Question:
You are given the full text of a scientific research paper.

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    Nature Climate Change | Volume 13 | January 2023 | 48–57
48
nature climate change
Article
https://doi.org/10.1038/s41558-022-01556-6
Cooperative adaptive management of the
Nile River with climate and socio-economic
uncertainties
Mohammed Basheer
  1, Victor Nechifor
  2,3, Alvaro Calzadilla
  2,
Solomon Gebrechorkos4, David Pritchard5, Nathan Forsythe
  5,
Jose M. Gonzalez
  1, Justin Sheffield4, Hayley J. Fowler5,6 & Julien J. Harou
  1,7
The uncertainties around the hydrological and socio-economic implications
of climate change pose a challenge for Nile River system management,
especially with rapidly rising demands for river-system-related services and
political tensions between the riparian countries. Cooperative adaptive
management of the Nile can help alleviate some of these stressors and
tensions. Here we present a planning framework for adaptive management
of the Nile infrastructure system, combining climate projections;
hydrological, river system and economy-wide simulators; and artificial
intelligence multi-objective design and machine learning algorithms.
We demonstrate the utility of the framework by designing a cooperative
adaptive management policy for the Grand Ethiopian Renaissance Dam that
balances the transboundary economic and biophysical interests of Ethiopia,
Sudan and Egypt. This shows that if the three countries compromise
cooperatively and adaptively in managing the dam, the national-level
economic and resilience benefits are substantial, especially under climate
projections with the most extreme streamflow changes.
Human activities have increased the atmospheric concentration of
GHGs, leading to warming of the Earth’s land, atmosphere and ocean1–4.
Global initiatives such as the Paris Agreement5 and the Sustainable
Development Goals6 aim to reduce the impacts of climate change by lim-
iting the rise in global temperature and stepping up adaptation efforts.
However, the global mean temperature continued to rise over the past
two decades, from 0.89 °C in 2001–2010 to 1.09 °C in 2011–2020 above
pre-industrial conditions1. The Nile Basin (Fig. 1) faces the threat of cli-
mate change alongside water scarcity, rapidly rising pressures on water
resources due to population and economic growth, and a politically
complex transboundary water management system. The Nile Basin is
located in northeastern Africa, occupies around 10% of the continent’s
area and extends over 11 countries. Supplementary Section 1 provides
further information on the Nile geography.
Since the beginning of the twentieth century, several large water
infrastructure projects have been constructed on the Nile River to
reduce the spatial and temporal variabilities of the river flow and to facil-
itate water supply, flood control and hydropower generation. Most of
these infrastructures are in Egypt, Sudan and Ethiopia. Currently, most
of the consumptive usage of the Nile streamflow, measured at Aswan, is
located in Sudan and Egypt7,8. Over the past two decades, Ethiopia has
been increasing its use of the Nile, primarily for hydropower generation.
The construction of the Grand Ethiopian Renaissance Dam (GERD) on
the Nile near the Ethiopian–Sudanese border (Fig. 1) flared up political
Received: 16 May 2022
Accepted: 10 November 2022
Published online: 9 January 2023
 Check for updates
1Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, UK. 2Institute for Sustainable Resources, University
College London, London, UK. 3Joint Research Centre, European Commission, Seville, Spain. 4School of Geography and Environmental Science,
University of Southampton, Southampton, UK. 5School of Engineering, Newcastle University, Newcastle upon Tyne, UK. 6Tyndall Centre for Climate
Change Research, Newcastle University, Newcastle upon Tyne, UK. 7Department of Civil, Environmental and Geomatic Engineering, University College
London, London, UK.
 e-mail: julien.harou@manchester.ac.uk

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Article
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GERD negotiations was a series of meetings held from November 2019
to February 2020, with the United States administration and the World
Bank as observers18. These meetings produced a proposal for the dam’s
initial filling and long-term operation (hereon referred to as the Wash-
ington draft proposal)19, but the proposal was unacceptable to Ethiopia,
which opined that the proposal would limit power generation from the
dam and restrain future development20.
Alongside this changing political landscape, the quantity and
intensity of rainfall and streamflow in the Nile Basin have changed
over the past two decades and are expected to continue to change due
to climate change. However, the direction and magnitude of future
changes to the Nile climate are uncertain, stemming from different
scenarios, modelling and downscaling choices21,22. In the Nile context,
these inconsistencies in climate projections diminish the value of using
one projection or a multi-model ensemble mean in climate adaptation
planning22,23.
tensions between Ethiopia, Sudan and Egypt on Nile water use9–12. The
construction of the GERD started in 2011, and when completed, the dam
will have a total storage capacity of 74 billion cubic meters (bcm) and an
installed power capacity of 5,150 MW. The total storage capacity of the
GERD is equivalent to 1.5 times the historical mean annual river flow at
the dam location. The dam is expected to result in a range of opportu-
nities and risks to Sudan and Egypt. Subject to coordination and data
sharing, hydropower generation, irrigation water supply reliability and
flood control in Sudan could improve because of the dam13–16. Still, the
dam will probably produce adverse environmental impacts and losses
to recession agriculture in Sudan17. For Egypt, the GERD is expected to
reduce hydropower generation and impose irrigation water deficits if
there is no coordination on managing multi-year droughts7,12.
Despite over a decade of negotiations, there is still disagreement
and political tension between Ethiopia, Sudan and Egypt on the GERD’s
initial filling and long-term operation. A milestone in the tripartite
0
200
400
600
800
100
km
Blue Nile
30° N
26° N
22° N
18° N
14° N
10° N
6° N
2° N
2° S
48° E
HAD
GERD
Other large dams
Lake
Nile River
Nile Basin
Blue Nile Basin
White Nile Basin
Tekeze-Atbara Basin
National boundary
Sudd wetlands
44° E
40° E
36° E
32° E
28° E
Egypt
N
24° E
Mediterranean Sea
Sudan
Democratic
Republic of
the Congo
Rwanda
Burundi
Ethiopia
Kenya
Uganda
Tanzania
White Nile
Bahr
El-Ghazal
Main Nile
Tekeze-Atbara
Lake
Tana
Red Sea
Eritrea
Lake
Victoria
South
Sudan
Sobat
Bahr
El-Jebel
Fig. 1 | The Nile Basin and its major tributaries and dams. The map shows the extents of the basins of the three main tributaries of the Nile: the Blue Nile, the White
Nile and the Tekeze-Atbara.

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The large uncertainties associated with the water resource implica-
tions of changes in socio-economic and climate systems motivate adap-
tive infrastructure development plans24–29. Several approaches have been
proposed to enable planning under deep uncertainty26,30; these allow
designing robust and flexible plans that maximize resilience and mini-
mize investment costs on the basis of, for example, adaptation tipping
points31, dynamic adaptive planning32 and dynamic adaptive policy path-
ways24,33. Several recent studies have applied such adaptive methods to
planning water resource systems in various contexts25,34–38. For instance, a
reservoir adaptive planning framework has been developed to explicitly
consider learning about climate uncertainty over time35, and other stud-
ies have optimized the indicators, actions and/or thresholds in the design
process of adaptive plans for water resource systems33,36,39,40. However,
none of the previous studies considered engineering performance
alongside economy-wide performance in the design process of climate
adaptation plans for large water infrastructure systems, even though
the ultimate goal of building and operating infrastructure is typically
to stimulate economic development and generate economy-wide gains.
Here we introduce a planning framework for adaptive management
of river infrastructure systems to consider both the socio-economic
and hydrological uncertainties of climate change and policy.
The framework uses climate and socio-economic data from the Coupled
Model Intercomparison Project 6 (CMIP6)41 to drive integrated hydro-
logical, economy-wide and river system simulators of the Nile Basin.
Our adaptive planning framework uses artificial-intelligence-based
algorithms to design efficient adaptive plans for climate change on
the basis of thousands of iterations between the algorithms and the
integrated simulators. We use the framework to design a coopera-
tive adaptive management policy for the GERD (for 2020–2045) that
considers economic and river system interests of Ethiopia, Sudan and
Egypt. The results demonstrate that if the GERD is adaptively managed,
the economic and resilience benefits to individual countries (especially
under extreme climate projections) are substantially larger than with
less adaptive responses such as the Washington draft proposal.
Adaptive planning framework for Nile
management
In this study, we introduce an adaptive planning framework for
managing the Nile infrastructure system in the face of climate and
socio-economic changes, including four interconnected stages
(Fig. 2): (1) selecting plausible climate change projections, (2) simu-
lating the hydrological implications of the selected projections,
Climate
projections
Hydrological
system
Economy
Climate
parameters
River
infrastructure
system
SSPs
Changes in water
and electricity
demands driven
by economic
performance
Changes in non-hydro
generation capacity
based on investment
behaviour
Water and
electricity supplies
River system
status
Monitor
Adaptation
decisions
Climate change scenarios
Interim
adaptation
measures
Short-term
plans
1
1
1
2
2
3
3
4
Long-term
adaptation
measures
Radiative forcings
Climate
policies
Population
growth
projections
Labour
growth
projections
Sectoral
productivity
projections
Projections of municipal
water demands driven
by population growth
River
streamflow
projections
Projections of evaporation
rates from open water bodies
Projections of agricultural
evapotranspiration demands
Fig. 2 | Adaptive planning framework for Nile infrastructure management in the face of climate and socio-economic changes. The framework includes four
stages numbered from 1 to 4.

