Document: 1 Introduction
Section: 1 Introduction
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There is increasing scientific and policy interest in detecting climate change impacts and assessing the extent to which these can be attributable to anthropogenic or natural causes. As a result, recent research demonstrating an anthropogenic fingerprint on a significant proportion of recent global glacier recession is an important step forward (Marzeion et al., 2014). The focus can now shift to glacier hazards but the complex nature of glacier–climate interactions (Roe et al., 2017) and their influence on hazards makes this a challenging task (Shugar et al., 2017).

Mountain glaciers have continued to recede (Kargel et al., 2014; Cramer et al., 2014) and thin from their late Holocene (Little Ice Age, LIA) positions and, in many cases, the rate of recession and thinning has increased over recent decades largely as a consequence of global warming (Marzeion et al., 2014). Thinning, flow stagnation and recession of glacier tongues have resulted in the formation of moraine-dammed lakes (Richardson and Reynolds, 2000). These moraines, some of which contain a melting ice core, are built from rock debris transported by glaciers. When they fail, large volumes of stored water can be released, producing glacial lake outburst floods (GLOFs). These floods have caused thousands of fatalities and severe impacts on downstream communities, infrastructure and long-term economic development (Mool et al., 2011; Riaz et al., 2014; Carrivick and Tweed, 2016).

Although much research has been carried out on the nature and characteristics of GLOFs and hazardous lakes from many of the world's mountain regions (e.g. Lliboutry et al., 1977; Evans, 1987; O'Connor et al., 2001; Huggel et al., 2002; Bajracharya and Mool, 2009; Ives et al., 2010; Iribarren et al., 2014; Lamsal et al., 2014; Vilímek et al., 2014; Westoby et al., 2014; Perov et al., 2017), there are significant gaps in our knowledge of these phenomena at the global scale and concerning their relationship to anthropogenic climate change. Detecting changes in the magnitude, timing and frequency of glacier-related hazards over time and assessing whether changes can be related to climate forcing and glacier dynamical responses is also of considerable scientific and economic interest (Oerlemans, 2005; Stone et al., 2013). Multiple case studies are insufficient to achieve a better understanding of the mechanisms leading to GLOF initiation so a more comprehensive understanding of the global frequency and timing of GLOFs is necessary. Testing such relationships at a global scale is also an important step toward assessment of the sensitivity of geomorphological systems to climate change.

Despite numerous inventories of GLOFs at regional scales (see Emmer et al., 2016), no global database has been created which focuses specifically on GLOFs relating to the failure of moraine dams. A global database is required to place GLOFs in their wider climatic context (Richardson and Reynolds, 2000; Mool et al., 2011). This means that we are unable to answer some important questions concerning


Document: 1 Introduction
Section: 1 Introduction
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to GLOF initiation so a more comprehensive understanding of the global frequency and timing of GLOFs is necessary . Testing such relationships at a global scale is also an important step toward assessment of the sensitivity of geomorphological systems to climate change . Despite numerous inventories of GLOFs at regional scales ( see Emmer et al . , 2016 ) , no global database has been created which focuses specifically on GLOFs relating to the failure of moraine dams . A global database is required to place GLOFs in their wider climatic context ( Richardson and Reynolds , 2000 ; Mool et al . , 2011 ) . This means that we are unable to answer some important questions concerning

their historic behaviour and therefore the changing magnitude and frequency of GLOFs globally through time, and their likely evolution under future global climate change. This latter point is made even more difficult by the lack of long-term climate data from many mountain regions. Given the size and impacts of GLOFs in many mountain regions, better understanding their links to present and future climate change is of great interest to national and regional governments, infrastructure developers and other stakeholders. We argue that glacier hazard research needs to be increasingly seen through the lens of change adaptation.

These issues and knowledge gaps can be addressed via a systematic, uniform database of GLOFs. Here we have compiled an unprecedented global GLOF inventory related to the failure of moraine dams. We discuss the problems involved in developing a robust attribution argument concerning GLOFs and climate change. This inventory covers only the subset of GLOFs that are linked to overtopping or failure of moraine dams. Our focus on moraine dams is motivated by (1) this type of event leaving clear diagnostic evidence of moraine-dam failures in the form of breached end moraines and lake basins, whereas ice-dammed lake failures commonly do not leave such clear and lasting geomorphological evidence and (2) the conventional hypothetical link between climate change, glacier response, moraine-dammed lake formation and GLOF production being more straightforward compared to the range of processes driving GLOFs from ice- and bedrock-dammed lakes.

Such GLOF events are often triggered by ice and rock-falls, rockslides or moraine failures into lakes, creating seiche or displacement waves, but also by heavy precipitation or ice melt/snowmelt events (Richardson and Reynolds, 2000). While climate change plays a dominant role in the recession of glaciers, downwasting glacier surfaces debuttress valley rock walls, leading to catastrophic failure in the form of rock avalanches or other types of landslides (Ballantyne, 2002; Shugar and Clague, 2011; Vilímek et al., 2014). Other climatically induced triggers of moraine dam failures include increased permafrost and glacier temperatures leading to failure of ice and rock masses into lakes and the melting of ice cores in moraine dams, which leads to moraine failure and lake drainage.

data/('Climate change and the global pattern of moraine-dammed', '.pdf')_extraction.md


Document: 1 Introduction
Section: 1 Introduction
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displacement waves , but also by heavy precipitation or ice melt / snowmelt events ( Richardson and Reynolds , 2000 ) . While climate change plays a dominant role in the recession of glaciers , downwasting glacier surfaces debuttress valley rock walls , leading to catastrophic failure in the form of rock avalanches or other types of landslides ( Ballantyne , 2002 ; Shugar and Clague , 2011 ; Vilímek et al . , 2014 ) . Other climatically induced triggers of moraine dam failures include increased permafrost and glacier temperatures leading to failure of ice and rock masses into lakes and the melting of ice cores in moraine dams , which leads to moraine failure and lake drainage .

Attribution of climate change impacts is an emerging research field and no attribution studies on GLOFs are available so far. Even for glaciers only very few attribution studies have been published to date (Marzeion et al., 2014; Roe et al., 2017). Follow-up studies from the IPCC 5th Assessment Report (Cramer et al., 2014) proposed a methodological procedure to attribute impacts to climate change (Stone et al., 2013). Based on that, a methodologically sound detection and attribution study needs first to formulate a hypothesis of potential impacts of climate change. In our case physical process understanding supports the association between climate change and GLOFs associated with morainedam failure by climate warming, resulting in glacier reces-


Document: 1 Introduction
Section: 1 Introduction
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so far . Even for glaciers only very few attribution studies have been published to date ( Marzeion et al . , 2014 ; Roe et al . , 2017 ) . Follow - up studies from the IPCC 5th Assessment Report ( Cramer et al . , 2014 ) proposed a methodological procedure to attribute impacts to climate change ( Stone et al . , 2013 ) . Based on that , a methodologically sound detection and attribution study needs first to formulate a hypothesis of potential impacts of climate change . In our case physical process understanding supports the association between climate change and GLOFs associated with morainedam failure by climate warming , resulting in glacier reces -

sion and glacial lake formation and evolution behind moraine dams which become unstable and fail catastrophically. The next step requires a climate trend to be detected, followed by the identification of the baseline behaviour of the system in the absence of climate change. The difficulty of identifying the baseline behaviour is related to several factors. The first is the existence of confounding factors, both natural and human related. For instance, the frequency of GLOFs from moraine dams also depends on factors such as the stability of the dam, including dam geometry and material or mitigation measures such as artificial lowering of the lake level (Portocarreo-Rodriguez and the Engility Corporation, 2014). Second, there are few long-term palaeo-GLOF records with which to assess baseline behaviour. Eventually, attribution includes the detection of an observed change that is consistent with the response to the climate trend, in our case a change in GLOF occurrence and the evaluation of the contribution of climate change to the observed change in relation to confounding factors. Our chief observational result is that there is an upsurge in GLOF frequency starting around 1930 and then a decline following roughly 1975 and persisting for decades (see also Carrivick and Tweed, 2014). At face value, when comparing this with the climate records, there seems to be no relationship between global GLOF frequency and concurrent climatic fluctuations, and a regional breakdown offers no solution; for example, strong climatic global (or Northern Hemisphere) warming during the period of declining GLOF frequency after 1975 appears to be counterintuitive. A simplistic inference would be that climate change does not influence GLOF incidence, but we reject this given our understanding of the physical drivers of glacier recession, lake development and drainage mechanisms. Although we know that GLOFs involve a complex set of dynamics, one of the important dynamical changes affecting GLOFs is the formation and growth of glacial lakes, and we know that there must be a relationship here to climatic warming. GLOF triggers also commonly involve extreme weather, such as extreme heat and extreme precipitation, which are intuitively linked to climate change as well, even if the attribution experiments have not yet been carried out. We thus have to dig deeper to see how GLOF frequency may be connected to climate change. The point arises that the conditions needed for a GLOF involve a long period of lake formation and growth, such that past climate changes are involved. In the Methods section we produce a model whereby the history of one climate variable and its time derivative – Northern Hemisphere mean temperature and warming rate – are linked to the GLOF record.


