2nd APWS IMPAC‐T Session (2013/5/18)

Development of the integratedDevelopment of the integrated water resources model

Kenji TanakaWater Resources Research Center

Disaster Prevention Research Institute, Kyoto University JapanKyoto University, Japan

Motivation for model development1. How the hydrological cycle in watershed will be

Motivation for model development

affected due to external disturbance such asclimate change?

2. How the hydrological cycle in watershed will beaffected due to the alteration of land surfaceaffected due to the alteration of land surfacecondition such as deforestation, urbanization?

3. What kind of watershed is strong/ feasible/sustainable under climate change ?

4. Human activity (flood control, irrigation, release f h t) i l i t t t f dof heat) is also an important part of energy and

water cycle.

Goal of IMPAC‐TT l t

Real time simulation Flood protection

Flooded area

Inundation model

TelemetryNumerical weather predictionReal time reanalysis Climate scenarios

Climate change

H08 model(NIES/UT)Meteorological data River discharge

Reservoir storage

R i l

Geographical data

Reservoir release

Irrigation requirementSiBUC model

(KU)

New reservoir operation New reservoir?

Irrigation requirement

Water resources management

River planning Reservoir management

Integrated Water Resources ModelWater Cycle YieldCrop Growth

Crop Dynamics

Human EffectsCrop calendarLAI

Land Surface IrrigationSoil moisture

SIMRIW(Horie,1985)

Soil moisture

Stream flow

SiBUC (Tanaka,2004) SiBUC (Tanaka,2004)Intake & drainage

Dam operationStream flow

Outflow demand

p

Kinematic waveOutflow & Inflow

Discharge Newly developed

Land Surface (SiBUC)Land Surface (SiBUC)Grid box is divided intothree landuse categories

Simple Biosphere including Urban Canopy

１．Broadleaf‐evergreen trees２．Broadleaf‐deciduous trees３ Broadleaf and needle leaf treesg

1. Green Area2. Urban Area

３．Broadleaf and needle leaf trees４．Needle leaf‐evergreen trees５．Needle leaf‐deciduous trees６ Short vegetation/C4 grassland3. Water Body ６．Short vegetation/C4 grassland７．Broadleaf shrubs with bare soil８．Dwarf trees and shrubs９ F l d ( i i d)

Land Surface Irrigation

Crop calendarLAI

YieldCrop Growth

Modeling…

９．Farmland (non‐irrigated)10. Paddy field (non‐irrigated)11. Paddy field (irrigated)

Land Surface

Stream flow

Irrigation

SiBUC (Tanaka,2004) SiBUC (Tanaka,2004)

Soil moisture

Runoff

Intake & drainage

Damoperation

12. Spring wheat (irrigated)13. Winter wheat (irrigated)14. Corn (irrigated)Stream flow

Kinematic wave

Runoff

Outflow & InflowDischarge

Dam operation

developed

( g )15. Other crops (irrigated)

Green area model（SiB）（ ）• Prognostic variables

temperature (canopy, ground, deep soil)p ( py g p )interception water (canopy, ground)soil wetness (surface, root zone, recharge)

• Time invariant parametergeometrical parametergeometrical parameteroptical parameterphysiological parametersoil physical propertiessoil physical properties

• Time varying parameter (LAI etc.)estimate from satellite data

• Physical processesPhysical processesradiative transferinterception losssoil hydrologycanopy resistancecanopy resistancetranspirationturbulent transfer,snow, freezing/melting,… etc., g g,

I i tiIrrigationCrop calendarLAI

YieldCrop Growth

Land Surface IrrigationSoil moisture

Intake & drainage

Modeling…

Basic concept is to maintain soilStream flow

SiBUC (Tanaka,2004) SiBUC (Tanaka,2004)

Kinematic wave

Runoff

Intake & drainage

Discharge

Dam operation

developed

Basic concept is to maintain soil moisture/water depth within appropriate ranges for optimal Kinematic wave

Outflow & InflowDischarge developedappropriate ranges for optimal 

crop growth.Application to wheat corn soyApplication to wheat, corn, soy bean, cotton etc…New water layer is added to treatNew water layer is added to treatpaddy field more accurately.

Water control in farmland

Soil moisture

Days

Water control in paddy field

Water depth

Days

Water maintain level for each growing stageCrop type Growing stage 1st 2nd 3rd 4th 5th

Spring Periods(%) 23 14 14 14 35p gwheat Soil wetness 0.70 0.60 0.80 0.80 0.55

Winter Periods(%) 25 20 22 13 20wheat Soil wetness 0.70 0.70 0.80 0.80 0.55

CornPeriods(%) 8 48 6 14 24

CornSoil wetness 0.75 0.65 0.70 0.75 0.65

RiPeriods(%) 25 13 33 13 16

Rice Water depth (mm)

20-50 none 20-60 moistening intermittent

Periods(%) 3 26 16 28 27soy bean

Periods(%) 3 26 16 28 27Soil wetness 0.75 0.65 0.65 0.70 0.65Periods(%) 4 21 13 26 36

cottonPeriods(%) 4 21 13 26 36Soil wetness none 0.5 0.55 0.55 0.5

Chart by required water for cultivation in China

Producing Crop Calendar from NDVI analysis

Time series NDVI represents growth of land surface 

B0    (430‐470nm)B2    (610‐680nm)B3    (780‐890nm)MIR (1580‐1750nm)

vegetation.To find parameter to describe 

f l dtimings of planting and harvesting.

