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A Distributed Biosphere- Hydrological Model System for Continental Scale River Basins 大大大大大大大大大大大大大大大大大大大大大大大大大 by Tang, Qiuhong 26 June 2006 Lab. meeting presentation

by Tang, Qiuhong 26 June 2006 Lab. meeting presentation

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A Distributed Biosphere-Hydrological Model System for Continental Scale River Basins 大陸河川のための分布型生物圈水文モデルに関する研究. by Tang, Qiuhong 26 June 2006 Lab. meeting presentation. Outline. ❶. ➢. Introduction. A Historical Perspective of Land Surface Hydrology. ❷. - PowerPoint PPT Presentation

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A Distributed Biosphere-Hydrological Model System for Continental Scale River

Basins大陸河川のための分布型生物圈水文

モデルに関する研究 by Tang, Qiuhong

26 June 2006

Lab. meeting presentation

Introduction❶

Outline

A Historical Perspective of Land Surface Hydrology❷

Development of a Distributed Biosphere-Hydrological Model❸

Forcing Data and Parameters Analysis❹

Application of the DBH Model System❺

A Comprehensive Application in a Continental Scale River Basin❻

Conclusions and Recommendations❼

The picture is adopted from Oki and Kanae (2006).

➁➂➃

➀ Land surface -atmosphere

➁ Vegetation-soil-groundwater

➂ Spatial/temporal heterogenieity

➃ Lateral redistribution of moisture

➄ Human activities

New challenges:

➀ Information from nontraditional data

➁ Develop a realistic model

➂ Investigate the effects of heterogeneities

➃ Runoff lateral redistributions

➄ Evaluate the effects of human activities and climate change

Research Objectives

Introduction❶ Tang, Qiuhong 26 June 2006 Slide 3

1D Land surface model

Lateral water distribution

Irrigation scheme

Result analysis

Nontraditional datasets

Scenario analysis

Data analysis

DBH model Forcing data and parameters

Applications

❸ ❹

❺ ❻

A Historical Perspective of Land Surface Hydrology

❼ Conclusions and Recommendations

Introduction❶ Tang, Qiuhong 26 June 2006 Slide 4

Introduction❶

Outline

A Historical Perspective of Land Surface Hydrology❷

Development of a Distributed Biosphere-Hydrological Model❸

Forcing Data and Parameters Analysis❹

Application of the DBH Model System❺

A Comprehensive Application in a Continental Scale River Basin❻

Conclusions and Recommendations❼

Conceptual Model: The first generation hydrological model (1960s – 1970s)

Use statistical relationship between rainfall and discharge

Integrate different components of hydrological processes in a lumped or fake-distributed way

Representative models and methodology: Stanford model, Xin’an jiang model, Tank model, Unit Hydrograph etc.

Meteorological observation

Hydrographic gauge

Empirical relationship

Lumped model

3-D saturated flow groundwater model

1-D unsaturated flow model

2-D overland flow model

Snow melt model

Canopy interception model

Rain and snow

Distributed Model: The second generation hydrological model (1980s – 1990s)

Recognize the effects of spatial heterogeneity with spatially varying data

Solve the differential equations with powerful computer

Representative models and methodology: SHE model, TOPMODEL, GBHM etc.

Distributed Biosphere-Hydrological (DBH) Model: The third generation hydrological model (2006)

Connect hydrological cycle with biosphere, climate system and human society.

Physically represent hydrological cycle with nontraditional data

Development of DBH model shows the new direction of hydrology science.

Few models can represent both biosphere and land surface hydrological cycle. (e.g. DHSVM, VIC, FOREST-BGC etc.)

This study will develop a DBH model system to bridge atmosphere-biosphere-land surface hydrology and human society.

The scope of hydrology will broaden from rainfall-runoff relationship to climatology, biosphere, ecosystem, geosphere, remote sensing, and human society.

SVAT scheme

Mass/Energy

Photosynthesis

CO2

Hydrologic scheme

Human activity

Nontraditional data sources

Climate model

Snow meltChemical tracers

Historical Perspective of Land Surface Hydrology❷ Tang, Qiuhong 26 June 2006 Slide 7

Introduction❶

Outline

A Historical Perspective of Land Surface Hydrology❷

Development of a Distributed Biosphere-Hydrological Model❸

Forcing Data and Parameters Analysis❹

Application of the DBH Model System❺

A Comprehensive Application in a Continental Scale River Basin❻

Conclusions and Recommendations❼

Flow intervals Sub-basin Basin

SiB2 Model

Outlet

bhr

River cross section

ha

qg

qs

hg

Surface layer

Root zone

Recharge zone

Canopy

D1

D2

D3

Z1

Z2

Zm Reference Height

Canopy Air Space

Groundwater

One dimensional modelOne dimensional model

River Routing SchemeRiver Routing Scheme

(Hydrotopes)