Document: 1 Introduction
Section: 2 Methods
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We produced a database of GLOFs developed from a collation of regional inventories and reviews (e.g. GAPHAZ1, WGMS2 and GLACIORISK3 databases and the GLOF Database provided under ICL database of glacier and permafrost disasters from the University of Oslo, Reynolds and Richardson, 2000; RGSL, 1997, 2002), regional overviews and reviews (e.g. Clague et al., 1985; Xu, 1987; Costa and Schuster, 1988; Reynolds, 1992; Ding and Liu, 1992; Clague and Evans, 2000; O'Connor et al., 2001; Zapata, 2002; Raymond et al., 2003; Jiang et al., 2004; Carey, 2005; Osti and Egashira, 2009; Narama et al., 2010; Ives et al., 2010; Wang et al., 2011; Carey et al., 2012; Mergili and Schneider, 2011; Fujita et al., 2012; Iribarren et al., 2014, Emmer, 2017) and case studies of individual GLOFs (e.g. Kershaw et al., 2005; Harrison et al., 2006; Worni et al., 2012). A complete list is available in the Supplement. The GLOF database was developed from a collation of regional inventories and reviews (see Supplement). Only GLOFs that could be dated to the year and to moraine failure were included. Past temperature trends from the glacier regions of interest were extracted from three independent global temperature reconstructions (CRUTEM4.2, Jones et al., 2012, NOAA NCDC (National Oceanic and Atmospheric Administration - National Climatic Data Center), Smith et al., 2008, and NASA GISTEMP (Goddard's Institute Surface Temperature Analysis), Hansen et al., 2010). These data sets provided temperature anomaly data relative to a modern baseline beginning in 1850 for CRUTEM4.2 and 1880 for NOAA NCDC and NASA GIS-TEMP.


Document: 1 Introduction
Section: 2 Methods > 2.1 Test of direct linkage between GLOF rate and climate change
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We concentrate exclusively on the subset of GLOFs associated with the failure of moraine-dammed lakes as these are a major hazard in many mountain regions but also represent the best candidates of outburst floods for attribution to climate change. We differentiate these from other glacially sourced outburst floods, such as those resulting from the failure of an ice dam (Walder and Costa, 1996; Tweed and Russell, 1999; Roberts et al., 2003), dam overflow, volcanically triggered jökulhlaups (Carrivick et al., 2004; Russell et al., 2010; Dunning et al., 2013) or the sudden release of water from englacial or subglacial reservoirs (Korup and Tweed, 2007).

The period over which climate data are available is dependent on the region but starts in 1850 in CRUTEM4.2 and 1880 in NOAA NCDC and NASA GISTEMP. The resolution of the data is generally 5°; however, NASA GISTEMP is provided at 1° resolution but it should be noted this does

<sup>1http://www.gaphaz.org/

<sup>2http://www.wgms.ch/

<sup>3https://cordis.europa.eu/project/rcn/54168\_en.html

not imply there are more observational data in this analysis. For each region, we extract all grid points that contain a glacier as defined in the World Glacier Inventory – Extended Format (WGI-XF). With the exception of the European Alps no data set contains a complete continuous record for the period 1900–2012. We therefore take all available data points to form time series for each data set and derive a mean linear trend for the 1990–2012 period. Given large uncertainties and data gaps no attempt is made to statistically test these trends. The trends presented here are therefore considered illustrative of past changes in temperature for these regions.


Document: 1 Introduction
Section: 2 Methods > Wavelet analysis of GLOF incidence
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Wavelets are a commonly used tool for analysing non-stationary time series because they allow the signal to be decomposed into both time and frequency (e.g. Lane, 2007). Here, we follow the methodology of Shugar et al. (2010), although we use the Daubechies (db1) continuous wavelet. The wavelet power shown here has been tested for significance at 95 % confidence limits, and a cone of influence is applied to reduce edge effects. We follow Lane (2007) in choosing an appropriate number of scales (S = 28, see his Eq. 28), which are related to the shape of the cone of influence.

2 Methods
Wavelet analysis of GLOF incidence


Document: 1 Introduction
Section: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis
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A potentially destructive GLOF may elapse after a glacial lake grows to a volume where a sudden release of glacial lake water can exceed a normal year's peak instantaneous discharge. There are timescales associated with the period between a climatic (or other) perturbation and the occurrence of a GLOF. The following thought experiment demonstrates the concept of the lagging responses of GLOF activity to climate change: an initialized stable condition allows glacierclimate equilibrium, where neither the climate nor the glacier has fluctuated much for some lengthy period and where no other strongly perturbing conditions exist; e.g. there are no significant supraglacial or ice-marginal or moraine-dammed lakes, and a steady state exists in the supply and removal of surface debris. We then impose a perturbation (climatic or other) which favours eventual lake development and growth and eventually a GLOF. We describe two successive time periods which must pass before a significant GLOF can occur, and then a third period before a GLOF actually occurs: lakeinception time  $(\tau_i)$ , lake growth time  $(\tau_g)$  and trigger time  $(\tau_t)$ . The first two sum to the GLOF response time  $(\tau_{GLOF})$ as we define it:  $\tau_{GLOF} = \tau_i + \tau_g$ . The terms are for illustrative purposes: many supraglacial ponds initially go through a lengthy period where they fluctuate and drain annually and thus do not have a chance to grow beyond one season. Furthermore, lakes can grow to a point where limnological processes take over from climate; hence lake growth becomes

detached from climate change. Even so, our set of definitions can be used to explain the lagging responses of glacier lakes and GLOFs to climatic history.

A GLOF does not necessarily occur upon climate step change date  $+\tau_{GLOF}$ , which is the timescale over which the metastable system establishes a condition where a significant GLOF could occur. A trigger is needed (e.g. a large ice or rock avalanche into the lake or a moraine collapse as an ice core melts). After a sizeable glacial lake has developed, suitable GLOF triggers may occur with a typical random interval averaging  $\tau_t$ , which depends on the topographic setting of the glacier lake, valley-side geology, steepness, moraine dam properties and climate. As a result,  $\tau_t$  could range from years to centuries. Furthermore, as a lake usually continues to grow after  $\tau_{GLOF}$  has elapsed,  $\tau_{t}$  can in principle change, probably shortening as the lake lengthens and as the damming moraine degrades. The time elapsing between a climatic perturbation and a GLOF then is the sum of three characteristic sequential periods,  $\tau_i + \tau_g + \tau_t$ .


Document: 1 Introduction
Section: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis
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$ \ tau_t $ , which depends on the topographic setting of the glacier lake , valley - side geology , steepness , moraine dam properties and climate . As a result , $ \ tau_t $ could range from years to centuries . Furthermore , as a lake usually continues to grow after $ \ tau_ { GLOF } $ has elapsed , $ \ tau_ { t } $ can in principle change , probably shortening as the lake lengthens and as the damming moraine degrades . The time elapsing between a climatic perturbation and a GLOF then is the sum of three characteristic sequential periods , $ \ tau_i + \ tau_g + \ tau_t $ .

The lake inception time  $\tau_i$  might be approximated by the glacier response time, which has been defined parametrically (Jóhannesson et al., 1989; Bahr et al., 1998) but in general describes a period of adjustment toward a new equilibrium following a perturbation. We take a simple parameterization (Jóhannesson et al., 1989) and equate  $\tau_1 = h/b$ , where h is the glacier thickness of the tongue near the terminus and b is the annual balance rate magnitude. The glacier response time approximating the lake inception time may be many decades for most temperate valley glaciers, but it can range between a few years and a few centuries. The glacier response time is a climate-change-forgetting timescale. After a few response times have elapsed, a glacier's state and dynamics no longer remember the climate change that induced the response to a new equilibrium. For illustration, we adopt  $\tau_1 = 60$  years, a value typical of many temperate valley glaciers.

A supraglacial pond may drain and redevelop annually (posing no significant GLOF risk), but at some point, if there is a sustained long-term negative mass balance, supraglacial ponds commonly grow, coalesce and form a water body big enough that rapid partial drainage can result in a significant GLOF. That lake growth period is defined here as  $\tau_g$ , for which we adopt 20 years, a value typical of many temperate glacier lakes of the 20th century (e.g. Wilson et al., 2018; Emmer et al., 2015) Hence,  $\tau_{GLOF} = \tau_1 + \tau_g \approx 80$  years for the favoured values. Hence, a significant GLOF may occur at any time from 80 years following a large climatic perturbation: what the GLOF waits on is  $\tau_t$ , which could be years or a century. This concept can be extended to the lagging response of a whole population of glaciers following a perturbation in regional climate (Fig. 1).

We distinguish between climate change, which may establish conditions needed for a GLOF to happen, and weather, which sometimes may be involved in a GLOF trigger. GLOF triggers are diverse; e.g. protracted warm summer weather may trigger an ice avalanche into the lake or moraine melt-


Document: 1 Introduction
Section: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis
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Figure

Image /page/4/Figure/2 description: A figure composed of six graphs, labeled (a) through (f), analyzing Northern Hemisphere temperature anomalies and warming rates over various time scales, and their connection to glacial lake outburst floods (GLOFs).


Document: 1 Introduction
Section: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis
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Graphs (a), (b), and (c) plot the 'Northern Hemisphere temperature anomaly' on the y-axis against 'Year' on the x-axis. They show the temperature anomaly data (blue line) along with smoothed versions for tau = 20 years (red), 40 years (gray), and 80 years (black).
- Graph (a) spans from year 600 to 2000, showing multi-proxy data until 1849 and instrumental data from 1850 onwards. The anomaly fluctuates around 0 until a sharp increase after 1850.
- Graph (b) focuses on the period from 1850 to 2100, showing instrumental data up to 2014 and model projections from 2015 to 2100, depicting a continuous and steep rise in temperature anomaly.
- Graph (c) is a zoomed-in view of the period from 1940 to 2100.

Graphs (d) and (e) plot the 'Warming rate, degrees yr⁻¹' on the y-axis against 'Year' on the x-axis. They show the warming rate for tau = 20 years (red line) and smoothed versions for tau = 40 years (gray) and 80 years (black).
- Graph (d) covers the years 500 to 2000, showing fluctuations in the warming rate that become more pronounced and positive in the later period.
- Graph (e) covers the years 1850 to 2100, showing a significant increase in the warming rate, with the model projection area shaded in light red.

