among several physical objects such as sensors and actuators that use different communication protocols and data formats. At the moment, it is a comprehensive ontology to deeply describe both sensors and actuators. Furthermore, its architecture enables domain-specialists to build every domain ontology they need using the top layer of our IoT ontology offering a high level of flexibility.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial Lake Outburst Floods (GLOFs)
Type: text

A **glacial lake outburst flood** (GLOF) is a release of meltwater from a moraine- or ice-dam glacial lake due to dam failure1,2. GLOFs often result in catastrophic flooding downstream, with major geomorphic and socioeconomic impacts3,4.

GLOFs have three main features:

- They involve **sudden** (and sometimes cyclic) releases of water.
- They tend to be **rapid events**, lasting hours to days.
- They result in **large downstream river discharges** (which often increase by an order of magnitude).


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial Lake Outburst Floods (GLOFs)
Type: figure
Figure

Image /page/0/Picture/11 description: An aerial photograph from 2002 captures a dramatic natural event at the terminus of the Hubbard Glacier in Alaska. The image shows a massive, textured glacier with white and light blue ice on the left, meeting a steep, dark, rocky mountainside on the right. A channel of turbulent, gray water with white foam flows between the glacier and the mountain, indicating a glacial lake drainage. The caption at the bottom reads, "Glacial lake drainage at the terminus of the Hubbard Glacier, Alaska, 2002. Photo: USGS."


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial Lake Outburst Floods (GLOFs)
Type: text

Glacial lake drainage at the margin of the Hubbard Glacier, Alaska, 2002. Photo: USGS, Wikimedia Commons.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Why are GLOFs important?
Type: text

Some of the largest floods in Earth's history have been GLOFs5,6. They have caused large-scale landscape change5, and even altered regional climate by releasing huge quantities of freshwater to the oceans7,8.

Today, GLOFs pose a risk downstream communities and infrastructure. In Peru alone, GLOFs were responsible for  $\sim$ 32,000 deaths in the 20th century3,4. They have killed hundreds to thousands of people in other mountain regions (e.g. the Himalayas), and destroyed roads, bridges, and hydroelectric developments3.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: An increasing hazard
Type: text

Importantly, the general global trend of glacier shrinkage through the 20th and 21st centuries has seen the number and size of glacial lakes increase9-11, at the same time as human activities have expanded further into glaciated catchments. The study of how GLOFs occur and their impacts is therefore important for future hazard mitigation.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: An increasing hazard
Type: figure
Figure

Image /page/1/Picture/6 description: An aerial photograph of a vast, mountainous landscape dominated by glaciers and snowfields. The mountains are rugged and brown, contrasting with the white snow and the light blue and white glaciers that flow down through the valleys. Several bodies of water, identified as "glacial lakes" by white text and lines, are visible. These lakes, varying in color from deep blue to turquoise, are situated at the terminus of the glaciers. The labels appear in three locations: the upper left, the upper center, and the lower right of the image.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: An increasing hazard
Type: text

Recent rapid growth of glacial lakes in the Bhutan-Himalaya in response to retreating glacier termini. Photo: NASA/USGS, <u>Wikimedia Commons</u>.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial lake settings
Type: text

There are two main settings in which glacial lakes form1,2: (1) behind **moraine dams**, and (2) behind **ice dams**.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial lake settings > Moraine-dammed lakes
Type: text

Moraine-dammed lakes form during periods of glacier retreat from a moraine1,12. As a glacier margin retreats, water collects in the topographic low between the ice-front and the abandoned frontal and/or lateral moraine. Most existing moraine-dammed lakes (such as the Imja Tsho glacial lake in Nepal; see below) formed when mountain glaciers began to retreat from large moraine ridges constructed during the Little Ice Age12.

Glacial lake settings
Moraine-dammed lakes


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial lake settings > Moraine-dammed lakes
Type: figure
Figure

Image /page/2/Picture/3 description: A photograph of a high-altitude mountain landscape illustrating various glacial features with labels. In the background, massive, jagged, snow-covered peaks rise against a clear blue sky. In the center of the image is a large, white, ice-covered body of water labeled 'lake'. To the upper left of the lake, a 'glacier' is shown descending from the mountains. To the right of the lake, a ridge of rock and debris is identified as a 'lateral moraine'. In the foreground, a vast, rugged area of rock, sediment, and small meltwater ponds is labeled as the 'terminal moraine complex'.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial lake settings > Moraine-dammed lakes
Type: text

Imja Tsho (or Imja Lake) in eastern Nepal, dammed by a terminal moraine complex. The lake has been growing rapidly since the 1960s as the Imja Glacier has retreated. Photo: Sharad Joshi, Wikimedia Commons, Edited by J.Bendle.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial lake settings > Ice-dammed lakes
Type: text

In contrast to moraine-dammed lakes, ice-dammed lakes form when drainage is blocked by a glacier that advances or becomes thicker13,14. Consequently, ice-dammed lake growth is closely related to glacier mass balance and climate. Ice-dammed lakes form wherever a glacier blocks the drainage of meltwater.

