INTERNATIONAL
SECRETARIAT
“Global Change SysTem for Analysis, Research and Training”
University of Kinshasa
Agronomy Sciences Department
Satellite-derived Rainfall Estimates (TRMM products) used for
Hydrological Predictions of the Congo River flow: Overview
and Preliminary Results
By
Yolande Munzimi
October 2008
"This material is based upon work supported by the National Science Foundation under Grant No. (NSF grant number)."
TABLE OF CONTENTS
PROJECT TITLE............................................................................................................................ 3
ABSTRACT.................................................................................................................................... 3
PROJECT INFORMATION........................................................................................................... 3
INTRODUCTION .......................................................................................................................... 5
Background Information ............................................................................................................. 5
Scientific significance ................................................................................................................. 6
Congo Basin – Description ......................................................................................................... 7
Objectives ................................................................................................................................... 7
Methodology .............................................................................................................................. 8
ACTIVITIES CONDUCTED ......................................................................................................... 9
1.
Research activities ............................................................................................................. 9
•
Formation in Remote Sensing applied to Water Resources ............................................. 9
•
Remote Sensed Data compilation .................................................................................... 9
•
Hydrological Modeling .................................................................................................. 10
•
Post-Processing Analysis and Mapping ......................................................................... 10
2.
1.
Basins Delineation .................................................................................................. 10
2.
Validation and Calibration ...................................................................................... 10
Publicize the research output......................................................................................... 11
OUTCOMES AND PRODUCTS ................................................................................................. 12
(i)
Operational validation and calibration with RVF concurrent data .......................... 17
(ii)
Historical validation with GRDC data ....................................................................... 21
(iii)
Discussion ...................................................................................................................... 24
(iv)
Research outcome publicized ...................................................................................... 26
(v)
Photos Gallery .............................................................................................................. 27
CONCLUSIONS........................................................................................................................... 29
FUTURE DIRECTIONS .............................................................................................................. 29
APPENDIX ................................................................................................................................... 30
2
PROJECT TITLE
Satellite-derived Rainfall Estimates (TRMM products) used for Hydrological Predictions of the
Congo River flow: Overview and Preliminary Results
ABSTRACT
The African continent is one of the most vulnerable to climate change (IPCC, 2007).
Regrettably, climate change will aggravate high water stress already experienced in some
regions. To arid and semi-arid countries facing water scarcity, the Congo River is considered to
be a potential asset for supplying water. However, very little of the River hydrology is known. A
deepened knowledge will provide opportunities to not only understand the dynamic balance of
the Congo River system but also to help decision-makers who work on issues related to water
management. The present study intended to contribute to the matter. However, the unavailability
of ground-based hydrometeorological data makes this research difficult.
Global and regional satellite-based observations facilitate terrestrial surface waters studies. To
comprehend the impact of climate change on Congo water systems, this study used the Tropical
Rainfall Measuring Mission (TRMM) precipitation data combined with other time series and
terrestrial data to predict the discharge of the Congo River and its tributaries. The Geospatial
Streamflow Model was the hydrologic model used to that end. The satisfying preliminary results
were the door to publicize the research methodology among African scientists involved in Congo
water studies and to trigger local capacity building in the field.
PROJECT INFORMATION
This project is the emanation of several years of thinking on how to help solving water stress
issue in Africa. While the Congo waters seem to be a plausible response to it by being a potential
asset for supplying water to arid areas, very little of the basin’s hydrology is known. Remote
Sensing applied to water resources combined with hydrological modeling appears to deliver
breakthrough capabilities not only in understanding the dynamic balance of the river system but
also in predicting and projecting its future dynamic face to global environmental change.
Though, the significance of this breakthrough has not yet been attested for the Congo Basin in
particular, this study explores the adequacy of satellite-derived Rainfall Estimates (TRMM
products) for stream flow modeling of the Congo.
Apart from its research study component, the project includes a no less significant part related to
meeting held in Africa to publicize the research methodology and tools among African
researchers and others scientists who work on issues related to water use and water regulation of
the Congo Basin. In a long term perspective, this project will likely be a steppingstone to
contribute to their capacity building through a transfer of technology by bringing Remote
Sensing applied to Congo water resources into general use.
3
The realization of this one-year project would not have been possible without the financial
support of the Global Change SysTem for Analysis, Research and Training (START/PACOM)
and the US National Science Foundation/ US Climate Change Science Program (NSF/USCCSP).
Indeed, the project proposal had been selected for a START/USCCSP Award of US$13,000
approved as a sub-award from NSF/USCCSP. It has been handled as a one-year visiting
fellowship going from October 9, 2007 to October 8, 2008.
The two phases of the project [research phase and publicizing phase] were spent in two different
locations. The research activities were run within the US in different Remote Sensing
laboratories. The second phase included tasks to publicize the research methodology and to
implement collaboration and exchange among actors involved in the management of water
resources within the Congo Basin. It was realized by means of workshops and other meetings
held in Africa.
A key partnering experience in the research team was essential for the project success. Beginning
with only two scientific contributors involved into the project, the partnership extended to three
institutions, ending up with not only the Faculty of Agronomy of the University of Kinshasa
(Democratic Republic of the Congo) and the Department of Geography of the University of
Maryland (USA), but also with the Geographic Information Science Center of Excellence
(GIScCE) of the South Dakota State University.