Graph (f) is a dual-axis plot for the 'Northern Hemisphere land' from 1904 to 2097.
- The left y-axis represents 'Warming rate, mK yr⁻¹' (red line and dots), ranging from -10 to 15.
- The right y-axis represents 'GLOFs/year' (blue line), ranging from 0 to 40.
- The plot is divided into a 'Data' section and a 'Model' section around the year 2008. It shows a correlation between the warming rate and the frequency of GLOFs, noting a '45 year glacial lake response time'.


Document: 1 Introduction
Section: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis
Type: figure
Figure: Figure 1. Reconciliation of GLOF and climate records. (a) Blue curve: composite record of Northern Hemisphere land surface temperature (merged from multi-proxy data and instrumental records, as described in the main text), plus a model of land surface temperature during the period 2015–2100. Red, grey and black curves: moving historical averages of the blue curve, as described in the text, using $\tau_{GLOF} = 20$ , 40 and 80 years. (b, c) Close-up of the more recent periods covered in (a). (d) Warming rate extracted from the moving historical averages using $\tau_{GLOF} = 20$ , 40 and 80 years. Periods of cooling and warming are shown with blue and red tints, respectively, using the $\tau_{GLOF} = 80$ years curve. (e) Close-up of (d) to a more recent period. (f) Comparison of a smoothed GLOF frequency curve (red line, GLOFs/year historical moving average) with the moving historical average Northern Hemisphere temperature (black curve) using $\tau_{GLOF} = 80$ years and shifted +45 years, where the 45-year shift is considered to be reflective of $\tau$ , the GLOF trigger timescale. See supplement text for more description and explanation.

Figure 1. Reconciliation of GLOF and climate records. (a) Blue curve: composite record of Northern Hemisphere land surface temperature (merged from multi-proxy data and instrumental records, as described in the main text), plus a model of land surface temperature during the period 2015–2100. Red, grey and black curves: moving historical averages of the blue curve, as described in the text, using  $\tau_{GLOF} = 20$ , 40 and 80 years. (b, c) Close-up of the more recent periods covered in (a). (d) Warming rate extracted from the moving historical averages using  $\tau_{GLOF} = 20$ , 40 and 80 years. Periods of cooling and warming are shown with blue and red tints, respectively, using the  $\tau_{GLOF} = 80$  years curve. (e) Close-up of (d) to a more recent period. (f) Comparison of a smoothed GLOF frequency curve (red line, GLOFs/year historical moving average) with the moving historical average Northern Hemisphere temperature (black curve) using  $\tau_{GLOF} = 80$  years and shifted +45 years, where the 45-year shift is considered to be reflective of  $\tau$ , the GLOF trigger timescale. See supplement text for more description and explanation.


Document: 1 Introduction
Section: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis
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through, or heavy winter snow may trigger an ice avalanche into the lake.

However, the relevant controlling climate in this example is that of the prior climatic history and the conditioning period defined by  $\tau_{GLOF}$  and the typical trigger interval  $\tau_t$ . Hence,  $\tau_{GLOF}$  is closely connected to climate, whereas  $\tau_t$  can be connected to weather for certain types of triggers.

The assessment above is for a single step-function climate change. Considering that climate changes continuously and glacier characteristics vary, populations of glaciers must have full distributions of  $\tau_i$ ,  $\tau_g$  and  $\tau_{GLOF}$ . Even while glaciers are still adjusting to any big recent historical climate change, more climate change accrues; glacier and lake dynamics take all that into account, either increasing the likelihood and perhaps size of a GLOF or decreasing or delaying it. Hence, the

overall GLOF frequency record cannot be synchronous with climatic fluctuations, and it also should not simply trace past climate change with a time lag; rather, the GLOF frequency record for any large population of glaciers should be definitely but complexly related to the recent climatic history.

The functional dependence on climate history is not known for any glacier or population of glaciers, but to explore the concept of a lagged GLOF response to accrued climate changes, we assert that the integration function will tend to weight recent climatic shifts more strongly than progressively older climatic shifts, the memory of which is gradually lost as the glacier population adjusts. That is, because of glacier dynamics and the responses of a population of glaciers to climatic changes, the population eventually loses memory of sufficiently older climatic changes and adjusts

asymptotically toward a new equilibrium. This should be true for any climate-sensitive glacier dynamics (Oerlemans, 2005). Though we do not know the functional form of the glacier responses (either for an individual glacier or a population), we nonetheless wish to illustrate our point while not driving fully quantitative conclusions. We propose that the integration of climate information into ongoing glacier dynamical adjustments occurs with exponentially declining weighting going backward in time from any given year. The exponential time-weighting constant may be similar to  $\tau_{GLOF}$ . We have computed a moving time-average Northern Hemisphere temperature with the weighting of the average specified by an assumed  $\tau_{GLOF} = 80$  years; the computed moving average pulls data, for any year, over the preceding period of  $\tau_{GLOF}$ ; i.e. it includes temperature information up to 240 years prior to any given year. The weighting of earlier years' temperatures within that  $\tau_{GLOF}$  is less than that of later years, according to the exponential. The cut-off at  $\tau_{GLOF}$  is arbitrary and was done for computational expediency, seeing that any climate fluctuation occurring before  $\tau_{GLOF}$  years earlier is inconsequential due to the exponential memory loss.


Document: 1 Introduction
Section: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis
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$ years ; the computed moving average pulls data , for any year , over the preceding period of $ \ tau_ { GLOF } $ ; i . e . it includes temperature information up to 240 years prior to any given year . The weighting of earlier years ' temperatures within that $ \ tau_ { GLOF } $ is less than that of later years , according to the exponential . The cut - off at $ \ tau_ { GLOF } $ is arbitrary and was done for computational expediency , seeing that any climate fluctuation occurring before $ \ tau_ { GLOF } $ years earlier is inconsequential due to the exponential memory loss .

We combined the Mann et al. (2008) multi-proxy Northern Hemisphere temperature anomaly from 501 AD to 1849, the Jones et al. (2012)4 Northern Hemisphere land instrumental temperature record from 1850 to 2014 and a model of expected warming from 2015 to 2100. It is the recent climate history at each glacier lake or region that is strictly relevant, but lacking such records and needing here to only establish the concept, we settle for the treatment described above involving the Northern Hemisphere temperature anomaly.

The model is a constant  $2.7\,^{\circ}\text{C}$  century-1 warming; noise was added from a naturally noisy but overall non-trending instrumental record from 1850 to 1899, with some years repeated to append the 2015–2100 period (Fig. 1). The Mann et al. (2008) and Jones et al. (2012) data sets were brought into congruence in 1850. Then we smoothed the composite record and model results using the  $\tau_{\text{GLOF}}$  exponentially weighted filter, as described above, where the natural logarithmic "forgetting" timescale  $\tau_{\text{GLOF}} = 20$ , 40 or 80 years for three illustrative cases. Smoothing was computed for  $\tau_{\text{GLOF}}$ , i.e. 240 years if  $\tau_{\text{GLOF}} = 80$  years. Our favoured value  $\tau_{\text{GLOF}} = 80$  years is based on large Himalayan and other temperate glacier lakes. The shorter response times would likely apply to small glaciers or those occurring in steep valleys.

Regardless of the functional form of the glacier response and lake dynamics, GLOF frequency in any given region or worldwide must lag the climate record. The historically filtered/smoothed temperature record and model incorporating  $\tau_{\rm GLOF} = 20$ , 40 and 80 years is shown in Fig. 1a–c together with the unsmoothed actual record and model temperature series. The temperature anomalies are plotted in panels (a), (b) and (c); and the warming rate in panels (d) and (e).

The historically averaged/smoothed temperature record lags fluctuations in the unsmoothed record. The lag is most easily seen where temperatures start to rise rapidly in the 20th and 21st centuries. The high-frequency temperature anomaly fluctuations also show concordantly but in damped form in the smoothed moving average curves because the curves are historical moving averages with the heaviest weighting toward the more recent years. The lagging responses are also seen at several times when the running average curves variously show warming and cooling for the same year depending on the value of  $\tau_{\rm GLOF}$ .


Document: 1 Introduction
Section: 2 Methods > 2.2 The Earth's recent climate record smoothed along glacier response timescales: development of the GLOF lag hypothesis
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warming rate in panels ( d ) and ( e ) . The historically averaged / smoothed temperature record lags fluctuations in the unsmoothed record . The lag is most easily seen where temperatures start to rise rapidly in the 20th and 21st centuries . The high - frequency temperature anomaly fluctuations also show concordantly but in damped form in the smoothed moving average curves because the curves are historical moving averages with the heaviest weighting toward the more recent years . The lagging responses are also seen at several times when the running average curves variously show warming and cooling for the same year depending on the value of $ \ tau_ { \ rm GLOF } $ .

We posit that the historically filtered warming rate (more than the temperature anomaly) drives GLOF frequency. In Fig. 1 we show GLOF frequency (smoothed over 10-year moving averages) together with the warming rate extracted from the historically filtered temperature and model temperature time series. To get a better match with the temperature treated as such, we applied a further 45-year shift. From a glacier and lake dynamics perspective, this shift might relate to the trigger timescale,  $\tau_t$ . Singular values of  $\tau_{GLOF}$ and  $\tau_t$  should not pertain globally to all glaciers but should span wide ranges. The adopted values  $\tau_{GLOF} = 80$  years and  $\tau_t = 45$  years nonetheless make for a plausible match between the GLOF and climate records. These numbers make sense in terms of glacier and lake dynamics timescales, but we reiterate that our purpose with this climate-GLOF fitting exercise is illustrative. In sum, a notable shift in GLOF frequency does not connote a concordant shift in climate, though prior climate change may still underlie the cause.