Point dataRS: LAIRS: FPAR

Land useSoil type

DEM

Input data (time varying) Geographic data

SiB2 Model

EvaporationRunoff

SiB2-DHM Model

Energy flux

River Routing

Gravity

Nontraditional Data

SVAT

DHM

Development of a DBH Model❸ Tang, Qiuhong 26 June 2006 Slide 9

DBH model strategy

New features of DBH model: Biosphere, Nontraditional data sources.

Development of a DBH Model❸ Tang, Qiuhong 26 June 2006 Slide 10

A B C D

A

D

CBO

O

➀ Vegetation condition-hydrology

➁ Climate (Energy part)-hydrology

➂ Human activity-hydrology

Contributions:

Biosphere (SVAT scheme)

New features:

New features of DBH model: Biosphere, Nontraditional data sources.

AV

HR

R / L

AI

SiB2 L

and Use

Global C

limate Station

s

Data sources used in the DBH model system:

Remote sensing (RS) : AVHRR/NDVI, LAI, FPAR, ISCCP-FD RadFlux, HYDRO1K, etc.

Ground observations: Global Surface Summary of Day Data, Global Soil Bank, etc.

Statistical survey data: Global Soil Map, Global Irrigation Area

Development of a DBH Model❸ Tang, Qiuhong 26 June 2006 Slide 11

1983-1-1 1985-1-1 1987-1-1 1989-1-1 1991-1-1 1993-1-10

500

1000

1500

2000

2500

3000

3500

Dis

char

ge (

m3/s

)

Tangnaihai_obv Tangnaihai_sim

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

300

600

900

1200

1500

1800

Dis

char

ge (m

3/s

)

Tangnaihai_obv Tangnaihai_sim

Monthly discharge comparison Averaged Monthly discharge comparison

Bias = -1.1% RMSE = 136 m3/s RRMSE = 0.2 MSSS =0.923Bias = -1.1% RMSE = 233 m3/s RRMSE = 0.3 MSSS =0.828

1983-1-1 1985-1-1 1987-1-1 1989-1-1 1991-1-1 1993-1-10

1000

2000

3000

4000

Dis

char

ge (

m3/s

)

Tangnaihai_obv Tangnaihai_sim

Daily discharge comparison

Bias = -1.1% RMSE = 297 m3/s RRMSE = 0.4 MSSS =0.759

MSSS (mean square skill score, Murphy, 1988, recommended by WMO)

Year Qobv Tpeak Qsim Tpeak Qsim-obv Tsim-obv

1983 3560 14-Jul 3253 14-Jul -307 0

1984 3660 17-Jul 3099 15-Jul -561 -2

1985 3350 21-Sep 3389 18-Sep 39 -3

1986 2620 4-Jul 2766 5-Jul 146 1

1987 2150 25-Jun 3252 27-Jun 1102 2

1988 1480 10-Oct 1340 7-Oct -140 -3

1989 4140 23-Jun 2670 26-Jun -1470 3

1990 1430 17-Sep 1309 13-Sep -121 -4

1991 1590 18-Aug 1751 17-Aug 161 -1

1992 2710 7-Jul 2322 22-Jun -388 -15

1993 2040 21-Jul 2264 23-Jul 224 2

Annual Largest Flood Peak comparison (m3/s, day)

Bias < 10% Bias > 50% Tdelay > 5 days

Performance of the DBH model system in the Yellow River Basin.

Development of a DBH Model❸ Tang, Qiuhong 26 June 2006 Slide 12

Introduction❶

Outline

A Historical Perspective of Land Surface Hydrology❷

Development of a Distributed Biosphere-Hydrological Model❸

Forcing Data and Parameters Analysis❹

Application of the DBH Model System❺

A Comprehensive Application in a Continental Scale River Basin❻

Conclusions and Recommendations❼

IDW

TS

TPS

Current available interpolation methods in the DBH model system:

Inverse Distance Weighted (IDW)

Thin Plate Splines (TPS)

Thiessen Polygons (TS)

Forcing Data and Parameters Analysis❹ Tang, Qiuhong 26 June 2006 Slide 14

Get time series coverage from in situ observation.

