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  <front>
    <journal-meta><journal-id journal-id-type="publisher">NHESS</journal-id><journal-title-group>
    <journal-title>Natural Hazards and Earth System Sciences</journal-title>
    <abbrev-journal-title abbrev-type="publisher">NHESS</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Nat. Hazards Earth Syst. Sci.</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1684-9981</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/nhess-18-1427-2018</article-id><title-group><article-title>The Norwegian forecasting and warning service for rainfall- and
snowmelt-induced landslides</article-title><alt-title>The Norwegian forecasting and warning service</alt-title>
      </title-group><?xmltex \runningtitle{The Norwegian forecasting and warning service}?><?xmltex \runningauthor{I. K. Kr{\o}gli et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Krøgli</surname><given-names>Ingeborg K.</given-names></name>
          <email>ikl@nve.no</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Devoli</surname><given-names>Graziella</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Colleuille</surname><given-names>Hervé</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Boje</surname><given-names>Søren</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Sund</surname><given-names>Monica</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Engen</surname><given-names>Inger Karin</given-names></name>
          
        </contrib>
        <aff id="aff1"><label>1</label><institution>Department of
Hydrology, Norwegian Water Resources and Energy Directorate (NVE), Oslo,
0368, Norway</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Department of Geosciences, University of Oslo, Oslo, 0316, Norway</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Ingeborg K. Krøgli (ikl@nve.no)</corresp></author-notes><pub-date><day>25</day><month>May</month><year>2018</year></pub-date>
      
      <volume>18</volume>
      <issue>5</issue>
      <fpage>1427</fpage><lpage>1450</lpage>
      <history>
        <date date-type="received"><day>30</day><month>November</month><year>2017</year></date>
           <date date-type="rev-request"><day>19</day><month>December</month><year>2017</year></date>
           <date date-type="rev-recd"><day>20</day><month>April</month><year>2018</year></date>
           <date date-type="accepted"><day>24</day><month>April</month><year>2018</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2018 Ingeborg K. Krøgli et al.</copyright-statement>
        <copyright-year>2018</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018.html">This article is available from https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018.html</self-uri><self-uri xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018.pdf">The full text article is available as a PDF file from https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018.pdf</self-uri>
      <abstract>
    <p id="d1e132">The Norwegian Water Resources and Energy Directorate (NVE) have run a
national flood forecasting and warning service since 1989. In 2009, the
directorate was given the responsibility of also initiating a national
forecasting service for rainfall-induced landslides. Both services are part
of a political effort to improve flood and landslide risk prevention. The
Landslide Forecasting and Warning Service was officially launched in 2013 and
is developed as a joint initiative across public agencies between NVE, the
Norwegian Meteorological Institute (MET), the Norwegian Public Road
Administration (NPRA) and the Norwegian Rail Administration (Bane NOR). The
main goal of the service is to reduce economic and human losses caused by
landslides. The service performs daily a national landslide hazard assessment
describing the expected awareness level at a regional level (i.e. for a
county and/or group of municipalities). The service is operative 7 days a
week throughout the year. Assessments and updates are published at the
warning portal <uri>http://www.varsom.no/</uri> at least twice a day, for the
three coming days. The service delivers continuous updates on the current
situation and future development to national and regional stakeholders and to
the general public. The service is run in close cooperation with the flood
forecasting service. Both services are based on the five pillars: automatic
hydrological and meteorological stations, landslide and flood historical
database, hydro-meteorological forecasting models, thresholds or return
periods, and a trained group of forecasters. The main components of the
service are herein described. A recent evaluation, conducted on the 4 years
of operation, shows a rate of over 95 % correct daily assessments. In
addition positive feedbacks have been received from users through a
questionnaire. The capability of the service to forecast landslides by
following the hydro-meteorological conditions is illustrated by an example
from autumn 2017. The case shows how the landslide service has developed into
a well-functioning system providing useful information, effectively and on
time.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><caption><p id="d1e143">Type of EWS for rapid mass movements and weather-induced
landslides, combined from Stähli et al. (2015) and Calvello (2017). Quotes are from Stähli et al. (2015).</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="3">
     <oasis:colspec colnum="1" colname="col1" align="justify" colwidth="42.679134pt"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="42.679134pt"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="284.527559pt"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry namest="col1" nameend="col2" align="left">Type of EWS  </oasis:entry>
         <oasis:entry colname="col3">Explanation</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Local</oasis:entry>
         <oasis:entry rowsep="1" colname="col2">Alarm</oasis:entry>
         <oasis:entry rowsep="1" colname="col3">“It detects process parameters of ongoing hazard events to initiate an alarm automatically, e.g., in the form of red flashing lights accompanied by sirens. The accuracy of the prediction is high, but the lead time is short. The alarm decision is based on a predefined threshold.”</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">Warning</oasis:entry>
         <oasis:entry colname="col3">“It aims to detect significant changes in the environment (time-dependent factors determining susceptibility with respect to mass release), e.g., crack opening, availability of loose debris material and potential triggering events (e.g., heavy rain), before the release occurs and thus allows specialists to analyse the situation and implement appropriate intervention measures. The information content of the data is often lower in this early stage, but the lead time is extended. The initial alert is based on predefined thresholds.”</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Territorial</oasis:entry>
         <oasis:entry colname="col2">Forecasting</oasis:entry>
         <oasis:entry colname="col3">“It predicts the level of danger of a rapid mass movement process, typically at the regional scale and at regular intervals. In contrast to warning systems, the data interpretation is not based on a simple threshold but is conducted on a regular basis, e.g., daily. Experts analyse sensor data and consult models to forecast the regional danger levels, which are communicated widely in a bulletin.”</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p id="d1e212">Early warning systems (EWSs) have been defined by UN/ISDR (2009) as “a set
of capacities needed to generate and disseminate timely and meaningful
warning information to enable individuals, communities and organization,
threatened by a hazard to prepare and to act appropriately and in sufficient
time to reduce losses”. They must comprise four elements: risk knowledge,
monitoring and warning services, dissemination and communication, and
response capability (UN/ISDR, 2006). A worldwide overview of existing EWSs
for rapid mass movements and for weather-induced landslides is available in
Stähli et al. (2015) and Calvello (2017). Based on the size of the area
covered by the system, landslide EWSs can be separated into (a) local, focusing on a single landslide at slope scale, and (b) territorial, focusing on
multiple landslides at regional scale, over a basin, municipality, region or
nation (Bazin, 2012; Calvello, 2017). Stähli et al. (2015) recognized
three main categories of EWSs for rapid mass movements: alarm, warning, and
forecasting systems (Table 1).</p>
      <p id="d1e215">The number of existing territorial and landslide forecasting systems seems
to have increased in recent years (Piciullo et al., 2018). Calvello (2017)
suggests that this can be due to better cost effectiveness, compared to<?pagebreak page1428?> the
realization of structural mitigation measures; easy applicability over large
and densely populated areas where the risk to people is widespread; upgraded
technologies; and more reliable models in weather forecasts. However, this
could also be explained by the fact that several territorial EWSs working
operationally have started to become visible in international literature
just recently, mainly in the last 5 years, like the EWS from Alerta-Rio,
from Brazil (D'Orsi, 2012) operating since 1997. Others are still not well
known outside their own region, typically due to a lack of international
publication and documentation. This is the case for the Norwegian service,
described in this document. It is challenging for territorial and local
operational EWSs to reconcile typical operational tasks with research
activities and dissemination of experiences to an international audience.
Often, especially for territorial services, operational activities and
continuous improvement of the service seem to have higher priority than
publicizing the latest development internationally. For some services,
frequent catastrophic events may also limit the required time and attention
to publish articles. Furthermore, documentation is often published in the
original language of the service first, sufficient for the direct users, but
less accessible to international readers.</p>
      <p id="d1e218">The existing operative services around the world focus on prediction,
warning and sending alarm to the population about possible occurrence of
fast-moving landslides, usually shallow, which are triggered by intense
rainfall and/or snowmelt. These landslides fall in the category of flow-type
landslides (Hungr et al., 2001) like debris flows, debris flood, debris
avalanches, but translational or rotational debris and soil slides
can also be observed (Hungr et al., 2014). They occur in steep slopes, usually
covered by Quaternary loose deposits (like tills deposits, volcanic
sediments, loess, lateritic soils). Because of their long runout and
high velocity, they are responsible of large damages and casualties
worldwide (Dowling and Santi, 2014). In regions covered by snow, slushflows,
another rapid mass movement, may also be triggered during rainfall and
snowmelt episodes. Slushflows are movements of water-saturated snow which
initiate in gentle slopes and are characterized by long runouts (Washburn
and Goldthwait, 1958; Hestnes, 1985). Their high density and velocity have
caused dozens of fatalities as well as the destruction of buildings and
closure of roads and railways (Hestnes, 1998).</p>
      <p id="d1e221">With the general name “rainfall- and snowmelt-induced landslides”, used herein, we refer to debris flows, debris flood, debris avalanches,
translational or rotational debris and soil slides and slushflows, because
they often occur under the same rainfall and/or snowmelt episodes. They
regularly occur in clusters, in large number and scattered over a large
area, happening frequently together with floods.</p>
      <p id="d1e225">These types of landslides cause yearly significant damages in Norway to
roads and railways, buildings, and other infrastructure. It is expected that
climate changes, with more intense rainfall and increased temperatures, will
contribute to an increase in landslide hazard (Gariano and Guzzetti, 2016;
Hanssen-Bauer et al., 2017). It is estimated that every year about 200 of
these events hit road sectors and about 30 hit railways (Hisdal et al.,
2017). Norway has a long tradition of building physical structures (i.e.
diversion dikes, tunnels, etc.) to protect road and railway lines in the
most critical sites. Protection measures are still useful, but their
maintenance is expensive and the building operations are time consuming. The
climatic and topographic conditions in Norway<?pagebreak page1429?> indicate that it is an
impossible task to provide 100 % protection of the national infrastructure. Therefore,
forecasting and warning have become a crucial mitigation option to reduce
risks.</p>
      <p id="d1e228">Here we present the Norwegian Landslide Forecasting and Warning Service
(known as “Jordskredvarslingen” in Norwegian). The service, or some of its
components, has been partly presented and described in conference
proceedings and previous articles (i.e. Devoli et al., 2014; Boje et al.,
2014a; Bell et al., 2014; Piciullo et al., 2017). The service,
presented herein, can be categorized as a “territorial” EWS following Calvello
(2017) and as “forecasting and warning type” based on Stähli et al. (2015). The service is designed to predict the level of danger of rainfall-
and snowmelt-induced landslides. The service predicts multiple landslides at
national scale, in particular over a region (which is commonly an
administrative county or a group of municipalities) on a regular basis
(every day). As for the majority of territorial systems described in
Calvello (2017) and Piciullo et al. (2018), the Norwegian one,
presented herein, is also managed by a governmental institution that uses warning
dissemination tools to warn multiple weather-induced hazards, including
floods and snow avalanches. The service uses specialists to analyse
meteorological and hydrogeological models and forecasts, sensor data, and
predefined national and regional thresholds. Finally, the regional danger
level is widely communicated through a bulletin.</p>
      <p id="d1e231">The Norwegian Water Resources and Energy Directorate (NVE) is a directorate
under the Ministry of Petroleum and Energy and is responsible for the
administration of Norway's water and energy resources and the coordination
of national efforts for landslide and snow avalanche risk prevention. NVE
operates three forecasting services (landslide, flood, and snow avalanche)
and several local warning systems for large rockslides (Engeset, 2013; Blikra
and Kristensen, 2013). NVE has run a national flood forecasting and warning
service since 1989. Back in 2009, the directorate was given the
responsibility of also initiating a national forecasting service for
rainfall-induced landslides (white papers: Meld. St. 22, 2007–2008; Meld.
St. 15, 2011–2012). Both services are part of a political effort to improve
flood and landslide risk prevention.</p>
      <p id="d1e234">The development of EWSs for landslides started in February 2010 based on
suggestions in Colleuille and Engen (2009). The landslide forecasting
service started an operational test phase in January 2012. This service was
officially launched in October 2013 and is run in close cooperation with
the National Flood Forecasting and Warning Service. Since then, the service
has operated continuously at regional scale for mainland Norway. The service
is developed as a joint initiative across public agencies between NVE, the
Norwegian Meteorological Institute (MET), the Norwegian Public Road
Administration (NPRA) and the Norwegian Rail Administration (Bane NOR).</p>
      <p id="d1e237">This work summarized the efforts made in the last 5 years by NVE and
collaborators to design, develop, and run a nationwide landslide forecasting
and warning service in close synergy with the Norwegian Flood Forecasting
and Warning Service. The main purpose of this article is to describe the
recent development and main components of the service, also indicating how
the service is organized and how daily assessments are performed. We present
the evaluation of the accuracy of assessments and use a case study as an
example. Finally we present some feedbacks from regional and local emergency
authorities on the usefulness of this new service.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><caption><p id="d1e243">Examples of landslides and flood types in Norway. <bold>(a)</bold> Debris slide.
Veikledalen, Oppland, May 2011. <bold>(b)</bold> Debris flow. Mjåland, Rogaland, June
2016. <bold>(c)</bold> Slushflow. Troms, May 2010. <bold>(d)</bold> Soil slide. Vennesla,
Vest-Agder,
October 2017. <bold>(e)</bold> Rain flood. River Opo in Odda, Hordaland, October 2014. <bold>(f)</bold>
Flash flood. Notodden, Telemark, July 2011. <bold>(g)</bold> Snowmelt-induced flood. The
river Reisa, Troms, May 2013. <bold>(h)</bold> Combined rainfall- and snowmelt-induced
flood. River Glomma, Hedmark, June 1995.</p></caption>
        <?xmltex \igopts{width=483.69685pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f01.jpg"/>

