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GLOWA VoltaPhase II completion report
Report period: 01.06.2003 – 30.05.2006
(Submitted in March 2007)
Förderkennzeichen 01/LW0302A
Charles Rodgers, Paul L.G. Vlek, Irit Eguavoen and Claudia Arntz
(Eds.)
submitted to
Bundesministerium für Bildung und Forschung
by the
Center for Development Research, Rheinische
Friedrich-Wilhelms-Universität
Bonn
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Outline
Abbreviations
...........................................................................................................................4
List of figures
.................................................................................................................................10
1. Introduction: The second project phase of GLOWA Volta: From
concepts to application .......13
1.1. The GLOWA Volta
Project.................................................................................................13
1.2. Planning and progress of the project
...................................................................................16
1.3. Achievements in the establishment of infrastructure and
capacity building.......................18
1.3.1. Technical infrastructure and research network
............................................................18
1.3.2. Doctoral
studies............................................................................................................22
1.3.4. Cooperation with local stakeholders
............................................................................23
2. Achievements and research results of the
sub-projects..............................................................25
2.1. The Atmosphere
Cluster..................................................................................................25
2.1.1. Sub-project A1: Regional Climate Simulations
...........................................................25
2.1.2. Sub-project A2: Hydro-Meteorological Monitoring System
.......................................32
2.1.3. Sub-project A3: Onset of the Rainy
Season.................................................................45
2.2. The Land Use
Cluster..........................................................................................................53
2.2.1. Sub-Project L1: Land Use Change Detection and
Quantification ...............................54
2.2.2. Sub-Project L2: Soil
Characterization..........................................................................60
2.2.3. Sub-Project L3: Vegetation
Dynamics.........................................................................64
2.2.4. Sub-Project L4: Modelling and Spatial and Temporal
Up-scaling of Erosion and
Hydrological Processes
..........................................................................................................68
2.2.5. Sub-project L5: Land Use Change
Prediction..............................................................75
2.3. The Water Use Cluster
........................................................................................................81
2.3.1. Sub-Project W1: Runoff and Hydraulic Routing
.........................................................82
2.3.2. Sub-Project W2: Water and Livelihood
.......................................................................93
2.3.3. Sub-Project W3: Institutional Analysis
........................................................................96
2.4. Technical Integration and Decision Support
.....................................................................102
2.4.1. Sub-Project D1: Technical Integration of Socio-Economic
and Environmental Models
..............................................................................................................................................102
2.4.2. Sub-Project D2: Household Decision-Making and Policy
Response ........................111
2.4.3. Sub-Project D3: Experimental Application of Scientific
Knowledge (Policy Pilot
Study)
...................................................................................................................................113
3. Internal progress evaluation, analysis and outlook to GVP
phase III ......................................118
3.1. Mechanisms for internal project evaluation
......................................................................118
3.2. Analysis of achievements in project phase II
....................................................................121
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3.3. Outlook to project phase III
..............................................................................................122
4. Completed Ph.D. projects, project publications and research
documentation .........................125
4.1. Completed Ph.D., M.Sc., M.Phil. and Diploma theses
.....................................................125
4.2. Publications, presentations and research documentation in
project phase II ....................128
4.3. Contributions at Status Conference 2005
..........................................................................139
5. Appendix
..................................................................................................................................141
5.1. Researchers and staff of the GLOWA-Volta project, Phase II
.........................................141
5.2. Selected publications (see appendix)
................................................................................153
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Abbreviations
AARSE African Association of Remote Sensing of
the Environment
AEJ African Easterly Jet
AMMA African Monsoon Multidisciplinary Analysis
ANN Artificial Neural Network
APSIM Agricultural Production Systems Simulator
ATCOR Atmospheric & Topographic Correction
AVN Aviation Model
BF Burkina Faso
BIGS-DR Bonn International Graduate School for
Development Research
BIOTA Biodiversity Monitoring Transect Analysis
BMBF Federal Ministry of Education and Resarch -
Germany
BON Biophysical Observation Network
CA Cellular Automata
CEC Cation Exchange Capacity
CGIAR Consultative Group on International
Agricultural Research
CIDA Canadian International Development Agency
CIRAD Centre de Coopération Internationale en
Recherche Agronomique pour le
Développement
COBIDS Component-Based Integration of Data and
Services
CP Circulation Pattern
CPU Central Processing Unit
CPWF Challenge Program for Water and Food
CR Clay Ratio
CRFS Centre de Recherches et de Formation
Scientifique
CRSN Centre de Recherche en Santé de Nouna
CsDM Diffusion and Migration Calibration Model
CSF GVP Common Sampling Frame
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CSIR Council for Scientific and Industrial
Research
CsMB1 Mass Balance 1 Calibration Model
CsMB2 Mass Balance 2 Calibration Model
CsPM Proportional Calibration Model
CWSA Community Water and Sanitation Agency
DA District Assembly
DAAC NASA Distributed Active Archive Center
DANIDA Danish International Development Agency
DBM Data-based mechanistic
DEM Digital Elevation Map
DGIRH Direction Générale de l'Investissement dans
les Ressources Humaines
DLR German Aerospace Center
DOY Day of Year
DSS Decision Support System
ECHAM4 Atmospheric General Circulation Model
ECMWF European Center for Medium Range Weather
Forecasting
ECOWAS Economic Community of West African
States
EM Expectation Maximization
EPA Environmental Protection Agency - Ghana
ESA European Space Agency
ET Evapotranspiration
etr Evapotranspiration (in WasSiM)
ETref Evapotranspiration (reference)
FAO Food and Agriculture Organization
GAMS General Algebraic Modelling System
GCM General Circulation Model
GEF Global Environment Facility
GIRE Gestion Intégrée des Ressources en Eau
GIS Geographic Information System
GLOWA Global Change in the Hydrological Cycle
GMP Governance and Modelling Project
GPS Global Positioning System
GUI Graphic User Interface
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GV LUDAS GLOWA Volta Land Use Dynamics
Simulator
GVP GLOWA Volta Project
GWCL Ghana Water Company Limited
GWP Global Water Partnership
GWP-WAWP Global Water Partnership West Africa
HMMS Hydro-Meteorological Monitoring System
HSD Hydrological Services Division - Ghana
ICOUR Irrigation Committee of Upper East Region
IFPRI International Food Policy Research Institute
IGBP International Geosphere-Biosphere
Programme
IMF International Monetary Fund
IMK-IFU Institute of Meteorology and Climate
Research - Atmospheric Environmental
Research Division
INERA Institut de l’Environnement et de Recherche
Agricoles
ITD Inter-Tropical Discontinuity
IUCN World Conservation Union
IWMI International Water Management Insitute
IWRM Integrated Water Resources Management
KACE Kofi Annan Center of Excellence in
Information and Communications
Technology
LAI Leaf Area Index
LANDSAT ETM LANDSAT Enhanced Thematic Mapper
LANDSAT TM LANDSAT Thematic Mapper
LAS Large-Aperture Scintillometry
LC Land Cover
LCCS Land Cover Classification System
LPJ-model Lund – Potsdam - Jena Dynamic Global
Vegetation Model
LSA-SAF Land Surface Analysis Satellite Application
Facility
LST Land Surface Temperature
LUC Land Use / Cover
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LUDAS Land Use Dynamics Simulator
LULC Land Use and Land Cover
MAS Multi-Agent System
MATA Multi-Level Analysis Tool for Agriculture
METOP Meteorological Operational Satellite
MM5 Mesoscale Meteorology Model 5
MODIS Moderate Resolution Imaging
Spectroradiometer
MoFA Ministry of Food and Agriculture - Ghana
MoWH Ministry of Works and Housing -Ghana
MSG Meteosat Second Generation
NAO North Atlantic Oscillation
NARMAX Nonlinear Autoregressive-moving Averages
Model with Exogenous Input
NASA National Aeronautics and Space
Administration
NCEP National Center for Environmental
Prediction
NCWSP National Community Water and Sanitation
Program - Ghana
NDVI Normalised Difference Vegetation Index
NGO Non-Governmental Organization
NOAA-AVHRR National Oceanic & Atmospheric
Administration - Advanced Very High
Resolution Radiometer
NUSLE Nomograph Universal Soil Loss Equation
PAGEV Projet pour l’Amélioration de la
Gouvernance de l’Eau dans le Bassin de la
Volta
