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 Qiuhong Tang 7

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 Qiuhong Tang

7 Nov 2006

Hydro Seminar @ Land surface hydrology group of UW

Introduction❶

Outline

Evolution of Hydrological Modeling❷

Analyses on Observed Data❸

Development of a Distributed Biosphere-Hydrological Model❹

Evaluation of the DBH Model System❺

Long Term Change of Hydrological Cycles in the Yellow River Basin❻

Conclusions and Recommendations❼

➢

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

➀

➁➂➃

➄

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 7 Nov 2006 Slide 3

Result analysis Scenario analysis

Validations and Applications

Nontraditional datasets

Data analysis

Analyses on Observed Data

1D Land surface model

Lateral water redistribution

DBH Model

Irrigation scheme

❷

❸ ❹

❺ ❻

Evolution of Hydrological Modeling

❼ Conclusions and Recommendations

Introduction❶ Tang, Qiuhong 7 Nov 2006 Slide 4

Introduction❶

Outline







➢

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.

Tang, Qiuhong 7 Nov 2006 Slide 6

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.

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

This study will develop a 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

Evolution of Hydrological Modeling❷ Tang, Qiuhong 7 Nov 2006 Slide 7

Introduction❶

Outline







➢

IDW

TS

TPS

Interpolation methods:

Inverse Distance Weighted (IDW)

Thin Plate Splines (TPS)

Thiessen Polygons (TS)

Analyses on Observed Data❸ Tang, Qiuhong 7 Nov 2006 Slide 9

Get time series coverage from in situ observation.

Harmonize variant data sources.

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


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


.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


.7-.8

.2-.3

.3-.4

.4-.5

.5-.6

.6-.7

.8-.9

.9-1.

0.-.1

.1-.2

Information extracted from nontraditional data is compared with traditional data.

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

Analyses on Observed Data❸ Tang, Qiuhong 7 Nov 2006 Slide 10 Tang, Q., Oki, T., 2006. J. Appl. Meteorol., accepted.

Data analysis.

Detect climate change magnitude (1960-2000) :

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

Analyses on Observed Data❸ Tang, Qiuhong 7 Nov 2006 Slide 11

Tang, Q., Oki, T., Kanae, S., Hu, H., 2006. Hydrol. Process., accepted.

Introduction❶

Outline







➢

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 Model❹ Tang, Qiuhong 7 Nov 2006 Slide 13

DBH model strategy

Tang, Q., Oki, T., Hu, H., 2006. Ann. J. Hydraul. Eng. JSCE 50, 37-42.

http://hydro.iis.u-tokyo.ac.jp/DBH/

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


A B C D

A

D

CBO

O

➀

➁

➂

➀ Vegetation condition-hydrology

➁ Climate (Energy part)-hydrology

➂ Human activity-hydrology

Contributions:

Biosphere (SVAT scheme)

New features:

Non-Irrigated Irrigated

1.0 IF

IF: Irrigation fraction

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


Introduction❶

Outline







➢

Evaluation of the DBH Model System❺ Tang, Qiuhong 7 Nov 2006 Slide 17

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;

##

#

#

#

##

#^

BohaiGulf

Tibetan Plateau

Loess PlateauNorth China Plain

Beijing

Tangnaihai (TNH)

Lanzhou (LZ)

Toudaoguai (TDG)

Huayuankou (HYK)

Lij in (LJ)Qingtongxia ID

Hetao ID

Lower reach IDsC h i n aC h i n a

M o n g o l i aM o n g o l i a

Qingtongxia (QTX)

Shizuishan

Longmen (LM)

Sanmenxia (SMX)Weihe ID

95°0'0"E

100°0'0"E

100°0'0"E 105°0'0"E

105°0'0"E

110°0'0"E

110°0'0"E

115°0'0"E

115°0'0"E

120°0'0"E

35°0'0"N

35°0'0"N

40°0'0"N

40°0'0"N

0 300 600150 Km±

River Basin

Irrigation district (ID)

Main stream

Tributary

Meteorological station

# Hydrologic gauge

^ Capital

Model Calibration and Validation

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

charg

e (

m3/s

)

Tangnaihai_obv Tangnaihai_sim

Monthly discharge comparison Bias = -1.1% RMSE = 233 m3/s RRMSE = 0.3 MSSS =0.828MSSS (mean square skill score, Murphy, 1988, recommended by WMO) MSSS: -∞ To 1.0

1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 19820

500

1000

1500

2000

2500

3000

3500

4000

BIAS=-6.1% RMSE=333m3/s RRMSE=0.48 MSSS=0.646

Dis

char

ge (

m3 /s

)

Tangnaihai_sim Tangnaihai_obv

Bias = -6% RMSE = 333 m3/s RRMSE = 0.48 MSSS =0.646

Calibration (1983-1993)

Validation (1962-1982)

Monthly discharge comparison

Slope: FAO soil map, slope f=2.0


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

300

600

900

1200

1500

1800

Dis

char

ge (m

3/s

)