      </fig>

</sec>
<sec id="Ch1.S2">
  <title>Major floods and landslides in Norway</title>
      <p id="d1e283">The mainland of Norway (Scandinavian Peninsula) covers an area of 324 000 km<inline-formula><mml:math id="M1" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula>,
with more than 490 000 km of rivers and streams and around 250 000 lakes. The country has large climatic contrasts, from maritime to
continental climate, because of rugged topography that causes large local
differences. The average annual precipitation is about 1400 mm, of which
about one-third is snow. The precipitation distribution is non-uniform. In Western
Norway, annual precipitation may exceed 5000 mm and daily values of 70 mm
are not uncommon. In the east, some valleys annually receive less than 300 mm. The Fennoscandian Shield constitutes the Precambrian bedrock of
Scandinavia. The oldest rocks, dating back 2.5 billion years, can be found in
Northern Norway. Above the bedrock lie remnants of the Caledonian mountain
range, while the youngest rocks are to be found in the Oslo Rift and provide
evidence of volcanic activity 250–300 million years ago (Solli and
Nordgulen, 2006). During the Quaternary, ice sheets covered Scandinavia several
times. This resulted in poorly weathered but fractured bedrock without
primary porosity, and young, sparse and thin sedimentary deposits. The
aquifers in Norway mainly consist of (a) small, highly permeable
glaciofluvial aquifers along streams and lakes, (b) small precipitation-fed
tills in mountainous areas and (c) overlying fractured bedrocks without
primary porosity, such as crystalline and metamorphosed hard rocks. The
tills have limited storage capacity and groundwater responds fast to water
input (rain and snowmelt). There are very few large and slowly responding
groundwater reservoirs in Norway. A recharge–discharge mechanism determined
by the physiographic and climatic conditions controls the groundwater level
(Colleuille et al., 2007). In winter, precipitation falls as snow and the ground
may freeze. This leads to a decrease in groundwater levels and increase
in soil water storage capacity and contributes to surface runoff in streams
and rivers. Following soil thaw and snowmelt in spring, groundwater levels
rise rapidly.</p>
      <p id="d1e295">Major natural hazards in Norway are extreme weather (wind storm, intense
rainfalls), floods and different types of mass movements. Rock fall, rock
slides, rock avalanches, mountain deformations (with a tsunamigenic
potential),<?pagebreak page1430?> debris avalanches, debris flows, debris slides, rotational clay
slides, and quick-clay slides are the most frequent landslide types in Norway
(NVE, 2011). Different types of snow avalanches can be observed and
slushflows are also common rapid mass movements (Fig. 1a–d).</p>
      <p id="d1e298">The main flood types in Norway are rain flood, flood due to snowmelt, the
combination of rainfall- and snowmelt-induced flood, and flash flood due to
intense rainfall, the last of which especially in summer (Fig. 1e–g). It is the
combination of rainfall- and snowmelt-induced flood that historically results
in the largest floods in Norway, both in return periods and extent (e.g.
south-eastern Norway, in 1995 and 2013). In coastal areas rain flood in autumn
usually results the largest floods. This especially is the case for Western
Norway and Northern Norway. In some glacial valleys, jøkulhlaup (glacier
lake outburst flood) is a reoccurring and potentially dangerous event. Flood
due to sudden release of water in ice dammed rivers, and flooding of
riverbanks due to ice dames, are also a phenomenon well known in Norway,
both during mild periods in winter and in springtime (Roald, 2013).</p>
      <p id="d1e301">Rainfall- and snowmelt-induced landslides are triggered by water. Intense or
long-duration water supply, caused by rain and/or snowmelt, increases the
water content in the soil or snow. The cohesiveness of soil or snow
particles decreases with higher water content, increasing the risk for mass
transportation. Not only steep natural slopes covered by loose Quaternary sediments
but also gentle slopes covered by snow as well as modified slopes and
filling along roads and railways are especially exposed to these kinds of
hazards. Climate scenarios for Norway indicate an increased occurrence of
extreme weather, and intense precipitation is also expected to increase
especially in the coastal areas of Norway (MET, 2013). Higher temperatures
have led to earlier spring floods, and there is a tendency to increased
frequency of rain floods. Future projections show that rain flood magnitude
will increase, while snowmelt floods will decrease over time. More frequent
and stronger intense rainfall events may in the future create specific
challenges in small, steep rivers and in urban areas. Weather conditions are
main triggers of certain types of landslides and snow avalanches; therefore
changes in climate may affect their future frequency. The risk of
slushflows will increase and may occur in areas where they have not
occurred previously (Hanssen-Bauer et al., 2017).</p>
      <p id="d1e305">The experience acquired from landslide events in Norway since 2011 shows
that they can occur all year round. Important and recent landslides events
are presented in Table 2.</p>

<?xmltex \floatpos{p}?><table-wrap id="Ch1.T2" specific-use="star"><caption><p id="d1e311">A selection of significant landslide events in Norway in
the period 2013–2017 listed from north to south. In the list the
landslide events that also occurred before 2013 and used for the definition of
thresholds are indicated. The general geomorphological and geological
conditions are also indicated, as well as the main landslide types in the
regions. Asterisks indicate that rainfall were remnants of tropical
cyclones.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.95}[.95]?><oasis:tgroup cols="7">
     <oasis:colspec colnum="1" colname="col1" align="justify" colwidth="56.905512pt"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="28.452756pt"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="56.905512pt"/>
     <oasis:colspec colnum="4" colname="col4" align="justify" colwidth="56.905512pt"/>
     <oasis:colspec colnum="5" colname="col5" align="justify" colwidth="56.905512pt"/>
     <oasis:colspec colnum="6" colname="col6" align="justify" colwidth="85.358268pt"/>
     <oasis:colspec colnum="7" colname="col7" align="justify" colwidth="85.358268pt"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Area</oasis:entry>
         <oasis:entry colname="col2">Year</oasis:entry>
         <oasis:entry colname="col3">Date</oasis:entry>
         <oasis:entry colname="col4">Name of <?xmltex \hack{\hfill\break}?>extreme <?xmltex \hack{\hfill\break}?>weather</oasis:entry>
         <oasis:entry colname="col5">Triggering</oasis:entry>
         <oasis:entry colname="col6">Max. 24 h rainfall in-<?xmltex \hack{\hfill\break}?>tensity in the area <?xmltex \hack{\hfill\break}?>(from xgeo.no)</oasis:entry>
         <oasis:entry colname="col7">Approx. number of <?xmltex \hack{\hfill\break}?>landslide events</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <?xmltex \mcwidth{426.791339pt}?><oasis:entry namest="col1" nameend="col7" align="left">Northern Norway: alpine relief or glacial relief, steep slopes (25–45<inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> and &gt; 45<inline-formula><mml:math id="M3" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>) with glacio-fluvial deposits; colluvial deposits; mainly debris slides and debris flows</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Nordland</oasis:entry>
         <oasis:entry colname="col2">2013</oasis:entry>
         <oasis:entry colname="col3">December<?xmltex \hack{\hfill\break}?>(10–12)</oasis:entry>
         <oasis:entry colname="col4">Ivar</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">80–100 mm <?xmltex \hack{\hfill\break}?>(locally up to <?xmltex \hack{\hfill\break}?>100–150 mm)</oasis:entry>
         <oasis:entry colname="col7"><inline-formula><mml:math id="M4" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 50–100 events</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Nordland and <?xmltex \hack{\hfill\break}?>Trøndelag</oasis:entry>
         <oasis:entry colname="col2">2014</oasis:entry>
         <oasis:entry colname="col3">December <?xmltex \hack{\hfill\break}?>(30–31)</oasis:entry>
         <oasis:entry colname="col4">Mons</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">40–60 mm <?xmltex \hack{\hfill\break}?>(locally up to<?xmltex \hack{\hfill\break}?>60–80 mm)</oasis:entry>
         <oasis:entry colname="col7">&lt; 50 events; many<?xmltex \hack{\hfill\break}?>slushflows</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <?xmltex \mcwidth{426.791339pt}?><oasis:entry namest="col1" nameend="col7" align="left">Central Norway: hilly terrain dominated by marine clay deposits, locally steep slopes (25–45<inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>); mainly debris and clay slides, some debris flows</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Trøndelag, Møre og Romsdal, Sogn<?xmltex \hack{\hfill\break}?>og Fjordane,<?xmltex \hack{\hfill\break}?>Hordaland</oasis:entry>
         <oasis:entry colname="col2">2013</oasis:entry>
         <oasis:entry colname="col3">November <?xmltex \hack{\hfill\break}?>(15–16)</oasis:entry>
         <oasis:entry colname="col4">Hilde</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">80–100 mm</oasis:entry>
         <oasis:entry colname="col7">&gt; 100 events</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Trøndelag</oasis:entry>
         <oasis:entry colname="col2">2015</oasis:entry>
         <oasis:entry colname="col3">October (1–2)</oasis:entry>
         <oasis:entry colname="col4">Roar</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">60–80 mm <?xmltex \hack{\hfill\break}?>(locally up to <?xmltex \hack{\hfill\break}?>100–150 mm)</oasis:entry>
         <oasis:entry colname="col7">&lt; 50 events</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <?xmltex \mcwidth{426.791339pt}?><oasis:entry namest="col1" nameend="col7" align="left">Western Norway: alpine relief or glacial relief, steep slopes (25–45<inline-formula><mml:math id="M6" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> and &gt; 45<inline-formula><mml:math id="M7" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>) with glacio-fluvial deposits; colluvial deposits; mainly debris flows and debris avalanches</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Sogn og<?xmltex \hack{\hfill\break}?>Fjordane, Hordaland, Rogaland</oasis:entry>
         <oasis:entry colname="col2">2005</oasis:entry>
         <oasis:entry colname="col3">September (14)</oasis:entry>
         <oasis:entry colname="col4">Kristin (*)</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">100–150 mm</oasis:entry>
         <oasis:entry colname="col7">50–100 events</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Møre og Romsdal, Sogn<?xmltex \hack{\hfill\break}?>og Fjordane, <?xmltex \hack{\hfill\break}?>Hordaland, Rogaland</oasis:entry>
         <oasis:entry colname="col2">2005</oasis:entry>
         <oasis:entry colname="col3">November<?xmltex \hack{\hfill\break}?>(14–15)</oasis:entry>
         <oasis:entry colname="col4">Loke (*)</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">100–150 mm</oasis:entry>
         <oasis:entry colname="col7">&gt; 100 events</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Sogn og Fjor-<?xmltex \hack{\hfill\break}?>dane, Horda-<?xmltex \hack{\hfill\break}?>land, Rogaland, Agder</oasis:entry>
         <oasis:entry colname="col2">2015</oasis:entry>
         <oasis:entry colname="col3">December <?xmltex \hack{\hfill\break}?>(4–6)</oasis:entry>
         <oasis:entry colname="col4">Synne</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">60–80 mm <?xmltex \hack{\hfill\break}?>(locally up to <?xmltex \hack{\hfill\break}?>80–100 mm)</oasis:entry>
         <oasis:entry colname="col7"><inline-formula><mml:math id="M8" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 50–100 events; some slushflows</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Sogn og<?xmltex \hack{\hfill\break}?>Fjordane, Hordaland, Rogaland</oasis:entry>
         <oasis:entry colname="col2">2016</oasis:entry>
         <oasis:entry colname="col3">January <?xmltex \hack{\hfill\break}?>(29–30)</oasis:entry>
         <oasis:entry colname="col4">Tor</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">40–60 mm</oasis:entry>
         <oasis:entry colname="col7">&lt; 50 events</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Sogn og<?xmltex \hack{\hfill\break}?>Fjordane, Hordaland, Rogaland</oasis:entry>
         <oasis:entry colname="col2">2017</oasis:entry>
         <oasis:entry colname="col3">December<?xmltex \hack{\hfill\break}?>(7–8)</oasis:entry>
         <oasis:entry colname="col4">Aina</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">60–80 mm <?xmltex \hack{\hfill\break}?>(locally up to <?xmltex \hack{\hfill\break}?>80–100 mm)</oasis:entry>
         <oasis:entry colname="col7">&lt; 50 events</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Møre og Romsdal, Sogn<?xmltex \hack{\hfill\break}?>og Fjordane,<?xmltex \hack{\hfill\break}?>Hordaland, <?xmltex \hack{\hfill\break}?>Rogaland</oasis:entry>
         <oasis:entry colname="col2">2017</oasis:entry>
         <oasis:entry colname="col3">December<?xmltex \hack{\hfill\break}?>(22–23)</oasis:entry>
         <oasis:entry colname="col4">Birk</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">60–80 mm <?xmltex \hack{\hfill\break}?>(locally up to<?xmltex \hack{\hfill\break}?>80–100 mm)</oasis:entry>
         <oasis:entry colname="col7"><inline-formula><mml:math id="M9" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 50–100 events</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table></table-wrap>

