• Methodology
• Key findings
• Conclusions
2
Forestry in Finland
1. Land area distribution 2. Species distribution
Total Forestry land 26.3 mill. ha
3. Growing stocks, increment and drain 4. Site type distribution
Source: Finnish Forest Research Institute, 2008
3
Forest management
Final felling
Timber
Energy biomass
Thinning
Timber
Pre-commercial or
energy biomass thinning
Regeneration Regeneration
4
/media/ces/Alam_Ashraful_CES_2010.pdf
1 10 100 1000
Return period (years)
P
e
a
k
d
a
i
l
y
d
i
s
c
h
a
r
g
e
(
m
3
/
s
)
1961-1990 Gumbel
2021-2050 Gumbel
2021 - 2050 annual maxima
1961 - 1990 annual maxima
X
X
35% increase
in 200-year
flood
Model uncertainty
Seasonal analysis - Rainfall-induced
peak flows in annual maximum series
1961 - 1990 2021-2050
Red – Type 1: > 67% of annual
maximum in mar-july (snowmelt
dominance
/media/ces/Lawrence_Deborah_CES_2010.pdf
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
%, respectively. For the best-estimate distribution
representing the year 2010, the probability of warm Decembers has increased to 72% and the
probability of very warm Decembers to 26%1; conversely the probabilities of cold and very
cold Decembers have been reduced. A part of these changes is already visible in the observed
distribution for 1961-2008.
1
/media/ces/CES_D2.4_task1.pdf
J600v berg 2.utg) were also used in this study.
Table 1. Main characteristics of river basins used in this study.
River Name Type Area Mean Percentage Mean annual Period
/ (km2) elevation glacier precipitation for
Gauging (m a.s.l) (mm) streamflow
station (1961-2014) data
vhm59 Ytri-Rangá L 622 365 0 1564 1961–2014
vhm64 Ölfusá L+D+J+S 5687 480 12.2 2003 1950–2014
vhm66 Hvítá (Borgarfirði) L+J 1577
/media/vedurstofan/utgafa/skyrslur/2015/VI_2015_009.pdf
and the organising of the participatory process.
2. BNs with stakeholder involvement and the NeWater
context
2.1. Bayesian networks
A Bayesian belief network, also called a BN, is a type of
decision support system based on probability theory which
implements Bayes’ rule of probability. This rule shows
mathematically how existing beliefs can be modified with
the input of new evidence.
BNs organise
/media/loftslag/Henriksen_Barlebo-2008-AWM_BBN-Journ_Env_Management.pdf
.............................................................................................. 11
4 Recalculation of magnitudes and pgx-distance relations without near-source effect .. 12
5 Other predictor variables .............................................................................................. 19
6 Amplitude variations between stations and instrument type/media/vedurstofan/utgafa/skyrslur/2009/VI_2009_012.pdf
in km2 and
km3, respectively, and c = 0:036, g = 1:36 for the valley glaciers. Figure 2 shows the same five
Icelandic ice caps and the same regression lines together with volume–area data for eight ice
caps and glaciers on Svalbard and in Scandinavia and the much larger ice sheets of Greenland
and Antarctica.
Figures 1 and 2 show that reasonable approximations for the ice volume stored in glaciers
/media/ces/ces-glacier-scaling-memo2009-01.pdf
cap and, in 2007, a rock avalanche fell on the Morsárjökull outlet glacier in southern Vatnajökull ice cap. Rockslides and rock avalanches on glaciers may break up the surface of the glacier, thereby adding ice to the moving material. In addition, the slide may sweep water from glacial lagoons on its way, creating a fast-flowing slurry of rock, ice, water and even air. Debris flows of this type/about-imo/news/fractures-in-svinafellsheidi-and-a-potential-rockslide-on-svinafellsjokull