IWMI
NBI
ENTRO
ILRI
WORLD FISH CENTER
Supported by: CPWF
19/09/2009, Chaingmai

Outline
1. Background
2. WP1 Poverty analysis
3. WP2: Assessment of Water Resources
4. WP3 Assessment of Water productivity
5. WP4 Institutional analysis
6. WP5 Intervention analysis
7. Conclusions
Supported by: CPWF
19/09/2009, Chaingmai

1. Background assessment of the basin
Nile BFP Project Objective:
To identify high potential water management
interventions to reduce poverty and increase
water productivity
Supported by: CPWF
19/09/2009, Chaingmai

Basin is highly variable
The Basin is highly variable, theSupported by: CPWF
river is very important, various interventions
19/09/2009, Chaingmai

Key ideas:
• Access to water is related to poverty, not availability
– need to differentiate access and availability
• Water productivity can be a key driver of wealth
generation
• Issues are different between Egypt and Northern part
of Sudan and the rest of the basin – access to water,
productivity, institutions, etc.
• In US Basin countries water access is limited, and
water productivity low – key to poverty reduction.
Supported by: CPWF
19/09/2009, Chaingmai

Project premise:
• These are missed opportunities because
agriculture water management for
rainfed, wetland, livestock, fisheries,
aquaculture tend to fall in a void.
• There are inadequate institutional
arrangements to support this.
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19/09/2009, Chaingmai

Project premise:
• There are numerous opportunities to
manage water better for agriculture in order to
improve productivity, food security and livelihoods.
• While most of the focus is on river water, we start
with rainfall to look for opportunities outside of
the river.
• Significant gains can be made through improving
rainfed production systems through better
agricultural water management
• Livestock, fisheries, aquaculture, wetlands
provide opportunities, but are generally absent in
Nile discourse.
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19/09/2009, Chaingmai

Baseline Conditions
• High poverty and low development 0.90 Egypt
Sudan
Kenya
• High Rainfall Poor Water Distribution-high loss Uganda
Hum developm index
0.78 Ethiopia
Tanzania
upstream Rwanda
ent
0.65 average, all countries
• Drought & flooding 0.53
• High rainfall variability
an
0.40
• High agriculture dependency, slow 0.28
transformation 0.15
1972 1978 1984 1990 1996 2002 2008
• Despite potential, low water usage Year
10000
3618
Agricultural Population in the Nile Basin
Pe rc en tag e o f A g ric u ltu ra l
936 1050 1012
Precipitation (km 3 yr -1 )
1000
285
402 100
1979-1981
Po p u la tio n 80
100 1989-1991
51 45 60
34 32 1999-2001
40
2003
10 20
2004
0
1 t
ia
i
n
ea
a
ia
da
da
yp
DR
nd
da
op
ny
an
it r
an
an
Eg
ru
Egypt
Eritrea
Rw anda
U ganda
Kenya
Ethiopia
Tanzania
Burundi
Sudan
D R Congo
Su
Ke
hi
o,
Er
nz
Ug
Bu
Rw
Et
ng
Ta
Co
Supported by: CPWF Countries
19/09/2009, Chaingmai

Nile Basin Study Sites:
Study Sites Nile Delta
Sudan
Transect
Basin Wide
Sudd
Ethiopian
Highlands
Cattle Corridor
Lake Victoria:
Ugandan
Highlands
Supported by: CPWF
19/09/2009, Chaingmai

Case Study Sites
Y
#
SU D A
M ong alla
Y
#
[
% N im ule
L aropi
As
w
a
P anyang o #
Y
Y
#
P akw ac h
# Paraa A lb
Y Y K am d ini
er t #
M u rchision Fa lls N il
e
Y
# Ma sind i P ort
B ut iaba Y
#
D R C Bu n ia L. Albert Y
#
[
% B ug ond o
L. K y og a
Kaf
u
Na ma sag ali
[
%
V ic
B we ram ule
Y
#
tori
ki
S em ili M bu lam u ti Y
#
aN
Fo rt P o rtal M u z izi
[
%
ile
N ga m b a O w e n Falls D am ia
N zo
S io
Y
# Y
#
Jinja
[
% Y ala
K at on ga
Ka m p ala
K asen y i # L. G e orge
Y E n teb b e %
[
Kis u m u So nd
u
Ish a ng o Y
# Y
# # K azing a C ha n ne l
Y [
%
K atw e
L. E d war d
Ka
ge L. Victoria
ra
Bu ko b a
[
%
[
% M ara
M u s om a
K ig ali
L. K ivu [
%
Nyaborongo
D im
[
% a
M w a nza
vuvu
Sim
yu
Ru
L. Ta ng any ik a
Y
# D isc harge Stations
[
% Tow ns
Falls
Equatorial La ke S ub- Basins
Supported by: CPWF
19/09/2009, Chaingmai Riv ers Sc a le 1 :4 , 25 0 , 00 0

The Nile Basin
Food or environment?
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19/09/2009, Chaingmai