Document: 1 Introduction
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Our global analysis identifies 165 moraine-dam GLOFs, recorded since the beginning of the 19th century (Fig. 2a). The vast majority of these GLOFs (n = 160; 97 %) occurred since the beginning of the 20th century, at a time of climate warming and increasing glacier recession (Figs. 2 and 5). None of these GLOFs were associated with repeat events from the same lake. Around 65 % of GLOFs occurred between 1930 and 1990. Thirty-six GLOFs occurred in the mountains of western North America between 1929 and 2002 (Table S1 in the Supplement). Fifteen of these occurred in western Canada, 15 in the Cascade Range of the USA and four in Alaska. One occurred in Mexico and 1 in the Sierra Nevada. In the South American Andes we identified 40 GLOFs. Eleven occurred in Chile between 1913 and 2009 (including the large one in Patagonia at Laguna del Cerro Largo in 1989); one in Colombia in 1995 and 28 in Peru between 1702 and 1998. Fourteen GLOFs are listed from the European Alps. Three are from Austria between 1890 and 1940, five from Switzerland between 1958 and 1993 one from France in 1944 and five from Italy between 1870 and 1993. In the Pamir and Tien Shan mountains in central Asia, we identified 20 GLOFs, with most of these dating from the

<sup>4https://crudata.uea.ac.uk/cru/data/temperature/\T1\textbackslash#datdow


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Figure

Image /page/6/Figure/2 description: A scientific figure composed of multiple panels, labeled (a) through (f), analyzing moraine-dammed glacial lake outburst floods (GLOFs) across different regions. Each row represents a region: (a) Global, (b) European Alps, (c) Pamir & Tien Shan, (d) Hindu Kush Himalayala, (e) Alaska & Canadian Rockies, and (f) South American Andes.


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For each region, the left panel plots data from 1860 to 2000. It shows the cumulative sum of events as a black line (left y-axis), the number of events each year as blue bars (inner left y-axis), and the discharge in 10^6 m^3 as red dots (right y-axis).

For regions (b) through (f), the right side contains three smaller plots showing regional temperature anomalies (°C) from 1850 to 2011 from three datasets: CRUTEM4.2 (blue), NOAA NCDC (green), and NASA GISTEMP (yellow). Each of these plots shows a linear trend line and its value in °C per 10 years. A grey shaded area marks the period from 1970 to 2011.

Key data points for each region:
- (a) Global: The left plot shows a cumulative sum of events approaching 200 by 2000, with a notable increase in event frequency after 1940. Discharge values reach up to 250 x 10^6 m^3.
- (b) European Alps: Cumulative events reach about 17. Temperature trends are: CRUTEM4.2: 0.148 C/10 yr, NOAA NCDC: 0.108 C/10 yr, NASA GISTEMP: 0.125 C/10 yr.
- (c) Pamir & Tien Shan: Cumulative events reach about 22. Temperature trends are: CRUTEM4.2: 0.149 C/10 yr, NOAA NCDC: 0.128 C/10 yr, NASA GISTEMP: 0.133 C/10 yr.
- (d) Hindu Kush Himalayala: Cumulative events reach about 45. Temperature trends are: CRUTEM4.2: 0.056 C/10 yr, NOAA NCDC: 0.095 C/10 yr, NASA GISTEMP: 0.109 C/10 yr.
- (e) Alaska & Canadian Rockies: Cumulative events reach about 70. Temperature trends are: CRUTEM4.2: 0.108 C/10 yr, NOAA NCDC: 0.101 C/10 yr, NASA GISTEMP: 0.099 C/10 yr.
- (f) South American Andes: Cumulative events reach about 38. The discharge axis is logarithmic. Temperature trends are: CRUTEM4.2: 0.047 C/10 yr, NOAA NCDC: 0.122 C/10 yr, NASA GISTEMP: 0.158 C/10 yr.


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Figure: Figure 2. (a–f) Left: temporal distribution of regional GLOF frequency and magnitude. At all locations, the cumulative sum of events (black line) indicates an upsurge in the number of events per year. The timing of this upsurge differs by location and likely reflects an increase in reporting, especially in the early part of the record, rather than a change in GLOFs, at least until the 1970s–1990s after which the GLOF rate reduces. Right: global time series climate data from the five regions using CRUTEM 4.2, NOAA NCDC and NASA GISTEMP. Grey columns represent the baseline against which temperature is measured.

**Figure 2.** (a–f) Left: temporal distribution of regional GLOF frequency and magnitude. At all locations, the cumulative sum of events (black line) indicates an upsurge in the number of events per year. The timing of this upsurge differs by location and likely reflects an increase in reporting, especially in the early part of the record, rather than a change in GLOFs, at least until the 1970s–1990s after which the GLOF rate reduces. Right: global time series climate data from the five regions using CRUTEM 4.2, NOAA NCDC and NASA GISTEMP. Grey columns represent the baseline against which temperature is measured.


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Image /page/7/Figure/2 description: The image displays three vertically stacked plots, labeled (a), (b), and (c), all sharing a common horizontal axis representing the 'Year' from 1860 to 2010.


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Plot (a) is a bar chart with the vertical axis labeled 'Count', ranging from 0 to 8. The chart shows the frequency of an event over the years. Before 1930, the counts are very low, mostly 0 or 1. After 1930, the frequency increases significantly, with numerous years having counts between 1 and 4. The highest count of 8 occurs around the year 1960.

Plot (b) is a wavelet power spectrum. The vertical axis, labeled 'Period (y)', is on a logarithmic scale, primarily showing values between 2 and 10^1 (10). A color bar on the left indicates the 'Wavelet power (10^3 W m^-2)', ranging from 0 (dark blue) to 6 (dark red). The plot shows areas of high power (red and yellow) concentrated in two main periods. The first is from approximately 1920 to 1960 at periods of 2 to 5 years. The second area of high power is from roughly 1955 to 1980 at periods of 5 to 8 years. Other smaller, less intense (blue and green) areas are scattered after 1960.

Plot (c) is a line graph. The vertical axis, labeled 'Wavelet power (Wm^-2)', is on a logarithmic scale, ranging from below 10^0 to 2. The line shows a low power level before 1920, followed by a sharp increase to a peak around 1935-1940. The power remains high with significant fluctuations until about 2000, with another notable peak around 1990, before declining towards 2010.


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Figure: Figure 3. (a) Record of all precisely dated GLOFs from 1860 to 2011. (b) Wavelet power spectrum of global GLOF record, significant at 5 %. (c) Frequency-integrated wavelet power spectrum.

**Figure 3.** (a) Record of all precisely dated GLOFs from 1860 to 2011. (b) Wavelet power spectrum of global GLOF record, significant at 5 %. (c) Frequency-integrated wavelet power spectrum.


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late 1960s to the early 1980s. The largest number of GLOFs (55) is reported from the Hindu Kush Himalaya (HKH) including the mountains of Bhutan and Tibet, dated from the 20th and 21st century. Thirty are from Tibet (between 1902 and 2009), 12 from Nepal between 1964 and 2011(and one is reported to have occurred in 1543) and five in Pakistan between 1878 and 1974. There is uncertainty in reporting some of these GLOFs and we discuss this further in the Supplement.

From around 1930 to about 1950, GLOFs occurred with regularity but a low frequency (Fig. 3). In other words, floods occurred with relatively long period variabil-

ity (50–60 years). Starting around 1960, the frequency of these events increased (period decreased to approximately 20 years), remaining relatively high until about 1975, after which the statistically significant periodicities end, though GLOFs continue to occur.

While incomplete data restrict a full analysis of GLOF triggers, precise date, magnitude and initiation at a global scale, many GLOFS triggered by ice avalanches and rockfalls occur during summer (see Fig. 4). The characteristics of GLOFs that could be influenced by climate change include changes in magnitude, frequency, timing (either changes in seasonality or changes over longer timescales) and trigger


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Image /page/8/Figure/2 description: A 3D bar chart showing the frequency of different types of failures across four seasons. The vertical axis is numbered from 0 to 35. The horizontal axis lists the categories: Total, Ice avalanche, Rock slope failure, Heavy rainfall, and Moraine failure. The legend indicates the seasons by color: blue for Spring, red for Summer, green for Autumn, and purple for Winter. The data presented is as follows: For the 'Total' category, Spring has a value of 2, Summer is 35, Autumn is 12, and Winter is 1. For 'Ice avalanche', Spring is 2, Summer is 27, Autumn is 5, and Winter is 1. For 'Rock slope failure', Spring is 0, Summer is 2, Autumn is 2, and Winter is 0. For 'Heavy rainfall', Spring is 0, Summer is 3, Autumn is 3, and Winter is 0. For 'Moraine failure', Spring is 0, Summer is 3, Autumn is 2, and Winter is 0.


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Figure: Figure 4. Seasonal variation in the occurrence of GLOF associated with the failure of moraine dams. Only a proportion of the GLOFs have seasonal data on timings.

**Figure 4.** Seasonal variation in the occurrence of GLOF associated with the failure of moraine dams. Only a proportion of the GLOFs have seasonal data on timings.


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mechanisms. In addition, many rock avalanches into lakes triggering a GLOF may represent a paraglacial response to deglaciation from the LIA or earlier times (Knight and Harrison, 2013; Schaub et al., 2013) and this delayed response demonstrates the need to account for lags between changes in forcing and responses in attribution studies.


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From this analysis, we highlight three key observations: (1) GLOFs became more common around 1930 but then their incidence was maintained at a quasi-steady level for a few decades thereafter; (2) since about 1975, GLOF periodicity has decreased globally; and (3) the periodicities of GLOF occurrence have changed throughout the 20th century. These observations are discussed below.