These three institutions committed to the project in line with some of their joined programs or
partners. They are the Observatoire Satéllitale des Forêts d’Afrique Centrale (OSFAC), a partner
of the Faculty of Agronomy of the University of Kinshasa (UNIKIN), the Central African
Regional Program for the Environment (CARPE) , a program sheltered by the Department of
Geography of the University of Maryland, and finally the Geographic Information Science
Center of Excellence (GIScCE) as a joint collaboration between South Dakota State University
(SDSU) and the United States Geological Survey's National Center for Earth Resources
Observation and Sciences (EROS). In compliance with the terms of their collaboration, UNEP/
EROS – USGS secured for the Principal Investigator, a four months living at South Dakota with
some additional funds.
The extended partnership increases the potential of the Research Team to execute the research
project. While their knowledge in GIS and in Remote Sensing for Land use and Land cover was
evident, more expertise was needed on Hydrological Modeling and on Remote Sensing applied
on water resources. This supplementary expertise was made available through the institutional
support brought by the SDSU GIScE and the UNEP/EROS – USGS. Their support contributed to
develop the Principal Investigator (PI) skills to operate and drive basin hydrology models, from
setup to satellite – derived climatic drivers’ ingestion.
That being said, we would like to express our gratitude to the START secretariat for the unique
opportunity given to us to accomplish a regional-scale analysis regarding global change impact
on Congo Basin water resources.
We thankfully acknowledge the support provided by the US National Science Foundation / US
Climate Change Science Program (NSF/USCCSP) as well as the contribution made by
UNEP/EROS – USGS. We are especially grateful to CARPE/UMD Department of Geography
and OSFAC for their assistance.
We also extend our gratitude and recognition to individuals who proved to be providential for the
project: Dr Christopher Justice and Diane Davies of CARPE/UMD for instigating the project
redaction and submission; Dr Raymond Lumbuenamo of UNIKIN for mentoring us in the
4
pursuit of our goals; Dr Matthew Hansen of the SDSU GIScE for allowing us to fly to South
Dakota and to benefit from a GIScE Remote Sensing class as well as from the expertise of the
EROS Faculties; Dr Gabriel Senay an EROS Faculty at GIScE and a USGS – EROS contractor,
for his enthusiast teachings; and finally, Dr Kwabena Asante, an EROS Faculty at GIScE and a
USGS – EROS contractor. Dr Asante has been willing to share with us his deep knowledge, his
expertise and his passion for surface water hydrology and hydrological modeling in Africa.
Our research interests have always been motivated by the issue of poor management of water
resources in Africa. This can be seen from our former water resources related B.Sc. Thesis, our
Master’s and training programs and our past professional activities. The present project was
handled with the hope that the research will set the stage for us to proceed with PhD studies. It
has thus provided a stepping stone to pursue a PhD in the same area at the GIScE Center of
South Dakota State University where we currently holds a graduate research assistantship
position. Now on leave to study, Yolande (PI) is still affiliated to the Agronomy Department of
the University of Kinshasa where she holds a teaching assistantship position.
Yolande can be reach at [email protected] , [email protected] or (001)
240 429 2732. Her mailing address is: 1021 Medary Ave, Wecota 506B, GIS Center of
Excellence, South Dakota State University, Brookings, SD 57007
INTRODUCTION
Background Information
In recent years, the stress of water resources deficiency has plagued the global village with no
exception for African countries. In addition, drought has been prevalent, exacerbated by the
process of desertification resulting from climate change. Coupled with the mentioned stress is the
rapid population growth which led to an increase in the demand for water resources. As a result,
water deficit has increased in both the Northern and Southern regions of the continent. This has
the potential to trigger armed conflicts among population living in these regions if the demand is
not met.
With its vast water reserves, the Congo Basin Water is considered to be a potential asset for
supplying water and is now pressured to meet water demand from arid and semi-arid countries in
order to address the problem of water scarcity. A series of proposals to export Congo River’s
water from the Democratic Republic of the Congo to both Northern and Southern water-deficient
areas has been explored. However, the economics of such an undertaking as well as the
environmental impacts may prove difficult to resolve, especially as the Congo River itself
currently faces the adverse effects of climate change. The consideration of climate change that
pushes the Sahara and Sahel deserts (North) as well as the Kalahari Desert (South), towards the
equator line, suggests a progressive strangulation of the Congo basin. This is evident in the
North-West part of the Basin where water levels within the Ubangi watershed, one of the
Congo’s subasins, have decreased significantly.
5
Scientific significance
This project is an effort to assess regional scale aspects of climate changes in Africa that may
have consequences on Congo Basin water systems. By its modest contribution, the project does
not pretend solving the whole issue of water stress in Africa, but it rather attempts to deepen the
existing knowledge of the Congo Basin hydrology taken integrally.
Deepening the knowledge of the Congo Basin Hydrology is totally relevant. However, there is a
shortage of ground-based hydrometeorological data that are necessary to study hydrological
processes. Streamflow estimation seems to be the only way around the lack of ground data
problem.
There is a need for global as well as regional satellite-based observations for terrestrial surface
waters studies to comprehend the impact of climate change on water systems, and the Congo
water system has brought the general attention in this regards.
Several researches have looked at the relationship between climate change and water levels
within river systems. Studies have related past and present precipitations to climate changes,
asserting that variability in precipitations are due to climate change effects. Other studies have
focused on changes in precipitation seasonality and have been able to establish a linkage between
seasonal precipitation and rivers discharges. The strong correlation between precipitations and
surface water in general, suggests a transitive relationship between surface water (river
discharge) and climate change. This has also motivated detailed research on the effect of
simulated climate change on the hydrology of major river basins.
