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Results:

• 11. VI_2017_009

  we take the corresponding data from the CMIP5 project. Table 1. All GCMs and RCMs used in this study. If a model is available for any of the domains Arctic-44, EURO-44, or EURO-11, it is marked with a v, but with an x if it is unavailable. Model name Type EURO-11 EURO-44 Arctic-44 CCCma-CanESM2 GCM x v v COSMO-CLM4-8-17 RCM v v x CNRM-CERFACS-CNRM-CM5 GCM v v x IHCEC-EC-Earth GCM v v v /media/vedurstofan-utgafa-2017/VI_2017_009.pdf

• 12. 2012-Refsgaard_etal-uncertainty_climate-change-adaptation-MITI343

  e in relatio n to climat echang eadapt ation .X ,X X ,XX X is a ge n era lguid eo n th e relativ e imp ortanc e leve lo fth e sourc es , alth oug h it mus tb e em phasise d tha tth e imp ortanc e o fth e indi vidua lsou rce s o fun certaint y is co n tex tspe cifi c St ep si n cl im at e ch an ge ad ap tat io n an al ys es (ch ain in u n ce rta in ty ca sc ad e, Fi g. 2) So ur ce s o fu n ce rta /media/loftslag/2012-Refsgaard_etal-uncertainty_climate-change-adaptation-MITI343.pdf

• 13. Public-Choice-2012---Teyssier---Inequity-and-risk-aversion-in-sequential-public-good-games

  individuals act like homo-œconomicus agents (see for example Andreoni 1988; Berg et al. 1995; Camerer 2003; Forsythe et al. 1994; Isaac et al. 1984). Recent developments in public-choice theory have taken a behavioral approach to broaden the analysis of collective action. The introduction of social preferences, such as altruism, inequity aversion or trust, may mean that optimal collective choices /media/loftslag/Public-Choice-2012---Teyssier---Inequity-and-risk-aversion-in-sequential-public-good-games.pdf

• 14. Kok_et_al._TFSC_published_2011

  ]. There is ample experience with backcasting, and consequently much has been said about the underlying principles (e.g. [20]), the methodological 838 K. Kok et al. / Technological Forecasting & Social Change 78 (2011) 835–851 Author's personal copy framework (e.g. [8,30–32]), and practical applications (e.g. [33–35]). Also the combination between backcasting and other types of scenarios has been /media/loftslag/Kok_et_al._TFSC_published_2011.pdf

• 15. Refsgaard_etal-2007-Uncertainty-EMS

  The advantage of Monte Carlo analysis is its general appli- cability and that it does not impose many assumptions on prob- ability distributions and correlations and that it can be linked to any model code. The key limitation is the large run times for computationally intensive models and the huge amount of outputs that are not always straightforward to analyse. 4.8. Multiple model simulation Multiple model /media/loftslag/Refsgaard_etal-2007-Uncertainty-EMS.pdf

• 16. Adalgeirsdottir-etal-tc-5-961-2011

  Korona et al., 2009) and 2010 (airborne LiDAR in autumn). The Cryosphere, 5, 961–975, 2011 www.the-cryosphere.net/5/961/2011/ G. Aðalgeirsdóttir et al.: 20th and 21st century evolution of Hoffellsjökull glacier 963 Fig. 2. (A) Measured bedrock topography of Hoffellsjökull (2001). Blue colours indicate elevation below sea level. (B–E) Surface to- pography at different times, showing retreat /media/ces/Adalgeirsdottir-etal-tc-5-961-2011.pdf

• 17. CES_D2.4_task1

  is projected to approach 90%. The impact of anthropogenic climate change on precipitation is still estimated to be very small at present. In the middle of this century, typically about 60% of all months are projected to have above-median precipitation in northern Europe, although with a substantial variation with the time of the year. An on-line appendix of this report provides detailed tables /media/ces/CES_D2.4_task1.pdf

• 18. VI_2020_008

  by the Peak-over-Threshold with MLE applied on daily and 24-hour accumulated precipitation from the ICRA. ............................... 58 8 Glossary 1M5 – Daily or 24-hour precipitation return level with a 5-year return period AMS – Annual Maxima Series CC – Closeness Coefficient CDO – Climate Data Operator EVA – Extreme Value Analysis GP – Generalized Pareto ICRA – Icelandic /media/vedurstofan-utgafa-2020/VI_2020_008.pdf

• 19. 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

• 20. vanRoosmalen_etal-2009-WRR_2007WR006760

  and Irrigationa Scenario Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Current 101 85 59 13
  6
  4
  1 6 39 79 84 97 A2 145 132 73 10
  10
  7
  6
  8 4 75 92 123 B2 137 119 75 16
  6
  6
  6
  5 21 74 110 141 aValues are in millimeters. 10 of 18 W00A15 VAN ROOSMALEN ET AL.: CLIMATE AND LAND USE CHANGE W00A15 time and larger area where groundwater levels rise above the drain levels. Table 6 shows the mean discharges /media/loftslag/vanRoosmalen_etal-2009-WRR_2007WR006760.pdf