<?xmltex \hack{\addtocounter{table}{-1}}?><?xmltex \floatpos{t}?><table-wrap id="Ch1.T3" specific-use="star"><caption><p id="d1e780">Continued.</p></caption><oasis:table frame="topbot"><?xmltex \begin{scaleboxenv}{.95}[.95]?><oasis:tgroup cols="7">
     <oasis:colspec colnum="1" colname="col1" align="justify" colwidth="56.905512pt"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="28.452756pt"/>
     <oasis:colspec colnum="3" colname="col3" align="justify" colwidth="56.905512pt"/>
     <oasis:colspec colnum="4" colname="col4" align="justify" colwidth="56.905512pt"/>
     <oasis:colspec colnum="5" colname="col5" align="justify" colwidth="56.905512pt"/>
     <oasis:colspec colnum="6" colname="col6" align="justify" colwidth="85.358268pt"/>
     <oasis:colspec colnum="7" colname="col7" align="justify" colwidth="85.358268pt"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Area</oasis:entry>
         <oasis:entry colname="col2">Year</oasis:entry>
         <oasis:entry colname="col3">Date</oasis:entry>
         <oasis:entry colname="col4">Name of <?xmltex \hack{\hfill\break}?>extreme <?xmltex \hack{\hfill\break}?>weather</oasis:entry>
         <oasis:entry colname="col5">Triggering</oasis:entry>
         <oasis:entry colname="col6">Max. 24 h rainfall in-<?xmltex \hack{\hfill\break}?>tensity in the area <?xmltex \hack{\hfill\break}?>(from xgeo.no)</oasis:entry>
         <oasis:entry colname="col7">Approx. mumber of <?xmltex \hack{\hfill\break}?>landslide events</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <?xmltex \mcwidth{426.791339pt}?><oasis:entry namest="col1" nameend="col7" align="left">Southern and Eastern Norway: hilly terrain dominated by marine clay deposits, gentle slopes (&lt; 25<inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> locally up to 45<inline-formula><mml:math id="M11" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>); mainly clay and debris slides</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Oslo, Akers-<?xmltex \hack{\hfill\break}?>hus, Buskerud, Telemark</oasis:entry>
         <oasis:entry colname="col2">2000</oasis:entry>
         <oasis:entry colname="col3">October–December</oasis:entry>
         <oasis:entry colname="col4">–</oasis:entry>
         <oasis:entry colname="col5">Prolonged rain-<?xmltex \hack{\hfill\break}?>fall and high <?xmltex \hack{\hfill\break}?>ground water <?xmltex \hack{\hfill\break}?>level</oasis:entry>
         <oasis:entry colname="col6">40–60 mm (many rain-<?xmltex \hack{\hfill\break}?>fall episodes during <?xmltex \hack{\hfill\break}?>3 months)</oasis:entry>
         <oasis:entry colname="col7"><inline-formula><mml:math id="M12" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 50–100 events</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Agder, Tele-<?xmltex \hack{\hfill\break}?>mark, Buskerud</oasis:entry>
         <oasis:entry colname="col2">2015</oasis:entry>
         <oasis:entry colname="col3">September<?xmltex \hack{\hfill\break}?>(14–17)</oasis:entry>
         <oasis:entry colname="col4">Petra</oasis:entry>
         <oasis:entry colname="col5">Prolonged rain-<?xmltex \hack{\hfill\break}?>fall and high <?xmltex \hack{\hfill\break}?>ground water <?xmltex \hack{\hfill\break}?>level</oasis:entry>
         <oasis:entry colname="col6">40–60 mm</oasis:entry>
         <oasis:entry colname="col7">&lt; 50 events; some<?xmltex \hack{\hfill\break}?>debris flows</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Rogaland, Agder, Tele-<?xmltex \hack{\hfill\break}?>mark</oasis:entry>
         <oasis:entry colname="col2">2017</oasis:entry>
         <oasis:entry colname="col3">September–October<?xmltex \hack{\hfill\break}?>(30–2)</oasis:entry>
         <oasis:entry colname="col4">(*)</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">100–150 mm <?xmltex \hack{\hfill\break}?>(locally &gt; 150 mm)</oasis:entry>
         <oasis:entry colname="col7"><inline-formula><mml:math id="M13" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 50–100 events; some debris flows</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Agder, Tele-<?xmltex \hack{\hfill\break}?>mark</oasis:entry>
         <oasis:entry colname="col2">2017</oasis:entry>
         <oasis:entry colname="col3">September–October <?xmltex \hack{\hfill\break}?>(21–22)</oasis:entry>
         <oasis:entry colname="col4">Ylva</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall</oasis:entry>
         <oasis:entry colname="col6">80–100 mm</oasis:entry>
         <oasis:entry colname="col7">&lt; 50 events</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <?xmltex \mcwidth{426.791339pt}?><oasis:entry namest="col1" nameend="col7" align="left">Eastern Norway: glacially scoured low mountains and valleys, steep slopes (25–45<inline-formula><mml:math id="M14" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> and &gt; 45<inline-formula><mml:math id="M15" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>) with glacio-fluvial deposits; mainly debris flows and debris slides</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Oppland, Hedmark, Buskerud, Telemark</oasis:entry>
         <oasis:entry colname="col2">2013</oasis:entry>
         <oasis:entry colname="col3">May <?xmltex \hack{\hfill\break}?>(15–16) <?xmltex \hack{\hfill\break}?>(22–23)</oasis:entry>
         <oasis:entry colname="col4">–</oasis:entry>
         <oasis:entry colname="col5">Intense rainfall and intense<?xmltex \hack{\hfill\break}?>snowmelt</oasis:entry>
         <oasis:entry colname="col6">40–60 mm</oasis:entry>
         <oasis:entry colname="col7">&gt; 100 events; some<?xmltex \hack{\hfill\break}?>slushflows</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup><?xmltex \end{scaleboxenv}?></oasis:table></table-wrap>

      <?pagebreak page1432?><p id="d1e1072">There are limited comprehensive estimates of human and economic losses
associated with natural hazards in Norway (e.g. floods and mass movements). In
terms of fatalities, about 2000 persons have lost their lives in the past
150 years because of mass movements. Most of these casualties have been due to
snow avalanches (Nadim et al., 2008). For landslides in soil, Aaheim et al. (2010) reported that 100 people have died since 1900 and most of the casualties
are related to clay slides and quick-clay slides, often triggered by
anthropogenic factors. Few data are available for casualties related to
rainfall- and snowmelt-induced landslides. An effort to document fatalities
associated with these landslide types was done by NVE in 2016 as part of the
work presented in Haque et al. (2017) where landslides fatalities have been
presented for the whole o Europe. For Norway the analysis showed that 42
people died in the period 1995–2016 due to 25 landslide events in the
category of debris flows, debris avalanches, clay slides, quick-clay slides,
rock falls, rock avalanches, and slushflows. The results indicated that 2005
and 2010 were the years with most recorded fatalities (ca. six persons).
Most of the fatalities were caused by rock falls and rock avalanches, 7
because of clay-type slides, while 12 people died because of rainfall- and
snowmelt-induced landslides (of these 7 due to slushflows and 5 due to
both debris flows and debris avalanches).</p>
      <p id="d1e1076">In terms of economic losses, there are no reliable estimates of the total
cost to society due to natural hazards, although insurance payments can
provide an indication of cost trends. Payments made by insurance companies
in Norway between 1980 and 2014 show both an increase in numbers of damaging
events and increase in number of total claims per year, reaching around NOK 2500
million (<inline-formula><mml:math id="M16" display="inline"><mml:mo lspace="0mm">∼</mml:mo></mml:math></inline-formula> EUR 275 million) in the flood and
landslides event of June 2011 in south-eastern Norway. However these numbers
are underestimated since they do not include events and costs associated
with public infrastructure (NIFS, 2016).</p>
</sec>
<sec id="Ch1.S3">
  <title>The Norwegian landslide forecasting and warning service</title>
<sec id="Ch1.S3.SS1">
  <title>Components</title>
      <p id="d1e1097">A sustainable EWS for rainfall-induced landslides requires not only strong and
reliable meteorological, hydrological, hydrogeological, or geotechnical
models as pointed out by Baum and Godt (2010) but also meteorological,
hydrological, hydrogeological, and geotechnical networks; a national
landslide database to support threshold development, probability analysis,
and verification; geographically specific warning thresholds; a uniform,
national-scale shallow susceptibility map or hazard map; computer and
communications networks to support the operation; and an operational
infrastructure and dedicated professional staff. Political commitments and
dedicated investments are also crucial. The service needs to be integrated
as part of national and local disaster risk management plans and budgets, and
enforceable legislation must define roles and responsibilities of local to
national authorities and agencies involved. Because of the multidisciplinary
characteristic of these types of landslides, the cooperation among agencies
should be effective. Finally, the service requires systematic feedback and
evaluation at all levels to ensure improvement, implementation/commitment
over time and systematic field verifications (UN/ISDR, 2006). The main
components of the Norwegian landslide forecasting and warning service are
described in the following sections.</p>
<sec id="Ch1.S3.SS1.SSS1">
  <title>Meteorological forecasts and hydrological models</title>
      <p id="d1e1105">The service uses daily meteorological quantitative gridded forecasts of
precipitation and temperature, obtained from the Norwegian Meteorological
Institute (MET). The forecasts are obtained from different weather models:
AROME-MetCoOp (short-term forecasts use in the Scandinavian<?pagebreak page1433?> regions in
cooperation MET Norway with Swedish Meteorological and Hydrological
Institute and Finnish Meteorological Institute; Müller et al., 2017)
and EC, which is a global long-term model from the European Center for
Medium-Range Weather Forecasts (<uri>https://www.ecmwf.int/</uri>). The short-term model's
resolution is 2.5 km and is used for the <inline-formula><mml:math id="M17" display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula>66 h forecast and updated
four times a day. The long-term model's resolution is 9 km, forecasts for
nine days ahead and is updated twice a day.</p>
      <p id="d1e1118">Due to the relatively sparse station network and relative short measurements
periods, hydrological models are a prerequisite to describe the water and
energy balances on a national scale. The service uses forecasted
hydro-meteorological variables obtained by a distributed version of the
hydrological HBV model (Beldring et al., 2003). The model divides Norway
into 1 km<inline-formula><mml:math id="M18" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> grid cells (total over 385 000 cells), where each cell is
treated as a separate basin with a corresponding simulation of the water
balance. The model simulates, for example, runoff, snowmelt, groundwater, soil
saturation and soil frost, based on two types of input data: temperature and
precipitation. Forecasted values are obtained from downscaling of the AROME
and EC weather prediction models, while observed values are based on
interpolated values from MET's nationwide network. The model is
automatically running four times per day. Several of the models simulated
variables can be found at <uri>http://www.xgeo.no</uri> as maps (see Sect. 3.1.6).</p>
      <p id="d1e1133">We use, in addition to the distributed HBV model, a one-dimensional soil
water and heat flow model (S-Flow) developed by NVE. This model simulates
water and heat dynamics in a layered soil column covered by vegetation.
S-Flow uses equations adapted mostly from the COUP (Jansson and Karlberg,
2014) and SHAW (Flerchinger, 2000) models. The model runs with a daily time
step, using precipitation, air temperature, wind speed, relative humidity
and sun radiation (or cloud cover) data as input. In addition, plant growth
characteristics and soil characteristics are necessary inputs to the model.
Simulations with S-Flow are performed only in areas where groundwater
stations are located (about 45 points), where observations are used for the
parameterization of the model. The model runs daily and the results, such as
water supply (snowmelt and rain), soil water deficit, groundwater level, and
soil frost, are available at <uri>http://www.xgeo.no</uri> (see Sect. 3.1.6). The S-Flow model has a
better physical description than the HBV model of the snowmelt and evaporation
process as it uses a physically based approach and all available
meteorological information. In addition to the estimation of soil-water
deficit, S-Flow includes soil-water depletion following the fall in
groundwater levels in winter caused by lack of recharge and groundwater
discharge into streams and lakes (Colleuille et al., 2007).</p>
</sec>
<sec id="Ch1.S3.SS1.SSS2">
  <title>Meteorological and hydrological network</title>
      <p id="d1e1145">The service uses several networks. We access data from meteorological
stations, equipped with rain gauge (hourly and daily data), temperature
sensors and snow and wind sensors, and operated mostly by MET but also by
NPRA and Bane NOR. Hydrological stations are used to measure discharge in
rivers, snow depth, and coverage (over 400 stations) and hydrogeological
stations to measure groundwater level (70 stations) and are operated by NVE.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2" specific-use="star"><caption><p id="d1e1150">The landslide hazard thresholds used by the Norwegian landslide early warning and forecasting service. <bold>(a)</bold> National thresholds, <bold>(b)</bold>
regional threshold for Southern Norway and <bold>(c)</bold> regional threshold for Eastern
Norway.</p></caption>
            <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f02.png"/>