Researchers in this area have been using extensively the Satellite derived precipitation from the
Tropical Rainfall Measuring Mission (TRMM) precipitation data of the U.S. National
Aeronautics and Space Administration (NASA). When ingested in hydrological model in
combination with other temporal and terrestrial remote sensed data, satellite-derived Rainfall
Estimates in general and TRMM products in partical, have proved to be adequate for stream flow
modeling (Artan et al, 2007). NASA TRMM estimates of precipitation, river gauge observations
and a regional hydrological model can be used to predict the discharge of the Congo River.
While, for this preliminary study, only remote sensed data are used to drive the model, station
data essentially serve for comparison with the estimated results. Complete calibration was not the
focus of this study. Any calibrated results provided in this study were just partial.
The USGS Geospatial Streamflow Model (GeoSFM) is the hydrological model that has been
used to generate streamflow estimates. It is a hydrologic modeling system run operationally to
identify and map wide-area stream flow anomalies. To monitor wide area hydrologic events, the
GeoSFM integrates a geographical information system (GIS) and dynamically linked libraries
(DLLs). In this study, NASA TRMM data are the primary input fluxes. The TRMM 3B42
product used is generated by merging three-hourly rainfall rates from TRMM’s space-borne
radar with all available microwave and infrared imagery. Daily accumulations of this dataset are
processed at USGS EROS, reformatted into GIS images which can readily be disseminated to
users. The NASA TRMM product (version 3B42) has complete spatial cover of Africa with a
spatial resolution of 0.25o by 0.25o. The archive of daily grids used in this study covers from the
beginning of 1998 to through the end of 2006 (Artan et al, 2007).
Global Daily Reference Evapotranspiration (GDET) dataset produced by USGS EROS are also
used in GeoSFM. GDET (a derived product of NOAA’s Global Data Assimilation System
6
(GDAS)) has full spatial coverage of Africa with a spatial resolution of 1o by 1o (Artan et al,
2007).
The GeoSFM processes the TRMM and evapotranspiration time series data to get rainfall and
streamflow estimates. Though NASA TRMM products are the major time series input used to
run the model, other terrestrial data such as land cover, soil, and elevation are also processed into
the model to balance precipitation and runoff estimates.
Congo Basin – Description
The Congo River drains areas from height African countries: Democratic Republic of the Congo,
Republic of Congo, Central African Republic, Angola, Zambia, Cameroon, Burundi and
Tanzania. It is a 3,690,750 km2 drainage area located in Central Africa (WWF, online resources).
The Basin extends from latitude 09°15’ N in the Central African Republic to 13°28’ S in Angola
and Zambia, and from longitude 31°10’ E to the Great African Lakes in the East African Rift to
11°18’ E on the Atlantic Ocean. The waters of the Congo River originate in the highlands and
mountains of the East Africa Rift, as well as Lake Tanganyika and Lake Mweru (Shahin, 2002)
and flows north towards Boyoma Falls. The Upper Congo (Lualaba River) ends at Boyoma Falls
where begins the Middle Congo. After the town of Kisangani, the Congo River turns west and
southwest following a great curve. The Middle Congo draws to its end at Malebo Pool, where the
capital cities of Kinshasa (DRC) and Brazzaville (Congo-Brazzaville) are located. There, the
river expands some 24 km across and the waters slow down considerably. After passing through
the Livingstone Falls, the River reaches the Atlantic Ocean between Banana Point (DRC) and
Sharks Point (Angola) from where it continues its course underwater.
The heart of the Basin area (the major part) enjoys an equatorial climate with no dry season
[whereas the rest benefit from a tropical climate]. The rainfall in this part varies between 1,500
and 2,000 mm a year. Flowing mainly in this equatorial area, the Congo is a more constant River
compared to other African rivers. However it shows some interannual variability with July and
August being the months of low flow whereas December is the month of high flow. The
tributaries from the South, such as the Kasai, have two periods of low water and two of high in
the year, but the tributaries from the north, such as the Ubangi, have a single maximum.
Consequently, the regime of the main river varies from place to place (Shahin, 2002).
Objectives
Originally, the work proposed was to identify and evaluate potential large scale hydrological
models, to drive them with TRMM precipitation as principal fluxes data and later, to train
institutions in their use.
However, as much as, the main goal of the project was to build local capacity of scientists and
actors of the water sector in the region with respect to the use of Satellite-based products
(TRMM) and hydrological modeling, after review, the objectives, as proposed, were considered
too broad to fulfill in 8 months.
7
The training program has been then replaced by a program to publicize the research outcome.
The aim became oriented to primarily inform and focus the attention of regional stakeholders
about the adequacy of exploring new venues for Congo Basin waters monitoring using remote
sensing. This work could reasonably be achieved in 8 months.
As for the modeling part of the research study, the sole large scale modeling system that has been
found adequate and that has been exploited is the GeoSFM. Indeed, GeoSFM has been selected
for three major reasons:
• GeoSFM can handle large scale hydrological modeling for assessment of large
watersheds such as the Congo
• GeoSFM has capabilities to be run for runoff estimation in absence or in presence of poor
quality ground information (difficult to access in real time in the Congo), such as rainfall
data, evaporation data and or streamflow data for model set up and calibration. GeoSFM
solely uses existing remotely sensed data. The reliance on remotely sensed data is the
most important advantage of GeoSFM.
• The third advantage of GeoSFM is the fact that it is fully integrated into a GIS system
which makes terrain analysis possible. This also allows change of input data to be
ingested in the model as needed.
