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a)
b)
d)
c) Gibe I
Gibe III
a)
b)
d)
e)
c)
The model (resolution: 1 km x 1 km, daily) can fairly
represent:
•  flow time series observed at stations representative of
the northern catchment dynamics (Fig. 4b);
•  lake level time series, representative of the whole
catchment dynamics (Fig. 4c)
The lake water budget is almost at equilibrium with
ET = P+Q, where Q is the dominant component (Tab. 1).
A change in the total inflow due to the hydropower reservoir
operations is likely to significantly affect the lake dynamics
(in line with Avery, 2010, Velpuri and Senay, 2012).
The simulated total water storage at the catchment level
(i.e., sub-surface water content and lake) shows fluctuations
similar to GRACE GRGS and CRS mascon (Fig. 4d),
although these estimates are uncertain because of the small
area and shape of the Turkana catchment.
HYDROLOGICAL MODEL
TOPKAPI-ETH
DEM
Soil
Land cover
ANALYSIS OF
INPUT DATA
TRMM-V7 precipitation
MERRA-2 temperature
MERRA-2 cloud cover
Precipitation
Temperature
Streamflow
ANALYSIS OF
HYDROLOGICAL
MODEL RESULTS
Lake
level
GRACE total water storage
GLDAS-NOAH
2m soil moisture
Streamflow
TOPEX/Poseidon
satellite altimetry data
Precipitation
Reservoir
operations
1st sub-surf. layer
2nd sub-surf. layer
Groundwater
layer
Evapotranspiration
Flow from/to
adjacent layer/cell
Groundwater
Soil moisture
a) b)
1. MOTIVATION AND OBJECTIVES
REFERENCES
•  Understand and model the hydrological processes of the poorly gauged Lake Turkana catchment.
•  Investigate how long-term water budget of the Lake Turkana catchment is affected by the operations of existing and
planned large hydropower reservoirs.
•  Understand the Water-Energy-Food nexus in the Lake Turkana catchment, to investigate the impact of planned
hydropower and irrigated agriculture expansion.
Modeling the hydrological regime of Turkana Lake (Kenya, Ethiopia)
by combining spatially distributed hydrological model and remote sensing datasets
H53J-1607
D. Anghileri1, A. Kaelin1, N. Peleg1, S. Fatichi1, P. Molnar1, C. Roques1, L. Longuevergne2, and P. Burlando1
1 ETH Zurich; 2 CNRS
3. DATA & METHODS
Ground datasets (northern catchment only):
•  Precipitation (14 stations, 1998-2008, daily)
•  Temperature (6 stations, 1998-2008, daily)
•  Flow (3 stations, 1998-2007, daily)
Remote sensing and reanalysis datasets:
•  TRMM-V7 (0.25°x0.25°, 3 hours)
•  MERRA-2 (0.5°x0.625°, hourly)
•  TOPEX/Poseidon (≈1 per month)
•  GRACE (GRGS and CRS mascon)
(300 km X 300 km, daily or monthly)
•  GLDAS-NOAH (0.25°x0.25°, hourly)
Hydropower reservoirs:
•  Technical features (e.g., capacity)
•  Target release/production
Funded under the H2020 Framework
Program of the EU, GA No. 690268
Hydrological model Topkapi-ETH (Fatichi et al., 2015):
•  Physically-based, fully distributed model
•  Vertical discretization of the subsurface in three layers
•  Kinematic approximation to route subsurface, overland, and river flow
•  Description of the anthropic component (reservoir operations)
Lake Turkana:
•  Endorheic or terminal lake
•  Area = 7’500 km2 (approx. 240 km x 30 km)
•  Volume = approx. 200 km3
Turkana catchment:
•  Area = 148’000 km2
•  Elevation = 320 – 2’700 m a.s.l.
•  Mean annual precip.= 250 – 1’800 mm/y
•  Mean annual temp.= 14 – 30 °C
Hydropower reservoirs in operation:
•  Gibe I: 184 MW, 0.7 km3 (commissioned in
2004)
•  Gibe II: 420 MW, (commissioned in 2010)
•  Gibe III: 1870 MW, 14.7 km3 (filling phase
started in 2016)
Hydropower reservoirs planned either:
•  Gibe IV and V: 1472 and 560 MW
•  Koysha: 2160 MW, 6 km3
5. RESULTS OF HYDROLOGICAL MODEL
•  Strong precipitation gradient over the catchment.
•  Different rainy patterns depending on the latitude.
•  Uncorrected MERRA-2 significantly overestimates
precipitation.
•  TRMM-V7 compares better than MERRA-2 to the
observations in the Northern Omo sub-catchment (both
at the monthly and daily time scale).
•  TRMM-V7 overestimates precipitation over the lake
(corrected by linear interpolation for the hydrological
simulation)
Figure 3: a) Map of TRMM-V7 mean annual precipitation over the
catchment; b-e) Comparison of mean monthly precipitation between
observations (when available), TRMM-V7, and MERRA-2 (uncorrected
and corrected) datasets.