          </fig>

      <p id="d1e1168">Real-time observations of rainfall, air temperature, water discharge, and
ground water level are used in the daily landslide hazard assessment to
check the performance of the hydro-meteorological conditions obtained from
the hydrological models. This is particularly important when the models
overestimate or underestimate certain parameters values (i.e., the soil
water saturation or the snowmelt) in certain regions or in certain seasons.
Real-time discharge data are used to automatically assimilate and correct
the modelled discharge in watercourses and are most used for flood
forecasting, but they can also give valuable information about the debris flow
hazards. Historical data on soil moisture, soil frost, and groundwater have
been mainly used to test and calibrate the physically based S-Flow model.</p>
</sec>
<sec id="Ch1.S3.SS1.SSS3">
  <title>Landslide database</title>
      <p id="d1e1177">Landslide records are essential for different types of analyses, e.g.
threshold establishment, calibration of models in warning systems and
evaluation of warning performance. Landslide data can be collected using two
interfaces: <uri>http://www.regobs.no/</uri> (see Sect. 3.1.9) and <uri>https://www.skredregistrering.no/</uri>. This last one is the web portal for the national
mass movement database, containing landslide and snow avalanche events and
was established in 2001. The database has registrations from historical
times, but 70 % of the registrations are recorded after 2000. The
database contains around 65 000 events in the categories of rock fall, rock
avalanche (of different sizes), debris flow, debris slide/debris avalanches
and shallow soil slide in artificial slopes, snow avalanche, icefall, and
landslide in clay (quick-clay slides and rotational clay slides). In
addition some events can be recorded as unspecified when the subtype is
unknown. The database is maintained by NVE, but several institutions have
registered data, among them the NPRA, the Geological Survey of Norway (NGU),
the Norwegian Geotechnical Institute (NGI) and Bane NOR. The data are
accessible through NVE Atlas (<uri>https://atlas.nve.no</uri>) and <uri>http://www.xgeo.no</uri> (see Sect. 3.1.6).
The landslides are represented by points positioned where the event caused
losses of life, damages or traffic interruptions. The database contains
valuable information for thresholds analyses. However, because of the many
limitations, a quality control is always performed before any type of
analysis.</p>
</sec>
<?pagebreak page1434?><sec id="Ch1.S3.SS1.SSS4">
  <title>Thresholds</title>
      <p id="d1e1199">Most landslide EWSs use intensity–duration curves for rainfall as thresholds
(Guzzetti et al., 2008; Piciullo et al., 2018; Segoni et al., 2018a; Bogaard
and Greco, 2018). Based on the threshold classification proposed by Guzzetti
et al. (2008), we can affirm that the Norwegian system uses “other
thresholds”. The development of the Norwegian forecasting system is based
on the principle that since hydro-meteorological parameters can be
predicted, forecasting of landslide hazard is possible. The knowledge of
these relationships is used to develop threshold values by investigating the
time of past landslide events and meteorological and hydrological variables.
Modelled hydro-meteorological variables obtained from a distributed 1 km<inline-formula><mml:math id="M19" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> grid version of the conceptual HBV model (Beldring et al., 2003)
and cross-checked with the time of previous landslides were used to
statistically derive thresholds (Colleuille et al., 2010; Cepeda et al.,
2012; Cepeda, 2013a, b; Boje et al., 2014b). A regression analysis was
performed, that used a decision tree classification technique similar to
Kirschbaum et al. (2015).</p>
      <p id="d1e1211">The best performance was obtained when the relative water supply and the
soil water saturation degree were combined. This result is not unexpected
and is in line with the findings of some recent works on landslide rainfall
thresholds (Capra et al., 2018; Segoni et al., 2018b). The relative water
supply is derived from simulated rain or snowmelt from the snowpack (in
which rain may percolate), as a percentage of an annual average value for a 30-year period. The degree of soil water saturation (%) describes the
relationship between simulated total water content in the soil (groundwater
and soil water) normalized by the maximum soil water content simulated for a
30-year period, which is assumed equal to a fully saturated soil. The
30-year reference period for both variables is 1981–2010. The HBV uses only
precipitation and air temperature in order to discriminate precipitation
into rain or snow and to simulate snow pack accumulation and snowmelt. The
model uses a one-dimensional soil bucket approach, accounting for the
storage of water in the soil (Bergstrøm, 1995; Beldring et al., 2003).</p>
      <p id="d1e1214">The thresholds are visualized in Fig. 2a–c. All thresholds are also
visualized in the form of raster data (with 1 km <inline-formula><mml:math id="M20" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 1 km resolution) and
available at <uri>http://www.xgeo.no</uri>. Figure 2a shows the national thresholds, while
Fig. 2b and c show regional thresholds. The development of the national
thresholds was done using relatively few weather events in the south of Norway,
but each with many landslides in the categories of rapid shallow slides,
debris avalanches<?pagebreak page1435?> and debris flows. These weather events occurred in
Southern and Eastern Norway (2000), Western Norway (2005), and Eastern Norway
(2008). The first one could be categorized as a low-intensity but prolonged
rainfall event over several months, responsible of the full saturation of
the soil, the second was a typical intense rainfall event that was the remnants of tropical cyclones reaching Norway and the third one was a typical snowmelt episode due
to very high temperatures.</p>
      <p id="d1e1227">The grey points are days without landslide events, while coloured triangles
and circles show days with landslide occurrence. The figure shows three
lines (yellow, orange, and red) that correspond to the minimum, medium, and
maximum threshold respectively. As pointed out by Aleotti (2004), for
practical, and hence operational, purposes, the minimum threshold is of
special interest, since above this threshold, landslides are expected to
occur. The medium and maximum thresholds were decided manually by evaluating
the spatial distribution of the threshold map and its impact colours (green,
yellow, orange, red) with regard to the abundance of expected landslides at
a regional scale (Cannon and Ellen, 1985). Below the yellow threshold,
landslides are not expected, while over the red line, many landslides are
expected to occur. A closer view of the figure shows that under the yellow
thresholds some landslides can be also observed. A recent quality control of
these data revealed their poor quality (i.e. uncertain date of occurrence;
many in modified slopes and not only rainfall-induced), suggesting that
these events should have been excluded from the dataset.</p>
      <p id="d1e1231">To these lines we assigned colours similar to our warning levels to
indicate that the hazard is high over the red line, while below the yellow
line the hazard is low. Looking at the plot of the landslide events it is
clear that there are regional differences because of the different
geomorphological and geological conditions, as well as for the
hydro-meteorological triggering conditions. The plotted landslides from
Western Norway are displayed almost separately from landslides from Eastern
Norway, and with Telemark and Oppland events somewhat in between. In Table 2, the differences between these regions are also illustrated.</p>
      <p id="d1e1234">Most recently, the thresholds are being adapted to take into account these
regional physiographic and climatic differences (Boje et al., 2017). New
thresholds have been defined for two regions: Southern Norway and Eastern
Norway (Fig. 2b–c), both where many false alarms were sent in recent years
based on the national thresholds. It has been used improved and high-quality
landslide records obtained since 2013, as well as assessed days without
landslides. A challenge for these two regions was too few recorded
landslides in order to carry out a statistically viable regression analysis.
The approach has been to manually adjust the original minimum yellow
threshold upwards. Based on the recent cases with false alarms, the
thresholds were simply increased until no impact was shown in the threshold
map. In this approach, the quality assessment of the days with no landslides
was crucial and based on the daily monitoring of landslide events performed
by the operational EWS.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3" specific-use="star"><caption><p id="d1e1239">Susceptibility map for landslides in soil at Sørfjorden,
Western Norway: <bold>(a)</bold> at first-order catchments from Bell et al. (2014); <bold>(b)</bold> at
<inline-formula><mml:math id="M21" display="inline"><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>:</mml:mo><mml:mn mathvariant="normal">50</mml:mn><mml:mspace width="0.125em" linebreak="nobreak"/><mml:mn mathvariant="normal">000</mml:mn></mml:mrow></mml:math></inline-formula> scale, from Fischer et al. (2014).</p></caption>
            <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f03.jpg"/>