Irrigation Schemes
Country Irrig. Water Irrigation Irrigated
Requirement, Potential, ha Area, ha
m3/ha/yr
Burundi 13,000 80,000 0
DRC 10,000 10,000 0
Egypt 13,000 4,420,000 3,078,000
Eritrea 11,000 150,000 15,124
Ethiopia 9,000 2,220,000 23,160
Kenya 8,500 180,000 0
Rwanda 12,500 150,000 2,000
Sudan 14,000 2,750,000 1,935,200
Tanzania 11,000 30,000 10,000
Uganda 8,000 202,000 9,120
Supported by: CPWF
19/09/2009, Chaingmai

Irrigation Schemes, current & future …
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19/09/2009, Chaingmai

Hydropower Plants,
current & future
Existing Sites
New Planned Sites
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19/09/2009, Chaingmai

Irrigated
A green-blue view
Rain = 1745 km3
Rainfed ET – 190 km3
Irrigated ET – 67 km3
Outflow – 10 to 30 km3
Limited options to expand Pastoral
irrigation – but gets attention Rainfed
Ample options to upgrade Wetlands
agriculture on rainfed lands –
gets little attention
Supported by: CPWF
19/09/2009, Chaingmai

Supported by: CPWF
19/09/2009, Chaingmai

Nile Wetlands
14 Ramsar Sites
All support agriculture
and/or fisheries
All sites listed as
threatened by these
activities
Image of the Sudd
CPWF, IWMI, WorldFish, ILRI, NBI Supported by: CPWF
19/09/2009, Chaingmai

The Sudd Wetland: Inundation Extent
Image courtesy of JAXA K&C
Image courtesy of JAXA K&C
ALOS PALSAR L-band SAR
RED: June 2008, GREEN: September 2008, BLUE: December 2008
Supported by: CPWF
19/09/2009, Chaingmai

Jonglei Canal
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19/09/2009, Chaingmai

Jonglei Canal
360 km long
7 5 m wide
4 to 8m deep
Supported by: CPWF
19/09/2009, Chaingmai

Irrigation Schemes
Country Irrig. Water Irrigation Irrigated
Requirement, Potential, ha Area, ha
m3/ha/yr
Burundi 13,000 80,000 0
DRC 10,000 10,000 0
Egypt 13,000 4,420,000 3,078,000
Eritrea 11,000 150,000 15,124
Ethiopia 9,000 2,220,000 23,160
Kenya 8,500 180,000 0
Rwanda 12,500 150,000 2,000
Sudan 14,000 2,750,000 1,935,200
Tanzania 11,000 30,000 10,000
Uganda 8,000 202,000 9,120
Supported by: CPWF
19/09/2009, Chaingmai

Irrigation Schemes, current & future …
Supported by: CPWF
19/09/2009, Chaingmai

Supported by: CPWF
19/09/2009, Chaingmai

Supported by: CPWF
19/09/2009, Chaingmai

2. WP1 Poverty analysis
Objectives:
• To establish a broad understanding of poverty
and how it relates to water access in production
systems in the Nile
• To create an overview of poverty and vulnerability
indicators relevant for the Nile basin
• To test links between water, agriculture and
poverty in the Nile basin
Supported by: CPWF
19/09/2009, Chaingmai

Research questions:
• What are the basin characteristics of water and
poverty and how are they linked?
• Where are the poor and what are their water related
problems?
• What are the water-related risks in crop-livestock
systems?
Supported by: CPWF
19/09/2009, Chaingmai

Methods:
• Literature review of the basin
• Mapping hotspots of poverty in agricultural systems
– We use food security, poverty level and poverty inequality to map poverty
in the rural agricultural production systems of the Nile Basin.
– Poverty in this case is related to household expenditure on food and non-
food items.
– Poverty line is drawn from expenditure required to purchase cost of a
basket of goods that allows minimum nutrition requirements
• Mapping vulnerability and water related risks
• Case study on mapping poverty indicators and water
access - Uganda
Supported by: CPWF
19/09/2009, Chaingmai

Poverty Hotspots:
±
±
KEY
KEY
Rivers
Water bodies Rivers
Poverty level (%) Poverty hotspots
<15 KEY Water bodies KEY
15 - 25
Poverty hotspots Mixed rainfed Rivers
25 - 35 Lakes
Production system Cereals
Nile Basin bnd
35 - 45
Agro-Pastoral Cereals+ Poverty level > 50%
45 - 55 Treecrops
Legumes
>55 Pastoral Rootcrops+
No data
Legumes+ Treecrops+
0 290 580 870 1,160 0 145 290 580 870 1,160 0 145 290 580 870 1,160 Rootcrops
Kilometers Kilometers Mixed rain Kilometers 0 130 260 520 780 1,040
Kilometers
Poverty in the Poverty in pastoral Poverty in cereal Poverty in tree and
basin and agropastoral and legume root crop systems
systems systems (banana, cassava &
cotton)
Supported by: CPWF
19/09/2009, Chaingmai

Mapping vulnerability and water
related risks
• Vulnerability as exposure to risk, ability to cope with
resulting impacts and the capacity to adapt to new
conditions
• Mapped several indicators of bio-physical and social
risks which results into vulnerability
• The outcomes of these cluster data were combined as
severity indices ranging from 4 to 5 levels depending on
the number of variables used
• Vulnerability maps indicate levels of exposure to risk.
These risks ranged from very high risk, high risk,
moderate risk, low risk and very low risk.
Supported by: CPWF
19/09/2009, Chaingmai