The main settings for ice-dammed lakes are shown in the diagram below. These include: where a tributary valley is blocked by a trunk glacier; where a glacier from a tributary valley advances across the main trunk valley; in openings between the lateral glacier margin and ice-free valley sides; and at the point where two glaciers join.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial lake settings > Ice-dammed lakes
Type: figure
Figure

Image /page/3/Picture/3 description: A diagram illustrating various features in a glacial environment. The image shows large, light-blue glaciers surrounded by gray land. Dotted arrows indicate the direction of 'ice flow'. Several features are labeled, including a 'tributary glacier' merging with a larger glacier at an 'ice confluence', and a 'retreating tributary glacier'. Different types of lakes, shown in a darker blue, are also labeled: a 'marginal lake' at the edge of the ice, a 'supraglacial lake' on top of the ice, and a 'proglacial lake' in front of the glacier. A 'river' is shown flowing between two large glacier masses. 'Streams' and 'drainage' are depicted as water flowing away from the glaciers onto the land. The bottom right corner includes the credit '© J.Bendle'.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Glacial lake settings > Ice-dammed lakes
Type: text

Settings of ice-dammed lake growth. Potential routed of lake drainage, beneath or around glacier margins, are shown with dashed blue lines. © J.Bendle.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams
Type: text

The failure of glacier and moraine dams depends on two main factors: (1) the **integrity of the dam**, and (2) the **nature of trigger mechanisms**1,2,12,13.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures
Type: text

Moraine dams tend to be narrow and sharp-crested12. As such, they are more likely to fail than broader dam types, such as ice-contact fans or landslides (see diagram below). Most moraines are made up of loose, poorly sorted, permeable sediment, and some contain ice cores12. Unconsolidated sediments are susceptible to failure, especially if saturated, while the melting of ice cores may cause moraines to subside over time. Despite these weaknesses, where a moraine is low, wide, and armoured by large rock material it may survive intact for hundreds or even thousands of years12.

The failure of ice or moraine dams
Moraine-dam failures


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures
Type: figure
Figure

Image /page/4/Figure/3 description: A diagram comparing the cross-sectional profiles of various geological landforms and man-made structures. At the top, three brown shapes are labeled "moraines". Below that is a long, tapering, light-yellow shape labeled "outwash fan". Next are two long, low-profile brown landforms labeled "Daguangbao rock avalanche" and "Yaoyingyan landslide", with the former being longer than the latter. At the bottom left, two gray cross-sections of dams are labeled "Man-made gravity dams". A scale is provided at the bottom right, indicating a horizontal distance of 1000 m and a vertical distance of 200 m, illustrated with a generic shape labeled "width" and "height". The image is credited to "© J.Bendle" in the bottom left corner.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures
Type: text

Approximate width and height of typical moraine, outwash, landslide (see ref: 15), and man-made gravity dams. Compared to other common dam types, moraines are narrow, and contain a relatively small volume of material. © J.Bendle


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures > Displacement waves
Type: text

Outburst floods in moraine-dammed settings are often caused by the sudden input of material into a lake causing displacement of water and overtopping of the dam12,16. Displacement (or *seiche*) waves are commonly triggered by avalanches or rockfalls, or calving of a lake-terminating glacier as shown below.

The failure of ice or moraine dams
Moraine-dam failures
Displacement waves


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures > Displacement waves
Type: figure
Figure

Image /page/4/Picture/7 description: A diagram illustrating how a seiche wave is formed in a moraine-dammed lake. On the left, a large light-blue glacier is shown on top of a brown landmass. The edge of the glacier is breaking off into a body of water in a process labeled "calving". This creates a large wave, labeled "seiche wave", in the "moraine-dammed lake". The wave travels to the right and crashes over a mound of earth labeled "moraine dam" in a process called "overtopping".


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures > Displacement waves
Type: text

Calving of ice blocks at the glacier terminus is a common cause for lake water displacement, seiche wave development, and overtopping of moraine dams. © J.Bendle.

Other triggers include, the rapid input of meltwater from an glacier upstream, heavy rainfall or snowmelt events that rapidly raise the lake level, or earthquakes that destabilise the moraine dam12,14.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures > Breach incision
Type: text

Once overtopped, a process called **breach incision** can occur. This occurs as water flowing across the dam surface erodes a channel into the moraine, starting a positive feedback process where channel incision allows more water to escape, and the higher discharges leaving the outlet encourage greater rates of erosion (see diagram below). This process allows a moraine dammed lake to drain

very rapidly12,16. It also adds large volumes of unconsolidated sediment to the flood waters, which may then develop into highly destructive debris flows12.