PbDM Diffusion and Migration Model for 210Pb
PbMB Mass Balance Model for 210Pb
PERL Practical Extraction and Report Language
PEST Parameter Estimation Tool
PPP Public Private Partnership
prec Precipitation
PSF Particle Size Fractions
PSP Private Sector Participation
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PTF Pedo-Transfer Function
PURC Public Utilities Regulatory Commission -
Ghana
qdir Direct runoff (in WaSiM)
qifl Interflow (in WaSiM)
qtot Total runoff (in WaSiM)
RCM Regional Climate Model
RMSE Root Mean Square Error
RN Net Radiation
RS Remote Sensing
SARI Savannah Agricultural Research Institute
SEBAL Surface Energy Balance Algorithm for Land
SMOS Soil Moisture and Ocean Salinity
SOFM Second-Order-First-Moment
SPOT Satellites Pour l’Observation de la Terre
SR Sand ratio
SRP Small Reservoirs Project
SRTM Shuttle Radar Topography Mission
SVAT Surface-Vegetation-Atmosphere-Transfer
SVN Social Venture Network
SVRT SMOS Validation and Retrieval Team
SWAT Soil & Water Assessment Tool
TEJ Tropical Easterly Jet
TU Technical University
UER Upper East Region - Ghana
UML Unified Modeling Language
UN United Nations
USP Unit Stream Power
USAID Unites States Agency for International
Development
USDS-ARS United States Department of Agriculture –
Agricultural Research Service
USGS United States Geological Survey
USLE Universal Soil Loss Equation
UTM Universal Transverse Mercator
VBA Volta Basin Authority - Ghana
VBTC Volta Basin Technical Committee
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VF Vegetation Fraction
VRA Volta River Authority -Ghana
WAPP West African Power Pool
WaSiM-ETH Water Balance Simulation Model
WaTEM/ SEDEM Water and Tillage Erosion Model / Sediment
Delivery Model
WATSAN Water and Sanitation Committee - Ghana
WEPP Water Erosion Prediction Project
WRC Water Resources Commission - Ghana
WRI Water Research Institute
WUA Water User Association - Ghana
WVPP White Volta Policy Pilot Project
ZEF Center for Development Research
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List of figures
Figure 1 Sap flow measurement equipment
...................................................................................21
Figure 2 Location of sub-catchments and gauges in the White
Volta catchment (resolution: 1km)
................................................................................................................................................22
Figure 3 Completed Ph.D., M.Sc. and M.Phil. theses in phase II
..................................................23
Figure 4 Calculation of evaporation in 24h time step, 3h time
step simulation and with improved
calculation with 24h time step, including an empirical factor
...............................................27
Figure 5 Model setup: Digital elevation model, sub-catchment
boundaries, river network and
location of the Sourou depression
..........................................................................................28
Figure 6 Calibration run, coupled simulation and observed
discharge values for the gauges
Bamboi and Saboba,
1968......................................................................................................28
Figure 7 Mean latitudal displacement (2030-2039 vs. 1991-2000)
of the Inter Tropical
Discontinuity (ITD): southward (northward) shift in April
(September) connected to a
decrease (increase) in precipitation
........................................................................................29
Figure 8 Climate change signal in precipitation (prec), direct
runoff (qdir), interflow (qifl), total
runoff (qtot) and evapotranspiration (etr), 2030-2039 vs.
1991-2000, left: percentage change,
right: absolute
change.............................................................................................................30
Figure 9 Monthly signal-to-noise ratio of precipitation (prec),
total runoff (qtot) and
evapotranspiration (etr)
..........................................................................................................31
Figure 10 Measured (blue) and simulated (green) discharge [m³/s]
and precipitation (black) in
[mm] for the gauges Nakong and Nawuni for the calibration period
1968 ...........................34
Figure 11 Simulated (green) vs. measured (blue) discharge [m³/s]
at Pwalugu and Nawuni for the
validation period 1961-67.
.....................................................................................................34
Figure 12 Simulated annual precipitation sum for 2004 (Domain 3)
for the Volta Basin .............35
Figure 13 Comparison of simulated and measured monthly
precipitation sums in 2004 for
gauging stations (Kaburi, Kpasenkpe, Pwalugu, Babile, Zuarungu)
in Ghana......................35
Figure 14 Coupled simulated (green) and measured (blue)
discharge [m³/s] and precipitation
(black) in [mm] for three gauging stations along the White Volta
in Ghana (Kaburi, Pwalugu
and Nawuni)
...........................................................................................................................36
Figure 15 Leaf Area Index (LAI) for the White Volta catchment:
mean MODIS LAI-grids for
2002........................................................................................................................................37
Figure 16 Annual evapotranspiration map simulated with a)
standard literature and b) MODIS
derived LAI- grids; c) difference of annual evapotranspiration:
a) - b) .................................37
Figure 17 Different components of actual evapo-transpiration,
example for November 15, 2002.
Incoming solar radiation (top left), vegetation fraction (top
right), reference temperature
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(bottom left) and resulting actual evapotranspiration (incl.
evaporation from interception
(bottom right)
.........................................................................................................................40
Figure 18 Maps of ET, sensible heat flux, and soil moisture
(degree of saturation) around Tono
dam at the end of the dry season (March 7, 2003) derived from
LANDSAT image. The size
of these maps is approximately 10 by 15 km. Spatial resolution
of heat fluxes and soil
moisture maps is 30
m............................................................................................................43
Figure 19 Map of ET at the end of the dry season (March 7, 2003)
derived from MODIS image.
The spatial resolution of this map is 1000 by 1000 m
...........................................................44
Figure 20 ET histograms of LANDSAT and MODIS ET maps on March 7,
2003.......................45
Figure 21 Spatial distribution of five derived regions within
the Volta Basin ..............................47
Figure 22 Mean Onset-Date, Julian Day (left); standard deviation
of onset dates (right) .............48
Figure 23 Mean normalized MF_U distribution in 500hPa of CP5
associated to the start of the
rains in PC1 (left) and mean normalized MF_U distribution in
500hPa of CP4, which is not
linked to the rainy season’s onset (right)
...............................................................................49
Figure 24 Zero- and first order Markov chain of
Accra.................................................................50
Figure 25 Observed (*) and fitted (-) proportion of a dry spell
of > 6 days at Bole for each day of
the year, based on rainfall data (1961-2001) with a threshold of
1 mm for a rainy day ........51
Figure 26 Supplementary information to assess the date of
optimal planting [DOY] for the major
rainy season, presenting the minimum probability of the
occurrence of a dry spell of > 6
consecutive days within the following 30
days......................................................................52
Figure 27 Sensing databases and products used within the L1
sub-project ...................................55
Figure 28 Workplan for the LC mapping period from January 2004 –
May 2006 ........................57
Figure 29 LCCS based LC map using 26 LANDSAT images from 1990
(left) and 26
corresponding LANDSAT tiles from 2000/2001 (right). The GLOWA
Volta Basin is shown
as a black solid line
................................................................................................................58
Figure 30 Description of soil sampling sites
..................................................................................61
Figure 31 Hydrotope map at Tamale study site (left), Histograms
of three hydrotope soil moisture
distributions
(right).................................................................................................................63
Figure 32 Patterns of diurnal anomalies (left), average soil
moisture distribution from 1992-2000
(right)......................................................................................................................................64
Figure 33 Tree spatial pattern maps in
30x30m2............................................................................66
Figure 34 Vegetation classification map of Bontiolo natural
reserve ............................................67
Figure 35 Comparison between simulated and observed latent heat
fluxes (left); dependency of
grid-scale effective roughness on subgrid scale mean and
standard deviation (right) ...........69
Figure 36 Land Cover Map of the Ioba- and the Dano catchment
based on classified Aster Image
2004........................................................................................................................................71
Figure 37 Spatial distribution of small reservoirs in the White
Volta Basin .................................72
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Figure 38 Soil erosion maps: 210
Pb (Pb), 137
Cs (Cs) and Sand ratio (SR) models
.........................74
Figure 39 Users select land conservation options by changing
management parameters, and
visualize consevation plan on slope map
...............................................................................78
Figure 40 Given the selected parameters, UPS model simulates
soil erosion (map and histogram)
as a consequences of the selected conservation option.