Figure 2: Modeling framework: ground, remote sensing, and reanalysis datasets
and schematization of the hydrological processes modeled in Topkapi-ETH (for one grid cell).
Figure 1: a) Satellite image of the Turkana catchment (source: Google map),
and b) map of the catchment including the lake, hydropower reservoirs, major rivers, and available gauges .
2. LAKE TURKANA CATCHMENT
P
(mm/d)
ET
(mm/d)
Q
(mm/d)
Q
(m3/s)
Land 2.85 2.44 0.37 634 (2)
Lake 0.87 8.32 (1) 7.30 634 (2)
Omo 3.86 3.19 0.67 562 (3)
Kerio 1.75 1.62 0.18 35 (4)
Turkwel 1.94 1.81 0.14 37 (5)
Previous estimates
(1) 7.6 mm/d Avery, 2010
(2) 650 m3/s Velpuri, 2012
(3) 555 m3/s Avery, 2013
(4) 5 m3/s Avery, 2010
(5) 26 m3/s Sogreah, 1982
Figure 4: a) Map of simulated mean annual actual evapotranspiration over the catchment; b) Comparison of daily simulated and observed flow at Abelti;
c) Comparison of simulated and observed lake level; d) Comparison of simulated sub-surface water storage and GRACE datasets.
Table 1: Water budget components over the whole catchment
(without the lake), lake, and sub-basins of major lake tributaries.
6. OUTLOOK
•  Simulation of the current and planned hydropower and irrigated
agriculture expansion to estimate the effects on the lake level
fluctuations.
•  Simulation of the hydrological budget when considering climate
change scenarios.
CONTACT: Daniela Anghileri | adaniela@ethz.ch
4. ANALYSIS OF PRECIPITATION INPUT DATA
•  Avery (2010). “Hydrological Impacts of Ethiopia’s Omo Basin on Kenya’s Lake Turkana Water
Levels & Fisheries. Final report prepared for the African Development Bank, Tunis.”
•  Avery (2013). “What Future For Lake Turkana”. In: African Studies Centre, Oxford University.
•  DAFNE: http://dafne-project.eu/
•  Fatichi et al. (2015), JoH 525, 362–382.
•  Sogreah, Consulting Engineers (1982). “Regional Development Plan for the Kerio Valley,
Water Resources Study, for Kerio Valley Development Authority, Kenya”.
•  Velpuri et al. (2012). HESS, 16, 1-18.
•  Velpuri and Senay (2012). HESS, 16.10, 3561–3578.

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Modeling the hydrological regime of Turkana Lake (Kenya, Ethiopia) by combining spatially distributed hydrological model and remote sensing datasets

  • 1. a) b) d) c) Gibe I Gibe III a) b) d) e) c) The model (resolution: 1 km x 1 km, daily) can fairly represent: •  flow time series observed at stations representative of the northern catchment dynamics (Fig. 4b); •  lake level time series, representative of the whole catchment dynamics (Fig. 4c) The lake water budget is almost at equilibrium with ET = P+Q, where Q is the dominant component (Tab. 1). A change in the total inflow due to the hydropower reservoir operations is likely to significantly affect the lake dynamics (in line with Avery, 2010, Velpuri and Senay, 2012). The simulated total water storage at the catchment level (i.e., sub-surface water content and lake) shows fluctuations similar to GRACE GRGS and CRS mascon (Fig. 4d), although these estimates are uncertain because of the small area and shape of the Turkana catchment. HYDROLOGICAL MODEL TOPKAPI-ETH DEM Soil Land cover ANALYSIS OF INPUT DATA TRMM-V7 precipitation MERRA-2 temperature MERRA-2 cloud cover Precipitation Temperature Streamflow ANALYSIS OF HYDROLOGICAL MODEL RESULTS Lake level GRACE total water storage GLDAS-NOAH 2m soil moisture Streamflow TOPEX/Poseidon satellite altimetry data Precipitation Reservoir operations 1st sub-surf. layer 2nd sub-surf. layer Groundwater layer Evapotranspiration Flow from/to adjacent layer/cell Groundwater Soil moisture a) b) 1. MOTIVATION AND OBJECTIVES REFERENCES •  Understand and model the hydrological processes of the poorly gauged Lake Turkana catchment. •  Investigate how long-term water budget of the Lake Turkana catchment is affected by the operations of existing and planned large hydropower reservoirs. •  Understand the Water-Energy-Food nexus in the Lake Turkana catchment, to investigate the impact of planned hydropower and irrigated agriculture expansion. Modeling the hydrological regime of Turkana Lake (Kenya, Ethiopia) by combining spatially distributed hydrological model and remote sensing datasets H53J-1607 