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Article
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SSP585 (ACCESS-CM2)
SSP245 (ACCESS-ESM1-5)
SSP585 (ACCESS-ESM1-5)
SSP126 (BCC-CSM2-MR)
SSP245 (CESM2)
SSP370 (CESM2)
SSP585 (CESM2-WACCM)
SSP126 (GFDL-ESM4)
SSP245 (GFDL-ESM4)
SSP245 (GISS-E2-1-G)
SSP370 (GISS-E2-1-G)
SSP370 (GISS-E2-1-G)
SSP585 (GISS-E2-1-G)
SSP370 (IPSL-CM6A-LR)
SSP245 (MIROC6)
SSP245 (MPI-ESM1-2-HR)
SSP370 (MRI-ESM2-0)
SSP585 (MRI-ESM2-0)
SSP126 (UKESM1-0-LL)
SSP585 (UKESM1-0-LL)
SSP245 (ACCESS-ESM1-5*)
SSP585 (ACCESS-ESM1-5*)
SSP245 (CESM2*)
SSP370 (CESM2*)
SSP585 (CESM2-WACCM*)
SSP245 (MIROC6*)
SSP126 (UKESM1-0-LL*)
SSP585 (UKESM1-0-LL*)
SSP585 (ACCESS-CM2*)
200
300
400
500
600
700
2050
Annual naturalized flow (bcm)
Year
SSP585 (UKESM1-0-LL)
SSP585(ACCESS-ESM1-5*)
Nile Basin
30
60
90
120
150
Annual naturalized flow (bcm)
Year
SSP585 (UKESM1-0-LL)
SSP585 (ACCESS-ESM1-5*)
Blue Nile Basin
200
250
300
350
400
Annual naturalized flow (bcm)
SSP585 (UKESM1-0-LL)
SSP585 (ACCESS-ESM1-5*)
 White Nile Basin
0
20
40
60
80
2010
2020
2030
2040
2010
2020
2030
2040
2050
2050
Year
Year
2010
2020
2030
2040
2010
2020
2030
2040
2050
Annual naturalized flow (bcm)
SSP585 (UKESM1-0-LL)
SSP585 (ACCESS-ESM1-5*)
Tekeze-Atbara Basin
–20
0
20
40
60
EOC precipitation change over the Nile (%)
1
2
3
4
5
6
a
b
c
d
e
f
EOC temperature change over the Nile (°C)
SSP585
(UKESM1-0-LL)
SSP585
(ACCESS-ESM1-5*)
SSP5 (8.5 W m–2)
SSP3 (7.0 W m–2)
SSP2 (4.5 W m–2)
SSP1 (2.6 W m–2)
–11
0
20
40
60
80
95
Nile streamflow change, 2021–2050 (%)
4
6
8
Mean annual economic growth, 2021–2050 (%)
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Mean annual population growth, 2021–2050 (%)
Ethiopia
Sudan
Egypt
Ethiopia
Sudan
Egypt
Ethiopia
Sudan
Egypt
Ethiopia
Sudan
Egypt
SSP5
SSP3
SSP2
SSP1
20
35
50
65
Mean percentage urban population, 2021–2050 (%)
Fig. 3 | Impacts of climate change on the Nile under multiple projections.
a, Changes in precipitation, naturalized streamflow and temperature. b, Socio-
economic implications for Ethiopia, Sudan and Egypt. c–f, 30-year moving
average of the annual naturalized streamflow. The labels in parentheses are
the names of the climate models. The change in naturalized streamflow is the
percentage change in the mean in 2021–2050 relative to 1981–2016. EOC change
in precipitation is the percentage change in the mean in 2071–2100 relative to
1981–2010. EOC change in temperature is the absolute change in the mean in
2071–2100 relative to 1981–2010. The climate projections marked with thick
black outlines in a and with asterisks in c–f were synthesized to address the
Eastern African Paradox (see Methods for the details).

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(3) simulating the economy-wide and river system infrastructure per-
formance under the selected projections, and (4) designing an adap-
tive plan for managing river system infrastructure. The four stages are
numbered in Fig. 2 from 1 to 4.
In the first stage, 29 transient climate change projections to
2100 were constructed on the basis of bias-corrected CMIP6 Tier 1
simulations41. Spanning the Shared Socio-economic Pathways (SSPs),
20 general circulation model (GCM) simulations were selected to
represent the joint distribution of end-of-century (EOC; 2071–2100)
changes in precipitation and temperature over the Nile Basin according
to the CMIP6 ensemble. Additionally, nine projections with decreas-
ing precipitation trends were synthesized to counteract the uncertain
Eficient design
a
b
c
d
e
f
g
h
i
Direction of better performance
Direction of better performance
Change in
GERD’s
mean annual
energy
(GWh)
Favourable Sudanese design
Favourable Ethiopian design
Favourable Egyptian design
Change in
Sudan’s mean
annual
irrigation
(bcm)
Change in
Sudan’s
mean annual
hydropower
(GWh)
Change in
Egypt’s mean
accumulated
real GDP
(US$ billion)
Change in Ethiopia's real GDP
(US$ billion)
Change in Sudan’s
mean annual irrigation (bcm yr–1)
Change in Sudan’s
mean annual hydropower (TWh yr–1)
Change in Egypt’s
mean annual irrigation (bcm yr–1)
Change in Egypt’s
mean annual hydropower (TWh yr–1)
Change in
Ethiopia’s mean
accumulated
real GDP
(US$ billion)
Change in
GERD’s
mean firm
power
(MW)
Change in
Sudan’s mean
accumulated
real GDP
(US$ billion)
Change in
Egypt’s mean
annual
irrigation
(bcm)
Change in
Egypt’s mean
annual
hydropower
(GWh)
Compromise design
Zero change line (i.e., Washington draft proposal)
2
1
0
–1
–2
–3
2
1
0
–1
–2
–3
2
1
0
–1
–2
–3
66.0
38.8
11.6
–15.6
–42.8
–70.0
490
300
110
–80
–270
–460
490
300
110
–80
–270
–460
490
300
110
–80
–270
–460
0.9
0.6
0.3
–0.1
–0.4
–0.7
0.9
0.6
0.3
–0.1
–0.4
–0.7
–5
0
5
10
15
–4
Change in Sudan’s real GDP
(US$ billion)
–8
–6
–4
–2
0
2
Change in Egypt’s real GDP
(US$ billion)
–2
0
2
4
6
–1
0
1
2
3
4
Change in GERD’s
mean annual energy (TWh yr–1)
–1.0
–1.0
–1.5
–1.0
–0.5
0
0.5
1.0
1.5
–0.5
0
0.5
–0.5
0
0.5
1.0
1.5
–0.5
0
0.5
Fig. 4 | Trade-offs and synergies between Ethiopian, Sudanese and Egyptian
economy-wide and river system performance objectives. a, Parallel
coordinates plot of the performance under efficient designs of an adaptive
GERD management policy across 29 climate change projections for 2020–2045.
b–i, Box plots of some of the metrics shown in a for selected designs across the
29 climate change projections. All change values are calculated from a baseline in
which the GERD is operated on the basis of the Washington draft proposal.
The box plots in b–i correspond to the lines with similar colours in a. The ends of
the boxes in b–i represent the upper and lower quartiles, the solid vertical lines
inside the boxes mark the medians, the dashed vertical lines mark the means,
the circles show the data points and the whiskers extend to the maximum and
minimum values, excluding the outliers. The firm power values are calculated on
the basis of a 90% reliability, and the GDP values are discounted at a 3% rate.

