Group D
An investigation into factors affecting a
flood forecasting model in a glaciated
region.
Hannah O’Reilly, Rachael Niblett, Pamela Murray, Richard Jenkins,
Nasir Mahmood, Edward Krussman, Tom Hickson and Kathryn McGrath.
1
Contents
By page number
3. Abstract
3. Aims
4. Objectives
4. Hypotheses
4-5. Introduction and background
6-7. Methods
7-15. Results
16-17. Discussion
17. Limitations
17. Conclusion
18. References
2
Abstract
The Odenwinkelkees glacier, Austria is a place of scientific interest as it provides
ample opportunities to study glacier behaviour such as ablation rates and proglacial
stream behaviour. Information obtained about the characteristics of the glacier and the
proglacial stream are vital in understanding the factors leading to flooding and the
prediction of flooding. Measurements taken at 500m intervals and 50m distance from
the stream showed a positive relationship between infiltration and slope and a
significant relationship between vegetation cover and infiltration rate. Using a DTM
model and flow accumulation model obtained from Salzburg University we were able
to see that as tree-line height at altitude increases discharge will decrease. The overall
accuracy between the data we collected, paper maps and the GIS data sets was fairly
good and our field measurements were at a higher resolution than that of the GIS thus
improving the accuracy of our study.
Aims
The aim of this project is to identify the factors that affect flood forecasting
downstream. We will do this by collecting the following data from various locations
on either side of the proglacial stream running out of the Odenwinkelkees glacier,
Austria:

Infiltration rates

Aspect

Vegetation type and percentage cover

Location

Elevation

Slope
We will then relate the collected data to melt rate results gathered over the whole
week by two other groups, secondary long term local meteorological data and
geological data. We will also map inputs in to the main river using GIS (Geographical
Information System) software. This will hopefully allow us to produce a flood
forecasting model data and calculate flood levels from given amounts of rainfall.
3
Objectives
1. Our first objective is to collect all the data from the chosen sites in the field.
2. The second objective is to collect local meteorological data and melt rate data
and input this into our model
3. The third objective is to input our data into a flood forecasting model.
4. The forth objective is to map the location of the sample sites and the inputs
into the main river using GIS and comparing GIS, GPS and OS map data in
order to test the reliability of the data put into our model.
5. The fifth objective is to investigate a relationship between altitude of the
treeline and discharge.
6. The sixth objective is to investigate a relationship between slope angle and
infiltration rates.
Hypotheses
Null Hypothesis 1: An increasing tree-line height at altitude will not decrease the
discharge.
Alternate Hypothesis 1: With increasing tree-line height at altitude discharge will
decrease.
Null Hypothesis 2: There is no statistical relationship between slope angle and
infiltration rates.
Alternate Hypothesis 2: There is a statistical relationship between slope angle and
infiltration rates.
Null Hypothesis 3: There is no significant difference between GIS, GPS and OS map
data.
Alternate Hypothesis 3: There is a significant difference between GIS, GPS and OS
map data.
Null Hypothesis 4: There is no statistical relationship between vegetation and
infiltration rates.
Alternate Hypothesis 4: There is a statistical relationship between vegetation and
infiltration rates.
Introduction and Background: Why is it important to investigate factors that affect
flood forecasting in a glaciated region?
4
The local area is situated in a glaciated region with heavy snow in winter and snow
melt in summer. These changes cause seasonal differences in melt water inputs into
the river. The ability for the river and surrounding flood plain to cope with these
changes has significant impacts and will depend on local geology, vegetation type and
cover, soil infiltration rates, slope angle and local meteorology as well as snow melt
rates.
One of the most probing questions that we are faced with today is the impacts of
global climate change on melt rates and subsequently flooding of the local area.
Proglacial flooding
As with normal river catchments, proglacial catchments experience periods of
flooding. However the causes of this flooding can be quite different. Glacial flood
events can be triggered by individual weather systems for example summer and
autumn storms. These determine not only the intensity of rainfall but also how much
increased ablation is likely to occur enabling more efficient melting by transferring
heat from the atmosphere (Benn and Evans 1998) Changes to the glacial drainage
systems also contributes to irregular flood events. The changes can include the
collapse of englacial and subglacial channels the release of stored melt water behind
sediment or ice jams or on a bigger scale ice dammed lakes. Such flood events have a
great geomorphological impact (Maizels 1995 cited in Benn and Evans 1998) and can
have in turn an effect on nearby towns and energy systems.
Tree-line and climate change
Changes in the climate have been long suspected of controlling the tree-line;
“Compared to the second half of the 19th century present mean temperatures present a
warming of 1.5K for the growing period (June to September) at tree-line elevations in
the Alps… A general warming of 1.5K… corresponds to 250 m of elevation”,
(Paulsen et al., 2000). This increase in elevation will lead to a decrease in discharge as
the tree cover will intercept the runoff and through flow of the rain water, where as
above the tree line less interception will occur.
5
Methods
To collect all the data in the field we used the following equipment:

GPS

Meter rule

1 bottle of water

Infiltration meter

OS map

Clinometer

Compass
To store and display our data we used the following equipment.