D. Anghileri1, A. Kaelin1, N. Peleg1, S. Fatichi1, P. Molnar1, C. Roques1, L. Longuevergne2, and P. Burlando1 1 ETH Zurich; 2 CNRS 3. DATA & METHODS Ground datasets (northern catchment only): •  Precipitation (14 stations, 1998-2008, daily) •  Temperature (6 stations, 1998-2008, daily) •  Flow (3 stations, 1998-2007, daily) Remote sensing and reanalysis datasets: •  TRMM-V7 (0.25°x0.25°, 3 hours) •  MERRA-2 (0.5°x0.625°, hourly) •  TOPEX/Poseidon (≈1 per month) •  GRACE (GRGS and CRS mascon) (300 km X 300 km, daily or monthly) •  GLDAS-NOAH (0.25°x0.25°, hourly) Hydropower reservoirs: •  Technical features (e.g., capacity) •  Target release/production Funded under the H2020 Framework Program of the EU, GA No. 690268 Hydrological model Topkapi-ETH (Fatichi et al., 2015): •  Physically-based, fully distributed model •  Vertical discretization of the subsurface in three layers •  Kinematic approximation to route subsurface, overland, and river flow •  Description of the anthropic component (reservoir operations) Lake Turkana: •  Endorheic or terminal lake •  Area = 7’500 km2 (approx. 240 km x 30 km) •  Volume = approx. 200 km3 Turkana catchment: •  Area = 148’000 km2 •  Elevation = 320 – 2’700 m a.s.l. •  Mean annual precip.= 250 – 1’800 mm/y •  Mean annual temp.= 14 – 30 °C Hydropower reservoirs in operation: •  Gibe I: 184 MW, 0.7 km3 (commissioned in 2004) •  Gibe II: 420 MW, (commissioned in 2010) •  Gibe III: 1870 MW, 14.7 km3 (filling phase started in 2016) Hydropower reservoirs planned either: •  Gibe IV and V: 1472 and 560 MW •  Koysha: 2160 MW, 6 km3 5. RESULTS OF HYDROLOGICAL MODEL •  Strong precipitation gradient over the catchment. •  Different rainy patterns depending on the latitude. •  Uncorrected MERRA-2 significantly overestimates precipitation. •  TRMM-V7 compares better than MERRA-2 to the observations in the Northern Omo sub-catchment (both at the monthly and daily time scale). •  TRMM-V7 overestimates precipitation over the lake (corrected by linear interpolation for the hydrological simulation) Figure 3: a) Map of TRMM-V7 mean annual precipitation over the catchment; b-e) Comparison of mean monthly precipitation between observations (when available), TRMM-V7, and MERRA-2 (uncorrected and corrected) datasets. Figure 2: Modeling framework: ground, remote sensing, and reanalysis datasets and schematization of the hydrological processes modeled in Topkapi-ETH (for one grid cell). Figure 1: a) Satellite image of the Turkana catchment (source: Google map), and b) map of the catchment including the lake, hydropower reservoirs, major rivers, and available gauges . 2. LAKE TURKANA CATCHMENT P (mm/d) ET (mm/d) Q (mm/d) Q (m3/s) Land 2.85 2.44 0.37 634 (2) Lake 0.87 8.32 (1) 7.30 634 (2) Omo 3.86 3.19 0.67 562 (3) Kerio 1.75 1.62 0.18 35 (4) Turkwel 1.94 1.81 0.14 37 (5) Previous estimates (1) 7.6 mm/d Avery, 2010 (2) 650 m3/s Velpuri, 2012 (3) 555 m3/s Avery, 2013 (4) 5 m3/s Avery, 2010 (5) 26 m3/s Sogreah, 1982 Figure 4: a) Map of simulated mean annual actual evapotranspiration over the catchment; b) Comparison of daily simulated and observed flow at Abelti; c) Comparison of simulated and observed lake level; d) Comparison of simulated sub-surface water storage and GRACE datasets. Table 1: Water budget components over the whole catchment (without the lake), lake, and sub-basins of major lake tributaries. 6. OUTLOOK •  Simulation of the current and planned hydropower and irrigated agriculture expansion to estimate the effects on the lake level fluctuations. •  Simulation of the hydrological budget when considering climate change scenarios. CONTACT: Daniela Anghileri | adaniela@ethz.ch 4. ANALYSIS OF PRECIPITATION INPUT DATA •  Avery (2010). “Hydrological Impacts of Ethiopia’s Omo Basin on Kenya’s Lake Turkana Water Levels & Fisheries. Final report prepared for the African Development Bank, Tunis.” •  Avery (2013). “What Future For Lake Turkana”. In: African Studies Centre, Oxford University. •  DAFNE: http://dafne-project.eu/ •  Fatichi et al. (2015), JoH 525, 362–382. •  Sogreah, Consulting Engineers (1982). “Regional Development Plan for the Kerio Valley, Water Resources Study, for Kerio Valley Development Authority, Kenya”. •  Velpuri et al. (2012). HESS, 16, 1-18. •  Velpuri and Senay (2012). HESS, 16.10, 3561–3578.