          </fig>

      <p id="d1e1269">The described procedure for the definition of the thresholds lacks in
objectivity and is not easily reproducible, like for many other published
empirical rainfall thresholds (Guzzetti et al., 2008; Segoni et al., 2018a).
However, we are working on a way to better describe and specify the
mathematical and statistical criteria used in the thresholds definition, as
well as to improve the quality of the landslide datasets to be used in the
analysis. Although a study of thresholds for different landslide types has
not been conducted yet, we consider that this could be of interest to test
in the future.</p>
</sec>
<sec id="Ch1.S3.SS1.SSS5">
  <title>Susceptibility maps</title>
      <p id="d1e1278">A susceptibility map shows the spatial probability of landslides, e.g. the
probability that a region will be affected by landslides given a set of
terrain conditions. Two maps (Fig. 3) that can be used to predict the
spatial occurrence of rainfall- and snowmelt-induced landslides in Norway
have been prepared, both of them covering the entire country. The first map
shows which first-order catchments are more susceptible to landslide in
soil (e.g. debris avalanches, debris flows, shallow soil slides, clay slides
and quick-clay slides) (Bell et al., 2014). The map was prepared combining
different variables, like Quaternary cover map, land cover, average yearly
rainfall, various water runoff variables, and various derivatives from the
15 m <inline-formula><mml:math id="M22" display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> 15 m digital elevation model (DEM), i.e. slope and aspect. It was
done using the Generalized Additive Models (GAM) (Fig. 3a). This map has
been used to improve the original threshold map (see Sect. 3.1.4) by
including information on landslide-prone areas and the result of this
combination was a new threshold map. This is used by the forecasters in the
initial phase to perform a more accurate assessment.</p>
      <p id="d1e1288">The second susceptibility map shows specifically where debris avalanches
and small debris flows may occur at <inline-formula><mml:math id="M23" display="inline"><mml:mrow><mml:mn mathvariant="normal">1</mml:mn><mml:mo>:</mml:mo><mml:mn mathvariant="normal">50</mml:mn><mml:mspace linebreak="nobreak" width="0.125em"/><mml:mn mathvariant="normal">000</mml:mn></mml:mrow></mml:math></inline-formula> scale (Fischer et al., 2012,
2014). The map displays the modelled potential source areas, tracks, and
runout areas. The source areas were discriminated based on an index
approach, which includes topographic parameters, obtained from a 10 m digital terrain model (i.e.
slope angle, planar curvature) and hydrological settings (i.e. drainage
area). For the runout modelling, the Flow-R model was used, which is based
on combined probabilistic and energetic algorithms for the assessment of the
spreading of the flow and maximum runout distances. This map is used in the
communication phase of the warning, since it can be viewed at <uri>http://www.varsom.no/</uri>
(see Sect. 3.1.8) together with the warning zone and warning level. The
user can zoom in on the map of the warning zone and see where landslides could
occur (Fig. 3b).</p><?xmltex \hack{\newpage}?>
</sec>
<?pagebreak page1436?><sec id="Ch1.S3.SS1.SSS6">
  <title>Web tools: xgeo – an analysis and decision making tool</title>
      <p id="d1e1316">Xgeo.no is a decision-making tool used by forecasters for snow avalanches, landslides, and
floods. Even though its use is aimed at specialists, data
are made available to the public thanks to an open data policy through <uri>http://www.senorge.no</uri> (Engeset et al., 2004). The web portal, developed and
maintained since 2008 is a map-centric tool for visualization of temporal
and spatial data (Barfod et al., 2013). The portal shows daily observations
and forecasts for meteorological and hydrological conditions as thematic
maps and time-series data. The maps, updated four times a day, show the
conditions for each day, and for 9 days ahead and reach back to 1957.
Landslide specialists use this tool during the daily evaluation to visualize, for example, real-time measurements, weather forecast, threshold values predictions,
water supply and groundwater simulations, data from the real-time database
regObs (see Sect. 3.1.9), landslide events from the national mass movement
database (see Sect. 3.1.3), roads closed because of landslides, and other
administrative data, such as existing infrastructure (Devoli et al., 2014).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4" specific-use="star"><caption><p id="d1e1324">Example of landslide warning bulletin, as viewed on <uri>http://www.varsom.no/</uri>. The example presented is for the bulletin
issued on 1 October 2017, the same case study as in Sect. 6.</p></caption>
            <?xmltex \igopts{width=455.244094pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f04.png"/>

          </fig>

      <p id="d1e1336">The hydro-informatics team at NVE has developed <uri>http://www.xgeo.no/</uri>, in cooperation with
the MET, NPRA, Bane NOR, and the Norwegian Mapping Authority (Kartverket).
The tool <uri>http://www.xgeo.no/</uri> is systematically updated.</p>
</sec>
<sec id="Ch1.S3.SS1.SSS7">
  <title>Operational infrastructure and staff</title>
      <p id="d1e1351">The organization of the landslide forecast service rests heavily on the
organization of the flood forecast service. It was important to maintain and
not disorganize the well-functioning flood forecast service during the
development of the landslide service. This was ensured by establishing a
parallel group of landslide forecasters. The landslide forecasting team
consists of people with different backgrounds, such as hydrologists,
geologists, geophysicists, hydrogeologists and physical geographers. The
team consists, in 2017, of 12 employees from NVE and 2 from NPRA. Five
of the landslide forecasters work also as flood forecasters and two of them
as snow avalanche forecasters. Landslide and flood forecasters closely discuss the daily landslide and flood assessments. This synergy effect leads to improvements and strengthening for both services. The assessment of
slushflows is done in collaboration with the snow avalanche forecasting
service, which provides additional information on snow structure and snow
condition. There is still an ongoing effort to synchronize the three
services groups where possible, and ideas and information are exchanged.</p>
      <p id="d1e1354">The service is operative 7 days a week, throughout the year, with a
rotating scheme with one forecaster on duty. Outside of working hours, forecasters can be reached by mobile phone
(08:00–21:00). Forecasters may have to be available 24/7 when there is a severe
situation. Courses and training workshops are organized yearly to educate
landslide forecasters, discuss new tools and exchange ideas. Many of
these courses and workshops are organized together with flood forecasters as
well.</p>
      <p id="d1e1357">Besides available and dedicated personnel as forecasters, the service
benefits from skilled IT personnel who also have a strong dedication. The real-time network and forecasting<?pagebreak page1437?> tools are set up with redundant systems. In the
case of internet failure, routines have been developed to secure minimum
communication both to ensure meteorological data and to convey the resulting
possible heightened warning level and situation report to the public.</p>
</sec>
<sec id="Ch1.S3.SS1.SSS8">
  <title>Communication network: varsom.no, SMS, and CAP</title>
      <p id="d1e1366">The national web portal for flood, landslides,
snow avalanches warning and ice conditions on regional scale is <uri>http://www.varsom.no/</uri>. The web was
chosen as the main channel for communicating bulletins and warning levels to
end-users according to the decision on open access. During development,
high priority was given to accessibility on mobile screens, according to
the need for making bulletins available to the users “on site” and because
of the rapidly increasing numbers of smartphone users (Johnsen, 2013). The
web portal displays both bulletins and related maps for the natural hazards
covered by the NVE's forecasting and warning services, but it also provides
additional information on precautions, educational literature, and videos and
relevant reports. Through <uri>http://www.varsom.no/</uri> the landslide service delivers
continuous updates on the current situation and development to national and
regional stakeholders and the public. Assessments are published at least
twice a day and contain the forecast for today, tomorrow, and the day after
tomorrow. The landslide forecast is valid from 07:00 the day of publication to
07:00 the following day (08:00 to 08:00 for daylight saving time).</p>
      <p id="d1e1375">The bulletin is provided in one version only, covering both the need of the
general public and the contingency personnel. Due to the regional aspect of
the EWS, the technical information provided is limited. The public and the
emergency authorities are recommended to make local assessments and decide on
the most appropriate measures. The forecaster on duty is available for
consulting by phone or e-mail. The bulletins are in Norwegian, but an
English version was launched in January 2018. An example of a landslide
warning bulletin is presented in Fig. 4.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5" specific-use="star"><caption><p id="d1e1380">The communication chain of the landslide hazard warnings.</p></caption>
            <?xmltex \igopts{width=455.244094pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f05.png"/>

          </fig>

      <p id="d1e1389">The software regVars has been developed to enable the publication of flood
and landslide bulletins in <uri>http://www.varsom.no/</uri>. It provides
possibilities for drafting bulletins before they are published, enabling
ample time for preparation and quality assurance. The bulletins for all
three forecasting services are available at <uri>http://api.nve.no/</uri> free of charge.
Since
early 2017, it has been possible to subscribe to warning messages published
at <uri>http://www.varsom.no/</uri>. The subscription available at <uri>https://abonner.varsom.no</uri> is easily managed and free of charge. Users choose which natural hazards they want to be notified about (e.g. flood, snow avalanche or landslides) and on what warning levels. They can also choose if they want to receive the notification as an SMS or e-mail or both. In any case, they receive an URL<?pagebreak page1438?> that leads them directly to the relevant warning bulletin at <uri>http://www.varsom.no/</uri>. It is possible to subscribe for all of Norway,
for landslide, flood and snow avalanche, for all warning levels, or just for
one municipality and one hazard. All local and regional emergency
authorities are encouraged to subscribe. In the case of the two highest
warning levels, NVE in addition uses a crisis information management tool
(CIM) to notify the relevant county's emergency division by e-mail that
warnings have been sent. The county has the responsibility to forward the
message to the respective municipalities. MET, NPRA's traffic service, and
NVE's regional offices are also contacted via CIM. In these cases, the
recipients must reply to NVE that the warning message is received, read, and
understood. Figure 5 describes the communication chain, from the assessment
done by the forecaster on duty to the publishing on web to the
dissemination to the citizens, via the regional and local administrations.</p>
      <p id="d1e1408">In 2017 NVE and MET started a project in order to use the Common Alerting
Protocol (CAP), which is an international data format for emergency alerting
and public warning, for distributing warning notifications on severe/extreme
weather, floods, landslides, and snow avalanches and to try to harmonize
warning procedures and products. The use of CAP is the first of its kind in
Norway and serves as the start of a Norwegian standard (CAP-NO) which may
be used for other types of alerts. The goal of this project is to improve
communication and effectiveness of the warning services. NVEs CAP fees are
available at <uri>https://cap.nve.no</uri>.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><caption><p id="d1e1416">Synthesis of how the daily landslide hazard assessment is
performed.</p></caption>
            <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f06.png"/>

          </fig>

</sec>
<sec id="Ch1.S3.SS1.SSS9">
  <title>Verification of landslide occurrence: regObs (a crowdsourcing tool)
and media monitoring</title>
      <p id="d1e1431">The landslide forecasting depends on the registration of landslide events.
Landslide events are used for both the development of thresholds and the
evaluation of a sent warning to confirm if the warning was correct or not.
Therefore it is important to confirm that a landslide event has occurred
after a specific triggering rainfall event (Devoli et al., 2017). We use different
sources to verify the occurrence of landslides. The website <uri>http://www.regobs.no/</uri> (the
abbreviation “regObs” standing for “register observations”) is a real-time registration tool for
observations, danger signs and events to be used by forecasters and
emergency personnel (Ekker et al., 2013). When regObs began in 2010,
it was a tool for submitting and sharing of snow avalanche observations.
Later, this real-time database was extended to register observations related
to other natural hazards like landslides, floods, and ice conditions. It was
designed as a public tool supporting crowdsourcing, which means that
everyone may contribute observations and all data are immediately
available to the public on the regObs website and in the app (<uri>http://www.regobs.no/</uri>). Both NVE and NPRA stand behind the development of regObs.
The data are treated as initial information and are subsequently quality-assured before they are stored in the national mass movement database (see
Sect. 3.1.3) and flood database.</p>
      <?pagebreak page1439?><p id="d1e1440">Information from local or national newspapers provides one of the fastest
sources for obtaining data on landslides affecting infrastructure.
Therefore, tools for media monitoring of events are also used as a part of
the daily routine to evaluate the issued warning levels. Nevertheless, the
accuracy of the reported event may be poor; therefore, a detailed aftermath
examination of the facts is essential. Proximity to important infrastructure
influences the media coverage rather than the severity of the landslide.
Events in more sparsely populated areas may not be covered by this
information source. Besides media, we can collect landslide information
through landslide specialists working at NVE's regional offices and
landslide specialists from NPRA and Bane NOR that monitor and report
landslide events, after field surveys.</p>
</sec>
</sec>
</sec>
<sec id="Ch1.S4">
  <title>Daily assessment and warning levels</title>
      <p id="d1e1452">The daily landslide hazard assessment is performed by a forecaster who uses
forecasted thresholds, forecasted hydro-meteorological parameters,
information from real-time observations, knowledge on historical events and
regional susceptibility, and personal experience. The daily landslide
assessment routine is summarized in Fig. 6 and includes the following
phases:
<list list-type="bullet"><list-item>
      <p id="d1e1457">weather forecast, also as input for the hydrological model;</p></list-item><list-item>
      <p id="d1e1461">model run, forecasted hydro-meteorological parameters, forecasted
threshold;</p></list-item><list-item>
      <p id="d1e1465">collection of real-time data;</p></list-item><list-item>
      <p id="d1e1469">interpretation of model results and use of additional information from
simulated hydro-meteorological parameters, i.e. snow and groundwater
conditions;</p></list-item><list-item>
      <p id="d1e1473">analysis of forecasted thresholds also corrected with susceptibility
information;</p></list-item><list-item>
      <p id="d1e1477">preparation of forecast information and warning messages with description of
possible events and expected impact;</p></list-item><list-item>
      <p id="d1e1481">communication and dissemination of messages to warn the public and local
authorities;</p></list-item><list-item>
      <p id="d1e1485">provision of hydrological situation updates and answers to questions from media or
another recipients.</p></list-item></list></p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T4" specific-use="star"><caption><p id="d1e1491">Awareness levels used in the Norwegian landslide forecast
and warning service.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="2">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="justify" colwidth="341.433071pt"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry namest="col1" nameend="col2">Significance of the awareness levels </oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Red awareness level</oasis:entry>
         <oasis:entry colname="col2">Very high landslide hazard. Many landslides and several large ones may occur; their long runout and extent may result in damage to settlements and infrastructures. Red awareness level is an extreme situation that occurs very rarely. Safety measures such as closed roads and evacuations can occur on short notice. Emergency response authorities should have implemented emergency plans and mitigation measures for carrying out evacuations and other contingency responses. Pay attention to the media and follow recommendations from the authorities.</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Orange awareness level</oasis:entry>
         <oasis:entry colname="col2">High landslide hazard. Many landslides and some large ones that can damage infrastructure and roads may occur. Exposed roads may be closed off. Emergency response authorities should be prepared to implement emergency plans and mitigation measures and evaluate the need for evacuations and other contingency responses. Mitigation measures such as clearing water channels should be carried out. Pay attention to the media and follow recommendations from the authorities.</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1">Yellow awareness level</oasis:entry>
         <oasis:entry colname="col2">Moderate landslide hazard, primarily shallow slides on artificial slopes that may affect roads, railways or river embankments. Isolated debris avalanches or debris flows can occur and could cause damages to infrastructure and people. At this level emergency authorities should increase vigilance related to landslides and pay attention to weather forecasts and landslide forecasts and information at <uri>http://www.varsom.no/</uri>. Preventive measures are recommended, such as clearing water channels in exposed areas.</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Green awareness level</oasis:entry>
         <oasis:entry colname="col2">Generally safe conditions. Debris avalanches, debris flows, shallow slides, and slushflows are not expected at this level; however other landslide types (like rock falls, clay slides, and quick-clay slides) may occur, caused by slow response processes, such as erosion, freeze–thaw weathering or human activity, such as deposition, digging or blasting. These incidents may occur at all awareness levels.</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7"><caption><p id="d1e1556">Popular representation of the awareness levels, symbolized by
rubber boots.</p></caption>
        <?xmltex \igopts{width=199.169291pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f07.png"/>