Microsoft Excel

GIS Software
Fieldwork Method
Due to the known terrain of the area, a stratified random sampling technique will be
used. The transect began at 2km from the snout of the proglacial stream. After this
point, a transect was undertaken on each side of the valley running parallel with the
stream. Measurements were taken every 500m at a 50m distance from the stream
where possible and from this we were able to decide on further sampling sites which
will reflect the general area the best. At each sampling site, the following is measured:
1. Aspect of each area was measured using a standard compass.
2. Slope was measured by placing a clinometer on a metre rule that is facing the
direction of greatest slope.
3. Vegetation coverage and type was estimated using a 1m2 quadrat.
4.
Grid reference location and elevation data was obtained using a handheld
GPS.
5. Infiltration was measured by placing a plastic bottle with its bottom cut off
into the soil and then recording the time taken for 200ml of water to infiltrate
completely.
6
Secondary data
Ablation stakes are used to measure the melt rates up the glacier.
GIS Method
Using GIS software we were able to use a DTM and a flow accumulation model to
create discharge levels when the same level of precipitation occurred and when the
tree line was at a different altitude.
Using GRID and ArcMap, the elevation, wetness, slope, and aspect data we collected
was entered into the GIS. This data was then used to compare with the GIS data that
was collected by Salzburg University. Using ArcMap, the GIS data for elevation
(DTM), slope, wetness index, and aspect was displayed, with the 12 site locations
displayed on top. By clicking on each site, the data we collected in the field is
displayed along with the GIS data, enabling us to compare the two data sets. This
allowed us to analyse the data and spot any relationships or differences between the
two.
Results
Table 1: Field Results Table
Site
Number
1
2
3
4
5
6
7
8
9
10
11
13
14
Aspect
NW311º
NW291º
N/A
NW300º
NE032º
W280º
E083º
E090º
E080º
SE119º
S180º
S170º
E065º
Slope
(º)
15º
1º
0º
1º
10º
30º
20º
4º
6º
0º
6º
9º
1º
Elevation (m)
2050
2072
2109
2112
2141
2172
2105
2088
2068
2085
2063
2239
2265
GPS
N47.13343: E012.263725
N47.13142: E012.63426
N47.13142: E012.63485
N47.13212: E012.63573
N47.13212: E012.63897
N47.12358: E012.64020
N47.12265: E012.63752
N47.12674: E012.63314
N47.13310: E012.63430
N47.12936: E012.63247
N47.13408: E012.63409
N47.13622: E012.63162
N47.13735: E012.63082
Soil Moisture (1-5)
3
3
2
2
2
2
2
0
2
2
2
3
2
7
Site
Number
1
2
3
4
5
6
7
8
9
10
11
13
14
Vegetation Cover
Grass, Rhoddendendum
Grass, Bushes
None
Grass
Moss
Grass
None
None
Daisies and shrubs
Mosses and Gentinia
Grass
Grass
Grass
Site Number
1
2
3
4
5
6
7
8
9
10
11
13
14
Infiltration (seconds)
25.055
13
19.86
11.625
7
4.25
4.54
3.64
38.03
31.32
3.5
9.08
33.57
Total Vegetation Percentage Cover
100%
50%
0%
100%
100%
50%
0%
0%
90%
80%
100%
100%
100%
Infiltration Velocity (cm/s)
0.1016
0.1958
0.1282
0.219
0.3637
0.5991
0.561
0.6995
0.0669
0.0813
0.7274
0.2804
0.0758
Using our data in table 2, we statistically tested the relationship between slope angle
and infiltration velocity. Figure 1 and figure 2 display our results which will be
explored in the discussion section.
Slope
(º)
15
1
0
1
10
30
20
6
0
9
1
Infiltration Velocity (cm/s)
0.1016
0.1958
0.1282
0.219
0.3637
0.5991
0.561
0.0669
0.0813
0.2804
0.0758
Table 2: Slope and Infiltration
8
Relationship between infiltration and slope
0.6
infiltration
0.5
0.4
0.3
0.2
0.1
0.0
0
5
10
15
slope
20
25
30
Fig 1: Graph showing relationship between slope angle infiltration velocity.
Fig 2: Minitab regression results
Regression Analysis: C1 versus C2
The regression equation is
C1 = 0.109 + 0.0158 C2
Predictor
Constant
C2
Coef
0.10918
0.015825
S = 0.117795
SE Coef
0.04792
0.003804
R-Sq = 65.8%
T
2.28
4.16
P
0.049
0.002
R-Sq(adj) = 62.0%
Analysis of Variance
Source
Regression
Residual Error
Total
DF
1
9
10
SS
0.24011
0.12488
0.36499
MS
0.24011
0.01388
F
17.30
P
0.002
Unusual Observations
Obs
1
6
C2
15.0
30.0
C1
0.1016
0.5991
Fit
0.3466
0.5839
SE Fit
0.0434
0.0893
Residual
-0.2450
0.0152
St Resid
-2.24R
0.20 X
R denotes an observation with a large standardized residual.
X denotes an observation whose X value gives it large influence.
9
Using a DTM model and flow accumulation model we were able to investigate the