Vulnerability hotspots:
KEY
KEY River Nile
KEY
River Nile River Nile Water bodies
Water bodies Bio-physical vulnerability
Water bodies
Bio-physical risk Very low
KEY Bio-physical risk Very low
River Nile Low
Very low Low
Water bodies
Medium
Medium
Bio-Physical risks Low
High High
Very low Medium Very high Very high
Low 0 145 290 580 870 1,160 0 145 290 580 870 1,160
High Kilometers Kilometers
Medium
High Very high 0 145 290 580 870 1,160
Kilometers
Very high
0 145 290 580 870 1,160
Kilometers
Rainfed cereals Rainfed tree crops Irrigated
Agropastoral
• hotspots of vulnerability in agricultural systems (biophysical risks estimated
from cluster data classification of human and livestock population, market
access, internal renewable water resources and area of crop suitability)
• population is a key driver of exposure to biophysical vulnerability especially in
the intensifying crop livestock systems throughout the highlands and in the
central belt of Sudan
Supported by: CPWF
19/09/2009, Chaingmai

Vulnerability:
KEY
River Nile KEY
KEY
Water bodies
River Nile River Nile
Social risk KEY
Water bodies Water bodies
Very low River_Nile
Social risk Social risks
Water bodies
Very low Low Very Low
Social risk
Low Medium Low Low
Medium High Medium
Medium
High Very high 0 145 290 580 870 1,160 High 0 145 290 580 870 1,160
0 145 290 580 870 1,160
High Kilometers Kilometers
Very high Kilometers
Agropastoral Rainfed cereals Rainfed tree crops Irrigated
-cluster data vulnerability in agricultural systems (social risks estimatedstunted
hotspots of
classification of disease prevalence; malaria HIV/AIDS and
from
growth and malnourished children below age 5)
- high vulnerability index in agropastoral areas reflects exposure and low
capacity to cope with disease and food insecurity due to high poverty rates
- low vulnerability index in irrigated systems reflects better institutional capacity
to cope with the impacts of disease and food insecurity
- exposure to disease and food insecurity is widespread in the rainfed agricultural
systems of the basin except along the lower nile and into the delta region
Supported by: CPWF
19/09/2009, Chaingmai

Water related risks: ±
KEY KEY
KEY River Nile KEY
River Nile
River Nile Water bodies River Nile
Water bodies
Water bodies Water bodies
Risk due to water
Risks due to water Risk due to water Risk due to water
Very low
Very low Low Ver Low
Low
Low Medium
Medium Low
Medium
High High Medium
High
Very high Very high 0 145 290 580 870 1,160
High 0 145 290 580 870 1,160
Very high 0 145 290 580 870 1,160 Kilometers
0 145 290 580 870 1,160 Kilometers Kilometers
Kilometers
Agropastoral Rainfed cereals Rainfed tree crops Irrigated
- hotspots of water related risks in agricultural systems (hazards estimated from
cluster data classification of drought index; rainfall variability as CV rain and
changes in the length of growing period; LGP)
- high risk index in agropastoral and rainfed areas reflects high variation due to
rainfall and changes in the length of growing period
- low risk index in irrigated systems reflectsCPWF dependency on rainfall
Supported by: less
19/09/2009, Chaingmai

Linking water, agriculture and poverty
Where are the poor? What are their water related problems?
• in hotspots with high population • Food insecurity due to high poverty
densities in the mixed rainfed rates and dependency on rainfed
agricultural systems particularly agriculture
those supporting cereal-legume
cropping and banana/cassava • high risk of rainfall variation and
changes in length of growing season in
systems pastoral and agropastoral systems
• These are concentrated in the • high exposure to disease and
highlands of east Africa (Kenya, malnutrition due to low institutional
Uganda, Rwanda, Burundi and capacity to cope with the negative
Ethiopia) impacts
• In pastoral and agropastoral • low risk of rainfall variation and
systems of the central belt of changes in length of growing season in
the highlands as well as lake Victoria
Sudan, northern Uganda and the sub-basin but widespread poverty still
lake region of Tanzania unexplained by good market access
• Low poverty in rice, wheat and
cotton systems
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19/09/2009, Chaingmai

3. WP2: Assessment of Water
Availability and Access
Egypt
Objectives:
– Assess Nile water availability (spatio-
temporal distribution)
– Assess water demands and use
– Assess water accessibility Eritrea
Sudan
Methodology Ethiopia
– Rapid Assessment through literature review
– Identify and fill in gaps of existing knowledge
– Statistical analysis (trends, frequencies) Uganda
Congo, DRC
Kenya
– Water accounting Rwanda Tanzania
Burundi
Supported by: CPWF
19/09/2009, Chaingmai

Nile Basin Databases
• Hydrological data base
• Climate (precipitation)
database (+ grid data)
• ET, soil moisture, biomass,
etc., (WaterWatch)
• Storage systems database Flow station
rainfall station
(under development)
Supported by: CPWF
19/09/2009, Chaingmai