Our first main observation is that GLOF frequency increased dramatically and significantly around 1930 globally and between 1930 and 1960 regionally (Figs. 1 and 2). We find no obvious reason for an abrupt improvement of GLOF reporting in 1930. While acknowledging that incompleteness of the record must be a pervasive factor throughout the early period covered by the database, we discount reporting variations as the cause of the abrupt shift. For instance, this pattern is observed in the European Alps, a region with a long history of mountaineering, glacier research and valley-floor habitation and infrastructure development. Given that we record individual GLOFs in the 19th and early 20th centuries we argue that the increase in GLOF frequency in the 1930s represents a real increase rather than an observational artefact. Following the increase around 1930, we observe a similar rate of GLOFs for the subsequent years, typically 1 per year in the following decade, increasing to 2-3 per year during the 1940s (e.g. Figs. 1a, 2a). Again, there is no evidence that incompleteness of data is a main cause of the observed pattern. We therefore conclude that the incidence of global GLOFs has remained generally constant between about 1940 and about 1960. In the 1960s and early 1970s, several years


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Image /page/8/Figure/8 description: A figure displaying five maps that show temperature anomalies in different mountain ranges, based on CRUTEM4.2 data for the period 1991–2012 relative to 1901–1920. A color scale at the bottom indicates the temperature anomaly in degrees Celsius, ranging from light orange (0 °C) to dark red (2.1 °C).


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The five maps are:
1. \*\*European Alps:\*\* Shows a high temperature anomaly, with the region colored in dark red (approximately 1.5 to 2.1 °C).
2. \*\*Pamir and Tien Shan:\*\* Shows moderate to high anomalies, with colors ranging from orange to red (approximately 0.6 to 1.5 °C).
3. \*\*Hindu Kush Himalaya:\*\* Shows lower temperature anomalies, with the region colored in light to medium orange (approximately 0.3 to 0.9 °C).
4. \*\*Alaska and Canadian Rockies:\*\* Shows a high temperature anomaly, with the region colored in dark red (approximately 1.5 to 2.1 °C).
5. \*\*South American Andes:\*\* Shows moderate anomalies, with the region colored in shades of orange (approximately 0.6 to 1.2 °C).

Each map includes latitude and longitude lines, and the mountain ranges are indicated by clusters of black plus signs.


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Figure: Figure 5. Temperature anomalies in the CRUTEM4.2 data set for each mountain region. For each region we extract all the grid points that contain a glacier as defined in the World Glacier Inventory – Extended Format (WGI-XF) and these are shown as black crosses.

**Figure 5.** Temperature anomalies in the CRUTEM4.2 data set for each mountain region. For each region we extract all the grid points that contain a glacier as defined in the World Glacier Inventory – Extended Format (WGI-XF) and these are shown as black crosses.


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saw more than five GLOFs. We argue below that the trend between 1940 and 1960 hides a more complex spatial and temporal pattern (Clague and Evans, 2000; Schneider et al., 2014).

Our second main observation is that, while there is considerable variability between regions, GLOF incidence rates have decreased since about 1975 globally (Fig. 2). There are both more and larger GLOFs during the 1970s and early 1980s in the Pamir and Tien Shan, in the 1960s in the HKH and in the 1990s in Alaska, the Coast Mountains and Canadian Rockies; and then decreases in both magnitude and frequency follow these periods. In the Andes however, GLOF incidence decreased after the early 1950s. The latter observation may be at least partly attributable to considerable GLOF mitigation measures in Peru, such as engineeringbased lake drainage or dam stabilization (Carey et al., 2012; Portocarreo-Rodriguez, 2014). Carrivick and Tweed (2014) propose several reasons why "glacial floods" may have decreased in frequency in recent decades. These include successful efforts to stabilize moraine dams and changes in the ability of fluvial systems to transmit floods over time. We argue, conversely, that this reduction may represent a "lagged" response to glacier perturbations following climate change. More research is clearly needed on this question, and we believe that our analysis, along with that of Carrivick and Tweed's, will stimulate further work and discussion.

Our third main observation is that for several decades in the 20th century, GLOF occurrence has been periodic, but that periodicity has varied. Since about 1975, and especially since 1990, the periodic nature of GLOF occurrence has diminished, even though GLOFs have continued. In other words, GLOFs since 1975 have become more irregular. We suspect that the switch to less periodic outburst floods in recent decades is related to an underlying mechanism such as topographic constraints and glacier hypsometries, with glaciers retreating into steeper slopes, implying a reduced rate of moraine-dammed lake formation – a phenomenon observed, for example, in the European Alps (Emmer et al., 2015).

The statistics of small numbers affect these regional, timeresolved records, but the overall validity of a similar mid-20th century increase and then decrease in the frequency of GLOFs can be further detected in the global record and is statistically significant (Fig. 3). We argue that the reduction in global GLOF frequency after the 1970s (especially in central Asia, HKH and North America) is real, because the contemporary reporting is likely to be nearly complete given the scientific and policy interest in glacier hazards from the late 20th century. Hence, our conclusion is that globally and regionally there have been interdecadal variations in the frequency of GLOFs, and in general the most recent couple of decades have seen fewer GLOFs than the early 1950s to early 1990s. The record's (in)completeness is not able to explain a decreasing incidence rate. This temporal variation in GLOF frequency and recent decrease is therefore a robust and surprising result and has occurred despite the clear trend of con-tinued glacier recession and glacier lake development in re-cent decades.


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reporting is likely to be nearly complete given the scientific and policy interest in glacier hazards from the late 20th century . Hence , our conclusion is that globally and regionally there have been interdecadal variations in the frequency of GLOFs , and in general the most recent couple of decades have seen fewer GLOFs than the early 1950s to early 1990s . The record ' s ( in ) completeness is not able to explain a decreasing incidence rate . This temporal variation in GLOF frequency and recent decrease is therefore a robust and surprising result and has occurred despite the clear trend of con - tinued glacier recession and glacier lake development in re - cent decades .

Our data allow us to test and refine the widespread assumption that GLOFs are a consequence of recent climate change (Bajracharya and Mool, 2009; Riaz et al., 2014). This is an important assumption because it implies that GLOF frequency will increase as the global climate continues to warm with potential major impacts for downstream regions.

The global increase in GLOF frequency after 1930 must be a response to a global forcing, considering global glacier retreat (Zemp et al., 2015), and physical process understanding suggests that this is a lagged response to the warming marking the end of the LIA (Clague and Evans, 2000). Although the global response appears sudden, in 1930 the region-by-region assessment shows that the response was asynchronous regionally and temporally over three decades (Fig. 2). This is consistent with the fact that the end of the LIA was not globally synchronous (Mann et al., 2009) and we also argue that this reflects regional variations in glacier response times.

We argue that as a climate shift occurs, after some period related to the glacier response time, previously stable or advancing glaciers start to thin and recede; after a further *limnological response time* proglacial ponds start to grow, coalesce and deepen into substantial moraine-dammed lakes. GLOFs typically occur after some additional period of time (the *GLOF response timescale*), but this time can be brief in glaciers with short response times, such as in the tropical Andes (Fig. 1).

In the HKH and central Asia the near-concordant formation of many Himalayan glacier lakes and the abrupt increase in GLOF rates in the 1950s and 1960s suggest that the GLOF response time is much smaller than the limnological response time. The moraine evidence here indicates that a shift from mainly glacier advance to recession and/or thinning occurred widely, though regionally asynchronously, between 1860 and 1910. The HKH underwent this shift by around 1860 (Owen, 2009; Solomina et al., 2015) in response to warming following the regional LIA. The limnological response time in the Himalayan–Karakoram region is thus around 100 years, i.e. substantially longer than in the tropical Andes.


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formation of many Himalayan glacier lakes and the abrupt increase in GLOF rates in the 1950s and 1960s suggest that the GLOF response time is much smaller than the limnological response time . The moraine evidence here indicates that a shift from mainly glacier advance to recession and / or thinning occurred widely , though regionally asynchronously , between 1860 and 1910 . The HKH underwent this shift by around 1860 ( Owen , 2009 ; Solomina et al . , 2015 ) in response to warming following the regional LIA . The limnological response time in the Himalayan – Karakoram region is thus around 100 years , i . e . substantially longer than in the tropical Andes .

We have arrived at a plausible explanation for the post-1930 (1930–1960) increases in GLOF rates. They are most likely heterogeneous, lagging responses to the termination of the LIA, with limnological response times of the order of decades to 100 years, depending on the region (e.g. Emmer et al., 2015). The limnological response times may be of a similar order to the glacier dynamical response times (Jóhannesson et al., 1989; Raper and Braithwaite, 2009) but are appended to them. Thus, measured from a climatic shift to increased GLOFs, the combined glaciological and limnological response times (plus GLOF response times, which may be the shortest of the three response times) may sum to roughly 45–200 years (Fig. 1). It cannot be much more

than this, because then we would not see the multi-decadal oscillations in GLOF rates in some regions or globally.

Some individual glaciers may have faster response times than estimated above (Roe et al., 2017), but taken on a broader statistical basis we infer that the most recent GLOFs are a delayed response to the end of the LIA. A fundamental implication is that anthropogenic climatic warming to date will likely manifest in increasing GLOFs in some regions of the world starting early this century and continuing into the 22nd century. In all the mountain regions considered here the available evidence indicates a warming trend over the last century around 0.1 °C decade<sup>-1</sup> (Figs. 2 and 5). The trend varies between data set and region, with the highest rates in the Pamir Tien Shan region and the lowest in the HKH. The most uncertain region is the Andes, where the sparseness of data prevents any meaningful assessment. The trends are consistent with the global mean land temperature trend  $0.95 \pm 0.02$  to  $0.11 \pm 0.02$  °C for 1901–2012, implying these regions have warmed at approximately the same rate as the global land surface.


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1 ° C decade < sup > - 1 < / sup > ( Figs . 2 and 5 ) . The trend varies between data set and region , with the highest rates in the Pamir Tien Shan region and the lowest in the HKH . The most uncertain region is the Andes , where the sparseness of data prevents any meaningful assessment . The trends are consistent with the global mean land temperature trend $ 0 . 95 \ pm 0 . 02 $ to $ 0 . 11 \ pm 0 . 02 $ ° C for 1901 – 2012 , implying these regions have warmed at approximately the same rate as the global land surface .