effects of the altitude of the tree line on discharge. Table 3 displays the data we used
and figure 2 displays our results. We moved the tree line up every 100m to explore the
effects and we could see that if there is a higher the tree line there is a decrease in
discharge.
Altitude of treeline
(m)
2000
2100
2200
2300
2400
2500
2600
2700
Discharge
(cumecs)
31916
31002
29776
28230
26366
24676
22756
20835
Table 3: Treeline Altitude and Discharge
Graph to show how the discharge differs with altitude
35000
30000
25000
20000
Discharge (cumecs)
15000
10000
5000
0
0
500
1000
1500
2000
2500
3000
Altitude (m)
Fig 3 A graph to show how discharge differs with altitude.
The relationship between total percentage cover and infiltration velocity was also
investigated using data from table 1 and the results are shown in figure 3.
10
Scatterplot of total percentage cover vs infiltration velocity
120
total percentage cover
100
80
60
40
20
0
0.0
0.1
0.2
0.3
0.4
0.5
infiltration velocity
0.6
0.7
0.8
Fig 4: A scatterplot to show how total percentage cover against infiltration velocity
GIS Results
elevation
ours
2050
2072
2109
2112
2141
2172
2105
2088
2085
2068
2063
2106
map
2040
2070
2110
2115
2140
2180
2110
2080
2090
2080
2060
2100
gis
2056
2073
2103
2109
2138
2158
2149
2105
2082
2059
2080
2106
aspect
ours
311
291
0
300
320
280
90
43
119
80
180
180
slope
ours
gis
0
5
5
5
4
4
1
0
0
0
2
2
gis
15
1
0
1
10
30
20
5
9
6
6
15
11
4
5
4
1
25
16
8
7
13
12
25
wetness
ours
16
12
8
8
8
8
8
8
8
8
8
16
gis
8
8
12
13
11
8
7
9
11
11
8
6
Table 4: GIS, Map and GPS results
11
Comparing slope measurements from GIS and
clinometer readings
35
Slope (degrees)
30
25
20
ours
15
gis
10
5
0
1
2
3
4
5
6
7
8
9
10
11
12
Site Number
Fig 5: A graph to show the slope measurements from GIS and clinometer readings
Comparing wetness measurements from GIS and field
measurements
18
16
Wetness Index
14
12
10
ours
gis
8
6
4
2
0
1
2
3
4
5
6
7
8
9
10
11
12
Site Number
Fig 6: A graph to show the wetness measurements from GIS and field measurements.
12
Comparing aspect measurements from GIS and GPS
350
Aspect (degrees)
300
250
200
ours
gis
150
100
50
0
1
2
3
4
5
6
7
8
9
10
11
12
Site Number
Fig 7: A graph to show the comparisons between aspect measurements
Comparing elevation measurments from GIS, GPS, and
maps.
2200
Elevation (M)
2150
ours
2100
map
2050
gis
2000
1950
1
2
3
4
5
6
7
8
9
10
11
12
Site Number
Fig 8: A graph to show the comparisons between elevation measurements from GIS,
GPS and maps.
We then statistically tested the elevation of our results, the GIS elevation results and
the elevation map using an anova test and the boxplot below displays our results.
13
Boxplot of elevation us, elevation gis, elevation map
2175
2150
Data
2125
2100
2075
2050
elevation us
elevation gis
elevation map
Fig 9: Boxplot of our field elevation results, GIS elevation results and the map
elevation.
One-way ANOVA: elevation us, elevation gis, elevation map
Source
Factor
Error
Total
DF
2
33
35
S = 35.21
SS
113
40903
41016
MS
57
1239
F
0.05
R-Sq = 0.28%
Level
elevation us
elevation gis
elevation map
N
12
12
12
P
0.955
R-Sq(adj) = 0.00%
Mean
2097.6
2101.5
2097.9
Individual 95% CIs For Mean Based on
Pooled StDev
---------+---------+---------+---------+
(----------------*----------------)
(----------------*----------------)
(----------------*----------------)
---------+---------+---------+---------+
2088
2100
2112
2124
StDev
34.5
33.6
37.4
Pooled StDev = 35.2
Boxplot of elevation us, elevation gis, elevation map
One-Sample T: slope us, slope gis
Variable
slope us
slope gis
N
12
12
Mean
9.83333
10.7500
StDev
8.89160
7.8964
SE Mean
2.56678
2.2795
95% CI
(4.18388, 15.48279)
( 5.7328, 15.7672)
14
Boxplot of slope us
Boxplot of slope gis
One-Sample T: slope us, slope gis
Variable
slope us
slope gis
N
12
12
Mean
9.83333
10.7500
StDev
8.89160
7.8964
SE Mean
2.56678
2.2795
95% CI
(4.18388, 15.48279)
( 5.7328, 15.7672)
Boxplot of slope us
Boxplot of slope gis
Two-Sample T-Test and CI: slope us, slope gis
Two-sample T for slope us vs slope gis
slope us
slope gis
N
12
12
Mean
9.83
10.75
StDev
8.89
7.90
SE Mean
2.6
2.3
Difference = mu (slope us) - mu (slope gis)
Estimate for difference: -0.916667
95% CI for difference: (-8.055688, 6.222354)
T-Test of difference = 0 (vs not =): T-Value = -0.27
21
P-Value = 0.792
DF =
Fig 4: Minitab display of our elevation results, the GIS results and the map elevation
15
Discussion
From the graph we can see there is a trend for infiltration rate to increase with