      </fig>

      <p id="d1e1566">The warning scale is applicable for both flood and landslide hazards and
consists of four levels using the same concept as meteoalarm (<uri>http://www.meteoalarm.eu/</uri>). The different levels show the landslide hazard and the
recommended awareness (Table 3), providing information on what is expected
to occur, the severity (qualitative estimation of numbers and dimensions of
landslides) and recommended actions that the users should undertake or which
measures should be initiated in order to reduce potential damages (Fig. 7).</p>
      <p id="d1e1572">The principle behind the awareness levels is that the highest level (red)
occurs very rarely, while the second lowest level (yellow) occurs more often.
Just for comparison, the red level corresponds to a flood with a return period of more than 50 years, while the yellow level corresponds to a flood with a return period of 2–5 years.</p>
      <?pagebreak page1440?><p id="d1e1575">Emergency response authorities should be prepared to implement emergency
plans, considering available resources, implementing preventive measures,
safeguarding exposed assets, and carrying out evacuations and other contingency
responses. One of the mitigation measures recommended is to ensure unhindered
water channels, e.g. that culverts are not obstructed by ice, snow,
sediments or other matter.</p>
</sec>
<sec id="Ch1.S5">
  <title>Validation of the forecasting service</title>
      <p id="d1e1584">Golnaraghi (2005) and UN/ISDR (2006) expressed that “one of the most
effective measures for disaster preparedness is a well-functioning early
warning system that delivers accurate information dependably and on-time”.
Therefore a useful EWS is the one capable of issuing correct warning
messages which are easily understood and provided early enough to lead municipalities
and contingency planners, emergency authorities to action in order to avoid
or reduce damages due to landslides. This implies that a successful service
requires periodically assessments of the technical performance and user
perception (Devoli et al., 2017). In our service, we evaluate the so-called
technical performance and the user perception.</p><?xmltex \hack{\newpage}?>
<sec id="Ch1.S5.SS1">
  <title>Technical performance</title>
      <p id="d1e1593">The technical performance is evaluated by measuring the accuracy of the
service, i.e. quantifying how well the landslide warning performs (correct
alarms, false alarms, missed events, wrong levels). It is assumed that a service that delivers warnings at a good time in advance will be perceived as credible and will trigger action by users. Bad hits, with many false alarms and/or more
unannounced events, will have the opposite effect.</p>
      <p id="d1e1596">The evaluation of the performance is based on the fact that the landslide
warning is a regional service that warns of landslides over a large area.
The daily assessment is considered as correct when the situation meets the
description of the awareness level presented in Table 3. This means, for
example, that if an orange warning has been issued for a particular region,
it is expected in this region that many landslides, some possibly large, have occurred, and that many roads have been closed. In short,
this
means that it was useful for municipalities and transport authorities to
have been prepared and to have implemented some mitigation measures. If the
situation was not so severe (no or few events or troubles), the warning may
be considered as a wrong level or as a false alarm. In the same way, a day
with “green awareness level” with several events may in a region be
considered as a “missed event”.</p>
      <?pagebreak page1441?><p id="d1e1599"><?xmltex \hack{\newpage}?>Each week, evaluation of the daily hazard assessment is
carried out based on a comparison of warning levels issued and the number of
events and hazard signs reported by the media or recorded by the road and
railway authorities and municipalities (see Sect. 3.1.9). Updated and new
information about landslide events may be available several weeks after the
event, and therefore the performance evaluation will undergo another quality
assurance after the first evaluation.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T5"><caption><p id="d1e1606">Percent of days with awareness level green, yellow, orange
and red.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="7">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:thead>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">2013</oasis:entry>
         <oasis:entry colname="col3">2014</oasis:entry>
         <oasis:entry colname="col4">2015</oasis:entry>
         <oasis:entry colname="col5">2016</oasis:entry>
         <oasis:entry colname="col6">2017</oasis:entry>
         <oasis:entry colname="col7"><italic>2013–2017</italic></oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Green</oasis:entry>
         <oasis:entry colname="col2">81.7</oasis:entry>
         <oasis:entry colname="col3">83.0</oasis:entry>
         <oasis:entry colname="col4">90.4</oasis:entry>
         <oasis:entry colname="col5">93.0</oasis:entry>
         <oasis:entry colname="col6">89.8</oasis:entry>
         <oasis:entry colname="col7"><italic>87.6</italic></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Yellow</oasis:entry>
         <oasis:entry colname="col2">16.0</oasis:entry>
         <oasis:entry colname="col3">15.0</oasis:entry>
         <oasis:entry colname="col4">8.5</oasis:entry>
         <oasis:entry colname="col5">7.0</oasis:entry>
         <oasis:entry colname="col6">8.0</oasis:entry>
         <oasis:entry colname="col7"><italic>10.9</italic></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Orange</oasis:entry>
         <oasis:entry colname="col2">2.0</oasis:entry>
         <oasis:entry colname="col3">2.0</oasis:entry>
         <oasis:entry colname="col4">1.1</oasis:entry>
         <oasis:entry colname="col5">0.0</oasis:entry>
         <oasis:entry colname="col6">2.2</oasis:entry>
         <oasis:entry colname="col7"><italic>1.4</italic></oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Red</oasis:entry>
         <oasis:entry colname="col2">0.3</oasis:entry>
         <oasis:entry colname="col3">0.0</oasis:entry>
         <oasis:entry colname="col4">0.0</oasis:entry>
         <oasis:entry colname="col5">0.0</oasis:entry>
         <oasis:entry colname="col6">0.0</oasis:entry>
         <oasis:entry colname="col7"><italic>0.1</italic></oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p id="d1e1765">Table 4 shows the percent of days when green, yellow, orange, and red
awareness levels have been issued in Norway, in the period 2013–2017. The
yellow, orange, and red levels are issued for a specific warning zone. The
green awareness is given for all of Norway when there is no landslide
hazard present. When a yellow, orange, and/or red warning is issued for a
specific warning area, the green awareness is given for the remaining of
Norway. The table shows that most of the time we give a green level for a
high percentage of days (87.6 % in the period 2013–2017), while the yellow
level has been issued for 10.9 % of the days in the same period and
orange for 1.4 % of the days. The table shows that a red level was seldom
issued, and only in 2013.</p>
      <p id="d1e1768">Table 5 shows two ways to estimate the technical performance: by using all
days or only “challenging days”. We define “challenging days” as days
with demanding assessment, i.e. days where, in addition to reliable
hydrological forecast, the expertise and experience of the forecaster on
duty is crucial. A demanding assessment may conclude in a green awareness
level, as well as yellow, orange or red. About 25–30 % of the days per
year are considered as “challenging”. This is often in periods with large
amounts of rain or high snowmelt, or both.</p>
      <p id="d1e1771">This statistical analysis shows a performance, at national scale, of about
96 % correct assessment using all days, and about 88 % considering
only “challenging days”, in the period 2013–2017. The performance
evaluation described here reflects four factors. How good are the threshold
values? How good are the hydrological simulations? How good are the weather
forecasts? How well did the forecaster on duty assess the situation? The
performance is of the same magnitude as for the flood forecasting service.
False alarms and unexpected events are in most cases due to changes in
weather forecasts. Some false alarms and unexpected events are also due to
errors in the hydrological models or incorrect interpretation of the model
results.</p>
      <p id="d1e1774"><?xmltex \hack{\newpage}?>The performance evaluation described above is challenging
because it is based on subjective qualitative assessments. Therefore
semi-quantitative classification criteria have been suggested to help the
daily performance evaluation. The performance of the landslide service was
also tested with the EduMaP (Event, Duration Matrix, Performance) method
proposed by Calvello and Piciullo (2016). It has been adapted to the
Norwegian landslide forecasting and warning service (taking into account the
variable warning areas) and tested for Western Norway for the years
2013–2014, and the results are presented in Piciullo et al. (2017). Based on
the results from both methods we have started to work on the regional
improvement of a landslide threshold, as has been done for Southern and
Eastern Norway, contributing in the reduction of false alarms in these
regions (Boje, 2017).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T6" specific-use="star"><caption><p id="d1e1781">Performance estimation (in %) for all days, and for
days with challenging assessment only.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="11">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="right"/>
     <oasis:colspec colnum="6" colname="col6" align="right" colsep="1"/>
     <oasis:colspec colnum="7" colname="col7" align="right"/>
     <oasis:colspec colnum="8" colname="col8" align="right"/>
     <oasis:colspec colnum="9" colname="col9" align="right"/>
     <oasis:colspec colnum="10" colname="col10" align="right"/>
     <oasis:colspec colnum="11" colname="col11" align="right"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry rowsep="1" namest="col2" nameend="col6" align="center" colsep="1">For all days </oasis:entry>
         <oasis:entry rowsep="1" namest="col7" nameend="col11" align="center">Only for “challenging days” </oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">2013</oasis:entry>
         <oasis:entry colname="col3">2014</oasis:entry>
         <oasis:entry colname="col4">2015</oasis:entry>
         <oasis:entry colname="col5">2016</oasis:entry>
         <oasis:entry colname="col6">2017</oasis:entry>
         <oasis:entry colname="col7">2013</oasis:entry>
         <oasis:entry colname="col8">2014</oasis:entry>
         <oasis:entry colname="col9">2015</oasis:entry>
         <oasis:entry colname="col10">2016</oasis:entry>
         <oasis:entry colname="col11">2017</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Correct</oasis:entry>
         <oasis:entry colname="col2">94.2</oasis:entry>
         <oasis:entry colname="col3">92.9</oasis:entry>
         <oasis:entry colname="col4">97.9</oasis:entry>
         <oasis:entry colname="col5">98.0</oasis:entry>
         <oasis:entry colname="col6">96.6</oasis:entry>
         <oasis:entry colname="col7">85.0</oasis:entry>
         <oasis:entry colname="col8">80.0</oasis:entry>
         <oasis:entry colname="col9">93.4</oasis:entry>
         <oasis:entry colname="col10">91.8</oasis:entry>
         <oasis:entry colname="col11">87.5</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">False alarm</oasis:entry>
         <oasis:entry colname="col2">3.3</oasis:entry>
         <oasis:entry colname="col3">5.2</oasis:entry>
         <oasis:entry colname="col4">1.4</oasis:entry>
         <oasis:entry colname="col5">0.8</oasis:entry>
         <oasis:entry colname="col6">1.9</oasis:entry>
         <oasis:entry colname="col7">8.5</oasis:entry>
         <oasis:entry colname="col8">14.5</oasis:entry>
         <oasis:entry colname="col9">4.4</oasis:entry>
         <oasis:entry colname="col10">3.1</oasis:entry>
         <oasis:entry colname="col11">7.0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Missed events</oasis:entry>
         <oasis:entry colname="col2">2.2</oasis:entry>
         <oasis:entry colname="col3">1.2</oasis:entry>
         <oasis:entry colname="col4">0.3</oasis:entry>
         <oasis:entry colname="col5">1.0</oasis:entry>
         <oasis:entry colname="col6">1.1</oasis:entry>
         <oasis:entry colname="col7">5.8</oasis:entry>
         <oasis:entry colname="col8">3.5</oasis:entry>
         <oasis:entry colname="col9">0.9</oasis:entry>
         <oasis:entry colname="col10">4.1</oasis:entry>
         <oasis:entry colname="col11">4.0</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Wrong level</oasis:entry>
         <oasis:entry colname="col2">0.3</oasis:entry>
         <oasis:entry colname="col3">0.7</oasis:entry>
         <oasis:entry colname="col4">0.4</oasis:entry>
         <oasis:entry colname="col5">0.2</oasis:entry>
         <oasis:entry colname="col6">0.4</oasis:entry>
         <oasis:entry colname="col7">0.7</oasis:entry>
         <oasis:entry colname="col8">2.0</oasis:entry>
         <oasis:entry colname="col9">1.3</oasis:entry>
         <oasis:entry colname="col10">1.0</oasis:entry>
         <oasis:entry colname="col11">1.5</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