The baseline behaviour of glacial lake systems in the absence of climate change is not known in detail, but the low rate of GLOFs prior to 1930 may indicate that without warming the frequency would be low. The difficulty of attributing individual GLOF behaviour to climate change relates to the presence of non-climatic factors affecting GLOF behaviour, such as moraine dam geometry and sedimentology, climate-independent GLOF triggers (e.g. earthquakes) and the timescales related to destabilization of mountain slopes, producing mass movements into lakes (Haeberli et al., 2017). This represents the period of paraglaciation (e.g. Ballantyne, 2002; Holm et al., 2004; Knight and Harrison, 2013). These system characteristics may vary regionally and temporally within the evolutionary stage of a receding mountain glacier, and non-climatic factors such as lake mitigation measures additionally influence GLOF frequency and magnitude (Clague and Evans, 2000; Portocarreo-Rodriguez, 2014). We argue that while the original driver of lake development is likely to involve climate change (resulting in glacier downwasting and slowed meltwater flux through glaciers systems as glacier surfaces reduce in gradient) other mechanical and thermodynamic processes likely assume more importance as the lakes evolve, and these includes small-scale calving and insolationinduced melting of ice cliffs (e.g. Watson et al., 2017).

We also recognize that contemporary mountain glaciers are dissimilar to those that existed in the LIA. They are, in the main, shorter, thinner and with prominent moraines. Assumptions that climate processes acted on similar glacial systems over time are therefore likely to be simplistic.

Based on the analysis of our global GLOF database we have shown that a clear trend is detectable globally and regionally diversified in the 20th century with a sharp increase in GLOF occurrence around 1930. This trend is attributable to the observed climate trend, namely the warming since the end of the LIA. The delayed response of GLOF occurrence

is an exemplar for the complexities of how natural systems respond to climate change, underlining the challenges of attribution of climate change impacts. We have shown here that attribution of GLOFs to climate change is possible, although the suite of factors influencing GLOF occurrence cannot be fully quantified.


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therefore likely to be simplistic . Based on the analysis of our global GLOF database we have shown that a clear trend is detectable globally and regionally diversified in the 20th century with a sharp increase in GLOF occurrence around 1930 . This trend is attributable to the observed climate trend , namely the warming since the end of the LIA . The delayed response of GLOF occurrence is an exemplar for the complexities of how natural systems respond to climate change , underlining the challenges of attribution of climate change impacts . We have shown here that attribution of GLOFs to climate change is possible , although the suite of factors influencing GLOF occurrence cannot be fully quantified .

In addition, lake outbursts following moraine failures are likely to be quite different in different regions. This reflects differences in a number of factors including ground thermal conditions, presence or absence of ground ice and permafrost, influence of extreme weather and seismic processes, topography and glacial history. To assess these we would need to better understand the geomorphological timescales involved in lake evolution and failure to design a more robust statistical analysis and to understand each region's GLOF history. We thus recommend close attention by the Earth surface processes science community to various process timescales using field studies, satellite remote sensing and theoretical modelling.

Our inventory and the global pattern of GLOFs that is derived from it lacks in many cases precise data on the processes responsible for GLOFs. This is a consequence of incomplete reporting of GLOFs in remote mountain regions, especially before the advent and wide use of remote sensing. In many cases the record is of a large flood being observed and then some time afterwards a collapsed moraine dam is seen and the flood is attributed to this collapse. Clearly the precise details of how the collapse occurred is not always available, and this uncertainty bedevils all similar detection and attribution studies, especially on those events associated with rapid geomorphological change. This intrinsic incompleteness in the record is problematic but should not prevent reasonable assertions on GLOF triggers to be made, especially if global-scale and consistent patterns in GLOF behaviour are observed.

Future research should therefore more systematically study the factors influencing GLOF frequency and magnitude and lake formation where a distinction between GLOF conditioning and triggering factors will be helpful (e.g. Gardelle et al., 2011).

If climate (such as temperature time series) influences GLOFs, as surely must be the case, long lag times are necessarily implied by the empirical data sets. With such lags as we have modelled, this brings the increase of GLOFs following 1930 into line with temperature increases at the end of the Little Ice Age. Subsequent changes in the GLOF rate (including a several-decades-long fall in GLOF rates) can similarly be attributed to fluctuations in global warming. If these conclusions are broadly correct, a further implication is that an acceleration in GLOF rates will probably occur in the 21st century, perhaps starting rather soon. Even though the actual global warming rate for the 21st century may be nearly constant, as modelled, the fitted warming rate as plotted in Fig. 1f accelerates because of the memory of a post-LIA, pre-anthropogenic quasi-stable climate. We are enter-

ing a stage where anthropogenic warming will increasingly dominate GLOF activity and attribution of GLOFs to anthropogenic global warming will be confirmed. For now, this remains a hypothetical projection or expectation and is not yet borne out in the GLOF record.


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We conclude that the global record of GLOF following the failure of moraine dams shows a dramatic increase in GLOF occurrences from 1930 to 1970, then a decline. We also observe that the GLOF frequency has not fluctuated directly in response to global climate. A reasonable premise is that climate, glaciers, glacier lakes and GLOFs are closely connected, but the connections between climate and GLOFs are hidden in response time dynamics. We argue that response times do not necessarily reflect linear processes and that lake growth may result in none, single or multiple GLOFs from the same lake systems. Accordingly, the response times must vary widely from region to region and glacier to glacier. From this we infer that the 1930 to 1970 increase in global GLOF activity is likely a delayed response following warming that ended the LIA and decreased the rate of morainedammed lake formation. We also infer that the decrease in GLOF frequency after 1970 is likely related to a delayed response to the stabilization of climate following the LIA. In addition, a minor cause (though important locally, for instance in Peru and Switzerland in particular), GLOF mitigation engineering, may have circumvented a few GLOFs, thus contributing to the downward trend in recent decades. We can expect a substantial increase in GLOF incidence throughout the 21st century as glaciers and lakes respond more dynamically to anthropogenic climate warming. This is corroborated by recent modelling studies projecting the location, number and dimension of new lakes in areas where glacier will recede over the coming decades in the Alps, the Himalayas or the Andes (Linsbauer et al., 2016).

As a result, we argue that the sharply increased GLOF rates starting from 1930 followed by reduced GLOF frequency from high levels in the mid-20th century are both real and we speculate these trends may reflect the failure of sensitive glacial lake systems in a lagged response to initial glacier recession from LIA limits. The apparent robustness of contemporary lake systems suggests that only the most resilient moraine-dammed lakes have survived recent climate change. Predicting their future behaviour is of great importance for those living and working in mountain communities and those developing and planning infrastructure in such regions.

Data availability. The database of GLOFs analysed in this paper is attached as a supplement.

*Supplement.* The supplement related to this article is available online at: https://doi.org/10.5194/tc-12-1195-2018-supplement.

Author contributions. The project was designed by SH following discussions with JK, CH and JR. Climate model data were provided by AW and RAB. Data analysis was carried out by SH, JK, DHS, LR and UH. JR, VV and AE provided inventory data. All authors helped to write and review the text.

Competing interests. The authors declare that they have no conflict of interest.


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is attached as a supplement . * Supplement . * The supplement related to this article is available online at : https : / / doi . org / 10 . 5194 / tc - 12 - 1195 - 2018 - supplement . Author contributions . The project was designed by SH following discussions with JK , CH and JR . Climate model data were provided by AW and RAB . Data analysis was carried out by SH , JK , DHS , LR and UH . JR , VV and AE provided inventory data . All authors helped to write and review the text . Competing interests . The authors declare that they have no conflict of interest .

Acknowledgements. Stephan Harrison was funded by a Leverhulme Research Fellowship. Stephan Harrison, Richard A. Betts and Andy Wiltshire acknowledge funding under the HELIX (European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no. 603864). Andy Wiltshire and Richard A. Betts acknowledge funding from the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). John Kennedy of the Met Office Hadley Centre provided advice on handling the temperature observation data sets used in this project. Contributions by Jeffrey S. Kargel, Umesh K. Haritashya, Dan H. Shugar, and Dhananjay Regmi were supported by NASA's Understanding Changes in High Mountain Asia programme, the NASA/USAID SERVIR Applied Science Team programme, and by the United Nations Development Program. We thank C. Scott Watson and an anonymous reviewer for their detailed and incisive reviews of the paper. We also thank Georg Veh, Jonathan Carrivik and Sergey Chernomorets for further comments and clarifications of the inventory.

Edited by: Chris R. Stokes

Reviewed by: C. Scott Watson and one anonymous referee


Document: Crisis and Disaster Situations on Social Media Str
Section: ABSTRACT
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Aim/Purpose Vis-à-vis management of crisis and disaster situations, this paper focuses on

important use cases of social media functions, such as information collection & dissemination, disaster event identification & monitoring, collaborative problem-solving mechanism, and decision-making process. With the prolific utilization of disaster-based ontological framework, a strong disambiguation system is realized, which further enhances the searching capabilities of the

user request and provides a solution of unambiguous in nature.

Background Even though social media is information-rich, it has created a challenge for

deriving a decision in critical crisis-related cases. In order to make the whole process effective and avail quality decision making, sufficiently clear semantics of such information is necessary, which can be supplemented through

employing semantic web technologies.

Methodology This paper evolves a disaster ontology-based system availing a framework

model for monitoring uses of social media during risk and crisis-related events. The proposed system monitors a discussion thread discovering whether it has reached its peak or decline after its root in the social forum like Twitter. The content in social media can be accessed through two typical ways: Search Application Program Interfaces (APIs) and Streaming APIs. These two kinds of API processes can be used interchangeably. News content may be filtered by time, geographical region, keyword occurrence and

Accepting Editor Iris A Humala | Received: June 23, 2019 | Revised: August 19, August 21, 2019 | Accepted: August 22, 2019.