The failure of ice or moraine dams
Moraine-dam failures
Breach incision


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures > Breach incision
Type: figure
Figure

Image /page/5/Picture/4 description: A high-angle photograph of a mountainous, glacial landscape with several features labeled. In the center is a milky, light-green body of water labeled "lake." This lake is dammed by a large ridge of rock and sediment, which is labeled "moraine." A V-shaped gap in this ridge is identified as a "moraine breach." Higher up the slope from the lake, a patch of snow and ice is labeled "glacier." A narrow, winding trail crosses the steep, rocky terrain, and two hikers with large backpacks are visible on the path.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures > Breach incision process
Type: figure
Figure

Image /page/5/Picture/6 description: A diagram illustrating the process of moraine incision over three time steps, labeled T1, T2, and T3. In T1, water is shown "overtopping" a moraine. In T2, this has led to a small "moraine incision," with water flowing through it. In T3, the incision has become a much larger, V-shaped channel. Arrows indicate that this process leads to an "increasing discharge" of water as the channel deepens.

The failure of ice or moraine dams
Moraine-dam failures
Breach incision process


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures > Breach incision process
Type: text

Example of moraine breach at Schoolroom Glacier lake, Grand Teton National Park, USA, with schematic to show breach incision process © J.Bendle. Photo: National Park Service, Wikimedia Commons.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Moraine-dam failures > Moraine degradation
Type: text

Piping (the seepage of water and sediment through a moraine weakening its internal structure) or the melting of ice cores can trigger moraine collapse, or lower the moraine to the point where a smaller displacement wave will cause overtopping and breach incision12.

The failure of ice or moraine dams
Moraine-dam failures
Moraine degradation


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Ice-dam failures
Type: text

Unlike moraine dam failures, the drainage of an ice-dammed lake does not necessarily end in dam destruction1,13,17. This is because water can drain through subglacial channels that close up when

water discharge drops, or because an ice-margin may temporarily float before regaining contact with the bed18,19. The time-lapse video below (provided by Joe Mallalieu) gives an example of glacial lake drainage at Russell Glacier, Greenland, due to temporary glacier flotation19.

http://www.antarcticglaciers.org/wp-content/uploads/2018/10/RG-GLOF-2015 JoeMallalieu.mp4

The failure of ice or moraine dams
Ice-dam failures


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation
Type: text

Ice-dam floatation occurs due to the difference in density between ice ( $\sim$ 0.9 g/cm³) and water ( $\sim$ 1.0 g/cm³)13,18,19. As shown in the diagram below, when lake level reaches 90% of the glacier dam height the ice will lift from the bed, and water can escape through subglacial conduits. However, as a lake empties, and its level falls below 90% of dam height, the glacier drops to the bed and subglacial conduits close up. This stops lake drainage and allows a lake to refill.

The failure of ice or moraine dams
Ice-dam failures
Ice dam flotation


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation
Type: figure
Figure

Image /page/6/Picture/7 description: A scientific diagram, labeled T₁ in the upper right corner, illustrates a cross-section of a glacier or ice sheet interacting with a body of water. The diagram shows a large mass of light blue ice on the right and a body of darker blue water on the left, both resting on a sloping dark gray ground surface. A dashed black line labeled "flotation level" is shown above the water level. A section where the ice is grounded is marked with a bracket and the text "no flotation". An arrow points from this label to an inset box below, which provides a magnified view of the ice-ground interface. This inset is labeled "sealed conduits" and shows dashed, arch-shaped channels at the base of the ice.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation
Type: text

hydrostatic pressure < ice overburden pressure


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation
Type: figure
Figure

Image /page/6/Picture/9 description: A scientific diagram illustrating the process of lake drainage under a glacier, labeled "T₂" in the upper right corner. The main diagram shows a cross-section of a body of water on the left, dammed by a large light-blue ice mass on the right, both resting on a downward-sloping grey ground. A section where the ice begins to lift off the ground is labeled "ice-dam flotation." An arrow points from this area to a smaller, inset diagram below. The main diagram also shows water flowing from the lake under the glacier, indicated by an arrow and the label "lake drainage." The channel under the glacier is labeled "subglacial conduit." The inset diagram, connected by an arrow from "ice-dam flotation," is a magnified view of the base of the ice, showing several rounded channels labeled "open conduits."


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Ice-dam failures > Ice dam flotation
Type: text

hydrostatic pressure > ice overburden pressure

© J.Bendle

Ice-dam flotation mechanism of glacial lake drainage. The ice dam lifts from the bed when hydrostatic (water) pressure is greater than ice overburden pressure. Lake water can then escape through subglacial conduits. © J.Bendle.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: The failure of ice or moraine dams > Ice-dam failures > Overspilling
Type: text

Lake drainage can also occur by simple overspilling13,17, for example, during a large rainfall or snowmelt event that causes lake level to rise. This is most common at cold-based glaciers, which are frozen to the bed and less permeable than warm-based glaciers2.