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precipitation increases often seen in GCMs over East Africa42,43 (Meth-
ods and Supplementary Table 1). The second stage of the framework
is to calibrate and use a distributed hydrological model for the Nile
driven by historical climate time series and the 29 climate projections
to generate naturalized historical and projected streamflow time series.
Furthermore, time series for projected potential evapotranspiration
(PET) from irrigation schemes and open water bodies were generated.
Because climate change scenarios have implications for river
systems and their economies, the third stage of the framework aims
to capture these implications through integrated economy-wide and
river system simulators7. The river system simulator accounts for all
major infrastructure in the Nile Basin in monthly time steps and uses
the naturalized streamflow and PET time series generated in the second
stage. Furthermore, dynamic-recursive annual computable general
equilibrium (CGE) models were developed for Ethiopia, Sudan and
Egypt to simulate their economies.
Climate projections may have different global socio-economic
development pathways associated with them41. Accordingly, the CGE
models were exogenously driven by national-level projections for
labour growth and population growth, total and sectoral productivi-
ties, and climate policies that differ with each SSP characterizing the
climate projections44–46. Population growth projections under the SSPs
were also used to change (that is, increase or decrease) municipal water
demands in the river system simulator.
The fourth stage of the framework seeks to design adaptive man-
agement policies for the Nile water infrastructure. Climate adaptation
planning is achieved using an artificial-intelligence-based approach
(multi-objective evolutionary algorithm) to design efficient adaptive
management plans. These plans involve adaptation mechanisms based
on new information gained about climate change impacts on the river
system and riparian countries as the future unravels. The design frame-
work identifies efficient adaptive plans that maximize the riparian
countries’ economy-wide and river system interests in different ways
and reveal trade-offs.
Implications of climate change for the Nile
Under the 29 examined projections, the results show varying impacts
of climate change on the naturalized streamflow of the Nile and some
socio-economic characteristics of Ethiopia, Sudan and Egypt (Fig. 3).
The lower the GHG emissions and EOC forcing levels, the lower the
change in precipitation and streamflow (Fig. 3a). The 30-year moving
average naturalized streamflow data shown in Fig. 3c–f indicate that the
mean Nile streamflow could change by between −13% and +90% by 2050
compared with 2020. The intra-annual variability of the naturalized
streamflow of the Nile and its main tributaries is projected to change
(Extended Data Fig. 1), with the biggest changes occurring under SSP5
and high EOC temperature projections. The inter-annual streamflow
variability shows varying changes depending on different SSPs, forcing
levels, GCMs and time horizons, as depicted in Extended Data Fig. 2.
The increase in temperature imposed by climate change is projected to
increase PET (Extended Data Figs. 3 and 4), which would increase future
irrigation water demands and evaporation from open water bodies.
Various SSPs that underpin climate projections have different
implications for baseline gross domestic product (GDP) and popu-
lation growth and urbanization in Ethiopia, Sudan and Egypt over
2021–2050 (Fig. 3b)44–46. The highest economic growth for each of the
three countries is projected under SSP5, whereas the lowest economic
growth occurs under SSP3.
Cooperative adaptive management policy for the
GERD
We use the Nile adaptive management framework to formulate and design
an adaptive management policy for the GERD’s initial filling and long-term
operation for 2020–2045, involving short-term rules and interim and
long-term adaptation measures to cope with climate change uncertainties.
The formulation is based on cooperative behaviour whereby the riparian
countries consider each other’s interests through adaptive measures. The
formulation of the GERD adaptive filling and operation policy is described
briefly below and detailed in Extended Data Fig. 5.
In the adaptive formulation, water retention during the GERD’s
initial filling phase is carried out in July and August and follows a
stage-based approach (Supplementary Table 2) while maintaining a
minimum outflow of 1.28 bcm per month, similar to the Washington
draft proposal19. From March to June, additional interim water releases
during the initial filling phase are made if the storage of the High Aswan
Dam (HAD) reservoir in Egypt falls below 60 bcm. This would enable
quick initial filling of the GERD, as data show consistently high water
storage in the HAD reservoir in recent years (2020–2022)47.
In the long-term operation phase of the adaptive formulation,
GERD water releases aim to generate a regular power target when res-
ervoir storage is above a level termed the power reduction threshold,
but this target is reduced as an interim precaution if GERD storage runs
lower than a power reduction threshold to allow storage recovery during
and following multi-year droughts. To help Egypt during droughts, an
interim minimum monthly drought mitigation water release from the
GERD is activated if the storage of the HAD reservoir stays below 60 bcm
over the past six months for an extended period; this period is termed
the drought trigger. This interim drought mitigation measure is acti-
vated only if the GERD storage is above a threshold termed the drought
outflow storage threshold. The operations of the seasonal storage dams
downstream of the GERD follow their historical rules during the GERD’s
filling phase, whereas their reservoirs are kept as high as possible during
the long-term operation phase, similar to assumptions made by previ-
ous studies, assuming data sharing between Ethiopia and Sudan7,14–16.
Long-term adaptation measures are applied to the GERD’s regular
and reduced power targets, the drought trigger of the HAD, and the
interim minimum monthly drought mitigation water release. The
long-term adaptation measures are triggered on a five-year interval,
involving increasing or decreasing these four GERD management vari-
ables on the basis of the change in the mean annual inflow to the dam
over the past five years in relation to the historical mean annual inflow
over 1980–2019. For instance, if the mean annual inflow to the GERD
increased over the past five years compared with the historical mean,
the four above-mentioned GERD operation variables are increased
proportionally, and vice versa.
The artificial-intelligence-based search component of the frame-
work was used to design the adaptive GERD management policy
described above. The search algorithm optimizes seven variables;
these variables and their upper and lower bounds are reported in
Supplementary Table 3. The variables are designed to maximize nine
objectives of Ethiopia, Sudan and Egypt over 2020–2045 across the
29 climate change projections: the accumulated GDP values of each
country (three objectives); the GERD’s 90% firm power (one objective);
the annual hydro-energy generation of the GERD, Sudan and Egypt
(three objectives); and the annual irrigation water use in Sudan and
Egypt (two objectives).
Economic and river system benefits of adaptive
GERD management
The results reveal differences between the aggregated economy-wide
and river system performance objectives over 2020–2045 under 1,032
efficient GERD adaptive management options and the performance
under the Washington draft proposal, as shown in the parallel coordi-
nates plot48 depicted in Fig. 4a. The reader is referred to Supplemen-
tary Section 2, Supplementary Table 2 and Extended Data Fig. 6 for
the implementation details of the Washington draft proposal. Four
efficient adaptive GERD policy designs are highlighted in Fig. 4a (the
green, blue, purple and cyan lines): a favourable design for each of
Ethiopia, Sudan and Egypt that results in the highest national accumu-
lated GDP benefits, and an example compromise design that results in