.........................................................79
Figure 41 Geology and groundwater potential in the Volta Basin
.................................................86
Figure 42 Hydrogeological concept model of the study area
(Atankwidi catchment)...................87
Figure 43 Daily groundwater recharge calculated by soil moisture
modelling (years 2003 – 2005)
................................................................................................................................................89
Figure 44 Spatial Infiltration Variation in Pwalugu and Tindama
Wetlands .................................91
Figure 45 Interaction between sub-surface and surface water in
Pwalugu site .............................91
Figure 46 Schematic of GVP Model Linkages and DSS
.............................................................104
Figure 47 Interface for linkage of simulation systems
.................................................................108
Figure 48 COBIDS-based model ensemble and interfaces
..........................................................109
Figure 49 Proposed Geo-database, web portal and file server
.....................................................111
Figure 50 Sub-catchments and outlets delineated for MIKE BASIN
..........................................117
Figure 51 GLOWA Volta Thematic Timeline
.............................................................................122
Figure 52 GLOWA Volta Project Phase III Planning Timeline
..................................................124
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1. Introduction: The second project phase of GLOWA Volta:
From concepts to application
1.1. The GLOWA Volta Project
The Volta River Basin, encompassing regions within six West
African nations, is in many
respects broadly representative of large basins within the
“developing” world. It embodies a wide
range of challenges to water resources management, including
static or declining water
availability as influenced by global and regional climatic and
land cover processes; increasing
demand for water resources over the coming decades in the
domestic, agricultural, industrial and
hydropower generation sectors, leading to increasing
intersectoral competition for water, and the
deterioration of water quality and supply of water to serve
critical ecosystem support functions.
Other water management challenges include a persistence of weak,
inefficient or conflicting
water rights and governance structures, an insufficient
hydro-climatic database, inadequate
scientific capacity to support effective political
decision-making, and low levels of investment
reflecting limitation in access to financial resources. Much of
the research conducted within the
framework of the GLOWA Volta Project has been designed to
address, or to work around these
constraints. Thus, our investigation of the physical and
socio-economic determinants of the
hydrological cycle within the Volta Basin, and the development
of model-based decision-making
tools, may strengthen efforts to manage water resources at river
basin level not only within the
study area, but more broadly throughout the region.
The two dominant riparian countries of the Volta Basin, Ghana
and Burkina Faso, provide the
research context for the GLOWA Volta Project. They occupy 40.18
% and 42.65 % of the total
400,000 square kilometre territory of the basin, respectively.
They also embody the full range of
climatic and land cover variability characterizing the Basin,
spanning at least four climatic zones
(sub-humid in the South to semi-arid in the North of the basin),
annual precipitation gradient
(2,000 mm in the southern lowland forest to less then 600 mm in
the northern Sahel) and
vegetation cover, as well as the full range of socio-cultural
and economic variation. Global
climate change is perceived as a factor likely to enhance the
existing, erratic and unreliable
character of rainfall within the basin. Projected annual
population growth of 2.8 % and likely
future expansion of irrigation schemes in northern parts of the
basin will lead to a situation in
which domestic water uses and irrigation compete directly with
water use for the generation of
hydropower at newly established dams in Burkina Faso, and at the
existing downstream
Akosombo hydropower dam on Volta Lake, which provides Ghana with
over 90 % of its
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electrical power. Reflecting on these conditions, it should be
clear that we are in a position not
only to contribute to the scientific understanding of linked
climate, hydrology and land use-land
cover patterns in a major West African basin, but also to make a
substantial contribution to the
solution of water sector- and related problems within the
basin.
The GLOWA Volta Project (GVP), conducted by the Center for
Development Research (ZEF),
Bonn University and research partners in Europe and West Africa,
is conceived as a nine-year,
multi- and trans-disciplinary study organized in three phases.
Phase I (June 2000 – May 2003)
emphasized the identification of methods and models; the
establishment of research infrastructure
on the ground in Ghana, the collection of baseline climatic,
hydrological and socio-economic
data, and the training of the first cadre of physical and social
scientists from the Volta Basin and
surrounding regions of West Africa. In phase II (June 2003 – May
2006), emphasis shifted from
scoping studies and data collection to the development, testing
and application of a range of
numerical simulation models; as well as process-oriented studies
of important bio-physical and
socio-economic phenomena. Phase III, currently underway, will
focus on integration of phase I
and II outputs, emphasis on aggregate economic analysis,
operationalization of Decision Support
System (DSS) components and transfer of activities and
responsibilities to institutions within the
Volta Basin.
This report is intended to provide a summary of activities
conducted, and completed, during the
second phase of the GVP. Given the size, scope and complexity of
the GVP, it is impractical to
attempt an exhaustive description of research activities and
outputs realized over a three-year
period, so this review is necessarily selective. We have
attempted to highlight the most
significant findings, and products, and we have in addition
pointed to the broader range of
contributions through the inventory of theses, publications and
presentations. Similarly, although
this completion report is organized according to the four
research clusters (Atmosphere, Land
Use, Water Use, Technical Integration) used to organize the GVP
phase II proposal (2003), an
additional range of research has been conducted or initiated
during phase II that cannot easily be
subordinated to these clusters. Examples include the
contributions to land use and land cover
change by the Wuerzburg project team, which bridge the structure
of the Land Use cluster sub-
projects; the coupled atmospheric-hydrological modelling,
(original work packages A1 and W1);
and the Multi-Agent Systems (MAS) model development conducted by
Dr. Quang Bao Le,
which spans Land Use and Water Use clusters, as originally
defined. The presentation of results
in this document still follows the structure of the phase II
proposal, but many activities described
link to several of these clusters, or occasionally fall outside
of them, as in the case of the
development of a GVP Geo-database, described under the Technical
Integration (D1) activities.
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As in any project as ambitious in scope and design as GVP, there
have been project areas in
which the quality and utility of research activities and outputs
exceeded expectation; areas where
expectations were largely met, and areas in which progress fell
short of expectation. Examples of
each will be documented, and the picture that emerges is that
setbacks relative to the original
research design have, on balance, been compensated by a range of
innovative activities that serve
to justify fully the time and resources committed to this
program. In taking stock of the progress
and accomplishments realized during phase II, as well as the
challenges and setbacks, it is useful
to consider the factors required to achieve success in meeting
project objectives. A list of such
factors would certainly include (i) financial resources; (ii)
human resources; (iii) data and/or
knowledge to support scientific enquiry; (iv) appropriate tools
and technologies, including field
instrumentation, numerical simulation tools, software
engineering capacity, modes of
communication and data storage and manipulation; (v) supporting
infrastructure, including
housing, transportation, logistics and related; (vi) appropriate
institutional configurations, linking
research partners, decision-makers and stakeholders; finally
(vii) a reasonably stable political
matrix within which to conduct the research and disseminate the
results.
Our perception is that GVP phase II was adequately funded, so
that availability of funds seldom
acted as the binding constraint to successful completion of
research activities (time was more
likely to constrain outcomes). Regarding human resources, the
greatest constraint to timely
completion of planned tasks during phase II was often staff
turnover. Several cases are described
below. This is not altogether unexpected, however, for at least
two reasons. First, the GVP has
made extensive use of young scientists, many from the Volta
Basin, who have earned their
Ph.D.’s in the context of GVP. Upon completion, they naturally
seek the widest possible set of
professional opportunities. As Ph.D. programs usually require
between three and four years, the
large cadre of students beginning their research during the
first phase of GVP completed their
work at some point during phase II. Although many have been
retained by GVP as postdoctoral
scientists or continue their collaboration via home institutions
in Ghana and Burkina Faso, many
entered the international job market, and were effectively lost
to the project just as their research
skills had matured. Second, a number of senior scientists moved
from research positions within
GVP to tenure-track positions at major research institutions; or
to international research centres
including the International Water Management Institute (IWMI).
Most have continued to
contribute actively to the GVP, but are no longer “dedicated” to
the project. Turnover has clearly
and regrettably resulted in discontinuities in several research
activities during phase II, but also
serves to demonstrate, via the success of alumni and senior
staff in securing increasingly
responsible positions, the quality and impact of GVP research
activities. Access to data – both
physical and socio-economic – continues to be an issue that
affects many areas of GVP research,
although many of the obvious gaps have been address through
phase I and early phase II data
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collection efforts. Given that the GVP methodological approach
was predicated on sparse or
nonexistent data, many of the impacts of data scarcity have been
fully factored into research
designs.
A complete review of project tools and technologies cannot be
presented here. Some points
appear relevant, however: First, decisions made early in the
project concerning the selection of
numerical simulation tools (“models”) have largely been
vindicated in terms of model suitability
and performance, with a few key exceptions. Models were not
chosen ex ante on the basis of
inter-operability, however, so that model linkage and
integration tasks encountered for the first
time during phase II have proven rather demanding. In this
context, the component-based
integration strategy, initiated in mid-phase II and described in
greater detail under work package
D1, appears as a promising approach, and indeed the only
approach identified that is likely to
enable the more ambitious task of full DSS integration underway
during phase III. The physical
infrastructure supporting GVP activities in Ghana and Burkina
Faso has served us well, which is
largely a reflection of the skill and experience of GVP project
managers within the Basin. In
Ghana, Dr. Boubacar Barry has served effectively since June of
2005, when he replaced Dr. Mark
Andreini, who moved to IWMI’s liaison office in Washington, D.C.