The failure of ice or moraine dams
Ice-dam failures
Overspilling


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Differences between moraine- and ice-dammed GLOFs
Type: text

For a given potential energy (think of this as lake volume) moraine dam outbursts typically produce higher magnitude floods (see diagram below)12. However, the threat of repeated GLOFs from a single lake is low because moraine dams are often destroyed during an outburst.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Differences between moraine- and ice-dammed GLOFs
Type: figure
Figure

Image /page/7/Figure/5 description: A log-log plot titled 'Flood size vs. potential energy of dammed lake water', which is noted as being 'Redrawn and modified from Costa & Schuster (1988)'. The x-axis is 'Potential energy (joules) of dammed lake water', with a scale from 10^7 to 10^16. The y-axis is 'Maximum flood discharge (m/sec)', with a scale from 10^1 to 10^5. The graph shows two positively correlated trend lines. The upper, green dashed line is labeled 'Moraine-dam outbursts (higher discharges for same potential energy)'. The lower, blue dashed line is labeled 'Ice-dam outbursts (lower discharges for same potential energy)'. A vertical dashed line at a potential energy of 10^13 joules highlights two points, one on each trend line, which are collectively labeled 'GLOF'. At this potential energy, the moraine-dam outburst has a discharge of approximately 8x10^3 m/sec, while the ice-dam outburst has a discharge of approximately 2x10^2 m/sec.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Differences between moraine- and ice-dammed GLOFs
Type: text

Flood size vs. potential energy of lake water (plotted on logarithmic scale). Moraine-dam outbursts produce, on average, GLOFs with higher flood discharges, than ice-dam outbursts. For example, a moraine- and ice-dammed lake with the same potential energy of  $10^13$  joules (black dots), will lead to a peak flood discharge of  $10^4$  and  $10^2$  m/sec respectively. Diagram created based on data originally presented in refs 12 and 13. © J.Bendle.

Ice-dammed lake drainage, by contrast, does not often result in dam destruction, meaning that ice-dammed lakes can drain and refill many times. A good example is Lago Catchet Dos in Patagonia (see image below), dammed by the Colonia glacier20. This lake has drained in subglacial tunnels beneath the ice at least 21 times in the last 10 years20.

So, while in the modern-day ice-dammed lake outbursts typically produce lower discharge, and less destructive, floods12, they may have long-term impacts on downstream communities and infrastructure20.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Differences between moraine- and ice-dammed GLOFs
Type: figure
Figure

Image /page/8/Picture/3 description: A satellite image showing the Colonia Glacier and Lake Cachet Dos. The main image is in color and shows the large, light-blue glacier flowing through a valley. An area to the right of the glacier is labeled "lake Cachet Dos". In the lower left corner, there is a smaller, inset grayscale image showing a close-up of the glacier, which is labeled "pre-GLOF".


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Differences between moraine- and ice-dammed GLOFs
Type: figure
Figure

Image /page/8/Picture/4 description: An annotated aerial photograph illustrating the aftermath of a glacial lake outburst flood (post-GLOF). The main image shows a large valley glacier flowing between dark, rocky mountains. Several features are labeled to explain the event. A label points to a small, dark body of water next to the glacier, identified as a "lake drained." A blue dashed line with an arrow, labeled "subglacial tunnel," traces the path of the water from the drained lake down the length of the glacier. In the top left, an inset image provides a magnified view of the glacier's terminus, showing numerous "stranded icebergs" in the water.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Differences between moraine- and ice-dammed GLOFs
Type: text

Lago Cachet Dos in Patagonia, southern South America, has repeatedly drained and refilled in recent times (ref: 20). During a GLOF event, lake water drains via subglacial conduits at the bed of the Colonia Glacier. Note the large icebergs left stranded on the former lake bed after drainage. Imagery: GoogleEarth, Edited by J.Bendle.


Document: Glacial Lake Outburst Floods (GLOFs)
Section: Will GLOF hazards increase or decrease in the future?
Type: text

An important and interesting question! The threat from moraine-dammed GLOFs is typically greatest during periods of glacier retreat, whereas ice-dammed GLOFs are highest during periods of glacier growth.

Therefore, we might expect the number of moraine-dammed GLOFs to increase as mountain glaciers continue to shrink worldwide. However, because moraine dams are normally destroyed in lake outbursts, the number of GLOFs will likely start to decrease over time, as the capacity for storing glacial meltwater is gradually lost.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Type: text

Identification of potentially dangerous glacial lakes and recognition of risks associated with them, including ranking of the critical lakes, has become a priority task. Once the critical lakes are identified, the planners, developers, and scientists involved need to develop and implement appropriate measures to reduce the potential risks from these lakes. Measures include: monitoring, to provide an early indication of changes; early warning systems, to provide downstream residents and owners of infrastructure time to take avoidance action; and mitigation measures, to physically change the situation and thus reduce the risk


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems
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The United Nations International Strategy for Disaster Reduction (UNISDR), in a 2006 report, defines early warning as "the provision of timely and effective information, through identified institutions, that allows individuals exposed to a hazard to take action to avoid or reduce their risk and prepare for effective response" (United Nations 2006).

Effective GLOF monitoring and early warning systems are an important part of disaster preparedness; they have the potential to greatly reduce loss of life and property. Any such system should involve application of remote sensing tools, such as the universally available earth observation satellite data, over-flight reconnaissance with small format cameras, telecommunication, and broadcasting systems.