Cite as: Narayanasamy, S., Muruganantham. D. & Elçi, A. (2019). Crisis and disaster situations on social media streams: An ontology-based knowledge harvesting approach. *Interdisciplinary Journal of Information, Knowledge, and Management*, 14, 343-366. https://doi.org/10.28945/4420

(CC BY-NC 4.0) This article is licensed to you under a Creative Commons Attribution-NonCommercial 4.0 International License. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes.

\* Corresponding author

availability ratio. With the support of disaster ontology, domain knowledge extraction and comparison against all possible concepts are availed. Besides, the proposed method makes use of SPARQL to disambiguate the query and yield the results which produce high precision.

Contribution

The model provides for the collection of crisis-related temporal data and decision making through semantic mapping of entities over concepts in a disaster ontology we developed, thereby disambiguating potential named entities. Results of empirical testing and analysis indicate that the proposed model outperforms similar other models.

Findings

Crucial findings of this research lie in three aspects: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event, but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms has been well designed for conventional news media, but due to shortness and sparseness of tweets, there remains a bottleneck for researchers.


Document: Crisis and Disaster Situations on Social Media Str
Section: ABSTRACT
Type: text

of this research lie in three aspects : ( 1 ) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event , but the rate of distribution among topics / categories differs . ( 2 ) On specific events such as disaster , crisis or any emergency situations , the volume of information that has been accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task . ( 3 ) Relational mapping / co - occurrence of terms has been well designed for conventional news media , but due to shortness and sparseness of tweets , there remains a bottleneck for researchers .

Recommendations for Practitioners

Though metadata avails collaborative details of news content and it has been conventionally used in many areas like information retrieval, natural language processing, and pattern recognition, there is still a lack of fulfillment in semantic aspects of data. Hence, the pervasive use of ontology is highly suggested that build semantic-oriented metadata for concept-based modeling, information flow searching and knowledge exchange.

Recommendation for Researchers

The strong recommendation for researchers is that instead of heavily relying on conventional Information Retrieval (IR) systems, one can focus more on ontology for improving the accuracy rate and thereby reducing ambiguous terms persisting in the result sets. In order to harness the potential information to derive the hidden facts, this research recommends clustering the information from diverse sources rather than pruning a single news source. It is advisable to use a domain ontology to segregate the entities which pose ambiguity over other candidate sets thus strengthening the outcome.

Impact on Society

The objective of this research is to provide informative summarization of happenings such as crisis, disaster, emergency and havoc-based situations in the real world. A system is proposed which provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd's perspective.

Future Research

In the future, one shall strive to help to summarize and to visualize the potential information which is ranked high by the model.

Keywords

disaster management, social media, ontological support, semantic search,

SPARQL, RDF


Document: Crisis and Disaster Situations on Social Media Str
Section: INTRODUCTION
Type: text

For long, there has been a huge demand to develop an efficient mechanism to effectively search and extract much-needed information from the social web. Manual annotation is effectively possible in information retrieval for a limited number of documents, but impractical for a large accumulation of content, particularly in social media. And moreover, automatic annotation processes are in an infant

stage. As Ritter, Etzioni and Clark (2012) indicated, automatic annotation has not reached its complete stage. There would be deemed requirements to properly utilize ontologies to precisely govern the types of knowledge to harvest. With the support of ontology (Sakaki, Okazaki, & Matsuo, 2010), domain knowledge extraction is very relevant and relates all possible concepts. Ontology-based knowledge extraction is expected to provide a boost to the domain of this research, disaster management.

Hence, pervasive use of ontology has been highly suggested (Ha-Thuc, Mejova, Harris, & Srinivasan, 2010; Luo, Osborne, & Wang, 2012) that builds semantic oriented metadata for concept-based modeling, information flow searching and knowledge exchanging. Once the semantic aspect of metadata is built for the content available over the Web on a domain of interest, then it will provide common grounds for understanding and sharing the information, as well as increasing relevancy and reducing, perhaps even minimizing inherent ambiguities. In the past, there were projects aimed at scavenging Web content with exemplary results for semantically annotating the metadata for domain-specific semantic searches. Several noteworthy examples may be mentioned; for instance, systems like PlanetOnto, ArtEquAKT, sparse kernel learning continuous relevance model for image annotation (Moran & Lavrenko, 2014), and integration of linked data in Knowledge-Based Systems for process planning (Rehage, Joppen, & Gausemeier, 2016). However, when it comes to working with the fast transient contents of social networks, a different paradigm is needed where this research comes in to contribute.

The next potential task of the operation is accessing the news contents from social media platforms. Though the task seems simple, it is inherently complicated due to interoperability conundrum and accessibility disabilities. Most of the social media platforms have accorded privileges to the programmers accessing the content through its appropriate APIs, but they differ from one another in what they provide and are mostly resource-limited (i.e., in the sense of the number of allowed request for a unit time). The content in the social media can be accessed through two typical ways: permitting users to access and archive the past messages, it is called Search APIs; and allowing users to subscribe for the real-time data feeds, it is known as Streaming APIs. These two kinds of API processes can be used interchangeably and allow expressing information needs, such as filtering the news content by time, geographical region, keyword occurrence and availability ratio (Celik, Abel, & Houben, 2011; Raman, Kuppusamy, Dorasamy, & Nair, 2014). The harvested data however still requires further pre-processing.


Document: Crisis and Disaster Situations on Social Media Str
Section: DATA PRE-PROCESSING AND NORMALIZATION
Type: text

Though we have effective information extraction processes and well-established APIs to gather the news source content, there still is a pertinent need for preprocessing the extracted data. The news content extracted from social media can be pre-processed by natural language processing (NLP) toolkit. Common pre-processing operations are tokenization and labeling, part-of-speech tagging, semantic role labeling, dependency parsing and named entity linking (Kumar & Muruganantham, 2016; Lima, Espinasse, & Freitas, 2017). The next challenge on data pre-processing is to reduce the amount of data identifying and eliminating duplicate messages. It is not an easy task since every message posted in the social media can be valuable; de-duplicating the messages requires thorough clustering and then prioritizing them based on the event context. In order to prune this whole process, semantic-based technologies are used.

Apart from that, there are various issues associated with handling social media messages. The most prominent issues are scalability and content. The scalability issue concerns Twitter stream size, volume, and velocity. Particularly during any large crisis or severe havoc (Otegi, Arregi, Ansa, & Agirre, 2015; Sakaki et al., 2010), a huge volume of tweets and millions of messages pertaining to that event may be posted. In these critical situations, the tweet velocity would never be at a constant rate. Instead, it grows drastically and records a huge response from people over the event. If it is observed at various times, it would be discovered that same/similar tweets were repeated and reposted again

and again for the same event. This is the foremost challenge for the scalability issue; redundancy avoidance is the core factor for decision making and enhances the level of understanding over the specified event. Next, the content issue deals with tweets that are very brief and canonical (Abel, Gao, Houben, & Tao, 2011); most of the tweets posted in the social media are akin to normal speech and they pose a seminal challenge for the computational methods to deliver the correct form.


Document: Crisis and Disaster Situations on Social Media Str
Section: OBJECTIVES OF THIS RESEARCH
Type: text

The objective of this research is to provide informative summarization of social network content concerning happenings such as crisis, disaster, emergency and havoc based situations in the real world. A system is proposed that provides the summarized views of such happenings and corroborates the news by interrelating with one another. Its major task is to monitor the events which are very booming and deemed important from a crowd's perspective (Samuel & Sharma 2018; Sheth, Thomas, & Mehra, 2010). Important events cannot be adjudged as such. Instead, the suggested events must have the root on the social media such as Twitter after the specified news inception, and it must take the serious impact on the social media through series of takes by the social media users (Lei, Rao, Li, Quan, & Wenyin, 2014). The system proposed and evolved in this research monitors a discussion thread whether it has reached its peak or decline after its root in the social forum like Twitter. Eventually, it would give us insight into the evolutionary trends of the specified events over time.

Thus this research aims to address the problems of entity ambiguity and its associated entity types for purposes of disaster management. We categorize the disaster based entity domains using ontology and enhance searching capability by incrementing the explicit connection which mutually exists between entity and ontology class. In order to achieve this task, we identify major issues to deal with and study them thoroughly for efficient processing of the following results: (1) Twitter streams and conventional news media tend to offer almost similar types of news coverage for a specified event but the rate of distribution among topics/categories differs. (2) On specific events such as disaster, crisis or any emergency situations, the volume of information accumulated between the two news media stands divergent and filtering the most potential information poses a challenging task. (3) Relational mapping/co-occurrence of terms suits well conventional news media but due to shortness and sparseness of tweets there remains a bottleneck for researchers. Therefore, we cover the details of the above-stated problems at length and propose algorithms and methods to exercise the cases conservatively.

In the following sections, we present semantic filtering of entities from Twitter streams and identifying the potential meaning of tweets using domain ontology. Besides, we highlight a semantic model for disaster situations and detail at length different ontological tools available for effective filtering of semantic content. In that connection, we propose the model by which to analyze the semantic mapping of entities/terms over the concepts and disambiguate the potential named entities. Eventually, we detail the analysis and present the empirical results obtained from our proposed model that lead us to the conclusions.


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS
Type: text

In recent years, social media has gained momentum over collecting real-time events, and it has been proven that it has given the appropriate responses over the time of crisis when compared to other sources of decision-making systems. Celik et al. (2011) and Lei et al. (2014) did carry out timely searches over the crisis-related events even when the access to the online events was dropped consistently due to network latency and data traffic on the social media sites. Also, Abel et al. (2011) stated that extracting the relevant content over the crisis-related situations turned mostly ambiguous and many times redundant information. The key challenges that they addressed in the paper are how to overcome the difficulties in avoiding ambiguous results and removing redundancy. Sakaki et al. (2010) empirically discovered that during the course of extracting crisis-related information, compre-

hending the phrases entered by social media users into the site is directly affecting the search results. Hence they used Wikipedia and other organized knowledge sources to map the canonical terms thus yielding better results through employing term dissimilarity.