Harmonize variant data sources of the DBH model system.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

3

6

9

12

15

18

21

24

27

30

(d)

(b)

(c)

(a)

SCI

NC

I valu

es

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

22222

VALID DATA POINTS 255626MISSING DATA POINTS 7414

R2 = 0.506

0.-.1 .1-.2 .2-.3 .3-.4 .4-.5 .5-.6 .6-.7 .7-.8 .8-.9 .9-1.

.7-.8

.2-.3

.3-.4

.4-.5

.5-.6

.6-.7

.8-.9

.9-1.

0.-.1

.1-.2

(a)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

3

6

9

12

15

18

21

24

27

30

21-24

6-9

9-12

12-15

15-18

18-21

24-27

Cloud amount

CLA

VR

valu

es

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

22222

VALID DATA POINTS 255626MISSING DATA POINTS 7414

R2 = 0.169

0.-.1 .1-.2 .2-.3 .3-.4 .4-.5 .5-.6 .6-.7 .7-.8 .8-.9 .9-1.

27-30

1-3

3-6

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

1

2

3

4

5

6

7

8

9

10

0.-.1 .1-.2 .2-.3 .3-.4 .4-.5 .5-.6 .6-.7 .7-.8 .8-.9 .9-1.

R2 = 0.407

Cloud amount

NC

I valu

es

0

2000

4000

6000

8000

10000

12000

14000

16000

22222

VALID DATA POINTS 255626MISSING DATA POINTS 7414

.7-.8

.2-.3

.3-.4

.4-.5

.5-.6

.6-.7

.8-.9

.9-1.

0.-.1

.1-.2

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

1

2

3

4

5

6

7

8

9

10

0.-.1 .1-.2 .2-.3 .3-.4 .4-.5 .5-.6 .6-.7 .7-.8 .8-.9 .9-1.

R2 = 0.572

Cloud amount

SC

I valu

es

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

22000

24000

26000

28000

VALID DATA POINTS 255626MISSING DATA POINTS 7414

.7-.8

.2-.3

.3-.4

.4-.5

.5-.6

.6-.7

.8-.9

.9-1.

0.-.1

.1-.2

Compare Cloud amount from variant data sources with the DBH model system

G: Ground observation

Rd: Data derived by DBH

Ro: Data from CLAVR

G1G1

G1G2

G2

Rd

Rd

Ro

Data from: AVHRR NDVI dataset

Spatial resolution: 16 km

Temporal resolution: daily

Study area: the Yellow River Basin

Study period: 1995-2000

Satellite data

Satellite data

Forcing Data and Parameters Analysis❹ Tang, Qiuhong 26 June 2006 Slide 15

Data analysis with the DBH model system.

Detect climate change magnitude (1960-2000) with the DBH model system:

Precipitation on the Loess Plateau decreases

Cloudy decreases, humidity decreases, Temperature and ET increase, in irrigation districts (Drier). LAI increase in irrigation districts.

Precipitation (%) Reference ET (%)

Relative humidity (%) Sunshine time (%)

Cloud amount (%) LAI (%)

Mean Temperature (K) Min. Temp. (K)

Max. Temp. (K)DTR (diurnal temp. range, K)

I

II

Temperature increases, LAI decreases on the Tibet PlateauThe Loess Plateau, the IDs, and the Tibet Plateau can be precipitation, human activity, and temperature hot spots of Yellow River drying up, respectively.

III

III

Forcing Data and Parameters Analysis❹ Tang, Qiuhong 26 June 2006 Slide 16

Introduction❶

Outline

A Historical Perspective of Land Surface Hydrology❷

Development of a Distributed Biosphere-Hydrological Model❸

Forcing Data and Parameters Analysis❹

Application of the DBH Model System❺

A Comprehensive Application in a Continental Scale River Basin❻

Conclusions and Recommendations❼

Application of the DBH Model System❺ Tang, Qiuhong 26 June 2006 Slide 18

DBH model application in the Yellow River Basin

The Yellow River BasinThe Yellow River BasinArea: 794,712 km2 River length: 5,464 km Topographic condition:Tibetan Plateau – Loess Plateau – North China PlainClimatic Condition:Annual precipitation < 200 – 800 mmSimulation:Spatial: 10*10 km; Time step: hourly; Period: 1983-2000

Application of the DBH Model System❺ Tang, Qiuhong 26 June 2006 Slide 19

Target: Effects of natural and anthropogenic heterogeneity

Methodology:

withdraw from nearest river section

withdraw from specific river section

Irrigated area data is from AQUASTAT dataset.