In Burkina Faso, Dr. Konrad
Vielhauer has served since project inception. Their
effectiveness has been greatly enhanced
through the network of participating and collaborating
institutions. IWMI, along with the
Ghanaian Council for Scientific and Industrial Research (CSIR)
through affiliated organizations
have provided strong support for our program in Ghana, as have
Foundation Dreyer and INERA
in Burkina Faso. More recent extensions of the GVP research
network are described in the phase
III Proposal (October 2005). Finally, the political matrix in
which the project operates within
West Africa, specifically the Governments of Ghana and Burkina
Faso and a range of
international actors, continues to provide a favourable setting
for project activities.
1.2. Planning and progress of the project
The overall objectives of the GLOWA Volta Project (GVP) remain
consistent with the initial
proposal (submitted in March 2000) and the updated Phase II
proposal (submitted in October
2002): (1) to provide an analysis of the physical and
socio-economic determinants of the
hydrological cycle within the Volt Basin, and (2) to develop a
scientifically sound Decision
Support System (DSS) for the assessment, sustainable use and
development of the Basin’s water
resources. The DSS is intended to provide a comprehensive
monitoring and simulation
framework, enabling decision makers to evaluate the impacts of
climatic and land use trends
overlaid on the consequences of deliberate policies, investments
and other interventions on the
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social, economic, and biological productivity of water
resources. Integral to this effort is the
development of scientific capacity and infrastructure within the
Basin to ensure the self-
sustainability of the DSS through the completion of formal GVP
activities. The development of
human capital via advanced education and training, co-operative
research and stakeholder
participation has been one of the GVP’s most successful
activities to date, and is now identified
as the third overall objective of the Project.
The GLOWA Volta phase II was initiated in June 2003, with four
strategic objectives identified:
• Successful completion of phase I activities;
• Expansion of significant project activities to Burkina
Faso;
• Technical integration of disciplinary models and knowledge
generation frameworks;
• Design, testing and preliminary application of a prototype DSS
for the White Volta Basin.
Broadly speaking, the first and second objectives have been
completed successfully, and
substantial progress has been made toward the third and fourth
objectives, respectively. These
accomplishments will be described in detail in the following
text. In addition, a number of
additional activities and accomplishments not explicitly
described or anticipated in the phase II
proposal were initiated. These activities include (i) a
multi-disciplinary evaluation of shallow
groundwater irrigation, an increasingly common practice in
Northern Ghana and throughout
Burkina Faso, conducted jointly with the International Water
Management Institute (IWMI),
Accra, Ghana; (ii) integrated analysis of small reservoirs,
found throughout the upper regions of
the Basin, in collaboration with the CGIAR Challenge Program for
Water and Food (CPWF),
Small Reservoirs Project; and (iii) sector-level analysis of the
agricultural and power sectors of
Ghana and Burkina Faso, respectively, utilizing the CIRAD MATA
(Multi-Level Analysis Tool
for Agriculture) bio-economic model and the West African Power
Pool (WAPP), developed via
USAID and currently under implementation throughout the ECOWAS
region. Although these are
largely intended as GVP phase III activities, and described in
detail in the phase III proposal,
groundwork was successfully laid during Phase II. In addition,
these activities have served to link
the GVP with the nascent Volta River Basin Authority, formally
constituted in 2006.
Although the GLOWA Volta Project is a large, multi-disciplinary
and multi-institutional project,
the presence and influence of a relatively small number of
senior scientists nevertheless act to
direct the GVP research agenda, as well as specific research
activities. A number of personnel
changes occurred during GVP phase II, some of which had
implications for the schedule and/or
orientation of research within the GVP. Among the most important
are the following: (1) in late
2003, Michael Schmidt of DLR – University of Wuerzburg was
promoted to a more senior and
supervisory position within DLR, thus creating a critical
vacancy within the lead partner
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18
responsible or retrieving, correcting and interpreting remote
sensing imagery for the GVP. A
suitable replacement for Dr. Schmidt was not identified until
late 2004, when Dr. Sasa Fistric
joined DLR-Wuerzburg. Dr. Fistric was in turn replaced by Dr.
Tobias Landmann in late 2005.
As a consequence of these discontinuities, the status of GVP RS
activities is only now, in early
2007, at the point where we anticipated we would be at the end
of Phase II. This has in turn
delayed completion of the cellular automata-based land use-land
conversion (LULC) model, also
envisioned for completion during Phase II. This will be complete
in late 2007. (2) In late 2004,
Thomas Berger moved from ZEF to University of Hohenheim as a
visiting Professor, leaving the
task of identifying and utilizing multi-agent system (MAS)-based
models of community-based
water resources management incomplete. Dr. Berger has continued
active collaboration with the
GVP through his role in the CGIAR CPWF Governance and Modelling
Project, although this
project is on an independent timeline. Dr. Berger’s position of
GVP lead economist was not filled
until Dr. Victor Afari-Sefa joined ZEF late in the phase II
project cycle. Dr. Afari-Sefa is an
agricultural economist whose expertise extends to the areas of
partial equilibrium economics, as
distinct from MAS, so the economic decision-making component of
the project has been re-
oriented accordingly. (3) Development of agent-based GV-LUDAS,
which had proceeded rapidly
during GVP phase I under Dr. Soojin Park, was interrupted
following Dr. Park’s return to Korea
in 2003, but resumed under Quang Bao Le, a student of Dr.
Park’s, in 2005. Dr. Le, a Forester
and Physical Geographer, has already developed a working
prototype community-scale model of
land and water resources use, and two Ph.D. students are
currently implementing this model at
locations in the Upper East Region (UER) of Ghana and the Dano
region of Burkina Faso,
respectively. Finally (4), Nick van de Giesen, the GVP
Scientific Coordinator, accepted a tenure-
track position at TU Delft in mid-2004. Dr. van de Giesen was
replaced by Dr. Charles Rodgers,
from the International Food Policy Research Institute (IFPRI),
Washington, D.C., in July of
2004. As there was overlap between Dr. van de Giesen and Dr.
Rodgers, no hiatus in project
scientific coordination occurred, and as Dr. van de Giesen
continues to participate in GVP
scientific research, no loss of continuity in project activities
or oversight was experienced.
1.3. Achievements in the establishment of infrastructure and
capacity
building
1.3.1. Technical infrastructure and research network
During the first phase of the GVP, research activities were
largely confined to the major
downstream riparian country, Ghana. During phase II, project
activities have expanded
significantly within Burkina Faso, primarily through
collaborative research with Institut de l’
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19
Environment et de Recherche Agricoles (INERA) and through the
establishment of research
networks linking GVP with BIOTA-West Africa and the Virtual
Institute – Foundation Dreyer.
The research network within Ghana has expanded as well. In early
2005, the White Volta Pilot
Project was formally initiated in collaboration with the
Ghanaian Water Resources Commission
(WRC), joined by the International Food Policy Research
Institution (IFPRI) via the Challenge
Program on Food and Water (CPWF), Governance and Modelling
Project (GMP). Shared project
offices were established in Bolgatanga, in the UER, and several
joint investigations initiated.
These include studies of small reservoirs, shallow groundwater
irrigation and riverine pump
irrigation. All are of high interest to the WRC, and to the
Ghanaian Ministry of Food and
Agriculture (MoFA).
GVP research infrastructure also expanded significantly though
the integration of Project
activities and research sites into regional research networks.
Important among these are the
formation of an integrative multi-scale monitoring concept in
Burkina Faso linking GVP with
BIOTA West Africa (BMBF) and Burkinabé counterpart INERA. The
Biophysical Observation
Network (BON) combines important features of biophysical ground
measurement and remote
sensing techniques in order to enable (a) monitoring of large
scale vegetation, hydrologic and
bio-geophysical dynamics and (b) evaluation of climate dynamics
based on observations of
biosphere – atmosphere interactions. 2004 also saw the
inauguration of the Centre de Recherches
et de Formation Scientifique (CRFS), an international research
facility constructed by the Dreyer
Foundation and shared by ZEF/GVP, BIOTA West Africa, Helmholtz
Institute and INERA. This
innovative research site in western Burkina Faso provides
residence and research facilities for
scholars conducting extended agricultural, environmental and
hydrologic studies. The Dreyer
Foundation field station at Dano (Burkina Faso) was used to
support the AMMA (African
Monsoon Multidisciplinary Analysis) 2006 field campaign. To
support the transfer and ongoing
development of computationally-intensive numerical modelling
activities, GVP established a
formal working relationship with the Kofi Annan Center of
Excellence in Information and
Communications Technology (KACE), a joint India – Ghana facility
located in Accra providing
regional access to high-performance computing and training in
hardware and software
development and utilization. KACE also provides broadband
satellite links to the Internet. Our
strategic objective is to transfer computationally intensive
aspects of the Volta Basin DSS,
including climate forecasting, to the KACE during GVP phase
III.