</sec>
<sec id="Ch1.S5.SS2">
  <title>User perception</title>
      <p id="d1e2020">A warning, if correctly received and understood, should contribute to a
better preparedness and generate a series of actions. User surveys will
provide the basis for an assessment of the value of the service. How do we
best use the forecasts, and other products, prepared by the service? How do
we communicate the risk?</p>
      <p id="d1e2023">We have performed two evaluations among users. The first survey was
conducted among emergency response officers in the municipalities, county
deputy chiefs and infrastructure owners, such as the NPRA and Bane NOR, for a
sample of 588 people (Epinion AS, 2017). We asked, among other questions, “How
important for the user is the NVE landslide forecasting?” and “How much
does
the user trust the NVE landslide forecasting?”. Results show that a large
majority of users consider the landslide forecasting service useful or very
useful and they have quite or very much confidence with the warning
notifications published at <uri>http://www.varsom.no/</uri>.</p>
      <p id="d1e2029">The second evaluation was conducted among a working group, with personnel
from NVE, MET, NPRA, Bane NOR, and a county emergency office, that was
assigned to carry out an evaluation of the snow avalanche and landslide
forecasting service (Hisdal et al., 2017). The group made the evaluation
based on the following criteria: development of the services, how the
services work today, costs, benefits for the users, measures to improve the
benefits, analysis of the number of snow avalanches, and the synergy between
flood and landslide services. The working group concluded that the
landslide service contributes to a more secure society. To improve the
accuracy of the notifications and utility of the service, four priority
areas are recommended: increased communication and building of capacity among
users, improvement of hazard assessment, improvement of models and tools, and better
landslide occurrence verification.</p><?xmltex \hack{\newpage}?>
</sec>
</sec>
<?pagebreak page1442?><sec id="Ch1.S6">
  <title>Case study: Southern Norway, autumn 2017</title>
      <p id="d1e2040">Southern Norway is the area that includes the counties of Rogaland and Agder
(e.g. Vest-Agder and Aust-Agder). As indicated in Devoli and Dahl (2014) and
later in Devoli et al. (2017) the region is characterized by predominant
hills and low relief with gentle slopes (&lt; 25<inline-formula><mml:math id="M24" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> locally up
to 45<inline-formula><mml:math id="M25" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>) along the coastlines, as well as moderate slopes to elevated
hills (&lt; 25<inline-formula><mml:math id="M26" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>, but locally 25–45<inline-formula><mml:math id="M27" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>) in the
interior. Alpine relief and steep slopes are observed in the valleys
oriented in a N–S direction. The area is covered with tills, but along the coast
and in the alluvial plains the soil coverage is thicker, with fluvial
deposits, used for agriculture. In the eastern parts of Agder, near the
coastline, there are also marine deposits.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T7" specific-use="star"><caption><p id="d1e2082">Daily assessments and issued flood and landslide warnings
for Southern Norway, between 30 September and 4 October 2017.</p></caption>
  <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-t06.png"/>
</table-wrap>

      <?xmltex \floatpos{t}?><fig id="Ch1.F8" specific-use="star"><caption><p id="d1e2092">Issued landslide warnings for southern Norway in the
period from 30 September to 3 October 2017 (source: xgeo.no).</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f08.jpg"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F9" specific-use="star"><caption><p id="d1e2104">Examples of floods and landslide in Southern Norway,
September–October 2017. <bold>(a)</bold> Flooded county road by the river Tovdalselv, at
Drangsholt, Kristiansand,  1 October 2017 (Photo: Turid Haugen, NVE). <bold>(b)</bold> Shallow
debris slide, close to house in Augland Kristiansand, 3 October 2017 (photos:
Ellen Davis Haugen, NVE).</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f09.jpg"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F10" specific-use="star"><caption><p id="d1e2121">Issued flood warnings for southern Norway in the period
from 30 September to 3 October 2017 and water discharge observations. The maps also show
locations where roads were closed due to flood and water discharge
stations, where the discharge level is classified after flood size at
various stations (source: xgeo.no).</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f10.jpg"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F11" specific-use="star"><caption><p id="d1e2132">Landslide threshold maps based on observed values
(regional landslide hazard threshold for Southern Norway) and distribution
of landslide events. Closed roads due to landslides are also viewed in the
same map (source: xgeo.no).</p></caption>
        <?xmltex \igopts{width=341.433071pt}?><graphic xlink:href="https://nhess.copernicus.org/articles/18/1427/2018/nhess-18-1427-2018-f11.png"/>