Much progress has already been made in this area, particularly in Nepal and Bhutan. The National Action Plan for Adaptation (NAPA) to Climate Change prepared by the Royal Government of Bhutan has placed considerable emphasis on GLOF vulnerability reduction efforts. Similarly, the Government of India has brought out a 'National Communication on Climate Change Mitigation and Adaptation' which has also pinpointed GLOF vulnerability reduction efforts. However the results are not always without problems. Some examples of early warning systems are given in the following.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Sutlei river basin, India
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Some measures have been put in place in the Sutlej River basin for monitoring, forecasting, and early warning to deal with flash floods, especially from cloudbursts. As GLOFs are one of the causative factors in propagating flash floods downstream, these measures also act as an early warning system for a GLOF. Telemetry stations set up by the Snow and Hydrology Division of the Central Water Commission in Sumdo, at the confluence of the Parechu and Spiti rivers, and Khaab, at the confluence of the Spiti and Sutlej rivers, and by the Naptha-Jhakri project at Dubling, are intended to monitor any increase in the water level and to relay information. They were introduced in response to the gap in early warning that was felt after the floods in 2000, and also for the protection of hydropower projects. Similarly, a wireless network at Reckong Peo, used by security personnel with connections to border outposts, and the Doordarshan Satellite Earth Station and All India Radio Relay Centre, have been very useful in generating warnings and in communicating during emergencies (UNDP 2008).


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Tsho Rolpa and Tamakoshi valleys, Nepal
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Lake Tsho Rolpa in the Tamakoshi sub-basin in eastern Nepal is fed by Trakarding glacier and is one of a small number of glacial lakes that have been studied in detail, including field investigations of the lake itself and the downstream area. This investigation led to a realisation in the 1990s that the risk associated with the lake, whose level had risen to the crest of its containing end moraine dam, was potentially serious (WECS 1993, 1994). It was feared that Rolwaling valley downstream of the lake could be inundated due to catastrophic outflow and that widespread loss of life and serious damage

to local infrastructure, including potential damage to the 60 MW Khimti Hydroelectric Project, was a high probability (Reynolds 1999).

Attempts to significantly reduce the risk of a GLOF occurring from Tsho Rolpa have been extensively documented. Although the early warning system(s) was initially to some extent successful, a serious problem arose as a result of interference from local people. Details of the early warning systems are given below.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems
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An intensive warning system is needed during short periods when construction projects are underway or for other reasons when a significant risk is identified. In June 1997, a manual early warning system was installed by the Nepal Government as an emergency measure for the local villagers and the Khimti Hydropower Project, under construction at that time. The emergency measure was needed in view of the rapid rate of deterioration of the moraine dam, and the construction activities of the Tsho Rolpa GLOF Risk Reduction Project (TRGRRP) which aimed to lower the lake level (see next section). Early warning systems were installed in the Rolwaling and Tamakoshi valleys. Army camps were established at the terminal moraine and at Naa village, the nearest village to the lake. Police posts were also established at Naa and Bedding villages. Each army camp and police post was provided with a high frequency radio transceiver; the army post at Naa also had a backup set. The police posts and the army camp in Naa were in regular radio contact with their respective headquarters in Kathmandu. The army posts were also provided with satellite telephones. The lakeside army post used one of the phones to contact the Disaster Prevention Cell at the Home Ministry twice a day to deliver status reports. In the event of a GLOF, Radio Nepal, the national broadcaster, would broadcast a warning (Reynolds, 1999; Bajracharya, et al., 2007).

In January 1998, MeteorComm and its partner, British Columbia Hydro International Ltd. (BCHIL) of Vancouver, signed a contract with the Government of Nepal, Department of Hydrology and Meteorology (DHM), to design, supply, install and commission an audible warning system downstream of Tsho Rolpa (www.MeteorComm – Wireless Communications Tsho Rolpa.mht). The project was financed by the World Bank (WB) at a total cost of US \$ 1,032,000. This first early warning system installed at Tsho Rolpa and the villages of the Tamakoshi valley in May 1998 was intended to warn people living in downstream areas in the case of a GLOF event, and consisted of a GLOF sensing and warning system. The sensors would detect the occurrence of a GLOF and transmit relevant information to the transmitter station thus setting in motion the warning process. The warning would sound to alert the local people downstream. This system was fully automated and required no human intervention.