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20
In anticipation of the European Space Agency’s (ESA) pending
launch of the Soil Moisture and
Ocean Salinity (SMOS) mission in early 2008
(http://www.esa.int/esaLP/LPsmos.html), GVP
submitted a proposal to participate, and we are now part of the
SMOS Validation and Retrieval
Team (SVRT), with Ph.D. student Jan Friesen serving as principal
investigator (ESA-SMOS PI
#3280). GVP has established a soil moisture monitoring network,
consisting of soil moisture
transects at three ongoing GVP research sites within Ghana and
Burkina Faso: Boudtenga,
Tamale, and Ejura. The field stations at Dano and Navrongo,
respectively, are also equipped with
automatic weather stations that record soil moisture levels at
fine intervals (Friesen 2005). These
instrumented sites will provide important in situ measurements
required to calibrate and validate
the SMOS mission, and will also serve to improve the calibration
and validation of the GVP
MM5 meso-scale climate model, operated by IMK-IFU and currently
providing near-term
forecasting for the basin. Remotely-sensed soil moisture fields
will become an important
component of the GVP climate monitoring system.
To provide additional points of validation for numerical climate
simulation (MM5) and
hydrological (WaSiM-ETH) modelling ensembles, four sap flow
measurements sites were
installed in the Nature Reserve of Bontioli, Burkina Faso in
collaboration with BIOTA West
Africa (Tia Lazare, Ph.D. student from Burkina Faso). The sites
were selected to provide
coverage of a large number of the dominant tree species within
the upper Basin, and will be
valuable in cross-validating wide-area evapotranspiration
estimates derived from numerical
models, Large-Aperture Scintillometry (LAS) and eddy covariance
measurements, and SEBAL
and related energy balance algorithms.
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21
Figure 1 Sap flow measurement equipment
To strengthen the understanding of groundwater recharge and flow
processes in the upper regions
of the Basin, where groundwater is relied upon extensively to
provide domestic water supplies of
acceptable quality and quantity throughout the year, four
stations within the Nakambe (White
Volta) sub-basin within Burkina Faso were equipped with rain
gauges, piezometers and divers
(Jean-Pierre Sandwidi, Ph.D. student from Burkina Faso). These
are monitored daily, and form
the basis for estimates of annual groundwater recharge. In
addition, 15 water quality sampling
points from 6 localities within the basin are monitored on a
daily basis to provide a continuous
evaluation of groundwater quality and quantity. In early 2006,
an additional study of groundwater
recharge was initiated in the Upper East Region of Ghana
(Emmanuel Obuobie, Ph.D. student
from Ghana) which will also establish a regional network of
climate and groundwater sensors,
supported by numerical modelling of the spatio-temporal patterns
of groundwater recharge using
the USDS-ARS model SWAT (Soil & Water Assessment Tool).
These studies build on the work
of Dr. Nicola Martin, who completed her Ph.D. study of
groundwater in the Atankwidi
catchment, UER Ghana, in 2005.
To facilitate the calibration and validation of coupled
meteorological (MM5) and hydrological
(WaSiM-ETH) models within the White Volta sub-basin, a dense
observational network for
precipitation and surface runoff was installed in spring 2004 in
the Upper East Region in Ghana,
involving scientists from ZEF-Bonn, IMK-IFU and the Ghanaian
Hydrological Services Division
(HSD). In October 2004, two Hydro Argos systems were installed
at locations selected in
consultation with HSD. The Hydro Argos units transmit water
levels measured by in situ divers
(pressure transducers) by dedicated satellite uplink to an
internet platform, so that this data can be
retrieved remotely and analyzed in near-real time from locations
in Germany and/or Ghana.
Sap Flow sites location
Sapflowfixedsystem
Boundaryof NRB
LEGEND
b
ÊÚ
ð
EddyCovariancestation
Micrometeorologystation
bSapflowmobilesystem
0 2 4Km
o
bb
b
bbbð
ÊÚLoc3
Loc4
Loc6
Loc5
Loc7
Loc8
Source: Fielddata
L. TIA©
Sap Flow fixed station on groundSap Flow mobile system on
tree
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22
Figure 2 Location of sub-catchments and gauges in the White
Volta catchment (resolution: 1km)
1.3.2. Doctoral studies
As in phase I, the project contributed extensively to local
capacity building in the form of Ph.D.,
Master and Diploma research. Most of the doctoral students
trained in the context of GVP were
affiliated with the Bonn International Graduate School for
Development Research (BIGS-DR),
located at ZEF – Bonn University. Others worked as student
assistants and supported the GVP
through both research and administrative and organizational
services. In return, the research
institute provided offices and other research infrastructure and
also consistent supervision during
the preparation, research and completion of individual studies.
During their research, students had
access to GVP infrastructure in Ghana und Burkina Faso and to
local project researchers who
provided support, scientific advice and supervision,
information, social contacts, and technical
instrumentation.
All Ph.D. projects started in the first project phase were
completed during phase II, while many
Ph.D. projects initiated during phase II will be finalized
before mid - 2007, during the first year
of phase III. As documented in the Annual Report for 2005, the
timelines for Ph.D. projects are
not necessarily aligned with project phases, but are rather
determined by the timeline of academic
programmes, the cycles of dry and rainy seasons in the Volta
basin (which govern research
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23
logistics as well as researched phenomena), vegetation cycles,
the occurrence of environmental
hazards, as well as by institutional calendars, such as periods
reserved for thesis defence and final
examinations as set by the relevant faculty. In most cases, the
three years allocated to the ZEF
doctoral programme resulted in a tight academic schedule, and
not all doctoral students are able
to complete within the allotted period. Extensions for an
additional 4 to 6 months became
effectively routinized. In spite of extended timelines, the
strategy of utilizing doctoral research
projects as a primary engine of GVP research proved to be
successful, and only one Ph.D. student
supported by the GVP was unable to complete the Ph.D. degree,
due largely to family issues. In
phase II, the project produced twenty-two completed Ph.D.
projects, seven MSc. theses, one
M.Phil., and four Diploma theses. Another fourteen Ph.D.
projects of phase II are ongoing and
will be finished in early GVP phase III.
Figure 3 Completed Ph.D., M.Sc. and M.Phil. theses in phase
II
0
2
4
6
8
10
12
14
16
Number of Students
Germany Burkina
Faso
Ghana Mali Eritrea Nigeria Netherlands Ethiopia USA
Country of Origin
GLOWA Volta Students
Number of students
1.3.4. Cooperation with local stakeholders
As the project proceeds and the DSS becomes increasingly
functional, the level of stakeholder
interest and the local requirements for informed decision-making
among water experts serve as
guidelines for the development of the user platform, the
selection of the use cases, as well as for
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24
the identification of applicable knowledge from our scientific
data sets, including policy
recommendations. Several stakeholder workshops were organized in
Ghana to elaborate on such
specific local interest for decision support, as well as for the
introduction of the GLOWA Volta
project and preliminary research results to local water experts
and stakeholders. Such workshops
further enhanced the creation of a local stakeholder - GVP
network, and the establishment of
professional partnerships. For example, during the “The White
Volta Basin Pilot Workshop”
(14.07.2005 in Bolgatanga), agricultural policies in northern
Ghana and public participation in
decision making in the water sector and possible contributions
of the GVP were debated with
local stakeholders, such as representatives of MoFA, WRC, IFPRI
and local farmers. During
2005, GVP scientists also initiated informal collaboration with
the Canadian International
Development Agency (CIDA) for improved understanding of
groundwater distribution and
behaviour in the Northern and Upper East regions of Ghana, where
CIDA is extensively involved
in supporting community water supply projects (for more details
on the workshops, see section
2.4.3. on D3 sub-project).
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25
2. Achievements and research results of the sub-projects
2.1. The Atmosphere Cluster
Primary objectives of the Atmosphere Cluster are to estimate the
impacts of climatic and land
use changes on the quantity and timing of precipitation; to
quantify feedback mechanisms
between land processes and climate and to establish a capacity
for simulating a range of
historical and hypothetical climatic conditions as an important
component of the Volta Basin
DSS toolkit. Significant accomplishments during phase II
included the successful generation of
regional climate scenarios corresponding to “present” and
“future” (2030-2040) conditions,
calibration, validation and utilization of distributed physical
hydrologic models at three nested
scales, coupling of mesoscale climate with physical hydrologic
models, preliminary analysis of
factors influencing the onset of the rainy season and initiation
of web-based short-term forecasts
for West Africa.