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Favourable Sudanese
design
Favourable Egyptian
design
Compromise
design
Favourable Ethiopian
design
SSP585 (ACCESS-CM2*)
SSP585 (UKESM1-0-LL*)
SSP585 (ACCESS-ESM1-5*)
SSP585 (CESM2-WACCM*)
SSP245 (ACCESS-ESM1-5*)
SSP126 (UKESM1-0-LL*)
SSP370 (CESM2*)
SSP245 (CESM2*)
SSP245 (MIROC6*)
SSP126 (GFDL-ESM4)
SSP245 (GFDL-ESM4)
SSP126 (BCC-CSM2-MR)
SSP245 (GISS-E2-1-G)
SSP370 (GISS-E2-1-G)
SSP245 (MPI-ESM1-2-HR)
SSP245 (ACCESS-ESM1-5)
SSP585 (MRI-ESM2-0)
SSP370 (MRI-ESM2-0)
SSP370 (GISS-E2-1-G)
SSP126 (UKESM1-0-LL)
SSP245 (MIROC6)
SSP245 (CESM2)
SSP370 (IPSL-CM6A-LR)
SSP585 (GISS-E2-1-G)
SSP370 (CESM2)
SSP585 (ACCESS-ESM1-5)
SSP585 (CESM2-WACCM)
SSP585 (ACCESS-CM2)
SSP585 (UKESM1-0-LL)
SSP585 (ACCESS-CM2*)
SSP585 (UKESM1-0-LL*)
SSP585 (ACCESS-ESM1-5*)
SSP585 (CESM2-WACCM*)
SSP245 (ACCESS-ESM1-5*)
SSP126 (UKESM1-0-LL*)
SSP370 (CESM2*)
SSP245 (CESM2*)
SSP245 (MIROC6*)
SSP126 (GFDL-ESM4)
SSP245 (GFDL-ESM4)
SSP126 (BCC-CSM2-MR)
SSP245 (GISS-E2-1-G)
SSP370 (GISS-E2-1-G)
SSP245 (MPI-ESM1-2-HR)
SSP245 (ACCESS-ESM1-5)
SSP585 (MRI-ESM2-0)
SSP370 (MRI-ESM2-0)
SSP370 (GISS-E2-1-G)
SSP126 (UKESM1-0-LL)
SSP245 (MIROC6)
SSP245 (CESM2)
SSP370 (IPSL-CM6A-LR)
SSP585 (GISS-E2-1-G)
SSP370 (CESM2)
SSP585 (ACCESS-ESM1-5)
SSP585 (CESM2-WACCM)
SSP585 (ACCESS-CM2)
SSP585 (UKESM1-0-LL)
Climate projections (higher Nile streamflow →)
Climate projections (higher Nile streamflow →)
Climate projections (higher Nile streamflow →)
SSP585 (ACCESS-CM2*)
SSP585 (UKESM1-0-LL*)
SSP585 (ACCESS-ESM1-5*)
SSP585 (CESM2-WACCM*)
SSP245 (ACCESS-ESM1-5*)
SSP126 (UKESM1-0-LL*)
SSP370 (CESM2*)
SSP245 (CESM2*)
SSP245 (MIROC6*)
SSP126 (GFDL-ESM4)
SSP245 (GFDL-ESM4)
SSP126 (BCC-CSM2-MR)
SSP245 (GISS-E2-1-G)
SSP370 (GISS-E2-1-G)
SSP245 (MPI-ESM1-2-HR)
SSP245 (ACCESS-ESM1-5)
SSP585 (MRI-ESM2-0)
SSP370 (MRI-ESM2-0)
SSP370 (GISS-E2-1-G)
SSP126 (UKESM1-0-LL)
SSP245 (MIROC6)
SSP245 (CESM2)
SSP370 (IPSL-CM6A-LR)
SSP585 (GISS-E2-1-G)
SSP370 (CESM2)
SSP585 (ACCESS-ESM1-5)
SSP585 (CESM2-WACCM)
SSP585 (ACCESS-CM2)
SSP585 (UKESM1-0-LL)
Year
Year
Year
–1.5
–1.0
–0.5
0
0.5
1.0
1.5
Change in Ethiopia’s real GDP (US$ billion)
–0.6
–0.4
–0.2
0
0.2
0.4
0.6
Change in Sudan’s real GDP (US$ billion)
–2
–1
0
1
2
Change in Egypt’s real GDP (US$ billion)
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
Year
Year
Year
Year
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
Year
Year
Year
Year
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
2020
2025
2030
2035
2040
2045
Year
Fig. 5 | Heatmap matrix of changes in the Ethiopian, Sudanese and Egyptian
real GDP for four adaptive policy designs. The GDP changes are for the four
designs of the GERD’s adaptive policy highlighted in Fig. 4a compared with a
baseline in which the GERD is operated on the basis of the Washington draft
proposal. Each row of the matrix shows GDP changes for one of the three
countries, whereas the matrix columns correspond to different designs of the
adaptive management policy for the GERD. The labels in parentheses are the
names of the climate models. The changes in real GDP are discounted at a 3% rate.
The climate projections marked with asterisks were synthesized to address the
Eastern African Paradox.

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at least 40% of the highest national accumulated GDP benefits for each
of the three countries. The specifications of the four highlighted GERD
policy designs are in Supplementary Table 4 and Supplementary Fig. 2.
The results show that the magnitudes of the economy-wide costs
and benefits to Ethiopia, Sudan and Egypt vary due to the different
economies and the spatial and temporal characteristics of the river
system in each country. There is a trade-off between the three countries
in achieving the highest possible GDP performance. Compared with the
Washington draft proposal, the compromise design results in
economy-wide benefits to the three countries.
Egypt’s
real GDP
a
b
c
d
e
f
g
h
i
Sudan’s
real GDP
Ethiopia’s
real GDP
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Interim additional filling
water release
Power reduction storage
Drought trigger
Regular power target
Interim reduced power
target
Drought outflow storage
threshold
Interim minimum drought
mitigation water release
Egypt’s
irrigation
water use
Egypt’s
hydro-energy
generation
Sudan’s
irrigation
water use
0
0.25
0.50
0.75
1.0
Relative influence
GERD’s
firm power
Sudan’s
hydro-energy
generation
GERD’s
hydro-energy
generation
0
0.25
0.50
0.75
1.0
Relative influence
0
0.25
0.50
0.75
1.0
Relative influence
0
0.25
0.50
0.75
1.0
Relative influence
0
0.25
0.50
0.75
1.0
Relative influence
0
0.25
0.50
0.75
1.0
Relative influence
0
0.25
0.50
0.75
1.0
Relative influence
0
0.25
0.50
0.75
1.0
Relative influence
0
0.25
0.50
0.75
1.0
Relative influence
Fig. 6 | Rankings of the variables of the adaptive management policy for the
GERD based on their relative influence on nine economic and river system
performance metrics. a–i, The relative influence values are based on machine
learning and can range from zero to one, with zero indicating that the variable
does not influence the performance metric, whereas one indicates that the
variable is the sole influencer of the performance metric. For each performance
metric, the sum of the relative influence values for all parameters is one.