increasing slope. From this we performed a regression analysis to determine how
much of the variance in infiltration could be explained by changes in slope. The result
we gained showed that 65% of the data could be explained by slope. The regression
was shown to be robust through testing for autocorrelation and normality within the
residuals. The p value of the regression of 0.002 meant this was a significant result.
This shows that there is a relationship of causality between slope and infiltration. This
positive correlation could be due to the condition of the ground at the start of the
rainfall. With steeper slopes the effect of gravity means through flow removes water
from the soil very quickly leaving an unsaturated soil. Thus as new rain falls it is able
to infiltrate rapidly into the soil. It could also be said however that slope would reduce
infiltration as water is more likely to be removed via overland flow on steeper slopes
although our results support the ideas of the positive correlation.
The relationship between infiltration velocity and percentage cover was statistically
tested using Pearson’s product moment correlation coefficient. The p value was 0.068
which was below the 95% confidence level. This shows that there was no significant
relationship between these two factors. However, if there was a significant
relationship, vegetation can decrease infiltration by providing a layer in the soil for
interception or it could increase because of the rooting system.
The results for the GIS section, displayed in figures 3-6 and in table 4, showed that
the elevation data that was collected in the field had very similar readings. The GPS
and GIS data were compared with an OS map which showed that the GPS was more
accurate than the GIS data, as it closely matched the map data. The aspect data was
very different to the GIS data, which may be due to the variation in terrain and the
fact that the results recorded in the field were at a much higher resolution than the
GIS. The wetness index was also very different to the GIS data, which is probably a
result of the weather conditions over the past few days. Again, the slope results were
similar to each other, but the field data was at a higher resolution, with the GIS data
accurate to 25m and GPS to 7m, therefore the field data being more accurate. Slope
16
and elevation was statistically tested by carrying out anova tests which showed no
significance. This is because the p values at 0.955 and 0.792 were below the 95%
confidence level.
Limitations
We came across the following limitations:
 Vegetation cover estimates are subjective as is vegetation identification.
 Infiltration rates were difficult to obtain in some areas because the soil was
already fully saturated from a previous storm.
 We were unable to take an equal amount of samples on both sides of the river
because we were limited by the size of the area, health and safety.
 Time constraints meant that we were unable to take multiple measurements at
each site hence we were restricted to 2 readings at each site.
 Time limitations also hindered the development of a complex flood
forecasting model.
 The GPS readings had an estimated 30 meter accuracy reading so a map would
have been more accurate.
 We also used 2 different models of GPS which may have had a significant
impact on our results.
Conclusion
In conclusion we found that the overall accuracy between the data we collected, paper
maps and the GIS data sets was fairly good and our field measurements were at a
higher resolution than that of the GIS thus improving the accuracy of our study, hence
showing the importance of going into the field for more accurate investigation. With
regards to the slope angle and infiltration we found that there was a statistical
relationship as slope angle increased the water infiltrated into the soil more quickly.
From using the flow accumulation model we have found that as altitude increases
there is a decline in discharge. If we were to further this investigation the results
would have been used in a flood forecasting model.
17
References
BENN AND EVANS,1998, Glaciers and Glaciation, Arnold
J PAULSEN, U.M WEBER, and CH KÖRNER, 2003, Tree Growth Near Treeline:
Abrupt or Gradual Reduction with altitude, Arctic, Antarctic and Alpine Research, 32,
1, 14-20.
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