Averaged Monthly discharge comparison

Bias = -1.1% RMSE = 136 m3/s RRMSE = 0.2 MSSS =0.9231983-1-1 1985-1-1 1987-1-1 1989-1-1 1991-1-1 1993-1-10

1000

2000

3000

4000

Dis

char

ge (

m3/s

)


Daily discharge comparison

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


1/1/1962 1/1/1965 1/1/1968 1/1/1971 1/1/1974 1/1/1977 1/1/19800

1000

2000

3000

4000

5000

6000 BIAS=-6.03% RMSE=459m3/s RRMSE=0.66 MSSS=0.419

Dis

char

ge (

m3 /s

)

Tangnaihai_Sim Tangnaihai_obv

Bias = -6% RMSE = 459 m3/s RRMSE = 0.6 MSSS =0.419

Daily discharge comparison

Calibration (1983-1993)

Validation (1962-1982)




1955 1960 1965 1970 19750

5

10

15

20

25

30

35

40

Wat

er w

ithdr

awal

s (1

09 m

3 )

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

1980 1985 1990 1995 20000

5

10

15

20

25

30

35

40

Wat

er w

ithdr

awal

s (1

09 m3 )


Validation (1960s-1970s)

Calibration (1980s-1990s)

Reported Simulated

1960s 17770 249801970s 19900 23181

Unit: 106m3/ year

Reported Simulated

1980s 29610 268861990-95 29960 29879

Unit: 106m3/ year

Canal coefficient: 0.3

The canal coefficient in Yellow River basin is about: 0.3 – 0.5. (Wang H., Cai P., Zhou H. Yellow River News, YRCC, 2005)

河套

青铜峡

尊村

宝鸡峡

汾河

泾惠渠

汾西

冯家山

镫口大黑河

洛惠渠交口抽渭

麻地毫

东雷抽黄（禹门口

靖会

萧河

兴电

石头河

固海扬水湟水流域

桃曲坡

景电一期

位山

潘庄

赵口

李家岸

彭楼

大功

陆浑

渠村

三义寨阎潭

韩墩簸箕李

谢寨

胡楼

王庄

刘庄

刑家渡

陶城铺

武嘉广利

人民胜利渠

95°0'0"E

100°0'0"E

100°0'0"E 105°0'0"E

105°0'0"E

110°0'0"E

110°0'0"E

115°0'0"E

115°0'0"E

35°0'0"N

35°0'0"N

40°0'0"N

40°0'0"NRiver

Basin

Irrigation Districts

Irrigation Districts in the Yellow River Basin


Target: Effects of natural and anthropogenic heterogeneity

Methodology:

withdraw from nearest river section

withdraw from specific river section

Irrigated Fraction 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 heterogeneityCase 3 : irrigation, with precipitation heterogeneity

Area

Pre

cipi

tati

on

Tang, Q., Oki, T., Kanae, S., Hu, H., 2006. J. Hydromet., accepted.

Review of studies on this topic: • Effect of natural, not anthropogenic, heterogeneity is presented.The new generation hydrological model makes it possible to represent both natural and anthropogenic heterogeneity.

TNH LZ QTX TDG LM SMX HYK0

500

1000

1500

2000

2500

Dis

charg

e a

long r

iver

(m3 /s

)

Observed Case 1 Case 2 Case 3

Case 2_ No irrigation

Case 3_ With irrigation

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

Run

off (

mm

/mon

th)

Case1_Surface Runoff Case1_Total Runoff Case2_Surface Runoff Case2_Total Runoff

Precipitation

Pre

cipi

tatio

n (m

m/m

onth

)

Case 2_ Runoff

Case 1_ Runoff

Case 2_ Surface

Case 1_ Surface


Results: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 : 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 Irrigation 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



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 MINAVG ID IF3 MAX MIN

Effects of human activities on energy components:

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

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


Introduction❶

Outline






Conclusions and Recommendations❼➢

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

Target: potential reasons for the Yellow River drying up

Long Term Change of Hydrological Cycles in YRB❻ Tang, Qiuhong 7 Nov 2006 Slide 26

Review of studies on this topic:

• Analyze hydro-climate data (Fu et al 2004; Yang et al 2004, Xu 2005)

• Analyze water use/irrigation data (Liu and Zhang 2002)

• Statistical relationship between climate data, water use, and discharge data

Climate condition

Human activity

Hydrology cycle

DBH

Distributed Numerical

The new generation hydrological model makes it possible to numerically simulate connections (internal relation) between climate condition, human activity and hydrology cycle.

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

50

100

150

200

250

Irrigate

d a

rea (

10

4hm

2)

Year

Upstream Midstream Downstream Upstream_no change Midstream_no change Downstream_no change

Downstream

Upstream

Midstream

Methodology:

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

Irrigated area change/ no change


To watch the hydrological response to hydrological forcing data. The simulation difference between ‘no change’ and ‘change’ forcing data shows the contribution of the hydrological components.


