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Results:

• 21. Isskyrsla_20100407

  N66°44.92' W025°08.94' 79. N66°45.01' W025°08.50' 80. N66°46.32' W025°10.27' 81. N66°46.73' W025°07.19' 82. N66°46.01' W025°04.86' 83. N66°46.22' W025°04.33' 84. N66°47.12' W025°06.45' 85. N66°47.26' W025°04.36' 86. N66°48.44' W025°03.60' 87. N66°49.10' W025°04.45' 88. N66°51.68' W025°03.27' 89. N66°53.36' W024°59.25' 90. N66°55.39' W024°58.89' 91. N66°55.82' W024°58.13' 92. N66°56.15' W025°01.15 /media/hafis/skyrslur_lhg/Isskyrsla_20100407.pdf

• 22. Traffc-maintenance_expenditures

  229 1 587 160 1970 18 591 42 413 2 166 86 101 - 892 5 131 2 310 201 - - - 157 805 1 652 904 1969 17 559 38 840 2 313 93 776 - 922 4 562 1 571 - - - - 159 543 1 832 354 1968 13 474 36 376 3 074 104 519 - 1 080 4 131 1 156 - - - - 163 811 1 964 368 1967 11 139 34 495 5 940 95 857 - 419 3 032 1 087 - - - - 151 968 1 936 249 1966 9 835 32 581 8 349 94 777 - 587 4 205 1 743 - - - - 152 077 2 032 936 /media/loftslag/Traffc-maintenance_expenditures.pdf

• 23. BIKF_windrose_2005-2014

  direction (%) Aerodrome Total observations: 14883 Calm: 2.2% Variable winds: 0.55% Average wind speed for wind direction Wind direction (°) Wind speed (knots ) 0 5 10 15 0 45 90 135 180 225 270 315 360 N E S W N (Number of observations for wind direction) Icelandic Met Office 02. júl. 2015 62 1 87 9 97 3 79 5 45 3 30 9 24 2 33 8 23 8 19 1 20 6 30 9 38 1 45 2 65 6 63 1 46 0 42 2 41 7 28 1 30 1 33 2 /media/vedur/BIKF_windrose_2005-2014.pdf

• 24. VI_2022_006_extreme

  77 91 109 Hraunaveita 132 116 136 159 117 140 169 Kvíslaveita 48 42 49 58 42 51 61 Sultartangi 66 57 68 80 58 69 84 Þingvallavatn 96 84 99 117 85 102 123 Þórisvatn 47 41 49 57 42 50 60 Tungnaá 76 67 79 92 67 80 98 Ufsarlón 104 92 108 126 93 112 134 36 Figure 19 – 1M5 maps for catchment Hálslón based on the ICRA dataset without projection (top left), with RCP 2.6 and 10th percentile /media/vedurstofan-utgafa-2022/VI_2022_006_extreme.pdf

• 25. Henriksen_Barlebo-2008-AWM_BBN-Journ_Env_Management

  the body of knowledge in any given area by mapping out cause-and-effect relationships among key variables and encoding them with numbers that represent the extent to which one variable is likely to affect another (Jensen, 2002). Factors, associations and probabilities can be adjusted and validated and BNs are powerful for integrating data and knowledge from different sources and domains, e.g /media/loftslag/Henriksen_Barlebo-2008-AWM_BBN-Journ_Env_Management.pdf

• 26. Water_resources_man_Veijalainen_etal

  range 90% 93.15–93.29 93.27–93.36 75.43–75.56 94.59–94.71 2040–2069 range 90% 93.09–93.31 93.28–93.40 75.41–75.56 94.72–94.83 2070–2099 range 90% 93.02–93.32 93.24–93.43 75.38–75.61 94.79–94.91 Lowest water level (in the 30 year period) (m) Reference period 92.86 92.72 75.20 94.27 2010–2039 range 90% 92.62–92.91 92.96–93.13 75.24–75.33 94.47–94.59 2040–2069 range 90% 92.55–92.82 92.88–93.12 75.19 /media/ces/Water_resources_man_Veijalainen_etal.pdf

• 27. The weather in Iceland in 2020

  to the 10-year average. It was relatively warmer in coastal areas but colder in the inland.JuneJune was warm and favorable. It was relatively warmest in the Northeast but cooler in the southwestern part. Wind and precipitation were close to average.July July was rather cold compared to recent years, although the mean was above the 1961 to 1990 average. Yet the weather was not unfavorable /about-imo/news/the-weather-in-iceland-in-2020

• 28. The weather in Iceland in 2021

  89 54.1 210 129 Sauðanesviti 944.7 107 101 41.8 222 127 Akureyri 636.4 111 98 29.5 172 103 Grímsstaðir 400.4 103 89 14.3 189 97 Skjaldþingsstaðir 1112.4 # 84 61.2 184 115 Dalatangi 1437.2 87 81 47.6 232 145 Höfn í Hornafirði 1327 # 83 /about-imo/news/the-weather-in-iceland-in-2021

• 29. BIGJ_windrose_2005-2014

  1 2 1 1 Wind rose BIGJ April 2005−2014 90 80 70 60 50 40 30 2010360350340 330 320 310 300 290 280 270 260 250 240 230 220 210 200 190 180 170 160 150 140 130 120 110 100 2 4 6 8 10 Frequency of wind direction (%) Aerodrome Total observations: 114 Calm: 0.88% Variable winds: 0% Average wind speed for wind direction Wind direction (°) Wind speed (knots ) 0 5 10 15 0 45 90 135 180 225 270 315 /media/vedur/BIGJ_windrose_2005-2014.pdf

• 30. Kok_2-scenarios-lecture-2

  extreem events C6; qual nat. systems C7; policies C8; price of resources C9; other sectors C10; industry C11; inrastructure Crimea - Ukraine Manaus - Brazil From FCM to model input FCM – strong points • Easy to develop and apply. The approach is highly intuitive, it can quickly be explained and applied to any new situation. • High level of integration. A FCM can contain any type of information /media/loftslag/Kok_2-scenarios-lecture-2.pdf