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The results reveal varying costs and benefits across climate projec-
tions (Fig. 4b–i). For instance, in the most favourable design for each
country taken individually, the discounted GDP over 2020–2045 could
increase by up to US$15.8, US$6.3 and US$3.0 billion for Ethiopia, Sudan
and Egypt, respectively, compared with the Washington draft proposal.
However, these country-centric designs lead to GDP losses for at least
one of the other two countries. In contrast, the mean GDP changes are
positive for the three countries under the compromise design.
The temporal evolution of GDP changes under different climate
projections and GERD policy designs (Fig. 5) shows that adaptive GERD
management benefits Ethiopia and Sudan the most under climate pro-
jections with the highest streamflow. The favourable Egyptian design
benefits Egypt during multi-year droughts in climate projections with
low streamflow, but it reduces the overall Sudanese and Ethiopian
benefits (Fig. 5). The compromise system design results in balanced
performance across the three countries.
The seven optimized variables of the GERD adaptive policy show
varying influence on the Ethiopian, Sudanese and Egyptian perfor-
mance objectives (Fig. 6). The Egyptian GDP is influenced the most by
the interim minimum drought mitigation water releases (Fig. 6a), as
most of Egypt’s benefits and costs materialize during droughts through
irrigation. In contrast, the most influential parameter for the Sudanese
and Ethiopian GDPs is the GERD’s reduced power target (Fig. 6b,c).
For Sudan, decreases in GERD water releases due to power reduction
influence irrigation (Fig. 6f) and hydropower (Fig. 6g) because of the
absence of multi-year storage dams to buffer this variability.
Discussion
Because the Nile River is crucial to its population’s economic develop-
ment and well-being, adaptation strategies are needed to cope with
the deep uncertainties associated with future climate change26,49. An
approach that can identify efficient options for transboundary adapta-
tion and demonstrate their economy-wide and river system benefits and
trade-offs could provide a platform for discussions on Nile adaptation
strategies. The adaptive planning framework introduced in this paper
can design adaptive policies for large infrastructures to cope with climate
change uncertainties. Using a meta-heuristic artificial-intelligence-based
algorithm for the search process provides twofold benefits. First, it ena-
bles finding multi-dimensionally efficient (Pareto-optimal) solutions for
complex and highly nonlinear interlinked river and economic systems.
Second, it optimizes on the basis of linked but independent simulation
models developed by different disciplines. Although the proposed frame-
work can capture direct and induced impacts of climate change and
infrastructure management policies on river and economy systems, it
should be complemented with approaches to assess other impacts on
groundwater, river ecology and riparian populations7,50.
The analysis of the GERD’s initial filling and long-term operation
shows that adaptively managing the dam to maximize the national
benefits of any of the three countries would be costly for at least one
of the other two countries. We show that a compromise adaptive man-
agement approach could produce balanced benefits for the three
countries. These results demonstrate the opportunity cost of not
implementing collaborative adaptive solutions, especially under
extreme climate change projections. It is high time to integrate climate
change adaptation into the decade-long negotiations between Ethio-
pia, Sudan and Egypt over the GERD and the broader Nile management
discussion between the 11 riparian countries.
Although the adaptive formulation examined in this study
includes some controlling features (for example, the 60-bcm HAD
drought threshold and the five-year adaptation period) that con-
strain basin-wide water management, it represents an incremental
step towards adaptive cooperation. Our goal is to show that any step
towards adaptive cooperation results in benefits. Such an incremental
approach can help build trust between riparian countries and make an
eventual unconstrained basin-wide adaptive management easier to
achieve, aiming to maximize basin-wide benefits, adopt benefit-sharing
and build on the comparative advantages of each riparian country.
Practical use of the proposed framework in Nile negotiations
requires riparian countries to negotiate an adaptive formulation and
define the ranges within which decisions can be optimized. The philoso-
phy behind the adaptive formulation should be guided by a long-term
vision to counter future climate and socio-economic uncertainties.
The efficient cooperative adaptive designs emanating from the agreed
formulation should then be negotiated to find a compromise solution
that balances national-level and basin-wide performance.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41558-022-01556-6.
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Article
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Methods
Here we introduce an adaptive planning framework for the Nile Basin
that combines climate change projections; integrated hydrological,
economy-wide and river system simulators; and multi-objective evo-
lutionary and machine learning algorithms. Below is a description of
the components and data of the framework.
Climate projections
Twenty climate projections were selected from CMIP6 Tier 1 GCMs
on the basis of uniform sampling to cover the full range of available
SSPs, radiative forcing and the joint distribution of EOC change in
precipitation and temperature over the Nile Basin. Processing, down-
scaling and bias correction of the GCM simulations were driven by
the requirement for transient (2017–2100) three-hourly forcing data
across seven climate variables that govern the surface mass and energy
balances in the hydrological model (described in the next section). The
seven variables are precipitation, temperature, incoming shortwave
radiation, incoming longwave radiation, humidity, wind speed and
surface pressure. Given the large spatial domain, the low availability
of subdaily model output for all of the relevant variables in CMIP6 and
challenges of multivariate bias correction51, we derived bias-corrected
transient projections by (1) resampling the 0.25° historical baseline
climate dataset to match the (detrended) relative variability of the
selected GCM projections and then (2) applying perturbation factors
to reintroduce the change signal extracted from the GCMs following
a quantile delta mapping approach52.
In more detail, we first detrended the basin-average monthly
future precipitation series for 2017–2100 in each GCM run on a
month-wise basis using smoothed 37-year moving averages. Second,
we ranked the detrended future series month-wise and matched the
ranks to their equivalents in the historical baseline precipitation data-
set of 1981–2016, allowing us to construct synthetic climate series for
2017–2100 at the three-hour temporal resolution of the historical
climate datasets by inserting the relevant month of data from the his-
torical dataset at the right place in the future series. The synthesized
series thus have the same overall sequencing of relative intra-annual
(seasonal), interannual and multi-annual variability as the original
GCM projections in rank terms, while preserving the statistical prop-
erties (including spatial, temporal and intervariable consistency53,54)
and resolution of the historical baseline dataset (that is, without GCM
bias). The historical baseline climate datasets used are the Multi-Source
Weighted-Ensemble Precipitation (MSWEP)55 for precipitation and
the Princeton Global Forcing (PGF)56 for the other climate variables.
The 0.1° MSWEP data were regridded to the 0.25° PGF grid before the
climate series was synthesized.
For each of the 20 climate projections, we then reintroduced the
climate change signal to the synthesized 2017–2100 climate series,
producing bias-corrected and perturbed projections. The transient
climate change signal was reintroduced on a month-wise basis using a
37-year moving window of quantile-based perturbations52. Smoothing
was applied to the perturbation factors via a moving average to avoid
any jumps between successive windows. We note that this method does
not adjust the frequency of wet periods/days within a month, and it is
not designed to focus on projected changes in precipitation extremes,
given the study’s focus on water resources management on a monthly
scale rather than flood frequency.
Several previous studies have highlighted how the wetting trend
in climate projections for eastern Africa stands in contrast to the
observed decline in precipitation from the 1980s to the late 2000s42.
This contradiction, known as the Eastern African Paradox, represents
a challenge for climate adaptation planning42,43. While the literature
shows a growing understanding of how this issue is linked with regional
circulation dynamics and their representation in models57,58, there is
not yet a consensus on how the projections might be constrained. Yet,
if the projections are taken at face value, there is a risk that adaptation
measures are designed for a highly uncertain wetter climate that might
not materialize, with potentially severe socio-economic consequences.
To address this issue, we synthesized nine additional scenarios
based on 9 of the 20 initial projections by removing the overall wetting
tendency in the CMIP6 ensemble for the East Africa region. The magni-
tude of the tendency was estimated by quantifying the mean increase
in EOC mean precipitation for a unit increase in mean temperature
according to the full GCM ensemble, given their strong relationship in
this context. The relationship was then used to adjust individual GCM
precipitation trends downwards, allowing us to select the nine projec-
tions representing the most pessimistic scenarios in terms of precipita-
tion decreases, which enables stress-testing adaptation policies across
a wide range of plausible futures (Fig. 3a). Supplementary Table 1 lists
the 29 projections and their main features. Daily PET was calculated
for the 29 projections using the FAO56 Penman–Monteith method59.
Hydrological simulator
To generate time series of historical and projected naturalized stream-
flow for the Nile Basin, we used and calibrated the Variable Infiltration
Capacity (VIC version 5) land surface model60 and the Routing Appli-
cation for Parallel Computation of Discharge (RAPID)61. The VIC and
RAPID models were originally developed in a previous study to recon-
struct vector-based global naturalized streamflow62. The hydrological
model has a spatial resolution of 0.25° and a three-hourly temporal
resolution. The historical streamflow time series was driven by MSWEP55
and temperature, incoming shortwave radiation, incoming longwave
radiation, humidity, wind speed and surface pressure data from the
PGF56. The 29 projected streamflow time series were driven by similar
climate variables obtained from the climate projections described
in the previous section. The VIC and RAPID models were calibrated
against a historical naturalized streamflow dataset of the Nile previ-
ously developed for the Eastern Nile Technical Regional Office63. The
hydrological model was calibrated using baseflow parameters, depth
of soil layers, variable infiltration curve and maximum soil moisture.
Supplementary Table 5 shows the ranges of the values of the calibra-
tion parameters for all grid cells in the hydrological model. The RAPID
model routes streamflow using the Muskingum method64 and requires
a river network to determine Muskingum parameters (that is, the
gradient coefficient and the weighting factor). The river network was
obtained from the Hydrological data and maps on the basis of SHuttle
Elevation Derivatives at multiple Scales (HydroSHEDS)65. The hydrolog-
ical model was calibrated over the period 1982–1992 and validated over
1993–2002 at four locations where naturalized flow data are available.
Supplementary Table 6 shows the performance of the hydrological
model in the calibration and validation periods, and Supplementary
Table 7 summarizes the model performance ranking criteria adopted.
As elaborated in the next section, the combined hydrological and river
system simulator was calibrated at additional locations.
River system simulator
To simulate the performance of the Nile River system infrastructure
under climate change and various adaptive management options, we
developed a monthly river system model for the basin using Python
Water Resources (Pywr)66. A river system model is a network representa-
tion of the supplies, demands and infrastructures of water resources in
a river system67. Pywr is an open-source Python library that simulates
resource networks. It enables representing water resources system
infrastructure (for example, dams, lakes, aquifer-based water sup-
ply and water abstraction locations) in a network structure driven
by water supplies (for example, hydrological inflows) and demands
(for example, irrigation, municipal and industrial water demands and
hydropower) and system operating rules. Pywr uses a linear program-
ming approach to simulate water allocations at each time step (monthly
in this study) subject to constraints imposed by system operation rules.
Supplementary Fig. 3 shows a schematic of the Nile River system model