The GLOF sensing system consisted of six water level sensors installed along the right bank of the river channel immediately downstream of the lake outlet at Sangma Kharka; this was designed to detect the onset of a breach. Three sensors were connected by armoured and shielded cables to each of the two independently functioning transmitting stations (Figure 5a) located at a higher elevation and within 80 m of the sensors. Each sensor was located at a different elevation above the previous high water mark such that different sensors would be able to indicate any progressively rising river stages. Thus, the sensing system would detect the occurrence of a GLOF immediately and signal directly to all warning stations located downstream within two minutes of initiation of a flood. The remote station at Naa village had the dual function of forming part of the GLOF sensing system and providing local warning to the village residents.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems
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were connected by armoured and shielded cables to each of the two independently functioning transmitting stations ( Figure 5a ) located at a higher elevation and within 80 m of the sensors . Each sensor was located at a different elevation above the previous high water mark such that different sensors would be able to indicate any progressively rising river stages . Thus , the sensing system would detect the occurrence of a GLOF immediately and signal directly to all warning stations located downstream within two minutes of initiation of a flood . The remote station at Naa village had the dual function of forming part of the GLOF sensing system and providing local warning to the village residents .

The warning system consisted of 19 warning and relay stations installed at the 17 villages of the Rolwaling and Tamakoshi valleys (Figure 5b). The warning stations located in the villages had Meteor Communication Corporation (MCC) 545-transceiver units mounted on 4.67m self-supporting standard galvanised iron power poles. The antennae were mounted on extensions to the poles approximately 5 m above the ground. Lightning rods and solar panels were mounted on the same poles. MCC 545 units, with battery and relay for the horns, were mounted inside sheet metal boxes with lockable shelters, also attached to the poles. All cables were protected by plastic conduits, covered by galvanised sheet metal and strapped to the poles. Air-powered horns, designed to operate off charged air cylinders for a period of two minutes, with a reserve for an additional one to two minutes, were also mounted on the poles. The air horns could provide a sound of 80 dB up to a minimum distance of 150 m under the most adverse conditions. They were backed up by electric horns which could operate for four minutes. The GLOF warning systems were based on 'extended line of sight' (ELOS) VHF radio technology. The warning signal would be transmitted via ELOS ground wave signals from remote station to remote station down the valley. Thus, an early warning signal would be triggered automatically if a GLOF was detected.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems
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Figure: Figure 5: The Tsho Rolpa/Tamakoshi valley early warning system: a) Sangma Kharka transmitter station outside lake Tsho Rolpa receives signals from sensors and transmits to other remote warning stations; b) the warning and relay station Gongar village, comprising solar panel, battery, antenna and amplifier with siren; c) destroyed siren pole at Sigati village; d) damaged siren at Bhorle village

Figure 5: The Tsho Rolpa/Tamakoshi valley early warning system: a) Sangma Kharka transmitter station outside lake Tsho Rolpa receives signals from sensors and transmits to other remote warning stations; b) the warning and relay station Gongar village, comprising solar panel, battery, antenna and amplifier with siren; c) destroyed siren pole at Sigati village; d) damaged siren at Bhorle village


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems
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Figure

Image /page/2/Picture/2 description: A photograph of a weather or communication station in a mountainous valley. A tall, grey metal pole is in the center, topped with a multi-element antenna. A small solar panel and a grey equipment box are attached to the side of the pole. The station is located on a rocky, grassy hillside, with steep, dark mountains in the background under a blue sky with white clouds. A partial metal fence is visible around the base of the pole. In the bottom left corner, there is a white circle with the letter 'a' inside.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems
Type: figure
Figure

Image /page/2/Picture/3 description: A close-up photograph of a solar-powered communication and alarm system mounted on a grey pole against a backdrop of dense green foliage on a hillside. At the top of the pole is a Yagi-Uda antenna with several horizontal elements. Below the antenna, a red horn-style siren is attached. Further down, there is a small solar panel tilted upwards, and below that, a larger, light-colored utility box is fixed to the pole. In the bottom left corner of the image, there is a white circle containing the letter 'b'. The entire image is framed with a blue border.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems
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Figure

Image /page/2/Picture/4 description: An outdoor, slightly blurry photograph of an overgrown, weedy area. A long, gray pipe lies on a grassy slope amidst dense green vegetation. In the background, there is a low, weathered stone or brick wall leading up to a building with a dark opening. The image is framed with a thick blue border, and there is a white circle with the letter 'c' in the bottom-left corner.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems
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Figure

Image /page/2/Picture/5 description: A photograph of a rural, hilly landscape. In the foreground, a person wearing a white cap and a backpack walks through green vegetation. In the center of the image stands a tall, grey pole with a red siren mounted on top. Behind the person and near the pole is a small building with a corrugated metal roof. The background consists of a steep, rocky hillside with patches of green foliage, suggesting a landslide area. A small white circle containing the letter 'd' is in the bottom left corner.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The early warning systems
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A second component of the system was the installation of an MCC meteor burst master station located in Dhangarhi, western Nepal. A meteor burst station uses the ionised trails of meteors to extend the range of transmitted radio signals to over 1,600 kilometres (1000 miles). Several of the warning stations, as well as a sensing station, were designed to transmit and receive signals from the master station, to provide further redundancy to the system. The master station also monitored the status of the entire warning system. Thus, the master station provided a communication link between remote stations located in the Rolwaling and Tamakoshi valleys and the monitoring station in Kathmandu.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > The outcome
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By 2002, four years after establishment, the early warning system was no longer operating (Figures 5c,d) despite the fact that it was a robust system commissioned with the latest technology. Lack of participation by the local communities and disruption of communications during Nepal's long period of political uncertainty, appears to have led to the system being ignored and then destroyed, with components being taken to use for other purposes locally. The people in the area thought that the lake had been reduced to a safe level and lost interest in the warning system. The tendency towards ignoring the importance of early warning was further intensified by the incidence of false alarms (Khanal et al. 2009).