2.1.1. Sub-project A1: Regional Climate Simulations
G. Jung, H. Kunstmann
Abstract
The Volta Basin is a semi-arid to sub-humid region in West
Africa, believed to be sensitive to broad
changes in global and regional circulation patterns. The
livelihood of much of the rural population
depends critically on rain-fed agriculture and is therefore
highly vulnerable to rainfall variability and
climate change. Coupled regional climate-hydrology simulations
are required to generate the
numerical data used in assessing a wide range of local
strategies for pre-empting, or adapting to
a changing climate. To investigate the likely impacts of
potential global climate change on regional
climate and surface- and sub-surface hydrological processes in
the region of the Volta Basin, coupled
regional climate-hydrology simulations were performed. Following
validation of the meso-scale
meteorological model MM5, the model was used to generate two
10-year reference time slices: 1991-2000
representing “present climate” and 2030-2039 representing
“future climate’. These regional climate
simulations were then coupled to the physically based,
distributed hydrological model WaSiM-ETH,
following the calibration and validation of the hydrological
model within the study region. This coupled
model ensemble provides the basis for a wide range of empirical
water management studies,
commencing in 2006 and continued in phase III.
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26
Milestones achieved:
Ø Validation of the meso-scale meteorological model MM5
Ø Calibration and validation of the distributed-parameter
physically based hydrological
model WaSiM at three nested levels within the region of the
Volta Basin
Ø Analysis of regional climate simulation results for 10 years
of present climate (1991-
2000) and 10 years of future climate (2030-2039)
Ø Performance and analysis of coupled regional climate -
hydrology simulations
Ø Preliminary statistical analysis of regional climate change
(rainfall, temperature) for the
recent historical period
Ø Statistical analysis of change of surface and subsurface water
balance components
Work in progress:
Ø Coupled simulations of climate and LUC changes
Research results
The meso-scale (regional) climate model was specified and
evaluated for the rectangular region
in West Africa containing the Volta Basin. Atmospheric boundary
conditions were provided by
the General Circulation Model (GCM) ECHAM4, which was
dynamically downscaled in two
stages, from the original 2.5 resolution of ECHAM4 (“Domain 1”)
to 27 km x 27 km (“Domain
2”), and finally to 9 km x 9 km pixels within the Volta Basin
model domain (“Domain 3”). A
comparison of the ECHAM4 output, as well as the Regional Climate
Model (MM5) output
corresponding to present-day climatic conditions relative to
historical observations indicated a
wet bias over the Sahel; although simulated temperatures in
ECHAM4 corresponded well with
historical observation (1961-1990). In the regional (MM5)
climate simulations, the displacement
of the Inter-Tropical Discontinuity (ITD) to the North at the
beginning of the rainy season, as
well as its displacement Southward at the end of the rainy
season occur early relative to historical
observation. Simulated rainfall showed a negative deviation
relative to observation along the
coast, but appeared sufficiently accurate within the Volta Basin
proper.
The physically based, distributed parameter hydrological model
WaSiM-ETH was specified and
calibrated for the topography, land surface cover and climate of
the Volta Basin. The calibration
period was 1962-68, although calibration on the basis of daily
gauging records was possible only
for the hydrological year 1967/68 due to limitations in stream
gauging records. Modifications
were required to resolve evapotranspiration when running WaSiM
at a daily time step (figure 4)
and to properly account for the impacts of the Sourou depression
(figure 5), a topographically
depressed region in which extensive water storage occurs during
many rainy seasons. The
calibration run, as well as a coupled MM5-WaSiM run for 1968
indicated good performance for
different sub-catchments (figure 6).
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27
Figure 4 Calculation of evaporation in 24h time step, 3h time
step simulation and with improved
calculation with 24h time step, including an empirical
factor
Source: Jung (2006)
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28
Figure 5 Model setup: Digital elevation model, sub-catchment
boundaries, river network and location of
the Sourou depression
Source: Jung (2006)
Figure 6 Calibration run, coupled simulation and observed
discharge values for the gauges Bamboi and
Saboba, 1968
Source: Jung (2006)
The results of the coupled regional climate-hydrology
simulations suggest an annual mean
temperature increase of 1.2-1.3°C in West Africa and the Volta
Basin over the period 1991-2000
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29
to 2030-2039. This temperature change is judged to be
significant relative to inter-annual
variability. Simulated mean annual precipitation increases over
both the Sahel and the coastal
regions of West Africa above the Gulf of Guinea. Spatial
averages are 840 mm for present
conditions and 894 for future conditions, an increase of roughly
6% over the 40-year period.
However, this increase, at basin-level at least, is relatively
small given inter-annual variability
within the region. Only in the Sahel does simulated change in
mean annual precipitation exceed
simulated inter-annual variability. The magnitude of increase is
highly heterogeneous spatially,
ranging from -20 % to +50 %. A dipole pattern of rainfall
variability in the Sahel and the Guinea
Coast region was detected for June and July. An overall increase
in precipitation was found for
September, and a strong decrease for April. This decrease is of
particular significance, since April
is historically the month in which seasonal rains begin over
much of the basin, and the simulated
decrease corresponds closely to anecdotal reports of delays in
the onset of rains, and reductions in
precipitation during the early parts of the rainy season. A
partial explanation for simulated
rainfall variability was found in the dynamics of the Tropical
Easterly Jet (TEJ), the African
Easterly Jet (AEJ) and in the position of the ITD (figure
7).
Figure 7 Mean latitudal displacement (2030-2039 vs. 1991-2000)
of the Inter Tropical Discontinuity
(ITD): southward (northward) shift in April (September)
connected to a decrease (increase) in
precipitation.
Source: Jung (2006)
Simulations for the Volta Basin indicate that the decreases in
April (at the beginning of the rainy
season) are associated both with lower rainfall accumulations
and with delays in the onset of the
rainy season, averaging around nine days within the Sahel region
of the basin. In addition,
simulated inter-annual variability in the Volta Basin increases
in the early stage of the rainy
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30
season. The simulations did not show strong changes in aridity,
as evaluated using the de
Martonne aridity index, which depends primarily on annual values
of temperature and
precipitation. The behaviour of this annual index (if
subsequently validated) is mildly
encouraging; since it suggests no major disruption in climatic
suitability for agricultural
activities. However, simulated increases in the inter-annual
rainfall variability in the early stage
of the rainy season, as well as the delay in the onset of the
rainy season increases the vulnerability
of local farming activities, even if annual rainfall amounts do
not change significantly.
Figure 8 Climate change signal in precipitation (prec), direct
runoff (qdir), interflow (qifl), total runoff
(qtot) and evapotranspiration (etr), 2030-2039 vs. 1991-2000,
left: percentage change, right: absolute
change
Using the MM5-WaSiM-ETH model ensemble, simulated changes in the
hydrologic budget can
be disaggregated into changes in precipitation (prec),
evapotranspiration (etr), direct runoff (qdir)
and inter-flow (qifl). Over the entire basin, simulated
increases in precipitation are to some
degree offset by increases in actual evapotranspiration, which
largely reflect increases in potential
ET via temperature effects. Overall (direct + interflow) runoff
is still projected to increase,
although by a smaller amount in percentage terms than either
rainfall or actual
evaoptranspiration. No significant changes in discharge follow
the precipitation decrease at the
onset of the rainy season. (figure 8).
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31
Figure 9 Monthly signal-to-noise ratio of precipitation (prec),
total runoff (qtot) and evapotranspiration
(etr)
Source: Jung (2006)
To summarize the results of integrated climate-hydrology
simulations at full basin scale over the
period 1991-2000 to 2030-2039, temperatures will increase, hence
potential evapotranspiration
will increase. Precipitation will increase modestly (subject to
a high level of spatial variability)
and thus actual evapotranspiration will increase due to
simultaneously increasing “supply” and
“demand” trends. Total runoff will increase only modestly,
composed of increases in surface
runoff overlaid on small decreases in inter-flow. However, with
respect to signal-to-noise ratios
(trends relative to inter-annual variability), only the
temperature trend clearly exceeds interannual
“noise”. The climate change signals of most of the other
reference climatic and hydrological
variables lie essentially within the range of inter-annual
variability (figure 9). This largely reflects
the modest increases in simulated precipitation. The future
impacts on water resources; and on
specific activities including rain-fed agriculture, are
therefore somewhat ambiguous, and will
likely depend on the actions (or inactions) taken in response to
a changing environment. Higher
rainfall is in general favourable for rainfed agriculture, but
increasing interannual variability and
delays in the onset of seasonal rains are not. Together with
increasing temperatures (the single
unambiguous trend) these trends suggest an increased
justification for, and a likely increase in the
coverage of supplemental irrigation as a likely response.