If data availability is a problem or the ability to change input datasets to reflect changing land
surface conditions is desired, then GeoSFM is the way to go (Asante, online discussion).
After careful consideration of the preceding, it was decided that the project specifically aims to:
1. Evaluate the usefulness, the efficiency and the performance of a regional hydrological
modeling system, the Geospatial Streamflow Model (GeoSFM), in the Congo Basin
2. Initiate a calibration exercise of the model since prior experience with runoff estimation
using satellite precipitation suggests that the resulting volumes cannot be used directly
without employing bias correction or flow calibration techniques (Asante et al, 2007)
3. Publicize the research project methodology and outcomes among researchers and other
professional of the water sector to focus their attention and motivate their interest and their
participation in the use of satellite-derived products such as NASA TRMM data and
hydrological modeling [as they could be adequate planning tools for water resources
management in the Congo Basin].
Methodology
1. After running the GeoSFM model for 7 years (2001-2007), comparison between the
GeoSFM simulated streamflow hydrograph and the observed hydrograph of corresponding
stations were made to visualize their discrepancies and their agreements. Depending on
ground data availability at given locations, the comparison was made with concurrent
streamflow observations (operational validation) or between mean simulated flows and
mean historical flows at each gauging station (historical data validation). The last
comparison was made in the assumption that long term averages flows have remained
unchanged between the two periods. In parallel, that also assumed that climate did not
change. The lack of concurrent ground data for validation obliges us to proceed that way.
8
This validation process was the first step in evaluating the model usefulness, efficiency and
performance.
2. The limited existing concurrent ground data were used to initiate a calibration exercise of
the model. River gauges observation data were made available only for Kinshasa station by
the Congolese agency, Régie des Voies Fluviales (RVF). The challenge in this study is that
the magnitude of biases must be estimated and scaled in the absence of sufficient
concurrent streamflow observations
3. Organize interviews and meetings and participate in workshops held within the region to
publicize the research project methodology and outcomes among researchers and other
professional of the water sector in the Congo Basin. That paved the way not only for future
intensive training program but also, from now on, for collaboration with experts based in
the Congo region.
ACTIVITIES CONDUCTED
1. Research activities
•
Formation in Remote Sensing applied to Water Resources
During the fall semester of 2007, Yolande attended a class on Remote Sensing applied to Water
Resources offered in the Geographic Information Science Center of Excellence (GIScCE) of
South Dakota State University. The material included lectures on diverse topics related to
Hydrology and Remote sensing of: Precipitation (Rainfall through IR-Based, Microwave, Spaceborne Radar), Evapotranspiration (Thermal Data), Soil Moisture, Topography (elevation through
SRTM, Lidar), Land cover Parameters, Groundwater, Surface Water, Snow Hydrology (IRbased extents, Microwave water equivalent) and Water Quality. Enough material was covered to
skill her to operate and drive basin hydrology models, from setup to satellite – derived climatic
drivers ingestion, for the execution the project.
•
Remote Sensed Data compilation
While taking that course, the required datasets that needed to be ingested in the selected
hydrological model (GeoSFM) were compiled and preprocessed. The proximity with
EROS/USGS Data center facilitated the access to data that are processed at USGS EROS and
reformatted into GIS images which can readily be disseminated to users. The major additional
preprocessing to do were some more formatting and the reprojection of all the data into LambertAzimuthal equal area.
The data used were:
- Precipitation data : Daily accumulations of Satellite derived precipitation from the Tropical
Rainfall Measuring Mission (TRMM) precipitation data of the U.S. National Aeronautics and
Space Administration (NASA), processed by USGS EROS
- Evapotranspiration data : Global Daily Reference Evapotranspiration (GDET) dataset
produced by USGS EROS
- Land use/ Land cover data : USGS Global Land Cover data
9
-
Elevation data : USGS digital elevation model, Global Topographic Data of 30 arc seconds
(GTOPO30)
Soil data : Food and Agriculture Organization (FAO) Digital soil data
•
Hydrological Modeling
This step consists on running 5 components of the GeoSFM, to end up with the daily streamflow
estimates. Later on, a GeoSFM utility served aggregate daily streamflow to monthly streamflow
to facilitate the comparison with the historical data available.
The 5 components of the model that were run were:
1. Terrain Analysis Module: using the elevation data, this analysis divided the study area
(3,690,750 km2 of the Congo Basin) into smaller hydrologic elements i.e. subbasins and
rivers. It also established the connectivity between hydrologic elements and computed
topography-dependent parameters
2. Basin Characterization Module: using soil and land cover data, this module estimated
surface runoff parameters in subbasins, estimated flow velocity and attenuation
parameters and computed unit hydrograph response for each subbasin
3. Data Assimilation Module: using precipitation and evapotranspiration data from 2001 to
2007, this module converted satellite rainfall and evapotranspiration estimates into a
common format and creates ASCII files for water balance and flow routing models.
4. Water Balance Module: this module generated saturation excess runoff, single subsurface
reservoir for interflow and baseflow generated from deep infiltration
5. Flow routing Module: this module aggregated the runoff contributions of each subbasin
at the subbasin outlet and moved the runoff from one subbasin to the next, through the
river network to the basin outlet. Indeed, it applied unit hydrograph to excess runoff to
obtain runoff at subbasin outlet
At the end of these five steps, one of the output obtained is a streamflow textfile containing one
line of discharge values of the estimated flows in m³/sec for each subbasin at each time step i.e.
for each day of the 7 years processed (2001-2007). Other spatially explicit products such as river
network and subbasin shapefiles are also obtained.
