demonstration: development of the …...demonstration: development of the hydrological model in...

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 ?

Industry

Agriculture

households

STAKEHOLDERS

Want to know…

Dam Storage

Well Storage

Yielding

Disaster

(Flood , Drought)

River Discharge

Ground Water

Soil Moisture

Land Productivity

Weather

Prediction

(rainfall)

MODEL OUTPUT

⇒ When we develop the hydrological model that can reproduce

above valuables in regional scale, we would face some difficulties.4

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. 5

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 2010 )6

1-3-3. Vegetation Dynamics

NDVI , Feb 2004NDVI , Sep 2004

⇒ 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 Wang

2010 )7

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]

8

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)

Rainfall on June at 4 different GCMs

⇒ But there are

big uncertainties

in the model

9

1-4. Summary of Scientific Challenges

• Spatial & Temporal Variety

of Climatology

• Land – Atmosphere Feedback

• Vegetation Dynamics

• Inundation Effect

• Uncertainties of Future Projection

of Climate Change

CALDAS⇒ regional climate model coupled

with land data assimilation system.

WEB-DHM⇒ distributed hydrological model

coupled with dynamic vegetation

model and inundation model.

10

1-5. Entire Outline of the Study

CALDASClimate model

with LDAS

Analyze……

West African Monsoon dynamics that

drives spatial & temporal variety.

Mechanism of Land – Atmosphere

Feedback in West Africa

PART_1

MODEL

DEVELOPMENT Validation by using satellite & ground

observation data

WEBDHMDistributed

hydrological

model

GROUND WATER VS

GRACE

satellite

observation

RIVER DISCHARGE VSGround

observation

LEAF AREA INDEX VS

MODIS

satellite

observation

PART_2

Future

ProjectionRun WEBDHM by

corrected GCM in

CMIP3

Make use of

validated

system

PART_3

11

2, Preliminary Demonstration –PART1-- Analysis of West African Monsoon Dynamics -

CALDASClimate model

with LDAS

Analyze……

West African Monsoon dynamics that

drives spatial & temporal variety.

Mechanism of Land – Atmosphere

Feedback in West Africa

PART_1

12

2-1. Methodology ; CALDAS [ Rasmy 2011]

⇒ Using land data

assimilation system, the

capacity to estimate soil

moisture is improved.

⇒ So, CALDAS is

suitable to analyze land

– atmosphere feedback.

Model Framework

13

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.14

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.

15

3, Preliminary Demonstration –PART2-- Validation of Distributed Hydrological Model, WEB-DHM -

MODEL


observation data

WEBDHMDistributed

hydrological

model

GROUND WATER VS

GRACE

satellite

observation


observation

LEAF AREA INDEX VS

MODIS

satellite

observation

PART_2

16

3-1. Methodology ; WEB-DHM [ Wang et. al. 2004 ]

• Well formulated 1D

simulation of water and

energy fluxes in SVAT

system

• Good prediction system of

photosynthesis and

respiration.SiB2

GBHM

• Distributed representation of

the spatial variation;

• Slope-driven runoff

generation and River

Routing;

• Groundwater dynamics

Coupling

17

3-1. Methodology ; WEB-DHM [ Wang et. al. 2004 ]

SiB2

GBHM

Coupling

Flood Model

• Formulate flood plain reservoir

to consider inundation effects

＋

Dynamic Vegetation Model

• Formulate vegetation dynamics

to calculate seasonal cycle

of vegetation and get the future

projection of distribution of it.

＋

18

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. 19

3-2-2. River Discharge

(considering an inundation effect)

⇒ Flood model can improve the result.

⇒ Improving the quality of input dataset is needed.

20

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

4, Preliminary Demonstration –PART3-

- Future Projection -

MODEL


observation data

WEBDHMDistributed

hydrological

model

GROUND WATER VS

GRACE

satellite

observation


observation

LEAF AREA INDEX VS

MODIS

satellite

observation

PART_2

Future

ProjectionRun WEBDHM by

corrected GCM in

CMIP3

Make use of

validated

system

PART_3

23

Other Forcings ( temperature, Radiation etc….)

4-1. Methodology ; Selecting GCMs and Bias Correction for

Future Projection in regional scale

CMIP323 GCMS

Scoring and Selectionby comparing observations & reanalysis

↓

cccma_cgcm3_1

cccma_cgcm3_1_t63

Miroc_hires

Rainfall – Bias Correction

1981-1992

Ground observed Rainfall

1981-1992

GCM simulated Rainfall

Bias corrected

2046-2058

GCM simulated Rainfall

WEB-DHM

24

4-2. Rainfall ( wet region )

⇒ One of the models reproduce the tendency of drought on August25


26


⇒ 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.

28

4-4. Leaf Area Index

⇒ Comparing past simulation, future projection

shows the decrease of LAI. 29

5, Conclusion

River Discharge

Ground Water

Soil Moisture

Land Productivity

Weather

Prediction

(rainfall)

MODEL OUTPUT

30

5, Conclusion0 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 ! [email protected]

31

APPENDIX

32

2-2-1. Model Input 1

- rainfall -

33


- Elevation -

⇒ We use 90m DEM that is

upscaled to 1km, same as

model resolution.

34


- Soil Type -

⇒ FAO dataset

⇒ There are 124 soil type

in the basin

⇒ The variety of soil types make

it difficult to calibrate the model.

35


- 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.

36

4-2-1. Rainfall ( dry region )

⇒ There are some gaps between GCMs.

⇒ It is difficult to derive a consistent tendency from future projection.37


38


⇒ Temporal variability of maximum rainfall would

increase in the dry region.39

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