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Early warning system in the Upper Bhote Koshi, Nepal
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Another example of an early warning system, similar to that of Tsho Rolpa, is in place in the Upper Bhote Koshi valley of eastern Nepal. The system was installed in 2001, mainly for the Upper Bhote Koshi Hydroelectric Project. It consists of two remote sensing stations with data loggers near the Friendship Bridge at the Nepal-China border that are designed to receive, analyse, and transmit data from sensors. If the water level increases significantly, the system will transmit warning signals to stations located at the intake and the powerhouse site of the power station. In this system, there are seven GLOF detection sensors at the Friendship Bridge, one ultrasonic water level measuring device, and six float type water level switches. It operates on short-burst VHF radio signals using meteor burst technology. Warning sirens are set off from compressed air horns which transmit a sound of 127 dB at a minimum distance of 33 m. There are five such stations along the river (Bajracharya et al. 2007), but the early warning system is only installed up to the border between Nepal and China. From there, there is only 6 minutes of warning time down to the Bhote Koshi hydropower station. To be really useful, the early warning system would need to be extended further upstream into China (TAR). As of 2009, the system was still fully functioning – presumably because of the interest of the hydropower project.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring and early warning systems > Poiqu basin (Sunkoshi – Bhote Koshi basin)
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The inventory by Mool et al. (2005) indicated that the number of lakes in the Poiqu basin, TAR, China (Sunkoshi – Bhote Koshi basin), increased from 119 in the 1980s to 139 in 2000, with an increase of 22% in lake area. Nine potentially dangerous lakes were identified from analysis of multi-temporal satellite images and use of GIS tools, based on different criteria. It was recommended that the lakes be monitored regularly and detailed field studies undertaken. Ten sites were selected for installation of early warning systems, some located in Nepal. As of 2009, no systems have been installed.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal
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Imja glacial lake has been one of the fastest growing lakes in the Himalayas and ICIMOD has been monitoring the lake as a basis for devising an early warning system. A remote sensing system (using geo-ICT tools and techniques) was developed in cooperation with the Department of National Parks and Wildlife Conservation (DNPWC) and Keio University of Japan (Bajracharya 2009). Two monitoring devices or field servers – the Internet field observation robot (Figures 6a,b) – were installed on the lake shore at 5,000 masl and close to the nearest big settlement, Namche Bazaar, in 2007 by a team led by Keio University, with other researchers from the National Agricultural Research Centre, Japan. It was connected to the Internet in collaboration with the Asia Pacific Advanced Network (APAN) and Nepal Research and Education Network (NREN). The field servers capture time lapse images of the lake and Namche Bazaar (Figures 6c, d) and meteorological data. These are transferred in real-time by Wi-Fi to a server located in Japan at http://fsds.dc.affrc.go.jp/data4/Himalayan/ (Asia Disaster Report 2007).

The World Wildlife Fund (WWF) – Nepal is also conducting a climate change impact assessment of the Everest region, especially Imja Tsho and downstream areas. In partnership with the Department of Hydrology and Meteorology, a simulation has been made of an Imja Tsho GLOF using a dam-break model. A detailed survey of Imja Tsho, and hazard mapping for


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal
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Figure: Figure 6: Monitoring of Imja glacial lake: a) field server the lake (2009); b) solar panel for field server (2009); c) part of image from field server at lake (26 June 2009); d) part of image from field server at Namche Bazar (1 Jan 2010)

Figure 6: Monitoring of Imja glacial lake: a) field server the lake (2009); b) solar panel for field server (2009); c) part of image from field server at lake (26 June 2009); d) part of image from field server at Namche Bazar (1 Jan 2010)


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal
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Figure

Image /page/4/Picture/2 description: A side-by-side comparison of two photographs, labeled 'a' and 'b', set in a rocky, mountainous environment. In image 'a', various pieces of scientific monitoring equipment are shown amidst a field of grey rocks. This includes a tall white pole topped with a weather-proof housing for a sensor or camera, a thinner white antenna, and a smaller sensor on a tripod. In the background is a steep, rocky mountain slope. Image 'b' displays a large solar panel mounted on an angled aluminum frame, secured among large rocks. In the background of image 'b', a milky-colored glacial lake is visible at the base of a mountain with a glacier.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal
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Figure

Image /page/4/Picture/3 description: A photograph of a rugged mountain landscape under a cloudy sky, framed by a blue border. In the foreground, there is a body of murky, greenish-brown water, likely a glacial lake, with steep, rocky banks. Behind the lake, a large, dark mountain with patches of snow on its upper slopes rises into the clouds. A small white circle with the letter 'c' is visible in the upper left corner.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal
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Figure

Image /page/4/Picture/4 description: A high-angle photograph captures a village nestled in a steep, snowy mountain valley. The rugged, rocky mountainside in the background is partially covered with snow. The village consists of several buildings with colorful roofs, including green, blue, and red, all lightly dusted with snow. The ground is also covered in a layer of snow. In the foreground, colorful prayer flags are visible. A small white circle with the letter 'd' is in the top-left corner.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal
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communities at risk related to rockfalls and landslides, are also planned. The Information Centre established by WWF Nepal at Ghat, near Lukla provides relevant information.