•
Post-Processing Analysis and Mapping
1. Basins Delineation
This step consisted on the delineation and the mapping of the real scale watersheds of the major
tributaries of the Congo basin (Ubangi, Sangha, Kasai, Maringa-Lopori, Tshuapa, Lualaba,
Lower Congo, Lomami and Tanganyika lake), using the subbasin shapefile produced by the
GeoSFM.
2. Validation and Calibration
10
Using ground data information of 7 gauges stations, validation was performed for 6 of the Congo
tributaries that had their watersheds previously delineated: the Ubangi, Sangha, Kasai, Tshuapa,
Lualaba (Upper Congo) and Lower Congo.
The estimation of biases magnitude between estimated and observed flows has been achieved by
exploiting two different observations data sources: the (Régie des Voies Fluviales) RVF database
and the (Global Runoff Data Centre) GRDC database.
The only available concurrent streamflow observations (2001 to 2007) were made accessible
only for the RVF Kinshasa station. At that location it was possible to not only observe the
magnitude of biases but also to initiate a first calibration exercise. Indeed, the results indicated a
higher tendency towards underestimation of the flow discharge in that location. To adjust the
flow magnitude, the soil depth parameter has been decrease to diminish infiltration and to
increase runoff volume at that location for the period of reference. However, as much as this
calibration exercise was just a start, much more need to be processed to calibrate more accurately
the GeoSFM flow estimate. At least, this calibration attempt had the advantage to demonstrate
the possibility to adjust GeoSFM streamflow output when sufficient concurrent streamflow
observations are made available, allowing complete sensitivity analysis of the model parameters.
Where concurrent streamflow observations were absent, the magnitude of biases was estimated
using historical data of 6 gauging stations of the GRDC database.
GRDC is currently one of the rare open access databases of global streamflow data available that
provides data upon request. However, for most of the stations in study, the newest consistent
streamflow records in the database are from 1983 which means that the records predate the
TRMM mission which began in 1998. To allow for the comparison of streamflow from the two
disparate periods (around1903 to 1983 of GRDC and 2001 to 2007 of the model run), it was
assumed that long term mean flows have remained unchanged between the two periods. This
assumption allowed means flows simulated with TRMM data to be compared with means flows
of the 7 GRDC gauging stations. Indubitably, this postulation limited the primary focus of this
study, namely addressing the change in the Congo water system over time caused by change in
rainfall regime. Indeed, assuming that the long term mean flows remained unchanged between
the two disparate periods annulled the chance of detection of change caused by global
environmental change at a regional-scale of analysis. This comparison through historical
validation rather gave an idea on the overall performance of the model without going deep into
its capability of capturing change in Congo water regime over a long time period.
2. Publicize the research output
The culmination of this project was about publicizing the research output and methodology
among Congo Basin Experts active in the water sector. The intent was to focus the attention of
regional stakeholders about the adequacy of exploring new venues for Congo Basin waters
monitoring using remote sensing particularly in a context where ground data availability is a
problem and where land surface conditions are not well known.
During almost a month and half, diverse meetings were organized at Kinshasa (Democratic
Republic of the Congo), Brazzaville (Republic of Congo) and Bangui (Central African
Republic). Due to time constraints of the special-interest groups, it was not possible to develop a
11
detailed agenda for a grand assembly. Instead, interacting with different special-interest groups at
their most convenient time on different occasions has proven to be a more judicious and flexible
approach. The presentation on the research varies from one audience to another including
French or English speakers. It was adapted to the audience background, knowledge and expertise
in the matter. The presentations were given either in French or English through interviews,
workshops, lectures or open discussion.
OUTCOMES AND PRODUCTS
The results of the application of GeoSFM to the Congo basin are included in this section. It
comprises maps and graphs and their scientific interpretation. Graphs show the discharge
volumes reported in the streamflow textfile output that has been produced, and compared with
observed discharges.
The section also contains pictures of some of the talk’s events we had with different audiences.
Figure 1: GeoSFM subbasins
and river network
Congo River basin subdivided
into subbasins and reaches.
The GeoSFM subbasins
shapefile (basply.shp) contains
369 subbasins with a minimum
drainage area of 10,000 km² for
each. 369 river reaches were
also delineated, each about 200
km. Each river reach’s segment
corresponds to a subasin which
is the hydrologic modeling units
for GeoSFM, containing all the
hydrologic information related
to that river reach’s segment.
Indeed, the subbasins were
defined for each stream
segment. Subbasins and reaches
were delineated from the
GTOPO digital elevation
models of 1 Km resolution).
12
Figure 2: Watersheds of major Congo Basin tributaries
The watersheds of the Congo Basin tributaries are delineated from the GeoSFM subbasins.
