Linking loss of agricultural land & embankment
breech to on-going development of flood warning
activities on the Indus
FAO/SUPARCO/University of Southampton
Objectives
 Produce a real-time
model of erosion
based land loss and
potential breech of
embankment during
flood events on the
Indus River
Chashma Barrage
Taunsa Barrage
 Link erosion model to
the river flow rates
 Build in-country
capacity in this field
Study site selected for the project
4 Key Activities
 Mapping of erosion based land loss and
agricultural impact on the river Indus due to
2010 floods
 Statistical analysis of 20 years of satellite data
for trend analysis of historical hot spots of
erosion
Huge losses of productive land and infra-structure
 Conduct field tests of characteristic
geotechnical properties of riverbanks to model
likely impacts of given flood events on
agricultural land and production as well as high
potential for embankment breech
 Build capacity in country to conduct analysis
and field work with joint production of erosion
models based upon flood work and associated
reporting/publications
Defensive Structures Protect Land and Infra-structure
but are costly and need to be optimally located
Key Emergency Response and Planning Outputs:
Breach Forecast
Substantial flood impact
on agriculture and lives
Single Point Breach
HYDERABAD: There are still a number
of river embankments in Thatta and
other parts of the province with visible
signs of wave-wash and erosion……
(Dawn.Com – local paper Pakistan)
Indus River Cross-sectional Data
• River cross-sectional
information is
essential for flood
simulations
– Ascertains the
capacity of the river
at a given cross
section.
• Use of ADCP for river
bed profiling along
with finer resolution
5
30,000
30,000
25,000
25,000
20,000
20,000
15,000
15,000
10,000
10,000
5,000
5,000
0
01-Jan
0
01-Feb
01-Mar
01-Apr
01-May
01-Jun
1999
2000
01-Jul
2001
01-Aug
2002
01-Sep
01-Oct
2009
01-Nov
01-Dec
2010
Land Lost
Land Lost
Flooded Area
1998
Flooded Area
Extreme event 2010 discharge hydrograph (cumes)
Discharge hydrograph (1998-2009) (cumecs)
Conceptual Model #1
Flood Discharge
Flood Discharge
Land lost is a function of flow > threshold for bank erosion + floodplain deposition
Conceptual model #2
Land lost
Much more likely that land loss is
not be linearly related to flood
discharge at all places or for all time
periods.
Flood discharge
Local and regional controls on bank migration and land loss –
Embankments, bank protection, floodplain confinement
Information (e.g. sediment flux) transfer from adjacent sections can
influence behaviour at a section.
Non-linear impacts of extreme events and “memory” imprints of
these events may control subsequent inundation and bank erosion
Autogenic processes (e.g. evolving river morphology) can alter
inundation and bank migration.
Measures required for
Conceptual model testing
• Detect bank migration (bank line change)
• Detect land loss
• Quantify threshold discharge above which land is lost
to bank erosion and deposition
• Identify local/regional controls
• Generate relationships & screen for evidence of
other controlling factors.
Flow data: defining metrics
30,000
20,000
Flow magnitude
Flow duration > threshold
Flow volume > threshold
25,000
20,000
15,000
15,000
10,000
10,000
5,000
5,000
0
01-Jan
0
01-Feb
01-Mar
01-Apr
1998
01-May
01-Jun
1999
2000
01-Jul
2001
01-Aug
2002
01-Sep
2009
01-Oct
01-Nov
2010
01-Dec
Extreme event 2010 discharge hydrograph (cumes)
Discharge hydrograph (1998-2009) (cumecs)
25,000
30,000
Source of data
•
•
•
•
•
Landsat data provided longest coverage in time
Free to use, downloaded from the USGS website
7GB of Landsat 5\7TM
Geo-referenced multispectral (7 bands) data at 30m resolution
Composite images from bands 4,5,3 were built
Issues with data
• Cloud cover
• Corrupted Landsat images from 2003-09
• Availability for specific time period
Year
1998
1999
2000
2001
2002
2003
2009
2010
2011
High
X
X
X
X
X
X
X
X
Low
X
X
X
X
X
X
X
X
X
SPOT
Landsat
100000
22/02/2000
06/08/1999
08/08/2000
04/03/2001
27/08/2001
19/02/2002
19/03/2002
29/07/2002
23/05/1998
27/08/1998
19/02/1999
10/03/2003
21/05/2009
08/07/2009
24/05/2010
09/06/2010
24/03/2011
02/10/2011
Discharge
800000
700000
01/01/2000
600000
01/01/2005
Date
SURPARCO provided unknown data from 89,92,94,96-05 at 30m resolution, possible pre-processed Landsat data, affected by clouds in some cases.