ICIMOD carried out GLOF simulation studies for Dig Tsho and Imja Tsho in Nepal, and Lunana lake in Bhutan (Bajracharya et al. 2007), and GLOF modelling studies including socioeconomic vulnerability assessments for Zangzangbo, Imja, Tsho Rolpa, and Thulagi lakes in their downstream areas in 2009 (Khanal et al. 2009, unpublished report). The studies provide a basis for ascertaining the arrangements that would be needed for setting up early warning systems in the valleys. A good first approximation was made for land and settlement classification according to four defined levels of risk. This facilitated an estimate of potential losses that can be anticipated in the event of a worst case GLOF occurrence.


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Monitoring of Imja lake in the Everest region, Nepal > Early warning system in the Lunana region, Bhutan
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The Lunana area at the head of the Pho Chhu River in west-central Bhutan has been of considerable concern to the Bhutanese authorities. The Luggye Tsho GLOF of 7 October 1994 caused heavy damage to the Dzong at Punakha and 23 deaths (Richardson and Reynolds 2000).

A manually-operated early warning system has been placed in the Lunana region by the Flood Warning Section (FWS) of the Department of Energy (DoE). Two staff members from the FWS are stationed in the Lunana lake area. They are equipped with wireless sets and satellite telephones to report lake water levels on a regular basis and to issue warnings to downstream inhabitants. Several gauges have also been installed along the main river as well as at the lakes. These are monitored at various stations at different times depending on the distance from the station and base camp. The station is in regular contact with other wireless stations in the downstream areas along the Puna Tsang Chu, including the villages and towns of Punakha, Wangduephodrang, Sunkosh, Khalikhola, and Thimphu (Bajracharya et al. 2007).

The Japan International Cooperation Agency (JICA) and the Gross National Happiness Commission (GNHC) signed an agreement for the study of GLOF phenomena in Bhutan. This aims to build a network for sharing satellite data for research, to complete an inventory of historical glacial lake expansions, to conduct a detailed analysis of hazardous lakes, to assess risk factors and triggers of GLOFs, and to recommend effective countermeasures, such as deployment of early warning systems. The project will also be extended to cover the Mangde Chu basin, and to recommend mitigation measures (Kuensel Online, 11 June 2009).


Document: 3 Early Warning Systems, Monitoring, and GLOF Mitigation
Section: 3 Early Warning Systems, Monitoring, and GLOF Mitigation > Mitigation measures
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Potential outburst flood hazards can be alleviated by various techniques. The primary objective is to reduce the risk of a flood from the lake. However, coordinated measures to protect life and property in the downstream area must also be undertaken. It is imperative to have monitoring systems in place prior to, during, and after the construction of infrastructure so that settlements in the downstream area are protected against unintentional creation of hazards. Mitigation measures to effect risk reduction can be structural or non-structural. In the following, mainly structural measures are discussed.

The most common structural mitigation measures are aimed at reducing the volume of water in the lake. Reduction of the volume of water in the lake should reduce the potential peak surge discharge as well as the hydrostatic pressure exerted on the moraine dam, and is the most effective mitigation measure. There are different ways to achieve this that can be used alone or in combination:

- 1. Controlled breaching of the moraine dam
- 2. Construction of an outlet control structure
- 3. Pumping or siphoning the water from the lake
- 4. Tunnelling through the moraine barrier or under an ice dam

Mitigation measures must be brought into play in such a way that no unintentional increase in danger occurs. Since moraine dam stability is a major part of the problem, it follows that artificial disturbance of the dam itself during construction activity could actually increase the degree of danger while mitigation measures are being put into place. Thus, choice of an appropriate method for each individual lake is critical. This necessitates careful evaluation of the lake, glacier, damming

materials, and surrounding landscape. Physical monitoring systems for the dam, lake, glacier, and surroundings are necessary at all stages of the mitigation process.

In addition to reducing the volume of lake water, there are other preventative measures around the area that can help reduce the likelihood, or impact of, a GLOF. These include removing masses of unstable rocks to guard against avalanches or rockfalls hitting the lake surface and causing a surge wave, and protecting infrastructure in the downstream area. Engineering work as a part of hazard reduction efforts, especially in such remote and high altitude areas, is very expensive, however.

Some examples of programmes, projects, and interventions related to mitigation measures that have been applied to reduce the impact of GLOF risk in the Himalayan region are described below.