Grolinger, Capretz, Shypanski and Gill (2011) empirically showed event identification using statistical methods to observe an event's history and concluded on some proximity-based estimation of preciseness. They proposed a robust system that employs an algorithmic method based on 'Latent Dirichlet Allocation', which monitors the events and omits the content that is not relevant. They computed the average Euclidean distance between events, segregated abnormal changes present in the streams so that unknown distribution of data would be neglected, and got the Bag-of-Words. Another approach that they proposed in the algorithm is the log-likelihood rate by which the statistical ratio of data and TF-IDF difference present in the user-generated streams can be normalized based on term weighting and similarity score. In Wirtz, Kron, Löw and Steuer (2014), classification algorithms were used largely for event identification and classification. Many supervised learning algorithms were applied to divide the user-generated streams into the already-defined topic categories and carry out the detection process much easier than before. Approaches such as text classification and named entity recognition were extensively employed for exploring hidden events and disambiguating the selection process (using a vector space model to increase the viability of event detection).

Liu, Brewster and Shaw (2013) explored the facts that covered the most common patterns on the disaster-related content and stored the details in a separate database. Weidong, Jidong, Jia, and Danni (2012) and Liu, Shaw, and Brewster, (2013) developed a system that detects online news events and searches related events on social media for the widespread collection of related event information. In contrast to our approach of ontology-based discrimination, all of the references mentioned above are statistical in nature.


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS
Type: text

and disambiguating the selection process ( using a vector space model to increase the viability of event detection ) . Liu , Brewster and Shaw ( 2013 ) explored the facts that covered the most common patterns on the disaster - related content and stored the details in a separate database . Weidong , Jidong , Jia , and Danni ( 2012 ) and Liu , Shaw , and Brewster , ( 2013 ) developed a system that detects online news events and searches related events on social media for the widespread collection of related event information . In contrast to our approach of ontology - based discrimination , all of the references mentioned above are statistical in nature .

Lei Li and Tao Li (2013) developed a domain ontology for analyzing multi-document summarization pertaining to disaster or any crisis management. They provided many experimental models for developing an efficient ontological framework specifically on Hurricane Wilma in 2005. With that ontology, they precisely demonstrated the ontology-based multi-document summarization that performed well compared to other existing models. Jerman-Blažič, Matskanis and Bojanc (2017) discussed differences between man-made and natural calamities in detail and delineated the strategic measures, such as preparation, response, and recovery of any crisis or disaster-related situations. Their model was approved by the European Union and funded for the project REDIRNET. Later Selvam, Balakrishnan and Ramakrishnan (2018) constructed an ontology for Social Event Detection (SED) and applied it for Flickr (online photo management and sharing application) website by extracting the metadata features, such as geolocation, photoID, tags, description, title, timestamp, etc. On the other hand, they extensively utilized the Linked Open Data (LOD), such as Last.fm, Eventful, GeoNames, FourSquare, and so on, for productive discrimination of ontological properties. Although ontology/metadata-based, these works considered full documents or photo metadata in contrast to our study of short bursty messages, for example of Twitter.


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT
Type: text

The primary objective of Semantic Web technologies is to pave the way for users to easily find relevant information, navigate among diverse data sources and integrate heterogeneous information. For example, usage of semantic technologies will be highly important in getting relevant content and linking data elements to search concepts in the case of Twitter content during any heavy crisis or mass convergence event (Schulz, Ristoski, & Paulheim, 2013; Yates & Paquette, 2011). Nevertheless, the task is complicated because all such processes should be machine-readable and automated; this is where the semantic Web technologies come handy in affecting ontological enrichments (see Table 1).


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT
Type: table
Table

| Component Name | Cached | Description                            |
|-------------------|--------|----------------------------------------|
| RDF, RDFS         | YES    | Vocabulary & Markup supported          |
| MicroFormats      | YES    | Coupling Vocabulary & Markup Languages |
| Data RSS Feeds    | YES    | Fetching Atom and Metadata             |
| XSLT              | NO     | Define data prototyping                |
| Web Services      | NO     | Interoperate with remote data objects  |


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC TECHNOLOGIES IN DISASTER MANAGEMENT
Type: text

Table 1. Semantic technologies for unambiguous data handling

In the rest of this section, we expand on the need to semantically enrich social media content, survey semantic Web technologies to that end, and how to integrate data from various sources towards facilitating crisis management.


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT
Type: text

As the semantic Web technologies play a seminal role in extracting meaningful information from social media and in the context of any crisis management (Heath & Bizer, 2011; Liu, Shaw, & Brewster, 2013), robust methods are required in dealing with different expressions of text that all in turn point to the central concepts. To get out this discrimination existing in the social media messages, the sole ways of solving this complexity is through making the Web machine-readable. To deal with the above-stated problems, semantic technologies provide an efficient technique called "named entity linking." Named entity linking is the process of detecting the entities prevailing in social media messages and associating the entities which are closely matched to the specified event (see Figure 1).


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT
Type: figure
Figure

Image /page/5/Figure/6 description: A flowchart illustrating a data processing pipeline. At the top, a component labeled 'Online News Extractor' is depicted as a stack of documents. This component feeds into two parallel processing streams. The left stream consists of two steps: 'Key Event Detection and Extraction' followed by 'Add Semantic Annotation to the Events'. The right stream also has two steps: 'Twitter Streams Entity Recognition & Filtering' followed by 'Semantic Labelling and Disambiguation of Events'. The outputs from both streams converge into a central process labeled 'Semantic Modelling & Ontology Mapping'. The final output of this process is directed into a cylindrical database symbol labeled 'Knowledge Repository' at the bottom of the flowchart.


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT
Type: figure
Figure: Figure 1. Semantic enrichment of news contents

Figure 1. Semantic enrichment of news contents


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT
Type: text

The named entity linking does two things. Firstly, it crawls the social media messages related to the crisis event detecting the potentially emerging entities like names, places, locations, organizations, category, etc. Table 2 displays ontological relations instrumental in extracting potential named entities of various genres using semantic technologies.


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT
Type: table
Table: Table 2. General domain-based ontological relations

| Event                | General Relation                                                                                |
|----------------------|-------------------------------------------------------------------------------------------------------------------------------|
| Identify Relation    | is-a, isAbout, defines, occurs, exists, classifies, express, describes, isRelatedTo, sameAs.                                  |
| Temporal Relation    | hasTime, timeInterval, time-span, timeStamp, during, eventDate, begin, end, since, nextTo.                                    |
| Spatial Relation     | place, region, space, location, hasBoundary, nearTo, direction, overlap, placeName                                            |
| Causal Relation      | cause, result, factor, agent, actor, action, activities, impact, consequence, result, participant, role, product, instrument. |
| Exceptional Relation | isPartOf, hasSubEvent, hasComponent, hasMember, unifies, includes, involves, transitive, symmetric, negative, opposite        |


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT
Type: text

Secondly, for every candidate event found, the named entity links searches for events in proximity to the named entity in that context. Unlike conventional search engines, it never searches based on a keyword. Instead, it makes use of the ontologies to augment the search process rendering the system to automate the process through enabling techniques such as RDF, RDFS, and OWL (Celik et al., 2011; Gruhl, Nagarajan, Pieper, Robson, & Sheth, 2009). Table 3 provides a sample of ontologies relevant in crisis management domain.


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT
Type: table
Table: Table 3. Domain specification of ontologies for crisis management

| Domain Specification | Ontology Name                 | SW Representation  |
|----------------------|-------------------------------|--------------------|
| Resources            | SoKNOS                        | OWL-DL             |
|                      | MOAC                          | RDF                |
|                      | SIADEX                        | Unknown            |
| People               | FOAF                          | RDF                |
|                      | BIO                           | RDF                |
| Organizations        | IntelLEO                      | RDF                |
|                      | Organization OL               | RDF                |
| Disaster             | EM-DAT                        | Online Query       |
|                      | UNEP-DTIE                     | Online query       |
|                      | Canadian Disaster Database | Application System |
| Damage               | HXL                           | RDF                |
| Infrastructure       | OTN                           | RDF                |
|                      | EPANET                        | Development        |
| Geography            | GeoNames                      | RDF                |
| Meteorology          | NEW weather ontology       | OWL                |
| Hydrology            | Ordnance Survey               | OWL                |
|                      | Hydrology Ontology         |                    |

Domain Specification
Ontology Name
SW Representation


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > SEMANTIC ENRICHMENT OF SOCIAL MEDIA CONTENT
Type: text

Once the named entity linking process is completed, thus having semantically enriched the messages present in the social media, then users can search for the information they want. This is called "faceted search". In faceted search, information is not crawled based on the keyword supplied but by associating the concepts for the term through proper ontological support (Grolinger et al., 2011; Malizia, Onorati, Díaz, Aedo, & Astorga-Paliza, 2010). For example, if we give the search word "virus", it will not search the information through using the keyword "virus"; instead, it finds relevant concepts for the keyword and associated terms in the ontology hierarchy and then related media messages based on the ontological concepts are returned.


Document: Crisis and Disaster Situations on Social Media Str
Section: RELATED WORKS > PERTAINING CHALLENGES
Type: text

Tweets extracted from Twitter entails a huge amount of challenges to overcome in order to put them to use for a particular purpose. Wirtz et al. (2014) pointed out precisely that there would be no metrics stipulated about how much of information need to be monitored, extracted and evaluated. The following are major problems that present various challenges in yielding useful results:

- a) Tweets extracted cover different aspects of the event and fail to make the distinction between the choices of the events.
- b) Many tweets tend to be very noisy and sometimes irrelevant to the event thus causing unnecessary computational problems.
- c) No measure to counteract rumors germinated during the events and it has been feared that it would spread vehemently over the short course of a period.
- d) Dealing with misspelled tweets is a big task since there had been no apt use of a dictionary to makeover.