SPOT
500000
400000
300000
200000
0
01/01/2010
02/10/2011
24/03/2011
24/05/2010
09/06/2010
21/05/2009
08/07/2009
10/03/2003
29/07/2002
19/02/2002
19/03/2002
27/08/2001
04/03/2001
08/08/2000
22/02/2000
06/08/1999
19/02/1999
23/05/1998
27/08/1998
Availability of Landsat images
Taunsa barrage upstream discharge and acquired Landsat images
900000
How was the satellite data processed?
• Study reach is approximately 304Km long
• GIS automation is really the only option due to channel complexity
Identifying vegetated land loss
Band 4
Band 5
Band 3
B453
Composite
Unsupervised
Classification into
10 classes
Reclassified
into 3 classes
Reclassified to:
Water (1)
Bare ground (10)
Vegetated ground (100)
Computer
year on year
change
Note: Year on Year may have gaps,
e.g. 2003 – 2009. There are 7 possible
outcomes (veg >bare/water,
bare > veg/water, water > bare/veg &
No change)
Using the standard ISO Cluster tool
Extract
vegetation
loss only
Tabulate area
loss
Using the High Flow mask of
the previous year clip out pixels
and recode veg > bare/water to 1
all other change as 0.
Tabulate vegetated land loss for
each 5Km clip grid row and convert
to a graph.
Identifying vegetated land loss
Landsat composite image (bands 4, 5 & 3)
Identifying vegetated land loss
Unsupervised Classification into 10 classes
Identifying vegetated land loss
Aggregate 10 classes into 3 classes (water, bare and vegetated)
Identifying vegetated land loss
Year on year difference, what changes to what…
Identifying vegetated land loss
A mask was created from Landsat data for high flow period
Identifying vegetated land loss
Vegetated land loss within high flow mask was identified.
Further Analysis
Further Analysis
Segment study reach into 5Km rows
Further Analysis
Extracting the centreline – stage 1
Further Analysis
Extracting the centreline – stage 2
Further Analysis
Terrace
Embankment
Canal
Capture terrace, embankments and major canalized channels
Further Analysis
Digitize left/right terracing and embankments
Further Analysis – Computing erosion rates for selected sites
ID
1
2
3
4
5
6
Erosion rate (m/yr)
131.564
131.114
176.101
178.693
169.381
169.967
Average erosion rate for 6 transects:
159.47 m/yr
Regional Context:
- Geological control
- Terraces
- Embankments
Chashma
barrage
Regional Context 3:
Infrastructure
Canals
Barrages
Taunsa
barrage
Floodplain Occupancy by channel and flooding
Low flow occupancy grid
High flow occupancy grid
Embankments
Occupancy grid
Terrace
Value
Chasma Taunsa 15Km Buffer
1
2
Elevation
3
Value
4
High : 3002
5
Low : 100
6
7
8
9
Despite wider available
floodplain between terraces,
channel occupancy is relatively
confined.
Important missing element is
high resolution digital elevation
data (e.g. LiDAR).
Flat nature of valley floor means
vertical resolution needs to be
better than ASTER/SRTM
Controls on Channel Dynamics
Effective channel dynamics (vegetated land lost) vs regional scale
controls (floodplain width)
350
Standard Deviation
300
250
200
150
100
50
0
10
12
14
16
18
20
22
24
26
28
Channel dynamics
Average width (km)
Conceptual Model
Some scaling with available floodplain
width
Clearly other controls operating:
- embankments?
- evolution of channel morphology
(autogenic controls)?
Available width
Downstream patterns of vegetated land loss:
“normal Monsoon”
u/s
d/s
Two main zones of higher land loss and lower
land loss
Embankment/barrage infrastructure controls
vegetated land loss
Downstream patterns of vegetated land loss:
“2010 event”
Patterns of land loss during an exceptional
event broadly follow lower event behaviour
though more extreme….
Dynamic reaches extended
Barrage/Embankment influence still detected
Continuing Analysis
• Autogenic controls – evidence for specific
patterns of channel evolution and their
relationship to bank migration and land loss
• Fill in gaps in time series (selecting shorter
reaches where behaviour varies but data is
complete)
• Improving definition of land “lost” to agriculture
e.g. how many years after deposition can land
be farmed?
Additional analysis and interpretation will be presented in session 3
IFAS – flood
extent model
UNESCO/PMD/SUPARCO
+
Bank erosion
Modelling – loss of
Ag. Land and
Breech
FAO/SUPARCO/SOTON
=
Full Warning
System
GoP