Sample results: Data collection
Nile Database: Monthly river flow: 1910 to 2000
Discharge Processed [m3/s]
11-1910 11-1920 11-1930 11-1940 11-1950 11-1960 11-1970 11-1980 11-1990 11-2000 11-2010
ASWAN QP BAHIR_DAR QP DONGOLA QP GIRBA QP HASSANAB QP J_AULIA QP JINJA QP
KESSIE QP KHARTOUM QP KILO_3 QP MALAKAL QP MANGALA QP ROSEIRES QP SENNAR QP
TAMANIAT QP
Supported by: CPWF
19/09/2009, Chaingmai

How much is the Nile Is it 84.5 billion m3
(Blue) water? (data from 1900 to 1950)
Long term mean: source
Sutcliffe and Parks, 1999
Supported by: CPWF
19/09/2009, Chaingmai

Nile trends: water flows
MAIN NILE
Monthly Flows: 1871/72 -2000/01
160.00 Q 1900 to 1950 = 86.3
140.00 What are the recent trends? More
Q 1900 to 1995 = 80.8 water? 88km3
120.00
Billion M3
100.00
TOTAL
80.00
5yr moving mean
60.00
40.00
Q 1951 to 1995 = 76.0
20.00
0.00
84
96
20
32
44
56
74
98
72
78
90
02
08
14
26
38
50
62
68
80
86
92
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
83
95
19
31
43
55
73
97
71
77
89
01
07
13
25
37
49
61
67
79
85
91
18
18
19
19
19
19
19
19
18
18
18
19
19
19
19
19
19
19
19
19
19
19
Supported by: CPWF
19/09/2009, Chaingmai

< 25
25 - 50
50 - 100
100 - 200
200-400
400 - 600
Mean P
600 - 800 Mean ET0
800 - 1000
1000 - 1200
1200 -1400
1400 - 1600
>1600
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19/09/2009, Chaingmai

What is the seasonal
variability?
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19/09/2009, Chaingmai

Nile water accounting: Methodology
• Based on water balance principle (inflow =
∆
outflow +∆S)
• Define indictors: supply, consumption,
beneficial (economical, environmental), non-
beneficial
• Boundary conditions (Inputs):
– Water Supply: Rain, River, Groundwater
– Water use: Consumptive (ET), non-
consumptive, beneficial (T), non-beneficial
(E), committed (treaties), etc.
• Scales:
– Spatial: catchment, production system, Source: Molden, 1997
sub-basin, basin, country
– Temporal: month, season, annual, long term
mean
• Output
– Water accounting water Supported by: CPWF
productivity
19/09/2009, Chaingmai

Input: Land and water
use classes
clas
No. Land use s
1 closed forest NL
2 open forest NL
3 shrub land NL
4 woody savanna NL
5 open savanna NL
6 sparse savanna NL
7 natural wetland NL
8 rainfed crops ML
9 Urban + industustry MW
10 desert NL
11 irrigated crop MW
12 reservoir
natural lakes and MW
13 rivers NL
14 managed wetland MW
15 saline sinks MW
Supported by: CPWF
19/09/2009, Chaingmai

Input: Land and water use classes
Productio
landus Area Area Rainfall ET T E n
o. Landuse e type Km2 % mm mm mm mm Kg/ha
1 Closed forest NL 85,821 3% 1350 1113 929 183 33818
2 Open forest NL 19,337 1% 900 791 613 177 17316
3 Shrub land NL 260,299 8% 290 227 162 65 5074
4 Woody savannah NL 373,785 12% 1090 919 699 220 23348
5 Open savannah NL 764,232 24% 780 699 510 189 16429
6 Sparse savannah NL 315,078 10% 685 612 504 107 8741
7 Natural wetland NL 14,077 0% 670 1299 1088 210 17447
8 Rainfed crops ML 235,526 7% 910 839 684 155 13672
Urban and
9 industrial MW 5,377 0% 350 227 121 105 5776
10 Desert NL 941,604 30% 60 53 21 32 328
11 Irrigated crop MW 51,493 2% 250 975 894 80 14758
12 Reservoir MW 5,991 0% 400 2916 0 2916 0
13 Lakes & rivers NL 88,832 3% 1250 1555 0 1555 0
14 Managed wetlands MW 501 0% 450 1704 0 1704 0
15 Saline sinks MW 313 0% 450 2132 0 2132 0
3,162,26
Total 6 Supported by: CPWF
19/09/2009, Chaingmai

Water balance for 2007 in km3
atural land cover Managed land use Managed water use
atural forest P, ET Forest plantation P, ET Irrigation P, ET
Savanna P, ET Rainfed crop P, ET Managed wetlands P, ET
Desert P, ET .. P, ET Drinking water P, ET
.. P, ET .. P, ET
81.4 5.0 -57.4
0.0
inflow 0.0
29.0 Outflow
Aquifer & reservoirs
Committed 9.8
Supported by: CPWF
19/09/2009, Chaingmai

Water balance indicators for 2007
water balance components
2000 1745 1716
1500
km3
1000
500
76.6 57.4 29.0 9.8 19.2
0 y
e
ed
ed
ow
ed
s
l
bl
pp
es
um
itt
rt
ila
tfl
su
xc
ve
m
va
ou
ns
om
E
di
er
A
co
at
C
w
Water Balance indicators
100%
75%
50%
25%
0%
Consumed Available Diverted Excess Committed
Supported by: CPWF
19/09/2009, Chaingmai