Precipitation heterogeneityCalibrate with Tangnaihai stationa=b=4

Anthropogenic heterogeneity

Experiments:Case 1 : no irrigation, no precipitation heterogeneity

Case 2 : no irrigation, with precipitation heterogeneity

Case 3 : irrigation, with precipitation heterogeneity

STN1 STN2 STN3 STN4 STN5 STN6 STN7

0

500

1000

1500

2000

2500

Dis

char

ge a

long

riv

er (

m3 /s

)

D_OBV D_SIM_irr D_SIM_noirr

Application of the DBH Model System❺ Tang, Qiuhong 26 June 2006 Slide 20

Results:

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

5

10

15

20

25Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

300

250

200

150

100

50

0

Runoff (

mm

) Case1_Surface Runoff Case1_Total Runoff Case2_Surface Runoff Case2_Total Runoff

Precipitation

Pre

cipita

tion (

mm

)

Case 1 : no precipitation heterogeneity

Case 2 : with precipitation heterogeneity

Case 1 : no precipitation heterogeneity

Case 2 : with precipitation heterogeneity

With consideration of natural heterogeneity, total runoff increase because surface runoff increase.

With consideration of natural heterogeneity, total runoff increase because surface runoff increase.

decreasing discharge

discharge increases

59%

41%

(RAZ)

Case 2

Case 3

Case 2 : no irrigation

Case 3 : with irrigation

Case 2 : no irrigation

Case 3 : with irrigation

With consideration of anthropogenic heterogeneity, Runoff Absorbing Zone (RAZ) can be simulated.

With consideration of anthropogenic heterogeneity, Runoff Absorbing Zone (RAZ) can be simulated.

Effects of human activities on water components:

Water shortage

Evaporation increase Runoff increase

Irrigation

Averaged (AVG) In Irrigated Districts (ID) Irrigated Fraction>0.3(IF3) MAX MIN

Annual mean water components (1983-2000) in the Yellow River Basin

65% 42% 44% 100% 0% 1.9 7.7 11.7 37.1 0

2.1 6.9 10.5 22 0 -0.25 0.8 1.2 26.4 -8.6

AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN

AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN

Application of the DBH Model System❺ Tang, Qiuhong 26 June 2006 Slide 21

Ground temperature change

Latent heat fluxes change Sensible heat fluxes change

Canopy temperature change-0.1 -0.32 -0.4 0 -1.6 -0.06 -0.23 -0.31 0 -1.2

3.3 11.2 15.5 43.3 0

-2.5 -.7.7 -10.2 0 -37.8

AVG ID IF3 MAX MIN AVG ID IF3 MAX MIN

AVG ID IF3 MAX MINAVG ID IF3 MAX MIN

Effects of human activities on energy components:

Averaged (AVG) In Irrigated Districts (ID) Irrigated Fraction>0.3(IF3) MAX MIN

Mean energy components in peak irrigation month (JJA, 1983-2000)

Application of the DBH Model System❺ Tang, Qiuhong 26 June 2006 Slide 22

Introduction❶

Outline

A Historical Perspective of Land Surface Hydrology❷

Development of a Distributed Biosphere-Hydrological Model❸

Forcing Data and Parameters Analysis❹

Application of the DBH Model System❺

A Comprehensive Application in a Continental Scale River Basin❻

Conclusions and Recommendations❼

A comprehensive application (Both data analysis and model simulation)Study area: the Yellow River Basin (1960-2000)

Target: what contributes to the Yellow River drying up?

Methodology:

1950-1959 1960-1969 1970-1979 1980-1989 1990-19950

50

100

150

200

250

Irriga

ted

area

(10

4 hm2 )

Year

Upstream Midstream Downstream Upstream_no change Midstream_no change Downstream_no change

The distribution of irrigated area data is from AQUASTAT dataset.The amount of irrigated area is obtained from reports or literatures.

A Comprehensive Application in YRB❻ Tang, Qiuhong 26 June 2006 Slide 24

Irrigated area change/ no change

A Comprehensive Application in YRB❻ Tang, Qiuhong 26 June 2006 Slide 25

Climate conditions linear change/ no linear change (mean value is the mean value of the 1960s) / no pattern change

Precipitation Mean Temp.

Min. Temp. Max. Temp.