13
Figure 3: Gauge Stations Locations and corresponding GeoSFM Subbasins
7 GRDC gauge stations identified by names are selected, as well as the 7 corresponding
GeoSFM subbasins are indentified with their GRID_ID numbers, for comparison between
observed and simulated flows
14
Figure 4: Gauge Stations Locations and corresponding GeoSFM river reaches
The 7 GRDC gauge stations are superposed to the streams network to situate the stations on the
rivers
15
Figure 5: Soil Humidity map of the Congo Basin
Distribution of soil moisture as simulated by the GeoSFM model using parameters extracted
from the terrestrial and temporal data
16
(i)
Operational validation and calibration with RVF concurrent data
Flow Discharge (m³/sec)
80000
simulated
observed
70000
60000
50000
40000
30000
20000
10000
1
112
223
334
445
556
667
778
889
1000
1111
1222
1333
1444
1555
1666
1777
1888
1999
2110
2221
2332
0
Times in days
Figure 6: Daily Simulated and Observed streamflow discharges (2001-2007) at Kinshasa
Traces of observed and simulated stream flows for the period from February 2001 to October
2007 for the Congo River at Kinshasa gauge station
Flow Discharge (m³/sec)
120000
100000
calibrated
observed
80000
60000
40000
20000
1
112
223
334
445
556
667
778
889
1000
1111
1222
1333
1444
1555
1666
1777
1888
1999
2110
2221
2332
0
Time in days
Figure 7: Daily Calibrated flow and Observed streamflow discharges (2001-2007) at
Kinshasa. Traces of observed and calibrated stream flows for the period from February 2001 to
October 2007 for the Congo River at Kinshasa gauge station; decreasing the soil depth has
increased significantly the runoff volume, particularly during the peak flows
17
Flow Discharge (m³/sec)
60000
50000
simulated
concurrent observed
40000
30000
20000
10000
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
Figure 8: Monthly mean of simulated flow and concurrent observed streamflow discharges
(2001-2007) at Kinshasa. The seasonality has been captured by aggregating daily observed and
simulated flows of series 2001-2007
70000
Flow Discharge (m³/sec)
60000
calibrated
concurrent observed
50000
40000
30000
20000
10000
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
Figure 9: Monthly mean of calibrated flow and concurrent observed streamflow discharges
(2001-2007) at Kinshasa. The seasonality has been captured by aggregating daily observed and
calibrated flows of series 2001-2007; this calibration try worked better for the peak flow periods
by increasing more significantly than the period of low flow
18
Flow Discharge (m³/sec)
60000
55000
50000
historical observed
45000
current observed
40000
35000
30000
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
70000
Flow Discharge (m³/sec)
60000
historical observed
concurrent observed
50000
40000
30000
20000
10000
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
Figure 10: Monthly mean of two different periods of observed flow at Kinshasa. Observed
flows have been aggregated from two different time series datasets, the current one from 2001 to
2007 and the historical one from 1903 to 1983; both the lines and the histogram show significant
increase of streamflow discharge during the rainy season (approximately September-March) and
slight decrease of streamflow discharge during the dry season (approximately May-August). This
may corroborate the assumption of change of the Congo River flow regime over a long period of
time and this maybe an indication of a climate change affecting trough a “drier” dry season and a
“wetter” rainy season. Yet, more investigations need to be done to test any of these two
statements.
19
Years
2001
2002
2003
2004
2005
2006
2007
600.0
Monthly Rain (mm)
500.0
400.0
300.0
200.0
100.0
0.0
J F MAM J J A S ON D J F MAM J J A S ON D J F MAM J J A S ON D J F MAM J J A S ON D J F MAM J J A S O N D J F MAM J J A S O N D J F MAM J J A S
Months of the year
Figure 11: Monthly rainfall volume observed at Kinshasa (2001-2007)
Traces of observed rainfall for the period from January 2001 to October 2007 at N’djili
meteorological station, the closest [upstream] to Kinshasa gauge station; this graph show the
increase of rainfall volume particularly during the two last years (2006 and 2007)
20
(ii)
Historical validation with GRDC data
Congo River at Kinshasa
Flow Discharge (m³/sec)
60000
simulated
50000
observed
40000
30000
20000
10000
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
(a)
Congo River at Ubundu (ex
Ponthierville)
Flow Discharge (m³/sec)
10000
8000
6000
4000
2000
simulated
observed
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
(b)
21
Flow Discharge (m³/sec)
Kasai Tributary at Ilebo (ex PortFrancqui)
4000
3500
3000
2500
2000
1500
1000
500
0
simulated
observed
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
(c)
Kasai Tributary at Kutu-Moke
Flow Discharge (m³/sec)
14000
simulated
12000
observed
10000
8000
6000
4000
2000
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
(d)
22
Flow Discharge (m³/sec)
Ubangi River at Bangui
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
simulated
1
2
3
4
observed
5
6
7
8
9
10
11
12
Months of the year
(e)
Sangha River at Ouesso
Flow Discharge (m³/sec)
3500
3000
simulated
observed
2500
2000
1500
1000
500
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Months of the year
(f)
23
Flow Discharge (m³/sec)
Luapula at Chembe Ferry
2000
1800
1600
1400
1200
1000
800
600
400
200
0
simulated
1
2
3
4
5
6
7
8
observed
9
10
11
12
Months of the year
(g)
Figure 12: Seasonal variation of streamflow discharge in the Congo Basin (model
versus observation)
(a), (b), (c), (d), (e), (f) and (g) show Observed mean monthly discharge series 1903-1983
of the Congo River at Kinshasa; 1932-1959 of Kasai tributaries at Kutu Muke and Ilebo;
1935-1975 of Ubangi River at Bangui; 1947-1983 of Sangha River at Ouesso; 1933-1959
of Congo River at Ubundu and 1956-2005 of Luapula River at Chembe Ferry. The
observed series are compared to simulated mean monthly discharge series 2001-2007 of
the corresponding rivers. The poor data quality of the Luapula series did not allow an
operational validation with concurrent data (2001-2005)
(iii)
Discussion
While the general trend i.e. the fluctuation of the flow estimates at the 7 stations were closed to
the observed flows, their magnitudes were different. The results have effectively shown the
interannual variability of the Congo River and its tributaries, and thus the diversified flow
regimes varying from place to place in the basin. This is verified in the previous graphs that
illustrated the bimodal flow pattern for the tributaries from the South, such as the Kasai (Figure
12 (c) (d)) that show two periods of high water in the year. The graphs also illustrated the
unimodal flow pattern for the tributaries from the north, such as the Ubangi (Figure 12 (e)) that
have a single maximum.