Water consumption for 2007
w ater consum ption
2000
1458 1305
ET, km3 1500
1000 716 588
411
500 189 69
0
l
al
nv
n
ia
t..
..
co
..
ici
f ic
-E
n.
wa
c.
-E
f
ne
la
ne
al
nd
al
ed
ed
be
Be
ici
l la
ici
ag
f
ag
n-
ne
f
ne
ra
an
No
an
Be
tu
Be
m
m
na
Water consumption indicators
100%
80%
60%
40%
20%
0%
LU
T
LU
T
U
T
.E
.E
lE
W
al
ed
nv
n
ia
ed
ur
co
ag
fic
E
ag
at
E
n_
ne
an
N
n_
an
Be
Be
M
Be
M
Supported by: CPWF
19/09/2009, Chaingmai

a n n u a l b io m a s s in 1 0 ^ 9 k g
0
500
1000
1500
19/09/2009, Chaingmai
C lo s e d
fo r e s t
O pen
fo r e s t
S h ru b
la n d
W oody
savannah
O pen
savannah
S p a rs e
savannah
N a tu r a l
w e tla n d
9
land and water use
Supported by: CPWF
Biomass production in 10 kg
R a in fe d
c ro p s
U rb a n a n d
in d u s tr ia l
D e s e rt
Ir r ig a te d
Water production for 2007
c ro p
R e s e r v o ir
Env.
Feed
Food
wood
Biomass
Lakes &
r iv e r s
M anaged
w e tla n d s
S a lin e
s in k s

4. WP3: Production Systems &
Productivity
Basin PS: Low to High Resolution
Supported by: CPWF
19/09/2009, Chaingmai

Water productivity mapping:
METHODOLOGY
Supported by: CPWF
19/09/2009, Chaingmai

Data sources
• Production data:
- Countries statistic departments
- FAO database in 2005
• Market prices of agricultural products
• RS images and secondary GIS data
- Waterwatch 2007 ETa and Ta maps
- Land use/land cover (LULC); GLC 2008/ Africover
- Admin and basin boundaries, road network, ecological zones
Supported by: CPWF
19/09/2009, Chaingmai

Standardized gross value of production
SGVP: is an index which helps to compare the economical
value of different crops regardless in which country or
region they are.
i  local price crop i  
SGVP = ∑  × production crop i  × International price base crop 
crops 
i =1  local price base crop
 
  
Wheat is the major crop in the basin and it is taken
as base crop.
Supported by: CPWF
19/09/2009, Chaingmai

Rainfall and Water stress
Supported by: CPWF
19/09/2009, Chaingmai

SGVP
SGVP/ha is highly variable
across the basin.
Egypt has the highest SGVP/ha,
1830 US$/ha
Sudan has the lowest SGVP/ha,
which goes down to about 20
US$/ha in Northern Darfur
Supported by: CPWF
19/09/2009, Chaingmai

WP – SGVP/ETa & SGVP/Ta
Supported by: CPWF
19/09/2009, Chaingmai

Conclusions
- More than half of the basin area is under high water stress
- SGVP and Water productivity are highly variable across
the Nile basin
- While Egypt has the highest SGVP and WP, Sudan has the
lowest
- Except Gezira and northern provinces of Sudan in which
irrigated farming is common practice, WP is very low in
other parts of the country where rainfed farming is
predominant.
Supported by: CPWF
19/09/2009, Chaingmai

Livestock Productivity: Where are the animals?
Tropical
Livestock Nile Basin
Units per Km2
<1
1-10
10-20
20-30
>30
Supported by: CPWF
19/09/2009, Chaingmai

Water productivity calculations for livestock for the Nile Basin.
Supported by: CPWF
19/09/2009, Chaingmai

Water Productivity of Aquaculture
Objective
• to estimate quantities of water used
per unit biomass of fish produced in
ponds in the Nile Delta
• to prepare water budgets for earthen
pond aquaculture to help guide future
water allocation policies
• to assess the water productivity
benefits of different aquaculture
technologies and incorporating
aquaculture with agriculture
– production and incomes
http://girlsoloinarabia.typepad.com/photos/egypt/water_wheel.jpg
– poverty
Supported by: CPWF
19/09/2009, Chaingmai

Experimental plans
Estimate net water use in pond
aquaculture throughout production
season at two sites in the Nile Delta
(WorldFish Center pond farm,
Abbassa, and at a commercial fish Site 2
farm, Kafr El-Sheikh)
Estimate water losses through
different routes (seepage,
evaporation, drainage etc )
Site 1
Determine the amount of fish
produced
Estimate water consumption rates
(m3) per kg fish production
Supported by: CPWF
19/09/2009, Chaingmai

Estimating water use
modified from Nath & Bolte (1998)
waterfeed + inflow = outflow + ∆S + waterfish
excluding rain, surface runoff, waterfeed, and
infiltration, inflow can be regarded as water added
excluding overflow and waterfish outflow can be regarded
as change in pond storage plus seepage and evaporation
i.e.
water consumption per kg fish production = kg fish pond-1/Ii – (E + S + Q ± ∆S)
water consumption per pond = Ii – (E + S + Q ± ∆S)
Supported by: CPWF
19/09/2009, Chaingmai