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and its elements, and Supplementary Table 8 shows the main features of
the dams included in the model. The model uses the naturalized stream-
flow time series of the Nile generated using the hydrological model
described in the previous section. Data for dams, lakes, reservoirs, and
irrigation and municipal water demands were collected from different
sources, including the Nile Basin Initiative and previous studies14,15,68–71.
In simulating future scenarios, the water demands of irrigation
schemes and the net evaporation rates of lakes, reservoirs and swamps
were modified (increased or decreased) on the basis of the annual
change ratio of PET at their locations relative to the historical annual
mean. Extended Data Figs. 3 and 4 show the projected annual change in
PET relative to the historical mean at some selected dams and irrigation
schemes in the Nile River system. Future municipal water demands were
calculated by applying the population growth rates projected under dif-
ferent climate change scenarios (that is, SSPs). The population growth
projections under different SSPs were obtained from the International
Institute for Applied Systems Analysis (IIASA) database44,45. Supple-
mentary Section 3 describes the initial water demand assumptions for
Ethiopia, Sudan and Egypt.
The Nile River system simulator was calibrated and validated at
ten locations over 1995–2010 using historical river flow observations
and reservoir and lake water levels. This calibration period was chosen
on the basis of the availability of common and continuous historical
observed data for the ten selected locations. Supplementary Fig. 4
and Supplementary Table 9 show the performance of the Nile River
system model over the calibration and validation periods, Supple-
mentary Table 10 reports the calibration parameters and their values,
and Supplementary Table 7 reports the performance ranking criteria.
Total non-hydro (for example, thermal) generation for each country
was represented as a super generator used to fill the gap between
hydropower generation and national electricity demand subject to
generation capacity. The non-hydro generation capacity and national
electricity demands are updated annually on the basis of an iterative
process between the river system and the economy-wide simulators,
as described in a later section.
Economy-wide simulator
The standard open-source CGE model of the International Food Policy
Research Institute72 was modified and used to develop economy-wide
models for Ethiopia, Sudan and Egypt. The production and consump-
tion specifications in the CGE models are shown in Supplementary Figs.
5 and 6. The CGE models were set up to run dynamically over multi-year
periods (that is, dynamic-recursive) following endogenous investment
behaviour, exogenous total and sectoral factor productivities, labour
growth, and energy use efficiency trends.
The CGE models of Ethiopia, Sudan and Egypt include five agent
types: households, the government, enterprises, industries (or eco-
nomic activities) and the rest of the world. Households are disaggre-
gated into rural and urban groups. Ethiopia’s, Sudan’s and Egypt’s
CGE models include 12, 15 and 14 economic activities, respectively.
Economic activities use the following factors of production: labour,
general capital, land, hydropower capital, non-hydro capital, renewa-
bles capital, water supply capital, oil capital and gas capital. Labour
and general capital are assumed mobile between sectors, whereas
the other factors are sector-specific. All capital types, except water
supply and hydropower capitals, grow or shrink over time on the basis
of investment behaviour according to relative rates of return. Water
supply and hydropower capitals were not included in the year-to-year
investment behaviour, as expansion in these infrastructures typically
requires abrupt investment in the Nile context.
We assume that commodity prices on the international market
are exogenous, following the small open-economy assumption73. The
government is assumed to spend a fixed share of total absorption. Sav-
ing propensities are fixed, and the exchange rate is variable. The CGE
models were calibrated to social accounting matrices for 2011 obtained
from the International Food Policy Research Institute74–76. We then
used the GTAP-Power 10 database for the year 201477 to disaggregate
the electricity sectors of the social accounting matrices. Five dynamic
baselines were calibrated for the CGE models of Ethiopia, Sudan and
Egypt, with each baseline corresponding to an SSP. The baseline total
factor productivity values were set up such that the baseline models fol-
low projected economic growth (obtained from the IIASA database) for
each SSP scenario46. After that, the total factor productivity values were
fixed and applied exogenously. Labour growth (16–64 age group)45,
urbanization78 and population growth45 projections for each of the
three countries were obtained from the IIASA database and applied
exogenously to the CGE models. Also, projections for national-level
sectoral productivity under each SSP were obtained from the Centre
d’Études Prospectives et d’Informations Internationales79 and applied
exogenously to the CGE models. Sectoral future energy use efficiency
values were calibrated such that the baseline simulated country-level
carbon dioxide emissions in the baselines follow the growth pattern
of the regional emission values of the Middle East and North Africa
projected by IIASA for each SSP80. Carbon dioxide emissions were
calculated in the CGE models by applying emission factors (tons of
carbon per terajoule) to the use of petroleum, gas and coal commodi-
ties in the economies.
Because the CGE models were calibrated on the basis of 2011 social
accounting matrices, they were first run dynamically over 2011–2019 to
bring the economies to the first year of the GERD’s initial filling before
scenarios for 2020–2045 were assessed. The temporal evolution of the
values of some of the key driving parameters and baseline outputs of
the CGE models are shown in Supplementary Fig. 7.
Economy and river system coupling
The river system infrastructure and economy-wide simulators were
connected using a generic co-evolutionary framework developed in a
previous study7. The framework enables linking river system simula-
tion models (with daily or monthly time steps) with dynamic-recursive
annual CGE models. At each annual time step, the coupling framework
performs an iterative bidirectional communication between the river
system and the CGE simulators to ensure coherence in the annual
national-scale irrigation water supply and demand, municipal water
supply and demand, hydropower generation, non-hydro generation
and capacity, and electricity demand. In each iteration over an annual
time step, the river system model quantifies and spatially aggregates
national-scale irrigation and municipal water supplies and hydro
and non-hydro generation on the basis of the river system’s spatial
and temporal constraints, infrastructure, and external drivers. This
information is then passed to the CGE models as an external shock
on the basis of which changes to the economy’s municipal and irriga-
tion water demands, electricity demand, and non-hydro capacity are
determined and passed back to the river system model for the next
iteration. Iterations over each annual time step can be terminated by
a maximum number of iterations and/or a convergence error. In this
work, we specified a maximum of three iterations between the river
system and the economy-wide simulators for each annual time step and
a convergence error of US$5 million measured using the Ethiopian real
GDP. The coupling framework is implemented using the open-source
Python Network Simulation framework81. The reader is referred to
Basheer et al.7 for further details about the co-evolutionary coupling
framework used in this study.
Multi-method artificial-intelligence-based design and
learning
In the Nile adaptive planning framework, an artificial-intelligence-based
multi-objective evolutionary algorithm (MOEA) provides the ability to
identify efficient adaptive management policies for river system infra-
structure. Supplementary Fig. 8 shows the interaction between the
integrated economy-wide and river system simulators and the MOEA.