The results have also shown diverse cases of inconsistencies between the flow estimates and the
information reported by RVF and GRDC for individual stations. Indeed, the results indicated a
slightly higher tendency towards under-prediction of the streamflow estimates.
The causes of the general tendency to this under-prediction need to be more investigated.
However, the discrepancies between simulated and observed flow can be explained by three
24
main reasons that may need to be explored: the quality of the input dataset, the structure of the
model and the performance of the model parameters.
Still, the good predictions obtained from the GeoSFM model in previous studies and the
correspondence between the scientific basis of the GeoSFM model and the actual hydrological
processes guarantee that GeoSFM is mechanistically correct. Thus, its internal structure should
not be in question.
Better resolution data would most likely provide better results though. Indeed, previous studies
asserted that the poor resolution of the elevation data used (GTOPO30 elevation datasets of 1 km
of resolution) had been identified as one of the possible sources of these inconsistencies and the
limited accuracy of the results (Asante and al, 2008). Using higher resolution elevation data
would improve the flow estimates accuracy with a better terrain analysis.
The well-known tendency towards an underestimation of precipitation volume from NASA
TRMM data should also be decisive in the accuracy of this satellite-derived product. Although
TRMM data have proven to be adequate in several hydrological modeling studies, more accurate
rainfall data would most likely improve the streamflow output.
However, for this preliminary study, in absence of alternate remote sensed data, calibration of
the model parameters should be the way out of the inconsistencies.
A try has been attempted using RVF concurrent data of Kinshasa gauge station. The RVF daily
concurrent streamflow discharge data helped adjusted daily streamflow estimated. The decrease
of soil depth that has been operated allows the augmentation of the runoff and the estimates got
closer to the observed data (Figure 7 and 9). However, much more should be done in term of
parameter sensitivity analysis, to adjust the estimates even more efficiently. This way, the model
output will meet the performance standards required for hydrological model’s purpose, namely,
produce the most consistent runoff possible.
A note should be added regarding the operational validation proceeded for the Kinshasa gauge
station. Although a general underestimation of the estimate was attested as re the seasonality, the
under-prediction was not even during the 7 years series. Comparison between the concurrent
daily simulated and observed flow shows that the simulated flow discharge increased
significantly during years 2006 and 2007. This increase is more significant than the increase of
the observed flow at the same period (Figures 6 and 8). In fact, it is observed that the
underestimation of the simulated streamflow is constant over the 5 first years (2001 to 2005) and
diminishes for the 2 last years 2006 and 2007.
In a temporal resolution perspective, the present GeoSFM implementation relies solely on a
water balance generated using satellite-derived precipitation and evapotranspiration data to
produce streamflow discharge. The evapotranspiration variability is almost zero when compared
to the precipitation variability, making the evapotranspiration data almost constant over the 7
years. So all the terrestrial data being constant, the variation of the water balance and streamflow
discharge is mostly proportional to the variation of precipitation data.
With the abrupt runoff increase in 2006 and 2007, the question is if there is a sudden relative
overestimation of the precipitation detected by TRMM or if the precipitation has indeed increase
for those two last years? Affirming the first will induce that the detection error of TRMM has
suddenly decreases for those 2 years, which is most unlikely. The inclination is towards
affirming the last. It is most probable that there was an increase in the augmentation rate of the
precipitation during 2006 and 2007. The graph of Figure 11 illustrates it for the N’djili
meteorological station, the closest upstream meteorological station to Kinshasa gauge station.
25
At this point the question is why the increase of the observed flow is not as much significant as
the increase of the simulated flow is for those 2 years knowing that observed rainfall shows a
similar significant increase for those 2 years? The increase rate for the observed flow should
have been proportional to the abrupt increase rate of the observed rainfall. Land use and land
cover change maybe part of the answer. Indeed, the simulated flow was generated with all
terrestrial conditions being constant. Consideration of their change over time in the model
implementation will most likely provides relatively better output.
(iv)
Research outcome publicized
The principal outcome of publicizing the research outcome among African researchers involved
in Congo water resources management is that now, the knowledge of the adequacy of remote
sensing and hydrological modeling is spread among this appropriate audience. African
researchers and other special-interest groups are indeed the appropriate audience, being directly
affected by water knowledge and water management practices in the Congo Basin. Those experts
know now that there are new venues that need to be explored in parallel with ground-based
research approach that they have been always carried out for Congo Basin waters monitoring.
Even though limited, the ground-based hydrometeorological needed for research are accessible
to them. The experts understood that interaction with remote sensed data with high capabilities in
their field of research will build their capacities and increase their productivity.
Those researchers were very interested by the prospect, and express their will to be trained in the
near future in the field of remote sensing applied to water resources.