Abbassa ponds
• 5 ponds, stocked 1 June 2008
Supported by: CPWF
19/09/2009, Chaingmai

Routine measurements
• pond water levels determined
weekly using fixed graduated
tube to measure pond tubes at three locations per pond
water column height
• water levels determined before
and after water was added to
compensate for losses
• fish sampled monthly to
determine growth
• fortnightly water samples taken to
determine DO, pH, Secchi disc
depth, N and P
• monthly analysis of phytoplankton
Supported by: CPWF
19/09/2009, Chaingmai

Fish growth in earthen ponds
over a five-month growing period
250
200
Average fis weight (gm)
Pond 1
150 Pond 5
Pond 10
Pond 13
100 Pond 16
50
0
1
2
3
4
5
t
ar
th
th
th
th
th
St
on
on
on
on
on
m
m
m
m
m
Groth period (month)
Supported by: CPWF
19/09/2009, Chaingmai

Water use – preliminary results
(Abbassa Site)
tube to measure pond
water column height
Supported by: CPWF
19/09/2009, Chaingmai

Seasonal variation in losses of
water
Supported by: CPWF
19/09/2009, Chaingmai

5. WP4: Institutional Analysis of
the Nile Basin
Research questions
• What are the water related institutions and policies
that shape agricultural outcomes in the Nile Basin?
• Do existing institutional and policy environment
support beneficial use of water for poverty
alleviation?
• Are basin wide priorities and nation wide institutions
and policies compatible?
• What are the agriculture related outcomes in the
basin?
Supported by: CPWF
19/09/2009, Chaingmai

Research methods
• Understanding institutions and policies at multiple scales
– Basin wide analysis: (Institutional analysis of the NBI and
CFA)
– Country analysis: Review of institutions and policies in
selected countries (Egypt, Sudan and Ethiopia)
– Micro level analysis at hotspots: Lake Victoria, Ethiopian
Highlands, Gezira scheme and Sudd wetlands
• Mixed methods: Literature review and primary data
collection
Supported by: CPWF
19/09/2009, Chaingmai

Institutional analysis of NBI
• What worked?
– Promoted the culture
of dialogue between
riparian states
– Attracted large donor
funding
– Basin wide
perspective
– Shared Vision and
Subsidiary Action
Program produced
important outputs
Supported by: CPWF
19/09/2009, Chaingmai

Institutional analysis of NBI
• What did not work as
expected?
– Not much evidence that power
balance between upstream and
downstream riparians have
indeed changed
– Absence of a clear regulatory
framework even after 10 long
years of negotiation
• Conclusion
– The future of cooperation in the
Nile Basin is not ‘black or white’:
the choice is not between, on the
one hand, fully-fledged
cooperation and non-
cooperation on the other. On the
contrary, there exists a large and
diverse grey-scale and the
different emerging scenarios
involve their own complexities.
Supported by: CPWF
19/09/2009, Chaingmai

Results from micro-level
institutional studies
• Insights on collective action for watershed management in Ethiopian
Highlands
–Compared successful watershed intervention with a not so successful one
–The inherent strength of local institutions & support given by implementing
agency (GTZ in this case) are the two crucial factors in success.
• Impact of institutional and policy change on productivity of Gezira
–Change in institutional regime from joint account to individual account to
economic liberalisation
–Production, productivity and cropping pattern changed with every change in
policy and institutions
–Area under cotton fell and gave away to wheat and other food crops.
Supported by: CPWF
19/09/2009, Chaingmai

Results from micro-level
institutional studies
• Institutional mechanisms in Lake Victoria
Multiplicity of institutions and overlap of authority and responsibilities;
–Fisheries management is the best coordinated activity among the 3
eastern Nile country
–Centrality of income from fisheries leads to such cooperation
–Provides employment to 3 million people
–Generates USD 400 million worth of income of which USD 250 is export
earning
Supported by: CPWF
19/09/2009, Chaingmai

6. WP5 Intervention Analysis
Objectives:
• To understand interventions that can have greater
impacts in the Nile Basin
• Specific objectives are to:
– Inventory and characterize existing interventions in
production systems
– document success and failures of interventions and map
intervention types
– detail performance analysis of existing interventions and
impacts
– undertake tradeoff analysis, ranking and modeling to select
and evaluate high impact interventions and implementation
strategy
– Develop problem tree & impact pathways through
interventions
Supported by: CPWF
19/09/2009, Chaingmai

Key Research Questions
• What are the existing water related interventions in
the basin under various production systems?
• Which interventions have succeeded and which
ones failed?
• What are the technical, economic, institutional
setups for successful or failed interventions under
various systems?
• Which future interventions are required to bring
high impact on poverty, water availability, access
and productivity for various target groups?
• Note: All questions may not be answered and some will
lead to future work
Supported by: CPWF
19/09/2009, Chaingmai