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The MOEA generates a design (that is, a set of decision variable values)
for the adaptive management policy (the yellow box in Supplemen-
tary Fig. 8), which is then passed to the integrated economy-wide and
river system simulators. The integrated model performs a dynamic
multi-year multi-scenario simulation considering socio-economic
and hydrological uncertainties related to climate change, resulting in
aggregated performance metrics (over time and projections) that are
indicated as objectives. The adaptive management design and objec-
tive values are then stored before proceeding to the next iteration, in
which the MOEA suggests new adaptive management designs. The
iteration between the MOEA and the integrated simulators contin-
ues until a stopping criterion is met. In this study, we use a maximum
number of iterations as a criterion to terminate iteration between
the integrated simulators and the MOEA. A maximum of 6,000 itera-
tions (or function evaluations) was specified as a stopping criterion
for the search process of adaptive GERD management. With every
iteration, the MOEA learns from the previous generations of itera-
tions and attempts to suggest an adaptive infrastructure management
design that improves performance as measured by the objectives.
Once the stopping criterion is met, a non-dominated sorting process
is performed to filter efficient adaptive management policies. We
used the open-source Non-dominated Sorting Genetic Algorithm III
(NSGA-III)82 as an MOEA for adaptive design. Platypus, an open-source
Python-based framework for evolutionary computing83, was used,
which supports NSGA-III.
The multi-objective search was performed for the adaptive man-
agement formulation for the GERD with five random seeds. Each seed
represents a unique starting condition for the search algorithm. The
use of multiple seeds allows checking that the search has converged
to a global, approximately optimal set of solutions. A total of 30,000
iterations were therefore performed during the search process (that is,
five seeds multiplied by 6,000 iterations). Optimization convergence
was tested by calculating the evolution of the hypervolume84 for each
random seed (Supplementary Fig. 9).
We used machine learning as a post-processing step for under-
standing the relative influence of adaptive policy variables on
economy-wide and river system performance, on the basis of a simi-
lar approach to a previous study on economy systems85. The values
of the adaptive policy variables and the objectives generated during
the search iterations between the MOEA and the integrated simula-
tors are used to train a machine learning model for each objective.
Accordingly, the features of each machine learning model are the
variables of the adaptive policy, and the target is each of the objec-
tives. The Random Forest Regression Machine Learning Algorithm86
was used. After the machine learning models were trained, feature
importance was calculated, which, for each model, represents the
relative influence of features on an objective. The Random Forest
Regression Algorithm was used through the open-source Scikit-learn
Python library87.
For the GERD application, 80% of the data on the objectives and
decision variables were used to train 100 tree predictors for each
machine learning model, and 20% of the data were used to test per-
formance. Maximum tree depth values from 1 to 30 were tested for
each machine learning model. The lowest tree depth that provided a
good prediction ability while avoiding overfitting or underfitting the
data was selected for each machine learning model. Supplementary
Fig. 10 shows the performance of the machine learning models with
the training and testing data with different maximum tree depths and
the chosen maximum tree depth values.
Implementation of the GERD adaptive management
formulation
The GERD adaptive management formulation optimized in this study
includes nine objectives and seven decision variables. The objectives
maximize the following aggregate metrics over 2020–2045:
	1.
The mean (over projections) accumulated (discounted over
time) Ethiopian real GDP
	2.
The mean (over projections) accumulated (discounted over
time) Sudanese real GDP
	3.
The mean (over projections) accumulated (discounted over
time) Egyptian real GDP
	4.
The mean (over projections) 90% firm (over time) power gen-
eration of the GERD
	5.
The mean (over projections and time) annual energy generation
of the GERD
	6.
The mean (over projections and time) annual energy from
hydropower in Sudan
	7.
The mean (over projections and time) annual energy from
hydropower in Egypt
	8.
The mean (over projections and time) annual irrigation water
supply in Sudan
	9.
The mean (over projections and time) annual irrigation water
supply in Egypt
The seven decision variables are (see Extended Data Fig. 5 for the
details):
	1.
Interim additional filling water release
	2.
Power reduction storage
	3.
Regular power target
	4.
Interim reduced power target
	5.
Drought trigger
	6.
Drought outflow storage threshold
	7.
Interim minimum drought mitigation water release
The upper and lower bounds of the seven decision variables are
reported in Supplementary Table 3. Long-term adaptation measures
are applied to the regular power target, the interim reduced power tar-
get, the drought trigger and the interim minimum drought mitigation
water release. The adaptation measures involve increasing or decreas-
ing these four GERD operation variables dynamically on a five-year
interval on the basis of how the mean annual inflow to the GERD over
the past five years changed in relation to the historical mean annual
inflow over 1980–2019.
Reporting summary
Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.
Data availability
The economy-related input data supporting this study’s findings are
available at Zenodo88: https://doi.org/10.5281/zenodo.5914757. The
Nile River system model and its data are not publicly available due to
state restrictions and contain information that could compromise
research participant privacy/consent. The Nile model data can be
made available upon presentation of the necessary permissions
from the relevant authorities that own the data. The CMIP6 climate
projections data can be accessed from https://esgf-node.llnl.gov/
search/cmip6/. The baseline population, labour, urbanization and
economic growth data of Ethiopia, Sudan and Egypt associated with
the SSPs can be accessed from the IIASA database at https://tntcat.
iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10. The sectoral
productivity projections of Ethiopia, Sudan and Egypt were pro-
duced on the basis of data from the Centre d’Études Prospectives
et d’Informations Internationales at http://www.cepii.fr/cepii/en/
bdd_modele/bdd.asp. The MSWEP data are available from http://
www.gloh2o.org/mswep/. The PGF data are available from https://
rda.ucar.edu/datasets/ds314.0/. The river network data used with
RAPID are freely accessible from https://www.hydrosheds.org/. The
World Bank data referred to in the manuscript are freely accessible
from https://data.worldbank.org/.

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Article
https://doi.org/10.1038/s41558-022-01556-6
Code availability
The Pywr simulation model (version 1.9.1) is open-source and freely
available at https://github.com/pywr/pywr, and building Pywr models
can be facilitated by www.waterstrategy.org. The standard CGE model
of the International Food Policy Research Institute is open-source and
freely accessible through the following link: https://www.ifpri.org/pub-
lication/standard-computable-general-equilibrium-cge-model-gams
-0. The VIC (version 5) model is freely accessible through the following
link: https://vic.readthedocs.io/en/master/. RAPID (version 1.8.0) is
freely accessible through the following link: http://rapid-hub.org/
index.html. The Python Network Simulation framework (version 0.1.5)
is open-source and freely available in the following repository: https://
github.com/UMWRG/pynsim. The multi-objective NSGA-III, the MOEA
used in the multi-objective search, is open-source and freely available
through Platypus (version 1.0.4) in the following repository: https://
github.com/Project-Platypus/Platypus. The Random Forest Regres-
sion Algorithm is open-source and freely available through Scikit-learn
(version 0.24.2) at https://github.com/scikit-learn/scikit-learn.
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Acknowledgements
M.B.’s doctoral degree is funded by the Faculty of Science and
Engineering of the University of Manchester. This work was supported
by the UK Research and Innovation Economic and Social Research
Council (grant no. ES/P011373/1) as part of the Global Challenges
Research Fund through the ‘Future Design and Assessment of
water-energy-food-environment Mega Systems’ (FutureDAMS)
research project to J.J.H., V.N., A.C., S.G., D.P., N.F., J.S. and H.J.F. We
thank GAMS Software GmbH for providing licences for mathematical
solvers compatible with parallel processing used for economy-wide
simulation on supercomputers. We acknowledge the use of the
Computational Shared Facility and High-Performance Computing of
the University of Manchester. The views expressed in this paper are the
sole responsibility of the authors and do not necessarily reflect those
of their institutions.
Author contributions
M.B. wrote the original manuscript. M.B. performed the visualization
of the results. All authors reviewed and edited the manuscript.
M.B. developed the adaptive framework linking various data and
model components. M.B. developed and calibrated the Nile River
system model. V.N., M.B. and A.C developed and calibrated the
economy-wide models of Ethiopia, Sudan and Egypt. S.G., M.B. and
J.S. developed and calibrated the hydrological model of the Nile
Basin. D.P., N.F. and H.J.F. processed and bias-corrected the climate
projections. M.B. performed the simulation and optimization work.
M.B., J.J.H., V.N. and A.C. conceptualized the study. M.B. formulated
the adaptive policy of the Nile. J.J.H. acquired the funding. All authors
contributed to the validation and interpretation of the results.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at https://doi.org/10.1038/
s41558-022-01556-6.
Supplementary information The online version contains
supplementary material available at https://doi.org/10.1038/s41558-
022-01556-6.
Correspondence and requests for materials should be addressed to
Julien J. Harou.
Peer review information Nature Climate Change thanks the
anonymous reviewers for their contribution to the peer review of this
work.
Reprints and permissions information is available at
www.nature.com/reprints.

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Range of projections
Climate projection (2021-2050)
Historical (1981-2010)
0
20
40
60
80
1
2
3
4
5
6
7
8
9
10 11 12
Percentage (%)
Month
(a) Nile Basin
0
20
40
60
80
1
2
3
4
5
6
7
8
9
10 11 12
Percentage (%)
Month
(b) Blue Nile Basin
0
20
40
60
80
1
2
3
4
5
6
7
8
9
10 11 12
Percentage (%)
Month
(c) White Nile Basin
0
20
40
60
80
1
2
3
4
5
6
7
8
9
10 11 12
Percentage (%)
Month
(d) Tekeze-Atbara Basin
Extended Data Fig. 1 | Mean monthly naturalized streamflow calculated as
a percentage of the mean annual naturalized streamflow. Mean monthly
naturalized streamflow of the Nile Basin (a) and its major sub-basins (b–d).
Tekeze-Atbara is projected to witness the biggest change in the intra-annual
naturalized streamflow variability under the most extreme scenarios, followed
by the Blue Nile.

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Extended Data Fig. 2 | Standardized streamflow index of the annual
naturalized streamflow of the Nile Basin relative to the mean and standard
deviation of 1981–2010 . Standardized streamflow index for different climate
change projections (a-ab). The index is calculated as follows: the annual
naturalized streamflow minus the mean annual naturalized streamflow in
1981–2010; this difference is divided by the standard deviation of the annual
naturalized streamflow in 1981–2010.