CICOS (the International Commission of the Congo-Oubangui-Sangha Basin) has been a big
facilitator for us to be in touch with water resources special-interest groups. CICOS is an
intergovernmental organization charged with managing the navigable waterways of the region
sustainably and promoting integrated water resources management for the Congo-OubanguiSangha Basin. AMESD (African Monitoring of the Environment for Sustainable Development)
also help us work efficiently back in Africa. AMESD is an EU funded program that aim to
develop new applications using satellite technologies and other ancillary data (including Remote
Sensing and Information and Communication Technologies) in Africa in support to the
Sustainable Development of the African. During our stay in Africa, both CICOS and AMESD
organized jointly a validation workshop of their Thematic Action for Central Africa in Bangui
(Central African Republic) to which we participate through diverse interventions. That was for
us an occasion to present with more details the outcome of a real application of remote sensing
on Congo water among a cosmopolite audience of experts from all Central Africa countries. We
also explained to them how auspicious would be the prospect of exploring this new venue in
collaboration. At the close of three days of talk, it was decided that, in the coming future,
AMESD and us will organize regional training sessions in the use of the GeoSFM model for
hydrological modelling of the Congo Basin, using Eumestat or/and other available remote sensed
data.
At the end of every talk, scientific collaboration was encouraged not only among African experts
but also with oversea institutions operational in Remote Sensing. Indeed, collaboration between
African experts that have access to ground data and Institutional entities equipped in Remote
26
sensing technologies [applied to water resources] will allow significant research progress
through data sharing.
(v)
Photos Gallery
AMESD – CICOS Conference at Bangui
(Central African Republic) where the START
Project was presented and where it contributed
to the thematic related to water balance
monitoring of the River basins working group
27
START Project presentation to
students during a Remote Sensing
Class in the Agronomy Department,
University of Kinshasa (Democratic
Republic of the Congo)
START Project presentation to water
sector professional and researchers in
OSFAC laboratory, Kinshasa
(Democratic Republic of the Congo)
CONCLUSIONS
The present project has shown the adequacy of TRMM precipitation Data and the capabilities of
the USGS GeoSFM model to simulate streamflow discharge for the Congo Basin. As much as
the simulated flows have shown agreement with observed flow as regards seasonal variation of
streamflow discharge, discrepancies are observed as regards flow magnitude. A first calibration
attempt demonstrated the extensive possibility of flow adjustment towards a better estimate. Yet,
the condition for a successful calibration is the availability of ground data. This is why
collaboration has been strongly encourage among Africans experts and with institutional entities
operational in Remote Sensing applied to water resources. This will insure not only the data
sharing but also the capacity building through transfer of technologies.
Partnership has been the key of the project success from its start to its end. Upstream, a strong
research team needed to be built to achieve the research purpose. That was made possible
through a developing partnership between scientific contributors and other institutions.
Downstream, partnering with regional experts and special-interest groups working in water
sector for the Congo Basin, insure the possibility to publicize the research outcome and to expose
new venues for Congo basin water management to appropriate audiences. That same partnership
philosophy may insure effectiveness in model calibration and opportune implementation of
Remote Sensing among African researchers of the water sector in the coming future.
FUTURE DIRECTIONS
Ongoing calibration exercises should be the main activities for a suitable parameterization of the
GeoSFM model for the Congo River Basin. This will adjust the magnitude of the GeoSFM
streamflow estimates to the observed data values and open the prospect of using these estimates
for forecast activities, for hydropower assessment and for many other applications.
Collaboration and partnership among researchers should be more encouraged in this regards.
Indeed, the inclusion of higher resolution remote sensed datasets and nationally-held ground
datasets can only be possible through partnership with international and local agencies with the
appropriate mandate.
Moreover, partnering should stimulate transfer of technology to the African interest parties,
specifically the directly concerned stakeholders that work in water sector in the Congo River
Basin. This would have the potential to insure the future implementation of water best
management practices in the region through the scientific appropriation of Remote Sensing and
Hydrological modeling new technologies. Indeed, through the appropriation processes the
concerned researchers will take both conceptual and operational control of Remote Sensing and
Hydrological modeling new technologies within the context of their own investigations.
Locally based hydrological modeling by trained experts of the Congo region will then ultimately
serve as decision support tools for decisions makers with respect to monitoring, prediction and
projection of Congo River Basin water resources.
APPENDIX
Content
Appendix 1: References
Appendix 2: Power Point Slides Presentation in English
Appendix 3: Power Point Slides Presentation in French
30
References
Artan, G.A., Asante, K., Smith, J., Pervez, S., Entenmann, D., Verdin, J., and Rowland, J., 2008,
Users Manual for the Geospatial Stream Flow Model (GeoSFM), USGS Open File Report, OF
2007–1440
Asante, K. O., Artan, G., Pervez, S., Bandaragoda, C. and Verdin, J., 2007, Technical Manual of
the Geospatial Streamflow Manual (GeoSFM), USGS Open File Report, OF 2007-1441
Asante, K. O., Munzimi, Y. and Fosnight, E., 2008, Assessment of Small Hydropower Potential
of Africa with Remotely Sensed Data, USGS File Report
Asante, K. O., Artan, G., Gadain, H., Bandaragoda, C., Smith, J., and Verdin, J., 2007, Adequacy
of satellite derived rainfall data for stream flow modeling, Nat Hazards 43:167–185
Pervez, S., Artan, G. and Asante, K.O., 2007, Streamflow Simulation Using Remotely Sensed
Hydrometerological Estimates, USGS
Shahin, M. 2002, Hydrology and water resources of Africa, Water Science and Technology
Library, Volume 41, Kluwer Academics Publishers, Dordrecht, The Netherlands.
31