Upload
zenaida-stephenson
View
23
Download
0
Embed Size (px)
DESCRIPTION
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
Citation preview
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).
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 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
❷
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
➁
➂
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.