      </fig>

      <p id="d1e2141">For landslide forecasters these areas have been challenging since the start of
the operations. The region is known to be an area with few landslide
records, even though the area may receive large amounts of rain in autumn
and sometimes in summer and winter. Along the coastline a few debris
slides and some soil slides in artificial slopes have been registered in
the database (<uri>https://www.skredregistrering.no</uri>). Many of these records had few
details (i.e. unknown landslide type, day of occurrence, etc.), and many
slides were not triggered by natural causes (i.e. rainfall/snowmelt) and
occurred in days without rainfall, possibly triggered by anthropogenic factors.
In the interior of the region, records of debris flows and debris slides are
almost absent, and the few ones present have poor quality and are very
uncertain. The lack of landslide records is also due to the low population density and transportation lines. From the experience acquired in the
last 5 years and evidenced by the warning performance evaluations realized
so far (see Sect. 5) it was clear that the thresholds were too high for
the area. In 2016–2017 we reviewed the thresholds and tuned and updated them
based on a few recent but most reliable events (Boje, 2017) (Fig. 2b).</p>
      <p id="d1e2147">At the end of September and beginning of October 2017 two powerful low-pressure systems, located initially north of Newfoundland, brought intense
rainfalls over 3–4 days starting on 29 September 2017. The
first low-pressure system was supposed to hit the western sector of the
region, while the second, which also carried the remnants of the tropical
cyclones Maria and Lee (<uri>http://www.noaa.gov</uri>), was supposed to hit the
eastern part of the region, including also the Telemark county and some of
the counties in southeastern sector of Norway. MET released a
meteorological warning for the region based on available forecasts. Flood
and landslide warnings were issued by NVE (Table 6).</p>
      <p id="d1e2154">The flood forecasting issued a warning at yellow level for Saturday
30 September on Thursday 28 September. On Friday 29 September the
flood warning was elevated to orange for the 30 September. On
Saturday 30 September the flood warning level was set to red, which stayed on red
level for most of the Agder counties for 3 days, followed by an orange
day (3 October) and one day yellow (4 October) before the river
discharge returned to normal (Table 6).</p>
      <p id="d1e2157">The landslides thresholds for the area showed high awareness level in Agder,
for a period of 2–3 days. The rail and road authorities had already been warned on Thursday 28 in an e-mail, before the first warning was
issued. The rail and road authorities use this early information as an
important input for the consideration of their contingency level and to initiate
mitigation measures such as planning the possible closure of railways and the use
of extra personnel.</p>
      <p id="d1e2160">The first issued warning, on Friday 29 September, was a yellow
level for the days of Saturday 30 September and Sunday 1
October (Table 6). On Saturday 30 September, the level for
landslide hazard was upgraded to orange and kept at orange for the two
following days for parts of the Agder counties, while the rest of the area,
including also the county of Telemark, had a yellow level (Fig. 8; Table 6).
The hazard level for Telemark was reduced to green on Monday 2
October, but Rogaland remained at a yellow level for one day longer than the
Agder counties, until 3 October. Based on these warnings the
regional offices of NVE started to interact with the respective counties and
municipalities to consider implementation of the emergency plans and to
discuss the risk of damage.</p>
      <p id="d1e2163">During these rainfall events, which lasted from 29 September to
3 October, some of the rain gauges in the area (the Agder
counties) received nearly 300 mm in 3–4 days. Many rain gauges measured
precipitation that corresponds to rain from a more than 100-year return period
(Gislefoss et al., 2017).</p>
      <p id="d1e2166">The first intense rainfall started around midnight on 29
September along the coastline of Rogaland, moving eastward through Agder
counties the following hours. Most of the rainfall fell on 30
September and 1 October until 04:00. After a break during the
day of 1 October, the second strong<?pagebreak page1443?> low-pressure system arrived in
the evening of 1 October around 22:00, and the most intense
rainfall fell until very early in the morning of 2 October (07:00–08:00).</p>
      <p id="d1e2169">These rainfall events triggered extensive floods and many landslides (Fig. 9) mainly in the counties of Vest-Agder and Aust-Agder, but some landslides
occurred also in Rogaland and Telemark. In total, 80 % of the damages were
to private buildings and many people had to evacuate. More than 3300 cases of
damages were reported, for a total value of NOK 500 million (EUR <inline-formula><mml:math id="M28" display="inline"><mml:mo>∼</mml:mo></mml:math></inline-formula> 50 million) (Holmqvist and Langsholt, 2017).</p>
      <p id="d1e2180">The observed return period of flood was between 50 and 100 years in many of the
large rivers of Agder. However, in some other rivers, the flood had even
longer return periods. In the river Mandalelva, for instance, the flood was
the highest registered since 1896. For many of the stations in this region,
with long time series, this was the largest flood ever recorded (Holmqvist
and Langsholt, 2017) (Figs. 9a, 10).</p>
      <p id="d1e2183">A preliminary registration (the verification is still in progress) shows
that around 60 landslides events occurred between 29 September and
2 October in the counties of Rogaland, Agder, and Telemark. They
were reported along the main roads, causing blockage, but houses were also
directly affected. The landslides registered were mainly shallow soil
slides and planar slides but also rotational and planar slides in clay
materials, mainly of marine origin (Fig. 9b). NVE's regional engineers were
attending several of the landslide sites. Because of the presence of marine
clay deposits in this area, one of the main concerns was the fear that some
of the small soil slides could develop into quick-clay slides. Most of
landslides occurred during the most intense rainfall, during 30
October and during the night between 1 and 2 October.</p>
      <p id="d1e2186">The newly updated landslide thresholds for southernmost part of Norway,
included Agder counties (Fig. 2b), proved very useful in this situation.
This made the forecasters more confident that the high awareness level was
necessary.<?pagebreak page1444?> However, since this event was the first in the area after the
correction of the thresholds, we did not have experience with the new
thresholds, which made the hazard assessment more complicated (Fig. 11). Daily communication between the regional NVE engineers on site and the
landslide forecaster on duty helped to understand the ground conditions at
local scale.</p>
      <p id="d1e2189">Due to the issued landslide and flood warnings the public and municipalities
became more aware of the severity of the situation, before and during the
event. Therefore they were more prepared to face damages and for
closing of roads and railways.</p>
</sec>
<sec id="Ch1.S7" sec-type="conclusions">
  <title>Summary</title>
      <p id="d1e2198">The development of the rainfall- and snowmelt-induced landslide forecasting
and warning service in Norway was possible thanks to a joint initiative
across governmental agencies, and due to the fact that we could take
advantage of existing IT tools, hydrological models and a hydrogeological
network available at NVE as part of the well-established flood forecasting
service.</p>
      <p id="d1e2201">The joint initiative with the MET, NPRA, and Bane NOR was crucial for the
establishment of the service and it is still important for the operation of
the service (in terms of economy, collection of landslide events, common
research and development). The synergy with the flood warning service was
significant for a rapid establishment and a rational operation
(organization, hydrological monitoring and models, automatic collection of
MET observations and forecasts, decision tools, warning routines and
communication).</p>
      <p id="d1e2204">The Norwegian landslide forecasting and warning service uses real-time
measurements of hydro-meteorological data (i.e. discharge, groundwater
level, soil water content<?pagebreak page1445?> and soil temperature, snow water equivalent,
meteorological data) and model simulations of the meteorological and
hydrological conditions. The thresholds used are based on statistical
analyses of historical landslides and simulated hydro-meteorological
variables (such as rainfall, snowmelt, soil saturation, and depth of frozen
ground) and shown as a hydro-meteorological index. The service identifies
potentially dangerous situations and notifies local emergency authorities
and the public up to 66 h ahead with the purpose that they can take
preventive measures. A case study from autumn 2017 has been presented
showing how the service is well functioning and useful in order to prevent
and reduce damages due to landslides and to save lives.</p>
      <p id="d1e2207">The first results after 4 years of operations indicate that the flood and
landslide services have succeeded as a tool for the road and railway
authorities in increasing awareness, preparedness, and risk reduction. NVE's
user survey from 2016/2017 (Epinion AS, 2017) confirms that warnings issued
by NVE (flood, landslide, snow avalanche) is considered as an “alarm
clock” for the municipalities and contingency planners. The service is wanted
and appreciated by our most important users, mainly those responsible for
emergency response at municipalities and counties, as well as the police.</p>
      <p id="d1e2211">Our aim and strategy is to provide correct forecasts of both spatial and
temporal landslide occurrence and systematically updated landslide
bulletins. Therefore, we need
<list list-type="bullet"><list-item>
      <p id="d1e2216">reliable weather forecasts;</p></list-item><list-item>
      <p id="d1e2220">reliable real-time data and hydrological models;</p></list-item><list-item>
      <p id="d1e2224">long-term records (data/events) and good hydrological statistics;</p></list-item><list-item>
      <p id="d1e2228">good-quality landslide data;</p></list-item><list-item>
      <p id="d1e2232">roles and responsibilities well defined, and agreed cooperation with key
agencies;</p></list-item><list-item>
      <p id="d1e2236">good internal and external coordination;</p></list-item><list-item>
      <p id="d1e2240">precise and understandable communication;</p></list-item><list-item>
      <p id="d1e2244">continuous evaluation, research and development, and improvement;</p></list-item><list-item>
      <p id="d1e2248">skilled and experienced personnel;</p></list-item></list></p>
      <p id="d1e2251">Even if the service is quite satisfactory, there are still many challenges and limitations. The hazard assessment, tools for decision making (xGeo,
hydrological models, indexes and thresholds) and communication (<uri>http://www.varsom.no/</uri>)
need continuous development and improvement. To improve the accuracy,
precision and usefulness of the service, the following areas should be
strengthened.</p>
      <p id="d1e2257"><italic>Hazard assessment</italic>. The usefulness of the issued warning can be
increased considerably by combining landslide hazard and vulnerability data.
Therefore hazard and risk maps represent important tools for local
authorities that assist them in setting priorities and where to implement the
required measures. However, hazard maps and risk maps are not available in
the Norwegian municipalities; therefore landslide susceptibility maps
available for the entire country could be used by local emergency
authorities. These maps have been used to improve the thresholds, but they
could support the municipalities by showing where landslides may occur. We need
to communicate better the importance of such maps to the users, from a lack of
other hazard maps.</p>
      <p id="d1e2262"><italic>Weather forecasts and hydrological models</italic>. Reliable warnings
require reliable meteorological observations and forecasts. It is still
challenging to predict landslides triggered by summer rain showers. The
cooperation between MET and NVE has contributed to improved grid data
(precipitation and temperature) of observations and forecasts, thus
improving estimates of snow, water flow, and other hydro-meteorological
variables.</p>
      <p id="d1e2267">The hydrological model used to calculate the water saturation, a parameter
used in landslide thresholds, still has a rough resolution in both time (24 h) and space. The model uses input grid data of precipitation and
temperature (observations and forecasts) based on a rough interpolation and
does not yet utilize the improved grid data provided by MET at the end of
2016 (Saloranta, 2016; Lussana et al., 2018). An improved version of the model
is scheduled to be operative in 2018/2019. It is also appropriate to implement
3 h resolution. Therefore, there is still considerable potential for
improving the basis used for landslide thresholds.</p>
      <p id="d1e2270"><italic>Better verification of landslide events</italic>. The service requires
reliable data of landslide events (e.g. correct type, date, place,
triggering). This is a prerequisite both for establishment of thresholds and
for
post-evaluation. For the first evaluation, it is enough to know if
landslides have occurred, but to tune the warning levels it is important to
know how many landslides occurred under a specific warning level. It is
essential to have a good overview of the number and dimension of landslide events that occurred after a rainfall and/or snowmelt episode. NVE maintain a
national database; however, the registration is still sparse; i.e. there is
no systematic record of events in all regions of Norway. The quality of
registrations also varies greatly. There is no consistency in data
collection and there are problems with the classification of different
landslide types. Release time and location can also be wrong and triggering
causes not reported.</p>
      <p id="d1e2276">A major issue for the verification of the landslide occurrence is that
we rely on media and not on systematic field observations. For events on
buildings or outside main roads and railways, the data collection is sparse
compared to registrations of landslides close to roads and railways.</p>
      <p id="d1e2279">Upgrading of landslide inventories is mandatory after each forecast in
order to have the correct type but also number and dimensions of landslide
events.</p>
      <p id="d1e2282">In Norway most of the data along roads (which is the majority of events) are
not recorded by specialists, and hence there is a degree of uncertainty in
the quality of the data. The NPRA is now working to ensure that all
contractors responsible for driving roads use a standard format and receive
periodic training. Because of the poor quality of landslide data and lack
of observations, it has been a challenge to tune the landslide thresholds in
some regions.</p>
      <p id="d1e2285"><italic>Increase communication and build the user's capacity</italic>. The greatest
opportunity to increase the benefit of the service is to build expertise
among users. Some of the challenges are communicating the warning on time
and with sufficient leading time to take actions and the communication of
the uncertainty. We have observed that rainfall- and snowmelt-induced
landslides are often considered as flood damages (i.e. debris flows and
debris slides/avalanches) or snow avalanche damages (i.e. slushflows). There
is a need to strengthen the dissemination work specifically aimed at
regional and local authorities, as well as the public and the media, so that
the warning service itself, the background for alerts and the different
landslide types are better understood. The goal is also to get users and
recipients of warnings to contribute significantly more, for example
registration of hazard signs and landslide events.</p>
</sec>

      
      </body>
    <back><notes notes-type="dataavailability">

      <p id="d1e2294">All the data presented in the document and shown in the
figures are publicly accessible. For accessing published landslide and flood
warnings <uri>http://www.varsom.no/</uri> (NVE,<?pagebreak page1447?> 2018a), xgeo.no (Barfod et al.,
2013) and api.nve.no (NVE, 2018b) may be used. Landslide data, as used in
Figs. 2 and 10, can be downloaded from
<uri>https://gis3.nve.no/map/rest/services/SkredHendelser/MapServer</uri>, or
through the application
<uri>https://temakart.nve.no/link/?link=SkredHendelser</uri> (NVE, 2018c). Data
used for Table 4 can be downloaded from
<uri>http://api.nve.no/doc/jordskredvarsling/</uri> (NVE, 2018b). Data from
Table 5 are available on request to the corresponding author. Data used in
Table 6 can be viewed at <uri>http://www.varsom.no/</uri> (NVE, 2018a) or
downloaded from <uri>http://api.nve.no/doc/jordskredvarsling/</uri> (NVE, 2018b)
and <uri>http://api.nve.no/doc/flomvarsling/</uri> (NVE, 2018b). Data shown in
Fig. 3 are available in <uri>http://www.xgeo.no/</uri> (Barfod et al., 2013). Data
shown in Fig. 8 can be viewed at <uri>http://www.xgeo.no/</uri> (Barfod et al.,
2013) and <uri>http://www.varsom.no/</uri> (NVE, 2018a). Data used for Fig. 10 can
be viewed at <uri>http://www.xgeo.no/</uri> (Barfod et al., 2013), and the
landslide data can be downloaded from
<uri>https://gis3.nve.no/map/rest/services/SkredHendelser/MapServer</uri>, or
through the application
<uri>https://temakart.nve.no/link/?link=SkredHendelser</uri> (NVE, 2018c).</p>
  </notes><notes notes-type="competinginterests">

      <p id="d1e2341">The authors declare that they have no conflict of
interest.</p>
  </notes><notes notes-type="sistatement">

      <p id="d1e2347">This article is part of the special issue “Landslide early
warning systems: monitoring systems, rainfall thresholds, warning models,
performance evaluation and risk perception”. It is not associated with a
conference.</p>
  </notes><ack><title>Acknowledgements</title><p id="d1e2354">Special thanks go to our partners: The Norwegian Meteorological
Institute (MET), the Norwegian Public Road Administration (NPRA), and the
Norwegian Rail Administration (Bane NOR).<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>
Edited by: Samuele Segoni<?xmltex \hack{\newline}?>
Reviewed by: Emanuele Intrieri and one anonymous referee</p></ack><ref-list>
    <title>References</title>

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<abstract-html><p>The Norwegian Water Resources and Energy Directorate (NVE) have run a
national flood forecasting and warning service since 1989. In 2009, the
directorate was given the responsibility of also initiating a national
forecasting service for rainfall-induced landslides. Both services are part
of a political effort to improve flood and landslide risk prevention. The
Landslide Forecasting and Warning Service was officially launched in 2013 and
is developed as a joint initiative across public agencies between NVE, the
Norwegian Meteorological Institute (MET), the Norwegian Public Road
Administration (NPRA) and the Norwegian Rail Administration (Bane NOR). The
main goal of the service is to reduce economic and human losses caused by
landslides. The service performs daily a national landslide hazard assessment
describing the expected awareness level at a regional level (i.e. for a
county and/or group of municipalities). The service is operative 7 days a
week throughout the year. Assessments and updates are published at the
warning portal <a href="http://www.varsom.no/" target="_blank">http://www.varsom.no/</a> at least twice a day, for the
three coming days. The service delivers continuous updates on the current
situation and future development to national and regional stakeholders and to
the general public. The service is run in close cooperation with the flood
forecasting service. Both services are based on the five pillars: automatic
hydrological and meteorological stations, landslide and flood historical
database, hydro-meteorological forecasting models, thresholds or return
periods, and a trained group of forecasters. The main components of the
service are herein described. A recent evaluation, conducted on the 4 years
of operation, shows a rate of over 95&thinsp;% correct daily assessments. In
addition positive feedbacks have been received from users through a
questionnaire. The capability of the service to forecast landslides by
following the hydro-meteorological conditions is illustrated by an example
from autumn 2017. The case shows how the landslide service has developed into
a well-functioning system providing useful information, effectively and on
time.</p></abstract-html>
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