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)


Vegetation conditions change / no change

LAI FPAR

Experiments:

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

Scenarios Climate Vegetation Irrigated Area

Scenario 1 / / /Scenario 2 -- / /Scenario 3 / -- /Scenario 4 / / --Scenario 5 -- -- --Scenario 6 O -- --

/ With change -- No linear change O No pattern and no linear change

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000

0

5

10

15

20

25

30

35

40

Wate

r w

ithdra

wals

(10

9 m3 )


Total

Lower reaches

Upper reaches

Mid reaches


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

Simulated and reported water withdrawals at the Yellow River basin

MSSS = 0.5 MSSS = 0.5

MSSS = 0.7 MSSS = 0.7

Scenario 1

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:


Runoff_Change ET_Change

Withdrawal_Change Tg_Change

Conclusion Remarks:

1) Climate change (75%) is dominated in upper/middle reaches, human activity is dominated in lower reaches.

2) Climate pattern change (30%) rather than linear change (10%) 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.

Long Term Change of Hydrological Cycles in YRB❻ Tang, Qiuhong 7 Nov 2006 Slide 32 Tang, Q. et al, 2006. xxx, xxx (manuscript ready for submission).

Introduction❶

Outline






Conclusions and Recommendations❼➢

Conclusions and Recommendations❼ Tang, Qiuhong 7 Nov 2006 Slide 34

Conclusions

1) A new generation hydrological model, DBH model, is developed and validated.

2) Spatial distribution of land characteristics and climate features can be captured by the DBH model with nontraditional datasets.

3) The new generation model can demonstrate the effects of natural and anthropogenic heterogeneity. 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 potential reasons for the Yellow River drying up. 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.

Recommendations

Conclusions and Recommendations❼ Tang, Qiuhong 7 Nov 2006 Slide 35

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

2) Model validation is needed for the new generation model. Data on the chemical composition of water can be used for modeling water flow paths.

3) Further, the model can extend to simulate 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.

Hydro Seminar @ Land surface hydrology group of UW



Publications (Accepted and Published):

Tang, Q., Oki, T., Hu, H., 2006. A distributed biosphere hydrological model (DBHM) for large river basin. Ann. J. Hydraul. Eng. JSCE 50, 37-42.

Tang, Q., Oki, T., 2006. Daily NDVI relationship to cloud cover. J. Appl. Meteorol., accepted.

Tang, Q., Oki, T., Kanae, S., Hu, H., 2006. The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation. J. Hydromet., accepted.

Tang, Q., Oki, T., Kanae, S., Hu, H., 2006. A spatial analysis of hydro-climatic and vegetation condition trends in the Yellow River Basin. Hydrol. Process., accepted.

Tang, Q., Hu, H., Oki, T., Tian, F., 2006. Water balance within intensively cultivated alluvial plain in an arid environment. Water Resource Management., accepted.

Tang,Q., Hu, H., Oki, T., 2006. Groundwater recharge and discharge in a hyperarid alluvial plain (Akesu, Taklimakan Desert, China), Hydrological Processes, accepted.

Tang, Q., Hu, H., and Oki, T., Hydrological processes within an intensively cultivated alluvial plain in an arid environment, Sustainability of Groundwater Resources and its Indicators (Pro- ceedings of symposium S3 held during the Seventh IAHS Scientific Assembly at Foz do Iguacu, Brazil, April 2005). IAHS Publ. 302, 2006.

Tang, Q., Tian, F., and Hu, H., Runoff-evaporation hydrological model for arid plain oasis II: the model application, Shuikexue Jinzhan/Advances in Water Science, 15 (2): 146-150, 2004. (in Chinese with English abstract)

Hu, H., Tang, Q., Lei, Z., and Yang, S., Runoff-evaporation hydrological model for arid plain oasis I: the model structure, Shuikexue Jinzhan/Advances in Water Science, 15 (2): 140-145, 2004. (in Chinese with English abstract)

Land Surface Model

Surface layer

Root zone

Recharge zone

Canopy

Reference Height

Canopy Air Space

Groundwater

Inter-layer exchangesDue to gravitation and hydraulic gradient

Potential w3

Potential gw

GW - River Water Interaction

Roff1: adjust canopy/ground water and canopy snowRoff2: overland flow (SE95 D9)

Roff3: gravitationally driven drainage (SE86)Roff4: excess, adjust www(3)

Groundwater-soil water transfer:

Relationship between soil moisture potential and soil moisture:

(Clapp and Hornberger, 1978)

Sloping impermeable bed

Water table

ha

h'g

h''g

qg

s

hr

B

h

D

ll/2

θb

Groundwater-River Interaction

sincos

ds

dhhKq g

gs

sincos

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Groundwater flow to a ditch over a sloping impermeable bed. Assuming that the flow lines are approximately parallel to the bed, according to the Dupuit-Forchheimer approximation, the flow of water per unit width of the river is estimated.

(Childs, 1971; Towner, 1975)

Overland flow on the Hillslope

Flow in river channel

ROFFs (qs)

RiverSLOPE ( S0 )

WaterDepth (hs)

Model Validation Criteria

Mean Error:

Relative Bias:

Mean Absolute Error:

Mean square error:

Relative RMSE:

Mean Square Skill Score:

(Murphy, 1988) Recommended by WMO.

Documents

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 Qiuhong Tang 7

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 Qiuhong Tang 7