Download Development of the Hydrological Model in Volta River Basin, West

DEMONSTRATION:
Development of the Hydrological Model
in Volta River Basin, West Africa
Yohei Sawada
The University of Tokyo
River and Environmental Engineering Laboratory
1
1, Introduction
2
1-1. Volta River Basin
[ Barry et. al. (2005) ]
3
1-2. What kinds of information do we need for
water resource management in Volta ?
STAKEHOLDERS
MODEL OUTPUT
Industry
Agriculture
Dam Storage
River Discharge
households
Wa
nt
to
kn
ow
…
Well Storage
Ground Water
Disaster
(Flood , Drought)
Soil Moisture
Yielding
Weather
Prediction
(rainfall)
Land Productivity
⇒ When we develop the hydrological model that can reproduce
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above valuables in regional scale, we would face some difficulties.
1-3-1. Spatial & Temporal Variety of Climatology
Distribution of Rainfall
in Volta River Basin
[ Barry et. al. (2005) ]
Seasonal Change of Rainfall by GPCP
[ Hagos and Cook (2007) ]
⇒ The wet southern region and the dry northern contrast sharply.
⇒ West African monsoon dynamics is strongly related
to water resource management in this region.
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1-3-2. Land – Atmosphere Feedback
[ Sawada (2011) ]
⇒ Land surface heat budget suddenly
changes after monsoon onset.
⇒ Distribution of surface albedo and
heat flux is supposed to drive not only
local circulation but also larger
general circulation. ( Charney 1975 ,
Taylor 2008 , Hagos and Zhang 20106)
1-3-3. Vegetation Dynamics
004
NDVI , Feb 2
004
NDVI , Sep 2
⇒ Distribution of vegetation in West Africa has seasonal cycle.
⇒ This vegetation dynamics has the feedback to climate.
⇒ Vegetation dynamics should be considered in numerical simulation
when you analyze the impact of climate change. ( Cox 2000 , Alo and Wang7
2010 )
1-3-4. Inundation Effect
⇒ Inundation has a big impact
to river discharge.
⇒ Considering inundation effect
is essential to estimate the discharge
of rivers on continent.
[ @Amazon river, Alsdorf et. al. 2011]
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1-3-5. Uncertainties of Future Projection of Climate Change
0 “Detailed, regional-scale research on the impact of, and
vulnerability to, climate change and variability with reference to
water is needed” (IPCC AR4)
ferent GCMs
if
d
4
t
a
e
n
Ju
n
o
Rainfall
⇒ But there are
big uncertainties
in the model
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1-4. Summary of Scientific Challenges
• Spatial & Temporal Variety
of Climatology
CALDAS
• Land – Atmosphere Feedback
⇒ regional climate model coupled
with land data assimilation system.
• Vegetation Dynamics
• Inundation Effect
• Uncertainties of Future Projection
of Climate Change
WEB-DHM
⇒ distributed hydrological model
coupled with dynamic vegetation
model and inundation model.
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PART_1
1-5. Entire Outline of the Study
CALDAS
Climate model
with LDAS
PART_2
An
al
yze
……
West African Monsoon dynamics that
drives spatial & temporal variety.
Mechanism of Land – Atmosphere
Feedback in West Africa
RIVER DISCHARGE
VS
WEBDHM
Distributed
hydrological
model
GROUND WATER
LEAF AREA INDEX
MODEL
DEVELOPMENT
VS
VS
Ground
observation
GRACE
satellite
observation
MODIS
satellite
observation
Validation by using satellite & ground
observation data
PART_3
Future
Projection
Run WEBDHM by
corrected GCM in
CMIP3
Make use of
validated
system 11
PART_1
CALDAS
Climate model
with LDAS
An
al
yze
……
West African Monsoon dynamics that
drives spatial & temporal variety.
Mechanism of Land – Atmosphere
Feedback in West Africa
2, Preliminary Demonstration –PART1- Analysis of West African Monsoon Dynamics 12
2-1. Methodology
;
CALDAS
[
Rasmy
2011]
rk
ewo
m
a
r
F
l
Mode
⇒ Using land data
assimilation system, the
capacity to estimate soil
moisture is improved.
⇒ So, CALDAS is
suitable to analyze land
– atmosphere feedback.
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2-2. Analysis of West African Monsoon Onset
⇒ By analyzing vertical and meridional circulation, and heating of
atmosphere, we found there are two processes of monsoon onset.
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2-3. Analysis of Land –Atmosphere Feedback and
Its Contribution of Monsoon Onset
⇒ Strong sensible heat flux from
land drives updraft on Sahel
region.
⇒ And this shallow updraft is
main driver of monsoon onset by
heating atmosphere due to
condensation of moisture.