Double Crop NDV

I

Growing periodhead headparameter

0.4

0.6

0.8

1

cnN

DVI

Double Crop

NDVI

NDVIfn

-0.2

0

0.2

0.4

12/3111/19/17/15/13/11/1

NDVI

, cn

time

NDVIst

Double cropland in India-0.2

12/3111/19/17/15/13/11/1

Date headplant harvest

Statistical crop calendar product (MIRCA 2000)

Statistical crop calendar products (country scale, monthly)

half data for calibrating parameter, half data for validation

NDVI  Product (Spot Vegetation)Estimated date

Good

allowable

Statistical month

Global crop calendar generated by NDVIC l d d t (S k t l 2010)Crop calendar product (Sacks et al., 2010)

Advantage over previous products

High spatial resolutionHigh temporal resolutionHigh temporal resolutionReliability in countries where no statistics available

Satellites are observing surface!

Annual IWR (Irrigation Water Requirement)  [mm/yr]

Annual IWR for each grids are

1000

100

im [G

t]

Model

Annual IWR for each grids are aggregated  into country, then compared with AQUASTAT

10

1

With

draw

al S

im

0.1

0.0110001001010.10.01

W

Withdrawal AQUASTAT [Gt]

AQUASTAT※ Using irrigation efficiency (Doll et al., 2002)

Crop calendarLAI

YieldCrop Growth

River Land Surface Irrigation

SiBUC (Tanaka,2004) SiBUC (Tanaka,2004)

Soil moisture

Intake & drainage

Modeling…RiverStream flow

Kinematic wave

Runoff

Outflow & InflowDischarge

Dam operation

developed

: the grid which have largest catchment

: the grid flow into from upper grid: the grid flow into from upper grid

mesh size of model

data resolutionChannel length

Actual channel network

D A MD A Min fQ

66.6505

disc

harg

e

order

floodQ lowQ

1 Flood Protection OperationFlood discharge, normal dischargeoufQ

1. Flood Protection Operation

flood inf floodbase

Q Q QQ when

Q else

Disc. Peak‐cut Operation

2. Water Supply Operation

normQ else floodQ

max ,ouf base reqQ Q Q Demand from downstreamTime

Crop calendarLAI

YieldCrop Growth

C Land Surface Irrigation

SiBUC (Tanaka,2004) SiBUC (Tanaka,2004)

Soil moisture

Intake & drainage

Modeling…CropStream flow

Kinematic wave

Runoff

Outflow & InflowDischarge

Dam operation

developedS I M R I WkLAI Empirical

f f

Simulation Model for Rice‐Weather relations

max

1 exp ( ) 1 jj j f cf

LAILAI LAI A K T T

LAI

LAIkSS )1()1(1Solar

LAI growth Empiricalequation

LAImkrrSSs )1(exp)1(1 00

C C for1 DVI 2C(1 B) DVI 2

CSDW Carbon

Solarabsorption

Cs C(1 B)1Bexp

, DVI 2t

for2 DVI 3

,ssj CSDW Carbon 

production

hi h & l t t t fl i tWhY high & low‐temp. stress at flowering stage Particle swarm optimization (PSO) was used to calibrate parameters.

WhY h Yield Harvest Index Dry weight

Seasonal Change （Asia）

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Water Balance check①WB② PREC

P E win② PREC③ EVAP④ R

ΔSWQsfwin

wout④ ROFF

⑤ TWS⑥ delTWS Qg

wout

⑥ delTWS

i

Qg

iTWS (Total water storage)=Soil moisture + surface water (snow)

catchment, , ,i t i t i tRoff Qsf Qg

, , , , ,i t i t i t i t i t iWB P E Win Wout S

Water Balance（Mekong & Chao Phraya）

What produces the difference pbetween flood and non flood year? 

Please see Mr. Kotsuki’s Poster

1800

Need for high quality data

1200

1400

1600

1800

400

600

800

1000

Full(C2)Average value depends on data 

0

200

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Limit(C2)used (selected)

割 降 量 違 がS ll diff i i f ll1割の降雨量の違いが1.5倍の流量過大となる

Small difference in rainfall makes large difference in runoff

Anomaly Correlation analysis (Quality check) Daily data  Monthly data  

( missing < 6 days)      var1monMonthly rainfall climatologyMonthly rainfall climatology

( at least 5years )         varclim

Anomaly = (var1mon – varclim)/varclimAnomaly  =  (var1mon – varclim)/varclim

X :  monthly rainfall anomaly at each stationY f thl i f ll lY :  area average of monthly rainfall anomalyCorrelation between   X  and Y

3

1.5

2

2.5

0

0.5

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

‐1.5

‐1

‐0.51 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Integrated Water Resources model (H08)1. Simulate both natural water 

cycle and human water activitiesHanasaki et al., 2006, J. of Hydrol.Hanasaki et al. ,2008a,b, HESSHanasaki et al., 2010, J. of Hydrolcycle and human water activities 

at daily basis2. Open source software

, , y

2. Open source software

Land Surface processRiver RoutingRiver RoutingEnvironmental flowWater withdrawalCrop GrowthReservoir Operation

Effectiveness of new operation rule pto mitigate flood and drought risk

Please see Miss Cherry’s Poster

Summary and ConclusionSummary and Conclusion• Integrated water resources model is developed.Integrated water resources model is developed.Land Surface, Irrigation, River, Dam, CropGl b l di ib i f l d f• Global distribution of land surface parameters are produced. Especially, crop calendar is produced from time series analysis of satellite data (NDVI).( )

• Quality of rainfall data is very important for b tt t f tbetter management of water resources.