Interventions Category/ Types
- Production/Farming system based
• Crop Based: Field Crops, Horticulture, Forestry/ agro-Forestry
• Animal based: Livestock, Fisheries/Aquaculture
• Rain fed, irrigation, mixed crop-livestock, etc
– Physical based
• Infrastructural interventions
• Water and land based interventions: eg watershed management
– Socio-economic based
• Ag trade, virtual water
• Hydropower-generation, power trade, interconnection
• Industrial – value addition
– Institutional and policy based
• Institutional innovations, basin, sub-basin institutions
• Benefit/water-sharing
Supported by: CPWF
19/09/2009, Chaingmai

Interventions Category
Regions/zones
– 5 specific detail case study sites
• Ethiopian Highlands
• Victoria Nile
• The Sudd
• Gezirra
• Delta
– One integrated basin wide analysis
Supported by: CPWF
19/09/2009, Chaingmai

Example 1: Ethiopian Highlands Agricultural Interventions
(Agriculture - main source of livelihood)
Challenges
• Extreme biophysical variations
Increased poverty, food insecurity &
– Elevation, soil, climate
Major chalenges to agriculture
• Population pressure and land
Vulnerability to climate change
degradation
⇒ Shortage of land
⇒ Encroachment to marginal lands
⇒ Exacerbating deforestation and erosion
⇒ Reduced land and water productivity
• Poor infrastructural development
• Limited use of modern technologies
– Lack of site specific technologies
– Lack of integrated approach
Required: Identification +Disseminate of Site specific Technologies
Pre-requisite: identify by: CPWF
Supported “Homogeneous Units”
19/09/2009, Chaingmai

Methodology
• “Homogenous” units of farming systems (FS) have been
mapped based on:
– Agro-ecology (Elevation, Soil, LGP) (BMPS, Woody
Biomass) data
– Major crops grown (BMPS, CSA reports)
• Current crop and livestock productivity of the FS examined
(BMPS,CSA reports)
• Major productivity limiting constraints identified
• Promising technologies identified (secondary data)
• Productivity & Poverty impacts analyzed (HH consumption
data)
77
Supported by: CPWF
19/09/2009, Chaingmai

Results- The FS
Cereal based
system
dominate
ed
ifi
nt
de
Single cropping
si
FS the largest of
FS
cereals
10
Livestock density changes with
small cereals
78
Supported by: CPWF
19/09/2009, Chaingmai

Results- the FS: Distribution and
Productivity
• Crop productivity too low
regardless of the FS
• Average grain yield < 1 T/ha
• Maize and sorghum are high
yielding
- much less than the potential &
national average
Some reasons:
• Low Soil Quality
• Lack of improved technologies
• AWM (SWC, irrigation, drainage)
• Soil fertility management
• Improved Crop varieties
79
Supported by: CPWF
19/09/2009, Chaingmai

Characterization
Supported by: CPWF
19/09/2009, Chaingmai

The way-out: Multi-faceted
Interventions
Technological
– Agricultural water management
• Conservation, Irrigation, Drainage Integrate
– Soil and Water Conservation Technologies:
• Biological + mechanical – S+S+W
– Soil fertility management
• Fertilizers +Liming
– IWSM
– Improved crop varieties
– Crop protection
• Pre +Post harvest tech.
81
Supported by: CPWF
19/09/2009, Chaingmai

3.5
Some examples of Interventions
3
2.5
Yl ( h )
1
i dt a
-
2
e
1.5
1
0.5
0
Traditional Tied ridge Traditional Tied ridge Traditional Tied ridge
Sorghum Mungbean Maize
Location Variety Management practices Increment (%)
Traditional Improved
Crop verities and Mgt
Jimma local 28.4 37.3 32
UCB 25.9 46.1 78
Beletech 26.3 39.8 51
BH_140 26.4 45.9 74
BH-660 25.8 57.6 124
kuleni 26.5 46.2 75
Adet BH-540 29.3 48.96 67
kuleni 50.6 81.8 61
Pawe BH-530 41.7 81.7 96
BH-140 41.7 76.7 84
Bako BH-140 29 34.2 82 18
Supported by: CPWF
Beletech 29 38.2 32
19/09/2009, Chaingmai

Inventory for AWM
Ex-situ
In-situ
Well + Treadle
Improved planting pits Trash lines pump + Drip kit
Well+motorised pump
Stone bunds Diversion
Spate irrigation
Large irrigation
Micro dam + canal + furrow
Supported by: CPWF
19/09/2009, Chaingmai

List of promising AWM technologies
Rank Tigray Amhara Oromia
1. River Diversion River diversion Wells
2. Micro dam Micro dams River diversion
3. Wells Wells Pond
4. Ponds Pond Spate
5. Tankers Terraces
6. Dams
Supported by: CPWF
19/09/2009, Chaingmai