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PART_2
RIVER DISCHARGE
VS
WEBDHM
Distributed
hydrological
model
GROUND WATER
LEAF AREA INDEX
MODEL
DEVELOPMENT
VS
VS
Ground
observation
GRACE
satellite
observation
MODIS
satellite
observation
Validation by using satellite & ground
observation data
3, Preliminary Demonstration –PART2- Validation of Distributed Hydrological Model, WEB-DHM 16
3-1. Methodology ; WEB-DHM [ Wang et. al. 2004 ]
GBHM
• Distributed representation of
the spatial variation;
• Slope-driven runoff
generation and River
Routing;
• Groundwater dynamics
Coupling
SiB2
• Well formulated 1D
simulation of water and
energy fluxes in SVAT
system
• Good prediction system of
photosynthesis and
respiration.
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3-1. Methodology ; WEB-DHM [ Wang et. al. 2004 ]
＋ Flood Model
• Formulate flood plain reservoir
to consider inundation effects
GBHM
Coupling
Dynamic Vegetation Model
＋
• Formulate vegetation dynamics
to calculate seasonal cycle
of vegetation and get the future
projection of distribution of it.
SiB2
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3-2-1. River Discharge
(not considering an inundation effect)
⇒ The simulation without flood model doesn’t have
the enough capacity to reproduce river discharge.
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3-2-2. River Discharge
(considering an inundation effect)
⇒ Flood model can improve the result.
⇒ Improving the quality of input dataset is needed.
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3-3. Ground Water
⇒ WEB-DHM basin averaged ground water depth can
reproduce its seasonal cycle.
⇒ GRACE satellite observation can make a
good information about total water storage on land area.21
3-4. Leaf Area Index
⇒ WEB-DHM coupling with dynamic vegetation model can show
the seasonal cycle of LAI.
⇒ In-situ observation is needed for a further investigation. 22
PART_2
RIVER DISCHARGE
VS
WEBDHM
Distributed
hydrological
model
GROUND WATER
LEAF AREA INDEX
MODEL
DEVELOPMENT
VS
VS
Ground
observation
GRACE
satellite
observation
MODIS
satellite
observation
Validation by using satellite & ground
observation data
PART_3
Future
Projection
Run WEBDHM by
corrected GCM in
CMIP3
Make use of
validated
system
4, Preliminary Demonstration –PART3- Future Projection 23
4-1. Methodology ; Selecting GCMs and Bias Correction for
Future Projection in regional scale
Scoring and Selection
CMIP3
23 GCMS
by comparing observations & reanalysis
↓
cccma_cgcm3_1
cccma_cgcm3_1_t63
Miroc_hires
Rainfall – Bias Correction
Other Forcings ( temperature, Radiation etc….)
1981-1992
Ground observed Rainfall
WEB-DHM
Bias corrected
1981-1992
GCM simulated Rainfall
2046-2058
GCM simulated Rainfall
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4-2. Rainfall ( wet region )
⇒ One of the models reproduce the tendency of drought on August
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4-2. Rainfall ( wet region )
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4-2. Rainfall ( wet region )
⇒ Generally, extreme event would increase in the future. 27
4-3. River Discharge
⇒ 2 models reproduce
huge extreme river
discharge compared
with the past simulation
case.
⇒ One of the models
reproduce high low
water discharge.
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4-4. Leaf Area Index
⇒ Comparing past simulation, future projection
shows the decrease of LAI.
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MODEL OUTPUT
River Discharge
Ground Water
Soil Moisture
Weather
Prediction
(rainfall)
Land Productivity
5, Conclusion
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5, Conclusion
0 Regional climate model with land data assimilation
system can contribute not only to realize accuracy
weather forecast but to get scientific knowledge about
the mechanism of climate system in west Africa.
0 Distributed hydrological model which include
inundation model and dynamic vegetation model has
the capacity to contribute to global & regional water
resource management.
0 Future projection still has a big uncertainties, but our
method makes it possible to start to discuss how to
adapt to climate change in regional scale.
Thank you for your listening !
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[email protected]
APPENDIX
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2-2-1. Model Input 1
- rainfall -
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2-2-2. Model Input 2
- Elevation -
⇒ We use 90m DEM that is
upscaled to 1km, same as
model resolution.
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2-2-3. Model Input 3
- Soil Type ⇒ FAO dataset
⇒ There are 124 soil type
in the basin
⇒ The variety of soil types make
it difficult to calibrate the model.
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2-2-4. Model Input 4
- Meteorological Forcing 0 JRA 25 reanalysis dataset - for Past run –
0 Spatial resolution : 110km (T106L40)
0 6-hourly
0 CMIP3 Multi – Model database - for Future Projection –
0 About 20 GCMs is included.
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4-2-1. Rainfall ( dry region )
⇒ There are some gaps between GCMs.
⇒ It is difficult to derive a consistent tendency from future projection.
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4-2-1. Rainfall ( dry region )
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4-2-1. Rainfall ( dry region )
⇒ Temporal variability of maximum rainfall would
increase in the dry region.
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