Moreover, simultaneous to projected
changes in climatic and hydrologic conditions, demographic and
economic trends will invariably
work to increase the demand for water resources while doing
little to influence overall supply.
These factors are likely to exacerbate water stress and reduce
water availability within the basin,
manifesting over the same time frame as the climatic processes,
and the relative magnitudes of
these impacts cannot be assumed.
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32
2.1.2. Sub-project A2: Hydro-Meteorological Monitoring
System
J. Friesen, J. Hendrickx; H. Kunstmann, A. Moene, D.
Schüttemeyer, S. Wagner
Abstract
The primary challenge for the development and testing of the
Hydro-Meteorological Monitoring
System (HMMS) is to develop systems and protocols by which real-
or near-real-time data can be
assimilated into the coupled model framework, so that e.g.,
predictions of river discharge and
reservoir storage can be updated with sufficient timeliness to
improve water management
decisions; or forecasts of the onset of seasonal rains can be
improved using updated climatic
indicators. Substantial progress was made within sub-project A2
toward an operational HMMS,
although much challenging work lies ahead in phase III. A
fundamental problem is data itself.
Although many types of climatological and hydrological data may
soon be available in near-real
time via a range of satellite remote sensing products, at
present most RS-based data must be
calibrated and/or validated before it can be utilized
successfully. One possibility for dealing with
this problem is to provide the required input data with a
meteorological model, and using
simulated climatic data to drive a coupled hydrological model.
We investigated the extent to
which meteorological models are able to provide the required
meteorological fields with
sufficient accuracy for predictive hydrological modelling. In
this study, the meso-scale
meteorological model MM5 and the distributed parameter water
balance simulation model
WaSiM were used in the setting of the White Volta (Nakambe)
Catchment. Prior to coupling, both
the meteorological and hydrological models were calibrated and
validated separately. In
addition to missing meteorological data, gridded information on
land surface properties (albedo,
LAI, etc.) is also difficult to obtain, although it is an
essential input to distributed hydrological
models. Satellite remote sensing can provide global, spatially
detailed information on land
surface properties, particularly valuable in areas such as the
Volta Basin where such information
is otherwise difficult to obtain.
Milestones achieved:
Ø Operational web-based climatic forecasts for the Volta Basin
region
Ø Installation and operationalization of real-time river
discharge measurements using
HydroArgos
Ø Successful testing of the Makkink-Vegetative Fraction method
of wide-area, remotely
sensed surface energy flux estimation
Ø Successful testing of the Surface Energy Balance Algorithm
(SEBAL) method of wide-
area, remotely sensed surface energy flux estimation using
LANDSAT and MODIS
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33
imagery
Work in progress:
Ø Energy flux estimates using Large Aperture Scintillometer
network; comparison with RS-
based methods and MM5 simulation
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34
Research results
To address the limited availability of meteorological and
hydrological data, an expanded
observational network for precipitation and surface runoff was
installed in the Upper East Region
in Ghana in 2004 (see section 1.3.1.) which provides
supplemental data for the calibration and
validation of the coupled meteorological and hydrological models
for the White Volta tributary.
The measurement campaign was conducted in close collaboration
with Ghanaian Hydrological
Services Division (HSD). The distributed parameter hydrological
model WaSiM-ETH was
specified for the White Volta catchment. Results of the
hydrological simulations driven by
measured values of the required meteorological inputs indicate
that for the calibration period
1968 (see figure 10) and validation period 1961-1967 (see figure
11) the overall discharge
hydrograph could be simulated quite satisfactorily, particularly
given the large catchment size
and available data basis.
Figure 10 Measured (blue) and simulated (green) discharge [m³/s]
and precipitation (black) in [mm] for
the gauges Nakong and Nawuni for the calibration period 1968
Source: Wagner et al. (2006)
Figure 11 Simulated (green) vs. measured (blue) discharge [m³/s]
at Pwalugu and Nawuni for the
validation period 1961-67.
Source: Wagner et al. (2006)
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35
The year 2004 was chosen as the time period for the coupled
meteorological- hydrological
simulations. Figure 13 shows one output of the MM5
meteorological simulations, specifically the
spatial distribution of annual precipitation [mm] for Domain 3
(9 x 9 km²) containing the Volta
Basin. The precipitation distribution shows a strong North-South
gradient with values of less than
300 mm in the North and over 1800 mm in the South. The scatter
plot in figure 12 provides a
comparison between the available observed and simulated monthly
precipitation sums. The
results of the coupled meteorological- hydrological simulations
(see figure 13) demonstrate that
the model ensemble is able to simulate the behaviour of the
discharge hydrographs satisfactorily.
Figure 12 Simulated annual precipitation sum for 2004 (Domain 3)
for the Volta Basin
Source: Wagner et al. (2006)
Figure 13 Comparison of simulated and measured monthly
precipitation sums in 2004 for gauging stations
(Kaburi, Kpasenkpe, Pwalugu, Babile, Zuarungu) in Ghana
Source: Wagner et al. (2006)
2 0 0 4 m i t F D D A
0
1 0 0
2 0 0
3 0 0
0 1 0 0 2 0 0 3 0 0
S i m u l a t e d [ m m ]
Ob
se
rv
ed
[
mm
]
K a b
K p a
P w a
B a b
Z u a
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36
In physical hydrological simulations, the land surface
parameters albedo and leaf area index
(LAI) are influential. To evaluate the utility of
satellite-derived gridded land surface data in
hydrological modelling, composites of albedo (16 days) and LAI
(8 days) grids for 2002 were
analyzed, aggregated and imported into the hydrological model.
The preliminary results indicate
that the hydrological simulations are relatively insensitive to
quarterly changes in the albedo
grids. However, the assimilation of seasonally-dependant LAI, as
shown in figure 15, has a
pronounced effect on all water balance variables, such as the
spatial distribution of annual
evapotranspiration (figure 16). Particularly within the southern
regions of the model domain,
higher annual evapotranspiration sums were calculated using
MODIS-derived LAI-grids.
Figure 14 Coupled simulated (green) and measured (blue)
discharge [m³/s] and precipitation (black) in
[mm] for three gauging stations along the White Volta in Ghana
(Kaburi, Pwalugu and Nawuni)
Source: Wagner et al. (2006)
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37
Figure 15 Leaf Area Index (LAI) for the White Volta catchment:
mean MODIS LAI-grids for 2002
January – March April – June July – September October -
December
Source: A2 sub-project
Figure 16 Annual evapotranspiration map simulated with a)
standard literature and b) MODIS derived
LAI- grids; c) difference of annual evapotranspiration: a) -
b)
Source: A2 sub-project
The objective of developing the coupled climate-hydrology model
complex is to develop the
capacity to simulate the terrestrial water balance in the
absence of directly measured
meteorological data. As it is likely that the situation with
regard to measured data is unlikely to
improve significantly in the near future, the model ensemble
will provide and important tool for
contemporary estimation of the spatial and temporal changes of
water balance variables, which
will in turn provide important information supporting water
resources management in the Volta
Basin. While the model complex described here is currently
operational, the various implications
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38
of using satellite-derived land surface parameters (here:
albedo, LAI) on coupled hydrological
simulations continues to be an area of active investigation.
Research results: Remote Sensing of Latent and Sensible Heat
Fluxes
In order to provide near-real-time forecasts of climate and
hydrology for the basin, information
concerning the accuracy of the wide-area surface energy balance
is also essential. As with
climatic and hydrological variables, direct measurement networks
are sparse in West Africa, and
remote sensing (RS) methods are required for broad scale
validation of water balance variables.
During phase II, we evaluated and applied a range of methods to
estimate latent and sensible heat
fluxes over the Volta Basin. These included (1) Large Aperture
Scintillometer (LAS), (2)
Makkink-Vegetative Fraction method and (3) the surface energy
balance algorithm (SEBAL).
The meso-scale climatic simulations generated by MM5 provide an
additional point of reference.
Research results: Scintillation
Scintillation refers generally to perceived changes in the
brightness of an object when viewed
through an atmosphere. Common examples include the twinkling of
stars, or “heat waves”
arising from asphalt or other surfaces heated by the sun. The
phenomenon occurs due to changes
in the refractive index of the air caused by fluctuations in
heat, humidity and pressure. Large
Aperture Scintillomenters (LAS) are instruments that measure the
turbulent intensity of the
refractive index fluctuations across an air mass on the basis of
fluctuations in the intensity of a
light beam passing through that air mass. Under suitable
conditions, sensible and latent heat
fluxes can be derived from these fluctuations if net surface
radiation, air temperature and vapour
pressure are known. In practice, the light beam is generated by
a light source placed from 0.5 to 5
km from the light receiver. The transect, or path of the
scintillometer beam, is chosen to be
representative of the zone (pixel, region) for which the heat
flux estimate is desired, with respect
to topography, vegetative cover (LAI), surface roughness, albedo
and related parameters. During
phase II, LAS pairs were installed and operated at the GVP field
sites lying along a south-north
transect in Ghana, at Ejura, Tamale and Navrongo, respectively;
each installation in proximity to
the micro-meteorological stations that were intended to provide
the required surface radiation and
climate data.