Poverty, HFS, Institutions & Impacts
Variables Incidence Depth Severity
(α=0) (α=1)
• Poverty (α=2)
– 22% less poverty incidence for users Access to irrigation
of AWMT Irrigators 0.585 0.322 0.226
– Treatment led to an increase in HH Non-irrigators 0.771 0.425 0.283
income - ca.ETB 670/ household
– deep wells, river diversions and micro
dams have led to 50, 32 and 25 %
reduction in poverty levels compared
to the reference, i.e. rain fed system.
∴The impacts of ponds and shallow
wells are relatively modest compared
to deep wells, diversions and small
dams.
• HH food security has significantly 50
improved
% of sample farmers reporting
45
40
• Institutional: 35
30
Irrigators
25
– Traditional irrigators higher efficiency 20
Non-Irrigators
15
– Modern irrigators have higher 10
production frontiers 5
0
– Institutional stabilities considerably Sales of
Cattle
Sales of Small
Animals
Off-farm
Employment
Consump tion
Credit
affecting performance, .
Supported by: CPWF Food shortage copping strategy
19/09/2009, Chaingmai

Example 2: Integrated Basin
Analysis
Integrated basin-wide modeling
to:
•Assess the current and future
large-scale intervention scenarios
•Evaluate the impacts of these
scenarios on water availability,
access and productivity
•Generate biophysical indicators
of interventions for socio-
economic and environmental
assessments
Supported by: CPWF
19/09/2009, Chaingmai

Integrated Basin Analysis
Infrastructural Interventions
• Control and
Management of
Natural Lakes (2)
• Large
Dams/Reservoirs
and Diversions (15)
• Small dams
• Ground Water
Storage and
Recharge
• Non-Conventional
Water Sources
Technologies
Supported by: CPWF
19/09/2009, Chaingmai

Large-scale Interventions and
Scenarios
• Large-scale interventions considered:
– Water control and storage infrastructures (single or multi-
purpose)
– Irrigation schemes
– Hydropower plants
– Environment and wetlands
• Simulation Scenarios:
– Current large-scale developments (Baseline)
– Medium-term intervention plans (2015)
– Long-term intervention plans (2025)
Supported by: CPWF
19/09/2009, Chaingmai

Modeling Framework
• WEAP water resources simulation model
applied at monthly time-step
• Monthly river flows are extended from
rainfall and ET using monthly water
balance model
• Annual irrigation demands are
disaggregated according to ET
• Wetland consumptions are treated as sinks
(environmental flow requirements)
• Storage release rules are represented as
stream flow requirements {Q = f(storage
head)}
Supported by: CPWF
19/09/2009, Chaingmai

Integrated Analysis:
River Schematization and Flows
Khartoum
Hawata
1,102 Rahad
2,797 Dinder
Giwasi
Lake Tana
Sennar
Bosheilo
SUDAN 3,809
Roseires 3,920
Beles Outlet Lake Tana
Border
4,345
Welaka
North Gojam 2,072
Wonbera South Gojam Jemma
ETHIOPIA 4,798
3,874 5,012 4,389
Kessie
Muger
2,440
6,246
Anger
2,355 1,719 2,187
Dabus Flow gauging station
5,673
Reservoir
Guder
Mean annual
Didessa 4,345
Finchaa discharge (Mm3)
Supported by: CPWF
19/09/2009, Chaingmai

All plans:
-Country specific
-SAP projects
Supported by: CPWF
19/09/2009, Chaingmai

Preliminary Results – Lake Victoria
ð
($
#
Supported by: CPWF
19/09/2009, Chaingmai

Preliminary Results – Wetlands
Supported by: CPWF
19/09/2009, Chaingmai

Aggregated Basin Conclusions
and Outlook
• The topology of the basin is configured for WEAP
simulation model
• Reliable information and data relevant to the integrated
modeling are almost collated
• Basin-wide simulations of large-scale intervention
scenarios are being conducted
• Finally, the integrated modeling experiment shall
generate biophysical indicators for impact assessment
and identification of potential interventions
Supported by: CPWF
19/09/2009, Chaingmai

WP5 Example: Capacity Building
• Tewdros : Water Resources Allocation of the Nile River Basin: A
cooperative Game Theoretic Approach
– Integrated economic-hydrologic-institutional modeling at the River Basin
Scale
• George: Developing Optimal Economic Incentives for Managing
Transboundary Water Externalities in the Blue Nile River Basin
– Application of economic instruments to review the past and present legal
documents on the Blue Nile and treaties governing the entire Nile River
Basin
– Modeling optimal allocation of water for maximizing use benefits among
the countries established
• Binyam: Equitable Distribution of Benefits in Transboundary
Waters
– Irrigation and Hydropower Benefits Sharing
– From Water Allocation and Cost Sharing to Benefit-Sharing: Implications
for Transboundary Rivers in the Nile Basin
• M.Sc. students
Supported by: CPWF
19/09/2009, Chaingmai

7. Conclusions
• Poverty is prevalent in high population, rainfed, pastoral and
agropstoral areas and less in irrigated systems and with
access to AWM
• Temporal and spatial variability of rainfall and runoff are high
and not sufficient mechanisms for improving water access
• Water productivity and productivity/ha are higher in managed
water system part of the basins and significant opportunities
to improve rainfed productivity
• Regional bodies such as NBI and water institutions give low
focus to rainfed production systems, livestock and fisheries.
Establishing relevance is important
• NBI Institutional Arrangement is progressing but the outcome
is uncertain
• Multiple interventions exist to improve rain fed productivity,
reduce poverty and enhance negotiations and economic
integration
Supported by: CPWF
19/09/2009, Chaingmai

Thank You
Supported by: CPWF
19/09/2009, Chaingmai