Relative Humidity Sunshine timeClimate conditions without pattern change (repeat the climate condition in the 1960s)

Climate conditions without pattern change (repeat the climate condition in the 1960s)

A Comprehensive Application in YRB❻ Tang, Qiuhong 26 June 2006 Slide 26

Vegetation conditions change / no change

LAI FPAR

Experiments:Scenario1 : control simulation with most realistic condition (all conditions are changing)

Scenario2 : non-climate linear change

Scenario3 : non-vegetation change

Scenario4 : non-irrigated area change

Scenario5 : stable without linear tendency (non-climate linear, no vegetation, no irrigated area change)

Scenario6 : stable without climate pattern change (non-climate pattern, no vegetation, no irrigated area change)

S1-S2: linear climate change contribution S1-S3: vegetation change contribution S1-S4: irrigated area change contributions

S1-S5: all linear changes contribution (S1-S5) – (S1-S6): climate pattern change contribution

S1-S2: linear climate change contribution S1-S3: vegetation change contribution S1-S4: irrigated area change contributions

S1-S5: all linear changes contribution (S1-S5) – (S1-S6): climate pattern change contribution

A Comprehensive Application in YRB❻ Tang, Qiuhong 26 June 2006 Slide 27

1955 1960 1965 1970 1975 1980 1985 1990 1995 20000

5

10

15

20

25

30

35

40

Wat

er w

ithdr

awal

s (1

09 m3 )

UP_rep. UP MID_rep. MID LOW_rep. LOW TOT_rep. TOT

Results:

Station BIAS RMSE m3/s RRMSE MSSS Station BIAS RMSE m3/s RRMSE MSSSTangnaihai -5% 121 0.18 0.5 Lanzhou -8% 158 0.16 0.5Qingtongxia -12% 163 0.20 0.5 Shizuishan -3% 141 0.14 0.6Toudaoguai 18% 191 0.26 0.4 Longmen 29% 254 0.36 -1.0Sanmenxia 15% 257 0.23 0.5 Huayuankou 6% 248 0.20 0.7Lijin 8% 317 0.32 0.7 MSSS >= 0.5

MSSS (mean square skill score, Murphy, 1988, recommended by WMO)

Model performance of annual discharge at main stem stations of the Yellow River

Simulated and reported water withdrawals at the Yellow River basin

Main results:

1) Climate change is dominated in upper/middle reaches, human activity is dominated in lower reaches.

2) Climate pattern change rather than linear change is more important for Yellow River drying up.

3) The reservoirs make more stream flow consumption for irrigation on one hand, and help to keep environment flow and counter zero-flow in the river channel on the other hand.

Hydrological components change contributed by climate, vegetation, irrigated area change. (S1-S5)

Hydrological components change contributed by climate, vegetation, irrigated area change. (S1-S5)

Results:

A Comprehensive Application in YRB❻ Tang, Qiuhong 26 June 2006 Slide 28

Introduction❶

Outline

A Historical Perspective of Land Surface Hydrology❷

Development of a Distributed Biosphere-Hydrological Model❸

Forcing Data and Parameters Analysis❹

Application of the DBH Model System❺

A Comprehensive Application in a Continental Scale River Basin❻

Conclusions and Recommendations❼➢

Conclusions and Recommendations❻ Tang, Qiuhong 26 June 2006 Slide 30

Conclusions

1) A new generation hydrological model, DBH model, is developed and validated. The model is intended to be as physically, biologically, and hydrologically realistic as possible. It can be used for hydrological simulation in continental scale river basin.

2) The agreement between nontraditional data and traditional ground observation suggests that spatial distribution of land characteristics and climate features can be captured by the DBH model. The data analysis in the Yellow River Basin indicates that the Loess Plateau, the Tibetan Plateau, and the irrigation districts are precipitation, temperature, and human activity hot spots of the Yellow River drying up, respectively.

3) The new generation model can demonstrate the effects of natural and anthropogenic heterogeneity. Accounting for precipitation heterogeneity improved the runoff simulation. Accounting for anthropogenic heterogeneity can simulate negative runoff contribution which cannot be represented by traditional models.

4) The DBH model was used to interpret the reasons for the Yellow River drying up. The results indicate climate change is dominated in upper/middle reaches, human activity is dominated in lower reaches. Climate pattern change rather than linear change is more important for Yellow River drying up.

Recommendations

Conclusions and Recommendations❻ Tang, Qiuhong 26 June 2006 Slide 31

1) Further data collection efforts would continuously benefit research on land surface hydrology. Hydrologists should improve communications with data maker community.

2) Data on the chemical composition of water can be used for modeling water flow paths. The transport processes of chemical traces could be incorporated into the third generation model and improve flow path simulation

3) Further, the model can extend to simulation hydrological cycle over the global land surface with global datasets. The ocean-land surface-atmosphere model system will explore and variability and predictability of climate and hydrological variations.

4) With the consideration of climate, biosphere, land surface hydrology and human activity, the new generation model has potential great societal benefits. The development and application of the new model will benefit both science and society.

Life was like football match. You never know what you're gonna get.

The picture is adopted from www.lqqm.org.