Research conducted during phase I indicated that LAS were
capable of providing ground-truth
validation of RS-based estimates of latent and sensible heat
fluxes. In these experiments, satellite-
derived H was computed from NOAA-AVHRR images for December 2001
under favourable
cloud conditions, and compared to LAS and MM5 simulation
results. An algorithm for
estimating actual evaporation from Meteosat-derived global
radiation and temperature and
MODIS-derived vegetation fraction was also applied and validated
for a complete drying-up
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39
period. The LAS-derived evapotranspiration (ET) and sensible
heat fluxes performed well with
respect to energy-balance closure, and in comparison with
eddy-covariance data, suggesting that
they can be used with confidence for validation of RS-derived
fluxes. Satellite derived H showed
good agreement with LAS data, with RMSE of 39 W/m² for the
Tamale site. Satellite derived H
showed substantial disagreement relative to MM5 due to
overestimation of latent heat flux in
MM5, with RMSE of 167 W/m² for the Tamale site. Uncertainty
analysis of satellite derived H
indicated relative uncertainties of 21 % for the Tamale site and
32 % for the Ejura site.
Uncertainty in LAS data is much lower, at 8 % for Tamale and 7 %
for Ejura. The Meteosat-
based procedure provides hourly estimates of actual ET under
both cloudless and cloudy
conditions. The agreement with ground-based data is generally
good, but in wet periods (higher
fluxes) the RS-based algorithm tends to underestimate real ET.
Long term bias of this method is
less than 0.5 mm/day. A significant advantage to this method is
that it can be used to monitor ET
at seasonal time scale, irrespective of cloud conditions.
Based on these favourable preliminary results, the intention was
to use the South-North transect
of LAS installations during phase II to provide near-real-time
validation for MM5-generated
energy flux estimates and ultimately, to update and correct
these fields as a component of the
HMMS. This objective has not realized to date, due primarily to
poor performance of the data-
logging modules and procedures associated with both LAS units
and the accompanying micro-
meteo stations. A number of problems were observed and
documented, including (i) data format
errors; (ii) data over-write errors; (iii) date and time errors;
(iv) implausible and/or out-of-range
values for key climatic variables; and (v) power supply failures
resulting in data loss. The
presence of multiple failure modes creates a serious problem,
since the effective use of LAS data
requires the simultaneous availability of the corresponding
radiation and climatic data. Thus, if
any of the required data series are missing, corrupt or suspect
on a given day, the LAS data itself
may be unusable. In addition, in order for the LAS-derived flux
estimates to be useful in
validating RS-based flux estimates, atmospheric conditions
(particularly cloud cover) must also
be favourable for RS interpretation. An assessment of the LAS
and supporting data collected at
the three Ghanaian sites during phase II (Moene, et al., 2006)
concluded that for each site, there
were only a limited number of dates (weeks in total) that met
the multiple criteria required for
successful interpretation and application of LAS results. As a
consequence, the objective of
utilizing the LAS transects for calibration and validation of
MM5 flux fields was not completed
during phase II. On balance, an extensive body of information
was obtained that should permit
the improved operation of the LAS network through phase III of
GVP. In addition, a number of
dates were identified for which all component data required for
successful energy flux estimation
and RS validation are present, inclusive of RS imagery. This
analysis is being conducted
currently by Dr. Jan Hendrickx.
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40
Research results: Optimal estimation of evapotranspiration at
the basin scale using
Makkink-Vegetation Fraction
Wageningen MAQ’s contribution to the development of the
Hydro-meteorological Monitoring
System (HMMS) is the development of algorithms to estimate
actual evapotranspiration at
regional scale using Meteosat imagery. The baseline algorithm
utilizes a combination of the
Makkink formula for reference evapotranspiration (ETref) and the
vegetation fraction (VF) in
order to scale down ETref to actual ET as: ET = VF ETref. The
underlying assumption is that
vegetation cover adjusts to the extent of available water; and
that this existing vegetation then
transpires at its maximum rate.
Figure 17 Different components of actual evapo-transpiration,
example for November 15, 2002. Incoming
solar radiation (top left), vegetation fraction (top right),
reference temperature (bottom left) and resulting
actual evapotranspiration (incl. evaporation from interception
(bottom right).
A first test of this algorithm was performed in 2005 for two
sites in Ghana (Schüttemeyer et al.,
2006). Although results for the baseline method are promising, a
number of limitations have also
been identified. Errors can occur on a limited number of days
due to ignoring bare-soil
evaporation and/or evaporation from canopy interception; and
consistent errors can occur on all
days due to the assumption that the crop factor equals one.
Moreover, the Makkink method does
not incorporate the effects of wind and atmospheric humidity on
evapotranspiration, and the
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41
baseline method is not able to account for the reduction in
evapotranspiration due to water stress
(in the short term, this stress has not yet resulted in a
decrease of vegetation fraction).
Based on these findings, the baseline algorithm has been
extended to incorporate the effects of
interception and bare soil evaporation, as well as the
variability of the crop factor. The inclusion
of rainfall information from climate models has also been
considered, but a validation study of
ECMWF re-analysis rainfall revealed that the timing, amount and
location of the modelled
rainfall were problematic. The same appears to hold for the MM5
simulated rainfall. Therefore
near real-time rainfall information obtained from remote sensing
data (RFE 2.0, a product of
NOAA) has been included to serve as input for the
parameterization of evaporation from
interception and bare soil.
The second modification involved the inclusion of different crop
factors for high and low
vegetation, as well as open water, based on the USGS land cover
database. The enhanced
baseline algorithm can now produce maps of daily
evapotranspiration (based on hourly global
radiation and temperature data) for the entire Volta Basin. The
resulting evapotranspiration map
for one day in November 2002 is shown in Figure 18. Incoming
solar radiation and near-surface
temperature are needed to determine ETref, and the vegetation
fraction is needed to scale ETref.
The resulting ET map reveals the signature of the North-South
gradient of vegetation fraction,
and the (small, on this particular day) variation in incoming
solar radiation. In the South-West, a
spot with high ET can also be identified which results from
evaporation of the previous day’s
intercepted rainfall. This spot of enhanced ET is also visible
via enhanced cloudiness at that
location in the global radiation image.
In order to study the feasibility of including atmospheric
information from a climate model , we
performed extensive experiments with a single-column boundary
layer model. This demonstrated
that in principle, useful information is available in the
boundary layer air temperature, which can
be combined with a remotely sensed surface temperature to
quantify water stress. However, this
simulated air temperature proved to be very sensitive to the
amount of soil moisture. Given the
difficulties of numerical weather models in predicting the
correct amount of rainfall at a specific
location (particularly in the West-African climate), it is
likely that the boundary layer
temperature, as well as its diurnal cycle, will be incorrect in
the model as well. Consequently, the
exploration of including model data in the remote sensing
algorithm was not pursued further.
The algorithm described above has been developed using products
for incoming solar radiation
and near-surface temperature provided by DLR-Stuttgart, in
combination with VF derived from
MODIS vegetation products, and RFE 2.0 from NOAA. However, data
from other sources can be
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42
utilized as well. Specifically, incoming solar radiation and
vegetation fraction products freely
available from the Land Surface Analysis Satellite Application
Facility (LSA-SAF) could be
utilized. The near-surface temperature data used in Makkink ET
(where errors of a few degrees
do not matter) could be replaced by data from an atmospheric
model (MM5) or freely available
NCEP forecasts.
Research results: SEBAL: Mapping Energy Balance Fluxes and Root
Zone Soil Moisture in
the White Volta Basin using Optical Imagery
The overall goal of this activity is to demonstrate that
important background information on the
components of the energy balance and soil moisture can be
obtained from optical imagery even in
data-scarce areas such as the Volta, without relying on ground
measurements. We evaluated the
method for the heterogeneous arid lands of the White Volta Basin
in Ghana, which represents a
challenging environment. The specific objectives were (i) to
test SEBALNM with LANDSAT
imagery in the White Volta Basin of Ghana without using any
ground measurements and (ii) to
compare evapotranspiration maps generated from LANDSAT images
with spatial resolution 60
m to those generated from MODIS images with spatial resolution
1,000 m.
Model Approach: SEBAL is a remote sensing flux algorit