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Extended Data Fig. 3 | Annual change in the mean potential evapotranspiration in large-scale irrigation schemes in the Nile Basin. The annual change values
(a–n) are calculated with respect to the mean potential evapotranspiration in 1981–2010, such that change values higher than one indicate an increase in annual
evapotranspiration.

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Extended Data Fig. 4 | Annual change in the mean potential evaporation
rates from open water bodies in the Nile Basin. The annual change values
(a–o) are calculated with respect to the mean potential evaporation rates in
1981–2010, such that change values higher than one indicate an increase in rates.
Potential evaporation rates from open water bodies were calculated following
FAO56 recommendations using a multiplication factor applied to potential
evapotranspiration. GERD stands for Grand Ethiopian Renaissance Dam, UASDC
stands for Upper Atbara and Setit Dam Complex, KED stands for Khashm Elgirba
Dam, D/S stands for downstream, and HAD stands for High Aswan Dam.

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01556-6
Extended Data Fig. 5 | Adaptive management policy of the Grand Ethiopian
Renaissance Dam. The management policy shows the operation decisions for
the Grand Ethiopian Renaissance Dam (GERD) in the initial filling and long-term
operation phases. HAD stands for High Aswan Dam. Labels numbered from 1 to 7
are the decision variables in the multiobjective optimization problem. The labels
numbered 3, 4, 5 and 7 are the decision variables adapted over time as climate
change unfolds.

Nature Climate Change
Article
https://doi.org/10.1038/s41558-022-01556-6
Extended Data Fig. 6 | Annual water release rules of the Grand Ethiopian Renaissance Dam under the Washington draft proposal. a, Initial filling. b,Long-term
operation. The Washington draft proposal contains additional rules to mitigate the impacts of droughts, as described in Supplementary Section 2.

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Corresponding author(s):
Julien Harou
Last updated by author(s): Nov 1, 2022
Reporting Summary
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Data analysis
The Python water resources simulation model (Pywr version 1.9.1) is open-source and freely available at: https://github.com/pywr/pywr, and
building Pywr models can be facilitated by www.waterstrategy.org. The standard CGE model of the International Food Policy Research
Institute is open-source and freely accessible through the following link: https://www.ifpri.org/publication/standard-computable-general-
equilibrium-cge-model-gams-0. The Variable Infiltration Capacity (VIC version 5) model is freely accessible through the following link: https://
vic.readthedocs.io/en/master/. The Routing Application for Parallel computatIon of Discharge (RAPID version 1.8.0) is freely accessible
through the following link: http://rapid-hub.org/index.html. The Python Network Simulation framework (Pynsim version 0.1.5) is open-source
and freely available in the following repository: https://github.com/UMWRG/pynsim. The multiobjective Non-dominated Sorting Genetic
Algorithm (NSGA-III), the Multi-Objective Evolutionary Algorithm (MOEA) used in the multiobjective search, is open-source and freely
available through Platypus version 1.0.4 in the following repository: https://github.com/Project-Platypus/Platypus. The Random Forest
Regression Algorithm is open-source and freely available through Scikit-learn version 0.24.2 at: https://github.com/scikit-learn/scikit-learn.
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Data
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The economy-related input data supporting this study's findings are available at Zenodo: http://doi.org/10.5281/zenodo.5914757. The Nile River system model and
its data are not publicly available due to state restrictions and contain information that could compromise research participant privacy/consent. The Nile model data
can be made available upon presentation of necessary permissions from the relevant authorities that own the data. The CMIP6 climate projections data can be
accessed from: https://esgf-node.llnl.gov/search/cmip6/. The baseline population, labor, urbanization, and economic growth data of Ethiopia, Sudan, and Egypt
associated with the SSPs can be accessed from the International Institute for Applied System Analysis (IIASA) database: https://tntcat.iiasa.ac.at/SspDb/dsd?
Action=htmlpage&page=10. The sectoral productivity projections of Ethiopia, Sudan, and Egypt were produced based on data from the Centre d'Études
Prospectives et d'Informations Internationales (CEPII): http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp. The Multi-Source Weighted-Ensemble Precipitation
(MSWEP) data are available from: http://www.gloh2o.org/mswep/. The Princeton Global Forcing (PGF) data are available from: https://rda.ucar.edu/datasets/
ds314.0/. The river network data used with the Routing Application for Parallel computatIon of Discharge (RAPID) are freely accessible from: https://
www.hydrosheds.org/. The World Bank data referred to in the manuscript is freely accessible from: https://data.worldbank.org/.
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Study description
This study introduces the first planning framework for adaptive management of river infrastructure systems to consider both the
socio-economic and hydrological uncertainties of climate change and policy. The framework uses climate and socio-economic data
from the Coupled Model Intercomparison Project 6 (CMIP6) to drive integrated hydrological, economy-wide, and river system
simulators of the Nile Basin. Our adaptive planning framework uses artificial intelligence-based algorithms to design efficient adaptive
plans for climate change. We use the framework to design a cooperative adaptive management policy for the Grand Ethiopian
Renaissance Dam (GERD) that considers the socio-economic and river system interests of Ethiopia, Sudan, and Egypt.
Research sample
The targeted region of the study is the Nile Basin. The Nile River Basin, located in northeastern Africa, faces the threat of climate
change alongside physical and economic water scarcities, rapidly rising pressures on water resources due to population and
economic growth, and a politically complex transboundary water management system
Sampling strategy
Twenty climate projections were selected from CMIP6 Tier-1 based on uniform sampling to cover the full range of available SSPs,
radiative forcing, and the joint distribution of EOC change in precipitation and temperature over the Nile Basin. Processing and bias
correction of the GCM simulations was driven by the requirement for transient (2017-2100) 3-hourly forcing data across seven
climate variables that govern the surface mass and energy balances in the hydrological model. We developed nine additional
projections based on 9 of the 20 initial projections by removing the overall wetting tendency in the CMIP6 ensemble for this region.
Data collection
The CMIP6 climate projections data were obtained from: https://esgf-node.llnl.gov/search/cmip6/. The baseline population, labor,
urbanization, and economic growth data of Ethiopia, Sudan, and Egypt associated with the SSPs were obtained from the International
Institute for Applied System Analysis (IIASA) database: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=10. The sectoral
productivity projections of Ethiopia, Sudan, and Egypt were produced based on data from the Centre d'Études Prospectives et
d'Informations Internationales (CEPII): http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp. The Multi-Source Weighted-Ensemble
Precipitation (MSWEP) data were obtained from: http://www.gloh2o.org/mswep/. The Princeton Global Forcing (PGF) data were
obtained from from: http://hydrology.princeton.edu/data.pgf.php.
Timing and spatial scale
The overall timeframe of the study is 1979-2100, including climate data downscaling and bias-correction (1979-2100), hydrological
model development and simulation (1979-2100), economy simulation (2011-2045), and river system infrastructure simulation
(1995-2045). The spatial domain of the study is the Nile Basin.
Data exclusions
No data were excluded from the analysis.
Reproducibility
The study experiments were designed based on numerical simulations. To ensure that the study experiments are reliable and
reproducible, we calibrated and validated the simulation models at multiple sites in the Nile Basin over multi-year periods.
Randomization
The Nile river system simulator was calibrated over the period 1995-2004  and validated over the period 2005-2010 at ten locations
over using historical river flow observations and reservoir and lake water levels. This calibration period was chosen based on the
availability of common and continuous historical observed data for the ten selected locations.

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Blinding
Binding is not relevant to the data because our data are acquired and processed systematically with established computational
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system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
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Dual use research of concern
Methods
n/a Involved in the study
ChIP-seq
Flow cytometry
MRI-based neuroimaging

    Policy Problem:

Answer: Designing management strategies for large dams requires the adoption of a multi-dimensional approach to foster synergies, identify efficient tradeoffs and optimize economic performance. Dam negotiations between the Nile riparian countries have traditionally used only biophysical metrics, such as irrigation water supplies and hydropower generation, even though governments often build dams to achieve wider economic goals. The implications of climate change uncertainty for the Nile hydrology (for example, streamflow and irrigation demands) and the economies of its riparian countries (for example, economic development pathways, population growth and climate policies) mean non-adaptive dam management can perform poorly. The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Nile triggered political tensions between Ethiopia, Sudan and Egypt, with negotiations between the countries yet to reach an agreement. While negotiations over the GERD have been ongoing since 2011, economy-wide metrics alongside climate change uncertainties have not been considered in developing and evaluating dam operation proposals for the Nile.